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 .
Miscellaneous
Claims pending: 1, 4-9, 11-15, 17-20,
Claims amended: 1, 4, 6-9, 11-13, 15, 17-18,
Claims cancelled: 2-3, 10, 16,
New claims: n/a
Response to Arguments
Applicant’s arguments, with respect to the rejection(s) of claim(s) 1 have been fully considered and are not persuasive.
Regarding applicant’s remarks dated 02/09/2026, regarding claim amendments 1, 9, 15, overcome currently cited references, specifically Ramos, and Cheng.
However, upon further consideration, a new ground(s) of rejection is made in view of Ramos, see below rejection for details.
Applicant’s arguments, with respect to the rejection(s) of dependent claim(s) 4-7, 11-13, 17-19, have been fully considered and are further rejected based on same reason(s) as claim(s) 1, 9, 15 above.
Claim Rejections - 35 USC § 112
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-5, 11, 17, are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
For claims 4, 11, 17, the claimed preamble claims “the group”, which lacks antecedence.
Further action is required.
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, 9, 15, is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20230326358) to (Ramos) in view of (US 20180322905) to (Cheng)
Regarding claim(s) 1, 9, 15, Ramos teach computer program product for controlling a
play speed of video content in a video player, determining segment complexity scores of segments of a video based on content of the segment and categories of the content in the segment; determining user comprehension score for the segment of the video for a viewer of the video with respect to the categories of the segment of the video; receiving, by a preferred speed, input comprising the segment complexity scores and the user comprehension scores for the categories of the video to output predicted play speeds for the segments of the video; and rendering the segments of the video in the video player according to the predicted play speeds of the segments. (Fig. 2, P. 38, 39, 42-43, 64-67, assign a comprehension score to a given topic based on the level of comprehension associated with the listener... adjust the assigned playback speed of these starting speed values upwards or downwards based on the comprehension score and interest score associated with the listener, and the complexity score associated with the content block...applied the effects of the complexity, comprehension, and interest scores, the system may assign the playback speed to the content block... based on real-time biometric information of the listener, dynamically modify the audio speed of the content blocks during playback)
Ramos further teach receiving a real-time comprehension score of viewer comprehension of a rendered segment based on computer program analysis of user behavioral responses to the rendered segment; determining whether a user comprehension score for a category of the rendered segment, of the determined user comprehension scores for the viewer with respect to the categories of the segments, used to calculate predicted play speed of the output predicted play speeds for the segments, differs from the real-time comprehension score; determining a play speed adjustment for the rendered segment based on the real-time comprehension score in response to user comprehension score, for the category of the rendered segment, differing from the real-time comprehension score received for rendering of render segment; and adjusting a current play speed of the rendered segment according to the play speed adjustment to modify the play speed. (Fig 2-3, P. 3, 13-15, 36-40, 43, 64, 66-67, determining a listener’s level of comprehension of the one or more topics; based on the level of complexity, the level of interest, and level of comprehension corresponding to the one or more topics, assigning a playback speed to the one or more content blocks associated with the one or more topics; and modifying the one or more content blocks to play at the assigned playback speed, and the system may assign a comprehension score to a given topic/content based on the level of comprehension associated with the listener given past behavior on the topic/content which reads on (determining whether a user comprehension score for a category of the rendered segment, of the determined user comprehension scores for the viewer with respect to the categories of the segments, used to calculate predicted play speed of the output predicted play speeds for the segments) , collect real-time biometric data to determine a listener’s level of comprehension based on a detected sentiment of the listener in real time as the listener is listening to the stream (real-time comprehension score of viewer comprehension of a rendered segment)...if it is determined based on the biometric sensor that the listener is expressing distraction or boredom, the playback pacing program may increase playback speed of the current content block to increase the listener’s stimulation and increase interest or may skip the current content block and/or move to a content block associated with a topic that has a higher interest score, and adjusting the complexity scores, interest scores, and/or the comprehension score of the user with respect to the topic of current content block based on the real-time biometric feedback. The system may, using sensors such as cameras or EEG machines, and subject to listener’s approval, collect real-time biometric data to determine a listener’s level of comprehension based on a detected sentiment of the listener in real time as the listener is listening to the audio stream, based on the sensor data, that the listener is expressing confusion or frustration, the system may decrease the playback speed of the current content block or repeat all or part of the current content block to allow the listener more time to process the content block, improving comprehension and reducing frustration. the system may adjust the complexity scores, comprehension scores of a listener with respect to the topic/content of the currently consumed content block (updating the comprehension score) which reads on (determining whether a user comprehension score for the rendered segment differs from real-time comprehension score…adjusting a current play speed of rendered segment according to the play speed adjustment to modify the play speed)).
Ramos further teach computer program product comprises a computer readable storage medium having computer readable program instructions that when executed perform operations; and a processor; and a computer readable storage medium having computer readable program instructions that when executed by the processor performs operations; computer implemented method for controlling a play speed of video content in a video player. (P. 44-56, 71-75)
Ramos fail to specifically teach machine learning model.
Cheng teach user comprehension score, used to calculate a predicted play speed for render segment, …preferred speed predictor machine learning model. (P. 14, 19-20, 25, 27-32, Cheng teach client devices 102A-C may receive input from a user via a user interface component or other input means, such as device sensors. Examples of input may include voice, visual, touch and text input, analyze the received input data/sensor data to determine a set of features and/or corresponding values for the input, represented as numeric values, apply the set of features and values to one or more logic sets, such as a rule sets, an algorithm, or a model/modelling utility, model, as used herein, may refer to a predictive or statistical language model that may be used to determine a probability distribution over one or more word, character sequences or events, and/or to predict a response value from one or more predictors, model may be a rule-based model, a machine-learning regression model, a machine-learning classifier, a neural network, or the like. In examples, the logic set may produce output determining, or used to determine, an adjusted video playback speed or value for the media content being played, default playback speed of the media content being played may be modified to, or in accordance with, the adjusted video playback speed/value. In aspects, client devices 102A-C may continue to apply the adjusted video playback speed/value to the playback of the media content while input continues to be received from the user. Which reads on (user comprehension score, used to calculate a predicted play speed for render segment), and further to modify or not to modify playback speed as the user input is continuously monitored for feedback.
Cheng further teach based on user interaction in real time, determining the modified video playback speed, may include using one or more rules sets or machine-learning techniques, which reads on (preferred speed predictor machine learning model))
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos by teach user comprehension score, used to calculate a predicted play speed for render segment, …preferred speed predictor machine learning model as taught by Cheng in order to benefit from developing technologies in improving the listening comprehension of audible media consumers.
Claim(s) 4, 11, 17, is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20230326358) to (Ramos) in view of (US 20180322905) to (Cheng) in view of (US 20220167052) to (Patel).
Regarding claim(s) 4, 11, 17, Ramos in view of Cheng teach computer program product, the system for controlling a play speed of video content in a video player, a computer implemented method for controlling a play speed of video content in a video player, the play speed adjustment.
Ramos further teach the user behavioral responses comprise a plurality of user behavioral responses in a set of user behavioral responses consisting of: facial expressions; eye movement; viewer engagement with the video; and biometric data gathered from the viewer, wherein the operations further comprise: processing the user behavioral responses to determine user behavioral metrics; and processing the user behavioral metrics to determine the real-time comprehension score of the viewer comprehension of the rendered segment.. (P. 38, 39, 42-43, 64-67, 68, sensors such as cameras or EEG machines...sensor data indicating dilated pupils, an elevated heart rate, furrowing of the brow, frowning, exasperated noises, et cetera. The listener’s sentiment may further include boredom, which may, for example, be inferred from sensor data indicating above-average eye movement, yawning, fidgeting, sighing, et cetera. The system may perform remedial actions based on detecting certain sentiments of the listener...if the system determines, based on the sensor data, that the listener is expressing confusion or frustration, the system may decrease the playback speed of the current content block or repeat all or part of the current content block to allow the listener more time to process the content block, improving comprehension and reducing frustration...adjust the complexity scores, interest scores, and/or comprehension scores of a listener with respect to the topic of the currently consumed content block, specifically P. 43, 66)
Ramos in view of Cheng fails to specifically teach head movements; viewer interaction with an input device during playing of the video; voice analysis of viewer verbal responses and comments during the rendering of the video.
Patel teach head movements; viewer interaction with an input device during playing of the video; voice analysis of viewer verbal responses and comments during the rendering of the video. (P. 13-14, 16, 21-22, 24-26, 35, 44-45, 47, user profile containing user preferences, and also user profile preferences in dynamic adaption routines responsive to user reactions to media content, and adjusting the media content to specific user, such as skipping over a gory scene based on user behavior changes and reactions, these user preferences can be derived from biometric sensors such as camera, microphones, etc. which are analyzing the user behavior while watching content to reflect the level of engagement, such as if the user looks down at his phone, distracted, bored, talking to other people during playback, etc. all while content is playing).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos by head movements; viewer interaction with an input device during playing of the video; voice analysis of viewer verbal responses and comments during the rendering of the video as taught by Patel in order to increase user engagement with content.
Claim(s) 5, is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20230326358) to (Ramos) in view of (US 20180322905) to (Cheng) in view of (US 20220167052) to (Patel) in view of (US 20240364970) to (Coskun) in view of (US 20210185405) to (Panchaksharaiah).
Regarding claim(s) 5, Ramos in view of Cheng in view of Patel teach computer program product, the system for controlling a play speed of video content in a video player, a computer implemented method for controlling a play speed of video content in a video player, the play speed adjustment, the processing the behavioral metrics, real-time comprehension score of viewers.
Ramos in view of Cheng in view of Patel fail to specifically teach applying weights to user behavioral metrics to produce weighted monitored behavior metrics, wherein the weights indicate strengths of the behavioral responses in predicting comprehension of the rendered segment.
Coskun teach applying weights to user behavioral metrics to produce weighted monitored behavior metrics, wherein the weights indicate strengths of the behavioral responses in predicting comprehension of the rendered segment. (P. 5, 75, weights for each metadata item of the user and determine the importance of each metadata, e.g., a re-watched scene might be more important than a skipped scene. Such weights may be predetermined by a domain expert or the system, or dynamically adjusted based on monitoring the user's consumption behavior. In some embodiments, the media application may determine the weights of users' metadata using neural networks or any other suitable computer-implemented technique).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos in view of Cheng in view of Patel by applying weights to user behavioral metrics to produce weighted monitored behavior metrics, wherein the weights indicate strengths of the behavioral responses in predicting comprehension of the rendered segment as taught by Coskun in order to provide relevant supplemental information.
Ramos in view of Cheng in view of Patel in view of Coskun fail to specifically teach aggregating monitored behavior metrics to produce real-time comprehension of viewer.
Panchaksharaiah teach aggregating monitored behavior metrics to produce real-time comprehension of viewer. (Fig 4A-4B, P. 14-20, 32, 35-43, 78, 99-103, A complexity score associated with each segment may be used to measure the complexity of a scene or segment, and the system used for generating the complexity score for each segment of the whole content, and Complexity scores may be dynamically calculated based on live or recent feedback from current viewers as aggregated via network, records the associated complexity score as a comprehension threshold).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos in view of Cheng by aggregating monitored behavior metrics to produce real-time comprehension of viewer as taught by Panchaksharaiah in order to identifying complex segments in content and providing enhanced content with subsequent complex segments.
Claim(s) 6, 12, 18, is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20230326358) to (Ramos) in view of (US 20180322905) to (Cheng) in view of (US 20210185405) to (Panchaksharaiah).
Regarding claim(s) 6, 12, 18, Ramos in view of Cheng teach computer program product, the system for controlling a play speed of video content in a video player, a computer implemented method for controlling a play speed of video content in a video player, the play speed adjustment.
Ramos further teach video content with complexity score. (P. 13, 16, 38, 39, 42-43, 64-67, 68,)
Ramos in view of Cheng fails to specifically teach inputting the video to a complexity analyzer to determine complexity scores for content in the video; and segmenting video into segments, wherein each of the segments has content with one of complexity scores.
Panchaksharaiah teach inputting the video to a complexity analyzer to determine complexity scores for content in the video; and segmenting video into segments, wherein each of the segments has content with one of complexity scores. (Fig 4A-4B, P. 14-20, 32, 35-43, 78, 99-103, A complexity score associated with each segment may be used to measure the complexity of a scene or segment, and the system used for generating the complexity score for each segment of the whole content reads on (complexity analyzer to determine complexity scores for content in the video)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos in view of Cheng by inputting the video to a complexity analyzer to determine complexity scores for content in the video; and segmenting video into segments, wherein each of the segments has content with one of complexity scores as taught by Panchaksharaiah in order to identifying complex segments in content and providing enhanced content with subsequent complex segments.
Claim(s) 7, 13, 19, is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 20230326358) to (Ramos) in view of (US 20180322905) to (Cheng) in view of (US 20230353820) to (Mcintosh) in view of (US 20210185405) to (Panchaksharaiah).
Regarding claim(s) 7, 13, 19, Ramos in view of Cheng teach computer program product, the system for controlling a play speed of video content in a video player, a computer implemented method for controlling a play speed of video content in a video player, the play speed adjustment, the determining the segment complexity scores.
Ramos in view of Cheng fails to specifically teach indexing the video according to content; inputting, to a categorizer machine learning model, the indexed video to categorize content of the indexed video.
McIntosh teach indexing the video according to content; inputting, to a categorizer machine learning model, the indexed video to categorize content of the indexed video. (P. 49, 67, 72, 79, 83, 89-90, 93, 96-97, 102, 108, categorizing content into different genre types such as comedy, etc. and using machine learning for the process)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos in view of Cheng by indexing the video according to content; inputting, to a categorizer machine learning model, the indexed video to categorize content of the indexed video as taught by McIntosh in order to provide contextual awareness with respect to the intended destination of the media content.
McIntosh teach machine learning model, the categorized content of the indexed video.
Ramos in view of Cheng in view of McIntosh fail to specifically teach inputting, to a comprehension analyzer, of the video to output complexity scores for the content; and segmenting the video into segments for the content.
Panchaksharaiah teach inputting, to a comprehension analyzer, of the video to output complexity scores for the content; and segmenting the video into segments for the content. (Fig 4A-4B, P. 14-20, 32, 35-43, 78, 99-103, A complexity score associated with each segment may be used to measure the complexity of a scene or segment, and the system used for generating the complexity score for each segment of the whole content reads on (complexity analyzer to determine complexity scores for content in the video)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ramos in view of Cheng in view of McIntosh by inputting, to a comprehension analyzer, of the video to output complexity scores for the content; and segmenting the video into segments for the content as taught by Panchaksharaiah in order to identifying complex segments in content and providing enhanced content with subsequent complex segments.
Allowable Subject Matter
Claim(s) 8, 14, 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
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 RONG LE whose telephone number is (571)270-7637. The examiner can normally be reached M-F (9 am - 6pm).
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/RONG LE/Primary Examiner, Art Unit 2421
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