CTNF 18/386,196 CTNF 84379 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim s 9 and 14 are objected to because of the following informalities: As to claim 9, the limitations “supervised generating the validation score of the machine learning model” should be changed to -- supervised generation of the validation score of the machine learning model --. As to claim 14, the limitations “the method further comprises unsupervised training a machine learning model” -- the method further comprises unsupervised training of a machine learning model --. Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step One The claims are directed to a method (claims 1 - 15) and one or more non-transitory computer readable media (claims 16 - 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). As to claims 1, Step 2A, Prong One The claim recites in part: generating, from an input, an inference that contains a plurality of probabilities respectively for a plurality of classes that contains a first class and a second class, wherein the plurality of probabilities contains a higher probability for the first class that is higher than a lower probability for the second class; For example, a human mentally assigns a higher likelihood to a first class over a second class based on a conclusion from observations classifying, in response to a threshold exceeding the higher probability, the input as the second class; For example, a human can mentally classify all the higher probabilities that are above a threshold into a single class. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: wherein the method is performed by one or more computers. which is recited at a high-level of generality with no detail of the performing process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) The claim further recites one or more computers which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application. Step 2B In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: wherein the method is performed by one or more computers. which is recited at a high-level of generality with no detail of the performing process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) The claim further recites one or more computers which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. As to claims 2, Step 2A, Prong One The claim recites in part: wherein: said threshold is a first threshold; the plurality of probabilities contains a third probability for a third class; said classifying is further in response to a second threshold exceeding the third probability. For example, a human can determine if a threshold exceeds a probability by comparing the two. Humans have been comparing data against a threshold before computers where invented. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 3, Step 2A, Prong One The claim recites in part: detecting the first threshold exceeds the higher probability in parallel with detecting the second threshold exceeds the third probability. For example, a human can evaluate multiple possibilities at once. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 4, Step 2A, Prong One The claim recites in part: selecting the first threshold and the second threshold based on a multi-objective optimization. For example, a human can choose the threshold based on past evaluations. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 5, Step 2A, Prong One The claim recites in part: generating based on the machine learning model, without retraining the machine learning model, a first validation score based on the first threshold and a second validation score based on the second threshold; For example, a human can generate a ranking score based on the value of the thresholds. selecting the first threshold based on the first validation score and the second threshold based on the second validation score. For example, a human can choose the threshold value based on the highest ranked score. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: training a machine learning model which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application. Step 2B In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: training a machine learning model which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. As to claims 6, Step 2A, Prong One The claim recites in part: selecting the second threshold based on the first threshold. For example, a human can choose the threshold based on past evaluations of the first threshold. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 7, Step 2A, Prong One The claim recites in part: selecting a third threshold based on a respective probability threshold of each class of multiple classes. For example, a human can choose the threshold based on past evaluations. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 8, Step 2A, Prong One The claim recites in part: selecting the threshold based on validation score of a machine learning model. For example, a human can choose the threshold based on past scores. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 9, Step 2A, Prong One The claim recites in part: supervised generating the validation score of the machine learning model For example, a human can choose the threshold based on past scores. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 10, Step 2A, Prong One The claim recites in part: selecting the threshold based on a one-dimensional search For example, a human can choose the threshold based on a focus on a single variable or parameter. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 10, Step 2A, Prong One The claim recites in part: wherein the one-dimensional search is uniform or not greedy For example, a human can choose the threshold based on a focus on a single variable or parameter. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 11, Step 2A, Prong One The claim recites in part: wherein the one-dimensional search is uniform or not greedy For example, a human can choose the threshold based on a focus on a single variable or parameter. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 12, Step 2A, Prong One The claim recites in part: said plurality of classes contains a first plurality of classes and a second plurality of classes that is disjoint from the first plurality of classes; the method further comprises assigning a distinct respective threshold to each class in the first plurality of classes; the method does not comprise assigning a threshold to a class in the second plurality of classes. For example, a human can choose the threshold for each and every sperate class. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 13, Step 2A, Prong One The claim recites in part: selecting the first plurality of classes based on a ranking of respective frequencies of said plurality of classes. For example, a human can choose a class based on the ranking of frequencies. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. As to claims 14, Step 2A, Prong One The claim recites in part: said generating the inference that contains the plurality of probabilities is performed by the machine learning model. For example, a human has been determining probabilities before computers where ever invented. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: the method further comprises unsupervised training a machine learning model; which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application. Step 2B In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: the method further comprises unsupervised training a machine learning model; which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. amount to “significantly more” to the judicial exception. As to claims 15, Step 2A, Prong One The claim recites in part: said input is a first input; the method further comprises by data parallelism, detecting that the first threshold exceeds a respective probability of the first class for each of the first input and a second input. For example, a human can evaluate multiple possibilities at once. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea. Step 2A, Prong Two The claim does not include additional elements that integrate the judicial exception into a practical application. Step 2B The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception. Claim 16 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above. The claim further recites one or more non-transitory computer-readable media, one or more processors, and one or more computers which is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Claim 17 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above. Claim 18 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above. Claim 19 has similar limitations as claim 12. Therefore, the claim is rejected for the same reasons as above. Claim 20 has similar limitations as claim 15. Therefore, the claim is rejected for the same reasons as above. Claim Rejections - 35 USC § 101 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1, 2, 4, 6, 7, 16, and 17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kulandai Samy et al (US 2024/0086429) . As to claim 1, Kulandai Samy et al teaches a method comprising: generating, from an input, an inference that contains a plurality of probabilities respectively for a plurality of classes that contains a first class and a second class, wherein the plurality of probabilities contains a higher probability for the first class that is higher than a lower probability for the second class ( paragraph [0121]… In step 720, the process 700 can include determining, by the building system 105, whether an ensemble probability is greater than a threshold. The result of executing the classification pipeline, by the timeseries analyzer 150, can be an ensemble probability that a tag or point is classified to a particular class associated that the model or models of the classification pipeline classify points or tags into. If the ensemble probability is less than the threshold, the process 700 can return to step 710 to retrieve a next specific class type pre-trained model to execute. The next selected model can be a next model in the order of models to execute. The next selected model can be associated with a next highest similarity between a tag and a particular class. )( Examiner’s Note: “the process 700 can include determining, by the building system 105, whether an ensemble probability is greater than a threshold. The result of executing the classification pipeline, by the timeseries analyzer 150, can be an ensemble probability that a tag or point is classified to a particular class associated that the model or models of the classification pipeline classify points or tags into” reads on “generating, from an input, an inference that contains a plurality of probabilities respectively for a plurality of classes that contains a first class and a second class” ; “If the ensemble probability is less than the threshold, the process 700 can return to step 710 to retrieve a next specific class type pre-trained model to execute. The next selected model can be a next model in the order of models to execute. The next selected model can be associated with a next highest similarity between a tag and a particular class.” reads on “wherein the plurality of probabilities contains a higher probability for the first class that is higher than a lower probability for the second class” ); and classifying, in response to a threshold exceeding the higher probability, the input as the second class ( paragraph [0121]… If the ensemble probability is greater than the threshold, the process 700 can proceed to the step 725. In the step 725, the process 700 can include mapping, by the building system 105, the point to the class. The timeseries analyzer 150 can map the point to the class of the model executed at the step 715 that produces an ensemble probability greater than the threshold. The timeseries analyzer 150 can generate a tag mapping 195. The tag mapping 195 can include data that indicates a mapping between the tag associated with the point analyzed in the process 700 and the class ) (Examiner’s Note: “If the ensemble probability is greater than the threshold, the process 700 can proceed to the step 725. In the step 725, the process 700 can include mapping, by the building system 105, the point to the class” reads on “classifying, in response to a threshold exceeding the higher probability, the input as the second class” ); wherein the method is performed by one or more computers ( paragraph [0071]… The building system 105 can be a cloud computing platform, a web-services platform, a server system, an Internet of Things (IoT) platform, an on-premises system within a building, an off-premises system outside a building, a building controller, a computing system, a desktop computer, a laptop computer, a smartphone, a console, or any other type of computing system ). As to claim 2, Kulandai Samy et al teaches the method wherein: said threshold is a first threshold; the plurality of probabilities contains a third probability for a third class: said classifying is further in response to a second threshold exceeding the third probability.( paragraph [0121]… If the ensemble probability is less than the threshold, the process 700 can return to step 710 to retrieve a next specific class type pre-trained model to execute. The next selected model can be a next model in the order of models to execute. The next selected model can be associated with a next highest similarity between a tag and a particular class. If the ensemble probability is greater than the threshold, the process 700 can proceed to the step 725. In the step 725, the process 700 can include mapping, by the building system 105, the point to the class )( Examiner’s Note: “The next selected model can be a next model in the order of models to execute. The next selected model can be associated with a next highest similarity between a tag and a particular class. If the ensemble probability is greater than the threshold, the process 700 can proceed to the step 725. In the step 725, the process 700 can include mapping, by the building system 105, the point to the class” reads on “the plurality of probabilities contains a third probability for a third class: said classifying is further in response to a second threshold exceeding the third probability” ). As to claim 4, Kulandai Samy et al teaches the method further comprising selecting the first threshold and the second threshold based on a multi-objective optimization ( paragraph [0173]…the optimization service 1510 can be a service that operates to implement an optimization of one or more variables based on one or more constraints. The optimization service 1510 could implement optimization for allocating loads, making control decisions, improving energy usage and/or occupant comfort etc ). As to claim 6, Kulandai Samy et al teaches the method further comprising selecting the second threshold based on the first threshold .( paragraph [0121]… If the ensemble probability is less than the threshold, the process 700 can return to step 710 to retrieve a next specific class type pre-trained model to execute. The next selected model can be a next model in the order of models to execute. The next selected model can be associated with a next highest similarity between a tag and a particular class. If the ensemble probability is greater than the threshold, the process 700 can proceed to the step 725. In the step 725, the process 700 can include mapping, by the building system 105, the point to the class ). As to claim 7, Kulandai Samy et al teaches the method selecting a third threshold based on a respective probability threshold of each class of multiple classes.( paragraph [0121]… If the ensemble probability is less than the threshold, the process 700 can return to step 710 to retrieve a next specific class type pre-trained model to execute. The next selected model can be a next model in the order of models to execute. The next selected model can be associated with a next highest similarity between a tag and a particular class. If the ensemble probability is greater than the threshold, the process 700 can proceed to the step 725. In the step 725, the process 700 can include mapping, by the building system 105, the point to the class ) . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 3, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulandai Samy et al (US 2024/0086429) in view of Tsirkin (US 2024/0211381) . As to claim 3, Kulandai Samy et al teaches a first and second threshold. Kulandai Samy et al fails to explicitly show/teach detecting the first threshold exceeds the higher probability in parallel with detecting the second threshold exceeds the third probability. However, Tsirkin teaches detecting the first threshold exceeds the higher probability in parallel with detecting the second threshold exceeds the third probability ( paragraph [0162]…determining whether a number of remaining versions in the updated plurality of versions is no more than a threshold value, responsive to determining that the number of remaining versions in the updated plurality of versions is more than the threshold value, testing a second set of versions in parallel, wherein the second set of versions is selected from the updated plurality of versions; determining whether a second complete number equals the first number, wherein the second complete number is a number of a second complete set of versions, and the second complete set of versions is versions that have been completely tested and are associated with the second set of versions; and responsive to determining that the second complete number equals the first number, updating the plurality of versions based on a result of testing the second complete set of versions ). Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made, for Kulandai Samy et al detecting of the first threshold exceeds the higher probability in parallel with detecting the second threshold exceeds the third probability, as in Tsirkin, for the purpose of efficient processing. As to claim 15, Tsirkin teaches said input is a first input; the method further comprises by data parallelism, detecting that the first threshold exceeds a respective probability of the first class for each of the first input and a second input ( paragraph [0162]…determining whether a number of remaining versions in the updated plurality of versions is no more than a threshold value, responsive to determining that the number of remaining versions in the updated plurality of versions is more than the threshold value, testing a second set of versions in parallel, wherein the second set of versions is selected from the updated plurality of versions; determining whether a second complete number equals the first number, wherein the second complete number is a number of a second complete set of versions, and the second complete set of versions is versions that have been completely tested and are associated with the second set of versions; and responsive to determining that the second complete number equals the first number, updating the plurality of versions based on a result of testing the second complete set of versions ). Therefore, it would have been obvious for said input is a first input; the method further comprises by data parallelism, detecting that the first threshold exceeds a respective probability of the first class for each of the first input and a second input, for the same reasons as above. Claim 20 has similar limitations as claim 15. Therefore, the claim is rejected for the same reasons as above . 07-21-aia AIA Claim (s) 8, 9, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulandai Samy et al (US 2024/0086429) in view of Venkayala et al (US 2003/0212679) . As to claim 8, Kulandai Samy et al teaches a first and second threshold. Kulandai Samy et al fails to explciytly show/teach selecting the threshold based on a validation score of a machine learning model. However, Venkayala et al teaches selecting the threshold based on a validation score of a machine learning model ( paragraph [0010]…the method may further comprise the step of validating the received scoring data to ensure active attributes and a target attribute specified for the data mining model are present in the received input data and the source attributes specified for the multi- category apply output are present in the input data. The selection criterion may comprise one of a topmost category including a class value having a highest associated probability, top N categories including N class values having highest associated probabilities, bottom N categories including N class values having lowest associated probabilities, or a set of select class values specified by the user and their associated probabilities and ranks ). Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made, for Kulandai Samy et al selecting the threshold based on a validation score of a machine learning model, as in Venkayala et al, for the purpose of producing output with multiple class values and their associated probabilities. As to claim 9, Venkayala et al teaches supervised generating the validation score of the machine learning model ( paragraph [0017]…An exemplary data flow diagram of a data mining process, including model building (supervised learning or unsupervised) and scoring of models (model apply), is shown in FIG. 1. The training/model building step 102 involves generating the models that are used to perform data mining recommendation and prediction ). Therefore, it would have been obvious for supervised generating the validation score of the machine learning model for the same reasons as above. As to claim 14, Venkayala et al teaches the method further comprises unsupervised training a machine learning model; said generating the inference that contains the plurality of probabilities is performed by the machine learning model. ( paragraph [0017]…An exemplary data flow diagram of a data mining process, including model building (supervised learning or unsupervised) and scoring of models (model apply), is shown in FIG. 1. The training/model building step 102 involves generating the models that are used to perform data mining recommendation and prediction ). It would have been obvious for the method further comprises unsupervised training a machine learning model; said generating the inference that contains the plurality of probabilities is performed by the machine learning model, for the same reasons as above . 07-21-aia AIA Claim (s) 10, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpa tentable over Kulandai Samy et al (US 2024/0086429) in view of Lynch et a l (US 7,587,374). As to claim 10, Kulandai Samy et al teaches a first and second threshold. Kulandai Samy et al fails to explicitly show/teach selecting the threshold based on a one-dimensional search. However, Lynch et al teaches selecting the threshold based on a one-dimensional search ( column 3, lines 1 - 15… FIG. 1B shows a more generalized illustration of the problem. In FIG. 1B there is a plot containing one thousand samples of one dimensional domain data (a single feature). The data for this figure was generated, for each dimension of each class (i.e., except those within the cluster), to be uniform, independent, and identically distributed. However, with respect to the features each data cluster was generated as Gaussian distributed, with a randomly generated mean, and constrained to be located around the specified center yield value. In FIG. 1B, the ordinate that defines the yield of each data point is plotted versus the domain, where a yield value of 0.5 is used to separate and define the five hundred samples of the target class (i.e., yield>0.5) identified as 10, and the five hundred samples of the nontarget class (yield<0.5) identified as 12 ) Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made, for Kulandai Samy et al selecting the threshold based on a one-dimensional search, as in Lynch et al, for the purpose of reducing the probability errors. As to claim 11, Lynch et al teaches the one-dimensional search is uniform or not greedy ( column 3, lines 1 - 15… FIG. 1B shows a more generalized illustration of the problem. In FIG. 1B there is a plot containing one thousand samples of one dimensional domain data (a single feature). The data for this figure was generated, for each dimension of each class (i.e., except those within the cluster), to be uniform, independent, and identically distributed. However, with respect to the features each data cluster was generated as Gaussian distributed, with a randomly generated mean, and constrained to be located around the specified center yield value. In FIG. 1B, the ordinate that defines the yield of each data point is plotted versus the domain, where a yield value of 0.5 is used to separate and define the five hundred samples of the target class (i.e., yield>0.5) identified as 10, and the five hundred samples of the nontarget class (yield<0.5) identified as 12 ). It would have been obvious for the one-dimensional search is uniform or not greedy, for the same reasons as above. Claim 18 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589 . 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRANDON S COLE/ Primary Examiner, Art Unit 2128 Application/Control Number: 18/386,196 Page 2 Art Unit: 2128 Application/Control Number: 18/386,196 Page 3 Art Unit: 2128 Application/Control Number: 18/386,196 Page 4 Art Unit: 2128 Application/Control Number: 18/386,196 Page 5 Art Unit: 2128 Application/Control Number: 18/386,196 Page 6 Art Unit: 2128 Application/Control Number: 18/386,196 Page 7 Art Unit: 2128 Application/Control Number: 18/386,196 Page 8 Art Unit: 2128 Application/Control Number: 18/386,196 Page 9 Art Unit: 2128 Application/Control Number: 18/386,196 Page 10 Art Unit: 2128 Application/Control Number: 18/386,196 Page 11 Art Unit: 2128 Application/Control Number: 18/386,196 Page 12 Art Unit: 2128 Application/Control Number: 18/386,196 Page 13 Art Unit: 2128 Application/Control Number: 18/386,196 Page 14 Art Unit: 2128 Application/Control Number: 18/386,196 Page 15 Art Unit: 2128 Application/Control Number: 18/386,196 Page 16 Art Unit: 2128 Application/Control Number: 18/386,196 Page 17 Art Unit: 2128 Application/Control Number: 18/386,196 Page 18 Art Unit: 2128 Application/Control Number: 18/386,196 Page 19 Art Unit: 2128 Application/Control Number: 18/386,196 Page 20 Art Unit: 2128 Application/Control Number: 18/386,196 Page 21 Art Unit: 2128 Application/Control Number: 18/386,196 Page 22 Art Unit: 2128 Application/Control Number: 18/386,196 Page 23 Art Unit: 2128 Application/Control Number: 18/386,196 Page 24 Art Unit: 2128 Application/Control Number: 18/386,196 Page 25 Art Unit: 2128 Application/Control Number: 18/386,196 Page 26 Art Unit: 2128 Application/Control Number: 18/386,196 Page 27 Art Unit: 2128 Application/Control Number: 18/386,196 Page 28 Art Unit: 2128 Application/Control Number: 18/386,196 Page 29 Art Unit: 2128 Application/Control Number: 18/386,196 Page 30 Art Unit: 2128 Application/Control Number: 18/386,196 Page 31 Art Unit: 2128
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