Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AlA first to invent provisions.
DETAILED ACTION
The instant application, as currently filed, 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 14/013,018 (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 02/17/2015.
See MPEP 2159.02.
Arguments and amendments filed on 6/18/2025 have been examined.
The following is the status of the claims:
Claims 1-122 are cancelled.
Claims 123-174 are added.
In this Office Action, Claims 123-174 are currently pending.
This Office Action is Final.
Examiner’s Note:
Claim 123, Line 11 recites:
“each page of the world wide web.”
However, it appears it was Applicant’s intent to rather claim:
“each page of the world wide web;” (i.e. the line ends in a semicolon, not a period.)
Claim 124, Line 10 recites:
“the popularity value of each page of the world wide web.”
However, it appears it was Applicant’s intent to rather claim:
“the popularity value of each page of the world wide web;” (i.e. the line ends in a semicolon, not a period.)
Appropriate correction is required.
Response to Arguments
Applicant's arguments have been fully considered and are not persuasive.
However, as seen from the rejections below, the specification including the claims recite new
matter that was not disclosed in a parent application. A continuation or divisional cannot include
new matter. A continuation-in-part, on the other hand, can include new matter with respect
to the parent application(s).
Furthermore, the Examiner searched the priority documents and relevant specification and
could not find support for the most recent amendments, please again see the detailed rejection
under 35 USC 112 below.
Additionally, as Applicant's arguments with respect to claim(s) 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.
As to Applicant's arguments/remarks (see pages 1-3) with respect to the
support for the independent claims, the Examiner respectfully disagrees.
With respect to the independent claims, as noted in the 35 USC 112 rejections, the cited
sections provide a discussion with no mapping to specification support and also do not appear
to illustrate comparing the claimed limitations to the asserted priority document disclosure, for
example the claimed limitations of:
Claim 123:
analyzing the content of each of said Internet Site nonduplicate web pages based upon a
set of predetermined semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
analyzing the content of each nonduplicate web pages of said Internet plurality of Sites that
are linked together given a corporate identification number, based upon a set of predetermined
semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
assigning a Site rank to each of said Internet plurality of Sites that are linked together given
a corporate identification number on said analysis;
Claim 124:
analyzing the content of each of said Internet web pages using eigenvectors to determine
the popularity value of each page of the world wide web.
assigning a page rank to each of said Internet web pages based on said analysis;
analyzing the content of each nonduplicate web pages of said Internet plurality of Sites that
are linked together given a corporate identification number, using eigenvectors to determine the
popularity value of each Site of the worldwide web;
Claim 125:
analyzing the content of each of said Internet web pages using eigenvectors to determine
the popularity value of each page;
assigning a page rank to each of said Internet web pages based on said analysis;
analyzing the content of each of said Internet Site and Internet plurality of Sites that are
linked together given a corporate identification number nonduplicate web pages using eigenvectors
to determine the popularity value of each Site of the worldwide web;
assigning a Site rank to each of said Internet Site and said Internet plurality of Sites that
are linked based on said analysis;
Claims 126:
superset of potential valid keyword regular expression requests;
keyword database;
link database;
analyzing the content of each of Internet Site nonduplicate web pages using eigenvectors
to determine the popularity and quality of each Site of the worldwide web;
assigning a Site rank and quality value to each of said Internet Site based on said analysis;
determining, by the search engine, a set of search results for each keyword regular
expression request; and
modifying the page rank of each web page of each search result based on the parent Site
quality given the Site rank;
Claim 151:
assigning for each Site a quality value given the site rank and the modified page rank for each page is determined based on the quality value assigned to the parent site of each page comprising:
analyzing the content of each of Internet Site nonduplicate web pages using eigenvectors
to determine the popularity value of each Site of the worldwide web;
determining for each respective Site, through searching the master index database, a
respective count of reference requests;
assigning a Site rank and quality value to each of said Internet Site based on said analysis;
determining, by the search engine, a set of search results for each keyword regular
expression request; and
modifying the page rank of each page of the set of search results based on the parent Site
quality value;
Claim 159:
establishing a real-time master index database with the latest calculated site and page rank,
and for each page using the quality partition of the parent site the weighted page rank comprising:
receiving data from a search engine identifying first search results as responsive to the first
query, where each of the first search results has a respective page rank;
determining, none of the first search results are substandard site content;
upon positive determination, none of the first search results are substandard site content:
providing first search results to the user, for presentation in an order based on the respective
weighted page rank;
upon negative determination, none of the first search results are substandard site content:
removing first search results identified as substandard site content: and
retrieving a second query from the knowledge database, providing pre-processed excellent
standard site search results without needing existing search engine capabilities;
in response to determining pre-processed excellent standard site search results given the
second query, providing said pre-processed excellent standard site search results given the second query in response to the first query;
Claim 160:
establishing a real-time master index database with the latest
calculated site and page rank, and for each page using the quality partition of the parent site the
weighted page rank comprising:
receiving a first query;
receiving data from a search engine identifying first search results as responsive to the first
query, where each of the first search results has a respective page rank;
determining, at least one of the first search results are substandard site content;
upon positive determination:
removing first search results identified as substandard site content; and
retrieving a second query from the knowledge database, providing pre-processed excellent
standard site search results without needing existing search engine capabilities;
in response to determining pre-processed excellent standard site search results given the
second query, providing said pre-processed excellent standard site search results given the second query in response to the first query
Furthermore, the Examiner searched the asserted priority documents and relevant specification
and could not find support for the most recent amendments, please again see the detailed
rejection under 35 USC 112 below.
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 123-174 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.
The dependent claims (claims 127-150, 152-158, and 161-174) are rejected as they do not correct the defect in the parent claims and thus inherit the defect as well.
The disclosure of the prior-filed application(s), see applications listed in the argued/asserted
support/priority from US Patent #7,809,659 from US Patent #7,908,263 (for example); fail(s) to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application, the disclosures failed to teach the techniques for implementing in the claim limitations listed below:
Claim 123:
analyzing the content of each of said Internet Site nonduplicate web pages based upon a
set of predetermined semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
analyzing the content of each nonduplicate web pages of said Internet plurality of Sites that
are linked together given a corporate identification number, based upon a set of predetermined
semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
assigning a Site rank to each of said Internet plurality of Sites that are linked together given
a corporate identification number on said analysis;
Claim 124:
analyzing the content of each of said Internet web pages using eigenvectors to determine
the popularity value of each page of the world wide web.
assigning a page rank to each of said Internet web pages based on said analysis;
analyzing the content of each nonduplicate web pages of said Internet plurality of Sites that
are linked together given a corporate identification number, using eigenvectors to determine the
popularity value of each Site of the worldwide web;
Claim 125:
analyzing the content of each of said Internet web pages using eigenvectors to determine
the popularity value of each page;
assigning a page rank to each of said Internet web pages based on said analysis;
analyzing the content of each of said Internet Site and Internet plurality of Sites that are
linked together given a corporate identification number nonduplicate web pages using eigenvectors
to determine the popularity value of each Site of the worldwide web;
assigning a Site rank to each of said Internet Site and said Internet plurality of Sites that
are linked based on said analysis;
Claims 126:
superset of potential valid keyword regular expression requests;
keyword database;
link database;
analyzing the content of each of Internet Site nonduplicate web pages using eigenvectors
to determine the popularity and quality of each Site of the worldwide web;
assigning a Site rank and quality value to each of said Internet Site based on said analysis;
determining, by the search engine, a set of search results for each keyword regular
expression request; and
modifying the page rank of each web page of each search result based on the parent Site
quality given the Site rank;
Claim 151:
assigning for each Site a quality value given the site rank and the modified page rank for each page is determined based on the quality value assigned to the parent site of each page comprising:
analyzing the content of each of Internet Site nonduplicate web pages using eigenvectors
to determine the popularity value of each Site of the worldwide web;
determining for each respective Site, through searching the master index database, a
respective count of reference requests;
assigning a Site rank and quality value to each of said Internet Site based on said analysis;
determining, by the search engine, a set of search results for each keyword regular
expression request; and
modifying the page rank of each page of the set of search results based on the parent Site
quality value;
Claim 159:
establishing a real-time master index database with the latest calculated site and page rank,
and for each page using the quality partition of the parent site the weighted page rank comprising:
receiving data from a search engine identifying first search results as responsive to the first
query, where each of the first search results has a respective page rank;
determining, none of the first search results are substandard site content;
upon positive determination, none of the first search results are substandard site content:
providing first search results to the user, for presentation in an order based on the respective
weighted page rank;
upon negative determination, none of the first search results are substandard site content:
removing first search results identified as substandard site content: and
retrieving a second query from the knowledge database, providing pre-processed excellent
standard site search results without needing existing search engine capabilities;
in response to determining pre-processed excellent standard site search results given the
second query, providing said pre-processed excellent standard site search results given the second query in response to the first query;
Claim 160:
establishing a real-time master index database with the latest
calculated site and page rank, and for each page using the quality partition of the parent site the
weighted page rank comprising:
receiving a first query;
receiving data from a search engine identifying first search results as responsive to the first
query, where each of the first search results has a respective page rank;
determining, at least one of the first search results are substandard site content;
upon positive determination:
removing first search results identified as substandard site content; and
retrieving a second query from the knowledge database, providing pre-processed excellent
standard site search results without needing existing search engine capabilities;
in response to determining pre-processed excellent standard site search results given the
second query, providing said pre-processed excellent standard site search results given the second query in response to the first query
Furthermore, the Examiner searched the asserted priority documents and relevant specification
and could not find support for the most recent amendments, please again see the detailed
rejection under 35 USC 112 above.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 123, 127 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over
Reitter et al., US Pub. No. 2007/0288514 A1, in view of Yun et al., US Pub. No. 2006/0074910 A1, in view of Adar et al., US Pub. No. 2006/0069663 A1, in view of
Ramer et al. US Pub. No. 2007/0061242 A1.
As to claim 123, Reitter discloses the method comprising the steps of:
defining a searchable environment;
(Reitter [0094] In an example embodiment, rankings will be produced for the following: Searches (keyword+category constraints) and Categories.)
providing a keyword database containing all of the recognized words for a given language;
(Reitter teaches a keyword store, see Fig. 1 item 9 “keyword store indexer”; see also Keyword
Store Builder [0122] This component will periodically update the histograms stored in the
Keyword Store from various data sources. The Keyword Store should be refreshed on a regular
basis.;
See also “The ability to configure a different set of rules for different languages” [0087] The
ability to configure a different set of rules for different languages or groups of sites should be
supported by the system.; see also [0124] The query index should support all languages used
by on platform sites.)
providing a link database of searchable Internet web pages, wherein the searchable Internet
web pages are provided within the searchable environment;
(Reitter teaches a URL-Keyword Cache/ URL Database i.e. “providing a link database of searchable Internet web pages,” [0063] This database contains the information that is generated by the Extractor Service for each URL and returns the data to the Editor Kit Server FE (Front End) when a particular URL is requested again in order to eliminate the latency associated with the fetch-and-extraction process.;
See also URL Database [0064] This component contains queues for unfetched and fetched
URLs as well as associated metadata for those URLs.; see also Pages Fetched [0066] The
fetcher will save content on only single pages identified by the URL in the URL.FETCH BES
event. The fetcher will not identify additional URLs (i.e. links) on the page in order to crawl
deeper into the site.)
Reitter does not disclose:
a method for simulating the entire superset of potential valid keyword regular expression requests constructed during an Internet search and converting the result sets into Environmental Bitmap data to enable efficient and accurate searching without requiring Search Engine supercomputer cluster searching capabilities,
analyzing the content of each of said Internet web pages based upon a set of predetermined
semantic guidelines for said language and using eigenvectors to determine the popularity value of each page of the world wide web;
assigning a page rank to each of said Internet web pages based on said analysis;
analyzing the content of each of said Internet Site nonduplicate web pages based upon a
set of predetermined semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
assigning a Site rank to each of said Internet Site based on said analysis;
analyzing the content of each nonduplicate web pages of said Internet plurality of Sites that
are linked together given a corporate identification number, based upon a set of predetermined
semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
assigning a corporate identification number to corporate organizational hierarchies
consisting of a plurality of Sites that are linked together;
assigning a Site rank to each of said Internet plurality of Sites that are linked together given
a corporate identification number on said analysis;
determining, by the search engine, a set of search results for each keyword regular
expression request;
However, Yun discloses
a method for simulating the entire superset of potential valid keyword regular expression requests constructed during an Internet search and converting the result sets into Environmental Bitmap data to enable efficient and accurate searching without requiring Search Engine supercomputer cluster searching capabilities,
(Yun [0102] The intrinsic rank generator 206 may read one keyword at a time from the
index database 118. The exemplary index database 118 stores a set of records where each record includes the URL identification number and bit fields to indicate the presence
and proximity of a given keyword in the title, the anchor text of the inbound link, text related to the anchor text as described earlier, in the plain text, and/or in the URL of the web page. Another bit field of the record may be set when the URL is the top-level of a given host.)
analyzing the content of each of said Internet web pages based upon a set of predetermined
semantic guidelines for said language and using eigenvectors to determine the popularity value of each page of the world wide web;
(Yun [0112-0113] [0112] The page-weight for the fetched pages may be found by solving the following matrix equation: X-GX
[0113] X is a (N +l)xl colunm matrix representing the page-weights for all N fetched pages plus one page-weight reservoir. (N+l) x(N+l) square matrix G represents the connectivity graph. Off-diagonal elements of G represent a link connectivity between the pages. In exemplary embodiments, diagonal elements of the matrix G are all equal to zero. The solution vector X is an eigenvector of the matrix G with the eigenvalue one. In principle, the solution vector
X may be obtained from solving this matrix equation xactly. In dealing with the World Wide Web, however, the number of total pages N is very large-order of hundreds of millions or even billions-and solving this matrix equation exactly may be impractical in terms of computer memory and CPU time. Thus, an iterative method is employed. Initially, a guess for X in the right-hand-side is made to obtain X in the left-hand-side. In general, the input and
output X will not be same and the input is combined with the output X to prepare new input X and iterate this process until the input and the input and output X become self-consistent
within the preset tolerance.)
assigning a page rank to each of said Internet web pages based on said analysis;
(Yun [0100] FIG. 2 is an exemplary architecture of the Yrank generator 120 of the search engine 100 (FIG. 1) according to one embodiment. The exemplary Yrank generator 120 comprises a page-weight generator 202, an intrinsic rank generator 206, a partial extrinsic rank generator 208, an extrinsic rank generator 210, an analytic rank generator 212, and a Yrank calculator 214.)
analyzing the content of each of said Internet Site nonduplicate web pages based upon a
set of predetermined semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
(Yun [0112-0113] [0112] The page-weight for the fetched pages may be found by solving the following matrix equation: X-GX
[0113] X is a (N +l)xl column matrix representing the page-weights for all N fetched pages plus one page-weight reservoir. (N+l) x(N+l) square matrix G represents the connectivity graph. Off-diagonal elements of G represent a link connectivity between the pages. In exemplary embodiments, diagonal elements of the matrix G are all equal to zero. The solution vector X is an eigenvector of the matrix G with the eigenvalue one. In principle, the solution vector
X may be obtained from solving this matrix equation xactly. In dealing with the World Wide Web, however, the number of total pages N is very large-order of hundreds of millions or even billions-and solving this matrix equation exactly may be impractical in terms of computer memory and CPU time. Thus, an iterative method is employed. Initially, a guess for X in the right-hand-side is made to obtain X in the left-hand-side. In general, the input and
output X will not be same and the input is combined with the output X to prepare new input X and iterate this process until the input and the input and output X become self-consistent
within the preset tolerance.)
assigning a Site rank to each of said Internet Site based on said analysis;
(Yun teaches Yranks/page ranks using site scores and editorial ranks/analytic ranks, i.e.
assigning a Site rank and quality value to each of said Internet Site
see [0037] In some embodiments, the search engine 100 ranks the pages. In exemplary
embodiments, the Yrank generator 120 reads the link structure from the link database 114,
calculates the page-weight, reads the indexed words (e.g., keyword) from the index database
118, and calculates the rank value for each keyword and page pair. The Yrank generator 120
may store the page-weight and the rank values in the index database 118.;
see also [0104] The Yrank calculator 214 reads the editorial rank database 216 and combines
the editorial rank with the analytic rank to get the final Yrank scores. The Yrank calculator 214
also may collect the top-ranked URLs (e.g., top 400 URLs) and store them in the Yrank
database 122 in descending order.;
see also [0025] A site, another concept used by Yrank, comprises a same meaning as a web
site in the Internet and may be extended to include any group of web pages that shares a
parent web page-with that web page included. This new abstraction adds a new layer in a
graph, making two layers, one for sites and another for web pages. The page layer computes an
equilibrium of page-weight distribution among the nodes. The site layer employees a similar
ranking scheme to compute an equilibrium of an endorsement distribution among the sites.
Combined with the concept of page-weight reservoir, this newly introduced site layer may
make it virtually impossible to manipulate Yrank scores for a few targeted pages. Every web
page may belong to a certain site in the Yrank system. Even though a targeted page may
receive many artificially created links from authority (high score) pages, site score for the site
containing the target page may be low, thereby making the target page's score low.;
see also Yrank [0041] In one embodiment, the overall rank of a web page is expressed in the
following form: YR=YR(p,s,q;t) meaning that the rank of a web page is a function of its page (p),
site (s), and a query (q) and topic (t) in question)
analyzing the content of each nonduplicate web pages of said Internet plurality of Sites that
are linked together given a corporate identification number, based upon a set of predetermined
semantic guidelines for said language and using eigenvectors to determine the popularity value of each Site of the worldwide web;
(Yun [0112-0113] [0112] The page-weight for the fetched pages may be found by solving the following matrix equation: X-GX
[0113] X is a (N +l)xl column matrix representing the page-weights for all N fetched pages plus one page-weight reservoir. (N+l) x(N+l) square matrix G represents the connectivity graph. Off-diagonal elements of G represent a link connectivity between the pages. In exemplary embodiments, diagonal elements of the matrix G are all equal to zero. The solution vector X is an eigenvector of the matrix G with the eigenvalue one. In principle, the solution vector
X may be obtained from solving this matrix equation xactly. In dealing with the World Wide Web, however, the number of total pages N is very large-order of hundreds of millions or even billions-and solving this matrix equation exactly may be impractical in terms of computer memory and CPU time. Thus, an iterative method is employed. Initially, a guess for X in the right-hand-side is made to obtain X in the left-hand-side. In general, the input and
output X will not be same and the input is combined with the output X to prepare new input X and iterate this process until the input and the input and output X become self-consistent
within the preset tolerance.)
assigning a corporate identification number to corporate organizational hierarchies
consisting of a plurality of Sites that are linked together;
(Yun [0031] The UMS 108 may assign an identification number to each URL. The UMS 108 may also maintain a database that stores the individual identification numbers and URLs.
In some embodiments, the UMS 108 associates one or more identification numbers to one or more URLs. In one example, the UMS 108 stores the associations within a hash table. In other embodiments, the UMS 108 stores the individual identification numbers, URLs, the URL's anchor text, and/or associations within the web page database 104)
assigning a Site rank to each of said Internet plurality of Sites that are linked together given
a corporate identification number on said analysis;
(Yun teaches Yranks/page ranks using site scores and editorial ranks/analytic ranks, i.e.
assigning a Site rank and quality value to each of said Internet Site
see [0037] In some embodiments, the search engine 100 ranks the pages. In exemplary
embodiments, the Yrank generator 120 reads the link structure from the link database 114,
calculates the page-weight, reads the indexed words (e.g., keyword) from the index database
118, and calculates the rank value for each keyword and page pair. The Yrank generator 120
may store the page-weight and the rank values in the index database 118.;
see also [0104] The Yrank calculator 214 reads the editorial rank database 216 and combines
the editorial rank with the analytic rank to get the final Yrank scores. The Yrank calculator 214
also may collect the top-ranked URLs (e.g., top 400 URLs) and store them in the Yrank
database 122 in descending order.;
see also [0025] A site, another concept used by Yrank, comprises a same meaning as a web
site in the Internet and may be extended to include any group of web pages that shares a
parent web page-with that web page included. This new abstraction adds a new layer in a
graph, making two layers, one for sites and another for web pages. The page layer computes an
equilibrium of page-weight distribution among the nodes. The site layer employees a similar
ranking scheme to compute an equilibrium of an endorsement distribution among the sites.
Combined with the concept of page-weight reservoir, this newly introduced site layer may
make it virtually impossible to manipulate Yrank scores for a few targeted pages. Every web
page may belong to a certain site in the Yrank system. Even though a targeted page may
receive many artificially created links from authority (high score) pages, site score for the site
containing the target page may be low, thereby making the target page's score low.;
see also Yrank [0041] In one embodiment, the overall rank of a web page is expressed in the
following form: YR=YR(p,s,q;t) meaning that the rank of a web page is a function of its page (p),
site (s), and a query (q) and topic (t) in question)
determining, by the search engine, a set of search results for each keyword regular
expression request;
(Yun [0013] The present invention relates to systems and methods of information retrieval within a specific topic. In one embodiment, a search engine and a method produces relevant search results to keyword queries.)
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 determining page ranks and site scores as taught by Yun to the
system of Reitter since it was known in the art that search systems provide that a site,
another concept used by Yrank, comprises a same meaning as a web site in the Internet and
may be extended to include any group of web pages that shares a parent web page-with that
web page included where this abstraction adds a new layer in a graph, making two layers,
one for sites and another for web pages where the page layer computes an equilibrium of page weight distribution among the nodes and the site layer employees a similar ranking scheme to
compute an equilibrium of an endorsement distribution among the sites where combined with
the concept of page-weight reservoir, the site layer may make it virtually impossible to
manipulate Yrank scores for a few targeted pages where every web page may belong to a
certain site in the Yrank system where even though a targeted page may receive many
artificially created links from authority (high score) pages, site score for the site containing the
target page may be low, thereby making the target page's score low. (Yun [0025]).
Reitter/Yun do not disclose:
correlating the content of each of said Internet Site, and identifying duplicate pages;
wherein reassigning each said duplicate pages a page rank of zero;
and identifying duplicate pages; wherein reassigning each said duplicate pages a page rank of zero;
However, Adar discloses:
correlating the content of each of said Internet Site, and identifying duplicate pages;
(Adar teaches duplicate/spamming detection see [0064] The ranking algorithm can include various features to control or compensate for spamming or other means to artificially inflate the score given to a network location. For example, a user could duplicate a website or weblog many times to artificially create popular URL infections. Alternatively, a user could automatically list many fresh links on a website each day in an effort to propagate the links. To address these artificial inflations, the ranking algorithm can filter out information or URLs that are not sufficiently cited (i.e. have not reached a certain popularity).)
wherein reassigning each said duplicate pages a page rank of zero;
(Adar teaches a ranking algorithm for filtering out redundant/duplicate URLs i.e. “wherein reassigning each said duplicate pages a page rank of zero;”
See [0064] The ranking algorithm can include various features to control or compensate for spamming or other means to artificially inflate the score given to a network location. For example, a user could duplicate a website or weblog many times to artificially create popular URL infections. Alternatively, a user could automatically list many fresh links on a website each day in an effort to propagate the links. To address these artificial inflations, the ranking algorithm can filter out information or URLs that are not sufficiently cited (i.e. have not reached a certain popularity).)
and identifying duplicate pages; wherein reassigning each said duplicate pages a page rank of zero;
(Adar teaches a ranking algorithm for filtering out redundant/duplicate URLs i.e. “reassigning each said duplicate pages a page rank of zero”
See [0064] The ranking algorithm can include various features to control or compensate for spamming or other means to artificially inflate the score given to a network location. For example, a user could duplicate a website or weblog many times to artificially create popular URL infections. Alternatively, a user could automatically list many fresh links on a website each day in an effort to propagate the links. To address these artificial inflations, the ranking algorithm can filter out information or URLs that are not sufficiently cited (i.e. have not reached a certain popularity).)
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 duplicate detection in the ranking algorithm as taught by Adar to the
system of Reitter/Yun since it was known in the art that search systems provide that to
address these artificial inflations, the ranking algorithm can filter out information or URLs that are not sufficiently cited (i.e. have not reached a certain popularity) where the ranking algorithm can have a threshold (example, 10 to 20 citations) to counter spamming by duplicate sites where more artificially created duplicates (such as a scheme creating a cluster of webpages) could be used to circumvent the threshold where to counter this scenario, the ranking algorithm
can be programmed to detect a cluster of websites or weblogs that consistently mention similar sets of URLs and give lower scores to those intra-cluster links and where in the case of a chronological inference technique, for example, the ranking algorithm can multiply the rating by a ratio determined by the fraction of leftover URLs cited by a weblog or website. (Adar [0064]).
Reitter/Yun/Adar do not disclose:
correlating the content of each of said Internet plurality of Sites that are linked together
given a corporate identification number,
organizing and distributing the Internet environment into ten concurrent quality partitions,
labeled from zero to ten, with zero representing substandard site information, with five
representing standard site information and ten representing excellent standard site information
based upon existing site ranking capacities;
assigning a related quality partition value within the Internet environment to each Site; and
modifying the page rank of each web page of each search result based on the parent Site
related quality partition value within the Internet environment;
However, Ramer discloses:
correlating the content of each of said Internet plurality of Sites that are linked together
given a corporate identification number,
(Ramer teaches grouping/correlating certain sites along dimensions of commonality and abbreviated, condensed, compressed source/index information based on category/classification, i.e. “correlating the content of each of said Internet plurality of Sites that are linked together given a corporate identification number " see [0084] Other coding techniques may group certain sites along dimensions of commonality, with navigation behavior analyzed using any number of Euclidean or other distance and/or matching techniques.; see also [0096] Other methods that may also be used successfully for statistical clustering of user preference groups include the weighted majority, Bayesian prediction, Pearson product correlation, and factor analysis.
see also para.[0235] The primary source information may be abbreviated, condensed, compressed, or encoded.;
See also [0272] The index of the mobile content may be stored in a unified or distributed
fashion. The index of the mobile content may be replicated, archived, compressed,
decompressed, transmitted, received, interpreted, processed, utilized, or otherwise associated
with any of the elements of the wireless search platform 100. In one example, the index of the
mobile content may represent relevant information that is provided to a user of the mobile
communication facility 102 in response to a query submitted by or on behalf of this user.;
See also [0270] the index production process may automatically generate an
index of the mobile content that is associated with the mobile content profile of the affirmative
result that may, for example and without limitation, represent or be associated with a hash
value, a priority, a relevancy, a market, a categorization, a classification, a rating, a grading, a
ranking, a designation, an assessment, an evaluation,)
organizing and distributing the Internet environment into ten concurrent quality partitions,
labeled from zero to ten, with zero representing substandard site information, with five
representing standard site information and ten representing excellent standard site information
based upon existing site ranking capacities;
(Ramer teaches ordered index production using a 10 point scale, i.e. " organizing and distributing the Internet environment into ten concurrent quality partitions,” see [0270] Based at least in part upon the affirmative result, the index production process may automatically generate an index of the mobile content that is associated with the mobile content profile of the affirmative result that may, for example and without limitation, represent or be associated with a .... a point rating ( such as on a ten -point scale);
see also [0271] A plurality of indexes may be generated. In some embodiments, the indexes
may be ordered based upon the value of the index. In one example, the value is a rank and the
indexes are ordered based upon the rank.).
assigning a related quality partition value within the Internet environment to each Site; and
modifying the page rank of each web page of each search result based on the parent Site
related quality partition value within the Internet environment
(Ramer teaches ordered index production using a 10 point scale, where indexes may be ordered based upon the value of the index. In one example, the value is a rank and
the indexes are ordered based upon the rank i.e. " organizing and distributing the Internet environment into ten concurrent quality partitions,” see [0270] Based at least in part upon the affirmative result, the index production process may automatically generate an index of the mobile content that is associated with the mobile content profile of the affirmative result that may, for example and without limitation, represent or be associated with a .... a point rating ( such as on a ten -point scale);
see also [0271] A plurality of indexes may be generated. In some embodiments, the indexes
may be ordered based upon the value of the index. In one example, the value is a rank and the
indexes are ordered based upon the rank.).
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 index production as taught by Ramer to the system of Reitter/Yun/Adar since it was known in the art that search systems provide that plurality of indexes may be generated where the indexes may be ordered based upon the value of the index where the value is a rank and the indexes are ordered based upon the rank and where
a database or a data facility such as and without limitation any of the database or data facilities associated with the wireless search platform where the index of the mobile content may be stored in a unified or distributed fashion where the index of the mobile content may be replicated, archived, compressed, decompressed, transmitted, received, interpreted, processed, utilized, or otherwise associated with any of the elements of the wireless search platform where in one example, the index of the mobile content may represent relevant information that is provided to a user of the mobile communication facility in response to a query submitted
by or on behalf of this user. (Ramer [0271-0272]).
As to claim 127, Yun as modified discloses: the method of claim 123,
wherein respective page rank refers to initial scores and
(Yun teaches calculating the page-weight for the fetched web pages/ intrinsic rank, i.e. “page rank refers to initial scores” see [0100] FIG. 2 is an exemplary architecture of the Yrank generator 120 of the search engine 100 (FIG. 1) according to one embodiment. The exemplary Yrank generator 120 comprises a page-weight generator 202, an intrinsic rank generator 206, a partial extrinsic rank generator 208, an extrinsic rank generator 210, an analytic rank generator 212, and a Yrank calculator 214.)
respective modified page rank refers to respective second scores.
(Yun teaches calculating extrinsic ranks/Yranks, i.e. modified “second scores” see Fig. 2 see also [0103-0104] [0103] The partial extrinsic rank generator 208 may read several input files including, but not limited to, files from the link database 114, the index database 118, and the pageweight database 204. The partial extrinsic rank generator 208 may also calculate the partial extrinsic rank values for each identical anchor text and URL pair. The partial extrinsic rank generator 208 may write the resulting partial extrinsic rank to the index database 118. In some embodiments, the partial extrinsic rank may be used for extrinsic rank for single and multi-word query. [0104] The exemplary extrinsic rank generator 210 collects the partial extrinsic rank for each keyword and URL pair. In the case of a multi-keyword query, the extrinsic rank generator 210 collects all partial extrinsic ranks for identical anchor text containing the keywords produced by partial extrinsic rank generator 208. In one embodiment, the analytic rank generator 212 combines intrinsic and extrinsic ranks to produce the analytic rank value, for each keyword and URL pair. The Yrank calculator 214 reads the editorial rank database 216 and combines the editorial rank with the analytic rank to get the final Yrank scores. The Yrank calculator 214 also may collect the top-ranked URLs (e.g., top 400 URLs) and store them in the Yrank database 122 in descending order.).
Claims 131, 135, 139, 143, 147 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Reitter et al., US Pub. No. 2007/0288514 A1, in view of Yun et al., US Pub. No. 2006/0074910 A1, in view of Adar et al., US Pub. No. 2006/0069663 A1, in view of
Ramer et al. US Pub. No. 2007/0061242 A1, in view of Panda et al., US Patent No. 8,682,892 B1.
As to claim 131, Yun as modified discloses the method of claim 123, the operations further comprising the steps of:
generating a respective second score for each of the search result;
(Yun teaches calculating extrinsic ranks/Yranks of pages, i.e. “second score for each of the search result” see Fig. 2 see [0104] The exemplary extrinsic rank generator 210 collects the partial extrinsic rank for each keyword and URL pair. In the case of a multi-keyword query, the extrinsic rank generator 210 collects all partial extrinsic ranks for identical anchor text containing the keywords produced by partial extrinsic rank generator 208. In one embodiment, the analytic rank generator 212 combines intrinsic and extrinsic ranks to produce the analytic rank value, for each keyword and URL pair. The Yrank calculator 214 reads the editorial rank database 216 and combines the editorial rank with the analytic rank to get the final Yrank scores. The Yrank calculator 214 also may collect the top-ranked URLs (e.g., top 400 URLs) and store them in the Yrank database 122 in descending order.;
See also [0040] Yrank database 122 and/or the index database 118 and returns the results to the query server 124. The query server 124 then sorts and ranks the results from these different nodes and presents the most relevant results.)
and
obtaining, using the link database, the initial score for each search results and its
corresponding parent site rank;
(Yun teaches a Yrank database for obtaining Yranks and using site-weights, i.e. “obtaining, using the link database, the page rank for each search results and its corresponding parent site rank” see Yrank [0041] In one embodiment, the overall rank of a web page
is expressed in the following form: YR=YR(p,s,q;t) meaning that the rank of a web page is a function of its page (p), site (s), and a query (q) and topic (t) in question.;
See also [0040] Yrank database 122 and/or the index database 118 and returns the results to the query server 124. The query server 124 then sorts and ranks the results from these different nodes and presents the most relevant results;
See also claim 5: “5. The method of claim 3, wherein the page popularity score of the page is a function of a page-weight of the page and a site-weight of a site.”)
Reitter/Yun/Adar/Ramer do not disclose:
and adjusting for each search result initial score by multiplying the initial score by the respective parent quality value given the parent site rank to obtain the respective modified page rank;
however, Panda discloses:
and adjusting for each search result initial score by multiplying the initial score by the respective parent quality value given the parent site rank to obtain the respective modified page rank;
(Panda teaches adjusting the initial score for each of the search result resources based on the group-specific modification factor
Col. 1 ln. 54-58: adjusting the initial score for each of the search result resources based on the group-specific modification factor for the group of resources to which the search result resource
belongs to generate a respective second score for each of the search result resources.
see also col. 9 ln. 27-34: For example, if the modification factor is multiplicative, the first modification factor f1 can be expressed as: