Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first
inventor to file provisions of the AIA .
The instant application, which has a filing date on or after March 16, 2013, is considered a
transition application because the application claims domestic benefit to a parent application
number 15/246,446, (filed on or after March 16, 2013), for example, which was examined with
AIA priority. Although the instant application does not contain a 37 CFR 1.55/1.78 statement
indicating that this application should be examined under the AIA (First Inventor to File), a
review of the disclosures of both the instant application and the parent application(s), by the
examiner, reveals that at least one claim presented or that has ever been presented in the
instant application appears to be drawn to an invention having an effective filing date on or after
March 16, 2013 as the claim(s) fail to have support in the earlier-filed application(s). Thus the
effective filling date of at least one claim in the application appears to be 06/7/2016. See
MPEP 2159.02.
Also, as noted below, specifically, claims 1-7; 18; 41; and 49-57 filed on 12/4/2025 lack support in the earlier filed applications (as further discussed below in the 35 USC 112 rejections below), because a review of those disclosures listed by applicant on record, it is found that the on record disclosures failed to teach the techniques for implementing in the below mentioned the independent claim(s) limitations.
See also the related rejections concerning the other independent claims (claims 1, 18 and 41, and 56) below.
Thus the effective filling date of at least one claim in the application appears to be 06/7/2016. See MPEP 2159.02
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/4/2025 has been entered.
Response to Amendments
Arguments and amendments filed 12/4/2025 have been examined.
Claims 1-7, 18, 41, 49-57 are amended
Claims 8-17; 19-40; 42-48 have been cancelled.
Thus, Claims 1-7; 18; 41; and 49-57 are currently pending.
Priority
Applicant states that this application is a continuation or divisional application of
the prior-filed application. A continuation or divisional application cannot include new
matter. Applicant is required to delete the benefit claim or change the relationship
(continuation or divisional application) to continuation-in-part because this application contains
the following matter not disclosed in the prior-filed application (a more detailed
discussion is shown in the 35 USC 112 rejections)
Applicant's claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35
U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or
more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as
follows:
The later-filed application must be an application for a patent for an invention which is also
disclosed in the prior application (the parent or original nonprovisional application or provisional
application). The disclosure of the invention in the parent application and in the later-filed
application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first
paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco
Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994)
Response to Arguments
Applicant’s arguments with respect to newly amended claim(s) and prior art rejections under 35 USC 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
To the extent the other arguments apply, they are responded to below.
As to the argument that: “To facilitate USC 112 affects the claims based on a verbatim copy paste of Panda US 9,135,307 claims.” & “US 9,135,307 Panda uses first query, second query.” & “It is obvious that Panda can permit a predetermined number N to be 10 e.g., 100% of the top ranked resources, and as such offer low-quality search results to the end user.”;
The Examiner respectfully reminds applicant that there is no Panda reference being used in the rejections below.
As to the argument(s) that: “COPY PASTING US 9,135,307 CLAIMS Examiner is right suggestion for an interference has not been completed, once the 112 rejections are overcome no new matter will exist. Until then examiner's remark of new matter is moot.”; “Cone [0031] does not teach irrelevancy and relevancy as conventional search engine do, both
Panda (is a technological improvement of Google search in case an interference specialist
requests at least one vanilla independent claim of US 9,135,307) and Paiz US 7,809,659 Col. 55 Line 51-Col. 56 Line 12 are Internet search engine where site rank is measure as (II) below.”; “While Panda US 8,682,892 performs this function, it is statutorily barred for being filed over a year after US 7,908,263 's publication, which triggers an interference suggestion.”; “Claim 1, 18, 41, 56 Reason suggesting an interference”; “These claim limitations show all the elements of a suggestion for an interference. The culling is performed by Site rank determining in real time what is probabilistically blacklisted and what is duplicate in the set of results and what not based on marketing web analytics, but on semantic structure will communicate to the end user their craving need..”; (see arguments, P. 1, 4, 44, 54, 58, 60)
However, as to the “suggestion for an interference” above, examination of this application has not been completed as required by 37 CFR 41.102(a). Consideration of a potential interference is premature. See MPEP § 2303.
As to the argument concerning 35 USC 101 (concerning arguments on pages 9-60):
“First the vanilla copy pasted claims that raises all the 35 USC § 112 are immunized
from the 35 USC § 101 in view of double jeopardy. In other words, based on the
35 USC§ 101 mumbo jumbo the examiner cannot suggest an invalidation of the
patent claims for being an abstract concept which it is not since it is a real-life state
of the art artificial intelligence driven supercomputer search engine for the web
The vanilla claims were also scrutinized under 35 USC§ 101 at least twice, in
particular claims 4-5.”;
The Examiner respectfully disagrees.
As to Applicant’s assertion that “35 USC§ 101 mumbo jumbo the examiner cannot suggest an invalidation of the patent claims for being an abstract concept which it is not since it is a real-life state of the art artificial intelligence driven supercomputer search engine for the web”; Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
As to the argument: “To those in the art organizing and distributing the internet environment with real-time page ranking capabilities addresses the scope of 35 USC§ 101, as it involves a (massive) large-scale, real-time search engine with immediate request-response functionality.”
Applicant asserts that the inventions direction towards “artificial intelligence driven supercomputer search engine for the web” implies that this “addresses the scope of 35 USC§ 101”; however, the test is not whether the claim is confined to a particular field of use or technological environment, see Intellectual Ventures ILLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015) (“[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment”). The relevant question, even at the first step of the Mayo/Alice analysis, is “whether the claims are directed to an improvement in computer functionality versus being directed to an abstract idea.” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016).
Here, the invention uses computer technology, but the Specification describes the claimed
solution as a scheme in collecting, storing and managing electronic records over time (see for
example specification para. [0174-0177] “If the deleted W _Rank is greater than
the TOP 1000, the Index value where the existing W _Rank is set to zero, all the W _Rank below it are improved by one, and the TOP 1000 is adjusted.”). And collecting, storing, and organizing information describes the abstract idea to which Appellants’ claims are directed, not an improvement in computer technology. Erie Indemnity Co., 850 F.3d at 1328 (“the heart of the claimed invention lies in creating and using an index to search for and retrieve data ... an abstract concept”).
Thus, as the test for patent eligibility is not whether the claim is confined to a particular field of
use or technological environment, (see, again Intellectual Ventures ILLC v. Capital One Bank
(USA)), the Examiner is unconvinced the claims are directed to eligible subject matter and thus
this argument is moot.
Similarly, arguments concerning claims 2--7; 18; 41; and 49-57 see for example: “Claim 2 was amended to overcome 35 USC § 101 by introducing to the search system a data warehouse and/or knowledge database with preprocessed and precalculated optimized search results and clarify” (concerning arguments on pages 9-60).
Here, the invention uses computer technology, but the Specification describes the claimed
solution as a scheme in collecting, storing and managing electronic records over time (see for
example specification para. [0174-0177] “If the deleted W _Rank is greater than
the TOP 1000, the Index value where the existing W _Rank is set to zero, all the W _Rank below it are improved by one, and the TOP 1000 is adjusted.”). And collecting, storing, and organizing information describes the abstract idea to which Appellants’ claims are directed, not an improvement in computer technology. Erie Indemnity Co., 850 F.3d at 1328 (“the heart of the claimed invention lies in creating and using an index to search for and retrieve data ... an abstract concept”).
Thus, as the test for patent eligibility is not whether the claim is confined to a particular field of
use or technological environment, (see, again Intellectual Ventures ILLC v. Capital One Bank
(USA)), the Examiner is unconvinced the claims are directed to eligible subject matter and thus
this argument is moot.
As to the argument concerning 35 USC 112 (concerning arguments on pages 4, 6, 10, 18, 27-29, 42, 56, 68):
In summary, the Examiner reviewed the cited sections provided with the filing of the instant application and also searched the parent application(s) and could not find any discussion related to the above noted claim limitations including the support/new matter limitations noted in the rejection under 35 USC 112.
Thus the above noted limitations would appear to be new matter with respect to the parent application(s) (see MPEP 211.05(1)(8)).
Additionally, the current amendments do not address and overcome the 35 USC 112(a) rejections nor do the various asserted “obvious tests” overcome the 35 USC 112(a) rejections (concerning arguments on pages 4, 6, 10, 18, 27-29, 42, 56, 68). The applicant in arguments above provides many discussions regarding support to the claim limitations. Upon further review of the arguments, the claim limitations still do not appear to have support from the parent applications based on how the claim is written. The See the discussion in the 35 USC 112 rejections above. In particular, the Examiner did not find clear evidence in the parent applications / asserted priority applications of the claimed limitations noted in the 35 USC 112 rejections.
As noted in previous Office Actions, certain claim elements did not appear to have support; even though those elements have since been cancelled, please note MPEP 2159 indicates "If there is ever even a single claim to a claimed invention in the application having an effective filing date on or after March 16, 2013, AIA 35 U.S.C. 102 and 103 apply in determining the patentability of every claimed invention in the application. This is the situation even if the remaining claimed inventions all have an effective filing date before March 16, 2013, and even if the claim to a claimed invention having an effective filing date on or after March 16, 2013, is
canceled."
As to the argument(s) (see page 5-6):
“According to CoPilot the ''definition of a search engine query term": A search query is a word or phrase that an internet user types into a search engine's search box to answer an inquiry or
question. A query consists of a series of words. a phrase, or full sentence - a ''long tail query” :.:
Queries a.re often viewed in terms of keywords.
According to Co Pilot a "definition of a search engine regular expression·':
A regular expression (regex) is a sequence of characters that defines a search pattern for matching text It is commonly used in programming and search engines for tasks such as string matching, input validation. and data extraction .. Regular expressions allow for flexible pattern matching. enabling users to find, extract, or replace specific strings of data efficiently. They are widely in various applications, including search engines, data analysis, and programming languages like Python, Pert and JavaScript..”;
“According to CoPilot a "search engine default results: "By default, the Search page is configured to display a maximum of the top 10 results. If you would like to raise this default limit, see the instructions below to change the number of results displayed per Google Search page. UPDATE Oct 2025: Google has removed the num=lO0 search ... " US 9,135,307 FIG. 2 and associated texts "The search system determines whether the first set of
resources includes at least a predetermined number N, e.g. I 0, 50, I 00, of top-ranked resources from low-quality sites (206). For example, the search system can compare sites on which the topranked resources are located to a list of sites that had been previously identified as being lowquality sites." It is obvious that N is not permitted to be I 00 and the lion share of end user is 10.”;
“According to CoPilot a "definition of top (n) results":
"The term "top (n) results" typically refers to a data analysis technique that identifies and ranks
the top N items in a dataset based on specific criteria."
US 9,135,307 FIG. 2 and associated texts "For example, the alternative query module 112 can
determine whether some number of the top-ranked resources, e.g., the 3, 5, 10, or 20, highestranked resources, responsive to the first query include at least a predetermined number N of resources from sites that have been previously identified as low-quality sites. The predetermined number N can be a number ofresources, e.g., 3, 5, 8, or 10, or a percentage of the top-ranked resources, e.g., 30%, 50%, 80%, or 90% of the top-ranked resources.
It is obvious that Panda can permit a predetermined number N to be 10 e.g., 100% of the top ranked resources, and as such offer low-quality search results to the end user.”
The Examiner respectfully disagrees.
See generally MPEP 2173.03:
“Correspondence Between Specification and Claims [R-07.2022] The specification should ideally serve as a glossary to the claim terms so that the examiner and the public can clearly ascertain the meaning of the claim terms. Correspondence between the specification and claims is required by 37 CFR 1.75(d)(1), which provides that claim terms must find clear support or antecedent basis in the specification so that the meaning of the terms may be ascertainable by reference to the specification. Glossaries of terms used in the claims are a helpful device for ensuring adequate definition of terms used in claims. If the specification does not provide the needed support or antecedent basis for the claim terms, the specification should be objected to under 37 CFR 1.75(d)(1). See MPEP § 608.01(o) and MPEP § 2181, subsection IV. Applicant will be required to make appropriate amendment to the description to provide clear support or antecedent basis for the claim terms provided no new matter is introduced, or amend the claim.”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 and 18, 41, 49-57 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ),
first paragraph, as failing to comply with the written description requirement. The claim(s)
contains subject matter which was not described in the specification in such a way as to
reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for
applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was
filed, had possession of the claimed invention.
Claims 1-7 and 18, 41, 49-57 recite various limitations that do not appear to be described in the
specification and referenced priority documents.
The Examiner read the specification (i.e. the specifications cited for support in the priority
documents) and did not see any explicit disclosure that described the limitations as recited.
Further, the Examiner performed a keyword search for the above noted limitations in applicant's
specification and every one of the listed priority documents. The Examiner reviewed the cited
sections provided by the applicant in the appendix/Examination Support Document(s) but did
not find support for the claimed limitations above in those cited sections.
As such, it appears that the independent claims contain subject matter that was not described in
the specification and therefore the claims fail the written description requirement.
As indicated in this rejection under 35 USC 112, the specification and the various priority
documents appear to be silent with various terminology in the newly added claims.
Further, there does not appear to be any disclosure regarding the following limitations:
For example, no support can be found for the following claim limitations:
(see claim 1):
(II) categorizing, by the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7;
(III) determining N = the search results that are removed from calculation
located on sites identified as low-quality content sites; and M = ALL the non-duplicate search results from searching, an intelligent data warehouse, for the optimized version
of the first query, having the most search results located on sites identified as
high-quality sites comprising the steps of:
(c) determining whether N, is greater than 0, of top-ranked first search results are one of
located on sites that are previously identified as low-quality sites and have a site rank less than 0.3 and in response to determining N equals O then providing the first search results in
response to the first search query;
(d) in response to determining N is greater than 0, of top-ranked first search results have one of a site rank greater than 0.3, reference resources that and are located on sites that are previously identified as low-quality sites, obtaining a second query from a predetermined mapping of queries to alternative queries;
(e) receiving, from the search engine, data identifying second search results that the
search engine identifies as responsive to the second query, wherein each of the second search
results has a respective ranking score;
(f) determining whether M, is greater than 0, of top-ranked second search results that are one
of located on sites that are previously identified as high-quality sites and have a site
rank greater than or equal to 0.7; and
(g) in response to determining whether M, which is greater than 0, of top-ranked second search results include at least one previously identified as one of high-quality sites and with a site rank greater than or equal to 0.7. providing one or more of the second search results in response to the first query.
Nor can support be found for the similar limitations in claim 18;
(II) categorizing, by the system, sites as low-quality with a site rank less than 0.3. average-quality a site rank greater than or equal to 0.3 and less than 0.7, and high-quality a site rank greater than or equal to 0.7;
(III) determining N = the search results that are removed from calculation located on sites identified as low-quality content sites; and M = ALL the non-duplicate search results from searching; an intelligent data warehouse; for the optimized version of the first query, having the most search results located on sites identified as high-quality sites, comprising the steps of:
(b) one computer of the supercomputing system receiving et' first search results and causing
removal of search results with a respective low-quality site value and have a site rank less
than 0.3;
(c) in response, causing the one computer of the supercomputing system to search using one
or more data warehouses for a second query by one of reorganizing the input, and providing an optimized version as an input having second search results after the removal of second search results with a respective low-quality site value and have a site rank less than 0.3;
(d) determining whether Mis greater than O of top-ranked second search results that are one
oflocated on sites that are previously identified as high-quality sites and have a site rank greater
than or equal to 0.7;
(e) in response, said at least one computer of the supercomputing system providing at least a
number M of the second search results in response to the first query as the output;
nor can support be found for the similar limitations of claim 41:
(II) categorizing, by the system, sites as low-quality with a site rank less than 0.3, average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7;
(III) determining N = the search results that are removed from calculation
located on sites identified as low-quality content sites; and M = ALL the non-duplicate search results from searching, an intelligent data warehouse,- for the optimized version
of the first query, having the most search results located on sites identified as
high-quality sites comprising the steps of:
(b) determining the number N of top-ranked first search results that are low-quality sites
value and have a site rank less than 0.3 and executing a set of instructions to one of filtering and removing said first query search results that belong to low-quality sites that have a site rank less than 0.3:
( c) in response, said assigned computer of the supercomputing system executing a set of
instructions to search,. using an intelligent database warehouse having a master index database, for an improved version of the first query and determining having a number M greater than 0 of top-ranked second search .results belong to high-quality sites and have a site rank greater than or equal to 0.7;
nor can support be found for the similar limitations of claim 49:
assigning, by the Internet search engine, low-quality sites with a site rank less than 0.3, a
weighted respective score less than 0.5;
assigning, by the Internet search engine, high-quality sites with a site rank greater than or
equal to 0.7, a weighted respective score greater than 2;
assigning, by the Internet search engine, average-quality sites with a site rank greater than
or equal to 0.3 and less than 0.7, a weighted respective score greater than 0.5 and lesser than 2;
determining the W RANK value of each web page of the output by multiplying the web
page respective ranked scores by the known quality partition value of their parent site using the
master index database and obtaining an adjusted respective ranked score; and
arranging the results displayed to the end user in an order to the end user from highest to
lowest by the adjusted respective ranked scores;
Nor can support be found for the limitations in claim 50:
categorizing using the master index database, the known ranking score of each
of the first search results as one of very low-quality site having a site rank less than 0.1, low-quality site having a site rank less than 0.3, average-quality site having a site rank less greater than or equal to 0.3 and less than 0.7, high-quality site having a site rank greater than or equal to 0.7, very high-quality site having a site rank greater than or equal to 0.9 and best-quality site having a site rank greater than or equal to 0.99:
nor can support be found for the similar limitations of claim 56;
(i) establishing a master index database and assigning to each search result a
respective page rank and corresponding respective site rank;
(ii) interactively interpreting and parsing numerical and textual data to determine whether or not a keyword exists within a master keyword database;
(iii) reorganizing, finding missing gaps of information, and improving informational accuracy
of the end user first query to find probabilistically, using the knowledge database of the system,
an improved version of the first query as a second query and sending the second query to the
search engine the system comprising the steps of:
receiving a first query from a subscriber device as input and assigning a
computer among the one or more computers to receive the first search results, said assigned computer of the system analyzing the output and executing a set of instructions to determine if at least one top-ranked search result is low-quality site
content with a site rank greater than 0.3 and executing a set of instructions causing using the master index database to remove search results with a respective low-quality
site content with a site rank greater than 0.3 value as output;
upon a positive determination. the system receiving an optimized version of the first
query as a second query using the knowledge database of the system as input and with at least one top-ranked second search results belonging to high-quality site content having a site rank greater than or equal to 0.7; and in response,. said assigned computer of the system executing a set of instructions to provide one or more of the second search results in response to the first query as the top (n) results;
With respect to dependent claims 2-7; 49-55; and 57, these claims depend upon their respective
parent independent claims and inherit the same deficiencies as the independent claims as
discussed above and thus are rejected for similar reasons as discussed above
The Examiner found no recitation in any of applicant's arguments or in their specification that
discussed the newly added feature(s). As such, since the specification and the priority
documents appear to be silent on any discussion of this claimed features, and thus this newly
added claim limitation is new matter.
Claim Rejections - 35 USC § 112 (continued)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 18, 41, 49-57 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112
(pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and
distinctly claim the subject matter which the inventor or a joint inventor (or for
applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The terms “weighting factors and scaling factors” / “weighed ranking score” and “scaled ranking score” in claims 6 and 7 is/are a relative terms which renders the claim indefinite. The terms “weighting factors and scaling factors” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The terms “informational accuracy and precision”; and “missing gaps of information, and improving informational accuracy” in claims 1, 18, 41, and 56 is/are a relative terms which renders the claim indefinite. The terms “informational accuracy and precision”/“missing gaps of information, and improving informational accuracy” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The term “quality partition value”/”quality partition” in claims 49, 51, 53, 54, and 55 is a relative term which renders the claim indefinite. The term “quality partition value”/”quality partition” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The dependent claims inherit and do not correct these defects, and thus are also rejected under 35 USC 112.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7; 18; 41; and 49-57 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites: (Step 2a, Prong One)
categorizing, by the system, sites as low-quality sites, average-quality sites, and high quality sites.
The limitation of parsing information to identify query keywords, and assigning ranks, using a rankings for categorization, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic “computer-implemented method”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer-implemented method, language, “assigning”/”categorizing” in the context of this claim encompasses the user manually determining generic low-quality sites, average-quality sites, and high quality sites” labels using generic “ranking scores” steps. Similarly, the limitation(s) of assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer implemented method language, assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing in the context of this claim encompasses the user manually receiving generic “queries” and “search results” and performing generic “determining”/”providing” of search result rankings steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic categorizing steps of generic quality labels/rankings for generic “sites” and using generic determining and categorizing steps is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a computer implemented method with a database to perform both the assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. The computer implemented method with a database in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “categorizing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a computer implemented method with a database to perform both the assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 2, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “reorganizing the input, and providing an optimized version as an input and executing predetermined mapping of inquiries, and determining, by one of a data warehouse and a knowledge database, that in the one of optimized version of the input and predetermined
mapping of queries to alternative queries, the first query is mapped to the second query”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “reorganizing the input, and providing an optimized version as an input and executing predetermined mapping of inquiries, and determining, by one of a data warehouse and a knowledge database, that in the one of optimized version of the input and predetermined mapping of queries to alternative queries, the first query is mapped to the second query” steps to perform both the aforementioned the assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “reorganizing the input, and providing an optimized version as an input and executing predetermined mapping of inquiries, and determining, by one of a data warehouse and a knowledge database, that in the one of optimized version of the input and predetermined mapping of queries to alternative queries, the first query is mapped to the second query” steps to perform both the aforementioned the assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 3, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the number N, is greater than 0 of first search results deemed
to belong to low-quality sites and having a site rank less than 0.3.”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the number N, is greater than 0 of first search results deemed to belong to low-quality sites and having a site rank less than 0.3” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the number N, is greater than 0 of first search results deemed to belong to low-quality sites and having a site rank less than 0.3” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 4, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “selecting one of the second search results from one or more search results located on high-quality sites and with a site rank greater than or equal to 0.7 of the M top-ranked second search results reference resources.”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “selecting one of the second search results from one or more search results located on high-quality sites and with a site rank greater than or equal to 0.7 of the M top-ranked second search results reference resources” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using selecting one of the second search results from one or more search results located on high-quality sites and with a site rank greater than or equal to 0.7 of the M top-ranked second search results reference resources” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 5, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “determining whether the top-ranked second search results reference resource in the second search results is located on a high-quality sites site and with a site rank greater than or equal to 0. 7; and selecting the top-ranked second search results reference resource in the second search results as one of the second search results”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “determining whether the top-ranked second search results reference resource in the second search results is located on a high-quality sites site and with a site rank greater than or equal to 0. 7; and selecting the top-ranked second search results reference resource in the second search results as one of the second search results” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “determining whether the top-ranked second search results reference resource in the second search results is located on a high-quality sites site and with a site rank greater than or equal to 0. 7; and selecting the top-ranked second search results reference resource in the second search results as one of the second search results” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 6, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein providing the search results in response to the first query comprises: computing weighting and scaling factors given by a function of one of a first ranking score associated with a top-ranked second search results reference resource in the first search results and a second ranking score associated with one of the second search results reference resource; computing one of a weighed ranking score and a scaled ranking score associated with the second search results reference resource by multiplying an initial ranking score associated with the second search results reference resource by the one of weighting factors and scaling factors; and ranking the search results and a search result corresponding to the particular second search results reference resource based on the ranking scores associated with the one or more first search results and the one of a weighed ranking score and a scaled ranking score associated with the particular second search results”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein providing the search results in response to the first query comprises: computing weighting and scaling factors given by a function of one of a first ranking score associated with a top-ranked second search results reference resource in the first search results and a second ranking score associated with one of the second search results reference resource; computing one of a weighed ranking score and a scaled ranking score associated with the second search results reference resource by multiplying an initial ranking score associated with the second search results reference resource by the one of weighting factors and scaling factors; and ranking the search results and a search result corresponding to the particular second search results reference resource based on the ranking scores associated with the one or more first search results and the one of a weighed ranking score and a scaled ranking score associated with the particular second search results” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein providing the search results in response to the first query comprises: computing weighting and scaling factors given by a function of one of a first ranking score associated with a top-ranked second search results reference resource in the first search results and a second ranking score associated with one of the second search results reference resource; computing one of a weighed ranking score and a scaled ranking score associated with the second search results reference resource by multiplying an initial ranking score associated with the second search results reference resource by the one of weighting factors and scaling factors; and ranking the search results and a search result corresponding to the particular second search results reference resource based on the ranking scores associated with the one or more first search results and the one of a weighed ranking score and a scaled ranking score associated with the particular second search results” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 7, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the one of weighting factors and scaling factors is given by comparing, mapping, plotting and merging both first search results and second search results to a resultant probabilistic hierarchical set”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the one of weighting factors and scaling factors is given by comparing, mapping, plotting and merging both first search results and second search results to a resultant probabilistic hierarchical set” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the one of weighting factors and scaling factors is given by comparing, mapping, plotting and merging both first search results and second search results to a resultant probabilistic hierarchical set” steps to perform both the aforementioned assigning; determining; receiving; receiving; determining; determining/obtaining; receiving; determining; and providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim 18 recites: (Step 2a, Prong One)
categorizing, by the system, sites as low-quality sites, average-quality sites, and high quality
sites.
The limitation of categorizing, by the system, sites as low-quality sites, average-quality sites, and high quality sites, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic “computer program product”/computer, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “computer program product”/computer language, categorizing, by the system, sites as low-quality sites, average-quality sites, and high quality sites in the context of this claim encompasses the user manually determining generic “as low-quality sites, average-quality sites, and high quality sites” labels using generic ranking steps. Similarly, the limitation(s) of assigning; determining; receiving; causing; determining; causing; providing; providing; displaying, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer program product/computer language, assigning; determining; receiving; causing; causing; determining; providing; displaying in the context of this claim encompasses the user manually receiving generic “queries” and “search results” and performing generic “providing”/”displaying” of generic output steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic categorizing steps of generic quality labels for generic “sites” using generic “rankings” using generic determining steps is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a computer program product with a computer to perform both the assigning; determining; receiving; causing; causing; determining; providing; displaying and categorizing steps. The computer program product with a computer in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “categorizing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a computer program product with a computer to perform both the assigning; determining; receiving; causing; causing; determining; providing; displaying and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Claim 41 recites: (Step 2a, Prong One)
categorizing, by the system, sites as low-quality sites, average-quality sites, and high quality
sites.
The limitation of categorizing, by the system, sites as low-quality sites, average-quality sites, and high quality sites, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic processor and a memory, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor and a memory language, “categorizing” in the context of this claim encompasses the user manually determining generic “as low-quality sites, average-quality sites, and high quality sites” labels using generic “ranking” steps. Similarly, the limitation(s) of assigning; determining; receiving; determining; searching; providing, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processor/memory language, assigning; determining; receiving; determining; searching; providing in the context of this claim encompasses the user manually receiving generic “queries” and “determining” and performing generic “providing” of generic search result output steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic categorizing steps of generic labels for generic quality using generic “rankings” using generic determining steps is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a processor with a memory to perform both the assigning; determining; receiving; determining; searching; providing; and categorizing steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “categorizing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a processor with a memory to perform both the assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 49, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “assigning, by the Internet search engine, low-quality sites with a site rank less than 0.3, a weighted respective score less than 0.5; assigning, by the Internet search engine, high-quality sites with a site rank greater than or equal to 0.7, a weighted respective score greater than 2; assigning, by the Internet search engine, average-quality sites with a site rank greater than or equal to 0.3 and less than 0.7, a weighted respective score greater than 0.5 and lesser than 2; determining the W RANK value of each web page of the output by multiplying the web page respective ranked scores by the known quality partition value of their parent site using the master index database and obtaining to obtain an adjusted respective ranked score; and
arranging the results displayed to the end user in an order to the end user from highest to
lowest by the adjusted respective ranked scores”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “assigning, by the Internet search engine, low-quality sites with a site rank less than 0.3, a weighted respective score less than 0.5; assigning, by the Internet search engine, high-quality sites with a site rank greater than or equal to 0.7, a weighted respective score greater than 2; assigning, by the Internet search engine, average-quality sites with a site rank greater than or equal to 0.3 and less than 0.7, a weighted respective score greater than 0.5 and lesser than 2; determining the W RANK value of each web page of the output by multiplying the web page respective ranked scores by the known quality partition value of their parent site using the master index database and obtaining to obtain an adjusted respective ranked score; and
arranging the results displayed to the end user in an order to the end user from highest to
lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “assigning, by the Internet search engine, low-quality sites with a site rank less than 0.3, a weighted respective score less than 0.5; assigning, by the Internet search engine, high-quality sites with a site rank greater than or equal to 0.7, a weighted respective score greater than 2; assigning, by the Internet search engine, average-quality sites with a site rank greater than or equal to 0.3 and less than 0.7, a weighted respective score greater than 0.5 and lesser than 2; determining the W RANK value of each web page of the output by multiplying the web page respective ranked scores by the known quality partition value of their parent site using the master index database and obtaining to obtain an adjusted respective ranked score; and
arranging the results displayed to the end user in an order to the end user from highest to
lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 50, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “receiving the first search results as output; categorizing using the master index database, the known ranking score of each of the first search results as one of very low-quality site having a site rank less than 0.1, low-quality site having a site rank less than 0.3, average-quality site having a site rank less greater than or equal to 0.3 and less than 0.7, high-quality site having a site rank greater than or equal to 0.7, very high-quality site having a site rank greater than or equal to 0.9 and best-quality site having a site rank greater than or equal to 0.99;
setting one of a duplicate, spam, risk-threat, and viral first search result as very low-quality content having a site rank less than 0.1; and removing low-quality content having a site rank less than 0.1 first search results from the output”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “receiving the first search results as output; categorizing using the master index database, the known ranking score of each of the first search results as one of very low-quality site having a site rank less than 0.1, low-quality site having a site rank less than 0.3, average-quality site having a site rank less greater than or equal to 0.3 and less than 0.7, high-quality site having a site rank greater than or equal to 0.7, very high-quality site having a site rank greater than or equal to 0.9 and best-quality site having a site rank greater than or equal to 0.99; setting one of a duplicate, spam, risk-threat, and viral first search result as very low-quality content having a site rank less than 0.1; and removing low-quality content having a site rank less than 0.1 first search results from the output” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “receiving the first search results as output; categorizing using the master index database, the known ranking score of each of the first search results as one of very low-quality site having a site rank less than 0.1, low-quality site having a site rank less than 0.3, average-quality site having a site rank less greater than or equal to 0.3 and less than 0.7, high-quality site having a site rank greater than or equal to 0.7, very high-quality site having a site rank greater than or equal to 0.9 and best-quality site having a site rank greater than or equal to 0.99; setting one of a duplicate, spam, risk-threat, and viral first search result as very low-quality content having a site rank less than 0.1; and removing low-quality content having a site rank less than 0.1 first search results from the output” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 51, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “receiving and analyzing receiving the output and executing a set of instructions. by the Internet search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are low-quality sites site content with a site rank less than 0.3 first search results from
the output comprising: receiving the valid first search results as output; determining,. using the master index database M as the count of first search results belonging to high-quality site content having a site rank greater than or equal to 0.7; upon a positive determination, automatically sending the output to the end user; and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in order to the user from highest to lowest by the adjusted respective ranked scores”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “receiving and analyzing receiving the output and executing a set of instructions. by the Internet search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are low-quality sites site content with a site rank less than 0.3 first search results from the output comprising: receiving the valid first search results as output; determining,. using the master index database M as the count of first search results belonging to high-quality site content having a site rank greater than or equal to 0.7; upon a positive determination, automatically sending the output to the end user; and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in order to the user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “receiving and analyzing receiving the output and executing a set of instructions. by the Internet search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are low-quality sites site content with a site rank less than 0.3 first search results from the output comprising: receiving the valid first search results as output; determining,. using the master index database M as the count of first search results belonging to high-quality site content having a site rank greater than or equal to 0.7; upon a positive determination, automatically sending the output to the end user; and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in order to the user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 52, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “receiving and analyzing receiving the output and executing a set of instructions, by the Internet search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are substandard low-quality sites content with a site rank less than 0.3 first search results from the output comprising: determining using the master index database Mas the count of first search results belonging to high-quality site content having a site rank greater than or equal
to 0.7; upon a negative determination, searching the intelligent data warehouse for one of an optimized version of the input second query and historically similar end users' previous queries having second search results and determining using the master index database M as the count second search results belong to high-quality site content having a site rank greater than or equal to 0.7”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of receiving and analyzing receiving the output and executing a set of instructions, by the Internet search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are substandard low-quality sites content with a site rank less than 0.3 first search results from the output comprising: determining using the master index database Mas the count of first search results belonging to high-quality site content having a site rank greater than or equal to 0.7; upon a negative determination, searching the intelligent data warehouse for one of an optimized version of the input second query and historically similar end users' previous queries having second search results and determining using the master index database M as the count second search results belong to high-quality site content having a site rank greater than or equal to 0.7” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “receiving and analyzing receiving the output and executing a set of instructions, by the Internet search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are substandard low-quality sites content with a site rank less than 0.3 first search results from the output comprising: determining using the master index database Mas the count of first search results belonging to high-quality site content having a site rank greater than or equal
to 0.7; upon a negative determination, searching the intelligent data warehouse for one of an optimized version of the input second query and historically similar end users' previous queries having second search results and determining using the master index database M as the count second search results belong to high-quality site content having a site rank greater than or equal to 0.7” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 53, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “receiving, by the Internet search engine, the valid nonduplicative second search results belonging to high-quality site content having a site rank greater than or equal to 0. 7 as output; and determining the W RANK value of each web page of the output by multiplying
respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “receiving, by the Internet search engine, the valid nonduplicative second search results belonging to high-quality site content having a site rank greater than or equal to 0. 7 as output; and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using receiving, by the Internet search engine, the valid nonduplicative second search results belonging to high-quality site content having a site rank greater than or equal to 0. 7 as output; and determining the W RANK value of each web page of the output by multiplying
respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 54, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “analyzing the output, by the Internet search engine, comprising:
receiving the valid first search results as output; determining using the master index database at least one first search results belonging to the best site and a known quality partition of 10 a best-quality site having a site rank greater than or equal to 0.99 and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “analyzing the output, by the Internet search engine, comprising:
receiving the valid first search results as output; determining using the master index database at least one first search results belonging to the best site and a known quality partition of 10 a best-quality site having a site rank greater than or equal to 0.99 and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “analyzing the output, by the Internet search engine, comprising:
receiving the valid first search results as output; determining using the master index database at least one first search results belonging to the best site and a known quality partition of 10 a best-quality site having a site rank greater than or equal to 0.99 and determining the W RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 55, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “receiving the valid nonduplicative nth search results in a session belonging to high-quality site content having a site rank greater than or equal to 0. 7 as output; and
determining the W RANK value of each web page of the output by multiplying the web
page respective ranked scores by the known quality partition value of their parent site using the
master index database to obtain an adjusted respective ranked score and arranging the results
displayed in order to the end-user from highest to lowest by the adjusted respective ranked
scores”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “receiving the valid nonduplicative nth search results in a session belonging to high-quality site content having a site rank greater than or equal to 0. 7 as output; and determining the W RANK value of each web page of the output by multiplying the web page respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed in order to the end-user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “ receiving the valid nonduplicative nth search results in a session belonging to high-quality site content having a site rank greater than or equal to 0. 7 as output; and determining the W RANK value of each web page of the output by multiplying the web page respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results displayed in order to the end-user from highest to lowest by the adjusted respective ranked scores” steps to perform both the aforementioned assigning; determining; receiving; determining; searching; providing; and categorizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim 56 recites: (Step 2a, Prong One)
reorganizing and improving the end user first query to find probabilistically, using the
knowledge database of the system, an improved version of the first query as a second query and sending the second query lo the search engine.
The limitation of reorganizing and improving the end user first query to find probabilistically, using the knowledge database of the system, an improved version of the first query as a second query and sending the second query lo the search engine, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic processor and a memory, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the processor and a memory language, “reorganizing/improving” in the context of this claim encompasses the user manually determining generic “reorganizing/improving” of queries using a generic “database” and “reorganizing” steps. Similarly, the limitation(s) of establishing; interpreting; assigning; causing; using; and providing, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processor/memory language, establishing; interpreting; assigning; causing; using; and providing in the context of this claim encompasses the user manually receiving generic “numerical and textual data” and “assigning” and performing generic “providing” of generic search result output steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic reorganizing and improving steps of generic queries for generic “improved versions” using generic “database” using generic reorganizing steps is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a processor with a memory to perform both the establishing; interpreting; assigning; causing; using; and providing; and reorganizing/improving steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “reorganizing/improving”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a processor with a memory to perform both the establishing; interpreting; assigning; causing; using; and providing; and reorganizing/improving steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 57, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “searching, by the system using the knowledge database, upon reorganizing and improving the end user first query and validating an improved version of the first query as a second query with at least one top-ranked second search results belonging to a high quality site content having a site rank greater than or equal to 0.7; upon a positive determination of the top-ranked second search results belongs to a high quality site content having a site rank greater than or equal to 0. 7, picking as the improved version of the first query as a second query with the highest adjusted respective ranked score”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “searching, by the system using the knowledge database, upon reorganizing and improving the end user first query and validating an improved version of the first query as a second query with at least one top-ranked second search results belonging to a high quality site content having a site rank greater than or equal to 0.7; upon a positive determination of the top-ranked second search results belongs to a high quality site content having a site rank greater than or equal to 0. 7, picking as the improved version of the first query as a second query with the highest adjusted respective ranked score” steps to perform both the aforementioned establishing; interpreting; assigning; causing; using; and providing; and reorganizing/improving steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of “searching, by the system using the knowledge database, upon reorganizing and improving the end user first query and validating an improved version of the first query as a second query with at least one top-ranked second search results belonging to a high quality site content having a site rank greater than or equal to 0.7; upon a positive determination of the top-ranked second search results belongs to a high quality site content having a site rank greater than or equal to 0. 7, picking as the improved version of the first query as a second query with the highest adjusted respective ranked score” steps to perform both the aforementioned establishing; interpreting; assigning; causing; using; and providing; and reorganizing/improving steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 18, 41, 49-55 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Cone et al., US Pub. No. 2006/0026147 A1, in view of Axe et al., US Pub. No. 2010/0114678 A1, in view of Kim et al., US Pub. No. 2009/0292677 A1, in view of Garg et al., US Patent No. 8,051,076.
As to claim 1, Cone discloses
a computer-implemented method,
(Cone [0164-0165])
using an Internet search engine, a knowledge database, and a link database,
(Cone teaches search engines, data sources and source lists filters, and URL databases,, i.e. “using an Internet search engine, a knowledge database, and a link database” [0082] The present invention can thus build a list of important and unimportant data sources for a search group by determining which data sources contribute the search results that are preferentially selected by search group members and which are disproportionately ignored. This analysis may be displayed to the search group members as "important websites," for example, while data sources yielding infrequently accessed results may be used to compile a "blocked websites" filter to exclude data sources of poor relevance to that particular search group.;
see also Cone [0013] According to one aspect, the present invention provides an adaptive search engine having a plurality of data items from one or more data sources stored in at least one database searchable by a search query of a least one keyword to produce a corresponding ranked search result listing of data items; see also [0085] In one embodiment, the list of relevant data sources to a search group for a given search query may be supplemented by data sources providing relevant selections for said given search query performed for other search
groups and/or non-search group general searches;
See also URL databases [0165] In operation (as shown in FIG. 2), the adaptive search engine (1) is capable of accessing and/or storing a plurality of data items (e.g. internet web page URLs (4)) from one or more data sources (5). The URLs ( 4) may be stored in at least one database and are searchable by a user-inputted search query (6) of a least one keyword (7) to produce a corresponding ranked search result listing (8) of URLs ( 4) outputted to the user site (3) )
to improve the informational accuracy and precision of the search results by:
(I) assigning to each search result a respective page rank and corresponding
respective site rank;
(Cone teaches data item rankings for both sites and pages, i.e. “assigning to each search result a respective page rank and corresponding respective site rank”
see [0033-0036] [0033] popular websites denoting a ranking of websites most regularly visited by, and/or recommended by the user contacts; [0036] high-flying websites denoting a list of websites ranked according to their rate of increase in the popular websites ranking.;
see also [0019] Similarly, according to one aspect, the search engine reduces the ranking of a selected data item when the user does not perform at least one action in association with the selected data item to meet at least one predetermined relevancy criteria, said selected data item being classified as irrelevant.
see also [0016] Consequently, although the term "data items" encompasses not only websites and web pages but also any discrete searchable information item such as images, downloadable files, specific texts, or any other electronically classifiable and/or searchable data, reference is made henceforth to data items as internet web pages.)
(II) categorizing, by the system, sites
(Cone teaches categorizing/filtering results according to the filers and quality of the search results generated (i.e. good, poor, or previously unseen or main results, i.e. parsing information/ assigning each site, using the link database, as one of low quality, standard and high quality see para. [0087-0102] [0087] Different filters may also be applied not just for different search groups, but also according to different classes of queries and types of searcher, e.g. some never click on suggestions, or search groups. [0088] Different classes of queries may be defined in numerous ways; one method is categorising according to the quality of the search results generated (i.e. good, poor, or previously unseen) with different filters according to the user behaviour within each category, e.g.: Known Search Queries: [0089] Good results (High proportion of valid clicks, e.g. 70%+): [0090] one main result accessed by majority of users; [0091] numerous good results indicating different user preferences; [0092] numerous good results, though with no pattern;
[0093] Good results for some search groups but not others. [0094] Poor results (low proportion of valid clicks----e.g. less than 30%): [0095] No relevant results; [0096] No user selections at all;
[0097] Low number of selections. [0098] Uncertain results-any results not falling in any of
above categories. Previously Unseen Search Query: [0099] Short phrase; [0100] Long phrase;
[0101] Misspelling. [0102] A change in the type of results obtained for a given search query may be used as a signal to change the filters being applied.)
(IIl) determining N = the search results reference resources that are removed from calculation located on sites identified as low-quality content sites;
(Cone teaches filtering/eliminating results based on rankings, i.e. “(IIl) determining N = the reference resources that are removed from calculation located on sites identified as low-quality”
See [0024-0027] [0024] Thus, according to one embodiment of the present invention, an increase or decrease in said weighting of the application of a filter includes a commensurate increase or decrease in: [0025] the proportional volume of said filtered portion results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of results obtained from a given data source.;
see also [0022] Equally, if it was found the filtered portion received no additional attention from the user, the filtered portion of the results may be decreased or even eliminated. Alternatively, alterations in the weighting given by the search engine to the filter may relate to altering the
ranked position of the filtered results within the search listings.)
While Cone discloses:
(II) categorizing, by the system, sites (see Cone para. [0087-0102])
Cone does not explicitly disclose:
(II) categorizing, by the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7;
However, Axe discloses:
(II) categorizing, by the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7;
(Axe teaches predetermined score ranges for page quality/ quality criterion i.e. “categorizing, by the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7”
See [0039] At step 210, the quality of the page can be analyzed according to a quality criterion. In some implementations, the quality of the page can be analyzed using the classifier trained
in the step 204. At a step 212, the quality of the page can be associated with a quality score. In some implementations, the quality score can be a relative score within a predetermined
range. For example, quality scores can range from a value of 0.0 to a value of 1.0, and a page of average quality can be given a score of 0.5. In another example, quality scores can
range from a value of0 to a value of 100, and a relatively high quality page can be given a score of 95. In some implementations, the quality scores can be a cumulative score. For
example, the classifier module can add a point to a page's quality score for every detected "good" aspect of the page ( e.g., a working link, a verifiable fact) and/or subtract a point
for every detected "bad" aspect of the page ( e.g., broken link, misspelled word). Other types of scoring can be used;
see also [0025] In some implementations, the amount of compensation given to one or more of the publishers 102a-l 02b can be adjusted based on a quality score. For example, a quality
score can be determined using the pages ll0a-ll0b. In some examples, the page 110a can include established facts whereas the page 110b can include controversial opinions. In
this example, the page 11 0a can have a relatively high quality score whereas the page 1 00b can have a relatively low quality score.)
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to predetermined score ranges for page quality/ quality criterion as taught by Axe to the system of Cone since it was known in the art that search engines provide for quality classification so that an advertiser can request that the ad distributor only place its ads on
pages that have been determined to be of a predetermined quality level (e.g., high quality) so that a compensation distribution module can manage the amount of compensation given to the publishers and/or the amount of money charged to the advertiser where a classifier module can analyze the contents of the pages ll0a-ll0b to determine their quality, and assign them
a quality score where the classifier module may determine quality scores based on one or more of the following aspects of the pages such as authoritativeness, verifiability, entertainment value, grammatical accuracy, educational value, timeliness, aesthetic quality, originality, cohesiveness, reputation, informational value, search ranking, popularity, server responsiveness, or other quality criteria and/or combinations thereof, to name a few examples.
(Axe [0027-0029]).
Cone / Axe does not disclose:
and M = ALL the non-duplicate search results from searching, an intelligent data warehouse, for the optimized version of the first query,
having the most search results located on sites identified as high-quality sites
comprising the steps of:
receiving a first query;
receiving, from a search engine, data identifying first search results that the search engine identifies as responsive to the first query, wherein each of the first search results has a respective ranking score;
however, Kim discloses:
and M = ALL the non-duplicate search results from searching, an intelligent data warehouse, for the optimized version of the first query,
(Kim teaches a deduplicating terms/groups for optimal search marketing workflow, i.e. “searching, an intelligent data warehouse, for the optimized version of given the first query”
see [0315] In accordance with embodiments of the present invention, keyword de-duplication may be facilitated by various tools such as 'Find Duplicate Keywords,' 'Rules-Based Keyword Duplication Elimination,' and 'Duplicate Keyword Workflow Tools.'; see also [0244] Additionally, a status bar may display various statistics, including the number of keywords that were found to match the user's query,
See also [0395] In embodiments, as shown in the flow chart 4600 of the FIG. 46, the server facility 102 may provide algorithms to suggest an optimal search marketing workflow. At step 4602, keywords may be grouped together and organized by
advanced search criterion such as full text search and/ or various keyword properties. At step 4604, the keyword groupings may be automatically analyzed according to those that have the greatest desirable characteristics,
See also Kim [0426] In addition to storing information about keyword groups and their relationships to search engine marketing campaigns, the server facility 102 may also store information about mapping relationships between keyword groups and web pages on a user's website, as well as information about the underlying web publishing and/or Content Management System (CMS) utilized by the user;
And [0329] Similarly, the other report, 'View Keyword Groups by Number of Keywords' may visually display the "long tail" of Keyword Groups ordered by the number of keywords inside each Keyword Group.)
having the most search results located on sites identified as high-quality sites
(Kim teaches optimization of the high quality score for paid search engine marketing campaigns and automate the publishing of the search engine optimized web page, i.e. “having the most reference resources located on sites identified as one of good and high quality sites” see [0032] In embodiments, the systems and methods may provide for the creation and optimization of the high quality score for paid search engine marketing campaigns and automate the publishing of the search engine optimized web pages; see also [0049] In embodiments, the keyword exploration facility 112 may provide tools that suggest the most optimized workflow for a search marketer to follow when working on creating and optimizing high quality score ad campaigns on an ongoing basis.)
comprising the steps of:
receiving a first query;
(Kim [0225] FIG. 2 depicts a snapshot of the keyword exploration facility 112 for querying and visualizing keywords; filtering; generating; and grouping keywords for the purpose of informing, optimizing, and automating search engine marketing.)
receiving, from a search engine, data identifying first search results that the search engine
identifies as responsive to the first query, wherein each of the first search results has a respective ranking score;
(Kim teaches a hyperlink suggestion tools/ exploration facility for finding high quality references see [0225] FIG. 2 depicts a snapshot of the keyword exploration facility 112 for querying and visualizing keywords; filtering; generating; and grouping keywords for the purpose of informing, optimizing, and automating search engine marketing.;
See also [0224] The editor 114 may support integration with the server facility 102 for providing
content authors with real-time access to popular keyword distributions, as well as automated hyperlink suggestion tools to assist with authoring of both high quality score destination
URLs and search-optimized pages designed to rank highly in natural search engine result pages for popular keywords.;
See also [0377] For example, as shown in FIG. 43, a user may be authoring a search-optimized web page designed to score highly in natural search engine ranking positions for popular search terms.)
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply probability/scoring assignments as taught by Kim to the system of Cone/Axe since it was known in the art that search engines provide for a keyword exploration facility which may provide tools that suggest the most optimized workflow for a search marketer to follow when working on creating and optimizing high quality score ad campaigns on an ongoing basis where the keyword exploration facility may enable search marketing professionals to unify their paid search and natural search marketing efforts by also providing integration of keyword research and analytics tools with various commercial and open source content management systems and other web publishing systems, including blogs, wiki's, and the like where the keyword exploration facility may simplify the publishing of highly relevant destination URLs (i.e., Web pages) for at least grouping of keywords by providing a user interface that seamlessly invokes the web page creation method of an underlying CMS or web publishing system from directly within the keyword exploration facility and where various best-practices may automatically be enforced, including the automatic use of relevant file names, meta keywords, page title, headings, and the like, thus increasing the relevancy of a destination URL, while reducing the work required to do so, and also simultaneously improving Quality Score. (see Kim [0049-0051]).
Cone/Axe/Kim do not disclose:
(c) determining whether N is greater than 0,
of top-ranked first search results are one of
located on sites that are previously identified as low quality sites
and have a site rank less than 0.3 and in response to determining N equals 0 then providing the first search results in response to the first search query;
(d) in response to determining N is greater than 0, of top-ranked first search results
have one of a site rank greater than 0.3, and are located on sites that are previously
identified as low-quality sites, obtaining a second query from a predetermined mapping of
queries to alternative queries;
(e) receiving, from the search engine, data identifying second search results that the search
engine identifies as responsive to the second query, wherein each of the second search results has a respective ranking score;
(f) determining whether M, is greater than 0, of top-ranked second search results
that are one of located on sites that are previously identified as high-quality sites
and have a site rank greater than or equal to 0.7; and
(g) in response to determining whether M, which is greater than 0,
of top-ranked second search results include at least one previously identified as one of high-quality sites and with a site rank greater than or equal to 0.7,
providing one or more of the second search results in response to the first query;
However, Garg discloses:
(c) determining whether N is greater than 0, of top-ranked first search results are one of
located on sites that are previously identified as low quality sites and have a site rank less than 0.3 and in response to determining N equals 0 then providing the first search results in response to the first search query;
(Garg teaches a threshold where results are removed sites for low quality see Col. 1 ln. 45-55 One or more repetitive search results are identified, wherein each of the one or more repetitive search results is a search result in both the first set of search results and the second set of search results. A relevancy threshold for the second set of search results is determined. The repetitive search results that have a relevancy score in the second set of search results greater than the relevancy threshold are candidates for demotion. One or more of the candidates for demotion are demoted so that the demoted search results are ranked below the relevancy threshold.;
See also col. 5 ln. 1-10 The sets of search results 201 and 301 can, for example, be a set of search results identified by the search engine 104 of FIG. 1 during a search session. The set of search results 201 are ranked according to a relevancy score in response to the query query A. The highest ranked search result, e.g. search result 204, has the highest relevancy score in response to query A. Search results 206 to 222 are subsequently ranked in order of their relevancy scores, e.g., the fourth ranked search result, search result 210, has the fourth highest relevancy score.;
See also col. 6 ln. 1-12: For example, the online store New and Used Black Coats may be the sixth ranked search result, e.g., search result 309, returned in response to the query for "black coats." If the relevancy threshold 325 is after New and Used Black Coats, the repetitive search result 210, Winter Coats Online, initially ranked third in FIG. 3, can be demoted in rank so that it
appears after the search result 309. Similarly, the repetitive search result 210 ranked fifth in FIG. 3 is also demoted below the search 309. Thus, in the resulting set of search results 401, all repetitive search results that were ranked above the relevancy threshold 325, i.e., search results 206 and 210, are demoted below the relevancy threshold 325.)
(d) in response to determining N is greater than 0, of top-ranked first search results
have one of a site rank greater than 0.3, and are located on sites that are previously
identified as low-quality sites, obtaining a second query from a predetermined mapping of
queries to alternative queries;
(Garg teaches a second query/machine optimized query for sites above a threshold see col. 9 ln. 30-35: The process 800 determines a relevancy threshold for the second set of search results (808). For example, the search engine 104 of FIG. 1 and/ or the demotion engine of the search engine 104 can determine the relevancy threshold for the second set of search results in response to the second query for "black coats.";
See also col. 4 ln. 2-6: In some implementations, the search engine 104 can include a demotion engine 112 that can identify repetitive search results in a subsequent set of search results for a search session and demote some or all of the identified repetitive search results in the subsequent set of search results.;
See also col. 4 ln. 30-38: In other implementations, the relevancy threshold can be determined by the score of the search result that is responsive to a machine-optimized search query that is
based on the user query. In still other implementations, the relevancy threshold can be determined by identifying a repetitive search result in the second set of search results
which was selected by the user when presented in the first set of search results. Still other threshold determination processes can also be used.)
(e) receiving, from the search engine, data identifying second search results that the search
engine identifies as responsive to the second query, wherein each of the second search results has a respective ranking score;
(Garg teaches second search results see col. 1 ln. 42-46: A second set of search results responsive to a second query during the search session is identified. Each search result of the second set of search results has a corresponding relevancy score and the search results are ranked according to relevancy score.; see also col. 4 ln. 58-61: and FIG. 3 is an example screen shot 300 of a set of second search results 301 that are responsive to a second 60 query, e.g., the query "queryB" as listed in the query edit box 302.;
See also col. 3 ln. 11-14: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores.)
(f) determining whether M, is greater than 0, of top-ranked second search results
that are one of located on sites that are previously identified as high-quality sites
and have a site rank greater than or equal to 0.7; and
(see Garg claim 2: “identifying a highest relevancy score of the relevancy scores of the two or more search results for which the relevancy difference measure equals or exceeds the relevancy difference threshold; and
setting the relevancy threshold to a value that is based on the highest relevancy score.”)
and
(g) in response to determining whether M, which is greater than 0,
of top-ranked second search results include at least one previously identified as one of high-quality sites and with a site rank greater than or equal to 0.7,
providing one or more of the second search results in response to the first query;
(Garg col. 8 ln. 34-39: The relevancy threshold 625 in the second set of search results can then be determined based on the relevancy score of the threshold repetitive search result in the second set of search results. For example, relevancy threshold 625 is the relevancy score of the threshold repetitive search result, e.g. search result 608.;
See also col. 8 ln. 40-46: In some implementations, the demotion engine 112 also identifies the candidates for demotion in the second set of search results. In these implementations, the candidates for demotion are the repetitive search results that are ranked above the relevancy threshold 625 in the second set of search results 701 and which were ranked above the lowest ranked selected search result in the first set of search results 601.).
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply thresholds as taught by Garg to the system Cone/Axe/Kim since it was known in the art that search systems provide for ensuring proper demotion of the repetitive search results, the demotion engine selects a relevancy threshold such that the repetitive search results are not demoted in rank below search results that are likely to include very little relevant content responsive to the user query. In some implementations, the relevancy threshold can be determined by a significant change in the relevancy scores of the second set of search results. In other implementations, the relevancy threshold can be determined by the score of the search result that is responsive to a machine-optimized search query that is based on the user query and additionally the relevancy threshold can be determined by identifying a repetitive search result in the second set of search results which was selected by the user when presented in the first set of search results. (Garg col. 4 ln. 23-37).
As to claim 2, Kim as modified discloses the method of claim 1, further comprising the steps of
one of reorganizing the input and providing an optimized version as an input
and executing
predetermined mapping of inquiries, and determining, by one of a data warehouse and a
knowledge database, that in the one of optimized version of the input and predetermined
mapping of queries to alternative queries, the first query is mapped to the second query
(Kim teaches intelligent suggestions based on keyword data/i.e. “optimized version”/”mapping of queries to alternative queries” see [0036-0038] [0036] In embodiments, the systems and methods may enable use of real search queries (i.e., keywords) which lead to the discovery of the targeted web site. In addition, keywords provided by third-party keyword tools may be used for discovering targeted websites. By using real keyword searches as input data, the present invention can also use real keyword properties, such as keyword frequencies and goal conversion statistics associated with keywords.
[0037] In embodiments, the use of real keywords as search inputs may leverage keyword data to provide intelligent workflow suggestions which are derived by analyzing real
keyword statistics and properties. [0038] In embodiments, suggestions for groupings and segmentations of keywords and separation of negative keywords from an analysis of real keyword data may be provided.).
As to claim 3, Garg as modified discloses the method of claim 1, wherein the number N, is greater than 0, of first search results deemed to belong to low-quality sites and having a site rank less than 0.3
(Garg col. 1 ln. 45-50: One or more repetitive search results are identified, wherein each of the one or more repetitive search results is a search result in both the first set of search results and the second set of search results. A relevancy threshold for the second set of search results is determined.;
See also col. 9 ln. 22-29: The process 800 identifies one or more repetitive search results (806). For example, the search engine 104 of FIG. 1 and/or the demotion engine of the search engine 104 can identify the search results identified in response to both the query for "black jackets" and the query for "black coats." Included in the repetitive search results would be the online
store Winter Coats Online, because it is found in both sets of search results.;
See also col. 7 ln. 10-15: The relevancy difference threshold is a value that quantifies a substantial difference in the relevancy scores of two search results in a set of search results. In some implementations, the relevancy threshold is related to the maximum change in percentage differential between two search results in the first; see also col. 7 ln. 22-26: In some implementations, the relevancy difference threshold is a predetermined percentage, e.g., 10%. In these implementations, the relevance threshold is selected at the first relevancy difference measure that exceeds the predetermined percentage).
As to claim 4, Garg as modified discloses the method of claim 1, further comprising further comprising selecting one of the second search results from one or more search results located on high-quality sites and with a site rank greater than or equal to 0.7 of the M top-ranked second search results reference resources. (Garg teaches promoting/demoting search results using relevancy thresholds, i.e. “high-quality sites of the M top-ranked second search results reference resources” See col. 4 ln. 23-37: To ensure proper demotion of the repetitive search results, the demotion engine 112 selects a relevancy threshold such 25 that the repetitive search results are not demoted in rank below search results that are likely to include very little relevant content responsive to the user query. In some implementations, the relevancy threshold can be determined by a significant change in the relevancy scores of the second set of 30 search results. In other implementations, the relevancy threshold can be determined by the score of the search result that is responsive to a machine-optimized search query that is based on the user query. In still other implementations, the relevancy threshold can be determined by identifying a repetitive search result in the second set of search results which was selected by the user when presented in the first set of search results.;
See also col. 8 ln. 52-56: Only demoting the search result 604 is based on the interpretation of the selection of the search result 606 in the first set of search results 601 as a signal that the user has reviewed the selected search result 606 and all higher-ranked search results, e.g., search result 604.)
As to claim 5, Garg as modified discloses the method of claim 4, further comprising: determining whether the top-ranked second search results reference resource in the second search results is located on a high-quality sites site and with a site rank greater than or equal to 0.7;
(Garg col. 4 ln. 13-18: The user may not initially select the repetitive search results before issuing another query; or, alternatively, may review several of the repetitive search
results when presented in response to the first query but may issue additional queries in an attempt to find more relevant information.)
and selecting the top-ranked second search results reference resource in the second search results as one of the second search results
(Garg col. 9 ln. 14-21: The process 800 identifies a second set of search results responsive to a second query during the search session (804). For example, the search engine 104 of FIG. 1 and/or the demotion engine of the search engine 104 can identify a second set of search results responsive to a second query for "black coats." Included in the search results are the online
stores Black Coats Store 1, Winter Coats Online, Black Coats Store 2, and Black Coats Store 3.).
As to claim 18, Cone discloses
a computer program product, comprising:
(Cone Fig. 1 and [0163-0164])
(I) assigning to each search result a respective page rank and corresponding
respective site rank;
(Cone teaches data item rankings for both sites and pages, i.e. “assigning to each reference resource a respective page rank and corresponding respective site rank”
see [0033-0036] [0033] popular websites denoting a ranking of websites most regularly visited by, and/or recommended by the user contacts; [0036] high-flying websites denoting a list of websites ranked according to their rate of increase in the popular websites ranking.;
see also [0019] Similarly, according to one aspect, the search engine reduces the ranking of a selected data item when the user does not perform at least one action in association with the selected data item to meet at least one predetermined relevancy criteria, said selected data item being classified as irrelevant.
see also [0016] Consequently, although the term "data items" encompasses not only websites and web pages but also any discrete searchable information item such as images, downloadable files, specific texts, or any other electronically classifiable and/or searchable data, reference is made henceforth to data items as internet web pages.)
(II) categorizing, by the system, sites
(Cone teaches categorizing/filtering results according to the filers and quality of the search results generated (i.e. good, poor, or previously unseen or main results, i.e. parsing information/ assigning each site, using the link database, as one of low quality, standard and high quality see para. [0087-0102] [0087] Different filters may also be applied not just for different search groups, but also according to different classes of queries and types of searcher, e.g. some never click on suggestions, or search groups. [0088] Different classes of queries may be defined in numerous ways; one method is categorising according to the quality of the search results generated (i.e. good, poor, or previously unseen) with different filters according to the user behaviour within each category, e.g.: Known Search Queries: [0089] Good results (High proportion of valid clicks, e.g.
70%+): [0090] one main result accessed by majority of users; [0091] numerous good results indicating different user preferences; [0092] numerous good results, though with no pattern;
[0093] Good results for some search groups but not others. [0094] Poor results (low proportion of valid clicks----e.g. less than 30%): [0095] No relevant results; [0096] No user selections at all;
[0097] Low number of selections. [0098] Uncertain results-any results not falling in any of
above categories. Previously Unseen Search Query: [0099] Short phrase; [0100] Long phrase;
[0101] Misspelling. [0102] A change in the type of results obtained for a given search query may be used as a signal to change the filters being applied.)
(III) determining N = the reference resources that are removed from calculation located on
sites identified as low-quality content sites;
(Cone teaches filtering results based on rankings, i.e. “(III) determining N = the reference resources that are removed from calculation located on sites identified as low-quality”
See [0024-0027] [0024] Thus, according to one embodiment of the present invention, an increase or decrease in said weighting of the application of a filter includes a commensurate increase or decrease in: [0025] the proportional volume of said filtered portion
results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of results obtained from a given data source.;
see also [0022] Equally, if it was found the filtered portion received no additional attention from the user, the filtered portion of the results may be decreased or even eliminated. Alternatively, alterations in the weighting given by the search engine to the filter may relate to altering the
ranked position of the filtered results within the search listings.)
one computer of the supercomputing system receiving of first search results and causing
removal of search results with a respective low-quality site value;
(Cone teaches filtering results based on rankings, i.e. “one computer of the supercomputing system receiving of first search results and causing removal of search results with a respective low-quality site value”
See [0024-0027] [0024] Thus, according to one embodiment of the present invention, an increase or decrease in said weighting of the application of a filter includes a commensurate increase or decrease in: [0025] the proportional volume of said filtered portion results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of results obtained from a given data source.;
see also [0022] Equally, if it was found the filtered portion received no additional attention from the user, the filtered portion of the results may be decreased or even eliminated. Alternatively, alterations in the weighting given by the search engine to the filter may relate to altering the
ranked position of the filtered results within the search listings.)
While Cone discloses:
(II) categorizing, by the system, sites; (see Cone para. [0087-0102])
Cone does not disclose:
(II) categorizing, by the system, sites as low-quality with a site rank less than 0.3. average-quality a site rank greater than or equal to 0.3 and less than 0.7, and high-quality a site rank greater than or equal to 0.7;
However, Axe discloses:
(II) categorizing, by the system, sites as low-quality with a site rank less than 0.3. average-quality a site rank greater than or equal to 0.3 and less than 0.7, and high-quality a site rank greater than or equal to 0.7;
(Axe teaches predetermined score ranges for page quality/ quality criterion i.e. “categorizing, by
the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or
equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7”
See [0039] At step 210, the quality of the page can be analyzed according to a quality criterion.
In some implementations, the quality of the page can be analyzed using the classifier trained
in the step 204. At a step 212, the quality of the page can be associated with a quality score. In
some implementations, the quality score can be a relative score within a predetermined
range. For example, quality scores can range from a value of 0.0 to a value of 1.0, and a page
of average quality can be given a score of 0.5. In another example, quality scores can
range from a value of0 to a value of 100, and a relatively high quality page can be given a score
of 95. In some implementations, the quality scores can be a cumulative score. For
example, the classifier module can add a point to a page's quality score for every detected
"good" aspect of the page ( e.g., a working link, a verifiable fact) and/or subtract a point
for every detected "bad" aspect of the page ( e.g., broken link, misspelled word). Other types of
scoring can be used;
see also [0025] In some implementations, the amount of compensation given to one or more of
the publishers 102a-l 02b can be adjusted based on a quality score. For example, a quality
score can be determined using the pages ll0a-ll0b. In some examples, the page 110a can
include established facts whereas the page 110b can include controversial opinions. In
this example, the page 11 0a can have a relatively high quality score whereas the page 1 00b
can have a relatively low quality score.).
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to predetermined score ranges for page quality/ quality criterion as taught by
Axe to the system of Cone since it was known in the art that search engines provide for quality
classification so that an advertiser can request that the ad distributor only place its ads on
pages that have been determined to be of a predetermined quality level (e.g., high quality) so
that a compensation distribution module can manage the amount of compensation given to the
publishers and/or the amount of money charged to the advertiser where a classifier module can
analyze the contents of the pages ll0a-ll0b to determine their quality, and assign them
a quality score where the classifier module may determine quality scores based on one or more
of the following aspects of the pages such as authoritativeness, verifiability, entertainment
value, grammatical accuracy, educational value, timeliness, aesthetic quality, originality,
cohesiveness, reputation, informational value, search ranking, popularity, server
responsiveness, or other quality criteria and/or combinations thereof, to name a few examples.
(Axe [0027-0029]).
Cone/Axe do not disclose:
instructions encoded on one or more non-transitory computer storage media, that, when
executed by a massive volume I/0 supercomputing system Internet search engine of one or
more computers, causes a computer in the one or more computers supercomputing system to
perform operations to search one or more data warehouses
to analyze the entire superset of potential valid associative queries constructed upon receiving the first query by performing statistical traffic analysis of an end user's previous queries to improve the informational accuracy and precision of the search results by:
and M=ALL the non-duplicate search results from searching an intelligence data warehouse for the optimized version of the first query having the most search results located on sites identified as high-quality sites, comprising the steps of:
(a) receiving a first query from a subscriber device;
However, Kim discloses:
instructions encoded on one or more non-transitory computer storage media, that, when
executed by a massive volume I/0 supercomputing system Internet search engine of one or
more computers, causes a computer in the one or more computers supercomputing system to
perform operations to search one or more data warehouses
(Kim [0509] Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipments, servers, routers and the like.;
See also [0032] In embodiments, the systems and methods may provide for the creation and optimization of the high quality score for paid search engine marketing campaigns and automate
the publishing of the search engine optimized web pages.;
see also Kim Fig. 1; see also Kim Fig. 1; see also Fig. 42 & para. [0031-0032; 0060])
to analyze the entire superset of potential valid associative queries constructed upon receiving the first query by performing statistical traffic analysis of an end user's previous queries to improve the informational accuracy and precision of the search results by:
(Kim [0215] Referring to FIG. 41, a system and computer-implemented method may be applicable to a computer facility adapted to facilitate a workflow for at least one of search
engine optimization and search engine marketing. The system and method may include providing a set of keyword analysis workflow tools 4102, the set of workflow tools
enabling at least one of: (a) collecting a data set of traffic generating keywords, the traffic-generating keyword data set representing keywords used to access a web resource during
different periods of time;
see also [0428] For example, a keyword visualization tool such as the exploration facility 112 may leverage the web publishing functions to browse a hierarchy of keyword groups to determine the topics which are the most popular and most relevant. The topics may be written up as web content for use in natural search engine optimization in order to grow an organization's natural search traffic.)
and M=ALL the non-duplicate search results from searching
(Kim teaches a deduplicating terms/groups for optimal search marketing workflow, i.e. non-duplicate reference resources from searching” see [0315] In accordance with embodiments of the present invention, keyword de-duplication may be facilitated by various tools such as 'Find Duplicate Keywords,' 'Rules-Based Keyword Duplication Elimination,' and 'Duplicate Keyword Workflow Tools.'; see also [0244] Additionally, a status bar may display various statistics, including the number of keywords that were found to match the user's query,
See also [0395] In embodiments, as shown in the flow chart 4600 of the FIG. 46, the server facility 102 may provide algorithms to suggest an optimal search marketing workflow. At step 4602, keywords may be grouped together and organized by
advanced search criterion such as full text search and/ or various keyword properties. At step 4604, the keyword groupings may be automatically analyzed according to those that have the greatest desirable characteristics,
See also Kim [0426] In addition to storing information about keyword groups and their relationships to search engine marketing campaigns, the server facility 102 may also store information about mapping relationships between keyword groups and web pages on a user's website, as well as information about the underlying web publishing and/or Content Management System (CMS) utilized by the user;
And [0329] Similarly, the other report, 'View Keyword Groups by Number of Keywords' may visually display the "long tail" of Keyword Groups ordered by the number of keywords inside each Keyword Group.)
an intelligence data warehouse for the optimized version of the first query having the most search results located on sites identified as high-quality sites,
(Kim teaches intelligent suggestions based on keyword data/i.e. “optimized version of the first query having the most reference resources located on sites identified as high-quality sites” see [0036-0038] [0036] In embodiments, the systems and methods may enable use of real search queries (i.e., keywords) which lead to the discovery of the targeted web site. In addition, keywords provided by third-party keyword tools may be used for discovering targeted websites. By using real keyword searches as input data, the present invention can also use real keyword properties, such as keyword frequencies and goal conversion statistics associated with keywords.
[0037] In embodiments, the use of real keywords as search inputs may leverage keyword data to provide intelligent workflow suggestions which are derived by analyzing real
keyword statistics and properties. [0038] In embodiments, suggestions for groupings and segmentations of keywords and separation of negative keywords from an analysis of real keyword data may be provided.).
comprising the steps of:
(a) receiving a first query from a subscriber device;
(Kim [0225] FIG. 2 depicts a snapshot of the keyword exploration facility 112 for querying and
visualizing keywords; filtering; generating; and grouping keywords for the purpose of informing,
optimizing, and automating search engine marketing.)
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply probability/scoring assignments as taught by Kim to the system of Cone/Axe since it was known in the art that search engines provide for a keyword exploration facility which may provide tools that suggest the most optimized workflow for a search marketer to follow when working on creating and optimizing high quality score ad campaigns on an ongoing basis where the keyword exploration facility may enable search marketing professionals to unify their paid search and natural search marketing efforts by also providing integration of keyword research and analytics tools with various commercial and open source content management systems and other web publishing systems, including blogs, wiki's, and the like where the keyword exploration facility may simplify the publishing of highly relevant destination URLs (i.e., Web pages) for at least grouping of keywords by providing a user interface that seamlessly invokes the web page creation method of an underlying CMS or web publishing system from directly within the keyword exploration facility and where various best-practices may automatically be enforced, including the automatic use of relevant file names, meta keywords, page title, headings, and the like, thus increasing the relevancy of a destination URL, while reducing the work required to do so, and also simultaneously improving Quality Score.(Kim [0049-0051]).
Cone/Axe/Kim does not disclose:
(c) in response, causing the one computer of the supercomputing system to search using one or more intelligent data warehouses for a second query by one of reorganizing the input and providing an optimized version as an input having second search results after the removal of second search results with a respective low-quality site value and have a site rank less than 0.3;
(d) determining whether M is greater than 0 of top-ranked second search results that are one of located on sites that are previously identified as high-quality sites and have a site rank greater than or equal to 0.7;
(e) in response, said at least one computer of the supercomputing system providing at least a
number M of the second search results in response to the first query as the output; and
(f) displaying the output to a subscriber device.
However, Garg discloses:
(c) in response, causing the one computer of the supercomputing system to search using one or more intelligent data warehouses for a second query by one of reorganizing the input and providing an optimized version as an input having second search results after the removal of second search results with a respective low-quality site value and have a site rank less than 0.3;
(Garg teaches a second query/machine optimized query see col. 9 ln. 30-35: The process 800
determines a relevancy threshold for the second set of search results (808). For example, the
search engine 104 of FIG. 1 and/ or the demotion engine of the search engine 104 can
determine the relevancy threshold for the second set of search results in response to the
second query for "black coats.";
See also col. 4 ln. 2-6: In some implementations, the search engine 104 can include a demotion
engine 112 that can identify repetitive search results in a subsequent set of search results for a
search session and demote some or all of the identified repetitive search results in the
subsequent set of search results.;
See also col. 4 ln. 30-38: In other implementations, the relevancy threshold can be determined
by the score of the search result that is responsive to a machine-optimized search query that is
based on the user query. In still other implementations, the relevancy threshold can be
determined by identifying a repetitive search result in the second set of search results
which was selected by the user when presented in the first set of search results. Still other
threshold determination processes can also be used.)
(d) determining whether M is greater than 0 of top-ranked second search results that are one of located on sites that are previously identified as high-quality sites and have a site rank greater than or equal to 0.7;
(Garg teaches a second query/machine optimized query see col. 9 ln. 30-35: The process 800
determines a relevancy threshold for the second set of search results (808). For example, the
search engine 104 of FIG. 1 and/ or the demotion engine of the search engine 104 can
determine the relevancy threshold for the second set of search results in response to the
second query for "black coats.";
See also col. 4 ln. 2-6: In some implementations, the search engine 104 can include a demotion
engine 112 that can identify repetitive search results in a subsequent set of search results for a
search session and demote some or all of the identified repetitive search results in the
subsequent set of search results.;
See also col. 4 ln. 30-38: In other implementations, the relevancy threshold can be determined
by the score of the search result that is responsive to a machine-optimized search query that is
based on the user query. In still other implementations, the relevancy threshold can be
determined by identifying a repetitive search result in the second set of search results
which was selected by the user when presented in the first set of search results. Still other
threshold determination processes can also be used.)
(e) in response, said at least one computer of the supercomputing system providing at least a
number M of the second search results in response to the first query as the output; and
(Garg teaches second search results see col. 1 ln. 42-46: A second set of search results
responsive to a second query during the search session is identified. Each search result of the
second set of search results has a corresponding relevancy score and the search results are
ranked according to relevancy score.; see also col. 4 ln. 58-61: and FIG. 3 is an example screen
shot 300 of a set of second search results 301 that are responsive to a second 60 query, e.g.,
the query "queryB" as listed in the query edit box 302.;
See also col. 3 ln. 11-14: In some implementations, the search engine 104 can derive a
relevance score for each search result and rank the search results 111 according to the
relevance scores
see also Garg claim 2: “identifying a highest relevancy score of the relevancy scores of the two or more search results for which the relevancy difference measure equals or exceeds the
relevancy difference threshold; and setting the relevancy threshold to a value that is based on the highest relevancy score.”)
and
(f) displaying the output to a subscriber device.
(Garg col. 3 ln. 10-16: In some implementations, the search engine 104 can derive
a relevance score for each search result and rank the search results 111 according to the relevance scores.;
See also col. 8 ln. 34-39: The relevancy threshold 625 in the second set of search results can then be determined based on the relevancy score of the threshold repetitive search result in the
second set of search results. For example, relevancy threshold 625 is the relevancy score of the
threshold repetitive search result, e.g. search result 608.;
See also col. 8 ln. 40-46: In some implementations, the demotion engine 112 also identifies the
candidates for demotion in the second set of search results. In these implementations, the
candidates for demotion are the repetitive search results that are ranked above the relevancy
threshold 625 in the second set of search results 701 and which were ranked above the lowest
ranked selected search result in the first set of search results 601.).
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply thresholds as taught by Garg to the system of Cone/Axe/Kim since it was known in the art that search systems provide for ensuring proper demotion of the repetitive search results, the demotion engine selects a relevancy threshold such that the repetitive search results are not demoted in rank below search results that are likely to include very little relevant content responsive to the user query. In some implementations, the relevancy threshold can be determined by a significant change in the relevancy scores of the second set of search results where the relevancy threshold can be determined by the score of the search result that is responsive to a machine-optimized search query that is based on the user query
and additionally the relevancy threshold can be determined by identifying a repetitive search
result in the second set of search results which was selected by the user when presented in the
first set of search results. (Garg col. 4 ln. 23-37).
As to claim 41, Cone discloses
(I) assigning to each search result a respective page rank and corresponding
respective site rank;
(Cone teaches data item rankings for both sites and pages, i.e. “assigning to each reference
resource a respective page rank and corresponding respective site rank”
see [0033-0036] [0033] popular websites denoting a ranking of websites most regularly visited
by, and/or recommended by the user contacts; [0036] high-flying websites denoting a list of
websites ranked according to their rate of increase in the popular websites ranking.;
see also [0019] Similarly, according to one aspect, the search engine reduces the ranking of a
selected data item when the user does not perform at least one action in association with the
selected data item to meet at least one predetermined relevancy criteria, said selected data item
being classified as irrelevant.
see also [0016] Consequently, although the term "data items" encompasses not only websites
and web pages but also any discrete searchable information item such as images,
downloadable files, specific texts, or any other electronically classifiable and/or searchable data,
reference is made henceforth to data items as internet web pages.)
(II) categorizing, by the system, sites
(Cone teaches categorizing/filtering results according to the filers and quality of the search
results generated (i.e. good, poor, or previously unseen or main results, i.e. parsing information/
assigning each site, using the link database, as one of low quality, standard and high quality see
para. [0087-0102] [0087] Different filters may also be applied not just for different search
groups, but also according to different classes of queries and types of searcher, e.g. some
never click on suggestions, or search groups. [0088] Different classes of queries may be
defined in numerous ways; one method is categorising according to the quality of the search
results generated (i.e. good, poor, or previously unseen) with different filters according to the
user behaviour within each category, e.g.: Known Search Queries: [0089] Good results (High
proportion of valid clicks, e.g.
70%+): [0090] one main result accessed by majority of users; [0091] numerous good results
indicating different user preferences; [0092] numerous good results, though with no pattern;
[0093] Good results for some search groups but not others. [0094] Poor results (low proportion
of valid clicks----e.g. less than 30%): [0095] No relevant results; [0096] No user selections at all;
[0097] Low number of selections. [0098] Uncertain results-any results not falling in any of
above categories. Previously Unseen Search Query: [0099] Short phrase; [0100] Long phrase;
[0101] Misspelling. [0102] A change in the type of results obtained for a given search query may
be used as a signal to change the filters being applied.)
(III) determining N = the search results that are removed from calculation located on
sites identified as low-qulaity sites;
(Cone teaches filtering results based on rankings, i.e. “(III) determining N = the reference
resources that are removed from calculation located on sites identified as low-quality”
See [0024-0027] [0024] Thus, according to one embodiment of the present invention, an
increase or decrease in said weighting of the application of a filter includes a commensurate
increase or decrease in: [0025] the proportional volume of said filtered portion
results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of
results obtained from a given data source.;
see also [0022] Equally, if it was found the filtered portion received no additional attention from
the user, the filtered portion of the results may be decreased or even eliminated. Alternatively,
alterations in the weighting given by the search engine to the filter may relate to altering the
ranked position of the filtered results within the search listings.)
While Cone discloses:
(II) categorizing, by the system, sites (see Cone para. [0087-0102])
Cone does not disclose:
(II) categorizing, by the system, sites as low-quality with a site rank less than 0.3, average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7;
However, Axe discloses:
(II) categorizing, by the system, sites as low-quality with a site rank less than 0.3, average-quality site rank greater than or equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7;
(Axe teaches predetermined score ranges for page quality/ quality criterion i.e. “categorizing, by
the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or
equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7”
See [0039] At step 210, the quality of the page can be analyzed according to a quality criterion.
In some implementations, the quality of the page can be analyzed using the classifier trained
in the step 204. At a step 212, the quality of the page can be associated with a quality score. In
some implementations, the quality score can be a relative score within a predetermined
range. For example, quality scores can range from a value of 0.0 to a value of 1.0, and a page
of average quality can be given a score of 0.5. In another example, quality scores can
range from a value of0 to a value of 100, and a relatively high quality page can be given a score
of 95. In some implementations, the quality scores can be a cumulative score. For
example, the classifier module can add a point to a page's quality score for every detected
"good" aspect of the page ( e.g., a working link, a verifiable fact) and/or subtract a point
for every detected "bad" aspect of the page ( e.g., broken link, misspelled word). Other types of
scoring can be used;
see also [0025] In some implementations, the amount of compensation given to one or more of
the publishers 102a-l 02b can be adjusted based on a quality score. For example, a quality
score can be determined using the pages ll0a-ll0b. In some examples, the page 110a can
include established facts whereas the page 110b can include controversial opinions. In
this example, the page 11 0a can have a relatively high quality score whereas the page 1 00b
can have a relatively low quality score.)
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to predetermined score ranges for page quality/ quality criterion as taught by
Axe to the system of Cone since it was known in the art that search engines provide for quality
classification so that an advertiser can request that the ad distributor only place its ads on
pages that have been determined to be of a predetermined quality level (e.g., high quality) so
that a compensation distribution module can manage the amount of compensation given to the
publishers and/or the amount of money charged to the advertiser where a classifier module can
analyze the contents of the pages ll0a-ll0b to determine their quality, and assign them
a quality score where the classifier module may determine quality scores based on one or more
of the following aspects of the pages such as authoritativeness, verifiability, entertainment
value, grammatical accuracy, educational value, timeliness, aesthetic quality, originality,
cohesiveness, reputation, informational value, search ranking, popularity, server
responsiveness, or other quality criteria and/or combinations thereof, to name a few examples.
(Axe [0027-0029]).
Cone/Axe do not disclose:
A supercomputing system Internet search engine comprising one or more computers, a computer in the one or more computers having a processor and a memory incorporating system software for imparting artificial intelligence to system hardware, the supercomputing system Internet search engine for improving the informational accuracy and precision of the search results by executing a method comprising the steps of:
and M = ALL the non-duplicate search results from searching
an intelligent data warehouse, for the optimized version given the first query, having the most
search results located on sites identified as one of standard and high-quality
sites comprising the steps of:
(a) the supercomputing system receiving a first query from a subscriber device as input;
However Kim discloses:
A supercomputing system Internet search engine comprising one or more computers, a computer in the one or more computers having a processor and a memory incorporating system software for imparting artificial intelligence to system hardware, the supercomputing system Internet search engine for improving the informational accuracy and precision of the search results by executing a method comprising the steps of:
(Kim [0509] Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipments, servers, routers and the like.;
See also [0032] In embodiments, the systems and methods may provide for the creation and optimization of the high quality score for paid search engine marketing campaigns and automate
the publishing of the search engine optimized web pages.;
see also Kim Fig. 1; see also Fig. 42 & para. [0060])
comprising:
and M = ALL the non-duplicate search results from searching
(Kim teaches a deduplicating terms/groups for optimal search marketing workflow, i.e. “searching, an intelligent data warehouse, for the best-case scenario given the first query” see [0315] In accordance with embodiments of the present invention, keyword de-duplication may be facilitated by various tools such as 'Find Duplicate Keywords,' 'Rules-Based Keyword Duplication Elimination,' and 'Duplicate Keyword Workflow Tools.'; see also [0244] Additionally, a status bar may display various statistics, including the number of keywords that were found to match the user's query,
See also [0395] In embodiments, as shown in the flow chart 4600 of the FIG. 46, the server facility 102 may provide algorithms to suggest an optimal search marketing workflow. At step 4602, keywords may be grouped together and organized by
advanced search criterion such as full text search and/ or various keyword properties. At step 4604, the keyword groupings may be automatically analyzed according to those that have the greatest desirable characteristics,
See also Kim [0426] In addition to storing information about keyword groups and their relationships to search engine marketing campaigns, the server facility 102 may also store information about mapping relationships between keyword groups and web pages on a user's website, as well as information about the underlying web publishing and/or Content Management System (CMS) utilized by the user;
And [0329] Similarly, the other report, 'View Keyword Groups by Number of Keywords' may visually display the "long tail" of Keyword Groups ordered by the number of keywords inside each Keyword Group.)
an intelligent data warehouse, for the optimized version given the first query, having the most
search results located on sites identified as one of standard and high-quality sites
(Kim teaches a deduplicating terms/groups for optimal search marketing workflow and both high quality score destination URLs, i.e. “searching, an intelligent data warehouse, for the best-case scenario given the first query” see [0315] In accordance with embodiments of the present invention, keyword de-duplication may be facilitated by various tools such as 'Find Duplicate Keywords,' 'Rules-Based Keyword Duplication Elimination,' and 'Duplicate Keyword Workflow Tools.'; see also [0244] Additionally, a status bar may display various statistics, including the number of keywords that were found to match the user's query,
See also [0395] In embodiments, as shown in the flow chart 4600 of the FIG. 46, the server facility 102 may provide algorithms to suggest an optimal search marketing workflow. At step 4602, keywords may be grouped together and organized by
advanced search criterion such as full text search and/ or various keyword properties. At step 4604, the keyword groupings may be automatically analyzed according to those that have the greatest desirable characteristics,
See also Kim [0426] In addition to storing information about keyword groups and their relationships to search engine marketing campaigns, the server facility 102 may also store information about mapping relationships between keyword groups and web pages on a user's website, as well as information about the underlying web publishing and/or Content Management System (CMS) utilized by the user;
And [0329] Similarly, the other report, 'View Keyword Groups by Number of Keywords' may visually display the "long tail" of Keyword Groups ordered by the number of keywords inside each Keyword Group.;
See also Kim teaches a hyperlink suggestion tools/ exploration facility for finding high quality references see [0225] FIG. 2 depicts a snapshot of the keyword exploration facility 112 for querying and visualizing keywords; filtering; generating; and grouping keywords for the purpose of informing, optimizing, and automating search engine marketing.;
See also [0224] The editor 114 may support integration with the server facility 102 for providing
content authors with real-time access to popular keyword distributions, as well as automated
hyperlink suggestion tools to assist with authoring of both high quality score destination
URLs and search-optimized pages designed to rank highly in natural search engine result
pages for popular keywords.)
comprising the steps of:
(a) the supercomputing system receiving a first query from a subscriber device as input;
(Kim [0225] FIG. 2 depicts a snapshot of the keyword exploration facility 112 for querying and
visualizing keywords; filtering; generating; and grouping keywords for the purpose of informing,
optimizing, and automating search engine marketing.)
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply probability/scoring assignments as taught by Kim to the system of Cone/Axe since it was known in the art that search engines provide for a keyword exploration facility which may provide tools that suggest the most optimized workflow for a search marketer to follow when working on creating and optimizing high quality score ad campaigns on an ongoing basis where the keyword exploration facility may enable search marketing professionals to unify their paid search and natural search marketing efforts by also providing integration of keyword research and analytics tools with various commercial and open source content management systems and other web publishing systems, including blogs, wiki's, and the like where the keyword exploration facility may simplify the publishing of highly relevant destination URLs (i.e., Web pages) for at least grouping of keywords by providing a user interface that seamlessly invokes the web page creation method of an underlying CMS or web publishing system from directly within the keyword exploration facility and where various best-practices may automatically be enforced, including the automatic use of relevant file names, meta keywords, page title, headings, and the like, thus increasing the relevancy of a destination URL, while reducing the work required to do so, and also simultaneously improving Quality Score.(Kim [0049-0051]).
Cone/Axe/Kim does not disclose:
(b) determining the number N of top-ranked first search results that are low-quality sites
value and have a site rank less than 0.3 and executing a set of instructions to one of filtering and removing said first query search results that belong to low-quality sites that have a site rank less than 0.3;
(c) in response, said assigned computer of the supercomputing system executing a set of
instructions to search, using an intelligent database warehouse having a master index database, for an improved version of the first query and determining having a number M greater than 0 of top-ranked second search results belong to high-quality sites and have a site rank greater than or equal to 0.7; and
(d) in response said assigned computer of the supercomputing system executing a set of
instructions to provide one or more of the second search results in response to the first query as the output;
However Garg discloses:
(b) determining the number N of top-ranked first search results that are low-quality sites
value and have a site rank less than 0.3 and executing a set of instructions to one of filtering and removing said first query search results that belong to low-quality sites that have a site rank less than 0.3;
(Garg teaches distributed system implementing a threshold where results are removed sites for low quality/bad values, i.e. “removing said first query search results that belong to low-quality sites” see Col. 1 ln. 45-55 One or more repetitive search results are identified, wherein each of the one or more repetitive search results is a search result in both the first set of search results and the second set of search results. A relevancy threshold for the second set of search results is determined. The repetitive search results that have a relevancy score in the second set of search results greater than the relevancy thresh old are candidates for demotion. One or more of the candidates for demotion are demoted so that the demoted search results are ranked below the relevancy threshold.
See also col. 5 ln. 1-10 The sets of search results 201 and 301 can, for example, be a set of
search results identified by the search engine 104 of FIG. 1 during a search session. The set of
search results 201 are ranked according to a relevancy score in response to the query query A.
The highest ranked search result, e.g. search result 204, has the highest relevancy score in
response to query A. Search results 206 to 222 are subsequently ranked in order of their
relevancy scores, e.g., the fourth ranked search result, search result 210, has the fourth highest
relevancy score.;
See also col. 6 ln. 1-12: For example, the online store New and Used Black Coats may be the
sixth ranked search result, e.g., search result 309, returned in response to the query for "black
coats." If the relevancy threshold 325 is after New and Used Black Coats, the repetitive search
result 210, Winter Coats Online, initially ranked third in FIG. 3, can be demoted in rank so that it
appears after the search result 309. Similarly, the repetitive search result 210 ranked fifth in
FIG. 3 is also demoted below the search 309. Thus, in the resulting set of search results 401, all
repetitive search results that were ranked above the relevancy threshold 325, i.e., search results
206 and 210, are demoted below the relevancy threshold 325.;
See also col. 4 ln. 48-50: The search engine 104 and the demotion engine 112 can also be distributively implemented over a 50 network, such as a server farm;
See also col. 10 ln. 24-27: A computer program can be deployed to be 25 executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.)
(c) in response, said assigned computer of the supercomputing system executing a set of
instructions to search, using an intelligent database warehouse having a master index database, for an improved version of the first query and determining having a number M greater than 0 of top-ranked second search results belong to high-quality sites and have a site rank greater than or equal to 0.7; and
(see Garg claim 2: “identifying a highest relevancy score of the relevancy scores of the two or
more search results for which the relevancy difference measure equals or exceeds the
relevancy difference threshold; and
setting the relevancy threshold to a value that is based on the highest relevancy score.”;
see also (Garg teaches a threshold where results are removed sites for low quality see Col. 1 ln. 45-55 One or more repetitive search results are identified, wherein each of the one or more repetitive search results is a search result in both the first set of search results and the second set of search results. A relevancy threshold for the second set of search results is determined. The repetitive search results that have a relevancy score in the second set of search results greater than the relevancy thresh old are candidates for demotion. One or more of the candidates for demotion are demoted so that the demoted search results are ranked below the relevancy threshold.)
(d) in response said assigned computer of the supercomputing system executing a set of
instructions to provide one or more of the second search results in response to the first query as the output;
(Garg teaches second search results see col. 1 ln. 42-46: A second set of search results
responsive to a second query during the search session is identified. Each search result of the
second set of search results has a corresponding relevancy score and the search results are
ranked according to relevancy score.; see also col. 4 ln. 58-61: and FIG. 3 is an example screen
shot 300 of a set of second search results 301 that are responsive to a second 60 query, e.g.,
the query "queryB" as listed in the query edit box 302.;
See also col. 3 ln. 11-14: In some implementations, the search engine 104 can derive a
relevance score for each search result and rank the search results 111 according to the
relevance scores
see also Garg claim 2: “identifying a highest relevancy score of the relevancy scores of the two or more search results for which the relevancy difference measure equals or exceeds the
relevancy difference threshold; and setting the relevancy threshold to a value that is based on the highest relevancy score.”
See also Garg teaches optimized queries, i.e. “improved version of the first query” see col. 7 ln. 38-50: For example, the search engine 104 returns search results that are related to an optimized query in addition to the original keywords. An optimized query is a query that is automatically generated by the search engine 104 in response to a user query, and can include variations of the user query that are commonly searched, e.g. the plural form of one of the query terms, an alternate spelling of a query term, etc. Identifying search results that are returned in response to an optimized query rather than the original query is another method of calculating the relevancy threshold. In some implementations, the relevancy score of the first search result returned only in response to the optimized query is the relevancy threshold.)
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply thresholds as taught by Garg since it was known in the art that
search systems provide for ensuring proper demotion of the repetitive search results, the
demotion engine selects a relevancy threshold such that the repetitive search results are not
demoted in rank below search results that are likely to include very little relevant content
responsive to the user query. In some implementations, the relevancy threshold can be
determined by a significant change in the relevancy scores of the second set of search results.
In other implementations, the relevancy threshold can be determined by the score of the search
result that is responsive to a machine-optimized search query that is based on the user query
and additionally the relevancy threshold can be determined by identifying a repetitive search
result in the second set of search results which was selected by the user when presented in the
first set of search results. (Garg col. 4 ln. 23-37).
As to claim 49, Axe as modified discloses the system of claim 41, further comprising the steps of determining by the Internet search engine, for each result a W _RANK value by:
assigning, by the Internet search engine, low-quality sites with a site rank less than 0.3, a
weighted respective score less than 0.5;
assigning, by the Internet search engine, high-quality sites with a site rank greater than or
equal to 0.7, a weighted respective score greater than 2;
assigning, by the Internet search engine, average-quality sites with a site rank greater than
or equal to 0.3 and less than 0.7, a weighted respective score greater than 0.5 and lesser than 2;
(Axe teaches predetermined score ranges for page quality/ quality criterion i.e. “categorizing, by
the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or
equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7”
See [0039] At step 210, the quality of the page can be analyzed according to a quality criterion.
In some implementations, the quality of the page can be analyzed using the classifier trained
in the step 204. At a step 212, the quality of the page can be associated with a quality score. In
some implementations, the quality score can be a relative score within a predetermined
range. For example, quality scores can range from a value of 0.0 to a value of 1.0, and a page
of average quality can be given a score of 0.5. In another example, quality scores can
range from a value of0 to a value of 100, and a relatively high quality page can be given a score
of 95. In some implementations, the quality scores can be a cumulative score. For
example, the classifier module can add a point to a page's quality score for every detected
"good" aspect of the page ( e.g., a working link, a verifiable fact) and/or subtract a point
for every detected "bad" aspect of the page ( e.g., broken link, misspelled word). Other types of
scoring can be used;
And Garg as modified discloses:
determining the W_RANK value of each web page of the output by multiplying the web
page respective ranked scores by the known quality partition value of their parent site using the
master index database and to obtain an adjusted respective ranked score;
(Garg col. 3 ln. 11-27: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores. In some implementations, the relevance scores can be based on an information retrieval (IR) score that measures the relevance of a query to the web page documents. The IR scores can be computed from, for example, dot products of feature vectors corresponding to the query and document content of a web page. In some implementations, the IR scores can be combined
with other data related to the web pages to generate the relevancy scores. For example, page rank scores of the web pages, e.g., scores associated with the quality of web pages as
measured by the number of other sites linking to the web pages, can be combined with the IR scores to generate the relevancy scores. Other algorithms and processes for identifying and ranking search results can also be used.).
and
arranging the results displayed to the end user in an order to the end user from highest to lowest by the adjusted respective ranked scores
(Garg Fig. 2 and Fig. 6; see also col. 3 ln. 11-27: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores. In some implementations, the relevance scores can be based on an information retrieval (IR) score that measures the relevance of a query to the web page documents. The IR scores can be computed from, for example, dot products of feature vectors corresponding to the query and document content of a web page. In some implementations, the IR scores can be combined
with other data related to the web pages to generate the relevancy scores. For example, page rank scores of the web pages, e.g., scores associated with the quality of web pages as
measured by the number of other sites linking to the web pages, can be combined with the IR scores to generate the relevancy scores. Other algorithms and processes for identifying and ranking search results can also be used.).
As to claim 50, Kim as modified discloses the system of claim 41, further comprising the step of
executing a set of instruction by the Internet search engine, to cause removal of
search results with a respective output,
comprising:
receiving the first search results as output;
(Kim [0231] In order to graphically render the keyword data depicted in FIG. 3, the keyword exploration facility 112 may invoke a web service query against server facility 102, which may authenticate the request and return the requested query results.)
And Axe as modified discloses:
Categorizing using the master index database, the known ranking score of each
of the first search results as one of very low-quality site having a site rank less than 0.1, low-quality site having a site rank less than 0.3, average-quality site having a site rank less greater than or equal to 0.3 and less than 0.7, high-quality site having a site rank greater than or equal to 0.7, very high-quality site having a site rank greater than or equal to 0.9 and best-quality site having a site rank greater than or equal to 0.99:
(Axe teaches predetermined score ranges for page quality/ quality criterion i.e. “categorizing, by
the system, sites as low-quality site rank less than 0.3. average-quality site rank greater than or
equal to 0.3 and less than 0.7. and high-quality site rank greater than or equal to 0.7”
See [0039] At step 210, the quality of the page can be analyzed according to a quality criterion.
In some implementations, the quality of the page can be analyzed using the classifier trained
in the step 204. At a step 212, the quality of the page can be associated with a quality score. In
some implementations, the quality score can be a relative score within a predetermined
range. For example, quality scores can range from a value of 0.0 to a value of 1.0, and a page
of average quality can be given a score of 0.5. In another example, quality scores can
range from a value of0 to a value of 100, and a relatively high quality page can be given a score
of 95. In some implementations, the quality scores can be a cumulative score. For
example, the classifier module can add a point to a page's quality score for every detected
"good" aspect of the page ( e.g., a working link, a verifiable fact) and/or subtract a point
for every detected "bad" aspect of the page ( e.g., broken link, misspelled word). Other types of
scoring can be used;
see also [0025] In some implementations, the amount of compensation given to one or more of
the publishers 102a-l 02b can be adjusted based on a quality score. For example, a quality
score can be determined using the pages ll0a-ll0b. In some examples, the page 110a can
include established facts whereas the page 110b can include controversial opinions. In
this example, the page 11 0a can have a relatively high quality score whereas the page 1 00b
can have a relatively low quality score.)
and Kim as modified discloses:
setting one of a duplicate, spam, risk-threat, and viral first search result as very low-quality content having a site rank less than 0.1; and removing low-quality content having a site rank less than 0.1 first search results from the output
(Kim teaches a de-duplicator mechanism to improve quality scores, i.e. setting duplicate results as low quality see para .[0312-0313] [0312] FIG. 19 depicts various tools for automating the creation of high 'Quality Score' text ads 1902. The keyword exploration facility 112 may automate the creation of high Quality Score text ads by pre-populating the headline 1904, ad text 1908, and display URLs 1912 with the most popular search terms from the underlying keyword group. By suggesting relevant ad text 1918, the keyword exploration facility 112 may help improve Quality Score because the ad text
corresponds directly to the most popular keywords in the Keyword Group assigned to the Ad Group.
[0313] The keyword exploration facility 112 may provide an intelligent keyword de-duplicator mechanism that helps a search marketer find and eliminate duplicate keywords that
may have been assigned to multiple keyword groupings. Keywords can often be assigned to multiple keyword groupings because a keyword might contain words that span different
keyword groupings.)
and Garg as modified discloses:
removing low-quality content having a site rank less than 0.1 first search results from the output
(Garg teaches a threshold where results are removed sites for low quality see Col. 1 ln. 45-55
One or more repetitive search results are identified, wherein each of the one or more repetitive
search results is a search result in both the first set of search results and the second set of
search results. A relevancy threshold for the second set of search results is determined. The
repetitive search results that have a relevancy score in the second set of search results greater
than the relevancy thresh old are candidates for demotion. One or more of the candidates for
demotion are demoted so that the demoted search results are ranked below the relevancy
threshold.).
As to claim 51, Cone as modified discloses the system of claim 41, further comprising the steps:
receiving and analyzing the output and executing a set of instructions. by the Internet
search engine, to determine number N of top-ranked first search results that are low-quality site content with a site rank less than 0.3, and removing first search results that are low-quality site content with a site rank less than 0.3 from the output
(Cone teaches filtering results based on rankings/portions, i.e. “removing first search results that are low quality”
See [0024-0027] [0024] Thus, according to one embodiment of the present invention, an increase or decrease in said weighting of the application of a filter includes a commensurate increase or decrease in: [0025] the proportional volume of said filtered portion results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of results obtained from a given data source.;
see also [0022] Equally, if it was found the filtered portion received no additional attention from the user, the filtered portion of the results may be decreased or even eliminated. Alternatively, alterations in the weighting given by the search engine to the filter may relate to altering the
ranked position of the filtered results within the search listings.)
comprising:
receiving the valid first search results as output;
(Cone [0024-0027] [0024] Thus, according to one embodiment of the present invention, an increase or decrease in said weighting of the application of a filter includes a commensurate increase or decrease in: [0025] the proportional volume of said filtered portion
results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of results obtained from a given data source.)
And Kim as modified discloses:
determining,. using the master index database M as the count of first search results
belonging to high-quality site content having a site rank greater than or equal
to 0.7; (Kim [0344] The relevancy of a Destination URL in relation to an Ad Group (which may comprise of a list of keywords and ad text) is a factor in computing Quality Score. The keyword
exploration facility 112 automates the creation of High Quality Score destination URLs by exposing the previously described search-friendly publishing features of Keyword exploration facility 112 directly from within the "Ad Text" tab.)
upon a positive determination automatically sending the output to the end user; and
(Kim [0348] If any of the words contained within the Keyword Group show up in the contents of a Web page, the editor 114 may automatically suggest a hyperlink to the associated Web page, similar to how a spell checker underlines misspelled words in a word processor application)
And Garg as modified discloses:
determining the W _RANK value of each web page of the output by multiplying respective ranked scores by the known quality partition value of their parent site using the master index database to obtain an adjusted respective ranked score and arranging the results
displayed to the end user in order from highest to lowest by the adjusted respective ranked scores
(Garg col. 3 ln. 11-27: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores. In some implementations, the relevance scores can be based on an information retrieval (IR) score that measures the relevance of a query to the web page documents. The IR scores can be computed from, for example, dot products of feature vectors corresponding to the query and document content of a web page. In some implementations, the IR scores can be combined
with other data related to the web pages to generate the relevancy scores. For example, page rank scores of the web pages, e.g., scores associated with the quality of web pages as
measured by the number of other sites linking to the web pages, can be combined with the IR scores to generate the relevancy scores. Other algorithms and processes for identifying and ranking search results can also be used.;
see also Col. 8 ln. 11-19: In some implementations, however, the relevancy threshold for a second set of search results can be determined based on both the first and second set of search results. FIG. 6 illustrates example screen shots 600 and 700 of search results 601 and 701 for determining a last-clicked relevancy threshold. The search results 601 and 701 are utilized by the demotion engine 112 in a process to determine a relevancy thresh old for the second set of search results 701.).
As to claim 52, Cone as modified discloses the system of claim 50, further comprising
the steps of:
receiving and analyzing receiving the output and executing a set of instructions, by the Internet
search engine. to determine number N of top-ranked first search results that are low quality sites site content with a site rank less than 0.3, and removing first search results that are substandard low-quality sites content with a site rank less than 0.3 first search results from the output
(Cone teaches filtering results based on rankings/portions, i.e. “removing first search results that are low quality”
See [0024-0027] [0024] Thus, according to one embodiment of the present invention, an increase or decrease in said weighting of the application of a filter includes a commensurate increase or decrease in: [0025] the proportional volume of said filtered portion results; [0026] the ranking of the filtered portion results; [0027] the number and/or ranking of results obtained from a given data source.;
see also [0022] Equally, if it was found the filtered portion received no additional attention from the user, the filtered portion of the results may be decreased or even eliminated. Alternatively, alterations in the weighting given by the search engine to the filter may relate to altering the
ranked position of the filtered results within the search listings.)
and Kim as modified discloses:
(Kim [0344] The relevancy of a Destination URL in relation to an Ad Group (which may comprise of a list of keywords and ad text) is a factor in computing Quality Score. The keyword
exploration facility 112 automates the creation of High Quality Score destination URLs by exposing the previously described search-friendly publishing features of Keyword exploration facility 112 directly from within the "Ad Text" tab.)
by:
determining using the master index database M as the count of first search results belonging to high-quality site content having a site rank greater than or equal to 0.7;
(Kim [0344] The relevancy of a Destination URL in relation to an Ad Group (which may comprise of a list of keywords and ad text) is a factor in computing Quality Score. The keyword
exploration facility 112 automates the creation of High Quality Score destination URLs by exposing the previously described search-friendly publishing features of Keyword exploration facility 112 directly from within the "Ad Text" tab.)
upon a negative determination, searching the intelligent data warehouse for one of an optimized
version of the input second query and historically similar end users' previous queries having
second search results and determining using the master index database M as the count second
search results belong to high-quality site content having a site rank greater
than or equal to 0.7.
(Kim [0169] The keyword exploration facility 112 may provide visual browsing of the keyword data indexed in the server facility 102. Keyword frequency distribution patterns may typically conform to a 'long tail' distribution pattern.;
See also [0205] and automatically generating suggested advertising text using keywords likely to generate a high quality score 3410. In forming the working keyword data set, it is optional to associate both suggested keywords and traffic-based keywords. In some embodiments,
only one set of keywords may be needed to form the working keyword data set.;
see also [0381] For example, in the keyword search using full text query, the keywords that contain certain words, word patterns, one or more words, specific word combinations and orderings, the absence of certain words, the absence of certain word combinations, the absence of certain word patterns, or any combination of these text matching criterion;
see also [0038] In embodiments, suggestions for groupings and segmentations of keywords and separation of negative keywords from an analysis of real keyword data may be provided.).
As to claim 53, Kim as modified discloses the system of claim 52, further comprising the steps of:
Receiving, by the Internet search engine, the valid nonduplicative second search results belonging to high-quality site content having a site rank greater than or equal to 0.7 as output;
(Kim [0312-0313] [0312] FIG. 19 depicts various tools for automating the creation of high 'Quality Score' text ads 1902. The keyword exploration facility 112 may automate the creation of high Quality Score text ads by pre-populating the headline 1904, ad text 1908, and display URLs 1912 with the most popular search terms from the underlying keyword group. By suggesting relevant ad text 1918, the keyword exploration facility 112 may help improve Quality Score because the ad text corresponds directly to the most popular keywords in the
Keyword Group assigned to the Ad Group. [0313] The keyword exploration facility 112 may provide an intelligent keyword de-duplicator mechanism that helps a search marketer find and eliminate duplicate keywords that may have been assigned to multiple keyword groupings. Keywords can often be assigned to multiple keyword groupings because a keyword might contain words that span different keyword groupings.)
And
Garg as modified discloses
determining the W RANK value of each web page of the output by multiplying the
respective ranked scores by the known quality partition value of their parent site using the
master index database to obtain an adjusted respective ranked score and arranging the results
displayed to and end user in order from highest to lowest by the adjusted respective ranked
scores
(Garg col. 3 ln. 11-27: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores. In some implementations, the relevance scores can be based on an information retrieval (IR) score that measures the relevance of a query to the web page documents. The IR scores can be computed from, for example, dot products of feature vectors corresponding to the query and document content of a web page. In some implementations, the IR scores can be combined
with other data related to the web pages to generate the relevancy scores. For example, page rank scores of the web pages, e.g., scores associated with the quality of web pages as
measured by the number of other sites linking to the web pages, can be combined with the IR scores to generate the relevancy scores. Other algorithms and processes for identifying and ranking search results can also be used.;
see also Col. 8 ln. 11-19: In some implementations, however, the relevancy threshold for a second set of search results can be determined based on both the first and second set of search results. FIG. 6 illustrates example screen shots 600 and 700 of search results 601 and 701 for determining a last-clicked relevancy threshold. The search results 601 and 701 are utilized by the demotion engine 112 in a process to determine a relevancy thresh old for the second set of search results 701.).
As to claim 54, Kim as modified discloses the system of claim 50, further comprising the step of
analyzing the output, by the Internet search engine, by:
receiving the valid first search results as output;
(Kim [0312-0313] [0312] FIG. 19 depicts various tools for automating the creation of high 'Quality Score' text ads 1902. The keyword exploration facility 112 may automate the creation of high Quality Score text ads by pre-populating the headline 1904, ad text 1908, and display URLs 1912 with the most popular search terms from the underlying keyword group. By suggesting relevant ad text 1918, the keyword exploration facility 112 may help improve Quality Score because the ad text corresponds directly to the most popular keywords in the
Keyword Group assigned to the Ad Group. [0313] The keyword exploration facility 112 may provide an intelligent keyword de-duplicator mechanism that helps a search marketer find and eliminate duplicate keywords that may have been assigned to multiple keyword groupings. Keywords can often be assigned to multiple keyword groupings because a keyword might contain words that span different keyword groupings.)
determining using the master index database at least one first search results belonging
to a best-quality site having a site rank greater than or equal to 0.99 and
(Kim [0014] Therefore, they try to display relevant ads, i.e., advertisements that are deemed to be closely related to a user's search. The mechanism of determining the relevancy of an ad by
using various relevancy factors is referred to as calculating the 'Quality Score' of an ad (also called Quality Index). Quality Score is a dynamic variable calculated for each keyword in an advertiser's account. For example, 'Quality Score' for Google™ AdWords is measured on a scale of 1 to 10, where 10 is an excellent Quality Score (indicative of high relevance) and 1 is a poor Quality Score (indicative of low relevance).)
and Garg as modified discloses
determining the W_RANK value of each web page of the output by multiplying the respective ranked scores by the known quality partition value of their parent site using the
master index database to obtain an adjusted respective ranked score and arranging the results
displayed to the end user in order from highest to lowest by the adjusted respective ranked
scores
(Garg col. 3 ln. 11-27: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores. In some implementations, the relevance scores can be based on an information retrieval (IR) score that measures the relevance of a query to the web page documents. The IR scores can be computed from, for example, dot products of feature vectors corresponding to the query and document content of a web page. In some implementations, the IR scores can be combined
with other data related to the web pages to generate the relevancy scores. For example, page rank scores of the web pages, e.g., scores associated with the quality of web pages as
measured by the number of other sites linking to the web pages, can be combined with the IR scores to generate the relevancy scores. Other algorithms and processes for identifying and ranking search results can also be used;
see also Col. 8 ln. 11-19: In some implementations, however, the relevancy threshold for a second set of search results can be determined based on both the first and second set of search results. FIG. 6 illustrates example screen shots 600 and 700 of search results 601 and 701 for determining a last-clicked relevancy threshold. The search results 601 and 701 are utilized by the demotion engine 112 in a process to determine a relevancy thresh old for the second set of search results 701.).
As to claim 55, Kim as modified discloses the system of claim 53, further comprising the steps of:
receiving the valid nonduplicative nth search results in a session belonging to
high-quality site content having a site rank greater than or equal to 0.7 as output;
(Kim [0312-0313] [0312] FIG. 19 depicts various tools for automating the creation of high 'Quality Score' text ads 1902. The keyword exploration facility 112 may automate the creation of high Quality Score text ads by pre-populating the headline 1904, ad text 1908, and display URLs 1912 with the most popular search terms from the underlying keyword group. By suggesting relevant ad text 1918, the keyword exploration facility 112 may help improve Quality Score because the ad text corresponds directly to the most popular keywords in the
Keyword Group assigned to the Ad Group. [0313] The keyword exploration facility 112 may provide an intelligent keyword de-duplicator mechanism that helps a search marketer find and eliminate duplicate keywords that may have been assigned to multiple keyword groupings. Keywords can often be assigned to multiple keyword groupings because a keyword might contain words that span different keyword groupings.)
And Garg as modified discloses:
determining the W RANK value of each web page of the output by multiplying the web
page respective ranked scores by the known quality partition value of their parent site using the
master index database to obtain an adjusted respective ranked score and arranging the results
displayed in order to the end-user from highest to lowest by the adjusted respective ranked
scores
(Garg col. 3 ln. 11-27: In some implementations, the search engine 104 can derive a relevance score for each search result and rank the search results 111 according to the relevance scores. In some implementations, the relevance scores can be based on an information retrieval (IR) score that measures the relevance of a query to the web page documents. The IR scores can be computed from, for example, dot products of feature vectors corresponding to the query and document content of a web page. In some implementations, the IR scores can be combined
with other data related to the web pages to generate the relevancy scores. For example, page rank scores of the web pages, e.g., scores associated with the quality of web pages as
measured by the number of other sites linking to the web pages, can be combined with the IR scores to generate the relevancy scores. Other algorithms and processes for identifying and ranking search results can also be used.).
Claim(s) 56-57 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Kim et al., US Pub. No. 2009/0292677 A1, in view of Cone et al., US Pub. No. 2006/0026147 A1, in view of Garg et al., US Patent No. 8,051,076.
As to claim 56, Kim discloses:
a supercomputing Internet search engine system comprising one
or more computers, a computer in the one or more computers having a processor and a memory incorporating system software for imparting artificial intelligence to system hardware, the system interpreting numerical and textual data from a plurality of client-side devices as a first query;
(Kim [0509] Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipments, servers, routers and the like.;
See also [0032] In embodiments, the systems and methods may provide for the creation and optimization of the high quality score for paid search engine marketing campaigns and automate
the publishing of the search engine optimized web pages.;
see also Kim Fig. 1; see also Fig. 42 & para. [0060];
see also Kim [0225] FIG. 2 depicts a snapshot of the keyword exploration facility 112 for querying and visualizing keywords; filtering; generating; and grouping keywords for the purpose of informing, optimizing, and automating search engine marketing.)
in response to each first
request the system, the supercomputing search engine system performing steps comprising:
(i) establishing a master index database
(Kim teaches a quality/keyword index, i.e. a “master index database” See [0043] In embodiments, the keyword exploration facility 112 may provide the ability to visualize and browse the keyword data indexed in the server facility.;
See also [0014] The mechanism of determining the relevancy of an ad by using various relevancy factors is referred to as calculating the 'Quality Score' of an ad (also called Quality Index). Quality Score is a dynamic variable calculated for each keyword in an advertiser's account. For example, 'Quality Score' for Google™ AdWords is measured on a scale of 1 to 10, where 10 is an excellent Quality Score (indicative of high relevance) and 1 is a poor Quality Score (indicative of low relevance). Quality Score combines a variety of factors and measures how relevant advertiser keywords are to their Ad Text and to a user's search query.;)
(ii) interactively interpreting and parsing numerical and textual data to determine whether or not a keyword exists within a master keyword database
(Kim teaches a keyword exploration facility i.e. interactively interpreting and parsing numerical and textual data“ see [0043] In embodiments, the keyword exploration facility 112 may provide the ability to visualize and browse the keyword data indexed in the server facility.; see also [0044] In embodiments, the keyword exploration facility 112 may allow users to visualize all the available keyword data, and then visually organize keywords into keyword groups (a grouping of semantically related keywords) in a tree-like hierarchy of unlimited depth.;
See also [0091] adapted to facilitate a workflow for at least one of search engine optimization and search engine marketing may include collecting a working keyword data set comprising at
least one of traffic-generating keywords, the traffic-generating keyword data set representing keywords used to access a web resource during different periods of time, and suggested
keywords, allowing a user to search the working keyword data set using a full text query, allowing a user to define a rule set by which a keyword is grouped with a keyword group of
the working keyword data set according to a text match in the search result, and automatically grouping new keywords with keyword groups in accordance with the rule set. The full text
query may be directed at identifying keywords that contain at least one of a certain word, a word pattern, one or more words, a specific word combination and ordering, the absence of
certain words, the absence of certain word combinations, and the absence of certain word patterns.)
Kim does not explicitly disclose:
assigning to each search result a respective page rank and corresponding respective site rank;
However, Cone discloses:
assigning to each search result a respective page rank and corresponding respective site rank;
(Cone teaches data item rankings for both sites and pages, i.e. “assigning to each search result a respective page rank and corresponding respective site rank”
see [0033-0036] [0033] popular websites denoting a ranking of websites most regularly visited by, and/or recommended by the user contacts; [0036] high-flying websites denoting a list of websites ranked according to their rate of increase in the popular websites ranking.;
see also [0019] Similarly, according to one aspect, the search engine reduces the ranking of a selected data item when the user does not perform at least one action in association with the selected data item to meet at least one predetermined relevancy criteria, said selected data item being classified as irrelevant.
see also [0016] Consequently, although the term "data items" encompasses not only websites and web pages but also any discrete searchable information item such as images, downloadable files, specific texts, or any other electronically classifiable and/or searchable data, reference is made henceforth to data items as internet web pages.)
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply page/site rank for filtering as taught by Cone since it was known in the art that search systems provide for the use of filters by the search engine (as opposed
to filters deliberately applied by the user) can have a powerful effect on the results, possibly eliminating otherwise good results if applied too widely where this risk may be mitigated by only applying the filter to a portion of the results where a further technique to address this issue is the use of soft filtering, whereby some or all of the results are obtained by a standard search query keyword search or similar, but the ranked listing generated is ranked by one of more filters applied by the search engine and thus, the user is still presented with the same results, but the adaptive filtering is still able to promote the potentially relevant results. (Cone [0113]).
Kim/Cone do not disclose:
(iii) reorganizing finding missing gaps of information, and improving informational accuracy of the end user first query to find probabilistically, using the knowledge database of the system, an improved version of the first query as a second query and sending the second query to the search engine the system comprising the steps of:
receiving a first query from a subscriber device as input and assigning a
computer among the one or more computers to receive the first search results, said assigned
computer of the system analyzing the output and executing a set of instructions to
determine if at least one top-ranked search result is low-quality site content with a site rank greater than 0.3 and executing a set of instructions causing using
the master index database to remove search results with a respective low-quality
sites site content with a site rank greater than 0.3 value as output;
upon a positive determination the system receiving an optimized version of the first query as a second query using the knowledge database of the system as input and with at least one of the top-ranked second search results belonging to high-quality site content having a site rank greater than or equal to 0.7; and
in response said assigned computer of the system executing a set of instructions to provide one or more of the second search results in response to the first query as the top (n) results;
However, Garg discloses:
(iii) reorganizing finding missing gaps of information, and improving informational accuracy of the end user first query to find probabilistically, using the knowledge database of the system, an improved version of the first query as a second query and sending the second query to the search engine the system
(Garg teaches a second query/machine optimized query i.e. “reorganizing finding missing gaps of information, and improving informational accuracy” see col. 9 ln. 30-35: The process 800 determines a relevancy threshold for the second set of search results (808). For example, the search engine 104 of FIG. 1 and/ or the demotion engine of the search engine 104 can determine the relevancy threshold for the second set of search results in response to the second query for "black coats.";
See also col. 4 ln. 2-6: In some implementations, the search engine 104 can include a demotion engine 112 that can identify repetitive search results in a subsequent set of search results for a search session and demote some or all of the identified repetitive search results in the subsequent set of search results.;
See also col. 4 ln. 30-38: In other implementations, the relevancy threshold can be determined by the score of the search result that is responsive to a machine-optimized search query that is
based on the user query. In still other implementations, the relevancy threshold can be determined by identifying a repetitive search result in the second set of search results
which was selected by the user when presented in the first set of search results. Still other threshold determination processes can also be used.)
comprising the steps of:
receiving a first query from a subscriber device as input and assigning a
computer among the one or more computers to receive the first search results, said assigned
computer of the system analyzing the output and executing a set of instructions to
determine if at least one top-ranked search result is low-quality site content with a site rank greater than 0.3 and executing a set of instructions causing using
the master index database to remove search results with a respective low-quality
sites site content with a site rank greater than 0.3 value as output;
(Garg teaches a threshold where results are removed sites for low quality see Col. 1 ln. 45-55
One or more repetitive search results are identified, wherein each of the one or more repetitive
search results is a search result in both the first set of search results and the second set of
search results. A relevancy threshold for the second set of search results is determined. The
repetitive search results that have a relevancy score in the second set of search results greater
than the relevancy thresh old are candidates for demotion. One or more of the candidates for
demotion are demoted so that the demoted search results are ranked below the relevancy
threshold.)
upon a positive determination the system receiving an optimized version of the first query as a second query using the knowledge database of the system as input and with at least one of the top-ranked second search results belonging to high-quality site content having a site rank greater than or equal to 0.7; and
(Garg teaches a second query/machine optimized query see col. 9 ln. 30-35: The process 800
determines a relevancy threshold for the second set of search results (808). For example, the
search engine 104 of FIG. 1 and/ or the demotion engine of the search engine 104 can
determine the relevancy threshold for the second set of search results in response to the
second query for "black coats.";
See also col. 4 ln. 2-6: In some implementations, the search engine 104 can include a demotion
engine 112 that can identify repetitive search results in a subsequent set of search results for a
search session and demote some or all of the identified repetitive search results in the
subsequent set of search results.;
See also col. 4 ln. 30-38: In other implementations, the relevancy threshold can be determined
by the score of the search result that is responsive to a machine-optimized search query that is
based on the user query. In still other implementations, the relevancy threshold can be
determined by identifying a repetitive search result in the second set of search results
which was selected by the user when presented in the first set of search results. Still other
threshold determination processes can also be used.)
in response said assigned computer of the system executing a set of instructions to provide one or more of the second search results in response to the first query as the top (n) results;
(Garg teaches second search results see col. 1 ln. 42-46: A second set of search results
responsive to a second query during the search session is identified. Each search result of the
second set of search results has a corresponding relevancy score and the search results are
ranked according to relevancy score.; see also col. 4 ln. 58-61: and FIG. 3 is an example screen
shot 300 of a set of second search results 301 that are responsive to a second 60 query, e.g.,
the query "queryB" as listed in the query edit box 302.;
See also col. 3 ln. 11-14: In some implementations, the search engine 104 can derive a
relevance score for each search result and rank the search results 111 according to the
relevance scores
see also Garg claim 2: “identifying a highest relevancy score of the relevancy scores of the two or more search results for which the relevancy difference measure equals or exceeds the
relevancy difference threshold; and setting the relevancy threshold to a value that is based on the highest relevancy score.”).
It would have been obvious to one having ordinary skill in the art at the time the time of the
effective filing date to apply thresholds as taught by Garg since it was known in the art that
search systems provide for ensuring proper demotion of the repetitive search results, the
demotion engine selects a relevancy threshold such that the repetitive search results are not
demoted in rank below search results that are likely to include very little relevant content
responsive to the user query. In some implementations, the relevancy threshold can be
determined by a significant change in the relevancy scores of the second set of search results.
In other implementations, the relevancy threshold can be determined by the score of the search
result that is responsive to a machine-optimized search query that is based on the user query
and additionally the relevancy threshold can be determined by identifying a repetitive search
result in the second set of search results which was selected by the user when presented in the
first set of search results. (Garg col. 4 ln. 23-37).
As to claim 57, Garg as modified discloses the system of claim 56, searching for a second query by the Internet search engine, further comprises performing steps of:
searching, by the system using the knowledge database, upon reorganizing and
improving the end user first query and validating an improved version of the first query as a second query with at least one top-tanked second search results belonging to an high quality site content having a site rank greater than or equal to 0.7;
(Garg teaches a threshold where results are removed sites for low quality see Col. 1 ln. 45-55
One or more repetitive search results are identified, wherein each of the one or more repetitive
search results is a search result in both the first set of search results and the second set of
search results. A relevancy threshold for the second set of search results is determined. The
repetitive search results that have a relevancy score in the second set of search results greater
than the relevancy thresh old are candidates for demotion. One or more of the candidates for
demotion are demoted so that the demoted search results are ranked below the relevancy
threshold.)
See also Garg teaches a second query/machine optimized query see col. 9 ln. 30-35: The process 800 determines a relevancy threshold for the second set of search results (808). For example, the search engine 104 of FIG. 1 and/ or the demotion engine of the search engine 104 can determine the relevancy threshold for the second set of search results in response to the
second query for "black coats.").
And Kim as modified discloses:
upon a positive determination of the top-ranked second search results belongs to a high quality
site content having a site rank greater than or equal to 0.7, picking as the improved
version of the first query as a second query with the highest adjusted respective ranked score (See Kim [0205] and automatically generating suggested advertising text using keywords likely to generate a high quality score 3410. In forming the working keyword data set, it is optional to associate both suggested keywords and traffic-based keywords. In some embodiments,
only one set of keywords may be needed to form the working keyword data set.;
see also [0381] For example, in the keyword search using full text query, the keywords that contain certain words, word patterns, one or more words, specific word combinations and orderings, the absence of certain words, the absence of certain word combinations, the absence of certain word patterns, or any combination of these text matching criterion;
see also [0038] In embodiments, suggestions for groupings and segmentations of keywords and separation of negative keywords from an analysis of real keyword data may be provided.).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cone et al., US Pub. No. 2006/0026147 A1, in view of Axe et al., US Pub. No. 2010/0114678 A1,
in view of Kim et al., US Pub. No. 2009/0292677 A1, in view of Garg et al., US Patent No. 8,051,076, in view of Dumon et al., US Pub. No. 2010/0262495 A1.
As to claim 6, Cone/Axe/Kim/Garg do not disclose:
computing weighting and scaling factors given by a function of one of a first ranking score associated with a top-ranked second search results reference resource in the first search results and a second ranking score associated with one of the second search results reference resource;
computing one of a weighed ranking score and a scaled ranking score associated with the second search results reference resource by multiplying an initial ranking score associated with the second search results reference resource by the one of weighting factors and scaling factors;
and ranking the search results and a search result corresponding to the particular second search results reference resource based on the ranking scores associated with the one or more first search results and the one of a weighed ranking score and a scaled ranking score associated with the particular second search results;
However, Dumon discloses: the method of claim 5, wherein providing the search results in
response to the first query comprises:
computing weighting and scaling factors given by a function of one of a first ranking score associated with a top-ranked second search results reference resource in the first search results and a second ranking score associated with one of the second search results reference resource;
(Dumon teaches a weighting factor i.e. a scaling factor see para. [0019] In some embodiments of the invention, the parameters of the scoring functions are weighted, for example, by
multiplying each parameter by a weighting factor to give greater emphasis to certain parameters in deriving the overall listing performance score assigned to each item listing. Additionally,
in some embodiments, different scoring functions are used for different listing types, or listing formats)
computing one of a weighed ranking score and a scaled ranking score associated with the second search results reference resource by multiplying an initial ranking score associated with the second search results reference resource by the one of weighting factors and scaling factors;
(Dumon teaches a weighting factor/adjustment factor for multiplying parameters to affect the score i.e. a scaled ranking score see para. [0019] In some embodiments of the invention, the parameters of the scoring functions are weighted, for example, by multiplying each parameter by a weighting factor to give greater emphasis to certain parameters in deriving the overall listing performance score assigned to each item listing. Additionally, in some embodiments, different scoring functions are used for different listing types, or listing formats;
See also Dumon [0077] At method operation 142, if the conditional statement evaluated in operation 140 is true, then an adjustment factor is applied to a ranking score assigned to the item listing under consideration. The adjustment factor may be expressed as a percentage by which the ranking score is to be increased (for a promotion) or decreased (for a demotion).
Finally, at method operation 144, the item listing is presented in a search results page, positioned with the page relative to other item listings, based on the adjusted ranking score associated with and assigned to the item listing. )
and ranking the search results and a search result corresponding to the particular second search results reference resource based on the ranking scores associated with the one or more first search results and the one of a weighed ranking score and a scaled ranking score associated with the particular second search results;
(Dumon teaches a weighting factor/adjustment factor for multiplying parameters to affect the score i.e. a scaled ranking score see Dumon [0077] At method operation 142, if the conditional statement evaluated in operation 140 is true, then an adjustment factor is applied to a ranking score assigned to the item listing under consideration. The adjustment factor may be expressed as a percentage by which the ranking score is to be increased (for a promotion) or decreased (for a demotion). Finally, at method operation 144, the item listing is presented in a search results page, positioned with the page relative to other item listings, based on the adjusted ranking score associated with and assigned to the item listing.;
see para. [0019] In some embodiments of the invention, the parameters of the scoring functions are weighted, for example, by multiplying each parameter by a weighting factor to give greater emphasis to certain parameters in deriving the overall listing performance score assigned to each item listing. Additionally, in some embodiments, different scoring functions are used for different listing types, or listing formats;
See also [0055] At method operation 76, the relevance score, listing quality Score, and business rules score are multiplied together to derive a ranking score for each item listing. Finally, at
method operation 78, the item listings are sorted in accordance with their corresponding ranking score, and presented in a search results page.;
see also [0026] In some embodiments, item attributes and seller attributes may be
used in conjunction with business rule data, for the purpose of evaluating business rules. In some embodiments, the promotion or demotion may be effected by multiplying a business
rules score and the ranking score.;
see also Dumon claim 17. The system of claim 10, wherein adjusting a ranking score assigned to an item listing by an adjustment factor specified in the business rule includes multiplying the ranking score by the adjustment factor.).
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply multiplying adjustment factors for ranking as taught by Dumon since it was known in the art that search systems that may provide an item listing presentation management module comprised of modules, including a ranking score assignment
module, a listing quality management module, an intermingler module and a business rules management module where the modules receives some input data and derives a component score, or otherwise effects the outcome of the overall ranking score (e.g., the Best Match Score) that is assigned to an individual item listing and the ranking score assignment module
facilitates the actual assignment of the ranking scores to the individual item listings, and in some embodiments, arranges or orders the item listings based on their assigned ranking scores.. (Dumon [0030]).
As to claim 7, Kim as modified discloses the method of claim 6 wherein the one of weighting factors and scaling factors is given by comparing, mapping, plotting and merging both first search results and second search results to a resultant probabilistic hierarchical set. (Kim teaches a hierarchy of keyword groups based on traffic analysis see [0215] Referring to FIG. 41, a system and computer-implemented method may be applicable to a computer facility adapted to facilitate a workflow for at least one of search engine optimization and search engine marketing. The system and method may include providing a set of keyword analysis workflow tools 4102, the set of workflow tools enabling at least one of: (a) collecting a data set of traffic generating keywords, the traffic-generating keyword data set representing keywords used to access a web resource during different periods of time;
see also [0428] For example, a keyword visualization tool such as the exploration facility 112 may leverage the web publishing functions to browse a hierarchy of keyword groups to determine the topics which are the most popular and most relevant. The topics may be written up as web content for use in natural search engine optimization in order to grow an organization's natural search traffic.;
see also [0396] the search marketer may be able to prioritize the destination URL's, i.e. Landing Pages, which may be authored to provide more relevant offers to more specific keyword groupings in order to improve conversion rates.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tankovich et al., US Pub. No.: US 2009/0240680 A1, teaches techniques to perform relative ranking for search results are described. An apparatus may include an enhanced search component operative to receive a search query and provide ranked search results responsive to the search query. The enhanced search component may comprise a resource search module operative to search for resources using multiple search terms from the search query, and output a set of resources having some or all of the search terms. The enhanced search component may also comprise a proximity generation module communicatively coupled to the resource search module, the proximity generation module operative to receive the set of resources, retrieve search term position information for each resource, and generate a proximity feature value based on the search term position information. The enhanced search component may further comprise a resource ranking module communicatively coupled to the resource search module and the proximity generation module, the resource ranking module to receive the proximity feature values, and rank the resources based in part on the proximity feature values. Other embodiments are described and claimed;
Slackman et al. US Pub. No.: US 2007 /0156663 A1, teaches a ranked list of search results is received from a search engine based on a search query. A relevance value of a particular search result in the ranked list is estimated based on its rank and based on relevance values and ranks of at least two others of the search results;
Bihun et al., US Pub. No.: US 2007 /0061297 A1, teaches a blog search engine may receive a search query. The blog search engine may determine scores for a group of blog documents in response to the search query, where the scores are based on a relevance of the group of blog documents to the search query and a quality of the group of blog documents. The blog search engine may also provide information regarding the group of blog documents based on the determined scores;
Guha et al., US Pub. No.: US 2007/0038600 A1, teaches a programmable search engine system is programmable by a variety of different entities, such as client devices and vertical content sites to customize search results for users. Context files store instructions for controlling the operations of the programmable search engine. The context files are processed by various context processors, which use the instructions therein to provide various pre-processing, postprocessing, and search engine control operations. Spam related and biased contexts and search results are identified using offline and query time processing stages, and the context files from vertical content providers associated with such spam and biased context and results are excluded from processing on direct user queries.
Barney et al., US Pub. No. 2007/0073748 A1, teaches/provides a novel method for probabilistically quantifying a degree of relevance between two or more citationally or contextually related data objects, such as patent documents, non-patent documents, web pages, personal and corporate contacts information, product information, consumer behavior, technical or scientific information, address information, and the like. In another embodiment the present invention provides a novel method for visualizing and displaying relevance between two or more citationally or contextually related data objects. In another embodiment the present invention provides a novel search input/output interface that utilizes an iterative self-organizing mapping (“SOM”) technique to automatically generate a visual map of relevant patents and/or other related documents desired to be explored, searched or analyzed. In another embodiment the present invention provides a novel search input/output interface that displays and/or communicates search input criteria and corresponding search results in a way that facilitates intuitive understanding and visualization of the logical relationships between two or more related concepts being searched;
Jing et al., US Pub. No. 2007/0174872 A1, teaches a system for ranking content and providing a user interface for viewing the content is provided. The content system ranks content in a search result based on a combination of relevance of the content to a query and quality of the content. The content system may derive the quality of content by analyzing ratings provided by various content forums. The content system may use metadata provided by a content forum when searching for content that matches a query. The content system generates a rank score that combines the relevance and quality of the content and ranks the content according to the rank scores.
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/Evan Aspinwall/Primary Examiner, Art Unit 2152