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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new ground(s) of rejection.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Phillips et al. (hereinafter ‘Phillips’, Patent No. 11,917,245) in view of Miller (Pub. No. 2022/0150589) in view of Van Os et al. (hereinafter ‘Van Os’, Patent No. 11,838,579) in view of Tsoy et al. (hereinafter ‘Tsoy’, Pub. No. 2022/0327134).
Regarding claims 1, 5, 6, 8, 12, 13, 15, 19 and 20, Phillips teaches a computing system for candidate ranking (100, Fig. 1; col. 2 lines 36-67) (with corresponding method and non-transitory computer-readable medium) for content recommendation, comprising:
one or more memories; and at least one processor (processor 202, Fig. 2; col. 7 lines 2-19; col. 13 lines 1-23) each coupled to at least one of the memories and configured to perform operations comprising:
identifying a plurality of category candidates, wherein each of the plurality of category candidates (col. 4 lines 29-45; col. 6 lines 51-63; col. 10 line 61 to col. 11 line 5; col. 19 lines 11-28, where the content is for different data and platforms);
identifying time series data associated with each of the plurality of content candidates, wherein the time series data comprises a time period of user interaction with a respective content candidate col. 8 line 59 to col. 9 line 6; col. 11 lines 60-65);
ranking the category candidates based on a machine learning model, wherein the machine learning model is trained using a learning algorithm based on the time series data (col. 5 lines 4-14; col. 9 lines 7-25; col. 10 lines 7-25);
ranking the content candidates in each of the category candidates based on the time series data (col. 4 lines 17-26; col. 11 lines 60-65); and
causing the ranked category candidates and the ranked content candidates to be outputted for display (col. 11 lines 60-65).
On the other hand, Phillips does not explicitly teach
corresponds to a respective theme comprising a plurality of content candidates associated with the theme
receiving feedback indicating a subset of the ranked content candidates have not been interacted with by one or more users.
However, in an analogous art, Miller teaches a system for deep reinforcement learning for personalized content optimization (Abstract; [0003]-[0007]). Miller teaches monitoring users interactions for an amount of time (102, Fig. 1; [0017]; [0047]; [0048]) with the content and selecting a set of content categories (106, Fig. 1; [0019]). The system could be implemented on a user device or at the provider side. The system monitors user’s interactions at the user device (if the system is based on the server [0005], the interaction data is fed back to the system (114, Fig. 1; [0022]; [0023]). Miller, through a neural network, predicts content likely to be watched and decreases/increases category scores based on the monitoring (118, fig. 1; [0022]; [0023]; [0056]; [0057]). The system adjusts the rankings and generates a display of recommended categories content based on the adjusted model (120 and 122, Fig. 1; [0024]; [0025]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Phillips’ invention with Miller’s feature of monitoring user’s interactions and optimize category rankings/recommendations by increasing/decreasing category score for the benefit of “improving explorative recommendations to a user while avoiding recommendation of content that is not new to the user” (Miller: [0007]).
Additionally, although Phillips and Miller teach monitoring user interactions (content watched, genre watched, user content requests, etc., 102, 118, Fig. 1), they do not explicitly teach
generating a popularity score for one or more of the content candidates, wherein the popularity score comprises a number of times a particular content candidate has been searched for;
and ranking candidates based on the popularity score
However, in an analogous art, Van Os teaches a system teaches a system that suggests content based on multiple factors such as content popularity, user preference, selection history, query (search) history, query (search) popularity, etc. (col. 44 lines 25-42). The queries can be input using pressing buttons, interfaces, or spoken commands (col. 43 lines 30-57).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Phillips and Miller’s invention with Van Os’ feature of considering search history/popularity and content popularity to be considered the monitored inputs and the reasons for recommending content for the benefit of including different methods of input that may be missed such as spoken searches.
For claims 5, 6, 12, 13, 19 and 20:
These steps are repeated multiple times based on the monitoring/feedback (Phillips: 422, 424, Fig. 4. Miller: Repeat 5; Fig. 1).
Finally, Phillips, Miller and Van Os do not explicitly teach
applying an impression discount algorithm to the subset of the ranked content candidates that have not been interacted with, wherein the impression discount algorithm reduces a ranking of an individual content candidate of the subset of the ranked content candidate based on a number of times the individual content candidate has been displayed without user interaction.
However, in an analogous art, Tsoy teaches a system and methods for determining rank positions of content elements to be displayed on a user interface ([0007]-[0010]; [0083]-[0088]). The system keeps record of user’s interactions with the content elements displayed on the interface. The system determines a win score and/or a loss score based on user’s interactions with the content elements, including elements that were displayed and not interacted with (Fig. 4; [0103]-[0107]). The system uses machine learning models to predict a relevance score to rank elements for presentation. The elements which records reflect the number of times they were/were not interacted with are lowered in the rankings or filtered out from presentation based on the final calculated score ([0104]-[0107]; [0114]; [0119]-[0137]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Phillips, Miller and Van Os’ invention with Tsoy’s feature of reducing the ranking of the content elements based on the number of times the content has been displayed without being interacted with for the benefit of maximizing the event when the user is not interested on a given content.
Regarding claims 2, 9 and 16, Phillips, Miller, Van Os and Tsoy teach wherein the learning algorithm comprises a reinforcement learning algorithm that maximizes an objective function (Phillips: the system also optimizes ad placement using the model, col. 12 lines 29-56. Additionally, in order to make the recommendation more accurate, selection/evaluation parameters are weighed. Miller: [0030]; [0051]).
Regarding claims 3, 10 and 17, Phillips, Miller, Van Os and Tsoy teach wherein the objective function is a weighted function based on one or more parameters (Phillips: col. 14 line 4 to col. 5 line 47).
Regarding claims 4, 11 and 18, Phillips, Miller, Van Os and Tsoy teach wherein the one or more parameters comprise a number of user clicks, a number of content launches, the time series data, streaming time data, or a business target associated with the content candidates (Phillips: col. 8 line 59 to col. 9 line 6).
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 OMAR S PARRA whose telephone number is (571)270-1449. The examiner can normally be reached M-F: Mostly 10-6PM.
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, Nathan Flynn can be reached at 571-2721915. 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.
/OMAR S PARRA/Primary Examiner, Art Unit 2421
Prosecution Insights