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 .
Claims 2-21 are pending in this application.
Claim Objections
Claim 4 is objected to for insufficient antecedent basis. The claim recites the limitation "the…online user" in line 5. Claim 1 has been amended to recite an “online consumer.” There is insufficient antecedent basis for this limitation in the claim. An appropriate correction is required.
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.
Claim(s) 2-6, 8-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong US 2013/0204871 (hereinafter Wong) in view of Ramer et al., US 2009/0240568 (hereinafter Ramer).
For claim 2, Wong teaches a method for collecting, scoring, and organizing online content, the method comprising:
characterizing, by a processor of a first server, based on a level of online interaction, an online consumer (see Wong, [0011] – [0012], “engagement data is collected and factored”), and searching, in response to a query received at a first client computing device by the consumer (see Wong [0028], identify “relevance of network objects identified in response to a user’s query,” [0030] – [0031], “the user initiates a search for information of interest by entering a query into his computing device”), a plurality of webpages presented at a plurality of websites hosted by at least a second server, each of the plurality of webpages displaying one or more content groupings including content items of potential interest to the consumer, the searching resulting in identifying a plurality of content items from one or more of the content groupings (see Wong, [0026], “restaurant may be represented by a set of URLs representing different web pages, for example, the home page of the restaurant, a menu page, a reservations page, a collection of reviews of the restaurant on sites like Yelp and/or Zagat,” where web sites that host content represents one or more second servers, [0027], [0034], “The ingested URLs are indexed and stored by the web service in step 212,” where objects grouped within the web pages represent plurality of content items), at least one of the identified plurality of content items having been interacted with by the online consumer (see Wong, [0011] – [0012], [0035], “the web service collects social engagement data from various social media sites for each URL identified in response to the query”);
collecting, by a processor of the first server, at least some of the identified plurality of content items having been interacted with by the consumer (see Wong, [0027], [0028], “utilize this social engagement data to score the relevance of network objects identified in response to a user’s query,” [0035], “the web service collects social engagement data from various social media sites for each URL identified in response to the query” where collection of social engagement data associated with URL represents second data set associated with objects);
Ramer teaches forming, by a processor of the first server, a content collection from the collected content items, the content collection including each collected content item (see Ramer, [0136], [0174], [0176], collecting “contextual website data parameters” that characterize “Entertainment” and “Weather” websites, [0487], [0885], [0907], [0991] – [0092], [1043], [1050], [01390], where collected “contextual website data parameters” that identify a website associated with categories such as florist, caterer, photographer, or sports represents first set of data associated with collected content items). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Wong with the teachings of Ramer to efficiently assess website characteristic data to return more relevant search results (see Ramer, [0176], [0972]).
The combination further teaches
weighting, by a processor of the first server, each respective content item, the weighting being at least partially based on the characterization of the consumer, so as to determine a relative weighting of each collected content item of the content collection with respect to its relevance to the online consumer (see Wong, [0040] – [0044], “count of all relevant "shares" identified for various social networks” and “considering other factors and weighting results accordingly” and active engagement score SPM takes into account “Site Unique” visitors representing weighting the first and second set data; see Ramer, [0146], [0407], [0903], [0907], represents weighting the first and second data sets);
calculating, by a processor of the first server, based on the relative weighting, a content item performance score for each of the collected content items, so as to produce a collection of scored content items (see Wong, [0013], [0040] – [0049], “This information is normalized and weighted in order to create a quality score...for each piece of content so it can be compared and ranked,” where normalizing active engagement score to create quality score represents calculating content item performance score; see Ramer, [0176], [0407], [0903], scored data; see Applicant’s Specification, US 2022/0067113, [0059], “A mapping function can be applied to the raw score in order to transform the raw score into a content item performance score within a minimum value and a maximum value, for example 0-10”); and
ordering, for use in subsequent communication generation, by a processor of the first server, in relation to its associated content score, each of the content items of the content collection to produce an ordered content collection, the ordering being based on a relation of each content item to one or more characteristics of the characterized online consumer (see Wong, [0028], [0037] – [0044], “a ranked list of the documents is generated by the web service, the ranking based on the quality score developed in the processing step”, [0040] – [0049], provide “ranking” of objects in response to user query representing ordered scored content collection, where generating ranking from top ranking to lowest ranking represents ordered content collection; see Ramer, [0131], [0176], [0407], return “rankings” of content collection that includes first and second data).
For claim 3, the combination teaches The method in accordance with claim 2, further comprising querying, via an API coupling the second server to the first server, in at least a passive manner, one or more of the plurality of webpages presented by at least the first server so as to continuously receive updated data, the updated data including one or more of updated content items of potential interest (see Wong, [0027], “analytical information is available through the application program interface (API) of the social network,” [0035], “collects social engagement data from various social media sites” that are collected through the API of these services for a specific URL” where collected social engagement data via API to social media servers represents querying, via API, for updated first data; see Ramer, [0488], “a mobile content website may be periodically analyzed for changes in content for purposes of assessing the relevance of keywords previously generated,” [01372], “connector may comprise an application programming interface or other code suitable to connect source and target data facilities” where application programming interface for periodically analyzing website changes represents passive querying via API to continuously receive updates for first and second data).
For claim 4, the combination teaches the method in accordance with claim 2, further comprising performing a mapping operation, by a processor of the first server, wherein the mapping operation includes delineating each of the plurality of content items with respect to its associated one or more characteristics of the characterized online user (see Wong, [0028], [0037] – [0044], generate “active engagement score SPM” and “This information is normalized and weighted in order to create a quality score...for each piece of content so it can be compared and ranked,” where normalizing and ranking score to create quality score represents mapping and delineating items,).
For claim 5, the combination teaches the method in accordance with claim 3, wherein the method further comprises adjusting respective content performance scores based on the updated data (see Ramer, [0176], [0488], where “changes” to assessed “relevance of keywords” adjusts “relevancy score” for affected content items).
For claim 6, the combination teaches The method in accordance with claim 5, wherein the method further comprises providing, for display at a graphical user interface of the first or a second client computing device, the ordered content collection (see Wong, [0029], [0037] – [0044], “results delivered to the client”).
For claim 8, the combination teaches the method in accordance with claim 3, wherein the method further comprises providing, for display at a graphical user interface of the first or the second client computing device, data encapsulating the content item performance score for each of the content items of the ordered content collection (see Wong, [0028] – [0031], [0037] – [0044], where display ranked results represents encapsulated performance score data).
For claim 9, the combination teaches the method in accordance with claim 8, wherein the data encapsulating the content item performance score includes one or more of the raw or normalized score, a webpage or website performance score, content data, webpage data, website data, an encoded file, and other data synthesized and/or extracted from the ordered content collection (see Wong, [0029], “results delivered to the client” comprise data content, [0040] – [0049], generate “active engagement score SPM” and “This information is normalized and weighted in order to create a quality score...for each piece of content so it can be compared and ranked,” representing normalizing score).
For claim 10, the combination teaches the method in accordance with claim 2, wherein the method further comprises receiving a selection, by the user, of a content item to produce a selected content item (see Wong, [0031], “presented with a list of topics for selection. The search engine then returns a list of URLs and/or HTML links in response to the query”).
For claim 11, the combination teaches the method in accordance with claim 10, wherein the method further comprises employing the selected content item for generating content for publishing on at least one webpage (see Wong, [0031], “presented with a list of topics for selection. The search engine then returns a list of URLs and/or HTML links in response to the query” where displaying of search results represents publishing on webpage to user for viewing).
For claim 12, the combination teaches the method in accordance with claim 9, wherein the characterization of the website includes a number of webpages, webpage views, a webpage size, a number, frequency and/or consistency of one or more content items on the webpage (see Ramer, [0815], “total # of page views” represents characterization of website via webpage views).
For claim 13, the combination teaches the method in accordance with claim 12, wherein the one or more metrics evaluating the content item includes one or more of a number of content views, a content size, a content type, a content origin, HTML tag, a “like,” a “forward,” a “comment,” an exclamation point, a question mark, or a number, frequency, and/or consistency of the content items on the page (see Wong [0027], “Facebook (Shares, Likes, Discussions), Twitter (Tweets, ReTweets), Google+ (+1s), Digg (Diggs), LinkedIn (Shares), Delicious, StumbleUpon (Stumbles), Reddit, and Pinterest (Pin count from button stats),” [0040] – [0044], “defines the polarity of appropriateness for each share, comment, etc.”).
For claim 14, Wong teaches a content scoring system for scoring communication content derived from a content collection for use in a generation of a communication to be published by a user, the system comprising:
at least one data processor of a first server; and
a memory storing instructions which, when executed by the at least one data processor, result in operations comprising:
a content database storing a content collection containing a plurality of content items collected from one or more social media platforms or online publications being hosted by a second server (see Wong, [0026] – [0028], “the processes described herein utilize this social engagement data to score the relevance of network objects identified in response to a user's query,” [0035] – [0036], “web service collects social engagement data from various social media sites for each URL identified in response to the query. For example, the number of shares, likes and discussions on Facebook, or tweets and retweets on Twitter, are active consumer engagement signals that can be collected through the API of these services for a specific UR” and “social sharing data are aggregated and processed by the web service,” [0038] – [0044], processing and storing social engagement data associated with relevant network objects), and a second set of data associated with particular content items, the second set of data including one or more metrics evaluating the content items (see Wong, [0035], “the web service collects social engagement data from various social media sites for each URL identified in response to the query,” [0038] – [0044], generating of “active engagement score SPM” that includes factor of “sentiment score” which defined “the polarity of appropriateness for each share, comment, etc., for example in a range from -100 (most negative) to +100 (most positive), based on a semantic analysis of the tone or attitude or context of the sharing event”);
Ramer teaches “the plurality of content items being associated with a first set of data related to the social media platform or online publication from which individual content items of the content collection were collected” (see Ramer, [0136], [0174], “Contextual information that may be associated with a website,” [0176], collecting “contextual website data parameters” that characterize “Entertainment” and “Weather” websites, [0487], [0885], [0907], [0991] – [0092], [1043], [1050], [01390], where collected “contextual website data parameters” that identify a website associated with categories such as florist, caterer, photographer, or sports represents first set of data associated with collected content items). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Wong with the teachings of Ramer to efficiently assess website characteristic data to return more relevant search results (see Ramer, [0176], [0972]).
The combination further teaches
scoring, by a scoring generator of the first server, the content items contained in the content collection to produce scored content items, the scoring being based in part on the first and second sets of data (see Wong, [0038] – [0044], scoring based on social engagement data related to collection from social media site and related to object(s) within media site; see Ramer, [0136], [0174], “Contextual information that may be associated with a website,” [0176], collecting “contextual website data parameters” that characterize “Entertainment” and “Weather” websites,), the scoring generator comprising:
a determination processor configured for:
weighting the first set of data relative to at least one parameter, and weighting the second set of data relative to the one or more metrics of the second set of data (see Wong, [0040] – [0044], “count of all relevant "shares" identified for various social networks” and “considering other factors and weighting results accordingly” and active engagement score SPM takes into account “Site Unique” visitors representing weighting the second set data; see Ramer, [0146], [0407], [0903], [0907], represents weighting the first and second data sets),
calculating a raw content item performance score for each collected content item of the content collection, the raw content item performance score being based at least partially on the weighting of the first and second data (see Wong, [0040] – [0044], “active engagement score SPM (Shares Per Thousand) that represents the number of sharing-events per thousand unique users for each URL” where active engagement score represent raw content item performance score; see Ramer, [0176], “The relevancy score may be a numerical summary of the statistical association between contextual website data parameters and mobile content parameters” and “receive a relevancy score for each of a plurality of websites. "Entertainment" websites may receive a higher relevancy score than the "Weather" websites,” [0407], [0903], calculating performance score), and
calculating a content item performance score by applying a normalization function to the raw content items performance score to produce a collection of scored content items (see Wong, [0040] – [0049], generate “active engagement score SPM” and “This information is normalized and weighted in order to create a quality score...for each piece of content so it can be compared and ranked,” where normalizing active engagement score to create quality score represents content item performance score; see Ramer, [0176], [0407], [0903], scored first and second data).
For claim 15, the combination teaches the platform in accordance with claim 14, further comprising a first server, the first server instantiating the at least one data processor (see Wong, [0022], “a computer system could include more than one processor”).
For claim 16, the combination teaches the platform in accordance with claim 15, further comprising a querying processor for querying, via an API coupling between a second server and the first server, in at least a passive manner, one or more of the plurality of webpages presented by at least the first server so as to continuously receive updated data, the updated data including one or more of updated content items of potential interest as well as updated first and second sets of data (see Wong, [0027], “analytical information is available through the application program interface (API) of the social network,” [0035], “collects social engagement data from various social media sites” that are collected through the API of these services for a specific URL” where collected social engagement data via API to social media servers represents querying, via API, for updated first data; see Ramer, [0488], “a mobile content website may be periodically analyzed for changes in content for purposes of assessing the relevance of keywords previously generated,” [01372], “connector may comprise an application programming interface or other code suitable to connect source and target data facilities” where application programming interface for periodically analyzing website changes represents passive querying via API to continuously receive updates for first and second data).
For claim 17, the combination teaches the platform in accordance with claim 16, further comprising a communication generator for selecting one or more scored content items for incorporation into a communication, the selecting being based at least in part on a score of each selected content item, and generating a communication, the generated communication comprising at least a portion of each of the selected scored content items (see Wong, [0029], [0032], select at least one stored search results for communication).
For claim 18, the combination teaches the platform in accordance with claim 17, further comprising a mapping processor for mapping and ordering each of the scored content items of the content collection in relation to its associated weighted first and weighted second set of data, to produce a functionally ordered and scored content collection (see Wong, [0028], [0037] – [0044], “normalized” score data represents mapping and where generating ranking from top ranking to lowest ranking represents ordering of quality score to produce functionally ordered and score content collection).
For claim 19, Wong teaches a method for retrieving and scoring online content for use by a consumer in evaluating communication content for use in generating a communication, the method comprising:
characterizing, by a first server computer, based on a level of online interaction, one or more interests of the consumer (see Wong, [0011] – [0012], “engagement data is collected and factored”);
searching, by the first server computer via a network, a webpage having one or more published items being hosted by a second server computer, each published item including one or more content items, the searching resulting in an identified content item of interest to the consumer (see Wong, [0011] – [0012], [0026], [0035], search “web pages”, in response to user queries” and where “the web service collects social engagement data from various social media sites for each URL identified in response to the query”);
retrieving, by the first server computer from the second server computer, the identified content item of interest from the webpage…a second set of data associated with the retrieved content item of interest…and the second set of data including one or more metrics evaluating the retrieved content item of interest (see Wong, [0026], “restaurant may be represented by a set of URLs representing different web pages, for example, the home page of the restaurant, a menu page, a reservations page, a collection of reviews of the restaurant on sites like Yelp and/or Zagat,” where web sites that host content represents one or more second servers, [0027], [0034], “The ingested URLs are indexed and stored by the web service in step 212,” where objects grouped within the web pages represent plurality of content items, [0035], “the web service collects social engagement data from various social media sites for each URL identified in response to the query,” [0038] – [0044], generating of “active engagement score SPM” that includes factor of “sentiment score” which defined “the polarity of appropriateness for each share, comment, etc., for example in a range from -100 (most negative) to +100 (most positive), based on a semantic analysis of the tone or attitude or context of the sharing event” representing second set of metric data);
Ramer teaches “retrieving…a first set of data associated with the webpage from which the content item is retrieved” (see Ramer, [0136], [0174], “Contextual information that may be associated with a website,” [0176], collecting “contextual website data parameters” that characterize “Entertainment” and “Weather” websites, [0487], [0885], [0907], [0991] – [0092], [1043], [1050], [01390], where collected “contextual website data parameters” that identify a website associated with categories such as florist, caterer, photographer, or sports represents first set of data associated with collected content items) and “the first set of data including a characterization of the webpage from which the content item was retrieved” (see Ramer, [0136], [0174], [0176], collecting “contextual website data parameters” that characterize “Entertainment” and “Weather” websites, [0487], [0885], [0907], [0991] – [0092], [1043], [1050], [01390], where collected “contextual website data parameters” that identify a website associated with categories such as florist, caterer, photographer, or sports represents first set of data associated with collected content items). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Wong with the teachings of Ramer to efficiently assess website characteristic data to return more relevant search results (see Ramer, [0176], [0972]).
The combination further teaches
calculating, by the first server computer, both a content item raw performance score (see Wong, [0040] – [0044], “active engagement score SPM (Shares Per Thousand) that represents the number of sharing-events per thousand unique users for each URL” and associated “a sentiment score...may be factored into the social engagement score SPM” and sentiment score “defines the polarity of appropriateness for each share, comment, etc., for example in a range from -100 (most negative) to +100 (most positive), based on a semantic analysis of the tone or attitude or context of the sharing event” where active engagement score and factored sentiment score represent raw content item performance score; see Ramer, [0176], “The relevancy score may be a numerical summary of the statistical association between contextual website data parameters and mobile content parameters” and “receive a relevancy score for each of a plurality of websites. "Entertainment" websites may receive a higher relevancy score than the "Weather" websites,” [0407], [0903], calculating performance score) and a relevance score for the retrieved content item of interest to produce a raw scored content item, the content item raw performance score being based in part on the first and second sets of data and one or more determined relevance factors (see Wong, [0013], [0040] – [0049], “This information is normalized and weighted in order to create a quality score...for each piece of content so it can be compared and ranked,” where normalizing active engagement score to create quality score represents calculating relevance performance score);
storing, at a database associated with the first server computer, the raw scored content item to produce a collection of raw scored content items (see Wong, [0040] – [0044]; see Ramer, [0176], [0407], [0903], scores are stored to analyze relevance to user queries); and
weighting, by the first server computer, based on the determined relevance score, the raw scored content item (see Wong, [0040] – [0044], “weighting results accordingly” in calculating active engagement score; see Ramer, [0146], [0407], [0903], [0907], represents weighting the first and second data sets for relevance scoring); and
mapping, by the first server computer, each weighted and scored content item of the content collection to produce a final ordered scored content collection, which order is based at least partially on the weighted score for each respective content item (see Wong, [0013], [0028], [0037] – [0044], “normalized” or transformed data for scoring data represents mapping and where generating “ranked” content items represents final ordered scored content collection; see Applicant’s Specification, US 2022/0067113, [0059], “A mapping function can be applied to the raw score in order to transform the raw score into a content item performance score within a minimum value and a maximum value, for example 0-10”).
For claim 20, the combination teaches the method in accordance with claim 19, further comprising querying, via an API coupling between a second server and the first server, in at least a passive manner, one or more of the plurality of webpages presented by at least the first server so as to continuously receive updated data, the updated data including one or more of updated content items of potential interest as well as updated first and second sets of data (see Wong, [0027], “analytical information is available through the application program interface (API) of the social network,” [0035], “collects social engagement data from various social media sites” that are collected through the API of these services for a specific URL” where collected social engagement data via API to social media servers represents querying, via API, for updated first data; see Ramer, [0488], “a mobile content website may be periodically analyzed for changes in content for purposes of assessing the relevance of keywords previously generated,” [01372], “connector may comprise an application programming interface or other code suitable to connect source and target data facilities” where application programming interface for periodically analyzing website changes represents passive querying via API to continuously receive updates for first and second data).
For claim 21, the combination teaches the method in accordance with claim 20, further comprising: evaluating the final ordered scored content collection, and selecting a final scored content item for use in a communication (see Wong, [0037], [0044]; see Ramer, [0131], [0405] – [0408], “ranked” scored items presented as search results).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wong US 2013/0204871 (hereinafter Wong) and Ramer et al., US 2009/0240568 (hereinafter Ramer) and further in view of Chien et al., US 2009/0055257 (hereinafter Chien).
For claim 7, Chien teaches the method in accordance with claim 5, wherein the relative weighting comprises characterizing a content-type dependent scaling of a pre-weighted raw content item performance score (see Chien, [0008], [0050], “the affinity score has a scale from 0.00 to 1.00, however, one of ordinary recognizes other scales such as, for example, 0 to 100, or another scale”). It would have been obvious to one skilled in the art at the time of the invention to modify the teachings of Wong and Ramer with the teachings of Chien to provide adjustment scaling of score data to reflect the variable data set being scored (see Chien, [0008], [0050]).
Response to Amendments and Arguments
Applicant’s filing of a terminal disclaimer with respect to nonstatutory double patenting rejections have been fully considered and are persuasive. The nonstatutory double patenting rejections have been withdrawn.
Applicant's arguments with respect to claims rejected under 35 U.S.C. 103 have been fully considered but they are not persuasive.
The applicant argues “Wong is deficient for failing to teach or suggest the characterizing of an online user based on the online activity of that user” because “Wong simply ranks the order of documents that are returned in response to a search query.” The applicant further contends “Wong is further defective for failing to teach the searching, identifying, collecting, storing, and eventually scoring of the content items with which that user interacts.” The examiner respectfully disagrees. Wong teaches a method of collecting “social engagement data” from an online user that includes “social check-ins” and “clicks” (see Wong, [0012], [0028]). As disclosed in the corresponding rejection above, this collected social engagement data (that characterizing an online user) is then in scoring and ranking documents when a search is conducted (see Wong, [0030] – [0031], [0040] – [0044], “count of all relevant "shares" identified for various social networks” and “considering other factors and weighting results accordingly”).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/JENSEN HU/Primary Examiner, Art Unit 2169
