DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) were submitted on 11/20/2025; 07/31/2025; 12/12/2024; and 09/05/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
(i) However, it is impractical for the examiner to review the references thoroughly with the number of references cited in this case. By initializing each of the cited references on the accompanying 1449 forms, the examiner is merely acknowledging the submission of the cited references and merely indicating that only a cursory review has been made of the cited references.
Examiner notes the length of the IDS filed on 09/05/2024 (10 Pages) consists of 114 NPLs, of which Applicant alleges to be pertinent prior art for consideration with no further discussion explaining the particular relevance of any documents.
Any subsequent reviewing judiciary official is reminded to use careful discretion when applying the presumption of administrative competence to grant deference to the fact finding of the examiner with respect to these documents as Examiner has made no factual findings with respect to their specific content.
(ii) In this regard, MPEP § 2004.13 states:
"It is desirable to avoid the submission of long lists of documents if it can be avoided. Eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant's attention and/or are known to be of most significance.
See Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aft'd, 479 F.2d 1338, 178 USPQ 577 (5th Cir. 1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d 1823 (Fed. Cir. 1995)."
Further, it should be noted that an applicant's duty of disclosure of material and information is not satisfied by presenting a patent examiner with "a mountain of largely irrelevant [material] from which he is presumed to have been able, with his experience and with adequate time, to have found the critical [material]. It ignores the real world conditions under which examiners work.
Also See; " Rohm & Haas Co. v. Crystal Chemical co., 722 F.2d 1556, 1573 [ 220 USPQ 289 ] (Fed. Cir. 1983), cert. Denied, 469 U.S. 851 (1984). Patent applicant has a duty not just to disclose pertinent prior art references but to make a disclosure in such a way as not to "bury" it within other disclosures of less relevant prior art; see Golden Valley Microwave Foods Inc. v. Weaver Popcorn Co. Inc., 24 USPQ2d 1801 (N.D. Ind. 1992); Molins PLC v. Textron Inc., 26 USPQ2d 1889, at 1899 (D.Del 1992); Penn Yan Boats, Inc. v. Sea Lark Boats, Inc. et al., 175 USPQ 260, at 272 (S.D. FI. 1972).
Furthermore, Applicant is reminded that "inequitable conduct requires not intent to withhold, but rather intent to deceive" (emphasis added). Dayco Prods., 329 F.3d at 1367. "The intent element of the offense is . . . in the main proven by inferences drawn from facts, with the collection of inferences permitting a confident judgment that deceit has occurred." Akron Polymer Container Corp. v. Exxel Container, Inc., 148 F.3d 1380, 1385 (Fed. Cir. 1998).
Double Patenting
3. Claim 1 of the instant Application is patentably indistinct from claims 1 of the issued Patent No. US 12,081,827 pursuant to 37 CFR 1.78(f) or pre-AIA 37 CFR 1.78(b).
The nonstatutory obviousness double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees.
When two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822
A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to; http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Although the claims at issue are not identical, they are not patentably distinct from each other as explained below;
Examiner’s Reasoning for the provisional Obviousness Double Patenting determination is based on the examination rules set below.
“A generic claim cannot be allowed to an applicant if the prior art discloses a species falling within the claimed genus.” The species in that case will anticipate the genus. In re Slayter, 276 F.2d 408, 411, 125 USPQ 345, 347 (CCPA 1960). See MPEP 2131.02.
Instant Application vs. Conflicting Patent - claim analysis
Specifically, all the limiting components of the method at the instant application claim 1, are defined and encompass the limitations claimed by the conflicting patent at claim 1.
The sole semantic difference between the instant application at claim 1 and the issued patent at claim 1, lies in the claimed limitation determines a provenience information for a video query reciting; “ receiving a query to determine provenance information for a query video; “, which is expressly recited at the conflicting claim 1 by; “identifying a known video of the plurality of known videos that corresponds to the query video from the determined video segments. “, which would have been obvious in view of the skilled artisan to represent a similar matter defined by a different syntax, where the provenience includes the metadata necessary to identify a known video as claimed.
A secondary difference is remarked between the instant claim 1 reciting; “1. A computer-implemented method comprising:” and the conflicting patent at claim 1 reciting; “1. A non-transitory computer readable medium comprising instructions that…” at the claim preamble, which is similarly disclosed at claims 9-16 of the instant application.
Thus, the invention at claim 1 of in the conflicting patent is in effect a “species” of the “generic” invention of the instant application claim 1 (and 9-16), where the ordinary skilled would have found obvious to interpret that a processor operates on instructions stored in a memory as a “non-transitory” medium.
It has been held by the Court that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Since the instant application claim 1 is anticipated by claim 1 of the conflicting pending application for patent, it is deemed patentably indistinct from the named claim 1 of the conflicting patent, as detailed below.
Corrective 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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
The applied reference does not have a common inventor with the instant application.
4, Claims 1-20, are rejected under 35 U.S.C. 103 as being obvious over Ben Ingel et al., (hereinafter Ingel) (US 2020/0213680) in view of Saurabh Sahu et al., (hereinafter Sahu) (US 2022/0300740) and further in view of Georgios Tzimiropoulos et al., (hereinafter Tzimiropoulos) “A TRANSFER LEARNING APPROACH TO HEATMAP REGRESSION FOR ACTION UNIT INTENSITY ESTIMATION”; © 2020 IEEE.
Re Claim 1. Ingel discloses, a computer-implemented method (Abstract) comprising:
receiving a query to determine provenance information for a query video (receiving a query at the system 600, from an input media stream 110, in Fig.6, Par.[0191, 0192] where the “provenance” is determined by receiving the original transcript at 640, from a video stream 615, to perform a Test Analysis at 635, e.g., by receiving a media stream including an individual speaking as the provenience source, speaking in an origin language being the provenance of a particular voice of the associated individual at Step 702 in Fig,7A, or by receiving the source of the audio data i.e., the provenance according “an individual speaking in origin language, wherein the individual is associated with the particular voice.”, per step 432 in Fig.4A, or step 702, and Par.[0148-0149, 0207, 0178]); and
determining, from a plurality of known videos, a known video that has been manipulated to generate the query video by (determining from video streams 615, in Fig.6, a data manipulation at step 706, which may be to “translate or transformed speech directly from the media stream received at step 702”, etc., as disclosed at Par.[0208] being used to generate the provenance query earlier established by determining a “voice profile” characteristics “uniquely related to the individual and may be used for identifying the individual.” i.e., the provenance of the data to be modified, as in Par.[0023, 0209, 0233, 0240], or for identifying the first language spoken, as a primary language, Par.[0231, 0241] etc.):
sub-dividing the query video into visual segments (sub-dividing the video query in separate video segments Par.[0196, 0458, 0490, 0508, 0514]) and audio segments (and audio segmentation, Par.[0147, 0195, 0202] by separating the media stream into an audio stream 610 and a video stream 615, at pre-processing unit 610, per Fig.6 Par.[0192] according to an image segmentation model Par.[0199]);
generating visual descriptors (generating i.e., “data is being received and/or captured and/or generated, Par.[0149] as video stream metadata, Par.[0192] by using classification algorithms to the machine learning models, i.e., descriptors, among which data regression algorithms, object or face or person detectors, motion or edge detectors Par.[0198] or visual descriptors for visual detection, recognition defined at Par.[0198, 0204]) for the visual segments of the query video utilizing a visual neural network encoder (for the visual segments per Fig.4C by using video segmentation algorithms at a machine learning model by artificial neural networks, Par.[0514, 0562] of an autoencoder Par.[0200]);
generating audio descriptors (generating i.e., “data is being received and/or captured and/or generated, Par.[0149] of audio descriptors, e.g., voice profiles to particular speaker identities, Par.[0150] or the metadata Par.[0192-0193, 0215]) for the audio segments of the query video utilizing an audio neural network encoder (extracting components of the source audio data and identifying speaker descriptors, utilizing audio data per Fig.4B, using audio neural network, Par.[0139-0149, 0151]); and
determining that video segments from the known video are similar to the query video based on the visual descriptors and audio descriptors (based on the obtained segments metadata information Par.[0215, 0301, 0352], matching the video of individual and audio voice profile of the individual speaking, Par.[0139, 0140, 0215], or by citing; “…by step 3106, step 3108 may generate a second output video depicting a second character (with the second at least one characteristic of the character) behaving identically or similarly to the first character.” Par.[0569-0571], or as relating to the voice generation, i.e., manipulated audio “such that the first revoiced audio stream 660 will sound similar to audio stream 610.” Par.[0193-0194, 0215]).
In an analogous art, Sahu also teaches about,
sub-dividing the query video into visual segments and audio segments (as may be interpreted from Fig.2, performing at a neural network machine training process a video segmentation function 402, is applied to the input video data 202 and the 404 segmentation of the audio input data 204, Par.[0058, 0081] from which a global representation 320, of the input data 202, is generated and by using a time split function 322 per Fig.3, splitting each attention head 308a-308c into multiple segments or chunks 324 containing part of the query matrix, having equal or substantial equal sizes, Par.[0066] where further deriving from the global output 312, generating multiple local representations 328 - for each chunk 324 of heads 308a-308c of a query matrix-, into a sub-divided local output representation 332, then into a local output representation 332, of the input video data 202, and of the input audio data 204, Par.[0064-0068]);
in order to clarify the generation process of visual descriptors and the associated audio descriptors of the video dataset query, the ordinary skilled would have sought alternative sematic definitions to the “descriptors” claimed,
as identified in the art to Tzimiropoulos teaching this matter as,
generating visual descriptors for the visual segments of the query video utilizing a visual neural network encoder (generating the visual descriptors underlying the facial expressions as Action Units (AU) at Abstract, Sec.1, depicted in Fig.1 and utilizing a neural network of a Variational Autoencoder framework, (VAE), Sec.2, Sub.2.1 at Pg.3 and the AU estimation model for feature adaptation network in Fig.6 and Sec.4);
generating audio descriptors for the audio segments of the query video utilizing an audio neural network encoder (the same reference would obviate a similar process for the intrinsic audio segments of the video datasets Sec.5.1); and
The ordinary skilled in the art would have found the method and processing apparatus in Ingel to represent similar techniques and implementation methods presented before the effective filing date of invention and by obviating every limitation of the claim, according to the query data subdivision represented in the art to Sahu, Pars.[0066-0068]), describing the detailed process of video/audio sub division into query segments in order to improve the accurate representation by which one time frame of the input is perceived in relation to the local content (Par.[0073]), wherein the “visual descriptors” are found to be expressly described in the art to Tzimiropoulos (at Sec.1, 2 and 4) hence further determining that the combined cited arts would have obtained predictable results in terms of the subject matter claimed.
Exemplary Rationales herein addressed are found at least in MPEP 2143.I (A).
See precedence in: “The Federal Circuit recognized Agrizap as "a textbook case of when the asserted claims involve a combination of familiar elements according to known methods that does no more than yield predictable results." Id. Agrizap exemplifies a strong case of obviousness based on simple substitution that was not overcome by the objective evidence of nonobviousness offered. It also demonstrates that analogous art is not limited to the field of applicant’s endeavor, in that one of the references that used an animal body as a resistive switch to complete a circuit for the generation of an electric charge was not in the field of pest control.”
Re Claim 2. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 1,
Ingel teaches that, further comprising generating one or more visual indicators identifying visual changes in the query video relative to the known video (generating virtual reality data Par.[0187] of “a character in a revoiced media stream” by maintaining the original volume changes, per Fig.20A, or where the neural network is used to generate the modified version of the depiction from the first part of the input frame including information related to the object, Par.[0550-0557] identifying the changes including visual depictions or textual description, Par.[0558]).
Re Claim 3. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 2,
Ingel teaches that, further comprising displaying the query video with the one or more visual indicators overlaid on frames of the query video to indicate locations of the visual changes in the query video relative to the known video (a user interface overlaying selected aspects presenting to user interface the ability to manipulate the video, Par.[0427]).
Re Claim 4. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 2,
Ingel teaches that, further comprising classifying one or more changes to the query video relative to the known video as benign changes or editorial changes (using classification algorithms to determine the visual properties, and analyze the depiction of the element in the video data, or identify properties of an element, step 466 in Fig.4C, over various properties of video data captured and/or generated after change by using classification algorithms, Par.[0180, 0198-0199, 0202, 0204 or 0496]).
Re Claim 5. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 4,
Ingel teaches that, wherein generating the one or more visual indicators comprises generating the one or more visual indicators for the editorial changes (the user interface i.e., generating the visual indicators for access to controls enabling the user to type and/or edit text or values, etc., Par.[0419, 0423-0424, 0447, 0449, 0478, 0481] Fig.27A or Fig.29 or 30 or 31 or 32, etc.).
Re Claim 6. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 5,
Ingel teaches that, wherein generating the one or more visual indicators comprises ignoring the benign changes (as one skilled in the art would have found obvious to consider that the negative statement of “ignoring the benign changes” at the comparing layers of the neural network find an analogous representation at Par.[0194] where the analysis unit 670, performs “comparison of the properties of the second revoiced audio stream 665, to the properties of the first revoiced audio stream 660, where the different properties of the edited overlay are not part of the neural network classification, therefore are ignored, Par.[0195-0196] and by having the neural network, machine training use the video and audio recognition but not the text content overlayed on the video media content, Par.[0198]).
Re Claim 7. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 2, but he does not expressly teach the video alignment method engaging the heatmap regression to localize the video discrepancies or changes,
It is determined that the analogous the art to Tzimiropoulos teaches about,
further comprising generating a heat map indicating locations of the visual changes in the query video (depicting a heatmap regression observing in a neural network the similarities in a face recognition alignment at Fig.2, Abstract and indicating the visual changes in Fig.1 e.g., by cheek raising, involving a six-point ordinal ranking of intensities in the facial action coding, per Sec.I, Introduction).
In consideration to the common application of neural network in determining image similarity between two images, when referencing the original image and a modified version of the same, the skilled artisan would have obvious to associate the method of comparing the inputs of the validated examples in the machine training algorithms corresponding to the desired outputs, suggested by Ingel: (Par.[0198-0199]) as applied to the image segmentation including a regression model, Ingel: (Par.[ 0199]) thus to further combine prior art elements according to detailed known methods identified in the art to Tzimiropoulos (at Abstract, Introduction and Figs.1 and 2), based on which, the rationale to combine would be motivated by the predictable results obtained.
Re Claim 8. Ingel, Sahu and Tzimiropoulos disclose, the computer-implemented method of claim 7,
Tzimiropoulos teaches that, wherein generating the heat map indicating locations of the visual changes in the query video comprises:
extracting one or more feature maps from the query video (extracting local representations i.e., features in the facial regions where the Action Units (AU) produce appearance changes, Sec.I, Fig.1);
extracting one or more feature maps from the known video (the features extracted are from Fig.1-Left image, as a set of RGB, WxH images for which the corresponding AU intensities which are known, Sec.3.1 Right-Column); and
generating the heat map from a combination of the one or more feature maps from the query video and the one or more feature maps from the known video (generating a heatmap regression from a combination of the one or more feature maps at the convolution blocks in Figs.4 and 5 described in Sec.3.3-3.4, corresponding AU intensities which are known, Sec.3.1 Right-Column, per cited section below,
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Re Claim 9. This claim represents the non-transitory computer readable medium (Ingel at Par.[0130-0132]) comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations each and every limitation of the method claim 1, hence it is rejected on the same evidentiary probe mutatis mutandis.
Re Claim 10. This claim represents the non-transitory computer readable medium (Ingel at Par.[0130-0132]) comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations each and every limitation of the method claim 2, hence it is rejected on the same evidentiary probe mutatis mutandis.
Re Claim 11. This claim represents the non-transitory computer readable medium (Ingel at Par.[0130-0132]) comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations each and every limitation of the method claim 3, hence it is rejected on the same evidentiary probe mutatis mutandis.
Re Claim 12. Ingel, Sahu and Tzimiropoulos disclose, the non-transitory computer readable medium (Ingel at Par.[0130-0132]) of claim 9,
Sahu teaches about, wherein sub-dividing the query video into visual segments and audio segments comprises subdividing the query video into equal-length segments ( equal or substantially equal length sizes of the split data chunks, Par.[0066, or 0082], or the even or substantially even size/length of the chunks 324, Par.[0089] Fig.5B).
Re Claim 13. Ingel, Sahu and Tzimiropoulos disclose, the non-transitory computer readable medium of claim 9, wherein determining that the video segments from the known video are similar to the query video based on the visual descriptors and the audio descriptors comprises:
Sahu teaches about, mapping the visual descriptors and the audio descriptors to codewords (mapping the global relevance 342, and the local relevance 344, Par.[0071] representing the weight matrix associated with the local representation 336, Par.[0070]); and
identifying the video segments based on the mapped codewords (identifying the features of the video data 202, and audio 204, data inputs, by using indicators 406 and 408 respectively of the start and end points, in Fig.4, Par.[0082, 0087, 0089], i.e., identified by the query matrix vectors or by matrix values at Par.[0061-0062]).
Re Claim 14. Ingel, Sahu and Tzimiropoulos disclose, the non-transitory computer readable medium of claim 13,
Tzimiropoulos teaches that, wherein the operations further comprise fusing the visual descriptors and audio descriptors prior to mapping the visual descriptors and audio descriptors to the codewords (using the neural network adaptation layers (b), prior to generating the (c) attention maps, at Abstract, or fusion to jointly predict the AU intensity levels, at Sec.I, Pg.1 Right-Col., or Pg.2 Right-Col., and Fig.6, Pg.6, Sec.4.3).
In consideration to the common application of neural network in determining image similarity between two images, when referencing the original image and a modified version of the same, the skilled artisan would have obvious to associate the method of comparing the inputs of the validated examples in the machine training algorithms corresponding to the desired outputs, suggested by Ingel: (Par.[0198-0199]) as applied to the image segmentation including a regression model, Ingel: (Par.[ 0199]) thus to further combine prior art elements according to detailed known methods of neural network adaptation and/or fusion processing layers, in the art to Tzimiropoulos (at Abstract, Sec.I, Pg.1 Right-Col., or Pg.2 Right-Col., and Fig.6, Pg.6, Sec.4.3), based on which, the rationale to combine would be motivated by the predictable results obtained.
Re Claim 15. Ingel, Sahu and Tzimiropoulos disclose, the non-transitory computer readable medium of claim 13, wherein mapping the visual descriptors and the audio descriptors to the codewords comprises:
Sahu teaches about, mapping the visual descriptors to visual codewords; and mapping the audio descriptors to audio codewords (mapping the global relevance 342, and the local relevance 344, Par.[0071] representing the weight matrix associated with the local representation 336, Par.[0070] and identifying the features of the video data 202, and audio 204, data inputs, by using indicators 406 and 408 respectively of the start and end points, in Fig.4, Par.[0082, 0087, 0089], i.e., identified by the query matrix vectors or by matrix values at Par.[0061-0062]).
Re Claim 16. Ingel, Sahu and Tzimiropoulos disclose, the non-transitory computer readable medium of claim 13, wherein:
Sahu teaches, the operations further comprise generating unified audio-visual embeddings from corresponding visual and audio descriptors utilizing a fully connected neural network layer (unifying the video and audio segments at unit 214, and obtaining a unified classification of a neural network at unit 218, in Fig.4); and
mapping the visual descriptors and audio descriptors to the codewords comprises mapping unified audio-visual embeddings to a codebook (combining audio/video representations, Figs.5A and 5B, Par.[0005-0010] and mapping the attention based values of the video features 208 and audio features, 212, representing feature maps, used in the machine learning, Par.[0031, 0051-0055]).
Re Claim 17. Ingel, Sahu and Tzimiropoulos disclose, a system comprising:
Ingel teaches about, one or more memory devices (Par.[0048, 0110, 0123], Figs.2, 3 or 4A); and
one or more processors coupled to the one or more memory devices, wherein the one or more processors cause the system to perform operations comprising (processors and memory at Par.[0118, 0197]):
receiving a query to determine provenance information for a query video; and determining, from a plurality of known videos, a known video that has been manipulated to generate the query video; generating one or more visual indicators identifying visual changes in the query video relative to the known video (as evidenced and mapped at claim 1 to the corresponding limitations); and
displaying the query video with the one or more visual indicators overlaid on frames of the query video to indicate locations of the visual changes in the query video relative to the known video (displaying the video query including changes and the visual indicators overlayed, Par.[0121] Fig.2, along with a user interface overlaying selected aspects presenting at a user interface the ability to manipulate the video, Par.[0427]).
Re Claim 18. This claim represents the system implementing each and every limitation of the computer method of claim 8, hence it is rejected on the same evidentiary premise mutatis mutandis.
Re Claim 19. This claim represents the system implementing each and every limitation of the computer method claims 4 and 5, hence it is rejected on the same evidentiary premises mutatis mutandis.
Re Claim 20. This claim represents the system implementing each and every limitation of the computer method claim 6, hence it is rejected on the same evidentiary premise mutatis mutandis.
Conclusion
5. The prior art made of record and not relied upon, is considered pertinent to applicant's disclosure as further listed below;
* Alexander Black; “Deep Image Comparator: Learning to Visualize Editorial changes”, CVSSP 2021, University of Surrey.
* Lorenzo De Donato; “Deep Learning for Audio detection and Video Analysis in Railway Applications”, Scuola Politecnica e delle Scienze, 2019/2020.
* Bolei Zhou “Interpretable Representation Learning for Visual Intelligence”; © Massachusetts Institute of Technology, June 2018.
* Ben Ingel et al., (US 2020/0169591)
* Zhang (US 2019/0197187)
See PTO-892 form.
Applicant is required under 37 C.F.R. 1.111(c) to consider these references when responding to this action.
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DRAMOS . KALAPODAS
Primary Examiner
Art Unit 2487
/DRAMOS KALAPODAS/