DETAILED ACTION
This action is in response to the amendment filed 09/10/2025. Claims 1-20 are pending and have been examined.
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 .
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0018, “often has imbalance data distribution” should read “often has imbalanced data distribution”.
In paragraph 0018, “The systems and methods can also detect the imbalance industrial data, auto address the imbalance data pattern” should read “The systems and methods can also detect the imbalanced industrial data, auto address the imbalanced data pattern”.
In paragraph 0019, “At step102” should read “At step 102”.
In paragraph 0026, “There are 90 data are identified, by the one or more identifiers, as normal class (or class negatives). There are 10 data are identified” should read “There are 90 data that are identified, by the one or more identifiers, as normal class (or class negatives). There are 10 data that are identified”.
Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a non-transitory computer-readable medium and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
Generating a label determination based on whether the set of industrial data is associated with one or more labels; (This limitation is a mental process as it encompasses a human mentally generating a label determination.)
generating a classification for each industrial data of the set of industrial data (This limitation is a mental process as it encompasses a human mentally generating a classification.)
generating an evaluation value for each of the one the one or more models of the plurality of models based on the classification for each industrial data of the set of industrial data; (This limitation is a mental process as it encompasses a human mentally generating an evaluation value for each of the set of models.)
selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination (This limitation is a mental process as it encompasses a human mentally selecting one or more models.)
making predictions related to the industrial system (This limitation is a mental process as it encompasses a human mentally making predictions.)
processing the raw industrial data (This limitation is a mental process as it encompasses a human mentally processing the raw data.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations (This element does not integrate the abstract idea into a practical application because it recites generic computing components to perform the abstract ideas (see MPEP 2106.05(f)).)
receiving a set of industrial data associated with one or more industrial components within an industrial system; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
using one or more models from a plurality of models; (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
having access to raw industrial data collected from the one or more industrial components within the industrial system (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
with the at least one chosen model based at least in part on the at least one chosen model (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receiving a set of industrial data associated with one or more industrial components within an industrial system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
using one or more models from a plurality of models; uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
having access to raw industrial data collected from the one or more industrial components within the industrial system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
with the at least one chosen model based at least in part on the at least one chosen model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
making predictions of the industrial system using industrial data (This limitation is a mental process as it encompasses a human mentally making predictions.)
Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
wherein the plurality of models are a set of machine learning models (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the plurality of models are a set of machine learning models specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
wherein the generating the evaluation value comprises, for each model of the one or more models: calculating a sensitivity value indicating correctly classified positives (This limitation is a mental process as it encompasses a human mentally calculating a value.);
calculating a specificity value indicating correctly classified negatives (This limitation is a mental process as it encompasses a human mentally calculating a value.);
calculating a precision value indicating a level of misclassified negatives (This limitation is a mental process as it encompasses a human mentally calculating a value.);
and calculating the evaluation value based at least in part on the sensitivity value, the specificity value, and the precision value (This limitation is a mental process as it encompasses a human mentally calculating a value.).
Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites an additional element of
wherein the set of industrial data are supervised data, each of the supervised data including one or more labels identifying one or more operating conditions of the industrial system (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the set of industrial data are supervised data, each supervised data including one or more identifiers identifying one or more operating conditions of the industrial system specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
determining, for each evaluation value of the one or more models, whether the evaluation value is larger than a threshold value (This limitation is a mental process as it encompasses a human mentally determining whether the evaluation value is larger than a threshold value.)
in response to determining that the evaluation value is larger than the threshold value, selecting a corresponding chosen model of the set of machine learning models (This limitation is a mental process as it encompasses a human mentally selecting the corresponding machine learning model.)
Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 does not have any additional elements and therefore, is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since claim 4 does not have any additional elements, claim 4 cannot provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
in response to determining that the evaluation value is less than or equal to the threshold value, applying a resampling method to the set of industrial data to produce a resampled set of industrial data (This limitation is a mental process as it encompasses a human mentally applying a resampling method.)
generating a classification for each of the resampled set of industrial data using each of the plurality of models (This limitation is a mental process as it encompasses a human mentally generating a classification for each of the resampled set.)
generating an evaluation value for each of the plurality of models based on the classification for each of the resampled set of industrial data (This limitation is a mental process as it encompasses a human mentally generating an evaluation value for each of the set of models.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 does not have any additional elements and therefore, is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since claim 5 does not have any additional elements, claim 5 cannot provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
generating a classification for each of the set of industrial data (This limitation is a mental process as it encompasses a human mentally generating a classification for each of the set of data.)
generating an evaluation value for each model of the second plurality of models based on the classification for each of the set of industrial data (This limitation is a mental process as it encompasses a human mentally generating an evaluation value for each of the set of models.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites
using each of a second plurality of models; (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
wherein the second plurality of models comprises one or more models for imbalanced data (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of claim 6 does not provide significantly more than the abstract idea itself, taken alone and in combination because
using each of a second plurality of models uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
wherein the second plurality of models comprises one or more models for imbalanced data specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
selecting one or more models, from a model group including the plurality of models and the second plurality of models, that have the highest of the set of evaluation values (This limitation is a mental process as it encompasses a human mentally selecting one or more models that have the highest evaluation values.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 does not have any additional elements and therefore, is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since claim 7 does not have any additional elements, claim 7 cannot provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites the same abstract idea as claim 1. Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites an additional element of
wherein the set of industrial data are unsupervised data, wherein each unsupervised data does not include any labels identifying one or more operating conditions of the industrial system (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of claim 8 does not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the set of industrial data are unsupervised data, wherein each unsupervised data does not include any identifiers identifying one or more operating conditions of the industrial system specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 8 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites
determining a subset of data, from the set of industrial data, that are classified as abnormal data by a percentage of models larger than a threshold value (This limitation is a mental process as it encompasses a human mentally determining a subset of data that are classified as abnormal.)
wherein the generating the evaluation value for each model of the one or more models comprises comparing a count of abnormal classifications that are present in the subset of data among the plurality of models (This limitation is a mental process as it encompasses a human mentally comparing a count of abnormal classifications.)
Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does not have any additional elements and therefore, is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since claim 9 does not have any additional elements, claim 9 cannot provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 9 is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 1:
Claim 10 recites a non-transitory computer-readable medium and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites
Generating a label determination based on whether the set of industrial data is associated with one or more labels; (This limitation is a mental process as it encompasses a human mentally generating a label determination.)
generating a classification for each industrial data of the set of industrial data (This limitation is a mental process as it encompasses a human mentally generating a classification.)
generating an evaluation value for each of the one the one or more models of the plurality of models based on the classification for each industrial data of the set of industrial data; (This limitation is a mental process as it encompasses a human mentally generating an evaluation value for each of the set of models.)
selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination (This limitation is a mental process as it encompasses a human mentally selecting one or more models.)
making predictions related to the industrial system (This limitation is a mental process as it encompasses a human mentally making predictions.)
processing the raw industrial data (This limitation is a mental process as it encompasses a human mentally processing the raw data.)
Therefore, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 further recites additional elements of
receiving a set of industrial data associated with one or more industrial components within an industrial system; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
using one or more models from a plurality of models; (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
having access to raw industrial data collected from the one or more industrial components within the industrial system (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
with the at least one chosen model based at least in part on the at least one chosen model (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 10 do not provide significantly more than the abstract idea itself, taken alone and in combination because
receiving a set of industrial data associated with one or more industrial components within an industrial system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
using one or more models from a plurality of models; uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
having access to raw industrial data collected from the one or more industrial components within the industrial system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
with the at least one chosen model based at least in part on the at least one chosen model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 10 is subject-matter ineligible.
Regarding claim 11, claim 11 recites substantially similar limitations as claim 2, and is therefore rejected under the same analysis.
Regarding claim 12, claim 12 recites substantially similar limitations as claim 3, and is therefore rejected under the same analysis.
Regarding claim 13, claim 13 recites substantially similar limitations as claim 4, and is therefore rejected under the same analysis.
Regarding claim 14, claim 14 recites substantially similar limitations as claim 5, and is therefore rejected under the same analysis.
Regarding claim 15, claim 15 recites substantially similar limitations as claim 6, and is therefore rejected under the same analysis.
Regarding claim 16, claim 16 recites substantially similar limitations as claim 7, and is therefore rejected under the same analysis.
Regarding claim 17, claim 17 recites substantially similar limitations as claim 8, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations as claim 9, and is therefore rejected under the same analysis.
Regarding Claim 19:
Subject Matter Eligibility Analysis Step 1:
Claim 19 recites a non-transitory computer-readable medium and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites
Generating a label determination based on whether the set of industrial data is associated with one or more labels; (This limitation is a mental process as it encompasses a human mentally generating a label determination.)
generating a classification for each industrial data of the set of industrial data (This limitation is a mental process as it encompasses a human mentally generating a classification.)
generating an evaluation value for each of the one the one or more models of the plurality of models based on the classification for each industrial data of the set of industrial data; (This limitation is a mental process as it encompasses a human mentally generating an evaluation value for each of the set of models.)
selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination (This limitation is a mental process as it encompasses a human mentally selecting one or more models.)
making predictions related to the industrial system (This limitation is a mental process as it encompasses a human mentally making predictions.)
processing the raw industrial data (This limitation is a mental process as it encompasses a human mentally processing the raw data.)
Therefore, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 further recites additional elements of
a memory that stores executable components (This element does not integrate the abstract idea into a practical application because it recites generic computing components to perform the abstract ideas (see MPEP 2106.05(f)).)
a processor, operatively coupled to the memory, that executes the executable components (This element does not integrate the abstract idea into a practical application because it recites generic computing components to perform the abstract ideas (see MPEP 2106.05(f)).)
receiving a set of industrial data associated with one or more industrial components within an industrial system; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
using one or more models from a plurality of models; (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
having access to raw industrial data collected from the one or more industrial components within the industrial system (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
with the at least one chosen model based at least in part on the at least one chosen model (This element does not integrate the abstract idea into a practical application because amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 19 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because
a memory that stores executable components uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a processor, operatively coupled to the memory, that executes the executable components uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
receiving a set of industrial data associated with one or more industrial components within an industrial system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
using one or more models from a plurality of models; uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
having access to raw industrial data collected from the one or more industrial components within the industrial system is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
with the at least one chosen model based at least in part on the at least one chosen model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 19 is subject-matter ineligible.
Regarding Claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites the same abstract idea as claim 19. Therefore, claim 20 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 20 further recites an additional element of
wherein the set of industrial data include supervised data and unsupervised data, wherein each supervised data includes one or more labels identifying one or more operating conditions of the industrial system (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 20 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional element of claim 20 does not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the set of industrial data include supervised data and unsupervised data, wherein each supervised data includes one or more labels identifying one or more operating conditions of the industrial system specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 20 is subject-matter ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 8, 10-14, 17, 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ralhan (US 2019/0354809 A1) (hereafter referred to as Ralhan).
Regarding Claim 1, Ralhan teaches
A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations (Ralhan, page 33, paragraph 0094, “Embodiments may also be at least partly implemented as instructions contained in or on a non-transitory computer-readable medium, which may be read and executed by one or more processors to enable performance of the operations described herein.”) comprising:
receiving a set of industrial data associated with one or more industrial components within an industrial system (Ralhan, page 31, paragraph 0072, “In block 502, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user.” where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038).);
generating a label determination based on whether the set of industrial data is associated with one or more labels (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the set of models are the models associated with various model objects such as features. Examiner further notes that the classification is the label);
generating a classification for each industrial data of the set of industrial data using one or more models from a plurality of models (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the plurality of models are the models associated with various model objects such as features.);
generating an evaluation value for each of the one or more models of the plurality of models based on the classifications for each industrial data of the set of industrial data (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy” where “ML algorithms 1008, data 1012, and/or engineered features 1014 may be used to determine candidate models 1016 via a model test/evaluation process 1018. Models 1020 may undergo continuous training and/or evaluation 1028” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the evaluation value is the degree of accuracy.); and
selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination (Ralhan, page 35, paragraph 0113, “In some embodiments, output from a sequence of ML algorithm selection 912, ML model training 914, and model evaluation 916 may be provided to a candidate selection process 918 to determine a candidate model 920” where “an apparatus may include a processor and a memory storing instructions, which, when executed by the processor, cause the processor to generate a candidate computational model for a model function based on an objective and function data, and provide the candidate computational model to a model assessment process to perform a champion-challenger process based on performance metrics, industrial metrics, and user metrics” (Ralhan, page 1, abstract) and “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” (Ralhan, page 31, paragraph 0077) and where “models evaluated using user metrics 948 may provide more precise evaluations for a … class of information” (Ralhan, page 35, paragraph 0111). Examiner notes that performance metrics and user metrics are the evaluation values)
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system having access to raw industrial data collected from the one or more industrial components within the industrial system (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in block 508, new data is received. In some examples, the new data is received from a remote database or a local database” and “network attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases” (Ralhan, page 27, paragraph 0038). Examiner notes that the new data is the raw data.)
making predictions related to the industrial system with the at least one chosen model based at least in part on the at least one chosen model processing the raw industrial data (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these”. Examiner notes that the new data is the raw data.)
Regarding claim 2, Ralhan teaches
The non-transitory computer-readable medium of claim 1, wherein the plurality of models are a set of machine learning models for making predictions of the industrial system using industrial data (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “model assessment process 905 may operate to evaluate challenger models against champion models. For example, model assessment process 905 may operate to evaluate challenger models against champion models. For example, model assessment process 905 may determine challenger predictions (pre-production) 940 for a model problem versus champion (production or in-production) models 942 using scoring evaluation data 938, performance metrics 944, industrial metrics 946, and/or other metrics 948” (Ralhan, page 35, paragraph 0113) and “another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid” (Ralhan, page 28, paragraph 0051). Examiner notes that the challenger and champion models are the set of machine learning models, industrial metrics are the industrial data, and the industrial system is an energy grid.).
Regarding claim 8, Ralhan teaches
The non-transitory computer-readable medium of claim 1, wherein the set of industrial data are unsupervised data, wherein each unsupervised data does not include any labels identifying one or more operating conditions of the industrial system (Ralhan, page 31, paragraph 0073, “the machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own.” Examiner notes that the desired outputs are the labels and the one or more operating conditions are the scalar, vector, or other data types.).
Regarding claim 10, Ralhan teaches
A method comprising: receiving a set of industrial data associated with one or more industrial components within an industrial system (Ralhan, page 31, paragraph 0072, “In block 502, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user.” where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038).);
generating a label determination based on whether the set of industrial data is associated with one or more labels (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the set of models are the models associated with various model objects such as features. Examiner further notes that the classification is the label);
generating a classification for each industrial data of the set of industrial data using one or more models from a plurality of models (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the plurality of models are the models associated with various model objects such as features.);
generating an evaluation value for each of the one or more models of the plurality of models based on the classifications for each industrial data of the set of industrial data (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy” where “ML algorithms 1008, data 1012, and/or engineered features 1014 may be used to determine candidate models 1016 via a model test/evaluation process 1018. Models 1020 may undergo continuous training and/or evaluation 1028” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the evaluation value is the degree of accuracy.); and
selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination (Ralhan, page 35, paragraph 0113, “In some embodiments, output from a sequence of ML algorithm selection 912, ML model training 914, and model evaluation 916 may be provided to a candidate selection process 918 to determine a candidate model 920” where “an apparatus may include a processor and a memory storing instructions, which, when executed by the processor, cause the processor to generate a candidate computational model for a model function based on an objective and function data, and provide the candidate computational model to a model assessment process to perform a champion-challenger process based on performance metrics, industrial metrics, and user metrics” (Ralhan, page 1, abstract) and “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” (Ralhan, page 31, paragraph 0077) and where “models evaluated using user metrics 948 may provide more precise evaluations for a … class of information” (Ralhan, page 35, paragraph 0111). Examiner notes that performance metrics and user metrics are the evaluation values)
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system having access to raw industrial data collected from the one or more industrial components within the industrial system (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in block 508, new data is received. In some examples, the new data is received from a remote database or a local database” and “network attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases” (Ralhan, page 27, paragraph 0038). Examiner notes that the new data is the raw data.)
making predictions related to the industrial system with the at least one chosen model based at least in part on the at least one chosen model processing the raw industrial data (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these”. Examiner notes that the new data is the raw data.)
Regarding claim 11, claim 11 recites substantially similar limitations as claim 2, and is therefore rejected under the same analysis.
Regarding claim 12, Ralhan teaches
The method of claim 10, wherein the set of industrial data are supervised data, each supervised data including one or more labels identifying one or more operating conditions of the industrial system (Ralhan, page 31, paragraph 0073, “the machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs” and “another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid” (Ralhan, page 28, paragraph 0051) Examiner notes that the desired outputs are the labels and the one or more operating conditions are the scalar, vector, or other data types.).
Regarding claim 13, Ralhan teaches
The method of claim 12, further comprising: determining, for each evaluation value of the one or more models, whether the evaluation value is larger than a threshold value (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy.” Examiner notes that the threshold value is degree of accuracy, 90%.); and
in response to determining that the evaluation value is larger than the threshold value, selecting a corresponding chosen model from the set of machine learning model (Ralhan, page 31, paragraph 0075 – paragraph 0076, “If the machine-learning model as an adequate degree of accuracy for the particular task, the process can continue to block 508. [0076] In block 508, new data is received” where “In block 510, the trained machine-learning model is used to analyze the new data and provide a result” (Ralhan, page 31, paragraph 0077) and where “selected models, such as challenger models 810 that been selected to be current champion models 812 may undergo an approval workflow process 814 before proceeding to a model online process 816 operative to prepare a model to be active. Examiner notes that the selecting the corresponding machine learning model is using the model to analyze the new data.).
Regarding claim 14, Ralhan teaches
The method of claim 13, further comprising: in response to determining that the evaluation value is less than or equal to the threshold value, applying a resampling method to the set of industrial data to produce a resampled set of industrial data (Ralhan, page 31, paragraph 0075, “if the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block 504, where the machine-learning model can be further trained using additional training data or otherwise modified to improve the accuracy” and “In block 504, a machine-learning model is trained using the training data The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.” (Ralhan, page 31, paragraph 0073) where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038) Examiner notes that the resampling method is the retraining using additional training data. Examiner further notes that the outputs are the resampled set of industrial data.);
generating a classification for each of the resampled set of industrial data using each of the plurality of models (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the plurality of models are the models associated with various model objects such as features.); and
generating an evaluation value for each of the plurality of models based on the classification for each of the resampled set of industrial data (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy” where “ML algorithms 1008, data 1012, and/or engineered features 1014 may be used to determine candidate models 1016 via a model test/evaluation process 1018. Models 1020 may undergo continuous training and/or evaluation 1028” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the evaluation value is the degree of accuracy.).
Regarding claim 17, claim 17 recites substantially similar limitations as claim 8, and is therefore rejected under the same analysis.
Regarding claim 19, Ralhan teaches
A system comprising: a memory that stores executable components; and a processor, operatively coupled to the memory that executes the executable components, (Ralhan, page 1, abstract, “In one embodiment, an apparatus may include a processor and a memory storing instructions, which, when executed by the processor, cause the processor to generate a candidate computational model for a model function”) the executable components comprising:
receiving a set of industrial data associated with one or more industrial components within an industrial system (Ralhan, page 31, paragraph 0072, “In block 502, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user.” where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038).);
generating a label determination based on whether the set of industrial data is associated with one or more labels (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the set of models are the models associated with various model objects such as features. Examiner further notes that the classification is the label);
generating a classification for each industrial data of the set of industrial data using one or more models from a plurality of models (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the plurality of models are the models associated with various model objects such as features.);
generating an evaluation value for each of the one or more models of the plurality of models based on the classifications for each industrial data of the set of industrial data (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy” where “ML algorithms 1008, data 1012, and/or engineered features 1014 may be used to determine candidate models 1016 via a model test/evaluation process 1018. Models 1020 may undergo continuous training and/or evaluation 1028” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the evaluation value is the degree of accuracy.); and
selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination (Ralhan, page 35, paragraph 0113, “In some embodiments, output from a sequence of ML algorithm selection 912, ML model training 914, and model evaluation 916 may be provided to a candidate selection process 918 to determine a candidate model 920” where “an apparatus may include a processor and a memory storing instructions, which, when executed by the processor, cause the processor to generate a candidate computational model for a model function based on an objective and function data, and provide the candidate computational model to a model assessment process to perform a champion-challenger process based on performance metrics, industrial metrics, and user metrics” (Ralhan, page 1, abstract) and “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” (Ralhan, page 31, paragraph 0077) and where “models evaluated using user metrics 948 may provide more precise evaluations for a … class of information” (Ralhan, page 35, paragraph 0111). Examiner notes that performance metrics and user metrics are the evaluation values)
implementing the at least one chosen model, wherein the implementing comprises: deploying the at least one chosen model into a computing system having access to raw industrial data collected from the one or more industrial components within the industrial system (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in block 508, new data is received. In some examples, the new data is received from a remote database or a local database” and “network attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases” (Ralhan, page 27, paragraph 0038). Examiner notes that the new data is the raw data.)
making predictions related to the industrial system with the at least one chosen model based at least in part on the at least one chosen model processing the raw industrial data (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these”. Examiner notes that the new data is the raw data.)
Regarding claim 20, Ralhan teaches
The system of claim 19, wherein the set of industrial data include supervised data and unsupervised data, wherein each supervised data includes one or more labels identifying one or more operating conditions of the industrial system (Ralhan, page 31, paragraph 0073, “the machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs” and “another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid” (Ralhan, page 28, paragraph 0051) Examiner notes that the desired outputs are the labels and the one or more operating conditions are the scalar, vector, or other data types.).
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ralhan in view of Poornaki et al. (US 2020/0210824 A1) (hereafter referred to as Poornaki).
Regarding claim 3, Ralhan teaches
The non-transitory computer-readable medium of claim 1, wherein the set of industrial data are supervised data, each supervised data including one or more labels identifying one or more operating conditions of the industrial system (Ralhan, page 31, paragraph 0073, “the machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs” and “another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid” (Ralhan, page 28, paragraph 0051) Examiner notes that the desired outputs are the labels and the one or more operating conditions are the scalar, vector, or other data types.).
Ralhan does not teach, but Poornaki does teach
Wherein the generating the evaluation value comprises, for each model of the one or more models: calculating a sensitivity value indicating correctly classified positives (Poornaki, page 35, paragraph 0138, “The model evaluation module 514 may evaluate the different failure prediction modules generated by the model training module 512. In various embodiments, the model evaluation module 514 applies macro-averaging of performance measures (e.g., accuracy, error rate, precision, recall and the like)” where “the model evaluation module 514 may utilize a failure forecasting performance measures (e.g., standard metrics in any detection/classification model) to generate a confusion matrix” (Poornaki, page 35, paragraph 0140) and where “Examples of the metrics may include the following: [0149] Sensitivity, Recall, Hit Rate, or True Positive Rate (TPR)” (Poornaki, page 35, paragraphs 0148-0149). Examiner notes that the True Positive Rate is the sensitivity value.);
Calculating a specificity value indicating correctly classified negatives (Poornaki, page 35, paragraphs 0148-0150, “Examples of the metrics may include the following: ….[0150] Specificity or True Negative Rate (TNR)” Examiner notes that the True Negative Rate is the specificity value.);
Calculating a precision value indicating a level of misclassified negatives (Poornaki, page 35, paragraphs 0148-0153, “Examples of the metrics may include the following: ….[0153] Miss Rate or False Negative Rate” Examiner notes that the False Negative Rate is the precision value.);
And calculating the evaluation value based at least in part on the sensitivity value, the specificity value, and the precision value (Poornaki, page 35, paragraph 0138, “The model evaluation module 514 may evaluate the different failure prediction modules generated by the model training module 512. In various embodiments, the model evaluation module 514 applies macro-averaging of performance measures (e.g., accuracy, error rate, precision, recall and the like).” Examiner notes that the macro-average of performance measure si the evaluation value.).
Ralhan and Poornaki are considered analogous to the claimed invention because they both classify industrial data. It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Ralhan to calculate a sensitivity, specificity, and precision value like in Poornaki. Doing so allows “the model application module 516 [to] compare new sensor data to classified and/or categorized states identified by the selected model to identify when sensor data indicates a failure state or a state associated with potential failure is reached.” (Poornaki, page 36, paragraph 0170).
Regarding claim 4, Ralhan in view of Poornaki teaches The non-transitory computer-readable medium of claim 3. Ralhan further teaches
wherein the operations comprise: determining, for each evaluation value of the one or more models, whether the evaluation value is larger than a threshold value (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy.” Examiner notes that the threshold value is degree of accuracy, 90%.); and
in response to determining that the evaluation value is larger than the threshold value, selecting a corresponding chosen model from the set of machine learning model (Ralhan, page 31, paragraph 0075 – paragraph 0076, “If the machine-learning model as an adequate degree of accuracy for the particular task, the process can continue to block 508. [0076] In block 508, new data is received” where “In block 510, the trained machine-learning model is used to analyze the new data and provide a result” (Ralhan, page 31, paragraph 0077) and where “selected models, such as challenger models 810 that been selected to be current champion models 812 may undergo an approval workflow process 814 before proceeding to a model online process 816 operative to prepare a model to be active. Examiner notes that the selecting the corresponding machine learning model is using the model to analyze the new data.).
Regarding claim 5, Ralhan in view of Poornaki teaches The non-transitory computer-readable medium of claim 4. Ralhan further teaches
wherein the operations comprise: in response to determining that the evaluation value is less than or equal to the threshold value, applying a resampling method to the set of industrial data to produce a resampled set of industrial data (Ralhan, page 31, paragraph 0075, “if the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block 504, where the machine-learning model can be further trained using additional training data or otherwise modified to improve the accuracy” and “In block 504, a machine-learning model is trained using the training data The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.” (Ralhan, page 31, paragraph 0073) where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038) Examiner notes that the resampling method is the retraining using additional training data. Examiner further notes that the outputs are the resampled set of industrial data.);
generating a classification for each of the resampled set of industrial data using each of the plurality of models (Ralhan, page 31, paragraph 0077, “The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these” where “in some embodiments, models 1020 may be associated with various model objects 1040 including, without limitation model files, model metadata (or “meta”), versions, parameters, hyper parameters, algorithm information, data, features, performance matrices, and/or the like” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the plurality of models are the models associated with various model objects such as features.); and
generating an evaluation value for each of the plurality of models based on the classification for each of the resampled set of industrial data (Ralhan, page 31, paragraph 0074, “In block 506, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database….The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy” where “ML algorithms 1008, data 1012, and/or engineered features 1014 may be used to determine candidate models 1016 via a model test/evaluation process 1018. Models 1020 may undergo continuous training and/or evaluation 1028” (Ralhan, page 35, paragraph 0117) and where “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)” (Ralhan, page 27, paragraph 0038). Examiner notes that the evaluation value is the degree of accuracy.).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ralhan in view Poornaki in further view of Posner et al. (US 11,568,179 B2) (hereafter referred to as Posner).
Regarding claim 6, Ralhan in view of Poornaki teaches the non-transitory medium of claim 5. Ralhan in view of Poornaki further teaches
the set of industrial data (Ralhan, page 27, paragraph 0038, “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)”).
Ralhan in view of Poornaki does not teach, but Posner does teach
generating a classification for each of the set of … data using each of a second plurality of models, wherein the second plurality of models comprises one or more models for imbalanced data (Posner, page 13, column 7, lines 49-52, “If the most accurate trained model is not accurate enough, then the model selector selects a default analytic model as the selected analytic model” where “information stored in association with a selected model may comprise an identifier (used to link the representative data with the target data) provided in the request to select a model” (Posner page 13, column 8, lines 28-31) and “Model filter 130 receives from distribution analyzer 120 a set of distribution types that matched the representative data. Model filter 130 has access to the associations between the set of machine learning models from which to choose and the set of distribution types that are unsuitable for use with the model” (Poser, page 11, column 4, lines 46-51). Examiner notes that the identifier is the classification and the second set of models is the default analytic model.); and
generating an evaluation value for each model of the second plurality of models based on the classifications for each of the set of … data (Posner, page 13, column 7, lines 44-52, “Model selector 160 uses the accuracy scores for the trained models to identify the trained model that is most accurate (Operation 255). The accuracy of the most accurate trained model is compared against an accuracy threshold to determine whether the most accurate trained model is accurate enough in an absolute sense (Operation 260). If the most accurate trained model is not accurate enough, then the model selector selects a default analytic model as the selected analytic model” where “information stored in association with a selected model may comprise an identifier (used to link the representative data with the target data) provided in the request to select a model” (Posner page 13, column 8, lines 28-31). Examiner notes that the evaluation value is the accuracy score, the identifier is the classification, and the second set of models is the default analytic model.).
Ralhan, Poornaki and Posner are considered analogous to the claimed invention because they classify data and train candidate models. It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Ralhan in view of Poornaki to use a second set of models like in Posner. Doing so “allows ruling out use of models for which the distribution of the representative data is unsuitable” (Posner, page 1, abstract).
Regarding claim 7, Ralhan in view of Poornaki and Posner teach the non-transitory computer-readable medium of claim 6.
Ralhan in view of Poornaki does not teach, but Posner does teach,
selecting one or more models, from a model group including the plurality of models and the second plurality of models, that have the highest of the set of evaluation values (Posner, page 13, column 7, lines 44-55, “Model selector 160 uses the accuracy scores for the trained models to identify the trained model that is most accurate (Operation 255). The accuracy of the most accurate trained model is compared against an accuracy threshold to determine whether the most accurate trained model is accurate enough in an absolute sense (Operation 260). If the most accurate trained model is not accurate enough, then the model selector selects a default analytic model as the selected analytic model 190 (Operation 270). If the most accurate trained model is accurate enough, then the model selector select the most accurate trained model as the selected analytic model 190.”Examiner notes that the evaluation value is the accuracy score.).
Ralhan, Poornaki and Posner are considered analogous because they classify data and train candidate models. It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Ralhan in view of Poornaki to use a second set of models like in Posner. Doing so “allows ruling out use of models for which the distribution of the representative data is unsuitable” (Posner, page 1, abstract).
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ralhan in view of Trinh et al. (US 2020/0379454 A1) (hereafter referred to as Trinh).
Regarding claim 9, Ralhan teaches the non-transitory computer-readable medium of claim 8. Ralhan does not teach, but Trinh does teach
determining a subset of data, from the set of industrial data, that are classified as abnormal data by a percentage of models larger than a threshold value (Trinh, page 35, paragraph 0082, “The predictive maintenance server 110 may receive a set of scoring sensor data of a piece of equipment 150 and use the set as input data 910. The predictive maintenance server 110 may provide the input data 910 to a neural network 900 to generate an output sample distribution 980 that can be used to determine the likelihood of observing the input data 910 based on a probability density 990. If the probability density 990 is high, it may imply that the likelihood of observing the input data 910 is high. Hence, the input data 910 likely represents sensor data measured from a piece of normal equipment 150. Conversely, if the probability density 990 is low, it may imply that the input data 910 is unlikely to be observed in a piece of normal equipment. Hence, anomaly might have been detected” where “The predictive maintenance server 110 may set a threshold to determine whether the observed data is considered highly unlikely. For example, p-value may be determined based on the probability density. If the set of observed scoring sensor data is more unlikely than a predetermined percentage of data (e.g., 99.99%), the predictive maintenance server 110 may generate 1070 the alert” (Trinh, page 37, paragraph 0091) and “the server uses one or more machine learning models to assign an anomaly score” (Trinh, page 1, abstract). Examiner notes that the subset of data is the anomaly detected, the percentage of models is the likelihood of observed scoring sensor data, the threshold is the predetermined percentage of data, and the industrial data is the sensor data of a piece of equipment.)
wherein the generating the evaluation value for each model of the one or more models comprises comparing a count of abnormal classifications that are present in the subset of data among the plurality of models (Trinh, page 34, paragraph 0074, “According to an embodiment, the PPP model may determine the anomaly of a piece of equipment 150 using one or more vitals. For the PPP model that uses more than one vital, multiple machine learning models may be trained. Each machine learning model may be specific to a particular vital. The predictive maintenance server 110 may repeat the process described in steps 520 to 550 for different vitals, as indicated by the arrow 555. For example, for a second vital, a second machine learning model may be used. The predictive maintenance server 110 may divide the set of scoring sensor data into a different way compared to the first vital. For the second vital, the first subset of scoring sensor data may exclude the measurements of the second vital but include the measurements of the first vital. For the second vital, the second subset of scoring sensor data includes the measurements of the second vital. One or more dissimilarity metrics for the second vital may also be generated based on the differences of pairs between a predicted value and an actual value. Since the dissimilarity metrics may be normalized, the dissimilarity metrics can be compared with those corresponding to the first vital. Similar processes may be performed for a third vital, a fourth vital, etc.” Examiner notes that the evaluation value is the anomaly and the comparing a count of abnormal classifications is comparing dissimilarity metrics.).
Ralhan and Trinh are considered analogous to the claimed invention because they train models on industrial data. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Ralhan to classify a subset of abnormal data like in Trinh. Doing so “reduce[s] the cost of determining whether the equipment is abnormal during the training of the machine learning model” (Trinh, page 27, paragraph 0033).
Regarding claim 18, claim 18 recites substantially similar limitations as claim 9, and is therefore rejected under the same analysis.
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ralhan in view of Posner et al. (US 11,568,179 B2) (hereafter referred to as Posner).
Regarding claim 15, Ralhan teaches the method of claim 14. Ralhan further teaches
the set of industrial data (Ralhan, page 27, paragraph 0038, “network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales)”).
Ralhan does not teach, but Posner does teach
generating a classification for each of the set of … data using each of a second plurality of models, wherein the second plurality of models comprises one or more models for imbalanced data (Posner, page 13, column 7, lines 49-52, “If the most accurate trained model is not accurate enough, then the model selector selects a default analytic model as the selected analytic model” where “information stored in association with a selected model may comprise an identifier (used to link the representative data with the target data) provided in the request to select a model” (Posner page 13, column 8, lines 28-31) and “Model filter 130 receives from distribution analyzer 120 a set of distribution types that matched the representative data. Model filter 130 has access to the associations between the set of machine learning models from which to choose and the set of distribution types that are unsuitable for use with the model” (Poser, page 11, column 4, lines 46-51). Examiner notes that the identifier is the classification and the second set of models is the default analytic model.); and
generating an evaluation value for each model of the second plurality of models based on the classifications for each of the set of … data (Posner, page 13, column 7, lines 44-52, “Model selector 160 uses the accuracy scores for the trained models to identify the trained model that is most accurate (Operation 255). The accuracy of the most accurate trained model is compared against an accuracy threshold to determine whether the most accurate trained model is accurate enough in an absolute sense (Operation 260). If the most accurate trained model is not accurate enough, then the model selector selects a default analytic model as the selected analytic model” where “information stored in association with a selected model may comprise an identifier (used to link the representative data with the target data) provided in the request to select a model” (Posner page 13, column 8, lines 28-31). Examiner notes that the evaluation value is the accuracy score, the identifier is the classification, and the second set of models is the default analytic model.).
Ralhan and Posner are considered analogous to the claimed invention because they both classify data and train candidate models. It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Ralhan to use a second set of models like in Posner. Doing so “allows ruling out use of models for which the distribution of the representative data is unsuitable” (Posner, page 1, abstract).
Regarding claim 16, Ralhan in view of Posner teach the method of claim 15. Ralhan does not teach, but Posner does teach,
selecting one or more models, from a model group including the plurality of models and the second plurality of models, that have the highest of the set of evaluation values (Posner, page 13, column 7, lines 44-55, “Model selector 160 uses the accuracy scores for the trained models to identify the trained model that is most accurate (Operation 255). The accuracy of the most accurate trained model is compared against an accuracy threshold to determine whether the most accurate trained model is accurate enough in an absolute sense (Operation 260). If the most accurate trained model is not accurate enough, then the model selector selects a default analytic model as the selected analytic model 190 (Operation 270). If the most accurate trained model is accurate enough, then the model selector select the most accurate trained model as the selected analytic model 190.”Examiner notes that the evaluation value is the accuracy score.).
Ralhan and Posner are considered analogous because they both classify data and train candidate models. It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Ralhan to use a second set of models like in Posner. Doing so “allows ruling out use of models for which the distribution of the representative data is unsuitable” (Posner, page 1, abstract).
Response to Arguments
The 112b rejection has been overcome in light of the instant amendments.
On page 10-11, Applicant argues:
The Office Action asserts that claim 1 is directed to the abstract idea of a mental process. The Applicant disagrees. A claim including an element that cannot practically be performed in the human mind does not recite a mental process. MPEP 2106.04(a)(2)(III)(A)
Claim 1 includes elements that cannot be practically performed by the human mind. For example, the human mind is not capable of implementing a model including deploying the model to a computing system as recited in claim 1. Claim 1 recites, inter alia:
"implementing the at least one chosen model, wherein the implementing comprises:
deploying the at least one chosen model into a computing system having
access to raw industrial data collected from the one or more industrial
components within the industrial system, and
making predictions related to the industrial system with the at least one
chosen model based at least in part on the at least one chosen model
processing the raw industrial data."
Additionally, the Applicant asserts that the implementing the at least one chosen model
limitation is not merely extra-solution activity. As set forth in the MPEP, "extra-solution
activity" is understood as activities that are only incidental to the primary process or nominal or tangential to the claimed process. MPEP 2106.05(g). By contrast, the "implementing the at least one chosen model" limitation is tightly integrated with the process of claim 1. In particular, this element reflects the implementation of the model selected in the previous limitations (based on the evaluation values and label determination). The implementing limitation is in no way "nominal" or "tangential" to the claimed invention. The Applicant submits that identifying the optimized model is rather irrelevant if it is never implemented. Since claim 1 recites subject matter that is not practically performed by the human mind and is not extra-solution activity, claim 1 is patent eligible.
Regarding the Applicant’s argument that claim 1 does not recite a mental process, the Examiner respectfully disagrees. Examiner notes that making predictions related to the industrial system and processing the raw data are mental processes. Additionally, implementing the at least one chosen model and deploying the at least one chosen model amount to mere “apply it on a computer” and cannot provide significantly more (see MPEP 2106.05(f)). Examiner further respectfully notes that claims can recite a mental process even if they are claimed as being performed on a computer. MPEP 2106.04(a)(2)(III)(C).
On page 11-12, Applicant argues:
Even if claim 1 is found to recite a mental process (which the Applicant does not concede), the limitations of claim 1 are integrated into a practical application. In particular, the limitations of claim 1 represent a multi-step machine learning (ML) framework that selects at least one model without human ML experts. Thus, the invention of claim 1 represents a technical solution to a technical problem. More specifically, the technical problem is that the reliance on manual processes in machine-learning frameworks has become impractical in increasingly complex environments. See Para. [0018] of the filed specification. The invention of claim 1 addresses this issue using a computer-implemented machine-learning framework including the selection of machine-learning models for both supervised and unsupervised learning data based on label determinations and evaluation values. The selected machine learning model is implemented to make predictions on raw data. Para. [0018]. The invention of claim 1 addresses the technical problem described above by providing for "more efficient data processing and reduce labor time by reducing duplicated data processing." See Para. [0018].
For at least these reasons, independent claim 1 is patent eligible. Independent claims 10 and 19 are patent eligible for the same or similar reasons. Accordingly, claims 2 - 9, 11 - 18, and 20 are patent eligible at least based on their dependence on an eligible base claim. The Applicant respectfully requests withdrawal of the 101 rejections.
Regarding the Applicant’s arguments that the limitations are integrated into a practical application, the Examiner respectfully disagrees. Examiner notes that selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determination is a mental process since a human can mentally select a model. Examiner further notes that making predictions related to the industrial system and processing the raw industrial data are also mental processes since a human can mentally make a prediction and mentally process data. Additionally, implementing the at least one chosen model amounts to mere “apply it on a computer” and therefore cannot integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
On page 13, Applicant argues:
Claim 1 has been amended to recite generating a label determination based on whether the set of industrial data is associated with one or more labels ... selecting at least one chosen model from the plurality of models based at least in part on the evaluation value and the label determinations. Ralhan does not teach generating a label determination or selecting a model based on a label determination. Rather, Ralhan discloses a model assessment process using various metrics, including, without limitation, performance metrics, industrial metrics, user metrics, and/or the like. Ralhan [0111]. These metrics refer to the performance of the model in specific situations (e.g., user metrics refer to the varying performance of models for specific users). Such performance-based metrics are not the same as determinations based on whether the data is labeled as recited in claim 1.
For at least these reasons, Ralhan does not anticipate claim 1. Accordingly, claim 1 is allowable over the prior art of record and such an indication is respectfully requested at the Examiner's earliest convenience. Independent claims 10 and 19 are allowable for the same or similar reasons as claim 1. The Applicant therefore requests their allowance at the Examiner's earliest convenience.
Dependent claims 1 - 5, 8, 10- 14, 17, and 19 - 20, while separately allowable over the art of record, depend on otherwise allowable independent claims. The Applicant therefore refrains from a discussion of the dependent claims for the sake of brevity.
Regarding the Applicant’s argument that Ralhan does not anticipate claim 1, Examiner respectfully disagrees. Specifically, Examiner notes that providing a result that includes a classification of the new data into a particular class (Ralhan, page 31, paragraph 0077) is generating a label determination based on whether the set of industrial data is associated with one or more labels. Examiner further notes that the classification is the label. Examiner further respectfully notes that performance metrics and user metrics, which are evaluation values, are used for a more precise evaluation of class of information used by the potential model (Ralhan, page 35, paragraph 0111). Examiner additionally notes that the class of information is the label determination. Therefore, Ralhan teaches selecting a model based at least in part on the evaluation value and the label determinations.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
On page 14, Applicant argues:
None of the cited references of Posner or Trinh remedies the deficiencies of Ralhan described above. Accordingly, claims 6 - 7, 9, 15 - 16, and 18 are allowable at least based on their dependence on an allowable base claim. The Applicant therefore requests their allowance at the Examiner's earliest convenience.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sidener et al (US 2021/0192384) also discusses training candidate models on industrial data to detect anomalies.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.R.H./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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