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
In response to communications filed on 21 May 2026, claims 1-8, 11-18, and 20-21 are presently pending in the application, of which claims 1, 11 and 20 are presented in independent form. The Examiner acknowledges amended claims 1, 6, 11, 18, and 20 and cancelled claims 9 and 19. Claim 10 was previously cancelled.
Response to Remarks/Arguments
All objections and/or rejections issued in the previous Office Action have been withdrawn, unless otherwise noted in this Office Action.
Applicant’s arguments with respect to claims 1-8, 11-18, and 20-21 have been considered but are not persuasive. Applicant’s arguments are directed to amended features and have been incorporated into the rejection below.
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.
Claims 1-9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable by Liu, Jingo (U.S. 2023/0244727 and known hereinafter as Liu) in view of Brukman, Michael, et al (U.S. 2014/0095495 and known hereinafter as Brukman) and in further view of Xie, Yubeng, et al (U.S. 2015/0066888 and known hereinafter as Xie)(newly presented).
As per claim 1, Liu teaches a computer-implemented method, comprising:
extracting, by the processor set, a user’s real-time context (e.g. Liu, see paragraphs [0058-0065], which discloses when the query is submitted, the query is extracted to determine the subset of queries that would need to be performed, where the subset of queries may be contextual queries to be performed or inputted by the machine learning engine.);
predicting, by the processor set via the machine learning module, a user preference based on the correlating the user’s real-time context to the historical query data (e.g. Liu, see paragraphs [0060-0069], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby predicting items that would be of greater interests to the user.), wherein correlating the user’s real-time context to the historical query data comprises comparing contextual data to the historical query data to identify pattern in a behavior of the user and the user preference with respect to specific times and locations (e.g. Liu, see paragraphs [0066-0067], which discloses one or more databases may also store historical search data, where historical search data can generally include any data associated with previous search queries submitted by users, where for each previously submissions of search queries, search event data is stored in a table, where, see paragraph [0080], the search event data and ranking features can be used to generate a training dataset, where the training dataset can comprise a plurality of training event samples, each sample corresponds to a record from that search event data. The Examiner notes the search event data also includes data that identifies individual engagement information (e.g. contextual data) in which, see paragraphs [0082-0083], where the training event samples are labeled to identify a pattern of behavior, which is then used to predict users’ preferences for corresponding items, whereby the greater scores indicate a higher likelihood that users will desire or prefer a particular result.);
calculating, by the processor set, a correlation score for the search result in a first list of search results based on the user preference (e.g. Liu, see paragraphs [0060-0069, 0082-0089], which discloses calculating a ranked score of the items to indicate or predict the user’s interest.);
re-ranking, by the processor set, the first list of search results based on the correlation score (e.g. Liu, see paragraphs [0082-0089], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby refining the ranking (e.g. re-ranking).); and
rendering, by the processor set, a re-ranked search result as a second list of search results based on the re-ranking (e.g. Liu, see paragraphs [0060-0069], which discloses the GUI displays to the user the items in the search results in a sequence or order that is based on item rankings.).
Liu does not explicitly disclose submitting, by the processor set, the search query to a search engine, wherein the search query does not include personal data of a user; and receiving, by the processor set, a first list of search results from the search engine.
Brukman teaches submitting, by the processor set, the search query to a search engine, wherein the search query does not include personal data of a user (e.g. Brukman, see paragraphs [0025-0026], which discloses a user from the client submits a search request to the information server, where the search request may include a search query comprising one or more query terms and unique identifiers to one or more of the following entities, the requesting user and the requesting client. The Examiner notes the unique identifiers do not contain personal data of a user, however contains contextualized information consistent with the Applicant’s disclosure.); and
receiving, by the processor set, a first list of search results from the search engine (e.g. Brukman, see paragraphs [0025-0028], which discloses a user from the client submits a search request to the information server, where the search request may include a search query comprising one or more query terms and unique identifiers to one or more of the following entities, the requesting user and the requesting client. Additionally, the front-end server passes the search query onto the search engine, where the search engine communicates with the content database and the document profile data to select a plurality of information items in response to the search query.).
Liu is directed to improving search result personalization and contextualization using machine learning models. Brukman is directed to promoting personalized search results based on personal information. It would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Liu with the teachings of Brukman to include the claimed features with the motivation to tailor personal search results.
The modified teachings of Liu and Brukman do not explicitly disclose monitoring, by a processor set of a client-side device, historical query data comprising a search query and a search result; and correlating, by the processor set via a machine learning module, the user’s real-time context to the historical query data stored in a local repository.
Xie teaches monitoring, by a processor set of a client-side device, historical query data comprising a search query and a search result (e.g. Xie, see paragraphs [0020-0021], which discloses a user feedback log is obtained based on the first set of query keywords, where the log is obtained from a database that includes historic query results using the first set of query keywords as the target of the query and the selection frequency by the users. Further, based on user feedback logs, the latent word meaning relationship between the query word and the historical query results that embody the user’s intention can be reliably and automatically found, therefore the user feedback log may include all previous historical query results that use the first set of query keywords as the search target and all previously click selecting frequencies of the users on the historical query results.); and
correlating, by the processor set via a machine learning module, the user’s real-time context to the historical query data stored in a local repository (e.g. Xia, see paragraphs [0020-0022], which discloses the user feedback log is gathered by the search engine, where keyword input by the users, past query results, click frequencies on the past query results, display frequencies of the past query results, etc, are all collected by the search engine and stored in the user feedback database.).
Liu is directed to improving search result personalization and contextualization using machine learning models. Brukman is directed to promoting personalized search results based on personal information. Xie is directed to querying information. All are directed to contextualizing query results and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Liu with the teachings of Brukman to include the claimed features with the motivation to tailor personal search results.
As per claim 11, Liu teaches a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
extracting, by the processor set, a user’s real-time context (e.g. Liu, see paragraphs [0058-0065], which discloses when the query is submitted, the query is extracted to determine the subset of queries that would need to be performed, where the subset of queries may be contextual queries to be performed or inputted by the machine learning engine.);
predicting, by the processor set via the machine learning module, a user preference based on the correlating the user’s real-time context to the historical query data (e.g. Liu, see paragraphs [0060-0069], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby predicting items that would be of greater interests to the user.), wherein correlating the user’s real-time context to the historical query data comprises comparing contextual data to the historical query data to identify pattern in a behavior of the user and the user preference with respect to specific times and locations (e.g. Liu, see paragraphs [0066-0067], which discloses one or more databases may also store historical search data, where historical search data can generally include any data associated with previous search queries submitted by users, where for each previously submissions of search queries, search event data is stored in a table, where, see paragraph [0080], the search event data and ranking features can be used to generate a training dataset, where the training dataset can comprise a plurality of training event samples, each sample corresponds to a record from that search event data. The Examiner notes the search event data also includes data that identifies individual engagement information (e.g. contextual data) in which, see paragraphs [0082-0083], where the training event samples are labeled to identify a pattern of behavior, which is then used to predict users’ preferences for corresponding items, whereby the greater scores indicate a higher likelihood that users will desire or prefer a particular result.);
calculating, by the processor set, a correlation score for the search result in a first list of search results based on the user preference (e.g. Liu, see paragraphs [0060-0069, 0082-0089], which discloses calculating a ranked score of the items to indicate or predict the user’s interest.);
re-ranking, by the processor set, the first list of search results based on the correlation score (e.g. Liu, see paragraphs [0082-0089], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby refining the ranking (e.g. re-ranking).); and
rendering, by the processor set, a re-ranked search result as a second list of search results based on the re-ranking (e.g. Liu, see paragraphs [0060-0069], which discloses the GUI displays to the user the items in the search results in a sequence or order that is based on item rankings.).
Liu does not explicitly disclose submitting, by the processor set, the search query to a search engine, wherein the search query does not include personal data of a user; and receiving, by the processor set, a first list of search results from the search engine.
Brukman teaches submitting, by the processor set, the search query to a search engine, wherein the search query does not include personal data of a user (e.g. Brukman, see paragraphs [0025-0026], which discloses a user from the client submits a search request to the information server, where the search request may include a search query comprising one or more query terms and unique identifiers to one or more of the following entities, the requesting user and the requesting client. The Examiner notes the unique identifiers do not contain personal data of a user, however contains contextualized information consistent with the Applicant’s disclosure.); and
receiving, by the processor set, a first list of search results from the search engine (e.g. Brukman, see paragraphs [0025-0028], which discloses a user from the client submits a search request to the information server, where the search request may include a search query comprising one or more query terms and unique identifiers to one or more of the following entities, the requesting user and the requesting client. Additionally, the front-end server passes the search query onto the search engine, where the search engine communicates with the content database and the document profile data to select a plurality of information items in response to the search query.).
Liu is directed to improving search result personalization and contextualization using machine learning models. Brukman is directed to promoting personalized search results based on personal information. It would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Liu with the teachings of Brukman to include the claimed features with the motivation to tailor personal search results.
The modified teachings of Liu and Brukman do not explicitly disclose monitoring, by a processor set of a client-side device, historical query data comprising a search query and a search result; and correlating, by the processor set via a machine learning module, the user’s real-time context to the historical query data stored in a local repository.
Xie teaches monitoring, by a processor set of a client-side device, historical query data comprising a search query and a search result (e.g. Xie, see paragraphs [0020-0021], which discloses a user feedback log is obtained based on the first set of query keywords, where the log is obtained from a database that includes historic query results using the first set of query keywords as the target of the query and the selection frequency by the users. Further, based on user feedback logs, the latent word meaning relationship between the query word and the historical query results that embody the user’s intention can be reliably and automatically found, therefore the user feedback log may include all previous historical query results that use the first set of query keywords as the search target and all previously click selecting frequencies of the users on the historical query results.); and
correlating, by the processor set via a machine learning module, the user’s real-time context to the historical query data stored in a local repository (e.g. Xia, see paragraphs [0020-0022], which discloses the user feedback log is gathered by the search engine, where keyword input by the users, past query results, click frequencies on the past query results, display frequencies of the past query results, etc, are all collected by the search engine and stored in the user feedback database.).
Liu is directed to improving search result personalization and contextualization using machine learning models. Brukman is directed to promoting personalized search results based on personal information. Xie is directed to querying information. All are directed to contextualizing query results and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Liu with the teachings of Brukman to include the claimed features with the motivation to tailor personal search results.
As per claim 20, Liu teaches a system comprising:
a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media (Liu, see Figures 1 and 2, which discloses a processor coupled ot memory and a communication interface.), the program instructions executable to:
extracting, by the processor set, a user’s real-time context (e.g. Liu, see paragraphs [0058-0065], which discloses when the query is submitted, the query is extracted to determine the subset of queries that would need to be performed, where the subset of queries may be contextual queries to be performed or inputted by the machine learning engine.);
predicting, by the processor set via the machine learning module, a user preference based on the correlating the user’s real-time context to the historical query data (e.g. Liu, see paragraphs [0060-0069], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby predicting items that would be of greater interests to the user.), wherein correlating the user’s real-time context to the historical query data comprises comparing contextual data to the historical query data to identify pattern in a behavior of the user and the user preference with respect to specific times and locations (e.g. Liu, see paragraphs [0066-0067], which discloses one or more databases may also store historical search data, where historical search data can generally include any data associated with previous search queries submitted by users, where for each previously submissions of search queries, search event data is stored in a table, where, see paragraph [0080], the search event data and ranking features can be used to generate a training dataset, where the training dataset can comprise a plurality of training event samples, each sample corresponds to a record from that search event data. The Examiner notes the search event data also includes data that identifies individual engagement information (e.g. contextual data) in which, see paragraphs [0082-0083], where the training event samples are labeled to identify a pattern of behavior, which is then used to predict users’ preferences for corresponding items, whereby the greater scores indicate a higher likelihood that users will desire or prefer a particular result.);
calculating, by the processor set, a correlation score for the search result in a first list of search results based on the user preference (e.g. Liu, see paragraphs [0060-0069, 0082-0089], which discloses calculating a ranked score of the items to indicate or predict the user’s interest.);
re-ranking, by the processor set, the first list of search results based on the correlation score (e.g. Liu, see paragraphs [0082-0089], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby refining the ranking (e.g. re-ranking).); and
rendering, by the processor set, a re-ranked search result as a second list of search results based on the re-ranking (e.g. Liu, see paragraphs [0060-0069], which discloses the GUI displays to the user the items in the search results in a sequence or order that is based on item rankings.).
Liu does not explicitly disclose submitting, by the processor set, the search query to a search engine, wherein the search query does not include personal data of a user; and receiving, by the processor set, a first list of search results from the search engine.
Brukman teaches submitting, by the processor set, the search query to a search engine, wherein the search query does not include personal data of a user (e.g. Brukman, see paragraphs [0025-0026], which discloses a user from the client submits a search request to the information server, where the search request may include a search query comprising one or more query terms and unique identifiers to one or more of the following entities, the requesting user and the requesting client. The Examiner notes the unique identifiers do not contain personal data of a user, however contains contextualized information consistent with the Applicant’s disclosure.); and
receiving, by the processor set, a first list of search results from the search engine (e.g. Brukman, see paragraphs [0025-0028], which discloses a user from the client submits a search request to the information server, where the search request may include a search query comprising one or more query terms and unique identifiers to one or more of the following entities, the requesting user and the requesting client. Additionally, the front-end server passes the search query onto the search engine, where the search engine communicates with the content database and the document profile data to select a plurality of information items in response to the search query.).
Liu is directed to improving search result personalization and contextualization using machine learning models. Brukman is directed to promoting personalized search results based on personal information. It would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Liu with the teachings of Brukman to include the claimed features with the motivation to tailor personal search results.
The modified teachings of Liu and Brukman do not explicitly disclose monitoring, by a processor set of a client-side device, historical query data comprising a search query and a search result; and correlating, by the processor set via a machine learning module, the user’s real-time context to the historical query data stored in a local repository.
Xie teaches monitoring, by a processor set of a client-side device, historical query data comprising a search query and a search result (e.g. Xie, see paragraphs [0020-0021], which discloses a user feedback log is obtained based on the first set of query keywords, where the log is obtained from a database that includes historic query results using the first set of query keywords as the target of the query and the selection frequency by the users. Further, based on user feedback logs, the latent word meaning relationship between the query word and the historical query results that embody the user’s intention can be reliably and automatically found, therefore the user feedback log may include all previous historical query results that use the first set of query keywords as the search target and all previously click selecting frequencies of the users on the historical query results.); and
correlating, by the processor set via a machine learning module, the user’s real-time context to the historical query data stored in a local repository (e.g. Xia, see paragraphs [0020-0022], which discloses the user feedback log is gathered by the search engine, where keyword input by the users, past query results, click frequencies on the past query results, display frequencies of the past query results, etc, are all collected by the search engine and stored in the user feedback database.).
Liu is directed to improving search result personalization and contextualization using machine learning models. Brukman is directed to promoting personalized search results based on personal information. Xie is directed to querying information. All are directed to contextualizing query results and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Liu with the teachings of Brukman to include the claimed features with the motivation to tailor personal search results.
As per claims 2 and 12, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 1 and the computer program product of claim 11, respectively, wherein the user’s real-time context comprises a user’s age, gender, family members and relations, personal preferences, search history, browsing behavior, location data, time data, and human-computer interaction patterns (e.g. Liu, see paragraphs [0051-0055], which discloses when the search engine receives the search query, it determines the type of query, by monitoring whether it is a head query, which is a frequent query, or a tail query, which is an infrequent query, where see paragraphs [0066-0069], which discloses one or more databases that stores historical search data that determines the frequency of the search query.).
As per claims 3 and 13, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 2 and the computer program product of claim 12, respectively, further comprising adjusting the user’s real-time context based on user feedback (e.g. Liu, see paragraphs [0058-0065], which discloses when the query is submitted, the query is extracted to determine the subset of queries that would need to be performed, where the subset of queries may be contextual queries to be performed or inputted by the machine learning engine.).
As per claims 4 and 14, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 1 and the computer program product of claim 11, respectively, wherein the historical query data comprises search keywords, selected uniform resource locators in the list of returned search results, daily web surfing history, real-time user interactions, social media account data, voice recordings, video recordings, and internet of things sensor data (e.g. Liu, see paragraphs [0060-0069], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby predicting items that would be of greater interests to the user.).
As per claims 5 and 15, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 1 and the computer program product of claim 11, respectively, wherein the predicting the user preference comprises learning, via natural language processing, a personal characteristic of the user associated with topics under different contexts associated with the user’s searching and clicking patterns (e.g. Liu, see paragraphs [0051-0055], which discloses when the search engine receives the search query, it determines the type of query, by monitoring whether it is a head query, which is a frequent query, or a tail query, which is an infrequent query, where see paragraphs [0066-0069], which discloses one or more databases that stores historical search data that determines the frequency of the search query.).
As per claims 6 and 16, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 1 and the computer program product of claim 11, respectively, wherein the predicting the user preference comprises performing sentiment analysis, topic modeling, user profiling, or collaborative filtering of the user’s real-time context and historical query data (e.g. Liu, see paragraphs [0058-0065], which discloses when the query is submitted, the query is extracted to determine the subset of queries that would need to be performed, where the subset of queries may be contextual queries to be performed or inputted by the machine learning engine.).
As per claims 7 and 17, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 1 and the computer program product of claim 11, respectively, further comprising: determining a user profile based on the historical query data and the user’s real-time context; and updating the user profile based on the user preference (e.g. Liu, see paragraphs [0060-0069], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby predicting items that would be of greater interests to the user.).
As per claims 8 and 18, the modified teachings of Liu, Brukman, and Xie teaches the computer-implemented method of claim 1 and the computer program product of claim 11, respectively, wherein the correlating the user’s real-time context to the historical query data occurs via word correlation, machine learning, or natural language processing (e.g. Liu, see paragraphs [0082-0089], which discloses the machine learning model provides rankings of search results and then personalizes the ranking based on previous historical search queries, thereby refining the ranking.).
As per claim 21, the modified teachings of Liu, Brukman, and Xie teaches the system of claim 20, wherein the program instructions are further executable to:
allow a user to configure and customize system settings including weights of reranking factors including a user’s family members and relations, personal preferences, search history, browse behavior, and interaction patterns (e.g. Liu, see paragraphs [0051-0055], which discloses when the search engine receives the search query, it determines the type of query, by monitoring whether it is a head query, which is a frequent query, or a tail query, which is an infrequent query, where see paragraphs [0066-0069], which discloses one or more databases that stores historical search data that determines the frequency of the search query.); and
adjust related setting according to user feedback (e.g. Liu, see paragraphs [0058-0065], which discloses when the query is submitted, the query is extracted to determine the subset of queries that would need to be performed, where the subset of queries may be contextual queries to be performed or inputted by the machine learning engine.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
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.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 August 11, 2026
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