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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/24/2026 has been entered.
Response to Arguments
Applicant's arguments regarding the 35 USC 103 rejections with respect to the amended limitations of claims 1-3, 5-10, 12-17 and 19-20 have been fully considered but they are not persuasive.
Applicant argues in pages 7-8 RE the amended limitations “extracting a facial feature vector from an image of a participant of a plurality of participants; generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character expression bases using the facial feature vector extracted for the participant;” of independent claims 1, 8 and 15 against the references individually. In response, the examiner contests that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Here Rabinovich is modified by Bouaziz and Wang to teach the limitations as a whole. As cited in the prior office action Rabinovich extracting a facial feature vector from an image of a participant of a plurality of participants (Rabinovich Figs 5,7,14, 22, abstract, [0175], [0183], [0188], [0375]-[0378], [0386] etc as the facial parameters, landmark positions etc).
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.); and
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)
Rabinovich is silent RE: generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character expression bases using the facial feature vector extracted for the participant, and applying the expression parameter vector to the 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 Bouaziz teaches generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character bases using the facial feature vector extracted for the participant in Figs 1-9, [0015], [0029]-[0030], [0039]-[0041], [0050]-[0066] etc, to generate an avatar character face model representing expressions incorporating the user facial features/geometry in each of the frames to generate facial animations, wherein the identity model comprising plurality of eigenvectors is equivalent to the claimed facial feature vector. In addition Wang teaches generating a set of virtual character expressions customized for the participant combining a set of virtual character expression bases using a set of template avatar expression bases in abstract, Figs 1-3, 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 generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character expression bases using the facial feature vector extracted for the participant, and applying the expression parameter vector to the 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 combining the teachings of Bouaziz and Wang, to generate the avatar expressions matching the user facial features directly from combining a set of virtual character expression bases and thereby increasing system effectiveness and user experience.
Therefore, as clearly set forth above, Rabinovich as modified by Bouaziz and Wang meets the requirements of the claim language, and rejections of the claims are maintained. Dependent claims 2-3, 5-7, 9-10, 12-14, 16-17 and 19-20 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 1-3, 5-10, 12-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rabinovich et al (US 20210392296 A1), in view of Bouaziz et al (US 20140362091 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:
extracting a facial feature vector from an image of a participant of a plurality of participants (Rabinovich Figs 5,7,14, 22, abstract, [0175], [0183], [0188], [0375]-[0378], [0386]).
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: generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character expression bases using the facial feature vector extracted for the participant, and applying the expression parameter vector to the 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 Bouaziz teaches generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character bases using the facial feature vector extracted for the participant in in Figs 1-9, [0015], [0029]-[0030], [0039]-[0041], [0050]-[0066] etc, to generate an avatar character face model representing expressions incorporating the user facial features/geometry in each of the frames to generate facial animations, wherein the identity model comprising plurality of eigenvectors is equivalent to the claimed facial feature vector. In addition Wang teaches generating a set of virtual character expressions customized for the participant combining a set of virtual character expression bases using a set of template avatar expression bases in abstract, Figs 1-3, 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 generating a set of virtual character expressions customized for the participant comprising combining a set of virtual character expression bases using the facial feature vector extracted for the participant, and applying the expression parameter vector to the 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 combining the teachings of Bouaziz and Wang, to generate the avatar expressions matching the user facial features directly from combining a set of virtual character expression bases and thereby increasing system effectiveness and user experience.
RE claim 2, Rabinovich as modified by Bouaziz and Wang teaches wherein the virtual character neutral face model is generated before a start of the video conference (Rabinovich [0178], [0375]-[0378], Wang Fig 1, page 348 col 1-2);
and wherein generating the virtual character neutral face model comprises: combining a set of virtual character face bases according to the facial feature vector to generate the virtual character neutral face mode (Bouaziz teaches in Figs 1-3, [0022], [0060] to generate the neutral character face model.)
RE claim 3, Rabinovich as modified by Bouaziz and Wang 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 5, Rabinovich as modified by Bouaziz and Wang 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 Bouaziz and Wang 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 Bouaziz and Wang 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 8-10, 12-14 recite limitations similar in scope with limitations of claims 1-3, 5-7 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]).
Claims 15-17, 19-20 recite limitations similar in scope with limitations of claims 1-3, 5-7 and therefore rejected under the same rationale. In addition Rabinovich teaches A non-transitory computer-readable medium comprising processor-executable instructions ([0008]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see attached 892).
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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