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 .
Remarks
For claims 19-20, Examiner makes the following findings.
Independent claim 19 is directed to “one or more computer storage media”.
To this end, Applicants’ Specification establishes a clear dichotomy between “computer storage media” and “communication media” (par. 104). “Communication media” is defined to include signals, carrier waves and other ineligible subject matter. Applicants’ Specification further describes “computer storage media” to exclude signals as described by the statement that “[c]omputer storage media does not comprise signals per se” (par. 104). Examiner accordingly finds that Applicants’ Specification has defined “computer storage media” in a manner to exclude signals are other ineligible subject matter so that “computer storage media” only encompasses statutory subject matter. Therefore, claims 19 and 20 are NOT rejected under 35 U.S.C. 101.
Claim Objections
Claims 4, 7, 9 and 15 are objected to because of the following informalities:
For claim 4, Examiner believes this claim should be amended in the following manner:
The system of claim 1, wherein the marginal model generates the third set of one or more frames by starting the diffusion process at an intermediate step based at least in part on providing noise as a portion of the input into the marginal model, and wherein the diffusion process is indicative of preventing artifacts from propagating over time as the fourth set of one or more [[images]] frames are generated.
For claim 7, Examiner believes this claim should be amended in the following manner:
The system of claim 1, wherein at least one of the first input or the second input includes at least one of a natural language text prompt, an audio signal, or a color request, and wherein the generating of the second set of one or more frames or the generating of the third set of one or more frames is based at least in part on at least one of[[,]] the natural language text prompt, the audio signal, or [[a]] the color request.
For claim 9, Examiner believes this claim should be amended in the following manner:
The system of claim 1, wherein the third set of one or more frames [[the]] generated by the marginal model represents a frame with one or more visual artifacts that have been removed from the second set of one or more frames.
For claim 15, Examiner believes this claim should be amended in the following manner:
The computer-implemented method of claim 10, wherein a marginal model generates the third set of one or more frames by starting the diffusion process at an intermediate step based at least in part on providing noise as a portion of [[the]] input into the marginal model, and wherein the diffusion process is indicative of preventing artifacts from propagating over time as the fourth set of one or more [[images]] frames are generated.
Appropriate correction 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 (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.
Claim(s) 1-3, 5-8, 10-14, 16, 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al., MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation, arXiv, September 2023 (hereinafter “Yan”) in view of Ho et al., Video Diffusion Models, 36th Conference on Neural Information Processing Systems, November 2022 (hereinafter “Ho”) and Ceylan Aksit et al. (U.S. Patent Application Publication 2025/0111866 A1, hereinafter “Aksit”).
For claim 1, Yan discloses a framework (page 1) to perform operations comprising: receiving a first set of one or more frames (disclosing acquisition of a first set of frames from source video (page 5/Figs. 2-3)); providing, as at least a portion of a first input, the first set of one or more frames into a conditional model, wherein the conditional model generates a second set of one or more frames based at least in part on the first input (disclosing the first set of frames is provided as first input into a PropDPM as a conditional generation model to generate a second set of frames (pages 5-6/Figs. 2-3)); providing the second set of one or more frames as input into a marginal model, wherein the marginal model generates, via at least a portion of a diffusion process, a third set of one or more frames (disclosing the second set of frames is provided into a Denoising Diffusion Probabilistic Model (DDPM) as a marginal model to generate, by a diffusion process, a third set of frames (pages 4 and 7)).
Yan does not disclose providing generated set of one or more frames as input back into a condition model wherein the condition model generates a subsequent set of one or frames based on the input.
However, these limitations are well-known in the art as disclosed in Ho.
Ho similarly discloses a method for conditional video generation with a conditional model (page 1). Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8). It follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so that its third set of one or more frames as a second input is provided into its conditional model to generate a fourth set of one or more frames based in its first input and second input to extend generated video to arbitrary lengths.
A person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention would find it obvious to modify Yan with the teachings of Ho. Ho is analogous art in dealing with a method for conditional video generation with a conditional model (page 1). Ho discloses its use of a conditional model for video prediction is advantageous in autoregressively extending generated video to arbitrary lengths as conditioned by user input and user prompts (page 4; and pages 6-8). Consequently, a PHOSITA would incorporate the teachings of Ho into Yan for autoregressively extending generated video to arbitrary lengths as conditioned by user input and user prompts.
Yan as modified by Ho does not specifically disclose a system comprising at least one computer processor and one or more computer storage media storing computer-useable instructions that, when used by the at least one computer processor, cause the at least one computer processor to perform the functions of the system.
However, these limitations are well-known in the art as disclosed in Aksit.
Aksit similarly discloses a system and method for generating images with image diffusion models (par. 1 and 26). Aksit explains it is known to implement a system with a computer processor and memory for storing instructions to be executed by the computer processor to perform the functions of the system (par. 92). It follows Yan and Ho may be accordingly modified with the teachings of Aksit to implement its framework as a system with a processor and storage media for storing instructions for execution by the processor.
A PHOSITA before the effective filing date of the claimed invention would find it obvious to modify Yan and Ho with the teachings of Aksit. Aksit is analogous art in dealing with a system and method for generating images with image diffusion models (par. 1 and 26). Aksit discloses its use of a processor and storage media is advantageous in appropriately carrying out the functions of a computing system and computer hardware (par. 92). Consequently, a PHOSITA would incorporate the teachings of Aksit into Yan and Ho for appropriately carrying out the functions of a computing system and computer hardware. Therefore, claim 1 is rendered obvious to a PHOSITA before the effective filing date of the claimed invention.
For claim 2, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the first, second, third, and fourth set of one or more frames represent one of, one or more digital images, one or more video frames, one or more single interlaced fields, one or more audio signals, or one or more files (Yan discloses its frames as video frames (page 1/Fig. 1)).
For claim 3, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the second set of one or more frames represent one or more frames that are predicted to be next in a sequential order after the first set of one or more frames, and wherein the third set of one or more frames represent a cleaned up version of the second set of one or more frames (Yan discloses the second set of frames is provide into a Denoising Diffusion Probabilistic Model (DDPM) as a marginal model to generate, by a diffusion process, the third set of frames where denoising is applied to clean up the second set of frames in generating the third set of frames(pages 4 and 7); Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input in generating a video sequence to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8); and it follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so that its second set of frames generated by its conditional model are predicted to be next in a sequential order after its first set of frames), and wherein the operations further comprising: excluding from providing the second set of one or more frames into the conditional model based at least in part on the generating of the third set of one or more frames, and wherein the fourth set of one or more frames represent at least one frame that is predicted to be next in the sequential order after the third set of one or more frames (Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input in generating a video sequence to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8); and it follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so its second set of one or more frames are excluded to avoid propagation of noise over time and its third set of one or more frames are provided in place of its second set of frames is provided into its conditional model to generate a fourth set of one or more frames predicted to be next in sequential order after the third set of frames).
For claim 5, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the conditional model is one of a probabilistic diffusion model or a deterministic prediction model trained on mean squared error (MSE) (Yan discloses PropDPM as its conditional generation model where PropDPM is also an appearance propagation diffusion probabilistic model (pages 5-6); Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains it is known to train a model on mean squared error (page 2); and it follows Yan may be accordingly modified with the teachings of Ho to train its conditional model on mean squared error to improve the quality of its conditional model).
For claim 6, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the conditional model generates the first set of one or more frames by running diffusion only part way through a second diffusion process (Yan discloses PropDPM as its conditional generation model where PropDPM is also an appearance propagation diffusion probabilistic model for generating its first set of frames as a second diffusion process to run diffusion only part way (pages 5-6)).
For claim 7, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein at least one of the first input or the second input includes at least one of a natural language text prompt, an audio signal, or a color request, and wherein the generating of the second set of one or more frames or the generating of the third set of one or more frames is based at least in part on at least one of, the natural language text prompt, the audio signal, or a color request (Yan discloses the first input includes a natural language text prompt for generating the second set of frames based on the natural language text prompt (pages 5-6/Figs. 2-3)).
For claim 8, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the operations further comprising: receiving user prompt that constrains at least one of the conditional model or the marginal model to generate content until a target final frame is generated, and wherein at least one of, the second set of one or more frames, the third set of one or more frames, or the fourth set of one or more frames includes the target final frame that is generated based at least in part on the user prompt (Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model receives a user prompt as a condition to constrain its condition model to generate a fixed number of frames until a target final frame is generated (page 3; and pages 6-7/Fig. 2); and it follows Yan may be accordingly modified with the teachings of Ho to receive user prompt for constraining its conditional model or its marginal model to generate content until a target final frame is generated so that its set of frames includes the target final frame generated based on the user prompt to generate video having a fixed number of frames).
For claim 10, Yan as modified by Ho and Aksit discloses a computer-implemented method (Yan discloses a method (pages 1-2); Aksit similarly discloses a system and method for generating images with image diffusion models (par. 1 and 26); Aksit explains it is known to implement a system with a computer processor and memory for storing instructions to be executed by the computer processor to perform the functions of the system (par. 92); and it follows Yan and Ho may be accordingly modified with the teachings of Aksit to implement its method with a computer to appropriately carry out the steps of its method) comprising: receiving a first set of one or more frames (Yan discloses acquisition of a first set of frames from source video (page 5/Figs. 2-3)); based at least in part on the first set of one or more frames, generating a second set of one or more frames, the second set of one or more frames represent one or more frames that are predicted to be next in a sequential order after the first set of one or more frames (Yan discloses the first set of frames is provided as first input into a PropDPM as a conditional generation model to generate a second set of frames (pages 5-6/Figs. 2-3); Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input in generating a video sequence to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8); and it follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so that its conditional model generates its second set of frames to be predicted next in a sequential order after its first set of frames); based at least in part on the second set of one or more frames, generating, via a least a portion of a diffusion process, a third set of one or more frames, the third set of one or more frames represent a different version of the second set of one or more frames (Yan discloses the second set of frames is provided into a Denoising Diffusion Probabilistic Model (DDPM) as a marginal model to generate, by a diffusion process, a third set of frames where the third set of frames represent a denoised version of the second set of frames (pages 4 and 7)); and based at least in part on the third set of one or more frames, generating a fourth set of one or more frames, and wherein the fourth set of one or more frames represent at least one frame that is predicted to be next in the sequential order after the third set of one or more frames (Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8); and it follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so that its third set of one or more frames as a second input is provided into its conditional model to generate a fourth set of one or more frames predicted to be next in sequential order after the third set of frames).
For claim 11, depending on claim 10, Yan as modified by Ho and Aksit discloses wherein the first, second, third, and fourth set of one or more frames represent one of, one or more digital images, one or more video frames, one or more single interlaced fields, one or more audio signals, or one or more files (Yan discloses its frames as video frames (page 1/Fig. 1)).
For claim 12, depending on claim 10, Yan as modified by Ho and Aksit discloses wherein the second set of one or more frames and the fourth set of one or more frames are generated by a conditional model, and wherein the third set of one or more frames are generated by a marginal model (Yan discloses PropDPM as a conditional generation model to generate the second set of frames (pages 5-6/Figs. 2-3); Yan discloses the second set of frames is provided into a Denoising Diffusion Probabilistic Model (DDPM) as a marginal model to generate, by a diffusion process, the third set of frames (pages 4 and 7); Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8); and it follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so that its third set of one or more frames as a second input is provided into its conditional model to generate a fourth set of one or more frames predicted to be next in sequential order after the third set of frames).
For claim 13, depending on claim 12, Yan as modified by Ho and Aksit discloses wherein the conditional model is one of a probabilistic diffusion model or a deterministic prediction model trained on mean squared error (MSE) (Yan discloses PropDPM as its conditional generation model where PropDPM is also an appearance propagation diffusion probabilistic model (pages 5-6); Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains it is known to train a model on mean squared error (page 2); and it follows Yan may be accordingly modified with the teachings of Ho to train its conditional model on mean squared error to improve the quality of its conditional model).
For claim 14, depending on claim 12, Yan as modified by Ho and Aksit discloses wherein the conditional model generates the first set of one or more frames by running diffusion only part way through a second diffusion process (Yan discloses PropDPM as its conditional generation model where PropDPM is also an appearance propagation diffusion probabilistic model for generating its first set of frames as a second diffusion process to run diffusion only part way (pages 5-6)).
For claim 16, depending on claim 10, Yan as modified by Ho and Aksit discloses wherein the generating of the second set of one or more frames or the third set of one or more frames is further based at least in part on at least one of a natural language text prompt, an audio signal, or a color request (Yan discloses the first input includes a natural language text prompt for generating the second set of frames based on the natural language text prompt (pages 5-6/Figs. 2-3)).
For claim 17, depending on claim 10, Yan as modified by Ho and Aksit discloses further comprising: receiving user prompt that constrains at least one of a conditional model or a marginal model to generate content until a target final frame is generated, and wherein at least one of, the second set of one or more frames, the third set of one or more frames, or the fourth set of one or more frames includes the target final frame that is generated based at least in part on the user prompt (Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model receives a user prompt as a condition to constrain its condition model to generate a fixed number of frames until a target final frame is generated (page 3; and pages 6-7/Fig. 2); and it follows Yan may be accordingly modified with the teachings of Ho to receive user prompt for constraining its conditional model or its marginal model to generate content until a target final frame is generated so that its set of frames includes the target final frame generated based on the user prompt to generate video having a fixed number of frames).
For claim 19, Yan as modified by Ho and Aksit discloses one or more computer storage media having computer-executable instructions embodied thereon that, when executed, by one or more processors (Yan discloses a method (pages 1-2); Aksit similarly discloses a system and method for generating images with image diffusion models (par. 1 and 26); Aksit explains it is known to implement a system with a computer processor and memory for storing instructions to be executed by the computer processor to perform the functions of the system (par. 92); and it follows Yan and Ho may be accordingly modified with the teachings of Aksit to implement its method with a computer to appropriately carry out the steps of its method), cause the one or more processors to perform operations comprising: generating, via a conditional model, a second set of one or more frames based at least in part on processing a first set of one or more frames (Yan discloses acquisition of a first set of frames from source video (page 5/Figs. 2-3); Yan discloses the first set of frames is provided as first input into a PropDPM as a conditional generation model to generate a second set of frames (pages 5-6/Figs. 2-3)); generating, via a diffusion process and a marginal model, a third set of one or more frames based on using the second set of one or more frames generated via the conditional model as input into the marginal model, wherein the third set of one or more frames representing the second set of one or more frames except that the third set of one or more frames include one or more frame elements that have different values than one or more frame elements of the second set of one or more frames (Yan discloses the second set of frames is provided into a Denoising Diffusion Probabilistic Model (DDPM) as a marginal model to generate, by a diffusion process, a third set of frames where the third set of frames represent a denoised version of the second set of frames so that the third set of frames includes frames with different values from denoising than frames of the second set of frames (pages 4 and 7)); and based at least in part on the marginal model generating the third set of one or more frames via at least a portion of the diffusion process, generating, via the conditional model, a fourth set of one or more frames based at least in part on using the third set of one or more frames as input instead of the second set of one or more frames (Ho similarly discloses a method for conditional video generation with a conditional model (page 1); Ho explains its conditional model implements video prediction to continually accept a set of frames as input to generate a subsequent set of frames based on the input to autoregressively extend generated video to arbitrary lengths (page 4; and pages 6-8); and it follows Yan may be accordingly modified with the teachings of Ho to implement its conditional model to support video prediction so its second set of one or more frames are excluded to avoid propagation of noise over time and its third set of one or more frames are provided in place of its second set of frames is provided into its conditional model to generate a fourth set of one or more frames predicted to be next in sequential order after the third set of frames).
Claim(s) 4, 9, 15, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan in view of Ho and Aksit further in view of Saharia et al. (U.S. Patent Application Publication 2023/0103638 A1, hereinafter “Saharia”).
For claim 4, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the marginal model generates the third set of one or more frames by starting the diffusion process at an intermediate step based at least in part on providing noise as a portion of the input into the marginal model (Yan discloses its marginal model generates its third set of frames by starting the diffusion process at an intermediate step by providing the second set of frames with noise as the input into the marginal model so that the Denoising Diffusion Probabilistic Model as the marginal model removes noise to denoise the second set of frames in generating the third set of frames (pages 4 and 7)).
Yan as modified by Ho and Aksit does not specifically disclose diffusion is indicative of preventing artifacts.
However, these limitations are well-known as disclosed in Saharia.
Saharia similarly discloses a system and method for image generation with diffusion models (par. 2 and 52). Saharia discloses a diffusion model to perform denoising to remove artifacts (par. 175 and 179). It follows Yan, Ho and Aksit may be accordingly modified with the teachings of Saharia to implement its marginal model for denoising to remove artifacts from its second set of frames in generating its third set of frames so that artifacts are prevented from propagating over time as its fourth set of frames are generated from its third set of frames.
A PHOSITA before the effective filing date of the claimed invention would find it obvious to modify Yan, Ho and Aksit with the teachings of Saharia. Saharia is analogous art in dealing with a system and method for image generation with diffusion models (par. 2 and 52). Saharia discloses its use of a denoising diffusion model is advantageous in removing artifacts to improve quality of generated images (par. 175 and 179). Consequently, a PHOSITA would incorporate the teachings of Saharia into Yan, Ho and Aksit for removing artifacts to improve quality of generated images. Therefore, claim 4 is rendered obvious to a PHOSITA before the effective filing date of the claimed invention.
For claim 9, depending on claim 1, Yan as modified by Ho and Aksit discloses wherein the third set of one or more frames the generated by the marginal model represents a frame with one or more visual artifacts that have been removed from the second set of one or more frames (Saharia similarly discloses a system and method for image generation with diffusion models (par. 2 and 52); Saharia discloses a diffusion model to perform denoising to remove visual artifacts (par. 175 and 179); and it follows Yan, Ho and Aksit may be accordingly modified with the teachings of Saharia to implement its marginal model for denoising to remove visual artifacts from its second set of frames in generating its third set of frames so that artifacts are prevented from propagating over time as its fourth set of frames are generated from its third set of frames).
For claim 15, depending on claim 10, Yan as modified by Ho and Aksit discloses wherein a marginal model generates the third set of one or more frames by starting the diffusion process at an intermediate step based at least in part on providing noise as a portion of the input into the marginal model, and wherein the diffusion is indicative of preventing artifacts from propagating over time as the fourth set of one or more images are generated (Yan discloses its marginal model generates its third set of frames by starting the diffusion process at an intermediate step by providing the second set of frames with noise as the input into the marginal model so that the Denoising Diffusion Probabilistic Model as the marginal model removes noise to denoise the second set of frames in generating the third set of frames (pages 4 and 7); Saharia similarly discloses a system and method for image generation with diffusion models (par. 2 and 52); Saharia discloses a diffusion model to perform denoising to remove artifacts (par. 175 and 179); and it follows Yan, Ho and Aksit may be accordingly modified with the teachings of Saharia to implement its marginal model for denoising to remove artifacts from its second set of frames in generating its third set of frames so that artifacts are prevented from propagating over time as its fourth set of frames are generated from its third set of frames).
For claim 18, depending on claim 10, Yan as modified by Ho and Aksit discloses wherein the third set of one or more frames represents a frame with one or more visual artifacts that have been removed from the second set of one or more frames (Saharia similarly discloses a system and method for image generation with diffusion models (par. 2 and 52); Saharia discloses a diffusion model to perform denoising to remove visual artifacts (par. 175 and 179); and it follows Yan, Ho and Aksit may be accordingly modified with the teachings of Saharia to implement its marginal model for denoising to remove visual artifacts from its second set of frames in generating its third set of frames so that artifacts are prevented from propagating over time as its fourth set of frames are generated from its third set of frames).
For claim 20, depending on claim 19, Yan as modified by Ho and Aksit discloses wherein the third set of one or more frames represents a frame with one or more visual artifacts that have been removed from the second set of one or more frames (Saharia similarly discloses a system and method for image generation with diffusion models (par. 2 and 52); Saharia discloses a diffusion model to perform denoising to remove visual artifacts (par. 175 and 179); and it follows Yan, Ho and Aksit may be accordingly modified with the teachings of Saharia to implement its marginal model for denoising to remove visual artifacts from its second set of frames in generating its third set of frames so that artifacts are prevented from propagating over time as its fourth set of frames are generated from its third set of frames).
Conclusion
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/CHARLES TSENG/Primary Examiner, Art Unit 2613