DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/12/2026 has been entered. Claims 1-20 remain pending in the application.
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.
Claim 1, 4-7, 9, 12-15, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella U.S. Patent Application 20220197306 in view of Graham U.S. Patent Application 20240346731, and further in view of Cheaz U.S. Patent Application 20240086546.
Regarding claim 9, Cella discloses a computing system for generating video content adhering to professional film standards using artificial intelligence (paragraph [1916]: support the theater and film productions; paragraph [0694]: the data, parameters and outcomes are fed into a machine learning process in the artificial intelligence system 1160 for further analysis), the system comprising:
one or more processors; and one or more memories having stored thereon instructions that when executed by the one or more processors (paragraph [0028]: a set of one or more processors that execute a set of computer-readable instructions... The set of job requirements is stored in a non-transitory computer readable memory that is accessible by at least one processor of the set of processors), cause the system to:
identify and prioritize essential components of a training dataset (paragraph [0720]: At 5202, a plurality of streams of machine related data from multiple data sources are received at the machine twin 1770... At 5205, the raw data is cleaned by removing any missing or noisy data, which may occur due to any technical problems in the machine at the time of collection of data);
selectively sample data that is most representative for foundational learning of an Al model (paragraph [0720]: At 5208, one or more models are selected for training by machine twin 1770. The selection of model is based on the kind of data available at the machine twin 1770 and the desired outcome of the model; paragraph [1551]: feature maps 8876 may be subsampled or down-sampled to generate an output matrix 8878);
generate simulations and synthetic data to expand a range of scenarios for Al learning (paragraph [0694]: At 5108, the outcomes of stress scenario simulations are determined, and the performance of value chain network and its different subsystems is estimated across various scenarios. At 5110, the data, parameters and outcomes are fed into a machine learning process in the artificial intelligence system 1160 for further analysis);
implement active learning and feedback loops to iteratively improve the model (paragraph [1809]: training may be done based on feedback received by the system, which is also referred to as “reinforcement learning.” The artificial intelligence system 10212 may receive a set of circumstances that led to a prediction (e.g., attributes of part, attributes of a model, and the like) and an outcome related to the part and may update the model according to the feedback; paragraph [1115]: the executive agent 8364 may record outcomes related to the performed/recommended actions, thereby creating a feedback loop with the expert agent system 8008; paragraph [0557]: the adaptive intelligent systems layer 614 may iteratively adjust one or more parameters of a digital twin and/or one or more embedded digital twins);
conduct quality control and iteration to ensure high standards of quality (paragraph [0720]: At 5210, the one or more models are trained using training dataset and tested for performance using testing dataset; paragraph [0560]: At each iteration, the digital twin simulation system 2020 may output the properties used to run the simulation to the machine learning system 2002... outcomes of the simulation are used to further train the model(s) used by the artificial intelligence system during the simulation);
monitor, evaluate, and project future applications of the Al model to maintain accuracy to filmmaking standards (paragraph [0720]: At 5212, the trained model is used for detecting faults and predicting future failure of the machine on production data; paragraph [1916]: support the theater and film productions).
Cella discloses all the features with respect to claim 9 as outlined above. However, Cella fails to disclose generating video content including filmmaking-specific metadata; implementing the Al model in real-world filmmaking environments explicitly.
Graham discloses training dataset for generating video content (paragraph [0019]: prompt the trained AI model(s) to generate video of the famous actor, John Smith (at any age), walking outside on a sunny day, and the AI model(s) may, in response to this prompt, generate the requested synthetic content without requiring the presence of John Smith at all, because the AI model(s) may have been trained with a robust dataset of images of John Smith to be able to generate a synthetic version of John Smith in the video content being displayed in real-time with the live prompting of the director);
implementing the Al model in real-world filmmaking environments (paragraph [0019]: One example application is filmmaking... the video content that is displayed in real-time can be generated entirely by the trained AI model(s); paragraph [0060]: Implementing specialized models 200(A), 200(B) may improve the quality of the output data 204(A), 204(B) to tailor the AI-generated synthetic content 102 for a particular application, and/or to help achieve real-time output of synthetic content 102 (e.g., on a display), as compared to tasking a single model with generating synthetic content 102 in multiple different styles).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella’s to implement Al model in filmmaking environments as taught by Graham, to create photoreal synthetic content.
Cella as modified by Graham discloses all the features with respect to claim 9 as outlined above. However, Cella as modified by Graham fails to disclose video content including filmmaking-specific metadata.
Cheaz discloses video content including filmmaking-specific metadata (paragraph [0041]: One purpose of analyzing the media data is to allow server 120 to generate metadata configured to be tagged to the plurality of frames of the video stream; Cheaz’s teaching of generating metadata can be combined with Cella and Graham’s device, such that to identify and prioritize training dataset for generating video content and filmmaking-specific metadata).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella and Graham’s to generate metadata as taught by Cheaz, to generate augmented content for media redaction.
Regarding claim 12, Cella as modified by Graham and Cheaz discloses the system of claim 9, wherein the instructions further cause the system to create virtual environments and actors when generating simulations and synthetic data (Graham’s paragraph [0013]: special effects, such as computer-generated imagery (CGI), are typically added to a scene of a movie after the scene has already been filmed; paragraph [0015]: the actor can be de-aged live, while a scene including the actor is being filmed, instead of de-aging the actor during a post-production phase of a filmmaking process).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella’s to implement Al model in filmmaking environments as taught by Graham, to create photoreal synthetic content; and combine Cella and Graham’s to generate metadata as taught by Cheaz, to generate augmented content for media redaction.
Regarding claim 13, Cella as modified by Graham and Cheaz discloses the system of claim 9, wherein the instructions further cause the system to query users and incorporate their responses when implementing active learning and feedback loops (Cella’s paragraph [1809]: training may be done based on feedback received by the system, which is also referred to as “reinforcement learning.” The artificial intelligence system 10212 may receive a set of circumstances that led to a prediction (e.g., attributes of part, attributes of a model, and the like) and an outcome related to the part and may update the model according to the feedback; paragraph [1115]: the executive agent 8364 may record outcomes related to the performed/recommended actions, thereby creating a feedback loop with the expert agent system 8008; Graham’s paragraph [0050]: the model(s) 200(A) can iteratively learn from the live prompting (e.g., user-provided prompt data 212(A))... a real-time, iterative prompting system with a learning feedback loop (e.g., the feedback loop 120) can be used to fine-tune train the model(s) 200(A) in an ad hoc, real-time sense against the live, iterative input (e.g., user-provided prompt data 212(A)) and/or output (e.g., output data 204(A))).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella’s to implement Al model in filmmaking environments as taught by Graham, to create photoreal synthetic content; and combine Cella and Graham’s to generate metadata as taught by Cheaz, to generate augmented content for media redaction.
Regarding claim 14, Cella as modified by Graham and Cheaz discloses the system of claim 9, wherein the instructions further cause the system to ensure the Al model meets film industry standards when conducting quality control and iteration (Graham’s paragraph [0034]: A training dataset 106 with a more abundant set of images to train from can allow for training the machine learning models described herein more efficiently by reducing the number of iterations that the machine learning models are retrained in order to fine tune the model(s). Furthermore, the output (e.g., the synthetic face 102(1)) generated by the trained machine learning model(s) 100 can be higher-quality output (e.g., a synthetic face 102(1) that is photoreal) due to being trained on a more robust set of training data 106).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella’s to implement Al model in filmmaking environments as taught by Graham, to create photoreal synthetic content; and combine Cella and Graham’s to generate metadata as taught by Cheaz, to generate augmented content for media redaction.
Regarding claim 15, Cella as modified by Graham and Cheaz discloses the system of claim 9, wherein the instructions further cause the system to focus on deploying the Al model for broader applications when implementing and scaling (Graham’s paragraph [0060]: Implementing specialized models 200(A), 200(B) may improve the quality of the output data 204(A), 204(B) to tailor the AI-generated synthetic content 102 for a particular application, and/or to help achieve real-time output of synthetic content 102 (e.g., on a display), as compared to tasking a single model with generating synthetic content 102 in multiple different styles; paragraph [0020]: the synthetic content is photoreal by virtue of fine-tuning the AI model(s) that is generating the synthetic content through initially training the AI model(s) on a specific dataset and through subsequently implementing a learning feedback loop where the AI model(s) can iteratively refine the generative output it is providing in response to prompts).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella’s to implement Al model in filmmaking environments as taught by Graham, to create photoreal synthetic content; and combine Cella and Graham’s to generate metadata as taught by Cheaz, to generate augmented content for media redaction.
Claim 1 recites the functions of the apparatus recited in claim 9 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 9 applies to the method steps of claim 1.
Claim 4 recites the functions of the apparatus recited in claim 12 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 12 applies to the method steps of claim 4.
Claim 5 recites the functions of the apparatus recited in claim 13 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 13 applies to the method steps of claim 5.
Claim 6 recites the functions of the apparatus recited in claim 14 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 14 applies to the method steps of claim 6.
Claim 7 recites the functions of the apparatus recited in claim 15 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 15 applies to the method steps of claim 7.
Claim 17 recites the functions of the apparatus recited in claim 9 as medium steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 9 applies to the medium steps of claim 17.
Claim 20 recites the functions of the apparatus recited in claim 12 as medium steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 12 applies to the medium steps of claim 20.
Claim 2-3, 10-11 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cella U.S. Patent Application 20220197306 in view of Graham U.S. Patent Application 20240346731, in view of Cheaz U.S. Patent Application 20240086546, and further in view of Turner U.S. Patent Application 20150286644.
Regarding claim 10, Cella as modified by Graham and Cheaz discloses prioritizing data when focusing on core elements (Cella's paragraph [0720]: At 5202, a plurality of streams of machine related data from multiple data sources are received at the machine twin 1770... At 5205, the raw data is cleaned by removing any missing or noisy data, which may occur due to any technical problems in the machine at the time of collection of data). However, Cella as modified by Graham and Cheaz fails to disclose based on focal lengths, camera settings, and shot types.
Turner discloses based on focal lengths, camera settings, and shot types (paragraph [0014]: providing metadata indicating the focal length of the camera capturing the content; paragraph [0059]: The metadata processor 440 includes a camera setting block; paragraph [0097]: the head and/or face size could be used to determine the type of shot or the quality of the identification).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella, Graham and Cheaz’s to consider camera settings and shot types as taught by Turner, to make it easy for crew members to monitor movie production.
Regarding claim 11, Cella as modified by Graham, Cheaz and Turner discloses the system of claim 9, wherein the instructions further cause the system to focus on common filmmaking scenarios and standard camera configurations when selectively sampling data (Graham's paragraph [0013]: Synthetic content is typically generated during a post-production phase of a content creation process, such as a video production process (e.g., filmmaking). For example, special effects, such as computer-generated imagery (CGI), are typically added to a scene of a movie after the scene has already been filmed; Turner’s paragraph [0014]: providing metadata indicating the focal length of the camera capturing the content; paragraph [0059]: The metadata processor 440 includes a camera setting block; Cella’s paragraph [0720]: At 5208, one or more models are selected for training by machine twin 1770. The selection of model is based on the kind of data available at the machine twin 1770 and the desired outcome of the model; paragraph [1551]: feature maps 8876 may be subsampled or down-sampled to generate an output matrix 8878).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella, Graham and Cheaz’s to consider camera settings and shot types as taught by Turner, to make it easy for crew members to monitor movie production.
Claim 2 recites the functions of the apparatus recited in claim 10 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 10 applies to the method steps of claim 2.
Claim 3 recites the functions of the apparatus recited in claim 11 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 11 applies to the method steps of claim 3.
Claim 18 recites the functions of the apparatus recited in claim 10 as medium steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 10 applies to the medium steps of claim 18.
Claim 19 recites the functions of the apparatus recited in claim 11 as medium steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 11 applies to the medium steps of claim 19.
Claim 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Cella U.S. Patent Application 20220197306 in view of Graham U.S. Patent Application 20240346731, in view of Cheaz U.S. Patent Application 20240086546, and further in view of Jobin U.S. Patent Application 20200342206.
Regarding claim 16, Cella as modified by Graham and Cheaz discloses foundational learning focuses on filmmaking scenarios (Cella’s paragraph [0694]: At 5108, the outcomes of stress scenario simulations are determined, and the performance of value chain network and its different subsystems is estimated across various scenarios. At 5110, the data, parameters and outcomes are fed into a machine learning process in the artificial intelligence system 1160 for further analysis). However, Cella as modified by Graham and Cheaz fails to disclose learning focuses on camera configuration.
Jobin discloses learning focuses on camera configuration (paragraph [0064]: different cameras and/or camera configurations from each other, and training the machine learning classification algorithm with the artificially augmented set of training images improves the reliability of the algorithm when applied to images captured by a range of different smartphones).
Therefore, it would have been obvious before the effective filing date of the claimed invention to combine Cella, Graham and Cheaz’s to focuses on camera configuration as taught by Jobin, to improve the reliability of the algorithm.
Claim 8 recites the functions of the apparatus recited in claim 16 as method steps. Accordingly, the mapping of the prior art to the corresponding functions of the apparatus in claim 16 applies to the method steps of claim 8.
Response to Arguments
Applicant's arguments filed 1/12/2026, page 6 - 15, with respect to the rejection(s) of claim(s) 1-20 under 103, have been fully considered and are moot upon a new ground(s) of rejection made under 35 U.S.C. 103 as being unpatentable over Cella U.S. Patent Application 20220197306 in view of Graham U.S. Patent Application 20240346731, and further in view of Cheaz U.S. Patent Application 20240086546, as outlined above.
Applicant argues on page 7-8 that “The Office Action Lacks Sufficient Articulated Reasoning and Bears the Burden of Establishing A Motivation to Combine”; and page 11-12 that “The Cited References Cannot Be Combined”; and page 13-15 that “Cella is Non-Analogous Art”.
In reply, Cella discloses the manufacturing platform using AI can be deployed to support the theater and film productions (paragraph [1916]), Graham discloses implementing the Al model in real-world filmmaking environments (paragraph [0019]), the motivation to combine Cella with Graham is to create photoreal synthetic content using Al model for theater and film productions.
Cella discloses an artificial intelligence system for learning on a training set of data (video and image data), determine and provide analyses to the artificial intelligence service; Graham discloses training multiple different artificial intelligence (AI) models on different types of training data, the artificial intelligence systems are pertinent art and are appropriate to be combined. The techniques and system in Cella (see fig. 115) use video/image data (digital twin system 10214), and can be used in various applications including filmmaking, to use photoreal synthetic content and support theater and film productions.
Applicant argues on page 8-9 that “The Art of Record Fails to Teach or Suggest Generating Video Content of Any Kind”.
In reply, Cella discloses the manufacturing platform can be deployed to support the theater and film productions (paragraph [1916]); paragraph [1018] discloses computer-generated display elements (such as animations and other computer-generated graphics). Graham discloses the video content that is displayed in real-time can be generated entirely by the trained AI model(s) (paragraph [0019]).
Applicant argues on page 9-10 that “The Art of Record Fails to Teach or Suggest Identifying and Prioritizing Essential Components of a Training Dataset For Generating Video Content Including Filmmaking-Specific Metadata”.
In reply, the rejection is based on Cella, Graham and Cheaz combined. Cella discloses identifying and prioritizing essential components of a training dataset (paragraph [0720]: At 5202, a plurality of streams of machine related data from multiple data sources are received at the machine twin 1770... At 5205, the raw data is cleaned by removing any missing or noisy data, which may occur due to any technical problems in the machine at the time of collection of data).
Graham discloses training dataset for generating video content (paragraph [0019]: prompt the trained AI model(s) to generate video of the famous actor, John Smith (at any age), walking outside on a sunny day, and the AI model(s) may, in response to this prompt, generate the requested synthetic content without requiring the presence of John Smith at all, because the AI model(s) may have been trained with a robust dataset of images of John Smith to be able to generate a synthetic version of John Smith in the video content being displayed in real-time with the live prompting of the director).
Cheaz discloses video content including filmmaking-specific metadata (paragraph [0041]: One purpose of analyzing the media data is to allow server 120 to generate metadata configured to be tagged to the plurality of frames of the video stream). Cheaz’s teaching of generating metadata can be combined with Cella and Graham’s device, such that to identify and prioritize training dataset for generating video content and filmmaking-specific metadata.
Applicant argues on page 10-11 that “The Art of Records Fails to Teach or Suggest Selectively Sampling Data That Is Most Representative for Foundational Learning”.
In reply, Cella discloses selectively sample data that is most representative for foundational learning of an Al model (paragraph [0720]: At 5208, one or more models are selected for training by machine twin 1770. The selection of model is based on the kind of data available at the machine twin 1770 and the desired outcome of the model; paragraph [1551]: feature maps 8876 may be subsampled or down-sampled to generate an output matrix 8878). The applicant can further specify to differentiate from cited prior art.
Applicant argues on page 11 that “The Art of Record Fails to Teach or Suggest Monitoring, Evaluating, and Projecting Future Applications of the AI Model to Maintain Accuracy to Filmmaking Standards”.
In reply, the rejection is based on Cella, Graham and Cheaz combined. Cella discloses monitoring, evaluating, and projecting future applications of the Al model to maintain accuracy to filmmaking standards (paragraph [0720]: At 5212, the trained model is used for detecting faults and predicting future failure of the machine on production data; paragraph [1916]: support the theater and film productions). Cella and Graham combined can detect and predict faults, and maintain accuracy to filmmaking standards. The applicant can further specify to differentiate from cited prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yi Yang whose telephone number is (571)272-9589. The examiner can normally be reached on Monday-Friday 9:00 AM-6:00 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Hajnik can be reached on 571-272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YI YANG/
Primary Examiner, Art Unit 2616
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