DETAILED ACTION
Response to Arguments
Applicant's arguments filed 04/06/2026 regarding the 35 USC 103 rejections with respect to the amended limitations of claims 1-20 have been fully considered but they are not persuasive.
Applicant argues in page 22 RE the amended limitations of claim 1 “determining an expression parameter vector from associated with a participant of the plurality of participants based on a facial expression of the participant detected from a video of the participant during the video conference” that “Rabinovich's "facial expression coefficient" is estimated based on speech audio. Rabinovich, paragraph [0158]. Specifically, a neural network learns the correspondence between speech audio (i.e., phonemes) and the corresponding face movements based on training data, so it can predict face movements, especially the lip movements, based on phonemes in speech audio. See Id. In other words, the "facial expression coefficient" in Rabinovich are coefficients (weights or biases) in the trained neural network model. In contrast, the "expression parameter vector" in claim is determined "based on a facial expression of the participant detected from a video of the participant during the video conference," as specified in amended claim 1. The "expression parameter vector" represents the facial expression, and it is not any coefficient or weight for a neural network model.”. In response the examiner contests that this appears to be Applicant’s own conclusion rather than evidence. Rabinovich clearly teaches the feature of determining an expression parameter vector associated with a participant of the plurality of participants based on a facial expression of the participant detected from a video of the participant during the video conference throughout the disclosure. For example Fig 7, [0183]-[0184] teaches characteristics 105 of the image that include texture parameters, expression parameters and/or shape parameters are outputted. [0158] merely teaches a neural network may be trained to estimate the facial expression coefficients based on the audio to further improve the natural appearance of the rendered model., wherein the network learns the correspondence between the audio (i.e. phonemes) and the corresponding face movements, especially the lip movements. The lip, face and throat movements are continuously detected from the video of the participants as Precise 3D Tracking of Faces Via Monocular RGB Video taught in [0303], [0365]-[0375] etc. In addition [0534]- [0549] applying video analysis to derive emotional parameters. Therefore Rabinovich meets the requirements of the claim language.
For the same reason as set forth above, independent claims 8 and 15 reciting limitations similar in scope stand rejected. Dependent claims 2-7, 9-14 and 16-21 stand rejected for depending on the rejected base claims.
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.
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, 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Rabinovich et al (US 20210392296 A1), and further in view of Wang et al (Wang, Jingying, et al. "Fully automatic blendshape generation for stylized characters." 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR). IEEE, March, 2023.).
RE claim 1, Rabinovich teaches A computer implemented method (abstract, Fig 1), comprising:
joining a video conference involving a plurality of participants (abstract, Figs 1, 5, [0060]);
determining an expression parameter vector from associated with a participant of the plurality of participants based on a facial expression of the participant detected from a video of the participant during the video conference (Fig 7, [0183]-[0184] teaches characteristics 105 of the image that include texture parameters, expression parameters and/or shape parameters are outputted. [0158] teaches a neural network may be trained to estimate the facial expression coefficients based on the audio to further improve the natural appearance of the rendered model., wherein the network learns the correspondence between the audio (i.e. phonemes) and the corresponding face movements, especially the lip movements. The lip, face and throat movements are continuously detected from the video of the participants as Precise 3D Tracking of Faces Via Monocular RGB Video taught in [0303], [0365]-[0375] etc. In addition [0534]- [0549] applying video analysis to derive emotional parameters.);
generating a virtual character customized for the participant from a virtual character face model comprising applying the expression parameter vector and incorporating a virtual character neutral face model customized for the participant, and the virtual character neutral face model customized for the participant describing a neutral face of the virtual character customized for the participant (abstract, Figs 1, 7, 15, [0074], [0088], [0102]-[0111], [0375]-[0377], [0141]-[0142], [0154], [0158], [0183]-[0188], [0264], [0417]-[0420] etc. wherein the customized 3D avatar model is created using the neutral 3DMM template model and mapping the determined shape, pose and expression parameters); and
rendering the virtual character customized for the participant in a video stream of the participant (abstract, Fig 1, [0060], [0074]-[0076]).
Rabinovich is silent RE: to a set of virtual character expressions customized for the participant each of the set of virtual character expressions customized for the participant describing a facial expression of the virtual character customized for the participant.
However Wang teaches in abstract, Figs 1-2, 7, page 349 col 1 to expression transfer for stylized characters with different topologies in real time.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in Rabinovich a system and method of to a set of virtual character expressions customized for the participant, initial 3D model as suggested by Wang, to expression transfer for stylized characters with different topologies in real time and thereby increasing system effectiveness and user experience.
Claim 8 recites limitations similar in scope with limitations of claim 1 and therefore rejected under the same rationale. In addition Rabinovich teaches A system comprising: a non-transitory computer-readable medium; and a processor communicatively coupled to the non-transitory computer-readable medium ([0009]).
Claim 15 recites limitations similar in scope with limitations of claim 1 and therefore rejected under the same rationale. In addition Rabinovich teaches A non-transitory computer-readable medium comprising processor-executable instructions ([0008]).
Claims 2-7, 9-14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rabinovich as modified by Wang, and further in view of Bouaziz et al (US 20140362091 A1).
RE claim 2, Rabinovich as modified by Wang teaches wherein the virtual character neutral face model is generated before a start of the video conference, and wherein generating the virtual character neutral face model comprises: extracting a facial feature vector from an image of the participant (Rabinovich [0178], [0375]-[0378], Wang Fig 1, page 348 col 1-2).
Rabinovich as modified by Wang is silent RE and combining a set of virtual character face bases according to the facial feature vector to generate the virtual character neutral face model. However Bouaziz teaches in Figs 1-3, [0022], [0060] to generate the neutral character face model.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include in Rabinovich as modified by Wang a system and method of combining a set of virtual character face bases according to the facial feature vector to generate the virtual character neutral face model, as suggested by Bouaziz, in order to effectively generate the neutral character face model and thereby increasing system effectiveness and user experience.
RE claim 3, Rabinovich as modified by Wang and Bouaziz teaches wherein the virtual character neutral face model customized for the participant comprises a base neutral model and one or more accessory models (Rabinovich [0258], [0818]).
RE claim 4, Rabinovich as modified by Wang and Bouaziz teaches wherein the set of virtual character expressions customized for the participant are generated by combining a set of virtual character expression bases based on the facial feature vector for the participant (Rabinovich [0178], [0375]- [0378], Wang abstract, Figs 1-2, 7, page 349 col 1).
RE claim 5, Rabinovich as modified by Wang and Bouaziz teaches wherein the set of virtual character face bases are generated by applying a deformation transfer to the virtual character face model, a human base face, and a set of human face bases (Rabinovich [0178], [0375]- [0378], [0418], Bouaziz Figs 1-4, [0023], Wang abstract, Figs 1-2, 7, page 349 col 1).
RE claim 6, Rabinovich as modified by Wang and Bouaziz teaches wherein the set of virtual character expression bases comprises a subset of expression bases for each virtual character face base in the set of virtual character face bases (Wang abstract, Figs 1-2, 7, page 349 col 1, Bouaziz Figs 1-4, [0023]).
RE claim 7, Rabinovich as modified by Wang and Bouaziz teaches wherein a k-th virtual character expression base in a subset of expression bases for a j-th virtual character face base is generated by applying the deformation transfer to the virtual character face model incorporated with the j-th virtual character face base, the human base face incorporated with a j-th human face base, and the human base face incorporated with the j-th human face base and a k-th human expression base of a set of human expression bases (Rabinovich [0178], [0375]- [0378], [0418], Bouaziz Figs 1-4, [0023], [0061]. Wang abstract, Figs 1-2, 7, page 349 col 1).
Claims 9-14 recite limitations similar in scope with limitations of claims 2-7 and therefore rejected under the same rationale.
Claims 16-20 recite limitations similar in scope with limitations of claims 2-6 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see attached 892).
THIS ACTION IS MADE FINAL. 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 SULTANA MARCIA ZALALEE whose telephone number is (571)270-1411. The examiner can normally be reached Monday- Friday 8:00am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Sultana M Zalalee/ Primary Examiner, Art Unit 2614
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