DETAILED ACTION
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 .
Status of Claims
This action is in response to amendment filed on 2 August 2026. Claims 1, 2, 5, 11, 12 and 15 have been amended. Claims 1-20 are currently pending and have been examined.
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 therefore, subject to the conditions and requirements of this title.
Step 1: The claims 1-10 are a method and claims 11-20 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20, 22 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: independent claims (1 and 11) recite identifying one or more contextual webpage having internet content having a corresponding context score indicating a probability of co-occurrence of the one or more context terms in the internet content and a frequency of occurrence in one or more data streams from server and context score of each contextual webpage satisfying a threshold context score; selecting from the plurlity of webpages to store within a cache database on the frequency of occurrence corresponding to each contextual webpage satisfying a threshold frequency score, wherein the cache database is a subset of the corpus database; generating campaign data comprising the one or more contextual webpages stored in the cache database and during the real-time automated selection process, receiving data stream including a candidate webpage having candidate internet content ; determining a placement score for the candidate webpage incited a degree of match between the candidate internet content and the webpage of the one or more webpages within the cache database, wherein any contextual webpage in the corpus database that is not in the cache database is excluded; and transmitting, by the computer to the RTB server, an identifier of the candidate webpage having the placement score satisfying a bid threshold score, wherein the identifier of the candidate webpage is configured for selection from the one or more contextual webpages during the real-time automated selection process. These steps are considered to be fundamental economic practices and methods of organizing human activity, which are an examples of abstract ideas. These limitations fall within “Certain Methods Of Organizing Human Activity” for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors. Simply put, these limitation merely describe standard data manipulation techniques for specifying context for placement of advertisement, which is clearly a business arrangement in its purest form. Claims 2-10 and 12-20, merely provide additional abstract concepts and narrow the abstract idea of claim 1 and 11. Further, claims 1-20, are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select content that meets a specified criteria, acknowledge an agreement to promote content that satisfying a bid threshold. That is other than reciting “by a computer “ , nothing in the claim precludes both the identifying and determining steps from practically being performed in the human mind. For example , but for “by a computer” language the claims encompasses the user thinking that the most co-occurrence of the one or more context term the highest scored terms. Thus, the limitation is a mental process.
Step 2A-Prong 2: The claims recite additional elements: a computer user to perform the obtaining, identifying, generating, receiving, deteriming and transmitting steps. The computer in these steps is recited at a high level of generality i.e., as a generic computer performing a generic computer functions of processing data (scoring of probability of co-occurrence of the context terms). Obtaining from client device via user interface, generating data, receiving a bidstream from server , and transmitting to an identifier. The obtaining, receiving and transmitting limitation are also reacted at a high level of gentility ( i.e., , as a general means of gathering data for use in the identifying, generating and determining steps), and amount to mere data gathering, which is a form of insignificant extra-solution activity. The, the user device , interface, the server and cache database are also recited at a high-level of generality (i.e., the user device via the interface for performing input data, the sever the database also used of storing data). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the computer, user device, the interface, server and database ). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claim is ineligible.
Further, the courts have consistently recognized that merely presenting the results of
abstract processes of collecting and analyzing information, without more (such as identifying a
particular tool for presentation), is abstract as an ancillary part of such collection and analysis.
See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709,
715 (Fed. Cir. 2014). Examiner asserts that the claim's use of data streams, calculations, and determinations is standard data processing. The steps of finding matching content, calculating a score, and transmitting data are routine functions performed by generic computers ” thus it offers no more than presenting anything that includes the content that resulted from data manipulation.
In sum, the combination of steps that obtaining, identifying, selecting, receiving and transmitting and stores a record are at best is doing no more than generally linking the claims to network environment that sends and receives communications– see MPEP 2106.05(h). See also, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
TLI Communications provides an example of a claim invoking computers and other
machinery merely as a tool to perform an existing process. The court stated that the claims
describe steps of recording, administration and archiving of digital images, and found them to be
directed to the abstract idea of classifying and storing digital images in an organized manner. 823
F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of
performing these functions using a telephone unit and a server and noted that these elements
were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and
operate as a digital camera including compressing images and transmitting those images, and the
server simply receives data, extracts classification information from the received data, and stores
the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at
1747-48.
Dependent claims 2-10 and 12-20, these claims recite limitation that further define the same abstract idea noted in claims 1 and 11, therefore, they are considered patent ineligible for the dame reason as above.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Henkin et al (US Pub., No., 2011/0213655 A1) in view of view of Cierniak (US Panent No., 11,055,332 B1)
With respect to claim 1, Henkin teaches a computer-implemented method for more efficiently selecting from a reduced dataset during a real-time automated selection process for loading content on a webpage ( paragraph [0051], discloses methods, systems, and computer program product for facilitating on-line contextual advertising operations.. and paragraph [1802], discloses MapReduce may be applied to significantly larger dataset ) , the method comprising:
prior to a real-time automated selection process(paragraph [0051], discloses real-time analysis may be used to select, in real-time, contextual relevant information, advertisement, and/or other content ),
obtaining, by a computer, one or more context terms from a client device via a user interface(Fig. 8. 811, discloses enter keywork, search [obtain one or more context term], paragraph [0061], discloses the server system obtains the text for the web page from the client (or in some embodiments, the server system may crawl the original web page from the publisher’s server) and paragraph [0063], discloses webpage obtained from client computer system);and
generating, by the computer, campaign data comprising the reduced set of contextual webpages stored in the cache e(Fig. 64H, discloses Campaign name, CPM, daily budget, categories [campaign data], Fig. 66E, discloses create campaign Wizard: set camping , Fig. 98, 195 discloses ad campaign’s creatives components ) database, wherein each contextual webpage of the reduced set of contextual webpages stored in the cache database has at least one of media content and third- party content removed to reduce a data storage volume of the cache database(paragraph [0051], discloses words and phrase on the web page may be determined and scored against a taxonomy of topics stored in a database .., a score indicting how related the web page is to each topic in the taxonomy .., and paragraph [0137], discloses storing selected KeyPhrase and/or related pages from the back end.., when front end receive page or URL request from client system…, and paragraph [1802], disclose MapReduce may be applied to significantly larger dataset [reduced set of contextual webpages] ) ;
during the real-time automated selection process(paragraph [0061], discloses in response the server system makes the final section among the candidate items of target content, paragraph [0067], discloses real-time web page context analysis and/or real-time insertion of textual markup objects and dynamic content may occur in real-time .., retrieving and/or rendering a request webpage for disply to a user and paragraph [0113], discloses automatically and dynamically adapted, in real-time, its algorithms and or other mechanisms for selecting and/or estimating potential revue related to one-line contextual advertising techniques),
receiving, by the computer, a data stream from a second server, the data stream including a candidate webpage having candidate internet content(paragraph [0061], discloses the source web page is downloaded from publisher web page to a client computer system…, candidate ads be obtained from ad server who bid on the ad placement opportunity, the candidate items of target content are also scored against the taxonomy, the related score of the source, key phrase and target and determined,…, in response the server system makes the final selection amount the candidates items of target content (for example, based on which ads remain available at that time) and sends those items to the client system for disply in a dynamic overlay layer. .., paragraph [0233], discloses the Dynamic Taxonomy Database and/or related content repository for identifying and/or retrieving (e.g., in real time a substantial real-time desired content such as for example a potential ad candidate, potentially related content candidate, potently related content and paragraph [0486], discloses identify and retrieve potential relevant ads candidates, potential related content candidates, potential related video candidates, and paragraph [0572], discloses potential target DOL elements (e.g., related content, page videos etc.);
determining, by the computer, a placement score for the candidate webpage indicating a degree of match between the candidate internet content of the candidate webpage against the internet content of each contextual webpage of the reduced set of contextual webpages within the cache database, wherein any contextual webpage in the corpus database that is not in the cache database is excluded(paragraph [0053], discloses the web pages are scored against each of the topics in the taxonomy databased on the server system.., the score for each topic may be normalized and represented by a number between 0 and 1.., the resulting list of score is a vector representing relatedness of the web page of the topics in the taxonomy database, paragraph [0061], discloses the server system determines which key phrases on the source page should be used for linking and send instructions back to browser .., paragraph [0062], discloses the taxonomy that is used for the above processing may be dynamic, .., paragraph [0063], discloses pages can be scored against the sport topic based on the occurrence of the key phrase and paragraph [0512]-[0520], discloses retravel from the index brings all (or selected ones of ) the result that pass different threshold values may be between 0-1, the default threshold example is 0.25,.., one or lore identify/score phrase option may be performed .., maximized relevancy and yield to the source the target page the score for each triplet of ..); and
transmitting, by the computer to the server, an identifier of the candidate webpage having the placement score satisfying a bid threshold score, wherein the identifier of the candidate webpage is configured for generating content for the contextual webpages during the real-time automated selection process (paragraph [0116], discloses transmitting the URL request info and/or web page content (corresponding to requested URL).., request the web page content paragraph [0821], discloses a unique Source Page ID for the received web page content and to transmit the source page ID, paragraph [0823], discloses webpage URL (an identifier of the candidate webpage] paragraph [0827], discloses a SourcePage ID represent a unique identifier for a specific page, paragraph [0901], dislcies topic-type ad bidding process provide topic input regarding one or more selected page topics which the advertiser has determined (and/or desires) to be related to a give AD and paragraph [0931], dislcies the relevancy and/or scoring value may be used to select and/or rank the most desirable and/or suitable ad candidate for an identified source web page).
Henkin teaches the above elements including identifying, by the computer, a plurality of contextual webpages having internet content stored in a corpus database(Fig. 2, 204 discloses store corpus (int corpus database )of webpage data or webpages or documents on the order of hundreds of millions of documents, paragraph [0016], discloses dynamic taxonomy databased and/or Related Content corpus , paragraph [0138], discloses analyzing selected webpages or other documents which have been identified for contextual analysis, bank end 250 may include a queue of URLs corrosinding to webpages (or other document) to be analyzed and paragraph [0153], discloses Dynamic Taxonomy Database (DTD) (e.g., organized by topic), paragraphs [0163], [0166], discloses representation of Dynamic Taxonomy Database, Related Content Corus, Index, ) , each contextual webpage having i) a corresponding context score indicating a probability of co-occurrence of the one or more context terms in the internet content (paragraph [0052], discloses the statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server.., a score indicting how related the web page is to each topic in the taxonomy is determined.., paragraph [0053], discloses occurrence of keywords/key phrase on the page that relate to each topic.., paragraph [0056], discloses a statistical distribution of the occurrences of the key phrase or phrase across the topics in the taxonomy…, the web pages are analyzed the count (the occurrences of the key phrase or phrase in each topic) may be dynamically updated …, paragraph [0057], discloses determine whether to use a particular key phrases or phrase, paragraph [0063], dislcies pages can then be scored against the sports topic based on the occurrence of that key phrase and the relative correlation of the key phrase to the topic of sport, paragraph [0141], discloses indexer 252a which, for example, may be operable for automatically and dynamically indexing the pages, titles, topics, phrase, etc., …, indexer may be congrued or designed to facilitate or able a quick retrieval or similar page (e.g., based on TF-IDF scoring .., and paragraph [0227], discloses identifying document/content (e.g., source pages, source page content, target pages, related content advertisement, advertismetn landing pages and etc. and paragraphs [0395]-[0401], discloses respective score value may be calculates for each word/phrase identified in the source document according to: Score (phrase-page) = a*Frequency +b *Tittle c*MCB + d *Link, where :…); selecting, by the computer from the plurality of contextual webpages identified within the corpus database , a reduced set of contextual webpages to store within a cache database based on the frequency of occurrence corresponding to each contextual webpage satisfying a threshold frequency score (paragraph [0053], discloses the related score can then use as a factor in selecting web pages or other items of content to be matched or linked for various purpose.., paragraph [0063], discloses pages can then be scored against the sports topic based on the occurrence of the key phrase and the relative correlation of that key phrase to the topic of sports, page related to sports can then be selected and linked to one another based on this key phrase (and other words/phrases appearing on the pages, paragraph [0110], discloses cache/index/repository system, and paragraph [0137], discloses caching or storing selected Key Phrase ..))) and stores the reduced set of contextual webpages configured to be accessed during the real-time automated selection process wherein the cache database is a subset of the corpus database and stores the reduced set of contextual webpages configured to be accessed during the real-time automated selection process(paragraphs [0488]-[0499], discloses indexing and/or scoring properties .., a respective relevance sore..), the publisher may be defined different threshold for each Ad/related elements type such as for example , one of the followings (or combination thereof): ads, video, audio, related information related content related article, etc. (see paragraphs [0500]-[0511]), the retravel from the index bring all (or selected ones of) the results the pass different threshold value for ads video and infoatmion. The thresh value maybe be between 0-1, the default threshold example is 0.25 (paragraph [0512]) and DOL related score value (paragraph [0883]).
Henkin failed to explicitly teach ii) a frequency of occurrence indicating a number of time each contextual webpage has been requested in one or more data strem from a server the context score of each contextual webpage satisfying a threshold context score; wherein the threshold frequency score corresponds to a threshold number of times a contextual webpage has been requested from the one or more data streams of the server.
However, Cierniak teaches ii) a frequency of occurrence indicating a number of times each contextual webpage has been requested in one or more data streams from a server, the context score of each contextual webpage satisfying a threshold context score(Col. 2, lines 25-40, discloses receiving data corresponding to a number of request for the web page and calculating the popularity score based on the number of request.., calculating the first and the second popularly score.., , and Col. 9, lines 1-16, discloses a number of times the resource is requested with a given time period .., ) ; wherein the threshold frequency score corresponds to a threshold number of times a contextual webpage has been requested from the one or more data streams of the server(Col. 9, lines 16-28, discloses the first resource is discussed 10,000 times over a 1-day period and a second resource second resource is discussed 15000 times over a 2-day period. The popularity score for the first resource is calculated as 10000 discussions per day.., Col. 12, lines 27-36, discloses a popularly score assocted with the web page is grater that a threshold popularity score ..), Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server of Henkin with calculating the popularity score based on the number of web page request of Cierniak in order provide a sort order (see, Cierniak, Col. 1, lines 37-38).
With respect to claim 2 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method further comprising identifying, by the computer, placement webpage (paragraph [821], discloses a unique SourcePage ID for the received web page content and to transmit the source page ID, paragraph [0823], discloses webpage URL [an identifier of the candidate webpage] paragraph [0827], discloses a SourcePage ID represent a unique identifier for a specific webpage and paragraph [0898], dislcies solicit bids for advertismtn to be displayed .., of the source web page ) , candidate ads may be obtained from ad server who bid on the ad placement opportunity .., the related scores of the source, key phrase and targe are determined ..(paragraph [0061]) . Henkin failed to teach the corresponding webpage is selected based on the placement score of the candidate webpage satisfying a threshold page score.
However, Cierniak teaches webpage is selected based on the placement score of the candidate webpage satisfying a threshold page score(Col. 2, lines 25-40, discloses receiving data corresponding to a number of request for the web page and calculating the popularity score based on the number of request.., calculating the first and the second popularly score.., , and Col. 9, lines 1-16, discloses a number of times the resource is requested with a given time period .., ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server of Henkin with calculating the popularity score based on the number of web page request of Cierniak in order provide a sort order (see, Cierniak, Col. 1, lines 37-38).
With respect to claim 3 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method further comprising: receiving, by the computer from the [[RTB]] server, an availability list of one or more available webpages requesting bids from a [[bid]]data stream system(paragraph [0063], discloses selected webpages or set of web pages may be manually designed); and
calculating, by the computer, a real-time page score for each available webpage in an availability list based, at least in part, upon a number of occurrences of one or more campaign terms comprising one or more updated beacon terms and one or more in-context terms in the available webpage(paragraph [0063], discloses pages being analyzed).
With respect to claim 4 Henkin in view of Cierniak teaches elements of claim 3, furthermore, Henkin teaches the method further comprising identifying, by the computer, a bidding list of webpages comprising each of one or more available webpages in the availability list satisfying a bid threshold(paragraph [0063], discloses analyzed designated sets of pages to the topic..).
With respect to claim 5 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method , further comprising storing, by the computer into a campaign database, the campaign data, wherein the campaign data is configured for executing a real-time automated selection operation for the candidate webpage during the real-time bidding selection operation(paragraph [0108], discloses real-time insertion of textual markup objects and dynamic content, identification and selection of related content and/or related elements, dynamic generation of dynamic overlay layers (DOLs) and paragraph [0113], discloses autotmcially and dynamically adapted, in real-time, its algorithms and or other mechanisms for selecting and/or estimating potential revenue relating to one-line contextual advertising techniques ).
With respect to claim 6 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method further comprising identifying, by the computer, a plurality of groups corresponding to the placement score associated with the candidate webpage, each group in the plurality of groups based on the campaign data (paragraph [0063], discloses pages can be scored against the sport topic based on the occurrence of the key phrase and paragraph [0512]-[0520], discloses retravel from the index brings all (or selected ones of ) the result that pass different threshold values may be between 0-1, the default threshold example is 0.25,.., one or lore identify/score phrase option may be performed .., maximized relevancy and yield to the source the target page the score for each triplet of ..) .
With respect to claim 7 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method , wherein the campaign data includes at least one of in-context phrases, out-of-context phrases, and beacon phrases (paragraph [0480], discloses using the various types of input data such as, for example: source page key phrase and page topic infoatmion ad campaign infoatmion).
With respect to claim 8 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method further comprising: prior to a future bid-time real-time automated selection process: selecting, by the computer from a plurality of corpus terms stored in the corpus database, one or more beacon terms using the one or more context terms, each corpus term selected for the one or more beacon terms has a co-occurrence probability with each context term of the one or more context terms that satisfies a threshold probability(paragraph [0063], discloses pages can then be scored against the sports topic based on the occurrence of the key phrase and the relative correlation of that key phrase to the topic of sports, page related to sports can then be selected and linked to one another based on this key phrase (and other words/phrases appearing on the pages, paragraph [0110], discloses cache/index/repository system, and paragraph [0137], discloses caching or storing selected Key Phrase ..)); and
presenting, by the computer, the one or more beacon terms selected using the one or more context terms to a user via the user interface of the client device(paragraph [0934], discloses selected KeyPhrase(s) for highlight/markup which is to be displayed at the client system).
With respect to claim 9 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method1, further comprising: calculating, by the computer, a performance score to identify a likelihood of an interaction with the campaign data(paragraph [1446], discloses facility performance tracking .., and paragraph [1585], discloses score for each phrase bed on past performance ). Henkin failed to teach transmitting, by the computer, an [[bid]] indication for the candidate webpage, responsive to the performance score satisfying a threshold.
However, Cierniak transmitting, by the computer, an indication for the candidate webpage, responsive to the performance score satisfying a threshold (Col. 2, lines 25-40, discloses receiving data corresponding to a number of request for the web page and calculating the popularity score based on the number of request.., calculating the first and the second popularly score.., , and Col. 9, lines 1-16, discloses a number of times the resource is requested with a given time period .., ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server of Henkin with calculating the popularity score based on the number of web page request of Cierniak in order provide a sort order (see, Cierniak, Col. 1, lines 37-38).
With respect to claim 10 Henkin in view of Cierniak teaches elements of claim 1, furthermore, Henkin teaches the method further comprising:
generating, by the computer, context data for the candidate webpage based upon user inputs received from the client device via the user interface(paragraph [0125], discloses generating contextual in-text KeyPhrase advertising for one or more selected KeyPhrase of the web page.., and paragraph [1549], disclose generating more content for the advertiser web site for better search ranking); and
transmitting, by the computer, the context data to the client device for display at the user interface(paragraph [0811], discloses transmitting or serving web page content including the tag information to the client system).
With respect to claim 11, Henkin teaches a system for more efficiently selecting from a reduced dataset during a real-time automated selection process for loading content on a webpage ( paragraph [0051], discloses methods, systems, and computer program product for facilitating on-line contextual advertising operations.. and paragraph [1802], discloses MapReduce may be applied to significantly larger dataset ) , the system comprising:
a server comprising a processor (Fig. 3G, ad server(s), Fig. 74 7403, server and paragraph [0052], discloses server on Internet by a client computing system)) configured to:
prior to a real-time automated selection process(paragraph [0051], discloses real-time analysis may be used to select, in real-time, contextual relevant information, advertisement, and/or other content ),
obtaining, one or more context terms from a client device via a user interface(Fig. 8. 811, discloses enter keywork, search [obtain one or more context term], paragraph [0061], discloses the server system obtains the text for the web page from the client (or in some embodiments, the server system may crawl the original web page from the publisher’s server) and paragraph [0063], discloses webpage obtained from client computer system);and
generating, campaign data comprising the reduced set of contextual webpages stored in the cache e(Fig. 64H, discloses Campaign name, CPM, daily budget, categories [campaign data], Fig. 66E, discloses create campaign Wizard: set camping , Fig. 98, 195 discloses ad campaign’s creatives components ) database, wherein each contextual webpage of the reduced set of contextual webpages stored in the cache database has at least one of media content and third- party content removed to reduce a data storage volume of the cache database(paragraph [0051], discloses words and phrase on the web page may be determined and scored against a taxonomy of topics stored in a database .., a score indicting how related the web page is to each topic in the taxonomy .., and paragraph [0137], discloses storing selected KeyPhrase and/or related pages from the back end.., when front end receive page or URL request from client system…, and paragraph [1802], disclose MapReduce may be applied to significantly larger dataset [reduced set of contextual webpages] ) ;
during the real-time automated selection process(paragraph [0061], discloses in response the server system makes the final section among the candidate items of target content, paragraph [0067], discloses real-time web page context analysis and/or real-time insertion of textual markup objects and dynamic content may occur in real-time .., retrieving and/or rendering a request webpage for disply to a user and paragraph [0113], discloses automatically and dynamically adapted, in real-time, its algorithms and or other mechanisms for selecting and/or estimating potential revue related to one-line contextual advertising techniques),
receiving, a data stream from a second server, the data stream including a candidate webpage having candidate internet content(paragraph [0061], discloses the source web page is downloaded from publisher web page to a client computer system…, candidate ads be obtained from ad server who bid on the ad placement opportunity, the candidate items of target content are also scored against the taxonomy, the related score of the source, key phrase and target and determined,…, in response the server system makes the final selection amount the candidates items of target content (for example, based on which ads remain available at that time) and sends those items to the client system for disply in a dynamic overlay layer. .., paragraph [0233], discloses the Dynamic Taxonomy Database and/or related content repository for identifying and/or retrieving (e.g., in real time a substantial real-time desired content such as for example a potential ad candidate, potentially related content candidate, potently related content and paragraph [0486], discloses identify and retrieve potential relevant ads candidates, potential related content candidates, potential related video candidates, and paragraph [0572], discloses potential target DOL elements (e.g., related content, page videos etc.);
determine a placement score for the candidate webpage indicating a degree of match between the candidate internet content of the candidate webpage against the internet content of each contextual webpage of the reduced set of contextual webpages within the cache database, wherein any contextual webpage in the corpus database that is not in the cache database is excluded(paragraph [0053], discloses the web pages are scored against each of the topics in the taxonomy databased on the server system.., the score for each topic may be normalized and represented by a number between 0 and 1.., the resulting list of score is a vector representing relatedness of the web page of the topics in the taxonomy database, paragraph [0061], discloses the server system determines which key phrases on the source page should be used for linking and send instructions back to browser .., paragraph [0062], discloses the taxonomy that is used for the above processing may be dynamic, .., paragraph [0063], discloses pages can be scored against the sport topic based on the occurrence of the key phrase and paragraph [0512]-[0520], discloses retravel from the index brings all (or selected ones of ) the result that pass different threshold values may be between 0-1, the default threshold example is 0.25,.., one or lore identify/score phrase option may be performed .., maximized relevancy and yield to the source the target page the score for each triplet of ..); and
transmit to the second server, an identifier of the candidate webpage having the placement score satisfying a bid threshold score, wherein the identifier of the candidate webpage is configured for generating content for the contextual webpages during the real-time automated selection process (paragraph [0116], discloses transmitting the URL request info and/or web page content (corresponding to requested URL).., request the web page content paragraph [0821], discloses a unique Source Page ID for the received web page content and to transmit the source page ID, paragraph [0823], discloses webpage URL (an identifier of the candidate webpage] paragraph [0827], discloses a SourcePage ID represent a unique identifier for a specific page, paragraph [0901], dislcies topic-type ad bidding process provide topic input regarding one or more selected page topics which the advertiser has determined (and/or desires) to be related to a give AD and paragraph [0931], dislcies the relevancy and/or scoring value may be used to select and/or rank the most desirable and/or suitable ad candidate for an identified source web page).
Henkin teaches the above elements including identifying, by the computer, a plurality of contextual webpages having internet content stored in a corpus database(Fig. 2, 204 discloses store corpus (int corpus database )of webpage data or webpages or documents on the order of hundreds of millions of documents, paragraph [0016], discloses dynamic taxonomy databased and/or Related Content corpus , paragraph [0138], discloses analyzing selected webpages or other documents which have been identified for contextual analysis, bank end 250 may include a queue of URLs corrosinding to webpages (or other document) to be analyzed and paragraph [0153], discloses Dynamic Taxonomy Database (DTD) (e.g., organized by topic), paragraphs [0163], [0166], discloses representation of Dynamic Taxonomy Database, Related Content Corus, Index, ) , each contextual webpage having i) a corresponding context score indicating a probability of co-occurrence of the one or more context terms in the internet content (paragraph [0052], discloses the statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server.., a score indicting how related the web page is to each topic in the taxonomy is determined.., paragraph [0053], discloses occurrence of keywords/key phrase on the page that relate to each topic.., paragraph [0056], discloses a statistical distribution of the occurrences of the key phrase or phrase across the topics in the taxonomy…, the web pages are analyzed the count (the occurrences of the key phrase or phrase in each topic) may be dynamically updated …, paragraph [0057], discloses determine whether to use a particular key phrases or phrase, paragraph [0063], dislcies pages can then be scored against the sports topic based on the occurrence of that key phrase and the relative correlation of the key phrase to the topic of sport, paragraph [0141], discloses indexer 252a which, for example, may be operable for automatically and dynamically indexing the pages, titles, topics, phrase, etc., …, indexer may be congrued or designed to facilitate or able a quick retrieval or similar page (e.g., based on TF-IDF scoring .., and paragraph [0227], discloses identifying document/content (e.g., source pages, source page content, target pages, related content advertisement, advertismetn landing pages and etc. and paragraphs [0395]-[0401], discloses respective score value may be calculates for each word/phrase identified in the source document according to: Score (phrase-page) = a*Frequency +b *Tittle c*MCB + d *Link, where :…); selecting, by the computer from the plurality of contextual webpages identified within the corpus database , a reduced set of contextual webpages to store within a cache database based on the frequency of occurrence corresponding to each contextual webpage satisfying a threshold frequency score (paragraph [0053], discloses the related score can then use as a factor in selecting web pages or other items of content to be matched or linked for various purpose.., paragraph [0063], discloses pages can then be scored against the sports topic based on the occurrence of the key phrase and the relative correlation of that key phrase to the topic of sports, page related to sports can then be selected and linked to one another based on this key phrase (and other words/phrases appearing on the pages, paragraph [0110], discloses cache/index/repository system, and paragraph [0137], discloses caching or storing selected Key Phrase ..))) and stores the reduced set of contextual webpages configured to be accessed during the real-time automated selection process wherein the cache database is a subset of the corpus database and stores the reduced set of contextual webpages configured to be accessed during the real-time automated selection process(paragraphs [0488]-[0499], discloses indexing and/or scoring properties .., a respective relevance sore..), the publisher may be defined different threshold for each Ad/related elements type such as for example , one of the followings (or combination thereof): ads, video, audio, related information related content related article, etc. (see paragraphs [0500]-[0511]), the retravel from the index bring all (or selected ones of) the results the pass different threshold value for ads video and infoatmion. The thresh value maybe be between 0-1, the default threshold example is 0.25 (paragraph [0512]) and DOL related score value (paragraph [0883]).
Henkin failed to explicitly teach ii) a frequency of occurrence indicating a number of time each contextual webpage has been requested in one or more data strem from a server the context score of each contextual webpage satisfying a threshold context score; wherein the threshold frequency score corresponds to a threshold number of times a contextual webpage has been requested from the one or more data streams of the server.
However, Cierniak teaches ii) a frequency of occurrence indicating a number of times each contextual webpage has been requested in one or more data streams from a server, the context score of each contextual webpage satisfying a threshold context score(Col. 2, lines 25-40, discloses receiving data corresponding to a number of request for the web page and calculating the popularity score based on the number of request.., calculating the first and the second popularly score.., , and Col. 9, lines 1-16, discloses a number of times the resource is requested with a given time period .., ) ; wherein the threshold frequency score corresponds to a threshold number of times a contextual webpage has been requested from the one or more data streams of the server(Col. 9, lines 16-28, discloses the first resource is discussed 10,000 times over a 1-day period and a second resource second resource is discussed 15000 times over a 2-day period. The popularity score for the first resource is calculated as 10000 discussions per day.., Col. 12, lines 27-36, discloses a popularly score assocted with the web page is grater that a threshold popularity score ..), Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server of Henkin with calculating the popularity score based on the number of web page request of Cierniak in order provide a sort order (see, Cierniak, Col. 1, lines 37-38).
With respect to claim 12 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system further comprising identifying, by the computer, the bid placement webpage (paragraph [821], discloses a unique SourcePage ID for the received web page content and to transmit the source page ID, paragraph [0823], discloses webpage URL [an identifier of the candidate webpage] , paragraph [0827], discloses a SourcePage ID represent a unique identifier for a specific webpage and paragraph [0898], dislcies solicit bids for advertismtn to be displayed .., of the source web page) , candidate ads may be obtained from ad server who bid on the ad placement opportunity .., the related scores of the source, key phrase and targe are determined ..(paragraph [0061]) . Henkin failed to teach the corresponding webpage is selected based on the placement score of the candidate webpage satisfying a threshold page score.
However, Cierniak teaches webpage is selected based on the placement score of the candidate webpage satisfying a threshold page score(Col. 2, lines 25-40, discloses receiving data corresponding to a number of request for the web page and calculating the popularity score based on the number of request.., calculating the first and the second popularly score.., , and Col. 9, lines 1-16, discloses a number of times the resource is requested with a given time period .., ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server of Henkin with calculating the popularity score based on the number of web page request of Cierniak in order provide a sort order (see, Cierniak, Col. 1, lines 37-38).
With respect to claim 13 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system further comprising: receiving, by the computer from the [[RTB]] server, an availability list of one or more available webpages requesting bids from a [[bid]]data stream system(paragraph [0063], discloses selected webpages or set of web pages may be manually designed); and
calculating, by the computer, a real-time page score for each available webpage in an availability list based, at least in part, upon a number of occurrences of one or more campaign terms comprising one or more updated beacon terms and one or more in-context terms in the available webpage(paragraph [0063], discloses pages being analyzed).
With respect to claim 14 Henkin in view of Cierniak teaches elements of claim 13, furthermore, Henkin teaches the system further comprising identifying, by the computer, a bidding list of webpages comprising each of one or more available webpages in the availability list satisfying a bid threshold(paragraph [0063], discloses analyzed designated sets of pages to the topic..).
With respect to claim 15 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system , further comprising storing, by the computer into a campaign database, the campaign data, wherein the campaign data is configured for executing a real-time automated bidding selection operation for the candidate webpage during the real-time bidding selection operation(paragraph [0108], discloses real-time insertion of textual markup objects and dynamic content, identification and selection of related content and/or related elements, dynamic generation of dynamic overlay layers (DOLs) and paragraph [0113], discloses autotmcially and dynamically adapted, in real-time, its algorithms and or other mechanisms for selecting and/or estimating potential revenue relating to one-line contextual advertising techniques ).
With respect to claim 16 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system further comprising identifying, by the computer, a plurality of groups corresponding to the placement score associated with the candidate webpage, each group in the plurality of groups based on the campaign data (paragraph [0063], discloses pages can be scored against the sport topic based on the occurrence of the key phrase and paragraph [0512]-[0520], discloses retravel from the index brings all (or selected ones of ) the result that pass different threshold values may be between 0-1, the default threshold example is 0.25,.., one or lore identify/score phrase option may be performed .., maximized relevancy and yield to the source the target page the score for each triplet of ..) .
With respect to claim 17 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system , wherein the campaign data includes at least one of in-context phrases, out-of-context phrases, and beacon phrases (paragraph [0480], discloses using the various types of input data such as, for example: source page key phrase and page topic infoatmion ad campaign infoatmion).
With respect to claim 18 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system further comprising: prior to a future bid-time real-time automated selection process: selecting, by the computer from a plurality of corpus terms stored in the corpus database, one or more beacon terms using the one or more context terms, each corpus term selected for the one or more beacon terms has a co-occurrence probability with each context term of the one or more context terms that satisfies a threshold probability(paragraph [0063], discloses pages can then be scored against the sports topic based on the occurrence of the key phrase and the relative correlation of that key phrase to the topic of sports, page related to sports can then be selected and linked to one another based on this key phrase (and other words/phrases appearing on the pages, paragraph [0110], discloses cache/index/repository system, and paragraph [0137], discloses caching or storing selected Key Phrase ..)); and
presenting, by the computer, the one or more beacon terms selected using the one or more context terms to a user via the user interface of the client device(paragraph [0934], discloses selected KeyPhrase(s) for highlight/markup which is to be displayed at the client system).
With respect to claim 19 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system further comprising: calculating, by the computer, a performance score to identify a likelihood of an interaction with the campaign data(paragraph [1446], discloses facility performance tracking .., and paragraph [1585], discloses score for each phrase bed on past performance ). Henkin failed to teach transmitting, by the computer, an [[bid]] indication for the candidate webpage, responsive to the performance score satisfying a threshold.
However, Cierniak transmitting, by the computer, an indication for the candidate webpage, responsive to the performance score satisfying a threshold (Col. 2, lines 25-40, discloses receiving data corresponding to a number of request for the web page and calculating the popularity score based on the number of request.., calculating the first and the second popularly score.., , and Col. 9, lines 1-16, discloses a number of times the resource is requested with a given time period .., ). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for a statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server of Henkin with calculating the popularity score based on the number of web page request of Cierniak in order provide a sort order (see, Cierniak, Col. 1, lines 37-38).
With respect to claim 20 Henkin in view of Cierniak teaches elements of claim 11, furthermore, Henkin teaches the system further comprising:
generating, by the computer, context data for the candidate webpage based upon user inputs received from the client device via the user interface(paragraph [0125], discloses generating contextual in-text KeyPhrase advertising for one or more selected KeyPhrase of the web page.., and paragraph [1549], disclose generating more content for the advertiser web site for better search ranking); and
transmitting, by the computer, the context data to the client device for display at the user interface(paragraph [0811], discloses transmitting or serving web page content including the tag information to the client system).
Prior arts:
Henkin et al (US Pub., No., 2011/0213655 A1) discloses different types of Hybrid contextual advertising and related content analysis and display techniques are disclosed for facilitating on-line contextual advertising operations and related content delivery operations implemented in a computer network. At least some embodiments may be configured or designed enabling advertisers to provide contextual advertising promotions to end-users based upon real-time analysis of web page content which may be served to an end-user's computer system.
Kingman, Jr, et al (US Patent No., 10,599,009 B1) focused on the system links Internet web page context with audience usage and location data to support advertising efficiency and effectiveness. An ontology of categories is created where domains and website pages are classified and scored against the links on those pages and the meta-tag key word pools that are harvested from those web pages. An ontology of high-level categories are derived from the frequency of the key words appearing within the domain URL addresses of the pages, the domain of the links on those pages or within the content of the pages themselves.
Freeman et al (US Pub., 2012/0066359 A1) focused on a method for valuing a link-hosting webpage is provided. The method includes the act of receiving, on a computer system, at least one keyword. The method also includes the act of receiving, on a computer system, at least one identifier of a webpage, the webpage having been previously identified as a link-hosting webpage. The method also includes the act of accessing information about the webpage over a computer network.
Cierniak (US Panent No., 11,055,332 B1) focused on Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a request, the request corresponding to a web page, identifying a plurality of annotation data sets associated with the web page in response to receiving the request, determining a popularity score associated with the resource, sorting the plurality of annotation data sets based on the popularity score to provide a sort order
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejection with respect to claims 1-20 filed on 2 August 2026 have been fully considered but they are not persuasive. Applicants’ arguments of the claims do not recite a judicial exception and are therefore patent-eligible under step 2A Prong 1 is not persuasive. The core of the claim covers collecting data ("obtaining... context terms"), analyzing data ("identifying... a plurality of contextual webpages... calculating a context score"), and filtering/storing data ("selecting... a reduced set... to store within a cache database"). The USPTO classifies these steps as the abstract idea of organizing, manipulating, or analyzing information. Thus, under 2A-Pong 1, the claims are ineligible.
Applicants’ arguments of the claims integrate any alleged abstract idea into a practical application because they receive a specific improvement to computer functionality in how computer’ cache database itself is structured and operates, rather than merely applying an abstract concept on a generic computer is not persuasive. The claim relies entirely on generic, routine hardware functioning in its standard capacity ("a computer," "a client device," "a user interface," "a corpus database," "a cache database," and "a server"). Simply stripping media or third-party content to save storage space is an administrative data-management choice rather than a fundamental breakthrough in the underlying physics or architecture of computer hardware.
Furthermore, the sequency of steps (collecting context → filtering a database → caching a subset → stripping heavy data → waiting for a real-time data stream) simply describes the generic workflow of a standard content-delivery network (CDN) or ad-serving pre-filtering system. It does not alter how the computer works; it just uses the computer as a tool to execute the abstract logic faster. Thus, the claims are ineligible under step 2A-prong 2 and 2B.
Applicants’ arguments of as amended, the claims recite features that describe an amount of technical detail and computing functions that can only interpreted as describing as mental process or generic automation of data selection and delivery is not persuasive. As updated by the above rejections the claimed invention is not patent-eligible because it is directed to a "judicial exception," such as an abstract idea. Abstract ideas include mathematical concepts, methods of organizing human activity, and mental processes.
The claims described are found to recite an abstract idea for the following reasons:
Organizing human activity: The claim describes a method for managing an advertising campaign and selecting content for real-time bidding (RTB). This falls under the category of a business method for organizing human activity, which is a recognized abstract idea.
Mathematical concepts: The steps involve determining a "context score" and a "placement score," which are mathematical concepts. While using mathematical formulas is not inherently abstract, relying on them to select web content based on co-occurrence and frequency is an abstract process.
Conventional computing: The claim describes using conventional computer components like a "server" and a "cache database" to implement the abstract idea. Simply using a generic computer to automate a business process does not make it patent-eligible.
Applicants’ arguments of forming a cache database using a large corpus databased and identifying a context score that is based on a co-occurrence of terms within contextual webpages and per-page frequency of occurrence in server data streams. Tracking the co-occurrence of terms and the frequence of occurrence of page in a plurality of data streams cannot be done by a human mind or are the claims directed to a method of organizing human activity, The PEG emphasize that a claim doss not receive a mental process with it control limitation that cannot practical be performed in human mind..” is not persuasive. The claimed limitation of “ selecting, by the computer from the plurality of contextual webpage identified within the corpus database , a reduced set of contextual webpages to store within a cache databased based on the frequency of occurrence corresponding to each contextual webpage satisfying a threshold frequency score, wherein the cache database is a subset of the corpus database and stores the reduced set of contextual webpages configured to be accessed during the real-time automated selection process” . This limitation as drafted, is processes, that, under its broadest reasonable interpretation, it convers identifying, analyzing, and storing data in a cache constitute abstract ideas, similar to organizing information or generic data manipulation.
Further, the claims lack inventive concept in step 2 when a selection and caching process use a generic computer functionality even if it performs the task faster. The claims must show a specific improvement in the computer’s functioning, such as a specialized caching architecture or an innovative algorithm for frequency calculation that goes beyond conventional steps. Clams that escribe “what “ the system does (e.g., selecting a reduce set .. to store”) rather than “how” it does it through a specific technical mechanism are often deemed ineligible.
In order to overcome the 35 U.S.C 101 rejections, the claims feature focuses on
Technical Improvement: The method must be framed as a solution to a technical problem (e.g., excessive latency, limited memory) rather than a business or data management goal.
Specific Implementation: Emphasize that the "reduced set of contextual webpages" is identified through a specialized, non-conventional algorithm that enhances computer performance.
Distinguished from Conventional Caching: Arguments should focus on how this particular frequency thresholding technique is unique compared to standard caching techniques. Thus, the claims are ineligible.
Applicant' s argument relating to the Enfish are misguided. Enfish is an example of something the courts found not to be abstract. The instant Application lacks the self-referential table of Enfish which proved to be an element that was determined to be non-abstract. Therefore, Enfish is not applicable to the instant case. Examiner used Alice/ Mayo two-part analysis used in the rejection above to determine that the claims are ineligible.
Applicant’s arguments of 35 U.S.C 103 rejection filed on 27 January 2026 with respect to claim(s) 1-20 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.
Applicants’ arguments of the cited reference do not teach or suggest at least the features of “ identifying, by the computer, a plurality of contextual webpages having internet content stored in a corpus database each contextual webpage having a corresponding context score indicating a probability of co-occurrence of the one or more context terms in the internet content and a frequency of occurrence of each contextual webpage in one or more data strem from a server the context score of each contextual webpage satisfying a threshold context score is not persuasive.
Henkin teaches identifying, by the computer, a plurality of contextual webpages having internet content stored in a corpus database (Fig. 2, 204 discloses store corpus (int corpus database )of webpage data or webpages or documents on the order of hundreds of millions of documents, paragraph [0016], discloses dynamic taxonomy databased and/or Related Content corpus , paragraph [0138], discloses analyzing selected webpages or other documents which have been identified for contextual analysis, bank end 250 may include a queue of URLs corrosinding to webpages (or other document) to be analyzed and paragraph [0153], discloses Dynamic Taxonomy Database (DTD) (e.g., organized by topic), paragraphs [0163], [0166], discloses representation of Dynamic Taxonomy Database, Related Content Corus, Index, ) , each contextual webpage having a corresponding context score indicating a probability of co-occurrence of the one or more context terms in the internet content and a frequency of occurrence of each contextual webpage in one or more data strem from a server the context score of each contextual webpage satisfying a threshold context score (paragraph [0052], discloses the statistical distribution of word and phrase of the web page may be determined and scored against a taxonomy of topics stored in a database on the server.., a score indicting how related the web page is to each topic in the taxonomy is determined.., paragraph [0053], discloses occurrence of keywords/key phrase on the page that relate to each topic.., paragraph [0056], discloses a statistical distribution of the occurrences of the key phrase or phrase across the topics in the taxonomy…, the web pages are analyzed the count (the occurrences of the key phrase or phrase in each topic) may be dynamically updated …, paragraph [0057], discloses determine whether to use a particular key phrases or phrase, paragraph [0063], dislcies pages can then be scored against the sports topic based on the occurrence of that key phrase and the relative correlation of the key phrase to the topic of sport, paragraph [0141], discloses indexer 252a which, for example, may be operable for automatically and dynamically indexing the pages, titles, topics, phrase, etc., …, indexer may be congrued or designed to facilitate or able a quick retrieval or similar page (e.g., based on TF-IDF scoring .., and paragraph [0227], discloses identifying document/content (e.g., source pages, source page content, target pages, related content advertisement, advertismetn landing pages and etc. and paragraphs [0395]-[0401], discloses respective score value may be calculates for each word/phrase identified in the source document according to: Score (phrase-page) = a*Frequency +b *Tittle c*MCB + d *Link, where :…).
Applicants’ arguments of Henkin is devoid of any contemplation of a frequency of occurrence indicating a presence of contextual webpage in one or more data streams from a server is not persuasive. Henkin in paragraph [1694], describes [score- score for phrase for topic – score (frequency of appearance of phrase on page, where it appeared (URL, title, MCB) -computed by classifier during classification – corresponds to score shown of frequency, also in paragraph [0141], discloses indexer 252a which, for example, may be operable for automatically and dynamically indexing the pages, titles, topics, phrase, etc., …, indexer may be congrued or designed to facilitate or able a quick retrieval or similar page (e.g., based on TF-IDF scoring .., which inherently indicates the presence of a contextual webpage in a data stream.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SABA DAGNEW whose telephone number is (571)270-3271. The examiner can normally be reached 9-6:45.
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/SABA DAGNEW/Primary Examiner, Art Unit 3621
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