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 para