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 all pending claims have been fully considered, but they are moot because of the new ground of rejection. Applicant argues that cited references failed to disclose wherein the user historical data comprises a metric indicative of an average of a percentage duration of the media content consumed by a user before the media content is stopped, wherein the percentage duration is a function of a duration watched by the user and a total duration of the media content.
However, Xu et al show in fig.3A to fig.4A a system being able to show or display progress bar or percentage of a video content being viewed currently by a user with respect to the total length of the video content as disclosed in para.0057;0032;0075. This action is made final.
Claims rejections-35 U.S.C. 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 Shanmugam(US.Pub.No.20250016411) in view of Lei(US.Pub.No.20170242571) and Phillips(US.Pub.No.20210193187) and Xu (US. Pub.NO. 20250203177).
Regarding claim 1, Shanmugam et al disclose a computer-implemented method for generating a recommendation for a media content of a first form of content based on user interactions with a second form of content, comprising(the system is able to use machine learning model to recommended clips of video contents related to video contents being viewed or watched by the users; 0017;
determining, by at least one computer processor, interaction based data associated with the second form of content based on an interaction of a user with a first media content, wherein the first media content is of the second form (the system is able to monitor or track interactions or viewing history of a user with respect to media contents and the system may shorten the length of the received video content from a first time duration to a second time duration to provide or generate media clips ; 0050; 0056;0064-0065;0017;0038);
providing, as an input to at least one machine learning model(the system is able to use machine learning model to process data; abstract; 0018; 0047) , the interaction based data, a representation of the first media content(user can input data via user interface; 0014; 0016), user historical data indicative of a user behavior with media contents of the first form of content or the second form of content(the system can provide viewing history related to the user behavior to machine learning model to process data accordingly; 0017; 0067; 0121), and metadata associated with the first media content(the system provides metadata or parameters related to clips of video content; 0017).
But did not explicitly disclose receiving, as an output from the at least one machine learning model, one or tags indicative of a user interest; identify a second media content of the first form of content based on the one or more tags, wherein the first form of content is of a different length than the second form of content and wherein the second media content is not associated with the first media content; wherein the user historical data comprises a metric indicative of an average of a percentage duration of the media content consumed by a user before the media content is stopped, wherein the percentage duration is a function of a duration watched by the user and a total duration of the media content.
However, Lei et al disclose receiving, as an output from the at least one machine learning model(an artificial intelligence,0091), one or tags indicative of a user interest(a user's personal interests,0010;0058); identify a second media content of the first form of content based on the one or more tags, wherein the first form of content is of a different length than the second form of content(the system is able to modify multimedia contents by providing a shorten version as summary of the multimedia content; 0015;0066-0067;0057).
It would have been obvious for any person of ordinary skill in the art at that time the invention was filed to incorporate the teaching of Lei to modify Shanmugam by providing options to present a shorten version as summary of a multimedia contents for the purpose of improving viewing experiences accordingly.
And Phillips et al disclose wherein the second media content is not associated with the first media content(the system is able to recommend scenes of other contents or descriptions of other contents to users based on scenes being currently watched or viewed in a video content;0100).
It would have been obvious for any person of ordinary skill in the art at that time the invention was filed to incorporate the teaching of Phillips to modify Shanmugam and Lei by providing options to recommend scenes of contents or description of scenes associated with other contents for the purpose of improving the satisfaction of the users accordingly.
And Xu et al disclose wherein the user historical data comprises a metric indicative of an average of a percentage duration of the media content consumed by a user before the media content is stopped, wherein the percentage duration is a function of a duration watched by the user and a total duration of the media content(See fig.3A to fig.4A for showing progress bar or percentage of a video content being viewed by a user with respect to the total length of the video content;0057;0032;0075).
It would have been obvious for any person of ordinary skill in the art at that time the invention was filed to incorporate the teaching of Xu to modify Shanmugam and Lei and Phillips by providing bookmark information and duration of portion of video content being watched or viewed by a user resulting in “ wherein the user historical data comprises a metric indicative of an average of a percentage duration of the media content consumed by a user before the media content is stopped, wherein the percentage duration is a function of a duration watched by the user and a total duration of the media content” for the purpose of controlling viewing history associated with the users accordingly.
Regarding claim 2, Shanmugam et al disclose further comprising: determining additional interaction based data associated with the first form of content based on interactions of the user with the second media content(the system is able to monitor or track interactions or viewing history of a user with respect to media contents;0050; 0056;0064-0065); and
retraining the at least one machine learning model based on the additional interaction based data(machine learning model can be trained to generate clips of media contents based 0on interaction related to the users; 0028;0032;0038).
Regarding claim 3, Shanmugam et al disclose further comprising: transforming the interaction based data associated with the second form of content and the additional interaction based data associated with the first form of content to a common representation(the system can embed multiple data to the machine learning model; 0071-0072;0017;0020).
Regarding claim 4, it is rejected using the same ground of rejection for claim 1.
Regarding claim 5, Shanmugam et al disclose wherein the media content of the first form of content is a subset of a media content of the second form of content(the system is able to video clips or segments related to the main video contents; 0102; 0015; 0017;0038).
Regarding claim 6, Shanmugam et al disclose wherein the at least one machine learning model includes a sequential machine learning model(see fig.1 and fig.2 having a sequence of machine learning models; 0018; 0020;0022;0028-0030).
Regarding claim 7, Shanmugam et al disclose wherein the output of the at least one machine learning model comprises a sequence of short form video contents(0038; 0074).
Regarding claim 8, Shanmugam et al disclose wherein the metadata associated with the first media content represents one of: a title of a first media content item; a category of the first media content item; a genre of the first media content item; a rating of the first media content; or cast information(the system is able to provide video contents by using different categories;0004; 0014; 0054).
Regarding claim 9, it is rejected using the same ground of rejection for claim 1.
Regarding claim 10, it is rejected using the same ground of rejection for claim 2.
Regarding claim 11, it is rejected using the same ground of rejection for claim 3.
Regarding claim 12, it is rejected using the same ground of rejection for claim 4.
Regarding claim 13, it is rejected using the same ground of rejection for claim 5.
Regarding claim 14, it is rejected using the same ground of rejection for claim 6.
Regarding claim 15, it is rejected using the same ground of rejection for claim 7.
Regarding claim 16, it is rejected using the same ground of rejection for claim 1.
Regarding claim 17, it is rejected using the same ground of rejection for claim 2.
Regarding claim 18, it is rejected using the same ground of rejection for claim 4.
Regarding claim 19, it is rejected using the same ground of rejection for claim 5.
Regarding claim 20, it is rejected using the same ground of rejection for claim 6.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN D SAINT CYR whose telephone number is (571)270-3224. The examiner can normally be reached 9-5.
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/JEAN D SAINT CYR/Examiner, Art Unit 2425
/Brian T Pendleton/Supervisory Patent Examiner, Art Unit 2425
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