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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/02/2026 has been entered.
Response to Arguments
101 Rejection: Applicant’s arguments, see remarks, filed 09/02/2026, with respect to claims 1-20 have been fully considered and are persuasive. The amended limitation “generating, using a generative adversarial network (GAN) that receives the plurality of topic videos and a comparison format prediction output by the deep neural network, the video comparing the plurality of topics identified in the comparison query in a format indicated by the comparison format” integrates the judicial exception into an improvement to technology. Accordingly, the 101 rejection of claims 1-20 has been withdrawn.
103 Rejection: Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Eklipse.GG in view of Pearson et al (US 20190303683 A1) hereafter Pearson further in view of Lemieux et al (US 20240267601 A1) hererafter Lemieux
Regarding claim 1, Eklipse.GG teaches a computer-implemented method, comprising: receiving a comparison query that requests generation of a video comparing a plurality of topics identified in the comparison query (Page 1, Open the Streams tab and select an unclipped stream for Eklipse to process); identifying a plurality of video content relevant to the plurality of topics (Page 2, Multi-kill, Clutch, Funny Moment. You name it! Eklipse AI’s automatic clipping captures every moment accurately and turns your stream into shareable clips in seconds); mapping at least one video content of the plurality of video content to respective topics of the plurality of topics, the mapped video content comprising a plurality of topic videos (Page 3, Convert Twitch clips to TikTok-ready content effortlessly with our simple video-editing tools, Eklipse Studio).
Eklipse.GG does not appear to explicitly teach predicting, using an algorithm that receives the plurality of topic videos and the comparison query, whether to generate an intra-frame comparison video or an inter-frame comparison video for the video comparing the plurality of topics identified in the comparison query, wherein the intra-frame comparison video displays the plurality of topic videos in a same frame and the inter-frame comparison video displays the plurality of topic videos in different frames; and generating, using an algorithm that receives the plurality of topic videos and a comparison format prediction output by the deep neural network, the video comparing the plurality of topics identified in the comparison query in a format indicated by the comparison format prediction.
In analogous art, Pearson teaches predicting, using an algorithm that receives the plurality of topic videos and the comparison query, whether to generate an intra-frame comparison video or an inter-frame comparison video for the video comparing the plurality of topics identified in the comparison query, wherein the intra-frame comparison video displays the plurality of topic videos in a same frame and the inter-frame comparison video displays the plurality of topic videos in different frames (Para 0048, a first face and a second face may be represented in the video data within proximity to each other and the cropped video data may comprise an area including the first face and the second face. However, in other examples the first face and the second face may be separated and the server 121 cannot crop the video data to include the first face and the second face due to an intervening area. FIG. 5 illustrates an example of generating a split screen according to embodiments of the present disclosure); and generating, using an algorithm that receives the plurality of topic videos and a comparison format prediction output by the deep neural network, the video comparing the plurality of topics identified in the comparison query in a format indicated by the comparison format prediction (Para 0045, The server 112 may generate (134) a video summarization and send (136) the video summarization to the device). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG to include the teaching of Pearson. One of ordinary skill in the art would be motivated to implement this modification in order to display video data, as taught by Pearson (Para 25, The video data may include a plurality of video frames and the device 102 may display a portion of the video data on the display 104 associated with a position of an object within the video data).
Eklipse.GG in view of Pearson does not appear to explicitly teach a deep neural network, a generative adversarial network (GAN).
In analogous art, Lemieux teaches a deep neural network, a generative adversarial network (GAN) (Para 0007, the content summarizing program is a program that comprises and/or calls one or more neural networks and/or deep neural networks, such as recurrent neural network, a generative adversarial network, and/or other neural networks). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG in view of Pearson to include the teaching of Lemieux. One of ordinary skill in the art would be motivated to implement this modification in order to process video content, as taught by Lemieux (Abs, A content summarizing program processes audio and/or visual content of a media item to determine a plurality of segments of the content; and determines a respective ranking for each of the plurality of segments).
Regarding claim 2, Eklipse.GG in view of Pearson further in view of Lemieux teaches the computer-implemented method of claim 1, wherein the identifying the plurality of video content relevant to the plurality of topics further comprises: retrieving, from a video repository, a plurality of video files associated with the plurality of topics; and extracting, from each video file of the plurality of video files, at least one video frame relevant to the respective topics of the plurality of topics (Lemieux, Para 0074, the media item is a song, a piece of music, suite of musical works, an audio book, a music video, a video, a movie, and/or other audio and/or visual work. In various embodiments, the run time of the intelligent content summary is determined based on the type and/or classification of the media item, user preferences determined based on user input and/or consumer demographics, and/or the like). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG in view of Pearson to include the teaching of Lemieux. One of ordinary skill in the art would be motivated to implement this modification in order to process video content, as taught by Lemieux (Abs, A content summarizing program processes audio and/or visual content of a media item to determine a plurality of segments of the content; and determines a respective ranking for each of the plurality of segments).
Regarding claim 3, Eklipse.GG in view of Pearson further in view of Lemieux teaches The computer-implemented method of claim 2, wherein the mapping the at least one video content of the plurality of video content to the respective topics of the plurality of topics further comprises: classifying the at least one video frame from each video file of the plurality of video files into the respective topics of the plurality of topics (Lemieux, Para 0074, the media item is a song, a piece of music, suite of musical works, an audio book, a music video, a video, a movie, and/or other audio and/or visual work. In various embodiments, the run time of the intelligent content summary is determined based on the type and/or classification of the media item, user preferences determined based on user input and/or consumer demographics, and/or the like). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG in view of Pearson to include the teaching of Lemieux. One of ordinary skill in the art would be motivated to implement this modification in order to process video content, as taught by Lemieux (Abs, A content summarizing program processes audio and/or visual content of a media item to determine a plurality of segments of the content; and determines a respective ranking for each of the plurality of segments).
Regarding claim 4, Eklipse.GG in view of Pearson further in view of Lemieux teaches The computer-implemented method of claim 1, further comprising: determining whether to generate a video summary for any topic of the plurality of topics based on the at least one video content mapped to the respective topics of the plurality of topics; and in response to determining to generate the video summary for at least one topic of the plurality of topics, integrating the video summary generated for the at least one topic of the plurality of topics into the video comparing the plurality of topics (Lemieux, Para 0074, the media item is a song, a piece of music, suite of musical works, an audio book, a music video, a video, a movie, and/or other audio and/or visual work. In various embodiments, the run time of the intelligent content summary is determined based on the type and/or classification of the media item, user preferences determined based on user input and/or consumer demographics, and/or the like). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG in view of Pearson to include the teaching of Lemieux. One of ordinary skill in the art would be motivated to implement this modification in order to process video content, as taught by Lemieux (Abs, A content summarizing program processes audio and/or visual content of a media item to determine a plurality of segments of the content; and determines a respective ranking for each of the plurality of segments).
Regarding claim 5, Eklipse.GG in view of Pearson further in view of Lemieux teaches The computer-implemented method of claim 1, wherein the intra-frame comparison video includes a single frame simultaneously displaying the plurality of topic videos for comparison (Pearson, Para 0048, a first face and a second face may be represented in the video data within proximity to each other and the cropped video data may comprise an area including the first face and the second face. However, in other examples the first face and the second face may be separated and the server 121 cannot crop the video data to include the first face and the second face due to an intervening area. FIG. 5 illustrates an example of generating a split screen according to embodiments of the present disclosure). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG to include the teaching of Pearson. One of ordinary skill in the art would be motivated to implement this modification in order to display video data, as taught by Pearson (Para 0025, The video data may include a plurality of video frames and the device 102 may display a portion of the video data on the display 104 associated with a position of an object within the video data).
Regarding claim 6, Eklipse.GG in view of Pearson further in view of Lemieux teaches the computer-implemented method of claim 1, wherein the inter-frame comparison video includes a sequence of frames consecutively displaying the plurality of topic videos for comparison (Pearson, Para 0048, a first face and a second face may be represented in the video data within proximity to each other and the cropped video data may comprise an area including the first face and the second face. However, in other examples the first face and the second face may be separated and the server 121 cannot crop the video data to include the first face and the second face due to an intervening area. FIG. 5 illustrates an example of generating a split screen according to embodiments of the present disclosure). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG to include the teaching of Pearson. One of ordinary skill in the art would be motivated to implement this modification in order to display video data, as taught by Pearson (Para 0025, The video data may include a plurality of video frames and the device 102 may display a portion of the video data on the display 104 associated with a position of an object within the video data).
Regarding claim 7, Eklipse.GG in view of Pearson further in view of Lemieux teaches the computer-implemented method of claim 1, wherein the receiving the query for the video comparing the plurality of topics included in the query further comprises: receiving at least one input video in the query; transforming the at least one input video into at least one topic of the plurality of topics; and integrating the at least one input video in the video comparing the plurality of topics (Lemieux, Para 0007, For example, in an example embodiment, an LSTM and/or clustering algorithm is used to determine and/or identify the plurality of segments of the audio and/or visual content of the media item). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Eklipse.GG in view of Pearson to include the teaching of Lemieux. One of ordinary skill in the art would be motivated to implement this modification in order to process video content, as taught by Lemieux (Abs, A content summarizing program processes audio and/or visual content of a media item to determine a plurality of segments of the content; and determines a respective ranking for each of the plurality of segments).
Claim 8 is the system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 9 is the system claim corresponding to the method claim 2, and is analyzed and rejected accordingly.
Claim 10 is the system claim corresponding to the method claim 3, and is analyzed and rejected accordingly.
Claim 11 is the system claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Claim 12 is the system claim corresponding to the method claim 5, and is analyzed and rejected accordingly.
Claim 13 is the system claim corresponding to the method claim 6, and is analyzed and rejected accordingly.
Claim 14 is the system claim corresponding to the method claim 7, and is analyzed and rejected accordingly.
Claim 15 is the computer program product claim corresponding to the method claim 8, and is analyzed and rejected accordingly.
Claim 16 is the computer program product claim corresponding to the method claim 9, and is analyzed and rejected accordingly.
Claim 17 is the computer program product claim corresponding to the method claim 10, and is analyzed and rejected accordingly.
Claim 18 is the computer program product claim corresponding to the method claim 11, and is analyzed and rejected accordingly.
Claim 19 is the computer program product claim corresponding to the method claim 12, and is analyzed and rejected accordingly.
Claim 20 is the computer program product claim corresponding to the method claim 13, and is analyzed and rejected accordingly.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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