DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 23 June 2026 has been entered. Claims 1-4, 6-11, 13-18, 20-25, and 27-28 have been examined and are pending.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
KR20220167056A
Pars. 44-48, 55-59
Learning a neural network for searching for a section in a video using video caption features and query textual features
20210109966
Abstract, par. 27
AI based video retrieval using embedded video metadata feature vectors and query embedded feature vectors
10678854
Abstract
Searching for start and end times of video sections that include queried textual content
20190266150
Pars. 34-35
Searching for segments of the video content that include textual content of interest.
20230418860
Abstract, par. 27
Video segment closed caption metadata analyzed to identify segments that correspond to queries
20210073551
Pars. 32-34, 42, 77
Receiving one video file with subtitles and a query, the video file having time based sequential frames/segments, the video’s time data and/or sequencing data and/or frame numbering data being associated with the subtitles
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-11, 13-18, 20-25, and 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Phillips et al., Pub. No.: US 20210193187 A1, hereinafter Phillips in view of Gibbon et al., Patent No.: US 6271892 B1, hereinafter Gibbon, and further in view of Brendel et al., Pub. No.: US 20200393915 A1, hereinafter Brendel.
As per claim 1, Phillips discloses A computer-implemented method for searching a video on a mobile device using an artificial neural network (ANN) (pars. 97, 130-131), comprising:
receiving, by the ANN, the video and a search query, the video comprising a sequence of frames and associated subtitle information (fig. 1, par. 46-47, 97 disclose a smartphone receiving a video and a query; par. 7, 60, 63, 67, 70 disclose OCR and obtaining on-screen text (i.e., associated subtitle information), captioned audio track data (associated subtitle information), and video captions which are further disclosures of associated subtitle information as seen in fig. 8A, item 830; at least pars. 5, 42, 67, 130-131 disclose the receiving is done by artificial neural networks on the smartphone) […].
generating, at the mobile device, by the ANN, first context representations based on […] each of a first set of words in the search query and second context representations based on […] each of a second set of words in the associated subtitle information (pars. 6, 7, 46, 47 disclose context-based encodings and representations of query terms and captions (subtitles));
determining, at the mobile device, by the ANN, a correlation between the first context representations and the second context representations (par. 47, 48), the ANN being trained to determine the correlation by distinguishing first words that are only in the first set of words in the search query from second words of the second set of words in the associated subtitle information (par. 83, 129-132 discloses a trained DNN model based semantic encoder; pars. 6, 9, 20, 21, 132 describe that the encoder encodes a query into a query vector and a video scene into a video scene vector, and that the video scene vector includes caption (subtitle) text. Par. 10, 132 makes it clear that they are compared semantically and with respect to similarity scores; this means they are not syntactically identical and that words that are only in one vector (along with all other words in the vector) will be correlated, distinguished, and compared with all words in the other vector to observe semantic (meaning) representations) […];
predicting, at the mobile device, by the ANN, a portion of the video including content responsive to the search query based on the correlation (par. 50, 64, 73, 100, 117); and
outputting, at the mobile device, an indication of a result of the search query based on the predicting (par. 93, 94, 100, 105, 109, 117).
Phillips does not explicitly disclose the subtitle information comprising preexisting closed captioning (CC) information, however, Gibbon in the related field of endeavor of image and video processing discloses this in at least col. 3, line 59 to col. 4, line 21. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Gibbon would have allowed Phillips to support receiving video that comprises frames with preexisting closed captioning information and to store video along with closed captioning and audio information in an efficiently compressed manner for subsequent retrieval.
Phillips and Gibbon do not explicitly disclose considering a first importance of each query word and/or a second importance of each subtitle/caption word, and that the first importance and the second importance determined by the ANN based on the first set of words and the second set of words, respectively. However, Brendel in the related field of endeavor of data analysis discloses these limitations in at least pars. 109-112. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Brendel would have allowed Phillips as modified to implement a trained word attention technique that applies weights to query and subtitle words according to their importance so that the resulting representations better reflect the most relevant words.
As per claim 2, Phillips as modified discloses The computer-implemented method of claim 1, in which the predicting further indicates a start time and an end time for the portion of the video including the content responsive to the search query (par. 61-63 disclose scene segment boundaries are associated with timepoints).
As per claim 3, Phillips as modified discloses The computer-implemented method of claim 2, further comprising displaying the portion of video included at the start time until the end time (see rejection of claim 2 as well as at least par. 100, 103, 108, 109).
As per claim 4, Phillips as modified discloses The computer-implemented method of claim 1, in which the ANN comprises a transformer neural network (pars. 5, 12, 19, 42, 67 disclose DNN’s which are transformer neural networks).
As per claim 6, Phillips as modified discloses The computer-implemented method of claim 1, in which the search query comprises one or more of a description of a scene, an event, a word or a phrase (see at least pars. 47, 87, 108).
As per claim 7, Phillips as modified discloses The computer-implemented method of claim 1, in which the search query is supplied via a speech input or text input of the mobile device (pars. 37, 47, 85, 87).
As per claims 1-4, 6-11, 13-18, 20-25, and 27-28, they are analogous to claims 1-7 and are therefore likewise rejected. The apparatus and medium of claims 8, 15 and 22 include hardware elements as per at least pars. 8-9 of the specification. See Phillips fig.’s 1 and 11 for the apparatus and mediums of claims 8-11, 13-18, 20-25, and 27-28.
Response to Arguments
Applicant’s 23 June 2026 arguments with respect to the claims have been considered; of Brendel et al., Pub. No.: US 20200393915 A1, has been applied in response to claim amendments.
Conclusion
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/SYED H HASAN/ Primary Examiner, Art Unit 2154
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