Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to Applicant’s amendments/remarks received on September 15,2025.
3. Claims 1-26 are pending in this application.
4. Claims 1-3, 6, 7, 13, 16-20, 22 and 24-26 have been amended.
Response to Arguments
5. Applicant's arguments filed September 15,2025 have been fully considered but they are deemed moot in view of a necessitated new grounds of rejection
Claim Rejections - 35 USC § 103
6. 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.
7. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. Claims 1-6, 10-16, 18 and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Lineback et al.(US 2025/0142183 A1)(hereinafter Lineback) in view of Codenie et al.(US 2021/0092493 A1)(hereinafter Codenie).
Regarding claim 1, Lineback discloses a system [See Lineback: at least Figs. 1-23 and par. 4 regarding system] comprising:
at least one execution unit to perform inference using a machine learning (ML) model to determine a genre associated with received frames of a media stream based at least in part on using ML model features associated with different genres [See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 182-198, 235-249, 280-290 regarding In some examples, to match targeted media content with a segment of media content, the content server 120 or the media device 106 can use an algorithm, such as a machine learning algorithm, to generate one or more embeddings encoding information about the content of the segment of the media content. The content server 120 or the media device 106 can generate the one or more embeddings based on one or more signals in one or more frames of the segment of the media content, such as a visual signal (e.g., image data), an audio signal (e.g., audio data), a closed-caption signal (e.g., text data), and/or any other signal. The content server 120 or the media device 106 can use the one or more embeddings to determine a category for the segment of the media content that describes, represents, summarizes, classifies, and/or identifies the segment of the media content, the content of the segment of the media content, a context(s) of the content of the segment of the media content, and/or one or more characteristics of the segment of the media content and/or the content of the segment of the media content… For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…In FIG. 9, a neural network 908 can process one or more media content items 906 of a segment 904B of media content 902 to generate embeddings 910A, 910B, 910N that represent and/or describe the one or more media content items 906 associated with the segment 904B, a content of the one or more media content items 906 associated with the segment 904B, one or more features in the one or more media content items 906 associated with the segment 904B, and/or a context of any content in the one or more media content items 906 associated with the segment 904B. The media content 902 can include video content (e.g., one or more video frames), audio content, text content (e.g., closed captions), and/or any other media content available for presentation (e.g., live or on-demand) at a device, such as media device(s) 106 illustrated in FIG. 1… Moreover, the neural network 908 can include any neural network configured to extract features from the one or more media content items 906 and generate one or more embeddings based on the extracted features… In some examples, the neural network 912 can classify the embeddings 910A, 910B, 910N to generate the one or more segment categories 914..]; and
a video encoder to dynamically encode the media stream to provide different encoding based at least in part on the determined genre, using a feedback loop between the ML model and the video encoder [See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 176-198, 235-249 regarding … For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…For example, the neural network 908 can use a visual signal (e.g., image data) in the one or more media content items 906 to generate an embedding 910A representing and/or encoding information from the visual signal in the one or more media content items 906, such as a depicted setting, a depicted object, a depicted actor, a depicted background, a depicted foreground, a depicted scene, a depicted action/activity, a depicted context, a depicted gesture, semantic information, and/or any other visual features/information…The embeddings 910A, 910B, 910N can include values encoding information from the respective signals in the one or more media content items 906 (e.g., the visual signal, the audio signal, the text signal, etc.), such as semantic information, contextual information, descriptive information, extracted features, sentiment/mood information, content information, and/or any other information about the one or more media content items 906 and/or the segment 904B associated with the one or more media content items 906…For example, in some cases, the neural network 908 can include a convolutional neural network (CNN), an encoder network, or a transformer network, among others...Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments...].
Lineback does not explicitly disclose a video encoder to dynamically encode the media stream to provide different encoding based at least in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder.
However, Codenie teaches a video encoder to dynamically encode the media stream to provide different encoding based at least in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder [See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-60, 65-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…].
Therefore, it would have been obvious before the effective filing date of the claimed inventio to a person having ordinary skill in the art to modify Lineback with Codenie teachings by including “a video encoder to dynamically encode the media stream to provide different encoding based at least in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder” because this combination has the benefit of providing a feedback control loop to further improve the parameters of the encoding approach[See Codenie: at least par. 40, 55].
Regarding claim 13, Lineback discloses at least one execution unit to be associated with a video encoder, to perform an inference using a machine learning (ML) model to determine a genre associated with received frames of a media stream based in part on using ML model features associated with different genres[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 182-198, 235-249, 280-290 regarding In some examples, to match targeted media content with a segment of media content, the content server 120 or the media device 106 can use an algorithm, such as a machine learning algorithm, to generate one or more embeddings encoding information about the content of the segment of the media content. The content server 120 or the media device 106 can generate the one or more embeddings based on one or more signals in one or more frames of the segment of the media content, such as a visual signal (e.g., image data), an audio signal (e.g., audio data), a closed-caption signal (e.g., text data), and/or any other signal. The content server 120 or the media device 106 can use the one or more embeddings to determine a category for the segment of the media content that describes, represents, summarizes, classifies, and/or identifies the segment of the media content, the content of the segment of the media content, a context(s) of the content of the segment of the media content, and/or one or more characteristics of the segment of the media content and/or the content of the segment of the media content… For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…In FIG. 9, a neural network 908 can process one or more media content items 906 of a segment 904B of media content 902 to generate embeddings 910A, 910B, 910N that represent and/or describe the one or more media content items 906 associated with the segment 904B, a content of the one or more media content items 906 associated with the segment 904B, one or more features in the one or more media content items 906 associated with the segment 904B, and/or a context of any content in the one or more media content items 906 associated with the segment 904B. The media content 902 can include video content (e.g., one or more video frames), audio content, text content (e.g., closed captions), and/or any other media content available for presentation (e.g., live or on-demand) at a device, such as media device(s) 106 illustrated in FIG. 1… Moreover, the neural network 908 can include any neural network configured to extract features from the one or more media content items 906 and generate one or more embeddings based on the extracted features… In some examples, the neural network 912 can classify the embeddings 910A, 910B, 910N to generate the one or more segment categories 914..], and
to enable the video encoder to dynamically encode the media stream to provide different encoding based in part on the determined genre, using a feedback loop between the ML model and the video encoder[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 176-198, 235-249 regarding … For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…For example, the neural network 908 can use a visual signal (e.g., image data) in the one or more media content items 906 to generate an embedding 910A representing and/or encoding information from the visual signal in the one or more media content items 906, such as a depicted setting, a depicted object, a depicted actor, a depicted background, a depicted foreground, a depicted scene, a depicted action/activity, a depicted context, a depicted gesture, semantic information, and/or any other visual features/information…The embeddings 910A, 910B, 910N can include values encoding information from the respective signals in the one or more media content items 906 (e.g., the visual signal, the audio signal, the text signal, etc.), such as semantic information, contextual information, descriptive information, extracted features, sentiment/mood information, content information, and/or any other information about the one or more media content items 906 and/or the segment 904B associated with the one or more media content items 906…For example, in some cases, the neural network 908 can include a convolutional neural network (CNN), an encoder network, or a transformer network, among others... Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments].
Lineback does not explicitly disclose to enable the video encoder to dynamically encode the media stream to provide different encoding based in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder.
However, Codenie teaches to enable the video encoder to dynamically encode the media stream to provide different encoding based in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder[See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-60, 65-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…].
Therefore, it would have been obvious before the effective filing date of the claimed inventio to a person having ordinary skill in the art to modify Lineback with Codenie teachings by including “to enable the video encoder to dynamically encode the media stream to provide different encoding based in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder” because this combination has the benefit of providing a feedback control loop to further improve the parameters of the encoding approach[See Codenie: at least par. 40, 55].
Regarding claim 18, Lineback discloses a video encoder to dynamically encode a media stream to provide different encoding based at least in part on a genre associated with a media stream as inferred using a machine learning (ML) model performed on at least one execution unit, using a feedback loop between the ML model and the video encoder[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 176-198, 235-249, 280-290 regarding … For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…For example, the neural network 908 can use a visual signal (e.g., image data) in the one or more media content items 906 to generate an embedding 910A representing and/or encoding information from the visual signal in the one or more media content items 906, such as a depicted setting, a depicted object, a depicted actor, a depicted background, a depicted foreground, a depicted scene, a depicted action/activity, a depicted context, a depicted gesture, semantic information, and/or any other visual features/information…The embeddings 910A, 910B, 910N can include values encoding information from the respective signals in the one or more media content items 906 (e.g., the visual signal, the audio signal, the text signal, etc.), such as semantic information, contextual information, descriptive information, extracted features, sentiment/mood information, content information, and/or any other information about the one or more media content items 906 and/or the segment 904B associated with the one or more media content items 906…For example, in some cases, the neural network 908 can include a convolutional neural network (CNN), an encoder network, or a transformer network, among others... Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments…],
the genre determined from received frames of the media stream based at least in part on using ML model features associated with different genres[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 182-198, 235-249 regarding In some examples, to match targeted media content with a segment of media content, the content server 120 or the media device 106 can use an algorithm, such as a machine learning algorithm, to generate one or more embeddings encoding information about the content of the segment of the media content. The content server 120 or the media device 106 can generate the one or more embeddings based on one or more signals in one or more frames of the segment of the media content, such as a visual signal (e.g., image data), an audio signal (e.g., audio data), a closed-caption signal (e.g., text data), and/or any other signal. The content server 120 or the media device 106 can use the one or more embeddings to determine a category for the segment of the media content that describes, represents, summarizes, classifies, and/or identifies the segment of the media content, the content of the segment of the media content, a context(s) of the content of the segment of the media content, and/or one or more characteristics of the segment of the media content and/or the content of the segment of the media content… For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…In FIG. 9, a neural network 908 can process one or more media content items 906 of a segment 904B of media content 902 to generate embeddings 910A, 910B, 910N that represent and/or describe the one or more media content items 906 associated with the segment 904B, a content of the one or more media content items 906 associated with the segment 904B, one or more features in the one or more media content items 906 associated with the segment 904B, and/or a context of any content in the one or more media content items 906 associated with the segment 904B. The media content 902 can include video content (e.g., one or more video frames), audio content, text content (e.g., closed captions), and/or any other media content available for presentation (e.g., live or on-demand) at a device, such as media device(s) 106 illustrated in FIG. 1… Moreover, the neural network 908 can include any neural network configured to extract features from the one or more media content items 906 and generate one or more embeddings based on the extracted features… In some examples, the neural network 912 can classify the embeddings 910A, 910B, 910N to generate the one or more segment categories 914..].
Lineback does not explicitly disclose a video encoder to dynamically encode the media stream to provide different encoding based at least in part on a genre and on changes to the genre associated with a media stream as inferred using a machine learning (ML) model performed on a least one execution unit, and the changes determined using a feedback loop between the ML model and the video encoder.
However, Codenie teaches a video encoder to dynamically encode the media stream to provide different encoding based at least in part on a genre and on changes to the genre associated with a media stream as inferred using a machine learning (ML) model performed on a least one execution unit, and the changes determined using a feedback loop between the ML model and the video encoder [See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-60, 65-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…].
Therefore, it would have been obvious before the effective filing date of the claimed inventio to a person having ordinary skill in the art to modify Lineback with Codenie teachings by including “a video encoder to dynamically encode the media stream to provide different encoding based at least in part on a genre and on changes to the genre associated with a media stream as inferred using a machine learning (ML) model performed on a least one execution unit, and the changes determined using a feedback loop between the ML model and the video encoder” because this combination has the benefit of providing a feedback control loop to further improve the parameters of the encoding approach[See Codenie: at least par. 40, 55].
Regarding claim 20, Lineback discloses at least one execution unit to train a machine learning (ML) model using features associated with different genres for media streams[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 182-198, 235-249, 280-290 regarding Additionally, the visual modality encoder 306, the audio modality encoder 308, and the timed text modality encoder 310 can use an applicable machine learning-based technique to encode features into the embedding space. Specifically, an applicable machine learning technique can be used to create lower dimensional, e.g., vector or matrix representations or embeddings, of features in units of the media content 302. More specifically, the visual modality encoder 306, the audio modality encoder 308, and the timed text modality encoder 310 can be trained using contrastive learning, e.g., contrastive self-supervised learning, to encode features into the embedding space…The neural network architecture 2200 is pre-trained to process the features from the data in the input layer 2220 using the different hidden layers 2222a, 2222b, through 2222n in order to provide the output through the output layer 2221…], wherein the ML model, once trained, is to enable a video encoder to dynamically encode a media stream to provide different encoding based in part on a genre inferred by the ML model for the media stream using a feedback loop between the ML model and the video encoder[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 176-198, 235-249, 280-290 regarding In some examples, to match targeted media content with a segment of media content, the content server 120 or the media device 106 can use an algorithm, such as a machine learning algorithm, to generate one or more embeddings encoding information about the content of the segment of the media content. The content server 120 or the media device 106 can generate the one or more embeddings based on one or more signals in one or more frames of the segment of the media content, such as a visual signal (e.g., image data), an audio signal (e.g., audio data), a closed-caption signal (e.g., text data), and/or any other signal. The content server 120 or the media device 106 can use the one or more embeddings to determine a category for the segment of the media content that describes, represents, summarizes, classifies, and/or identifies the segment of the media content, the content of the segment of the media content, a context(s) of the content of the segment of the media content, and/or one or more characteristics of the segment of the media content and/or the content of the segment of the media content… For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…In FIG. 9, a neural network 908 can process one or more media content items 906 of a segment 904B of media content 902 to generate embeddings 910A, 910B, 910N that represent and/or describe the one or more media content items 906 associated with the segment 904B, a content of the one or more media content items 906 associated with the segment 904B, one or more features in the one or more media content items 906 associated with the segment 904B, and/or a context of any content in the one or more media content items 906 associated with the segment 904B. The media content 902 can include video content (e.g., one or more video frames), audio content, text content (e.g., closed captions), and/or any other media content available for presentation (e.g., live or on-demand) at a device, such as media device(s) 106 illustrated in FIG. 1… Moreover, the neural network 908 can include any neural network configured to extract features from the one or more media content items 906 and generate one or more embeddings based on the extracted features… In some examples, the neural network 912 can classify the embeddings 910A, 910B, 910N to generate the one or more segment categories 914.. Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments].
Lineback does not explicitly disclose wherein the ML model, once trained, is to enable a video encoder to dynamically encode the media stream to provide different encoding based at least in part on a genre inferred by the ML model for the media stream and on changes to the genre, the changes determined using a feedback loop between the ML model and the video encoder.
However, Codenie teaches wherein the ML model, once trained, is to enable a video encoder to dynamically encode the media stream to provide different encoding based at least in part on a genre inferred by the ML model for the media stream and on changes to the genre, the changes determined using a feedback loop between the ML model and the video encoder [See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-60, 65-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…].
Therefore, it would have been obvious before the effective filing date of the claimed inventio to a person having ordinary skill in the art to modify Lineback with Codenie teachings by including “wherein the ML model, once trained, is to enable a video encoder to dynamically encode the media stream to provide different encoding based at least in part on a genre inferred by the ML model for the media stream and on changes to the genre, the changes determined using a feedback loop between the ML model and the video encoder” because this combination has the benefit of providing a feedback control loop to further improve the parameters of the encoding approach[See Codenie: at least par. 40, 55].
Regarding claim 22, Lineback discloses a method for a video encoder[See Lineback: at least Figs. 1-23 and par. 2-5 regarding method for media content encoder], the method comprising:
performing a machine learning (ML) model to infer a genre associated with received frames of a media stream based at least in part on using ML model features associated with different genres[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 182-198, 235-249, 280-290 regarding In some examples, to match targeted media content with a segment of media content, the content server 120 or the media device 106 can use an algorithm, such as a machine learning algorithm, to generate one or more embeddings encoding information about the content of the segment of the media content. The content server 120 or the media device 106 can generate the one or more embeddings based on one or more signals in one or more frames of the segment of the media content, such as a visual signal (e.g., image data), an audio signal (e.g., audio data), a closed-caption signal (e.g., text data), and/or any other signal. The content server 120 or the media device 106 can use the one or more embeddings to determine a category for the segment of the media content that describes, represents, summarizes, classifies, and/or identifies the segment of the media content, the content of the segment of the media content, a context(s) of the content of the segment of the media content, and/or one or more characteristics of the segment of the media content and/or the content of the segment of the media content… For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…In FIG. 9, a neural network 908 can process one or more media content items 906 of a segment 904B of media content 902 to generate embeddings 910A, 910B, 910N that represent and/or describe the one or more media content items 906 associated with the segment 904B, a content of the one or more media content items 906 associated with the segment 904B, one or more features in the one or more media content items 906 associated with the segment 904B, and/or a context of any content in the one or more media content items 906 associated with the segment 904B. The media content 902 can include video content (e.g., one or more video frames), audio content, text content (e.g., closed captions), and/or any other media content available for presentation (e.g., live or on-demand) at a device, such as media device(s) 106 illustrated in FIG. 1… Moreover, the neural network 908 can include any neural network configured to extract features from the one or more media content items 906 and generate one or more embeddings based on the extracted features… In some examples, the neural network 912 can classify the embeddings 910A, 910B, 910N to generate the one or more segment categories 914..]; and
dynamically encoding the media stream to provide different encoding using the video encoder based at least in part on the determined genre using a feedback loop between the ML model and the video encoder[See Lineback: at least Figs. 1-23 and par. 50-52, 70-88, 114-145, 176-198, 235-249, 280-290 regarding … For example, in some cases, the system 300 can additionally or alternatively implement an encoder(s) that accounts for a genre of the media content 302, a general description of the media content 302, a synopsis of the media content 302, any other aspects of the media content 302, or a combination thereof…For example, the neural network 908 can use a visual signal (e.g., image data) in the one or more media content items 906 to generate an embedding 910A representing and/or encoding information from the visual signal in the one or more media content items 906, such as a depicted setting, a depicted object, a depicted actor, a depicted background, a depicted foreground, a depicted scene, a depicted action/activity, a depicted context, a depicted gesture, semantic information, and/or any other visual features/information…The embeddings 910A, 910B, 910N can include values encoding information from the respective signals in the one or more media content items 906 (e.g., the visual signal, the audio signal, the text signal, etc.), such as semantic information, contextual information, descriptive information, extracted features, sentiment/mood information, content information, and/or any other information about the one or more media content items 906 and/or the segment 904B associated with the one or more media content items 906…For example, in some cases, the neural network 908 can include a convolutional neural network (CNN), an encoder network, or a transformer network, among others... Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments].
Lineback does not explicitly disclose dynamically encoding the media stream to provide different encoding using the video encoder based at least in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder.
However, Codenie teaches dynamically encoding the media stream to provide different encoding using the video encoder based at least in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder [See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-60, 65-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…].
Therefore, it would have been obvious before the effective filing date of the claimed inventio to a person having ordinary skill in the art to modify Lineback with Codenie teachings by including “dynamically encoding the media stream to provide different encoding using the video encoder based at least in part on the determined genre and on changes to the determined genre, the changes determined using a feedback loop between the ML model and the video encoder” because this combination has the benefit of providing a feedback control loop to further improve the parameters of the encoding approach[See Codenie: at least par. 40, 55].
Regarding claim 2, Lineback and Codenie teach all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Further on, when combined, Lineback teaches wherein an encoded media stream output from the video encoder comprises at least two video sequences that are associated with the different genres[See Lineback: at least Figs. 1-17, 23 and par. 47-52, 70-88, 114-145, 182-198, 235-249 regarding The embeddings 910A, 910B, 910N can include values encoding information from the respective signals in the one or more media content items 906 (e.g., the visual signal, the audio signal, the text signal, etc.), such as semantic information, contextual information, descriptive information, extracted features, sentiment/mood information, content information, and/or any other information about the one or more media content items 906 and/or the segment 904B associated with the one or more media content items 906… context analysis module 1802 may identify parts (e.g., segments, sections, sequences, frames, etc.) of a video and identify contextual information that corresponds to one or more of the parts…Also, in Fig. 4, an example portion of media content 400 segmented into a plurality of shots. A shot can include a contiguous sequence of frames that are captured from or generated by an applicable source...].
Regarding claim 3, Lineback and Codenie teach all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Further on, when combined, Lineback teaches wherein an encoded media stream output from the video encoder comprises different video sequences that are associated with different encoding parameters representing the different genres[See Lineback: at least Figs. 1-17, 23 and par. 47-52, 70-88, 114-145, 182-198, 235-249, 252, 262 regarding in Fig. 4, an example portion of media content 400 segmented into a plurality of shots. A shot can include a contiguous sequence of frames that are captured from or generated by an applicable source... The information about the one or more segments can include, for example and without limitation, contextual information, a type and/or genre of content in the one or more segments, a type of scene (e.g., a scenic scene, a sports scene, a scene with dialogue, a slow or fast scene, an indoors scene, an outdoors scene, a city scene, a rural scene, a holiday scene, a vacation scene, a scene with certain weather, a scene with a certain amount of lighting, and/or any other scene) in the one or more segments, a background and/or setting depicted in the one or more segments, any activity and/or events in the one or more segments, an actor(s) included in the one or more segments (and/or associated demographics of the one or more actors), a mood and/or sentiment associated with the one or more segments, a type of audio in the one or more segments (e.g., dialogue, music, noise, certain sounds, etc.) or lack thereof, any objects included in the one or more segments (e.g., a product and/or brand, a device, a structure, a tool, a toy, a vehicle, etc.), noise levels in the one or more segments, a landmark and/or architecture depicted or described in the one or more segments, a message conveyed in the one or more segments, a type of encoding associated with the one or more segments, a time and/or date associated with content of the one or more segments, one or more characteristics of content in the one or more segments, and/or any other information associated with the one or more segments.].
Regarding claims 4, 14, 21 and 23, Lineback and Codenie teach discloses all of the limitations of claims 1, 13, 20 and 22, and are analyzed as previously discussed with respect to those claims. Further on, when combined, Lineback teaches wherein the features comprise one or more of different noise features for the different genres, different distributions of motion vectors for the different genres, different intensity levels of pixels for the different genres, or different edge features for the different genres[See Lineback: at least Figs. 1-23 and par. 169-175, 239, 250-265 regarding The neural network 1304 can use the adjacent and/or neighboring pixels or blocks of pixels to predict the missing pixels in the video 1302 based on motion, intensity values, patterns, pixel values, and/or other information derived from the adjacent and/or neighboring pixels or blocks of pixels (and/or other portions of content such as any previous video frames, content in the video 1302, etc.). In some examples, the neural network 1304 can determine one or more motion vectors associated with the video 1302 based on motion calculated from the video 1302 (and/or motion calculated from one or more previous video frames). .. In some aspects, the contextual features can include a type and/or genre of content, a type of scene, a background and/or setting, any activity and/or events, an actor or actors, demographic information, a mood and/or sentiment, a type of audio or lack thereof, any objects, noise levels, a landmark and/or architecture, a geographic location, a keyword, a message, a type of encoding, a time and/or date, any other characteristic associated with media content 1804, and/or any combination thereof.].
Regarding claims 5 and 15, Lineback and Codenie teach all of the limitations of claims 1 and 13, and are analyzed as previously discussed with respect to those claims. Further on, when combined, Lineback teaches wherein the ML model is trained using supervised training, unsupervised training, or semi-supervised training [See Lineback: at least Figs. 1-23 and par. 81, 121, 124 regarding In some cases, the neural network 908 can be trained using unsupervised or self-supervised learning. In other cases, the neural network 908 can be trained using supervised learning based on a training dataset containing labels provided by human experts/labelers.].
Regarding claims 6 and 16, Lineback and Codenie teach all of the limitations of claims 1 and 13, and are analyzed as previously discussed with respect to those claims. Further on, when combined, Lineback and Codenie teach or suggests further comprising the feedback loop which is from the video encoder to the at least one execution unit to indicate a scene cut event to the at least one execution unit, wherein an encoded media stream output from the video encoder comprises different encoding parameters that are provided dynamically for the encoded media stream based at least in part on the scene cut event to allow the different encoding / further comprising an input to receive feedback, through the feedback loop, from the video encoder, the feedback to indicate a scene cut event to the at least one execution unit, wherein an encoded media stream output from the video encoder comprises different encoding parameters that are provided dynamically for the encoded media stream based at least in part on the scene cut event[See Lineback: at least Figs. 1-23 and par. 50-52, 66-90, 108-145, 176-198, 235-249, 265 regarding To illustrate, in some aspects, the content server 120 or the media device 106 can segment media content based on identified boundaries or breaks between portions (e.g., segments) of the media content. The content server 120 or the media device 106 can adjust a segment of media content to include and/or present targeted media content matched with the segment, in addition to any media content of the segment… Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments… See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… Using the adaptive bitrate streaming approach, edge computing system 133 may prepare multiple segments of video content 301 encoded for different VQ depending on a bandwidth of the connection between system 133 and a user device (e.g., device 153 as shown in FIG. 1)…In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…]
Regarding claim 10, Lineback and Codenie teach all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Further on, when combined, Lineback teaches wherein the inference using the ML model is performed using one or more sub-regions of one or more of the received frames[See Lineback: at least Figs. 1-23 and par. 70-88 regarding The content segmentation system 304 functions to access the media content 302 and segment the media content 302 into different units to form a sequence of units. A unit (also referred to as a segment), as used herein, can include an applicable section that media content can be divided into as part of a sequence of sections that ultimately form the media content. Specifically, a unit can include frames of media content, shots in media content, scenes in media content, subframes of media content, and spatial regions within frames of media content…]
Regarding claim 11, Lineback and Codenie teach all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Further on, when combined, Lineback teaches wherein the ML model is controlled by an application to perform the inference based in part on an input from the application[See Lineback: at least Figs. 1-23 and par. 47, 48, 62, 90, 280-290, 294 regarding the media content can be segmented into a sequence of units through application of one or more machine learning models…] and wherein the video encoder is controlled by a processing infrastructure to perform the encoding of the media stream based in part on capabilities associated with the processing infrastructure[See Lineback: at least Figs. 1-23 and par. 2-5, 70-88, 280-294, 301 regarding The system 300 includes accessed media content 302, a content segmentation system 304, a visual modality encoder 306, an audio modality encoder 308, a timed text modality encoder 310, and a sequence classifier 312…The various components of the system 300 can be implemented at applicable places in the multimedia environment 102 shown in FIG. 1…].
Regarding claim 12, Lineback and Codenie teach all of the limitations of claim 11, and are analyzed as previously discussed with respect to that claim. Further on, when combined, Lineback teaches wherein the application and the processing infrastructure share memory of the system to enable the inference and to enable the encoding of the media stream[See Lineback: at least Figs. 1-23 and par. 2-5, 47, 48, 62, 70-88, 90, 280-303 regarding the media content can be segmented into a sequence of units through application of one or more machine learning models…The system 300 includes accessed media content 302, a content segmentation system 304, a visual modality encoder 306, an audio modality encoder 308, a timed text modality encoder 310, and a sequence classifier 312…The various components of the system 300 can be implemented at applicable places in the multimedia environment 102 shown in FIG. 1…].
Regarding claim 24, Lineback and Codenie teach all of the limitations of claim 11, and are analyzed as previously discussed with respect to that claim. Further on, when combined, Lineback and Codenie teach or suggest further comprising: enabling the feedback loop from the video encoder to the at least one execution unit performing the ML model; and determining a scene cut event from feedback in the feedback loop, wherein the different encoding is provided dynamically for the media stream based at least in part on the scene cut event [See Lineback: at least Figs. 1-23 and par. 50-52, 66-90, 108-145, 176-198, 235-249, 265 regarding To illustrate, in some aspects, the content server 120 or the media device 106 can segment media content based on identified boundaries or breaks between portions (e.g., segments) of the media content. The content server 120 or the media device 106 can adjust a segment of media content to include and/or present targeted media content matched with the segment, in addition to any media content of the segment… Fig. 14 shows a feedback loop used to make adjustments to content categorization, content matching, and/or data augmentation. The performance metrics can be used to generate feedback 1406 for the neural network 908, the neural network 912, the matching system 1106, and/or the LLM 1204. The feedback 1406 can indicate, based on the performance metrics, whether the targeted media content 1402 was correctly categorized (or should be categorized differently) and/or matched with the media content segment provided with the targeted media content 1402, and/or whether the categorization and/or matching of the targeted media content 1402 (and any other targeted media content) can or should be adjusted. The neural network 908 can use the feedback 1406 to adjust how it generates embeddings encoding information about a media content segment, the neural network 912 can use the feedback 1406 to adjust how it generates categories based on the embeddings from the neural network 908, and/or the matching system 1106 can adjust how it matches targeted media content with media content segments… See Codenie: at least Figs. 3-12 and par. 3-5, 26, 31-32, 40-46 ,50-70 regarding at least one processor of an encoding system (e.g., system 160) may be configured to select an encoding method for content 121 based on a program characteristic related to content 121. For example, the characteristic may be a title of content 121 (e.g., a text string describing a name of a broadcast channel, a text string describing a title of an episode of a broadcast series, a movie title, and the like), a description of content 121 (e.g., a text data briefly describing a plot of a movie) a description of a genre (e.g., drama, comedy, action etc.) of content 121, a description of a type of content 121 (e.g., sports channel, news channel, a broadcast series, and the like),… Using the adaptive bitrate streaming approach, edge computing system 133 may prepare multiple segments of video content 301 encoded for different VQ depending on a bandwidth of the connection between system 133 and a user device (e.g., device 153 as shown in FIG. 1)…In various embodiments, when the encoding method is not determined (e.g., when broadcasting system 101 was not able to select an encoding method for a broadcast (e.g., content 121) based on a program characteristic related to content 121, encoding system 140 and/or system 160 may use a default encoding method and may also utilize a feedback control loop to dynamically adjust parameters of the default encoding method to optimize a metric related to quality of the encoded broadcast and a bitrate of the encoded broadcast…Returning to FIG. 6A, when a particular content (e.g., live feed 507) cannot be classified (e.g., if live feed 507 is a nonrecurrent and dissimilar content such as a movie), at step 605, analytics system 509 may determine a genre of live feed 507. Analytics system 509 may determine genres for previously broadcasted programs and will suggest encoding parameter corresponding to the genres. In an example embodiment, action movies may have different encoding parameters than, for instance, historical documentaries. In some embodiments, a broadcast program may be broadcasted for the first time. For such a case, at step 609 of process 600, as shown in FIG. 6A, parameters of the encoding method may be optimized using the feedback control loop approach as previously discussed. The determined parameters may be stored in database 511, as shown in FIG. 5, for further use for similar broadcast programs, or broadcast programs of the same category or genre…].
9. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lineback et al.(US 2025/0142183 A1)(hereinafter Lineback) ) in view of Codenie et al.(US 2021/0092493 A1)(hereinafter Codenie) and in further view of AHN et al.(US 2025/0088632 A1)(hereinafter Ahn).
Regarding claim 9, Lineback and Codenie teach all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim.
Lineback and Codenie do not explicitly disclose wherein the inference using the ML model is performed on processed versions of one or more of the received frames.
However, pre-processing the images before executing the machine learning model was well known in the art at the time of the invention was filed as evident from the teaching of Ahn[See Ahn: at least Figs. 1 and par. 72-77 regarding The image pre-processor may generate an input tensor by receiving an image as input and performing pre-processing to fit the input form of the neural network. The generated input tensor may be transmitted to the neural network. Here, the tensor may refer to three-dimensional data or three-dimensional or more data.].
Therefore, it would have been obvious before the effective filing date of the claimed inventio to a person having ordinary skill in the art to modify Lineback and Codenie with Ahn teachings by including “wherein the inference using the ML model is performed on processed versions of one or more of the received frames” because this combination has the benefit of providing a processed image to fit the machine learning model and aiming to improve the coding efficiency of the video compression system[See Ahn: at least par. 3-20, 72-73].
Allowable Subject Matter
10. Claims 7-8, 17, 19 and 25-26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Pertinent Prior Art Citation
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vairavan et al.(US 12,284,364 B2)
Moussaoui et al.(US 2023/0199204 A1)
Liu et al.(US 2025/0329349 A1)
Fogg et al.(US 2025/0139969 A1)
Cutter et al.(US 2025/0139942 A1)
LEE et al.(US 2025/0131537 A1)
Mao et al.(US 2025/0080758 A1)
Conclusion
11. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANA J PICON-FELICIANO whose telephone number is (571)272-5252. The examiner can normally be reached Monday-Friday 9:00-5:00.
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, Christopher Kelley can be reached at 571 272 7331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ana Picon-Feliciano/Examiner, Art Unit 2482
/CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482