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 filed 4/20/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Issa et al (US 2010/0195975 A1).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 7, 9, 10-12, 14, 16, and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Issa et al (US 2010/0195975 A1).
As to claim 1, Issa et al discloses a system and method for semantic trick play. In a media player, a semantic trick play command is received from a user while a user is experiencing a current content of a media item. Metadata is detected with respect to a current playback position of the media item, and at least one further playback position is determined in the current content of the media item. The further playback position is semantically related to the metadata of the current playback position. Playback is then moved to the at least one further playback position (Abstract, see also [0032]). Claim 1 is met as follows: A method comprising: receiving, by a computing system, a transcript for a video; ([0028-0030], metadata information for each scene or segment may include a script, closed captioning information, sub-titles, or the like) receiving, by the computing system, an input indicative of a request to adjust a playback position of the video, wherein the request does not specify a timestamp of the video to which to adjust the playback position; ([0034-40], an additional set of trick play controls is also available based on semantics instead of time) applying, by the computing system, and based on the request to adjust the playback position, a first machine learning model to the transcript and a current timestamp of the video to identify one or more noncurrent time stamps; ([0027], semantic analysis can use machine learning among various techniques) applying, by the computing system, a second machine learning model to the transcript, the current timestamp, and the one or more noncurrent time stamps to rank, based on user data, the one or more noncurrent time stamps; and (see [0052]+, ‘Example 2: Semantic Rewind with the Same Video’, and in particular: [0057], when a user selects a semantic trick play function, the system has to determine what is semantically relevant at that point in playback, there may be several semantic items identified and each of these items will take different paths through a semantic content tree; [0057-0061], the system determines which path to take using any combination of several techniques, including user history and profile whereby the system makes a calculated judgement about what is most relevant to the user using history of previous selections or user profile; [0062], establishing semantic relevance may include Causal Graphs or Networks; [0065], constructing such data structures through automated video analysis may be accomplished using Machine Learning) adjusting, by the computing system and based on the ranking of the one or more noncurrent timestamps, the playback position to a noncurrent timestamp from the one or more noncurrent timestamps. ([0063], a semantic rewind operation on a scene S2 would travel this causal relationship backwards to scene S1)
As to claim 2, Issa et al disclose: The method of claim 1, wherein the noncurrent time stamp is a first-ranked noncurrent timestamp. (as indicated above with respect to [0057-0061], the system determines which path to take using any combination of several techniques, including user history and profile whereby the system makes a calculated judgement about what is most relevant to the user using history of previous selections or user profile; this determination/calculated judgement results in a ‘first-ranked noncurrent timestamp’ from among what might be several semantic items identified)
As to claim 3, Issa et al discloses: The method of claim 1, wherein the input indicative of the request is a first input, wherein the noncurrent timestamp is a first noncurrent timestamp, the method further comprising: responsive to receiving a second input indicative of a request to adjust a playback position of the video, adjusting, by the computing system and based on the ranking of the one or more noncurrent timestamps, the playback position to a second noncurrent timestamp from the one or more noncurrent timestamps. ([0042), a user is taken back from a scene in Episode 34 to a scene in Episode 25, the user continues to hit the semantic rewind button, and the system continues to take him back to various connected plot elements in Episodes 21 and 15)
As to claim 5, Issa et al discloses: The method of claim 1, wherein the one or more noncurrent time stamps include at least one of a start time stamp for a current sentence, a start time stamp for a current dialogue, a start time stamp for a current scene, start time stamp for a future sentence, a start time stamp for a future dialogue, and a start time stamp for a future scene. (as established above, the reference discloses that the semantic trick play function moves playback to another video segment or scene; [0038], the semantic trick play function Semantic Fast Forward moves forward in the semantic tree---this equates to the claimed ‘start time stamp for a future scene’)
As to claim 7, Issa et al disclose: The method of claim 1, further comprising: applying, by the computing system, a third machine learning model to the transcript to generate an augmented transcript including information indicative of one or more scenes included in the video; and providing, by the computing system, the augmented transcript to the first machine learning model as input. ([0027], as indicated in the rejection of claim 1, semantic analysis leverages machine learning; [0028, 0030], metadata analysis per se, including the analysis of scripts, closed-captioning information, sub-titles, or the like, establishes semantic links which ‘augment’ the metadata and ‘include information indicative of one or more scenes’, as claimed)
As to claim 9, The method of claim 1, wherein the first machine learning model is a transcript matching model, as discussed above with respect to claim 1, the process of using metadata information for each scene or segment (that includes a script, closed captioning information, sub-titles, or the like) to establish semantic pathways to related metadata information in other scenes or segments is ‘transcript matching’.
Claims 10-12, 14, 16, and 18 are met by that discussed above for claims 1-3, 5, 7, and 9, respectively.
Claims 19 and 20 are met by that discussed above for claims 1 and 3. Also, see [0019].
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) 4, 6, 8, 13, 15, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Issa et al (US 2010/0195975 A1).
As to claim 4: The method of claim 3, wherein the second noncurrent timestamp is a second-ranked noncurrent timestamp. ([0057, 0059, 0060, 0061], The system determines which semantic path to take using any combination of techniques, including ‘explicit’ user choice in which a user obtains multiple options and then makes the choice, user selection of semantically relevant points, and a calculated judgement based on user history and profile. The former two techniques allow the user to select any of multiple options. The latter technique is a judgement about what is most relevant to the user and implies a range of options from most relevant to least relevant. Issa et al does not explicitly teach a ranked list of semantics paths based on relevance to a user’s profile or history. However, this is not considered to be a patentable distinction. The examiner takes Official Notice that it was notoriously well-known in the art prior to the effective filing date of the invention to present ranked options based on a user’s profile or history, and it would have been clearly obvious to one of ordinary skill in the art to implement Issa et al in this manner to facilitate the selection process.
As to claim 6: The method of claim 1, wherein the user data includes data indicative of one or more of a number of requests for rewinding the video and a number of requests for fast- forwarding the video, and wherein the second machine learning model is trained on the user data. The reference discloses at [0057, 0061] that the determination of which semantic path to take is based at least in part on user history of previous selections. Issa et al does not explicitly disclose that the machine learning model is trained on user data indicative of the number of requests of semantic rewind and fast-forward. However, this is not considered to be a patentable distinction. The examiner takes Official Notice that it was notoriously well-known in the art prior to the effective filing date of the invention to employ continual learning to machine learning models in order to adapt to new data and real-world environments over time. Accordingly, it would have been clearly obvious to one of ordinary skill in the art, prior to the effective filing date of the invention, to apply this well-known teaching to the system of Issa et al to allow the model to adapt to use as it relates to the frequency of rewinds and fast-forwards for the stated advantages.
As to claim 8, The method of claim 1, wherein the first machine learning model and the second machine learning model are the same machine learning model, [0027], Issa et al does not explicitly disclose that semantic analysis, indexing, retrieval of video, and storyline detection leverage machine learning techniques through a single model, however this is not considered to be a patentable distinction. The examiner takes Official Notice that it was notoriously well-known in the art prior to the effective filing date of the invention to employ end-to-end learning to reduce cumulative errors and optimize the entire pipeline.
Claims 13, 15, and 17 are met by that discussed above for claims 4, 6, and 8, respectively.
Conclusion
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/JOHN W MILLER/ Supervisory Patent Examiner, Art Unit 2422
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