CTNF 18/947,375 CTNF 85674 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim s 1, 4, 6, 8, 10, 13, 19 and 20 are objected to because of the following informalities: For claim 1, Examiner believes this claim should be amended in the following manner: A device comprising: a memory configured to store data corresponding to a diffusion model; and one or more processors coupled to the memory and configured to: obtain multiple image frames; generate multiple latent representation frames based on the multiple image frames, the multiple latent representation frames include latents; obtain multiple output latent representations generated based on multiple diffusion sampling operations performed on the multiple latent representation frames, the multiple diffusion sampling operations performed based on the diffusion model; for a pair of latent representation frames of the multiple latent representation frames, determine flow values based on the multiple diffusion sampling operations performed on the pair of latent representation frames; and perform, based on the flow values, a video generation operation. For claim 4, Examiner believes this claim should be amended in the following manner: The device of claim 1, wherein: the diffusion model includes a latent diffusion model (LDM); the diffusion model has a U-Net architecture including a plurality of blocks; the diffusion model includes one or more transformers; the video generation operation includes a warping operation; the diffusion model includes a latent diffusion model (LDM); or a combination thereof. For claim 6, Examiner believes this claim should be amended in the following manner: The device of claim 3, wherein: each latent representation frame of the pair of latent representation frames is associated with a plurality of tokens; and the one or more processors are configured to, for the pair of latent representation frames: determine a set of distance values based on the activations obtained from the at least one diffusion sampling operation, the set of distance values associated with a first plurality of tokens associated with the first latent representation frame and a second plurality of tokens associated with the second latent representation frame. For claim 8, Examiner believes this claim should be amended in the following manner: The device of claim 6, wherein the one or more processors are configured to, for the pair of latent representation frames of the multiple latent representation frames: identify a first index value of a first token of [[a]] the first plurality of tokens of the first latent representation frame; identify, based on the set of distance values, a shortest distance value for the first index value of the first token of the first plurality of tokens; based on the identified shortest distance value, identify a second index value of a second token of the second plurality of tokens; determine an offset value based on the first index value of the first token of the first plurality of tokens and the second index value of the second token of the second plurality of tokens; and determine, based on the offset value, a flow value for the first token of the first plurality of tokens. For claim 10, Examiner believes this claim should be amended in the following manner: The device of claim 9, wherein: the first latent representation frame is associated with a first plurality of tokens, and the second latent representation frame is associated with a second plurality of tokens; and the one or more processors are configured to, for the pair of latent representation frames: for each sampling operation of at least two sampling operations of the multiple diffusion sampling operations, determine a first set of distance values based on the activations obtained from [[the]] that sampling operation, the first set of distance values associated with the first plurality of tokens and the second plurality of tokens; and generate a second set of distance values for the pair of latent representations based on an average of the multiple sets first set of distance values associated with the first plurality of tokens and the second plurality of tokens . For claim 13, Examiner believes this claim should be amended in the following manner: The device of claim 1, further comprising one or more cameras coupled to the one or more processors and configured to generate the multiple image frames, wherein video content is generated by the one or more processors at least partially based on the multiple image frames from the one or more cameras. For claim 19, Examiner believes this claim should be amended in the following manner: A method of operating a processor of a video generation device, the method comprising: obtaining multiple image frames; generating multiple latent representation frames based on the multiple image frames, the multiple latent representation frames include latents; obtaining multiple output latent representations generated based on multiple diffusion sampling operations performed on the multiple latent representation frames, the multiple diffusion sampling operations performed based on a diffusion model; for a pair of latent representation frames of the multiple latent representation frames, determining flow values based on the multiple diffusion sampling operations performed on the pair of latent representation frames; and performing, based on the flow values, a video generation operation. For claim 20, Examiner believes this claim should be amended in the following manner: A non-transitory computer-readable medium storing instructions that are executable by one or more processors to cause the one or more processors to: obtain multiple image frames; generate multiple latent representation frames based on the multiple image frames, the multiple latent representation frames include latents; obtain multiple output latent representations generated based on multiple diffusion sampling operations performed on the multiple latent representation frames, the multiple diffusion sampling operations performed based on a diffusion model; for a pair of latent representation frames of the multiple latent representation frames, determine flow values based on the multiple diffusion sampling operations performed on the pair of latent representation frames; and perform, based on the flow values, a video generation operation . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 4, 8, 10 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. For claim 4, parent claim 1 establishes “a diffusion model” and claim 4 goes on to establish “a latent diffusion model”. Claim 4 goes on to recite the phrase “the diffusion model” and it is unclear and ambiguous to which of the previously established “diffusion model” and “latent diffusion model” is being referenced by the phrase “the diffusion model”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities. For claim 8, parent claim 6 establishes a first “a first plurality of tokens” and claim 8 goes on to establish a second “a first plurality of tokens”. Claim 8 goes on to recite the phrase “the first plurality of tokens” and it is unclear and ambiguous to which of the previously established first “first plurality of tokens” and second “first plurality of tokens” is being referenced by the phrase “the first plurality of tokens”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities. For claim 10, this claim establishes “the multiple sets of distance values”. However, neither claim 10 nor its parent claims provide antecedent basis for “the multiple sets of distance values”. Therefore, the phrase “the multiple sets of distance values” is indefinite. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities. For claim 13, parent claim 1 establishes a first “multiple image frames” and claim 13 goes on to establish a second “multiple image frames”. Claim 13 goes on to recite the phrase “the multiple image frames” and it is unclear and ambiguous to which of the previously established first “multiple image frames” and second “multiple image frames” is being referenced by the phrase “the multiple image frames”. Examiner has suggested amendments in the claim objections discussed above to resolve the ambiguities. Appropriate correction is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 1-5, 13, 14 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ni et al, Conditional Image-to-Video Generation with Latent Flow Diffusion Models , 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 (hereinafter “Ni”), Huang et al., Motion-aware Latent Diffusion Models for Video Frame Interpolation , Proceedings of the 32nd ACM International Conference on Multimedia, October 2024 (hereinafter “Huang”) and Acuna Marrero et al. (U.S. Patent Application Publication 2025/0292497 A1, hereinafter “Acuna Marrero”) . For claim 1, Ni discloses a device (disclosing a computer as a device (page 18444)) comprising: one or more processors (disclosing a graphics processing unit (GPU) (page 18450)) configured to: obtain multiple image frames (disclosing acquisition of multiple image frames (page 18447Fig. 3)) ; generate multiple latent representation frames based on the multiple image frames, the multiple latent representation frames include latents (disclosing generation of latent maps as multiple latent representation frames from the multiple image frames where the latent maps include latents (page 18447)) ; obtain multiple output latent representations generated based on multiple diffusion sampling operations performed on the multiple latent representation frames, the multiple diffusion sampling operations performed based on a diffusion model (disclosing acquisition of output images as multiple output latent representations generated from multiple diffusion sampling operations performed on the latent maps where the sampling operations performed on a diffusion model (pages 18447 and 18449)) ; for a pair of latent representation frames of the multiple latent representation frames, determine flow values based on the multiple diffusion sampling operations performed the pair of latent representation frames (disclosing, for a pair of latent maps corresponding to a pair of image frames, determination of flow values of a flow map based on the sampling operations performed on the pair of latent maps (pages 18447 and 18449)) ; and perform, based on the flow values, a video generation operation (disclosing the flow values enable an operation to generate video (pages 18449-18450/Fig. 4)) . Examiner finds Ni discloses generating multiple latent representation frames based on multiple image frames, the multiple latent representation frames including latents for the reasons discussed above. In any case, these limitations are well-known in the art as disclosed in Huang. Huang similarly discloses a system and method for generating video with a diffusion model (page 1043). Huang likewise discloses generation of latent representation frames based on multiple image frames where each latent representation frame includes a respective latent to facilitate determination of motion hints (pages 1046-1047). It follows Ni may be accordingly modified with the teachings of Huang to generate multiple latent representation frames for its multiple image frames for determination of motion hints in improving generation of video. A person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention would find it obvious to modify Ni with the teachings of Huang. Huang is analogous art in dealing with a system and method for generating video with a diffusion model (page 1043). Huang discloses its use of latent representation frames is advantageous in facilitating determination of motion hints to appropriately improve video generation (pages 1046-1047). Consequently, a PHOSITA would incorporate the teachings of Huang into Ni for facilitating determination of motion hints to appropriately improve video generation. Ni as modified by Huang does not disclose a device comprising a memory configured to store data and one or more processors coupled to the memory. However, these limitations are well-known in the art as disclosed in Acuna Marrero. Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44). Acuna Marrero explains its system may be implemented in a computing device including a memory for storing data for its diffusion model and processor(s) coupled to the memory for performing the functions of the system (Fig. 5; par. 72-75). It follows Ni and Huang may be accordingly modified with the teachings of Acuna Marrero to implement its device with a memory to store data corresponding to its diffusion model and to couple the memory to its processor to perform its functions. A person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention would find it obvious to modify Ni and Huang with the teachings of Acuna Marrero. Acuna Marrero is analogous art in dealing with a system and method for generating video with a diffusion model (par. 11-12 and 44). Acuna Marrero discloses its use of a memory is advantageous in storing data for a diffusion model to appropriately perform the functions of the diffusion model in generating video (Fig. 5; par. 11-12, 44 and 72-75). Consequently, a PHOSITA would incorporate the teachings of Acuna Marrero into Ni and Huang for storing data for a diffusion model to appropriately perform the functions of the diffusion model in generating video. 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, Ni as modified by Huang and Acuna Marrero discloses wherein: the multiple image frames include a sequence of image frames of video content (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may acquire multiple images frames as a sequence of image frames of video content (par. 29); and it follows Ni and Huang may be accordingly modified with the teachings of Acuna Marrero to implement its multiple image frames as a sequence of image frames) ; the flow values are associated with a flow map that represents a flow of the pair of latent representation frames (Ni discloses its flow values are associated with a flow map to represent a flow of the pair of latent maps (pages 18447 and 18449)) ; the one or more processors include an autoencoder (Ni discloses an auto-encoder (page 18447)) ; and wherein the one or more processors are configured to: generate the multiple latent representation frames based on the autoencoder (Ni discloses generation of its latent maps based on the auto-encoder (page 18447)) ; and decode the multiple output latent representations to generate multiple output image frames (Ni discloses a decoder to decode the latent maps to generate multiple output image frames (page 18447)) . For claim 3, depending on claim 1, Ni as modified by Huang and Acuna Marrero discloses wherein the one or more processors are configured to: for at least one diffusion sampling operation of the multiple diffusion sampling operations, obtain activations; and for the pair of latent representation frames of the multiple latent representation frames, determine the flow values based on first activations obtained for a first latent representation frame of the pair of latent representation frames and second activations obtained for a second latent representation frame of the pair of latent representation frames (Huang similarly discloses a system and method for generating video with a diffusion model (page 1043); Huang likewise discloses generation of latent representation frames based on multiple image frames where each latent representation frame includes a respective latent to facilitate determination of motion hints (pages 1046-1047); Huang explains its system performs diffusion sampling to obtain motion hints as activations (page 1049); Huang further explains its system determines flows values based on motion hints obtained for a first latent representation frame of a pair of latent representation frames and based on motion hints obtained for a second latent representation of the pair of latent representation frames (page 1049); and it follows Ni may be accordingly modified with the teachings of Huang to generate multiple latent representation frames for its multiple image frames for determination of motion hints as activations for determining its flow values in improving generation of video) . For claim 4, depending on claim 1, Ni as modified by Huang and Acuna Marrero discloses wherein: the diffusion model includes a latent diffusion model (LDM); the diffusion model has a U-Net architecture including a plurality of blocks; the diffusion model includes one or more transformers; the video generation operation includes a warping operation ; or a combination thereof (Ni discloses its video generation operation includes a warping operation (page 18447)) . For claim 5, depending on claim 3, Ni as modified by Huang and Acuna Marrero discloses wherein: the flow values are based on a first set of diffusion sampling operations of the multiple diffusion sampling operations performed on the multiple latent representation frames (Ni discloses a first set of diffusion sampling operations as down-sampling performed by an encoder on latent maps to determine flow values (pages 18447 and 18449)) ; and the video generation operation is performed in association with a second set of diffusion sampling operations of the multiple diffusion sampling operations (Ni discloses a second set of diffusion sampling operations as up-sampling performed by a decoder to perform video generation pages 18447 and 18449)) . For claim 13, depending on claim 1, Ni as modified by Huang and Acuna Marrero discloses further comprising one or more cameras coupled to the one or more processors and configured to generate multiple image frames, wherein video content is generated by the one or more processors at least partially based on the multiple image frames from the one or more cameras (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented with cameras for generating multiple image frames for subsequent generation of video content (par. 28-29); and it follows Ni and Huang may be modified with the teachings of Acuna Marrero to implement a camera for generating its multiple image frames for subsequent generation of video content) . For claim 14, depending on claim 1, Ni as modified by Huang and Acuna Marrero discloses further comprising a display device coupled to the one or more processors and configured to output video content generated based on the multiple image frames (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented with a display device for outputting video content (Fig. 5; par. 28-29 and 72); and it follows Ni and Huang may be modified with the teachings of Acuna Marrero to implement a display device coupled to its processor for output of video content generated based on its multiple image frames). For claim 17, depending on claim 1, Ni as modified by Huang and Acuna Marrero discloses further comprising a speaker configured to output audio associated with video content generated based on the multiple image frames (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented with a speaker to output audio generated in associated with video content generated based on multiple image frames (par. 28-29, 46 and 84); and it follows Ni and Huang may be modified with the teachings of Acuna Marrero to implement a speaker to output audio associated with video content generated based on its multiple image frames) . For claim 18, depending on claim 1, Ni as modified by Huang and Acuna Marrero discloses wherein the one or more processors are integrated in a mobile phone, a tablet computer device, a wearable electronic device, a virtual reality headset, a mixed reality headset, an augmented reality headset, or a camera device (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented in a smartphone, tablet, virtual reality headset or camera (par. 100); and it follows Ni and Huang may be accordingly modified with the teachings of Acuna Marrero to integrate its processor in various client devices) . For claim 19, Ni as modified by Huang and Acuna Marrero discloses a method of operating the device of claim 1 and comprising steps corresponding to the functions performed by the device of claim 1 (see above as to claim 1) . For claim 20, Ni as modified by Huang and Acuna Marrero discloses a non-transitory computer-readable medium storing instructions that are executable by one or more processors (Ni discloses a computer as a device (page 18444) with a graphics processing unit (GPU) (page 18450); Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented in a computing device including a memory for storing instructions and processor(s) coupled to the memory for executing the instructions to perform the functions of the system (Fig. 5; par. 72-75); and it follows Ni and Huang may be accordingly modified with the teachings of Acuna Marrero to implement its device with a memory to store data corresponding to its diffusion model and to couple the memory to its processor to perform its functions) to cause the one or more processors to perform functions corresponding to the functions performed by the device of claim 1 (see above as to claim 1) . 07-21-aia AIA Claim (s) 6, 7, 9, 11 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ni, Huang and Acuna Marrero further in view of Geyer et al., TokenFlow: Consistent Diffusion Features for Consistent Video Editing , arXiv, November 2023 (hereinafter “Geyer”) . For claim 6, depending on claim 3, Ni as modified by Huang and Acuna Marrero does not disclose each latent frame is associated with a plurality of tokens and determine a set of distance values associated with a first plurality of tokens associated with a first latent frame and a second plurality of tokens associated with a second latent frame. However, these limitations are well-known in the art as disclosed in Geyer. Geyer similarly discloses a system and method for generating video with a diffusion model (page 1). Geyer explains its system associates tokens for each latent frame (page 6). Geyer further explains its system determines distance values associated with a first plurality of tokens associated with a first latent frame and associated with a second plurality of tokens associated with a second latent frame (page 7). It follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Geyer to associate each latent representation frame with a plurality of tokens and to determine a set of distance values based on its activations where the set of distance values is associated with a first plurality of tokens associated for its first latent representation frame and a second plurality of tokens associated for its second latent representation frame. A PHOSITA before the effective filing date of the claimed invention would find it obvious to modify Ni, Huang and Acuna Marrero with the teachings of Geyer. Geyer is analogous art in dealing with a system and method for generating video with a diffusion model (page 1). Geyer discloses its use of tokens is advantageous in establishing inter-frame correspondences between diffusion features to appropriately improve video generation (page 6). Consequently, a PHOSITA would incorporate the teachings of Geyer into Ni, Huang and Acuna Marrero for establishing inter-frame correspondences between diffusion features to appropriately improve video generation. Therefore, claim 6 is rendered obvious to a PHOSITA before the effective filing date of the claimed invention. For claim 7, depending on claim 6, Ni as modified by Huang, Acuna Marrero and Geyer discloses wherein, to determine the set of distance values, the one or more processors are configured to: determine a cosine distance based on the activations obtained for the first latent representation frame and the activations obtained for the second latent representation frame; and wherein the set of distance values are arranged in a first dimension according to index values of the first plurality of tokens and in a second dimension according to index values of the second plurality of tokens (Geyer similarly discloses a system and method for generating video with a diffusion model (page 1); Geyer explains its system associates tokens for each latent frame (page 6); Geyer further explains its system determines distance values associated with a first plurality of tokens associated with a first latent frame and associated with a second plurality of tokens associated with a second latent frame (page 7); Geyer discloses a distance value may be a cosine distance and the set of distance values are arranged in a first dimension p according to index values for the first plurality of tokens and a second dimension q according to index values for the second plurality of tokens (page 7); and it follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Geyer to associate each latent representation frame with a plurality of tokens and to determine a set of distance values based on its activations where the set of distance values is associated with a first plurality of tokens associated for its first latent representation frame and a second plurality of tokens associated for its second latent representation frame) . For claim 9, depending on claim 5, Ni as modified by Huang, Acuna Marrero and Geyer discloses wherein: each latent representation frame of the pair of latent representation frames is associated with a plurality of tokens; and the one or more processors are configured to obtain the activations from a transformer of one or more transformers of the diffusion model (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented with transformers (par. 46); and it follows Ni and Huang may be accordingly modified with the teachings of Acuna Marrero to implement transformers for generating its activations; Geyer similarly discloses a system and method for generating video with a diffusion model (page 1); Geyer explains its system associates tokens for each latent frame (page 6); Geyer further explains its system determines distance values associated with a first plurality of tokens associated with a first latent frame and associated with a second plurality of tokens associated with a second latent frame (page 7); and it follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Geyer to associate each latent representation frame with a plurality of tokens and to determine a set of distance values based on its activations where the set of distance values is associated with a first plurality of tokens associated for its first latent representation frame and a second plurality of tokens associated for its second latent representation frame of its pair of latent representation frames) . For claim 11, depending on claim 1, Ni as modified by Huang, Acuna Marrero and Geyer discloses wherein the one or more processors are configured to: receive an input that includes a request to perform a text-based video generation , a text-based video content editing operation , a video enhancement operation, video compression, a data augmentation operation, or a combination thereof; and one or more activations are obtained based on the input (Geyer similarly discloses a system and method for generating video with a diffusion model (page 1); Geyer discloses its system receives a text prompt as in input to request a text-based video generation or text-based video content editing operation (page 5); and it follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Geyer to implement input for text-based video generation or video editing to obtain its activations based on the input) . For claim 12, depending on claim 1, Ni as modified by Huang, Acuna Marrero and Geyer discloses further comprising: one or more cameras coupled to the one or more processors and configured to generate the multiple image frames; and an input device configured to receive an input and provide the input to the one or more processors, wherein the input includes a request to generate output video content based on the diffusion model and the multiple image frames from the one or more cameras (Acuna Marrero similarly discloses a system and method for generating video with a diffusion model (par. 11-12 and 44); Acuna Marrero explains its system may be implemented with cameras for generating multiple image frames (par. 28-29) and an input device to receive and provide input to a processor (Fig. 5; par. 82); and it follows Ni and Huang may be accordingly modified with the teachings of Acuna Marrero to implement cameras for generating its multiple image frames and to implement an input device for receiving user input; Geyer similarly discloses a system and method for generating video with a diffusion model (page 1); Geyer discloses its system receives a text prompt as in input to request a text-based video generation or text-based video content editing operation (page 5); and it follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Geyer to implement user input of a request to generate output video based on its diffusion model and its multiple image frames from its cameras) . 07-21-aia AIA Claim (s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ni, Huang and Acuna Marrero further in view of Laksono (U.S. Patent Application Publication 2015/0089550 A1) . For claim 15, depending on claim 1, Ni as modified by Huang and Acuna Marrero does not disclose a modem to transmit video content to a second device for output by the second device. However, these limitations are well-known in the art as disclosed in Laksono. Laksono similarly discloses a system and method for generating video content (par. 84). Laksono explains its system implements a modem for facilitating client-to-client communications so that the video content may be transmitted to a second device for output by the second device (par. 177-178). It follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Laksono to implement a modem coupled to its processor to transmit video content generated based on its multiple image frames to a second device for output by the second device. A PHOSITA before the effective filing date of the claimed invention would find it obvious to modify Ni, Huang and Acuna Marrero with the teachings of Laksono. Laksono is analogous art in dealing with a system and method for generating video content (par. 84). Laksono discloses its use of a modem is advantageous in facilitating distribution of video content to other users and devices for appropriate playback (par. 177-178). Consequently, a PHOSITA would incorporate the teachings of Laksono into Ni, Huang and Acuna Marrero for facilitating distribution of video content to other users and devices for appropriate playback. Therefore, claim 15 is rendered obvious to a PHOSITA before the effective filing date of the claimed invention . 07-21-aia AIA Claim (s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ni, Huang and Acuna Marrero further in view of Ji et al. (U.S. Patent Application Publication 2025/0104376 A1, hereinafter “Ji”) . For claim 16, depending on claim 1, Ni as modified by Huang and Acuna Marrero does not disclose a microphone configured to provide an input signal to cause generation of content, performing a voice-to-text operation on the input signal to generate text data; and identifying a content generation request based on the text data. However, these limitations are well-known in the art as disclosed in Ji. Ji similarly discloses a system and method for generating image content with a diffusion model (par. 63). Ji explains its system implements a microphone to provide an input signal for causing generation of image content (par. 60). Ji further explains its system performs automatic speech recognition as a voice-to-text operation on the input signal to generate text data and identify a content generate request from the text data (par. 60). It follows Ni, Huang and Acuna Marrero may be accordingly modified with the teachings of Ji to implement a microphone to provide an input signal to its processor for generating video content based on its multiple image frames and to perform a voice-to-text operation on the input signal to generate text data for identifying a video content generation request. A PHOSITA before the effective filing date of the claimed invention would find it obvious to modify Ni, Huang and Acuna Marrero with the teachings of Ji. Ji is analogous art in dealing with a system and method for generating image content with a diffusion model (par. 63). Ji discloses its use of a microphone is advantageous in receiving and determining requests for appropriate content generation as desired by a user (par. 60). Consequently, a PHOSITA would incorporate the teachings of Ji into Ni, Huang and Acuna Marrero for receiving and determining requests for appropriate content generation as desired by a user. Therefore, claim 16 is rendered obvious to a PHOSITA before the effective filing date of the claimed invention. Allowable Subject Matter Claims 8 and 10 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims and to address any claim objections raised above in the Detailed Action. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TSENG whose telephone number is (571)270-3857. The examiner can normally be reached 8-5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached at (571) 272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CHARLES TSENG/ Primary Examiner, Art Unit 2613 Application/Control Number: 18/947,375 Page 2 Art Unit: 2613 Application/Control Number: 18/947,375 Page 3 Art Unit: 2613 Application/Control Number: 18/947,375 Page 4 Art Unit: 2613 Application/Control Number: 18/947,375 Page 5 Art Unit: 2613 Application/Control Number: 18/947,375 Page 6 Art Unit: 2613 Application/Control Number: 18/947,375 Page 7 Art Unit: 2613 Application/Control Number: 18/947,375 Page 8 Art Unit: 2613 Application/Control Number: 18/947,375 Page 9 Art Unit: 2613 Application/Control Number: 18/947,375 Page 10 Art Unit: 2613 Application/Control Number: 18/947,375 Page 11 Art Unit: 2613 Application/Control Number: 18/947,375 Page 12 Art Unit: 2613 Application/Control Number: 18/947,375 Page 13 Art Unit: 2613 Application/Control Number: 18/947,375 Page 14 Art Unit: 2613 Application/Control Number: 18/947,375 Page 15 Art Unit: 2613 Application/Control Number: 18/947,375 Page 16 Art Unit: 2613 Application/Control Number: 18/947,375 Page 17 Art Unit: 2613 Application/Control Number: 18/947,375 Page 18 Art Unit: 2613 Application/Control Number: 18/947,375 Page 19 Art Unit: 2613 Application/Control Number: 18/947,375 Page 20 Art Unit: 2613 Application/Control Number: 18/947,375 Page 21 Art Unit: 2613 Application/Control Number: 18/947,375 Page 22 Art Unit: 2613 Application/Control Number: 18/947,375 Page 23 Art Unit: 2613
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