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 .
DETAILED ACTION
This action is responsive to the Application filed on 06/12/2026
Claims 1, 3-4, 6-11, 13-14 and 16-24 are pending in the case. Claims 1, 11 and 21 are independent claims. Claims 1 and 11 have been currently amended. Claims 21-23 are newly added.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1, 3-4, 6-11, 13-14 and 16-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claim(s) 1, 3-4 and 6-10 are drawn to a system and claims 11, 13-14, 16-24 are drawn to a method, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1 and 10 are nonverbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites: A system comprising: a processing hardware; and
a system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas;
the processing hardware configured to execute the software code to:
receive an input including a the plurality of video segments;
map, using the ML model based embedder, each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments;
perform, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings;
classify, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric;
determine an encoding schema suitable for encoding the first content type;
and
encode the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 1 recites in part:
map, […] each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments
The limitation above is broadly and reasonable interpreted as a mental. For example, one can observe/match each video segments and decide whether two videos are similar/dissimilar, involving a mathematical transformation of data into numerical vector representations. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
classify, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric
The limitation above is broadly and reasonable interpreted as a mental concept. For example, a person could conceptually review video segments, compare their characteristic/similarity, and categorize then as sports, news, animation, movie, etc. . See MPEP § 2106.04(a)(2)(III).
determine an encoding schema suitable for encoding the first content type
The limitation above is broadly and reasonable interpreted as a mental concept. For example, one can mentally evaluate information and make a judgement. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
A system comprising:
a processing hardware; and
a system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas; as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
the processing hardware configured to execute the software code to:
receive an input including the plurality of video segments, as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
[…] using the ML model based embedder, […], as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
perform, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
encode the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “encoding schema” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
A system comprising:
a processing hardware; and
a system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
the processing hardware configured to execute the software code to:
receive an input including the plurality of video segments, as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
[…] using the ML model based embedder, […], as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
perform, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
encode the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “encoding schema” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 11
Claim 11 recites: A method for use by a system including a processing hardware, and a system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas, the method comprising:
receiving, by the software code executed by the processing hardware, an input including the plurality of video segments;
mapping, by the software code executed by the processing hardware and using the ML model based embedder, each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments;
performing, using the ML model based embedder or another trained ML model, one of a classification or a regression of the content plurality of video segments, by the software code executed by the processing hardware, using the plurality of mapped embeddings;
classifying, by the software code executed by the processing hardware based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric;
determining, by the software code executed by the processing hardware, an encoding schema suitable for encoding the first content type; and
encoding, by the software code executed by the processing hardware, the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 11 is directed to an abstract idea, specifically, a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 11 recites in part:
mapping, […] each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments
The limitation above is broadly and reasonable interpreted as a mental concept. For example, one can match each video segments, involving a mathematical transformation of data into numerical vector representations. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
classifying, […] the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric
The limitation above is broadly and reasonable interpreted as a mental concept. For example, with pen and paper one can sort video segments into one main type from a group of different types. See MPEP § 2106.04(a)(2)(III).
determining, […] an encoding schema suitable for encoding the first content type
The limitation above is broadly and reasonable interpreted as a mental concept. For example, one can mentally evaluate information and make a judgement. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 11 recites in part:
A method for use by a system including a processing hardware, and a system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas, the method comprising: as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
receiving, by the software code executed by the processing hardware, an input including the plurality of video segments, as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
[…] by the software code executed by the processing hardware and using the ML model based embedder, […], as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
performing, using the ML model based embedder or another trained ML model, one of a classification or a regression of the content plurality of video segments, by the software code executed by the processing hardware, using the plurality of mapped embeddings, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
[…] by the software code executed by the processing hardware based on the one of the classification or the regression, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “software code” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
encoding, by the software code executed by the processing hardware, the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “encoding schema” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 11 recites in part:
A method for use by a system including a processing hardware, and a system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas, the method comprising: as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
receiving, by the software code executed by the processing hardware, an input including the plurality of video segments, as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
[…] by the software code executed by the processing hardware and using the ML model based embedder, […], as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
performing, using the ML model based embedder or another trained ML model, one of a classification or a regression of the content plurality of video segments, by the software code executed by the processing hardware, using the plurality of mapped embeddings, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
[…] by the software code executed by the processing hardware based on the one of the classification or the regression, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “software code” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
encoding, by the software code executed by the processing hardware, the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type, as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “encoding schema” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 21
Claim 21 recites: A method for use with at least one machine learning (ML) model trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas, the method comprising:
receiving an input including the plurality of video segments;
mapping, using the at least one ML model, each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments;
performing one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings;
classifying, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric;
determining, based on the video content type, at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments; and
encoding the plurality of video segments using the at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) a bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 21 is directed to an abstract idea, specifically, a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 21 recites in part:
mapping, […] each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments
The limitation above is broadly and reasonable interpreted as a mental concept. For example, one can match each video segments, involving a mathematical transformation of data into numerical vector representations. See MPEP § 2106.04(a)(2)(III) & See MPEP § 2106.04(a)(2)(I)(C).
classifying, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric
The limitation above is broadly and reasonable interpreted as a mental concept. For example, a person could conceptually review video segments, compare their characteristic/ similarity, and categorize then as sports, news, animation, movie, etc. . See MPEP § 2106.04(a)(2)(III).
determining, based on the video content type, at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments
The limitation above is broadly and reasonable interpreted as a mental concept. For example, a person could evaluate the content type and decide which known encoding parameters are appropriate, recognizing high-motion sport videos and select a higher bitrate. This is an evaluation/judgement/selection, which fall within the mental concept. See MPEP § 2106.04(a)(2)(III).
performing one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings
The limitation above is broadly and reasonable interpreted as a mental concept. For example, classification involves examining information and deciding which category it belongs to an evaluation/judgement that a person conceptually perform.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 21 recites in part:
A method for use with at least one machine learning (ML) model trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas, the method comprising: as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
receiving an input including the plurality of video segments, as drafted, extra-solution activity (pre-solution activity is a step of gathering data). See MPEP §§ 2106.04(d), 2106.05(g).
[…] using the at least one ML model, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
encoding the plurality of video segments using the at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) a bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments
as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “encoding schema” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 21 recites in part:
A method for use with at least one machine learning (ML) model trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas, the method comprising: as drafted, amount to the judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components.
receiving an input including the plurality of video segments, as drafted, extra-solution activity (pre-solution activity is a step of gathering data). See MPEP §§ 2106.04(d), 2106.05(g).
[…] using the at least one ML model, […] as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
encoding the plurality of video segments using the at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) a bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments
as drafted, amount to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “encoding schema” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-10 which are dependent on claim 1 and claims 12-20 which are dependent on claim 11, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claims 3 and 13 are dependent on claims 1 and 11 respectively, and include mental concept- concepts performed on pen and paper (including an observation, evaluation, judgement, opinion). For example, one can with pen and paper group similar data points corresponding to a distinct category.
Claims 4 and 14 are dependent on claims 3 and 13 respectively, and include an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “NN” or “unsupervised process” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Claims 6 and 16 are dependent on claims 1 and 11 respectively, and include an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Claims 7 and 17 are dependent on claims 1 and 11 respectively, and include an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Claims 8 and 18 are dependent on claims 1 and 11 respectively, and include additional elements recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Claims 9 and 19 are dependent on claims 1 and 11 respectively, and include additional elements recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Claims 10 and 20 are dependent on claims 1 and 11 respectively, and include additional elements recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Claim 22 is dependent on claim 21, and include additional elements recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Claim 23 is dependent on claim 21, and include additional elements recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
Claim 24 is dependent on claim 21, and include additional elements recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
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.
Claims 1, 7, 11, 17 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over NGUYEN et al. (Pub No.: 2020022958 A1), hereinafter referred to as NGUYEN, in view of Otto et al. (Pub No.: 20190335192 A1), hereinafter referred to as Otto and further in view of SULZER et al. (Pub No.: 20200364468 A1), hereinafter referred to a SULZER.
With respect to claim 1, NGUYEN disclose:
A system comprising: a processing hardware (In Col. 35, lines 18–31, NGUYEN discloses that the user device includes one or more processors capable of executing instructions/programs. The processor executes the software used for the disclosed video-content validation and may include a processing unit that runs computer programs and backend applications.)
A system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas (In col.23, lines 18–25, NGUYEN discloses video content is converted into machine-understandable image features, so a neural network can compare videos. During training, features are extracted from videos, so the neural network can learn a model that calculates a similarity value between two or more videos. In col. 24, lines 5–20, NGUYEN discloses that given an original video and multiple transcoded videos, the system learns a video similarity function using relationships between groups/triplets of videos. Each video is mapped by an embedding function onto a point in a Euclidean embedding space, and similarity between two videos is measured using their squared Euclidean distance. In Col. 24, lines 35–47, NGUYEN discloses that each video is represented by a feature vector, and the triplet establishes a relative similarity relationship: the original video should be more similar to the positive video than to the negative video. In Col. 28, lines 32–45, NGUYEN discloses a threshold to decide whether two audio files are similar. The system calculates a threshold similarity/confidence level and compares the calculated similarity value against that threshold. If the similarity value satisfies the threshold condition, the two files can be determined to be similar.)
The processing hardware configured to execute the software code to: receive an input including a the plurality of video segments (In Col.36, lines 42–53, NGUYEN disclose using a trained neural network to verify whether a video has been correctly transcoded. Extracting a video frame descriptor from each frame of the original video.)
Map, using the ML model based embedder, each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments (In Col.24, lines 5–29, NGUYEN discloses how metric learning maps videos into an embedded space so their similarity can be measured. Maps each video through an embedding function to a point in Euclidean space.)
With respect to claim 1, NGUYEN does not appear to explicitly disclose:
Perform, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings
Classify, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric
Determine an encoding schema suitable for encoding the first content type
Encode the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type
However, it is known by Otto to disclose:
Classify, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric (In paragraph [0049], Otto discloses a machine-learning classifier that analyzes previous/current video information to predict characteristics of upcoming video segments and determine appropriate encoding parameters.)
Determine an encoding schema suitable for encoding the first content type (In paragraph [0037], Otto discloses the system trains and uses a machine-learning classifier to predict characteristics of video segments so that the encoder can make better encoding decisions.)
Encode the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type (In paragraph [0035], Otto discloses the difference between single-pass and multi-pass video encoding. In multi-pass encoding, the video is first analyzed during a first pass, information/data about the video is collected.)
NGUYEN in view of Otto are analogous pieces of art because both concern machine-learning based analysis of video data for improving or controlling video encoding/transcoding. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify NGUYEN, with determining from the similarity value whether the performance of the transaction is correct as taught by NGUYEN, with providing the at least one piece of frame data to a machine learning classifier using the media server system as taught by Otto. The motivation for doing so would have been to improve the effectiveness of video content similarity comparison for proof of transcoding (See (Col. 25, lines 28-33) of NGUYEN.)
With respect to claim 1, NGUYEN in view of Otto does not appear to explicitly disclose:
Perform, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings
However, it is known by SULZER to disclose:
Perform, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings (In paragraph [0135], SULZER discloses that one or more video frames are annotated and labeled with classification data. The classification data can represent categories such as color, lighting, object type, etc.)
NGUYEN in view of Otto and SULZER are analogous pieces of art because both concern machine-learning based analysis of video data for improving or controlling video encoding/transcoding. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify SULZER, with classification/annotation of data/labels relevant to the object of real-time analytics on a live video stream. The motivation for doing so would have been to improve the performance of the detection model, such as, for example, how fast or slow the detection model learns or how many times the detection model analyzes a particular frame/image (See [0114] of SULZER).
Regarding claim 7, NGUYEN in view of Otto and SULZER disclose the elements of claim 1. In addition, NGUYEN disclose:
The system of claim 1, wherein the similarity metric comprises one of a quantitative similarity metric or a perceptual similarity metric (In Col.27, lines 5–20, NGUYEN discloses a video similarity function, computes a similarity score of content between the original video and the transcoded videos. )
With respect to claim 11, NGUYEN disclose:
A method for use by a system including a processing hardware (In Col. 35, lines 18–31, NGUYEN discloses that the user device includes one or more processors capable of executing instructions/programs. The processor executes the software used for the disclosed video-content validation and may include a processing unit that runs computer programs and backend applications.)
A system memory storing a software code and a machine learning (ML) model based embedder trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas (In col.23, lines 18–25, NGUYEN discloses video content is converted into machine-understandable image features, so a neural network can compare videos. During training, features are extracted from videos, so the neural network can learn a model that calculates a similarity value between two or more videos. In col. 24, lines 5–20, NGUYEN discloses that given an original video and multiple transcoded videos, the system learns a video similarity function using relationships between groups/triplets of videos. Each video is mapped by an embedding function onto a point in a Euclidean embedding space, and similarity between two videos is measured using their squared Euclidean distance. In Col. 24, lines 35–47, NGUYEN discloses that each video is represented by a feature vector, and the triplet establishes a relative similarity relationship: the original video should be more similar to the positive video than to the negative video. In Col. 28, lines 32–45, NGUYEN discloses a threshold to decide whether two audio files are similar. The system calculates a threshold similarity/confidence level and compares the calculated similarity value against that threshold. If the similarity value satisfies the threshold condition, the two files can be determined to be similar.)
The method comprising: receiving, by the software code executed by the processing hardware, an input including a the plurality of video segments (In Col.36, lines 42–53, NGUYEN disclose using a trained neural network to verify whether a video has been correctly transcoded. Extracting a video frame descriptor from each frame of the original video.)
Mapping, by the software code executed by the processing hardware and using the ML model based embedder, each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments (In Col.24, lines 5–29, NGUYEN discloses how metric learning maps videos into an embedded space so their similarity can be measured. Maps each video through an embedding function to a point in Euclidean space.)
With respect to claim 11, NGUYEN does not appear to explicitly disclose:
Performing, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments, by the software code executed by the processing hardware, using the plurality of mapped embeddings
Classify, by the software code executed by the processing hardware based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric
Determining, by the software code executed by the processing hardware, an encoding schema suitable for encoding the first content type
Encoding, by the software code executed by the processing hardware, the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type
However, it is known by Otto to disclose:
Classify, by the software code executed by the processing hardware based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric (In paragraph [0049], Otto discloses a machine-learning classifier that analyzes previous/current video information to predict characteristics of upcoming video segments and determine appropriate encoding parameters.)
Determining, by the software code executed by the processing hardware, an encoding schema suitable for encoding the first content type (In paragraph [0037], Otto discloses the system trains and uses a machine-learning classifier to predict characteristics of video segments so that the encoder can make better encoding decisions.)
Encoding, by the software code executed by the processing hardware, the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type (In paragraph [0035], Otto discloses the difference between single-pass and multi-pass video encoding. In multi-pass encoding, the video is first analyzed during a first pass, information/data about the video is collected.)
NGUYEN in view of Otto are analogous pieces of art because both concern machine-learning based analysis of video data for improving or controlling video encoding/transcoding. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify NGUYEN, with determining from the similarity value whether the performance of the transaction is correct as taught by NGUYEN, with providing the at least one piece of frame data to a machine learning classifier using the media server system as taught by Otto. The motivation for doing so would have been to improve the effectiveness of video content similarity comparison for proof of transcoding (See (Col. 25, lines 28-33) of NGUYEN.)
With respect to claim 11, NGUYEN in view of Otto does not appear to explicitly disclose:
Performing, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments, by the software code executed by the processing hardware, using the plurality of mapped embeddings
However, it is known by SULZER to disclose:
Performing, using the ML model based embedder or another trained ML model, one of a classification or a regression of the plurality of video segments, by the software code executed by the processing hardware, using the plurality of mapped embeddings (In paragraph [0135], SULZER discloses that one or more video frames are annotated and labeled with classification data. The classification data can represent categories such as color, lighting, object type, etc.)
NGUYEN in view of Otto and SULZER are analogous pieces of art because both concern machine-learning based analysis of video data for improving or controlling video encoding/transcoding. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify SULZER, with classification/annotation of data/labels relevant to the object of real-time analytics on a live video stream. The motivation for doing so would have been to improve the performance of the detection model, such as, for example, how fast or slow the detection model learns or how many times the detection model analyzes a particular frame/image (See [0114] of SULZER).
Regarding claim 17, NGUYEN in view of Otto and SULZER disclose the elements of claim 11. In addition, NGUYEN disclose:
The method of claim 11, wherein the similarity metric comprises one of a quantitative similarity metric or a perceptual similarity metric (In Col.27, lines 5–20, NGUYEN discloses a video similarity function, computes a similarity score of content between the original video and the transcoded videos. )
With respect to claim 21, NGUYEN disclose:
A method for use with at least one machine learning (ML) model trained using contrastive learning based on a similarity metric to map each of a plurality of video segments to a respective embedding in a continuous vector space, wherein the similarity metric is a video encoding metric, and the ML model based embedder is trained to label a pair of video segments as similar in response to the pair of video segments both meeting a same threshold performance criterion for a same encoding schema, and to label the pair of video segments as dissimilar in response to the pair of video segments meeting one of different threshold performance criteria for a corresponding one of different encoding schemas (In col.23, lines 18–25, NGUYEN discloses video content is converted into machine-understandable image features, so a neural network can compare videos. During training, features are extracted from videos, so the neural network can learn a model that calculates a similarity value between two or more videos. In col. 24, lines 5–20, NGUYEN discloses that given an original video and multiple transcoded videos, the system learns a video similarity function using relationships between groups/triplets of videos. Each video is mapped by an embedding function onto a point in a Euclidean embedding space, and similarity between two videos is measured using their squared Euclidean distance. In Col. 24, lines 35–47, NGUYEN discloses that each video is represented by a feature vector, and the triplet establishes a relative similarity relationship: the original video should be more similar to the positive video than to the negative video. In Col. 28, lines 32–45, NGUYEN discloses a threshold to decide whether two audio files are similar. The system calculates a threshold similarity/confidence level and compares the calculated similarity value against that threshold. If the similarity value satisfies the threshold condition, the two files can be determined to be similar.)
The method comprising: receiving an input including the plurality of video segments (In Col.36, lines 42–53, NGUYEN disclose using a trained neural network to verify whether a video has been correctly transcoded. Extracting a video frame descriptor from each frame of the original video.)
Mapping, using the at least one ML model, each of the plurality of video segments to the respective embedding in the continuous vector space to provide a plurality of mapped embeddings corresponding respectively to the plurality of video segments (In Col.24, lines 5–29, NGUYEN discloses how metric learning maps videos into an embedded space so their similarity can be measured. Maps each video through an embedding function to a point in Euclidean space.)
With respect to claim 21, NGUYEN does not appear to explicitly disclose:
Performing one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings
Classifying, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric
Determining, based on the video content type, at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments
Encoding the plurality of video segments using the at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) a bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments
However, it is known by Otto to disclose:
Classifying, based on the one of the classification or the regression, the plurality of video segments into a video content type among a plurality of video content types with respect to the similarity metric (In paragraph [0049], Otto discloses a machine-learning classifier that analyzes previous/current video information to predict characteristics of upcoming video segments and determine appropriate encoding parameters.)
Determining, based on the video content type, at least one of (i) a pre-processing algorithm for pre-processing the plurality of video segments, (ii) bitrate ladder for the plurality of video segments, or (iii) encoding parameters for the plurality of video segments (Examiner selects: encoding parameter for the plurality of video segments. In paragraph [0037], Otto discloses the system trains and uses a machine-learning classifier to predict characteristics of video segments so that the encoder can make better encoding decisions.)
Encoding, by the software code executed by the processing hardware, the plurality of video segments using the encoding schema determined to be suitable for encoding the first content type (In paragraph [0035], Otto discloses the difference between single-pass and multi-pass video encoding. In multi-pass encoding, the video is first analyzed during a first pass, information/data about the video is collected.)
NGUYEN in view of Otto are analogous pieces of art because both concern machine-learning based analysis of video data for improving or controlling video encoding/transcoding. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify NGUYEN, with determining from the similarity value whether the performance of the transaction is correct as taught by NGUYEN, with providing the at least one piece of frame data to a machine learning classifier using the media server system as taught by Otto. The motivation for doing so would have been to improve the effectiveness of video content similarity comparison for proof of transcoding (See (Col. 25, lines 28-33) of NGUYEN.)
With respect to claim 21, NGUYEN in view of Otto does not appear to explicitly disclose:
Performing one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings
However, it is known by SULZER to disclose:
Performing one of a classification or a regression of the plurality of video segments using the plurality of mapped embeddings (In paragraph [0135], SULZER discloses that one or more video frames are annotated and labeled with classification data. The classification data can represent categories such as color, lighting, object type, etc.)
NGUYEN in view of Otto and SULZER are analogous pieces of art because both concern machine-learning based analysis of video data for improving or controlling video encoding/transcoding. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify SULZER, with classification/annotation of data/labels relevant to the object of real-time analytics on a live video stream. The motivation for doing so would have been to improve the performance of the detection model, such as, for example, how fast or slow the detection model learns or how many times the detection model analyzes a particular frame/image (See [0114] of SULZER).
Regarding claim 23, NGUYEN in view of Otto and SULZER disclose the elements of claim 21. In addition, Otto disclose:
The method of claim 21, further comprising: selecting, based on the video content type, the bitrate ladder for the plurality of video segments; and encoding the plurality of video segments using the selected bitrate ladder (In paragraph [0038], Otto disclose learning video encoders can also be utilized to encode a single piece of video data at a variety of resolutions, frame rates, and/or bit rates, thereby providing a number of alternative streams of encoded video data that can be utilized in adaptive streaming systems.)
Regarding claim 24, NGUYEN in view of Otto and SULZER disclose the elements of claim 21. In addition, Otto disclose:
The method of claim 21, further comprising: selecting, based on the video content type, the encoding parameters for the plurality of video segments; and encoding the plurality of video segments using the selected encoding parameters (In paragraph [0045], Otto disclose learning video encoding processes to analyze incoming video data, identify characteristics within the video data, utilize machine learning classifiers to adjust encoding parameters for encoding the video data.)
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over NGUYEN, in view of Otto, SULZER and further in view of Paluri et al. (Pub No.: 20170132510A1),hereinafter referred to a Paluri.
Regarding claim 3, NGUYEN in view of Otto and SULZER disclose elements of claim 1. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The system of claim 1, wherein the classification comprises grouping each of at least one of the plurality of mapped embeddings into one or more clusters each corresponding respectively to a distinct category of the similarity metric
However, Paluri disclose the limitation (In paragraph [0041], Paluri discloses generating clusters of embeddings of content items in an embedding space. Each cluster may be associated with a class of content items (e.g., a categorization or subset of content items). As used herein, a cluster may be a set of one or more points corresponding to embeddings of content items in an embedding space, and the content items whose embeddings are in the cluster may belong to the same class.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Paluri, with a first embedding of the first content item may be determined and may corresponds to a first point in an embedding space as taught by Paluri. The motivation for doing so would have been to identify similar content items in an embedding space (See [0036] of Paluri.)
Regarding claim 13, NGUYEN in view of Otto and SULZER disclose elements of claim 11. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 11, wherein the classification comprises grouping each of at least one of the plurality of mapped embeddings into one or more clusters each corresponding respectively to a distinct category of the similarity metric
However, Paluri disclose the limitation (In paragraph [0041], Paluri discloses generating clusters of embeddings of content items in an embedding space. Each cluster may be associated with a class of content items (e.g., a categorization or subset of content items). As used herein, a cluster may be a set of one or more points corresponding to embeddings of content items in an embedding space, and the content items whose embeddings are in the cluster may belong to the same class.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Paluri, with a first embedding of the first content item may be determined and may corresponds to a first point in an embedding space as taught by Paluri. The motivation for doing so would have been to identify similar content items in an embedding space (See [0036] of Paluri.)
Claims 4, 14 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over NGUYEN, in view of Otto, SULZER and further in view of Raveendran et al. (Pub No.: 20070081587 A1),hereinafter referred to a Raveendran.
Regarding claim 4, NGUYEN in view of Otto and SULZER disclose elements of claim 1. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The system of claim 1, wherein the processing hardware is further configured to execute the software code to: select, based on the first content type, a pre-processing algorithm for pre-processing the plurality of video segments
Pre-process the plurality of video segments using the selected pre-processing algorithm
However, Raveendran disclose the limitation:
The system of claim 1, wherein the processing hardware is further configured to execute the software code to: select, based on the first content type, a pre-processing algorithm for pre-processing the plurality of video segments (In paragraph [0086], Raveendran discloses a preprocessor 226 that can use content information for one or more preprocessing operations. Preprocessor 226 receives metadata 222 and decoded "raw" video data 224 from the parser/decoder 202. The preprocessor 226 is configured to perform certain types of processing on the video data 224 and the metadata 222 and provide processed multimedia (e.g., base layer reference frames, enhancement layer reference frames, bandwidth information, content information) and video to the encoder.)
Pre-process the plurality of video segments using the selected pre-processing algorithm (In paragraph [0087], Raveendran discloses that the preprocessor 226 can be configured to determine content information and use the content information for preprocessing operations and/or provides content information to other components of the transcoder 200, e.g., the decoder 228. In some aspects, the preprocessor 226 can use such content information to influence GOP partitioning, determine appropriate type of filtering, and/or determine encoding parameters that are communicated to an encoder )
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Raveendran, with processing multimedia data includes receiving multimedia data, and encoding the multimedia data into a first data group and a second data group based on content of the multimedia data as taught by Raveendran. The motivation for doing so would have been to improve the performance of existing encoding systems (See [0118] of Raveendran.)
Regarding claim 14, NGUYEN in view of Otto and SULZER disclose elements of claim 11. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 11, further comprising: selecting, based on the first content type, a pre-processing algorithm for pre-processing the plurality of video segments
Pre-processing the plurality of video segments using the selected pre-processing algorithm
However, Raveendran disclose the limitation:
The method of claim 11, further comprising: selecting, based on the first content type, a pre-processing algorithm for pre-processing the plurality of video segments (In paragraph [0086], Raveendran discloses a preprocessor 226 that can use content information for one or more preprocessing operations. Preprocessor 226 receives metadata 222 and decoded "raw" video data 224 from the parser/decoder 202. The preprocessor 226 is configured to perform certain types of processing on the video data 224 and the metadata 222 and provide processed multimedia (e.g., base layer reference frames, enhancement layer reference frames, bandwidth information, content information) and video to the encoder.)
Pre-processing the plurality of video segments using the selected pre-processing algorithm (In paragraph [0087], Raveendran discloses that the preprocessor 226 can be configured to determine content information and use the content information for preprocessing operations and/or provides content information to other components of the transcoder 200, e.g., the decoder 228. In some aspects, the preprocessor 226 can use such content information to influence GOP partitioning, determine appropriate type of filtering, and/or determine encoding parameters that are communicated to an encoder. )
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Raveendran, with processing multimedia data includes receiving multimedia data, and encoding the multimedia data into a first data group and a second data group based on content of the multimedia data as taught by Raveendran. The motivation for doing so would have been to improve the performance of existing encoding systems (See [0118] of Raveendran.)
Regarding claim 22, NGUYEN in view of Otto and SULZER disclose elements of claim 21. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 21, further comprising: selecting, based on the video content type, the pre-processing algorithm for pre- processing the plurality of video segments
Pre-processing the plurality of video segments using the selected preprocessing algorithm
However, Raveendran disclose the limitation:
The method of claim 21, further comprising: selecting, based on the video content type, the pre-processing algorithm for pre- processing the plurality of video segments (In paragraph [0086], Raveendran discloses a preprocessor 226 that can use content information for one or more preprocessing operations. Preprocessor 226 receives metadata 222 and decoded "raw" video data 224 from the parser/decoder 202. The preprocessor 226 is configured to perform certain types of processing on the video data 224 and the metadata 222 and provide processed multimedia (e.g., base layer reference frames, enhancement layer reference frames, bandwidth information, content information) and video to the encoder.)
Pre-processing the plurality of video segments using the selected preprocessing algorithm (In paragraph [0087], Raveendran discloses that the preprocessor 226 can be configured to determine content information and use the content information for preprocessing operations and/or provides content information to other components of the transcoder 200, e.g., the decoder 228. In some aspects, the preprocessor 226 can use such content information to influence GOP partitioning, determine appropriate type of filtering, and/or determine encoding parameters that are communicated to an encoder. )
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Raveendran, with processing multimedia data includes receiving multimedia data, and encoding the multimedia data into a first data group and a second data group based on content of the multimedia data as taught by Raveendran. The motivation for doing so would have been to improve the performance of existing encoding systems (See [0118] of Raveendran.)
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over NGUYEN, in view of Otto, SULZER and further in view of Duffy et al. (Pub No.: 20200081906 A1),hereinafter referred to a Duffy.
Regarding claim 6, NGUYEN in view of Otto and SULZER disclose elements of claim 1. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The system of claim 1, wherein the continuous vector space is multi-dimensional
However, Duffy disclose the limitation (In paragraph [0186], Duffy discloses that the embedded space comprises a multidimensional vector space.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Duffy, with identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents as taught by Duffy. The motivation for doing so would have been to identify documents in association with embedding information (See [0239] of Duffy.)
Regarding claim 16, NGUYEN in view of Otto and SULZER disclose elements of claim 11. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 11, wherein the continuous vector space is multi-dimensional
However, Duffy disclose the limitation (In paragraph [0186], Duffy discloses that the embedded space comprises a multidimensional vector space.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Duffy, with identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents as taught by Duffy. The motivation for doing so would have been to identify documents in association with embedding information (See [0239] of Duffy.)
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over NGUYEN, in view of Otto, SULZER and further in view of Mao et al. (Pub No.: 20200202145 A1),hereinafter referred to a Mao.
Regarding claim 8, NGUYEN in view of Otto and SULZER disclose elements of claim 1. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The system of claim 1, wherein the one of the classification or the regression is performed using a respective one of a trained classification ML model or a trained regression ML model, and wherein the ML model based embedder and the respective one of the trained classification ML model or the trained regression ML model are trained independently of one another
However, Mao disclose the limitation (In paragraph [0073], Mao discloses the embedding neural network and a classification neural network, and the context embedding neural network are trained separately.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Mao, with a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment as taught by Mao. The motivation for doing so would have been to generate classifications of objects represented in data acquired by one or more sensors on a vehicle (See [0001] of Mao.)
Regarding claim 9, NGUYEN in view of Otto and SULZER disclose elements of claim 1. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The system of claim 1, wherein the one of the classification or the regression is performed using a respective one of a trained classification ML model or a trained regression ML model, and wherein the respective one of the trained classification ML model or the trained regression ML model comprises a trained neural network (NN)
However, Mao disclose the limitation (In paragraph [0073], Mao discloses the embedding neural network and a classification neural network, and the context embedding neural network are trained separately.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Mao, with a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment as taught by Mao. The motivation for doing so would have been to generate classifications of objects represented in data acquired by one or more sensors on a vehicle (See [0001] of Mao.)
Regarding claim 18, NGUYEN in view of Otto and SULZER disclose elements of claim 11. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 11, wherein the one of the classification or the regression is performed using a respective one of a trained classification ML model or a trained regression ML model, and wherein the ML model based embedder and the respective one of the trained classification ML model or the trained regression ML model are trained independently of one another
However, Mao disclose the limitation (In paragraph [0073], Mao discloses the embedding neural network and a classification neural network, and the context embedding neural network are trained separately.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Mao, with a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment as taught by Mao. The motivation for doing so would have been to generate classifications of objects represented in data acquired by one or more sensors on a vehicle (See [0001] of Mao.)
Regarding claim 19, NGUYEN in view of Otto and SULZER disclose elements of claim 11. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 11, wherein the one of the classification or the regression is performed using a respective one of a trained classification ML model or a trained regression ML model, and wherein the respective one of the trained classification ML model or the trained regression ML model comprises a trained neural network (NN)
However, Mao disclose the limitation (In paragraph [0073], Mao discloses the embedding neural network and a classification neural network, and the context embedding neural network are trained separately.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Mao, with a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment as taught by Mao. The motivation for doing so would have been to generate classifications of objects represented in data acquired by one or more sensors on a vehicle (See [0001] of Mao.)
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over NGUYEN, in view of Otto, SULZER and further in view of Urtasun et al. (Pub No.: 20210012116 A1),hereinafter referred to a Urtasun.
Regarding claim 10, NGUYEN in view of Otto and SULZER disclose elements of claim 1. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The system of claim 1, wherein the one of the classification or the regression is performed using a respective one of a classification block or a regression block of the ML model based embedder, and wherein the ML model based embedder including the respective one of the classification block or the regression block is trained using end-to-end learning
However, Urtasun disclose the limitation (In paragraph [0027], Urtasun discloses that end-to-end via backpropagation, the machine-learned feature embedding model, the machine-learned instance scoring model, and the machine-learned category-agnostic instance model can be jointly trained end-to-end via back propagation.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Urtasun, with determining a feature embedding and at least one of an instance embedding, class embedding, and/or background embedding for each of the plurality of three-dimensional points as taught by Urtasun. The motivation for doing so would have been to jointly trained end-to-end via backpropagation (See [0027] of Urtasun.)
Regarding claim 20, NGUYEN in view of Otto and SULZER disclose elements of claim 11. NGUYEN in view of Otto and SULZER do not explicitly disclose:
The method of claim 11, wherein the one of the classification or the regression is performed using a respective one of a classification block or a regression block of the ML model based embedder, and wherein the ML model based embedder including the respective one of the classification block or the regression block and the trained NN is trained using end-to-end learning
However, Urtasun disclose the limitation (In paragraph [0027], Urtasun discloses that end-to-end via backpropagation, the machine-learned feature embedding model, the machine-learned instance scoring model, and the machine-learned category-agnostic instance model can be jointly trained end-to-end via back propagation.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of NGUYEN in view of Otto and SULZER to include Urtasun, with determining a feature embedding and at least one of an instance embedding, class embedding, and/or background embedding for each of the plurality of three-dimensional points as taught by Urtasun. The motivation for doing so would have been to jointly trained end-to-end via backpropagation (See [0027] of Urtasun.)
Response to Arguments
The applicant's arguments filed 06/12/2026 have been fully considered, but in part are not persuasive.
Pertaining to Rejection under 101
On page 12, applicants arguments have been fully considered but are not persuasive. Although applicant contends that the claimed machine learning operation cannot practically be performed in the human mind, the underlying claim concept, absent the recited ML model implementation, encompasses observing and evaluating video content, comparing characteristics or performance of video segments, determining similarities or difference between the video segments, classifying the video segments based on those similarities or differences, and determining a suitable encoding scheme based on the resulting classification. Such activities constitute observation, evaluation, and judgement that conceptually be performed in the human mind and therefore fall within the mental-process grouping of abstract idea identified in MPEP 2106.04(a)(2)(iii).
On page 13, I agree with applicants arguments to the extent that claim 1 does not recite a mathematical concept. However, the claimed Machine Learning based embedder uses contrastive learning based on similarity metric map video segments into respective embedding in a continuous vector space and subsequently performs classification or regression using those mapped embeddings. These operations ordinarily involve mathematical relationship between numerical vector representation. Although claim 1 refers to contrastive learning, the claim does not recite mathematical formula, equation defining how those operations are mathematically performed.
Pertaining to the improvement of technology
On page 12, Applicants arguments have been considered but are not persuasive because, although the specification (page 4-5) describes potential optimizations within a video-encoding pipeline, claim 1 does not recite a specific improvement to the operation of a computer, video encoder, or other technology. Rather , claim 1 recites using a ML-based embedder to evaluate vide segments, classifying…and encoding schema suitable for that content type. The specification itself explains that content classification was conventionally performed through human inspection and that selection of an appropriate encoding workflow could involve trial and error. While such automation may improve efficiency or reduce human involvement, the claim does not recite how the underlying encoding technology itself is improved, such as a particular modification to an encoding algorithm, bitrate ladder, etc. Accordingly, the claim does not integrate the judicial exception into a practical application by reflecting an improvement to the functioning of a computer or to another technology or technical field.
.
Pertaining to Rejection under 103
Applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection
Conclusion
THIS ACTION IS MADE FINAL. 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.
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EVEL HONORE
Examiner
Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
Prosecution Insights