DETAILED ACTION
This office action is responsive to the above identified application filed 09/20/2022. The application contains claims 1-20, all examined and rejected.
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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Almeida et al. (US 20170357650 A1) in view of Lawrence (US 20120233142 A1).
Regarding claim 1, Almeida et al. (US 20170357650 A1) discloses:
a computer-implemented method for filtering search engine results for a user, at least by (paragraph [0031] describes re-ranking search results for a user from a search server (e.g. search engine results for a user)
comprising: maintaining a filtration layer that is at least by (paragraph [0039] describes client re-ranker module separate from client browser/search module and search server, which creates a separate re-ranking/filtration layer; paragraph [0060] further describes “opt in” or “opt out” options related to what kind of personal information is used)
building a user search interaction model, operatively coupled to the filtration layer, based on a user's profile and historic search results by performing a topic analysis on a user's interactions with the historic search results, at least by (paragraph [0031] “interest model reflects user interest based on the user's browsing history and engagement history”)
and selecting a subset of relevant topics based on respective amounts of user interaction, where the user interaction includes interactions at least by (paragraph [0044] “converts each URL in the browser history into a set of topics using the topic model. In this embodiment, process 400 uses the topic model, which is a map of URLs to topics, to generate a set of topics from the browser history URLs…. the set of topics generated from the browser history reflects the type of topics the user frequents by the browser”, see also paragraph [0049] “a user's top K topics and topic scores” are subset of relevant topics based on respective amounts of user interaction (see para. 0046-0048 where the K topics and topic scores are calculated based on user interactions)
and filtering search results produced for a particular user search query on the client device using the user search interaction model and the filtration layer, at least by (paragraph [0057] “a client re-ranker module 108 that re-scores search results using the interest model… client re-ranker module 108 includes a receive results module 1002, calculate personalization score module 1004, adjust ranking score module 1006, determine new rankings module 1008, and return results… return results module 1010 returns the re-ranked search results”)
Regarding maintaining a filtration layer that is opted into by a search engine and a client device
Lawrence teaches the above limitations at least by (paragraph [0073-0074] which describes two options including client side implementation or server-side implementation, where the server-side implementation does the personalization of search results based on user profile (e.g. filtration layer) on the server side (e.g. search engine layer) and the client-side implementation does the personalization of search results based on user profile (e.g. filtration layer) on the client (e.g. client device), as such, depending on the implementation, the personalization of search results needs to be implemented/“opted into” either by the search server or the client.
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of Almeida with option of either client or server side system of Lawrence as both may have their own advantages, where client-side implementation may reduce the server's workload, and server side may be able to process larger number of documents using server resources as opposed to the limited resources on the client side (Lawrence, para. 0075).
Regarding: interactions on different devices.
Lawrence teaches the above limitations at least by (paragraph [0042] “ a user profile is created and stored on a server (e.g., user profile server 108) associated with a search engine. The advantage of such deployment is that the user profile can be easily accessed by multiple computers” where the user profile incudes interactions related to the user, accessed by multiple computers (e.g. interactions on different devices)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of Almeida with profiles provided by Lawrence to be able to access the user profile from multiple computers (Lawrence, para. 0042)
As per claim 2, claim 1 is incorporated and Almeida further discloses:
wherein the filtration layer is a client- side layer in a client device browser, at least by (paragraph [0035] “the client 102 includes a browser 104, client search module 106, client re-ranker 108”)
As per claim 3, claim 1 is incorporated and Almeida fails to discloses:
wherein the filtration layer is a search engine layer associated with the user.
However, Lawrence (US 20120233142 A1) teaches the above limitations at least by (paragraph [0073-0074] which describes two options including client side implementation or server-side implementation, where the server-side implementation does the personalization of search results based on user profile (e.g. filtration layer) on the server side (e.g. search engine layer)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of Almeida with the server-side system of Lawrence to be able to process larger number of documents using server resources as opposed to the limited resources on the client side (Lawrence, para. 0075).
As per claim 4, claim 1 is incorporated and Almeida further discloses:
wherein the topic analysis on the user's interactions with the historic search results is based on common search terms, browser usage, emails, and other opt-ed in user profile determinants, at least by (paragraph [0044] “converts each URL in the browser history into a set of topics using the topic model… set of topics generated from the browser history reflects the type of topics the user frequents by the browser” paragraph [0046-0048] further describes how user’s interactions with search results including browser usage is used in the topic analysis, paragraph [0060] further describes other personal information that the user can select to “opt in” or “opt out”)
As per claim 5, claim 1 is incorporated and Almeida further discloses:
wherein the common search terms, the browser usage, the emails, and the other opt-ed in user profile determinants are clustered and classified to extract features indicative of the user as represented by the at least by (paragraph [0038] “this interest model remains on client 102 so that the user's browsing interests remain private and located on the client 102… interest model module 110 builds an interest model using the browser history and search engagement by creating a set of feature vectors from the documents referenced in the browser history and search engagements, since these feature vectors are calculated for the user and kept on user’s client device these features are indicative of the user as represented by the user's profile)
But Almeida fails to specifically recite user profile
However, Lawrence teaches the above limitations at least by (paragraph [0042] “ a user profile is created and stored on a server (e.g., user profile server 108) associated with a search engine. The advantage of such deployment is that the user profile can be easily accessed by multiple computers” where the user profile incudes interactions related to the user, accessed by multiple computers (e.g. interactions on different devices)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of Almeida with user profiles provided by Lawrence to be able to access the user profile from multiple computers (Lawrence, para. 0042)
As per claim 6, claim 1 is incorporated and Almeida further discloses:
further comprising, for the particular search query, finding, by the user search interaction model, distances of search terms and disambiguations of the search terms with associated search keywords to the subset of relevant topics, at least by (paragraph [0049] “receives the search results from the search server. In one embodiment, the search results include a set of URLs and a ranking score for each of these URLs. In one embodiment, process 500 further applies the topic model to each of the documents returned from the server as part of the search results. In this embodiment, applying the topic model to each of the search result documents enables a client-side similarity calculation between the user interest model topics and the search results and to compute the “result_score_for_feature” value used” where the topic model includes a cluster of terms appearing is a plurality of seed documents (claim 2, para. 0010), as such the similarity calculation disambiguates the terms appearing in the document to particular topics)
As per claim 7, claim 6 is incorporated and Almeida further discloses:
wherein the associated keywords are extracted features from the historic search results, at least by (paragraph [0046] “converts each document in the browser history and the search engagements into a feature vector” and wherein the disambiguations are the extracted features from the current search results, at least by (paragraph [0049] “ applies the topic model to each of the documents returned from the server as part of the search results. In this embodiment, applying the topic model to each of the search result documents enables a client-side similarity calculation between the user interest model topics and the search results and to compute the “result_score_for_feature”” By applying the similarity calculation between the user interest model topics and the search results, the terms in current search results are disambiguated into particular matching topics extracted form historic search results and user interaction.)
As per claim 8, claim 6 is incorporated and Almeida further discloses:
wherein finding the distances of the search terms and the disambiguations of the search terms comprises running each of the historic search results through a process which finds a cosine distance of a search keyword to concepts and features found within with the historic search results such that if the extracted features are similar to ones the user searches and interacts with more than a threshold amount, then the extracted features will be assigned a shorter distance with increasing interaction resulting in decreasing distance, at least by (paragraph [0046-0048] which describes topic scores based on user interactions, where the weighting of the score increases based on the number of interactions, and the similarity calculations being based on the user interest model topics which includes the weighted topic scores and the search results, as such based on higher interaction and higher weights the similarity calculation also increase, “decreasing the distance” (e.g. more similar))
As per claim 9, claim 1 is incorporated and Almeida further discloses:
further comprising capturing, by the user search interaction model, user re-searching and interaction with results to determine model accuracy and improves upon itself via a feedback loop trained on itself, at least by (paragraph [0035-0037] which further describes how user’s interaction with search results, tracking whether search results are engaged, rendered, or abandoned, are used in a feedback loop to influence re-ranking)
As per claim 10, claim 1 is incorporated and Almeida further discloses:
wherein search criteria is monitored, measured, and weighted, the search criteria comprising search string entered, similarity of search strings after an initial search query, a number of pages returned, an inter arrival time entering the page, a number of hyperlinks clicked on, a number of hyperlinks ignored, a number of back and forth clicks between pages, and a length of time on a page, at least by (paragraph [0037] “ feedback package includes the information <query, result, render counts, engagement counts, abandonment counts>, where query is the input query and context information such as, device type, application, locale, and geographic location, result is the render result, render counts is the number of times the result is rendered for that query, engagement counts is the number of times the result is engaged for that query, and abandonment counts is the number of times that result is abandoned.”)
Lawerence further describes, an inter arrival time entering the page a number of back and forth clicks between pages, and a length of time on a page, at least by (paragraph [0034] “information about a user's activities 209 with respect to the user selected documents (sometimes herein call the identified documents), such as how long the user spent viewing the document, the amount of scrolling activity on the document, and whether the user has printed, saved or bookmarked the document, also suggests the importance of the document to the user as well as the user's preferences” paragraph [0050] “Each preferred URL may be further weighted according to the time spent by the user and the user's scrolling activity at the URL, and/or other user activities
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of Almeida with other user activities provided by Lawrence to be able to select only document that received significant user activity (Lawrence, para. 0034).
Claims 11-19 recite equivalent claim limitations as claims 1-9 above, except that they set forth the claimed invention as a computer program product comprising a non-transitory computer readable storage medium; Claim 20 recite equivalent claim limitations as claim 20 above, except that they set forth the claimed invention as a system, as such they are rejected for the same reasons as applied hereinabove.
Conclusion
Related references not relied upon:
US 8661029 B1: describes ranking documents based on user feedback such as how long the views are for each document (abstract)
US 20050216434 A1: A user interest profile identifies topics of interest to a user. Results can be ranked by their unboosted information retrieval score, thus reflecting no personalization, or by their fully or partially boosted information retrieval scores. This allows the user to selectively control how their interests affect the ranking of the documents. (abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS TRUONG whose telephone number is (571)270-3157. The examiner can normally be reached Monday - Friday 7:00 am - 3:30 pm PT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jail can be reached at 571-270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS TRUONG/Primary Examiner, Art Unit 2152 10/16/2025