DETAILED ACTION
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 .
Claim Objections
Claims 1 and 14 are objected to because of the following informalities: respective lines 11 and 14 partially recite “…content is selected based a comparison to the metric;”. The Examiner believes this to be a typo. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 2-3 and 19-20 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.
Claim 2, lines 3 and 4 recite “combinations of encoding parameter values”. The Examiner is unclear on whether said “combinations of encoding parameter values” are different combinations of encoding parameters as the ones recited/claimed in claim 1 or are the same “combinations of encoding parameter values” as in claim 1.
Claim 2, lines 4-5 recite “instances of encoded content to the metric”. The Examiner is unclear on whether said “instances of encoded content to the metric” are the same or different instances of encoded content to the metric as recited/claimed in claim 1.
Claim 3, line 1 recites “prior sampling of combinations of encoding parameter values”. The Examiner is unclear on whether said prior sampling of combinations of encoding parameter values is a different or the same prior sampling of combinations of encoding parameter values as the one recited/claimed in claim 2.
Claim 19, lines 2 and 9 respectively recite “an instance of content” and “predicted encoding parameter values” are the same or different “an instance of content” and “predicted encoding parameter values” as they are recited/claimed in claims 15 and 18.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 14-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zvezdakov et al. ("Machine-learning-based method for content-adaptive video encoding." 2021 Picture Coding Symposium (PCS). IEEE, 2021.).
Regarding claim 1 Zvezdakov discloses a method comprising:
determining an instance of content and a metric to evaluate a quality of an encoding of the instance of content (for each preset p and video V, the relative bitrate Q(p,V) with some objective quality of the encoded file is obtained – B. Encoders and presets, last paragraph; the video file is a video-sequence dataset – abstract);
extracting a set of features for the instance of content (feature extraction from input video – Section IV, C. Video Features);
performing an optimized search process to evaluate different combinations of encoding parameter values that are used to encode the content to generate instances of encoded content, wherein the instances of encoded content are compared to the metric to determine a next combination of encoding parameter values to use (using a grid search to select the best configuration of video features, classifiers (and parameters), and number of predefined presets – D. Hyperparameter Selection and Training Details; for each preset p and video V, the relative bitrate Q(p,V) with some objective quality of the encoded file is obtained – B. Encoders and presets, last paragraph; the main idea of content-adaptive encoding (CAE) is to replace a reference preset pref (by which we encoded all previous video groups) with other presets that will more efficiently encode an input video – Section IV. A. Problem Definition);
selecting an optimal combination of encoding parameter values that is associated with one of the instances of encoded content, wherein the one of the instances of encoded content is selected based a comparison to the metric (for each video encoder a set of highly successful encoding presets were created. For this task we used option-popularity statistics, codec-developer presets from the MSU Codec Comparison 2016–2019, and our algorithm for iteratively discovering Pareto optimal presets – Section III, B. Encoders and Presets; for each preset p and video V, the relative bitrate Q(p,V) with some objective quality of the encoded file is obtained – B. Encoders and presets, last paragraph);
outputting predicted encoding parameter values from a model using model parameters based on an input of the set of features (For each reference preset, we can train a model that will take video features as input and predict the best preset from a list of predefined presets chosen in accordance with Eq. 2 – Section IV, B. Converting to Classification); and
training the model using the optimal combination of encoding parameter values and the predicted encoding parameter values, wherein the model parameters are adjusted in the training (running the codec several times to train the machine learning model, allowing it to predict the results for other presets (such as video quality and encoding speed) and to choose the Pareto optimal set – Section II. Related Work; For each reference preset, we can train a model that will take video features as input and predict the best preset from a list of predefined presets chosen in accordance with Eq. 2 – Section IV, B. Converting to Classification).
Claim 14 corresponds to the non-transitory computer-readable storage mediu8m having stored thereon computer executable instructions, which when executed by a computing device, causes the computing device to perform the method of claim 1.
Claim 15 is being rejected on the same basis as claim 1.
Regarding claim 16 Zvezdakov discloses the method of claim 15, wherein the predicted encoding parameters are determined without encoding the new instance of content (the main idea of content-adaptive encoding (CAE) is to replace a reference preset pref (by which we encoded all previous video groups) with other presets that will more efficiently encode an input video – Section IV. A. Problem Definition).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2, 3, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zvezdakov et al. ("Machine-learning-based method for content-adaptive video encoding." 2021 Picture Coding Symposium (PCS). IEEE, 2021.) in view of Crabtree et al. (US 2025/0267296).
Regarding claim 2 Zvezdakov discloses the method of claim 1, wherein performing the optimized search process comprises:
iteratively determining combinations of encoding parameter values based on respective comparisons of instances of encoded content to the metric (for each video encoder a set of highly successful encoding presets were created. For this task we used option-popularity statistics, codec-developer presets from the MSU Codec Comparison 2016–2019, and our algorithm for iteratively discovering Pareto optimal presets – Section III, B. Encoders and Presets; for each preset p and video V, the relative bitrate Q(p,V) with some objective quality of the encoded file is obtained – B. Encoders and presets, last paragraph).
However, fails to explicitly disclose determining combinations of parameter values based on prior sampling of combinations of parameter values and respective comparisons of instances of encoded content to the metric.
In his disclosure Crabtree teaches determining combinations of parameter values based on prior sampling of combinations of parameter values and respective comparisons of instances of encoded content to the metric (Intelligent adaptive compression system incorporates a comprehensive feedback mechanism that operates across multiple subsystems. This closed-loop feedback system continuously evaluates the quality of the compressed output on a frame-by-frame basis, comparing it to the original input and desired quality parameters. The feedback mechanism interfaces primarily with content-adaptive encoding subsystem and AI-driven optimization subsystem, providing real-time data on compression performance and perceptual quality. This information is used to dynamically adjust encoding parameters, AI model weights, and optimization strategies – [0176]; Adaptive control subsystem then monitors overall system performance, utilizing feedback loop component to analyze real-time compression results and performance metrics database to compare against historical data and predefined thresholds – [0384]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Regarding claim 3 Zvezdakov discloses the method of claim 2. However, fails to explicitly disclose wherein prior sampling of combinations of encoding parameter values is used to focus a search for another combination of encoding parameter values.
In his disclosure Crabtree teaches prior sampling of combinations of encoding parameter values is used to focus a search for another combination of encoding parameter values (Intelligent adaptive compression system incorporates a comprehensive feedback mechanism that operates across multiple subsystems. This closed-loop feedback system continuously evaluates the quality of the compressed output on a frame-by-frame basis, comparing it to the original input and desired quality parameters. The feedback mechanism interfaces primarily with content-adaptive encoding subsystem and AI-driven optimization subsystem, providing real-time data on compression performance and perceptual quality. This information is used to dynamically adjust encoding parameters, AI model weights, and optimization strategies – [0176]; Adaptive control subsystem then monitors overall system performance, utilizing feedback loop component to analyze real-time compression results and performance metrics database to compare against historical data and predefined thresholds – [0384]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Regarding claim 17 Zvezdakov discloses the method of claim 15. However, fails to explicitly disclose wherein the predicted encoding parameters for the new instance of content are used by the encoder to encode the new instance of content a single time for a target bitrate.
In his disclosure Crabtree teaches wherein the predicted encoding parameters for the new instance of content are used by the encoder to encode the new instance of content a single time for a target bitrate (content adaptation and optimization subsystem ensures efficient delivery of the processed content. It includes dynamic bitrate adjustment component that adapts the bitrate based on scene complexity. Content-adaptive encoding component utilizes a decision tree-based model, for example random forest or gradient boosting machine. This model takes aggregated features from video segments as input and predicts optimal encoding parameters – [0298]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zvezdakov et al. ("Machine-learning-based method for content-adaptive video encoding." 2021 Picture Coding Symposium (PCS). IEEE, 2021.) in view of Kalasibail Seetharam et al. (US 2024/0244224 hereinafter ‘224).
Regarding claim 4 Zvezdakov teaches the method of claim 1. However, fails to explicitly disclose building a surrogate model to model an encoder function to encode the instance of content; using an acquisition function to determine where to sample encoding parameter values next based on the surrogate model; encoding the instance of content based on the encoding parameter values to generate an instance of encoded content; and updating the surrogate model based on comparing the instance of encoded content to the metric.
In ‘224, it is disclosed building a surrogate model to model an encoder function to encode the instance of content; using an acquisition function to determine where to sample encoding parameter values next based on the surrogate model; encoding the instance of content based on the encoding parameter values to generate an instance of encoded content; and updating the surrogate model based on comparing the instance of encoded content to the metric (the constrained optimization solver 360 can execute a surrogate optimization algorithm that implements a surrogate model to approximate the objective function 340. Some examples of other constrained optimization techniques that can be used to generate candidate encoding ladders include branch and bound techniques, cutting planes techniques, and surrogate model techniques – [0128]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of ‘224 into the teachings of Zvezdakov because such incorporation reduces storage footprint and would provide the same or higher streaming QoE (paragraph 48).
Regarding claim 5 Zvezdakov discloses the method of claim 1. However, fails to explicitly disclose determining the instance of encoded content that is associated with a highest ranked value of the metric; and selecting the combination of encoding parameters that were used to generate the instance of encoded content that is associated with the highest ranked value of the metric.
In ‘224, it is disclosed determining the instance of encoded content that is associated with a highest ranked value of the metric; and selecting the combination of encoding parameters that were used to generate the instance of encoded content that is associated with the highest ranked value of the metric (a quality score can be a value for any type of metric that correlates to visual quality in any technically feasible fashion. In some embodiments, each quality score is a value for a visual quality metric – [0042]; the ladder evaluation and selection engine 480 generates a metric value set 470(1,1)—a metric value set 470(F,T) based on the request sequence 460(1,1)—a request sequence 460(F,T) and the synthetic streaming header 430(1)—synthetic streaming header 430(F). More precisely, the ladder evaluation and selection engine 480 generates the metric value set 470(ft) based on the request sequence 460(f,t) and the synthetic streaming header 430(f). Each of the metric value set 470(1,1)—the metric value set 470(F,T) specifies a different set of values for a streaming evaluation metric set – [0141]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of ‘224 into the teachings of Zvezdakov because such incorporation reduces storage footprint and would provide the same or higher streaming QoE (paragraph 48).
Regarding claim 6 Zvezdakov discloses the method of claim 1. However, fails to explicitly disclose inputting the set of features into the model; outputting predicted encoding parameter values; and comparing the predicted encoding parameter values to the optimal combination of encoding parameter values to adjust the model parameters of the model based on a difference between the predicted encoding parameter values and the optimal combination of encoding parameter values.
In ‘224, it is disclosed inputting the set of features into the model; outputting predicted encoding parameter values; and comparing the predicted encoding parameter values to the optimal combination of encoding parameter values to adjust the model parameters of the model based on a difference between the predicted encoding parameter values and the optimal combination of encoding parameter values (the constrained optimization solver can execute a surrogate optimization algorithm that implements a surrogate model to approximate the objective function. Some examples of other constrained optimization techniques that can be used to generate candidate encoding ladders include branch and bound techniques, cutting planes techniques, and surrogate model techniques – [0128]; the ladder evaluation and selection engine performs any number and/or types of evaluations and/or comparisons between the metric value set groups corresponding to any number of the candidate encoding ladders in the filtered candidate encoding ladder set; based, at least in part, on the results of the evaluations and/or comparisons, the ladder evaluation and selection engine can select any number of the associated candidate encoding ladders for further evaluation and/or deployment via the production encoding pipeline – [0058]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of ‘224 into the teachings of Zvezdakov because such incorporation reduces storage footprint and would provide the same or higher streaming QoE (paragraph 48).
Claim(s) 7-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zvezdakov et al. ("Machine-learning-based method for content-adaptive video encoding." 2021 Picture Coding Symposium (PCS). IEEE, 2021.) in view of Kalasibail Seetharam et al. (US 2024/0244224 hereinafter ‘224) further in view of Crabtree et al. (US 2025/0267296).
Regarding claim 7 Zvezdakov discloses the method of claim 6. However, fails to explicitly disclose the model parameters are trained using a regression training process or classification training process.
In his disclosure Crabtree teaches the model parameters are trained using a regression training process or classification training process (AI and machine learning subsystem includes content-adaptive encoding component, which dynamically adjusts encoding settings based on content type and complexity; This component utilizes a convolutional neural network (CNN) model trained on a diverse dataset of video content to classify video scenes and determine optimal encoding parameters – [0261]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Regarding claim 8 Zvezdakov discloses the method of claim 1. However, fails to explicitly disclose using the model to determine predicted encoding parameter values for a new instance of content, wherein the predicted encoding parameter values are used by an encoder to encode the new instance of content.
In his disclosure Crabtree teaches using the model to determine predicted encoding parameter values for a new instance of content, wherein the predicted encoding parameter values are used by an encoder to encode the new instance of content (predicting optimal encoding settings – [0075]; this model takes aggregated features from video segments as input and predicts optimal encoding parameters – [0298]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Regarding claim 9 Zvezdakov discloses the method of claim 8, wherein using the model comprises: extracting feature values of the new instance of content (feature extraction from input video – Section IV, C. Video Features).
However, fails to explicitly disclose inputting the feature values into the model; and outputting the predicted encoding parameters for the new instance of content based on the model parameters that were adjusted.
In his disclosure Crabtree teaches inputting the feature values into the model; and outputting the predicted encoding parameters for the new instance of content based on the model parameters that were adjusted (content adaptation and optimization subsystem ensures efficient delivery of the processed content. It includes dynamic bitrate adjustment component that adapts the bitrate based on scene complexity. Content-adaptive encoding component utilizes a decision tree-based model, for example random forest or gradient boosting machine. This model takes aggregated features from video segments as input and predicts optimal encoding parameters – [0298]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Regarding claim 10 Zvezdakov discloses the method of claim 8, wherein the predicted encoding parameters for the new instance of content are determined without encoding the new instance of content (the main idea of content-adaptive encoding (CAE) is to replace a reference preset pref (by which we encoded all previous video groups) with other presets that will more efficiently encode an input video – Section IV. A. Problem Definition).
Regarding claim 11 Zvezdakov discloses the method of claim 8. However, fails to explicitly disclose wherein the predicted encoding parameters for the new instance of content are used by the encoder to encode the new instance of content a single time for a target bitrate.
In his disclosure Crabtree teaches the predicted encoding parameters for the new instance of content are used by the encoder to encode the new instance of content a single time for a target bitrate (content adaptation and optimization subsystem ensures efficient delivery of the processed content. It includes dynamic bitrate adjustment component that adapts the bitrate based on scene complexity. Content-adaptive encoding component utilizes a decision tree-based model, for example random forest or gradient boosting machine. This model takes aggregated features from video segments as input and predicts optimal encoding parameters – [0298]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Regarding claim 12 Zvezdakov discloses the method of claim 8. However, fails to explicitly disclose further comprising: encoding the new instance of content using the predicted encoding parameter values.
In his disclosure Crabtree teaches encoding the new instance of content using the predicted encoding parameter values (AI and machine learning subsystem includes content-adaptive encoding component, which dynamically adjusts encoding settings based on content type and complexity; This component utilizes a convolutional neural network (CNN) model trained on a diverse dataset of video content to classify video scenes and determine optimal encoding parameters – [0261]; Content-adaptive encoding component utilizes a decision tree-based model, for example random forest or gradient boosting machine. This model takes aggregated features from video segments as input and predicts optimal encoding parameters – [0298]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Crabtree into the teachings of Zvezdakov because such incorporation creates a highly efficient and responsive video, gaming, and content compression and delivery system that significantly improves performance while reducing the overall load of processing, data transport, storage, and energy consumption (paragraph 50).
Claim(s) 13, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zvezdakov et al. ("Machine-learning-based method for content-adaptive video encoding." 2021 Picture Coding Symposium (PCS). IEEE, 2021.) in view of Soltanayev et al. (EP 4723625 A2).
Regarding claim 13 Zvezdakov discloses the method of claim 1. However, fails to explicitly disclose training the model using a condition that sets a bitrate in a plurality of bitrates, wherein the model is trained to output predicted encoding parameter values for bitrates in the plurality of bitrates.
In his disclosure Soltanayev teaches training the model using a condition that sets a bitrate in a plurality of bitrates, wherein the model is trained to output predicted encoding parameter values for bitrates in the plurality of bitrates (the machine learning model is trained to predict encoder settings which correspond to an optimal rate-quality convex hull for encoding video data. Causing the external encoder to operate at (or close to) the rate-quality convex hull for encoding video data, by selecting encoder settings which correspond to points on the rate-quality convex hull, enables the rate-quality performance of the external encoder to be optimized. In some cases, the convex hull may comprise multiple bitrate zones, and the machine learning model may be trained to predict encoder settings which correspond to optimal rate-quality points in each bitrate zone – [0017]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Soltanayev into the teaching of Zvezdakov because such incorporation improves the performance of the encoder (paragraph 8).
Claim 18 is being rejected on the same basis as claim 13.
Claim 19 is being rejected on the same basis as claim 1.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zvezdakov et al. ("Machine-learning-based method for content-adaptive video encoding." 2021 Picture Coding Symposium (PCS). IEEE, 2021.) in view of Soltanayev et al. (EP 4723625 A2) further in view of Kalasibail Seetharam et al. (US 2024/0244224 hereinafter ‘224).
Regarding claim 20 Zvezdakov discloses the method of claim 19. However, fails to explicitly disclose building a surrogate model to model an encoder function to encode the instance of content; using an acquisition function to determine where to sample encoding parameter values next based on the surrogate model; encoding the instance of content based on the encoding parameter values to generate an instance of encoded content; and updating the surrogate model based on comparing the instance of encoded content to the metric.
In ‘224, it is taught building a surrogate model to model an encoder function to encode the instance of content; using an acquisition function to determine where to sample encoding parameter values next based on the surrogate model; encoding the instance of content based on the encoding parameter values to generate an instance of encoded content; and updating the surrogate model based on comparing the instance of encoded content to the metric (the constrained optimization solver 360 can execute a surrogate optimization algorithm that implements a surrogate model to approximate the objective function 340. Some examples of other constrained optimization techniques that can be used to generate candidate encoding ladders include branch and bound techniques, cutting planes techniques, and surrogate model techniques – [0128]).
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of ‘224 into the teachings of Zvezdakov because such incorporation reduces storage footprint and would provide the same or higher streaming QoE (paragraph 48).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA E VAZQUEZ COLON whose telephone number is (571)270-1103. The examiner can normally be reached M-F 7:30 AM-3:30 PM.
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/MARIA E VAZQUEZ COLON/Examiner, Art Unit 2482
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