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 .
DETAILED ACTION
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6-7, 10, 14-15 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Weisz et al (10,904,488).
Consider claims 1, 10 and 17, Weisz et al teach a method, system and non-transitory computer-readable medium comprising: joining, by a client device, a video conference (col. 6 lines 53-55; “A participant may be the initiator of a video conference and/or a participant in a video conference in any of the approaches described herein”); accessing, by the client device, a source video clip comprising a set of source video frames related to a user associated with the client device (col. 6 lines 46-53; “The high-level architecture 300 includes participant input data 302. Participant input data 302 includes audio input, video input, image input, graph input, statistical input, or any combination thereof. Any input as referred to herein may refer to data and vice versa. In a preferred aspect, the participant input data 302 comprises at least audio input data and video input data. The audio and/or video input data may be historical input, live-streaming input, or any combination thereof”); receiving, by the client device, source audio data related to the user (col. 6 lines 46-53; “In a preferred aspect, the participant input data 302 comprises at least audio input data and video input data”); generating, by the client device, target video data based on the set of source video frames and the source audio data using a trained video generator model (col. 6 lines 56-63; “In preferred aspects, a participant model 304 is trained on participant input data 302 and/or any additional new input data 306 according to any of the various approaches described herein”; col. 7 lines 3-30; “In preferred approaches, the participant model 304 is trained on as much participant input data 302 and/or any additional new input data 306 as possible… In various aspects, the deep net 308 generates a synthetic video representation (e.g., video output 310) based on the participant model 304 and environmental parameters 312. Environmental parameters 312 may be selected and/or configured by the participant during training of the participant model 304”); and streaming, by the client device, the target video data during the video conference (col. 10 lines 52-56; “Optional operation 414 may include transmitting the synthetic video representation for inclusion in a live video conference. The synthetic video representation may be transmitted to a live video conference in any manner known in the art”).
Consider claims 6 and 14, Weisz et al teach further comprising: receiving a selection of one or more digital assets for customizing an appearance of the user in the target video data, wherein the one or more digital assets corresponds to hair style, beard style, eyeglass style, or makeup; and generating the target video data further based on the selection of one or more digital assets (col. 8 lines 51-62; col. 9 lines 38-45; “Operation 404 includes training the participant model on environmental parameters. In one approach, the environmental parameters belong to one or more categories including physical background appearance, participant clothing, participant makeup, participant hair color, lighting, etc”).
Consider claims 7 and 15, Weisz et al teach wherein the source video clip comprises a pre-recorded video depicting the user speaking utterances comprising a unique identifier associated with the user, wherein the unique identifier comprising a string of numerals or characters randomly generated for the user (col. 8 lines 22-35; “In a preferred aspect, the participant model is trained on at least video streams of a participant. For example, the participant model may be pre-trained on a collection of existing images and/or video input of the participant. The images and/or video input may be available through a web search, prior video conference recordings, input by the user, etc. In various approaches, every time the user participates in a video conference, the participant model is updated based on the new video input to refine the participant model to realistically represent how the participant looks and/or how the participant's face and/or upper body features move as the participant speaks. In other aspects, the participant model may be trained on any other video input data known in the art”; it should be noted that the unique identifier is inherent as the participant is unique to each participant).
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 of this title, 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 2-5, 8-9, 11-13, 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Weisz et al (10,904,488) in view of Waibel (2025/0315631).
Consider claims 2, 11 and 18, Weisz et al suggest where training is performed using various type of generative adversarial network (GAN) (col. 8 lines 9-50). Weisz et al did not explicitly suggest training a video generator model comprising an encoder model and a decoder model to obtain the trained video generator model by: accessing training video data comprising a set of training video frames and corresponding training audio data; encoding the set of training video frames to obtain a set of training image features in a latent space using an encoder model; mapping a set of training audio features of the training audio data to the set of training image features to obtain a set of training alignment features; reconstructing the training video data by decoding the set of training alignment features using a decoder model to obtain reconstructed training video data; and adjusting one or more parameters of the encoder model or the decoder model by comparing the reconstructed training video data and the training video data using a generative adversarial network to obtain a trained encoder model and a trained decoder model. In the same field of endeavor, Waibel suggest such (Fig. 2; par. 0048-0052; i.e., GAN based encoder-decoder). Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date to substitute the GAN based encoder-decoder taught by Waibel with Weisz et al and the results would have been predictable and resulted in improving model training and performance.
Consider claims 3-4 and 12, the combination teaches wherein the encoder model comprises a first transformer model, wherein the decoder model comprises a second transformer model and wherein the generative adversarial network comprises the video generator model and a video discriminator, wherein the video discriminator comprises an image discriminator and an audio discriminator (par. 0034-0035; 0044; 0050 of Waibel; “A Transformer model can feature, for example, in various embodiments, 24 encoder layers and 8 decoder layers. The overall structure of a Transformer-based model is shown in FIG. 7. The encoder and decoder of the Transformers are constructed by layers, each of which contains self-attentional sub-layers coupled with feed-forward neural networks. To adapt the encoder to long speech utterances, a reshaping practice may be used by grouping consecutive frames into one step. Subsequently, the input features can be combined with sinusoidal positional encoding”; “A HiFi-GAN can comprise one generator and two discriminators: multi-scale and multi-period discriminators. The generator and discriminators are trained adversarially, along with two additional losses for improving training stability and model performance”).
Consider claims 5, 13 and 19, the combination teaches wherein generating the target video data based on the set of source video frames and the source audio data using the trained video generator model comprises: generating a plurality of mouth region images for the user corresponding to the source audio data based on the set of training image features in the latent space using the trained decoder model; blending the plurality of mouth region images with the set of source video frames respectively iteratively to generate a set of target video frames; and synchronizing the set of target video frames and the source audio data to generate the target video data (par. 0052 of Waibel; “The lip generation module 24 preferably synchronizes as closely as possible the lip movements of the speaker in the video frames generated by the lip generation module 24 (and ultimately in the output video 26) to adapted speech from the voice conversion module 20. The lip generation module 24 can also be trained to preserve facial expressions of the speaker in the input video 12 in the output video 26. Note that the speakers in the input and output videos could be the same or different”).
Consider claims 8-9, 16, and 20, Weisz et al did not explicitly suggest of receiving a text script; and generating the source audio data based on the text script using a trained text-to-speech model and further comprising receiving the text script from a user input device associated with the client device during the video conference. In the same field of endeavor, Waibel suggest such (par. 0012; 0031). Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date to incorporate the teaching Waibel into view of Weisz et al and the results would have been predictable and resulted in improving model training and performance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/Quoc D Tran/
Primary Examiner, Art Unit 2691
July 14, 2026
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