CTNF 19/047,988 CTNF 82707 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-5, 7, 8, 10-14 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (hereinafter ‘Li’, Pub. No. 2021/0264106) in view of Omar et al. (hereinafter ‘Omar’, Patent No. 12,105,755) . Regarding claims 1 and 11, Li teaches a method comprising: generating, using a teacher artificial intelligence (Al) model, a set of soft labels for a first training dataset, wherein the first training dataset reflects characteristics of a first plurality of media items, wherein the set of soft labels reflect predicted values of one or more metrics associated with the first plurality of media items ([0042]) ; and training a student Al model on the first training dataset using the set of soft labels generated by the teacher Al model and a set of observed labels associated with the first plurality of items, wherein the student Al model is trained to predict a score reflecting a relevance of a given media item to a user acting in a current user context of the media platform ([0043]-[0046]) . On the other hand, Li does not explicitly teach that the media items are accessible via a media platform. However, in an analogous art, Omar teaches a system that allows searching, classifying and retrieving media content after user searching input. The content is accessible through a media platform (col. 2 lines 31 to col. 4 line 26) . Omar uses different artificial models for the different steps and services; including teaching and learning models (col. 21 line 65 to col. 22 line 51). For claim 16, Omar teaches: a system (100, Fig. 1) (with respective method), comprising: a processing device (106, 116, Fig. 1) ; and a memory, coupled with the processing device, comprising instructions that when executed by the processing device (108, 118; Fig. 1), perform operations comprising: responsive to a user of a media platform accessing a selected media item of the media platform on a client device, identifying a set of candidate media items of the media platform (col. 23 line 53 to col. 25 line 31, Figs. 10-14; 1602, Fig. 16; col. 27 lines 1-14. 1702, Fig. 17; col. 28 lines 33-44); ordering at least a subset of the set of candidate media items based on the plurality of scores 1604, Fig. 16; col. 27 lines 15-56. 1704, 1706, Fig. 17; col. 28 line 38 to col. 29 line 15); and causing at least a portion of the subset of the set of candidate media items to be provided to the client device for presentation as media item recommendations for the user accessing the selected media item (col. 2 line 56 to col. 3 line 18. 310, Fig. 3; col. 18 line 59 to col. 19 line 12). Therefore, it would have been obvious to one of ordinary skill in the art before the effective fling date of the claimed invention to have modified Li’s invention with Omar’s feature of accessing the media content through a video platform for the benefit of allowing the user to retrieve the media content that fulfills his/her preferences. Regarding claims 2 and 3, Li and Omar teach wherein the teacher Al model and the student Al model share a common architecture comprising a plurality of neural network layers, and wherein a size of a layer of the teacher Al model is a multiple of a size of a corresponding layer of the student Al model (Li: [0021]; [0037]-[0039]) . Regarding claim 4, Li and Omar teach further comprising pre-training the teacher Al model on a second training dataset until the teacher Al model achieves a threshold convergence, wherein the second training dataset reflects characteristics of a second plurality of media items accessible via the media platform (Li: [0018]; [0038]. Omar: col. 6 lines 56-67; col. 21 line 65 to col. 22 line 51; col. 28 line 25 to col. 29 line 40, where the dataset is iteratively input/filtered until it reaches a threshold) . Regarding claim 5, Li and Omar teach wherein training the student Al model on the first training dataset comprises a plurality of iterations, each iteration comprising: calculating a distillation loss metric based on an output of the student Al model and a distillation weight; updating parameters of the student Al model based on the distillation loss metric; and increasing the distillation weight (Li: [0040]-[0045]) . Regarding claim 7, Li and Omar teach wherein two or more student Al models are co-trained with the teacher Al model to facilitate a selection of a best performing student Al model for inference (Li: [0040]-[0045]) . Regarding claims 8, 14 and 19, Li and Omar teach wherein the one or more metrics associated with the first plurality of media items comprise one or more engagement metrics and one or more satisfaction metrics (Li: Table 1, accuracy, training time) . Regarding claim 10, Li and Omar teach wherein the teacher Al model and the student Al model form part of a knowledge distillation framework (Li: [0043]; [0048]; [0049]) . Regarding claims 12 and 17, Li and Omar teach wherein the second Al model is a teacher Al model that is co-trained with one or more student Al models comprising the trained first Al model (Li: [0040]-[0045]) . Regarding claims 13 and 18, Li and Omar teach wherein the trained first Al model comprises at least one of : a first classification head configured to predict a first score reflecting a relevance of a given media item to the user acting in a current user context of the media platform, wherein the first classification head uses direct distillation (Omar: col. 18 line 60 to col. 19 line 39) ; or a second classification head configured to predict a second score reflecting the relevance of the given media item to the user acting in a current user context of the media platform, wherein the first classification head uses auxiliary distillation . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 6, 9, 15 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion 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 Application/Control Number: 19/047,988 Page 2 Art Unit: 2421 Application/Control Number: 19/047,988 Page 3 Art Unit: 2421 Application/Control Number: 19/047,988 Page 4 Art Unit: 2421 Application/Control Number: 19/047,988 Page 5 Art Unit: 2421
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