DETAILED ACTION
This office action is in response to communication filed on July 8, 2026.
Response to Amendment
Amendments filed on July 8, 2026 have been entered.
The specification has been amended.
Claims 1, 3-6, 8-14 and 16-19 have been amended.
Claims 2, 7 and 15 have been canceled.
Claims 21-23 have been added.
Claims 1, 3-6, 8-14 and 16-23 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 12), filed on 07/08/2026, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn. However, upon further consideration, new objections to the specification are presented below to address additional informalities.
Applicant’s arguments, see Remarks (p. 12), filed on 07/08/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented below to address additional informalities introduced by the amendments.
Applicant’s arguments, see Remarks (p. 12-13), filed on 07/08/2026, with respect to the rejections of the claims under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, have been fully considered. In view of the amendments to the claims addressing the issues raised in the previous office action, the corresponding rejections of the claims have been withdrawn. However, upon further consideration, new rejections of claims are presented below to address additional issues introduced by the amendments.
Applicant’s arguments, see Remarks (p. 13-15), filed on 07/08/2026, with respect to the rejection of claims 1, 3-6, 8-14 and 16-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues (p. 13) that the claims are not directed to an abstract idea. The claims recite a specific technical process for anomaly detection in industrial assets using machine learning. The claimed invention addresses the technical problem of detecting anomalies in complex industrial equipment with unknown configurations … The claims as a whole are directed to a specific improvement in the technology of industrial asset monitoring. The claims recite receiving an initial set of measurement data from a sensor configured to monitor an industrial asset, processing the data through specific technical steps (generating cleaned training data by automatically removing outliers based on at least one standard deviation range and at least one dynamic threshold, grouping related measurement values using a correlation algorithm, training a machine learning model configured to generate predicted parameters associated with normal operation of the industrial asset), and using the trained model to detect anomalies and transmit a control signal to a controller to modify operation of the industrial asset. This is not mere data manipulation-it is a technological solution to a technological problem that results in improved physical control of industrial equipment.
These arguments are not persuasive.
The examiner submits that, as indicated by applicant’s arguments, the claimed invention recites data collection (“the claims recite receiving an initial set of measurement data from a sensor configured to monitor an industrial asset”) and data analysis using a series of mental/mathematical steps (“processing the data through specific technical steps (…)”) to obtain a result (“detect anomalies”), which as indicated in the October 2019 Patent Eligibility Guidance Update:
“A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (p. 4, section “iii. “Mathematical Calculations””, par. 1); and
“claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.;
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC” (p. 7-8).
Based on these guidelines, the examiner submits that the claimed invention recites limitations that fall under the “Mental Processes” and “Mathematical Concepts” groupings of Abstract Ideas (see rejection below).
Moreover, the examiner submits that the invention applies a particular analysis (e.g., series of mental/mathematical steps) to monitor any industrial asset using any type of sensor and any type of measurement, as well as performing any type of action that modifies the operation of the industrial asset, as evidenced by the applicant’s own disclosure (see specification at [0018]-[0020], [0023], [0025], [0033]-[0034], [0055]), which according to the current guidance:
“Even if the judicial exception is narrow (e.g., a particular mathematical formula or detailed mental process), the Court has held that a claim may not preempt that judicial exception” (see 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, “III. Update on Certain Areas of the USPTO’s Patent Subject Matter Eligibility Guidance Applicable to AI Inventions”, section “A. Evaluation of Whether a Claim Is Directed to a Judicial Exception (Step 2A)”);
“In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception” (see MPEP 2106.04(d)); and
“Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: … vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)” (see MPEP 2106.05(h)).
Applicant also argues (p. 14) that the claims recite specific physical components and actions that go beyond mere data manipulation. The claims require: (1) a sensor configured to monitor an industrial asset that acquires measurement values; (2) a data processor that performs specific data processing steps; (3) training a machine learning model configured to generate predicted parameters associated with normal operation of the industrial asset; and (4) a controller that modifies operation of the industrial asset based on inferred severity of anomalies identified by the data processor during continued operation of the industrial asset. These limitations tie the claims to the specific technical field of industrial asset monitoring and preclude the claims from preempting all uses of any abstract idea. The claims culminate in physical control of industrial machinery-analogous to Diamond v. Diehr, 450 U.S. 175 (1981), where the Supreme Court found claims patent-eligible because they recited a specific industrial process that used calculations to control physical equipment.
These arguments are not persuasive.
The examiner submits that adding a generic sensor to collect data for analysis does not transform the judicial exception into a practical application as indicated in the MPEP: “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more” (see MPEP 2106.05(b); see also MPEP 2106.05(g)).
Regarding the arguments about the data processor and training a machine learning model, the examiner submits that these features append generic computer components/implementation used to facilitate the application of the judicial exception, which as indicated in the Office guidance: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more” (see MPEP 2106.05(f)).
Regarding the argument about modifying the operation of the industrial asset based on inferred severity of anomalies, the examiner submits that this feature appends transformations at a high level of generality such that substantially all practical applications of the judicial exception are covered, which according to the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
Regarding the argument about the limitations tying the claims to the specific technical field of industrial asset monitoring and preclude the claims from preempting all uses of any abstract idea, and the claims culminating in physical control of industrial machinery-analogous to Diamond v. Diehr, 450 U.S. 175 (1981), where the Supreme Court found claims patent-eligible because they recited a specific industrial process that used calculations to control physical equipment, the examiner submits that:
“the additional elements in Diamond v. Diehr as a whole provided eligibility and did not merely recite calculating a cure time using the Arrhenius equation “in a rubber molding process”. Instead, the claim in Diehr recited specific limitations such as monitoring the elapsed time since the mold was closed, constantly measuring the temperature in the mold cavity, repetitively calculating a cure time by inputting the measured temperature into the Arrhenius equation, and opening the press automatically when the calculated cure time and the elapsed time are equivalent. 450 U.S. at 179, 209 USPQ at 5, n. 5. These specific limitations act in concert to transform raw, uncured rubber into cured molded rubber. 450 U.S. at 177-78, 209 USPQ at 4” (see MPEP 2106.05(h));
Whereas the current claims in the application recite a particular analysis (e.g., series of mental/mathematical steps) that can be applied to any industrial assets using any sensor and any measurement, while limiting the application of the judicial exception to a field of use and appending generic transformations that encompass all possible practical applications as indicated above.
Applicant further argues (p. 14-15) that the Examiner asserts that “training a model” merely adds the words “apply it” or uses a computer as a tool to perform an abstract idea. Applicant respectfully disagrees. The claims do not merely recite training a generic model-rather, the claims recite specific steps of preparing data for model training (generating cleaned training data by automatically removing outliers based on at least one standard deviation range and at least one dynamic threshold, grouping related measurement values using correlation algorithms) and using the model for a specific purpose (generating predicted parameters associated with normal operation of the industrial asset for anomaly detection). Most importantly, the claims require physical action in response to the anomaly detection: transmitting a control signal to a controller coupled to the industrial asset to modify operation of the industrial asset. This is not mere computer implementation; it is a specific technical process for improving industrial asset monitoring that results in tangible physical effects on industrial equipment.
These arguments are not persuasive.
The examiner submits that using the argued specific steps of preparing the data for model training recite the judicial exception as indicated in the rejection, while appending generic computer implementation (e.g., training a machine learning model, as evidenced by applicant’s specification at [0019], [0043]) does not integrate the judicial exception into a practical application (see MPEP 2106.05(f); see also Example 47, claim 2 of the July 2024 Subject Matter Eligibility Examples).
Regarding the argument about the claims requiring a physical action in response to the anomaly detection, the examiner submits that as indicated above, appending generic transformations that encompass all possible practical applications do not integrate the judicial exception into a practical application or amount to significantly more.
Applicant’s arguments, see Remarks (p. 15-17), filed on 07/08/2026, with respect to the rejections of the claims under 35 U.S.C. 103 have been fully considered. In view of the amendments to the claims, the prior art rejections of the claims have been withdrawn.
Applicant argues (p. 15-16) that In Chan, cross correlation is used for feature selection and dimension reduction-to identify redundant inputs and eliminate them from the dataset before model training. Chan explicitly states that “cross-correlation analysis identifies highly correlated inputs of the dataset and groups them to limit these highly-correlated (redundant) inputs from all being selected as final inputs to the model or solution.” Chan, [0070]. Chan further teaches that “only one variable is kept from each highly correlated group and other variables are dropped.” Chan, [0113]. In contrast, the method and system of claims 1 and 14 uses a correlation algorithm to group related measurement values together for training the machine learning model. This is the opposite of Chan’s approach … Chan’s approach removes correlated inputs to avoid redundancy in the dataset before model training. The claimed invention groups correlated measurement values together for use in model training. These are fundamentally different operations yielding fundamentally different results. Chan’s cross-correlation analysis results in a reduced dataset with redundant inputs eliminated; the claimed invention’s grouping using a correlation algorithm results in grouped cleaned training data that is used for model training
These arguments are persuasive.
Specification
The disclosure is objected to because of the following informalities:
[0033]: Language “The data can include a (static) vibration signal recorded over a period of time and/or process data recorded over a period of time or a multiple time points” should read “The data can include a (static) vibration signal recorded over a period of time and/or process data recorded over a period of time or [[a]] multiple time points” in order to correct for minor informalities.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “receiving, by the at least one data processor, from the sensor, a new set of measurement data characterizing measurement values acquired by the sensor during continued operation of the industrial asset” should read “receiving, by the at least one data processor, from the sensor, a new set of measurement data characterizing new measurement values acquired by the sensor during continued operation of the industrial asset” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Claim language “determining, by the at least one data processor, deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with normal operation of the industrial asset over time” should read “determining, by the at least one data processor, deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with the normal operation of the industrial asset over time” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read “The method of claim 1, wherein the sensor is affixed to the industrial asset in an industrial environment and the initial set of measurement data further characterizes a state of health of the industrial asset” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 4 is objected to because of the following informalities:
Claim language should read “The method of claim 3, wherein the sensor is included in a sensor health monitoring system associated with the industrial environment and the initial set of measurement data further characterizes [[a]]the state of health of the sensor” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
Claim language should read “The method of claim 5, further comprising: determining the at least one dynamic threshold for the at least one portion of the initial set of measurement data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 10 is objected to because of the following informalities:
Claim language should read “The method of claim 9, wherein the machine learning model comprises one or more machine learning models trainable to generate the predicted parameters associated with the normal operation of the industrial asset” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 13 is objected to because of the following informalities:
Claim language should read “The method of claim 12, wherein the data validation rule verifies association between a data type of the initial set of measurement data from the sensor and a pre-determined data tag” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 14 is objected to because of the following informalities:
Claim language “receiving, from the sensor, a new set of measurement data characterizing measurement values acquired by the sensor during continued operation of the industrial asset” should read “receiving, from the sensor, a new set of measurement data characterizing new measurement values acquired by the sensor during continued operation of the industrial asset” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Claim language “determining deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with normal operation of the industrial asset over time” should read “determining deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with the normal operation of the industrial asset over time” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 16 is objected to because of the following informalities:
Claim language should read “The system of claim 14, wherein the sensor is affixed to the industrial asset in an industrial environment and the initial set of measurement data further characterizes a state of health of the industrial asset” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 17 is objected to because of the following informalities:
Claim language should read “The system of claim 16, wherein the sensor is included in a sensor health monitoring system associated with the industrial environment and the initial set of measurement data further characterizes [[a]]the state of health of the sensor” in order to clarify the recited subject matter and provide appropriate antecedence basis.
Appropriate correction is required.
Claim 18 is objected to because of the following informalities:
Claim language “determining at least one dynamic threshold for the at least one portion of the initial set of measurement data” should read “determining the at least one dynamic threshold for the at least one portion of the initial set of measurement data” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 19 is objected to because of the following informalities:
Claim language should read “The system of claim 18, wherein the data processor is further configured to provide one or more of the initial set of measurement data, the cleaned training data, the one or more states of the industrial asset, the at least one dynamic threshold, the predicted parameters associated with the normal operation of the industrial asset over time, and the deviation data to a graphical user interface display” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 22 is objected to because of the following informalities:
Claim language should read “The method of claim 1, wherein modifying the operation of the industrial asset comprises at least one of adjusting a rate of speed of the industrial asset, modifying a frequency of the operation of the industrial asset, or terminating the operation of the industrial asset to prevent a malfunction” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 23 is objected to because of the following informalities:
Claim language should read “The system of claim 14, wherein modifying the operation of the industrial asset comprises at least one of adjusting a rate of speed of the industrial asset, modifying a frequency of the operation of the industrial asset, or terminating the operation of the industrial asset to prevent a malfunction, and wherein the data processor is further configured to generate an alert in response to inferring that the severity exceeds the predetermined threshold” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-4, 13, 16-17 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites “the data further characterizes a state of health of the industrial asset” which is unclear as to what “data” is being referred to: “the initial set of measurement data”, “the cleaned training data”, “the at least one portion of the initial set of measurement data”, “the new set of measurement data” or “the deviation data” recited in independent claim 1?
Similar language is recited in claim 16 with reference to independent claim 14.
Claim 4 recites “the measurement data further characterizes a state of health of the sensor” which is unclear as to what “measurement data” is being referred to: “the initial set of measurement data”, “the at least one portion of the initial set of measurement data”, or “the new set of measurement data” recited in independent claim 1?
Similar language is recited in claim 17 with reference to independent claim 14.
Claim 13 recites “the data validation rule verifies association between a data type of the measurement data from the sensor and a pre-determined data tag” which is unclear as to what “measurement data” is being referred to: “the initial set of measurement data”, “the at least one portion of the initial set of measurement data”, or “the new set of measurement data” recited in independent claim 1?
Claim 19 recites “the data processor is further configured to provide one or more of the measurement data …” which is unclear as to what “measurement data” is being referred to: “the initial set of measurement data”, “the at least one portion of the initial set of measurement data”, or “the new set of measurement data” recited in independent claim 14?
The original disclosure does not provide clarification as to what data is referred to in the above descriptions (see [0006]-[0007], [0009], [0028], [0031], [0035], [0038], [0056]).
For examination purposes, claim language is interpreted as indicated in the Claim Objections section.
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, 3-6, 8-14 and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Regarding claim 1, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test (see italic text for abstract idea):
the limitation “generating, by the at least one data processor, cleaned training data by automatically removing outliers from the initial set of measurement data based on at least one standard deviation range and at least one dynamic threshold for at least one portion of the initial set of measurement data” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data for filtering purposes (e.g., remove outliers; see specification at [0007], [0041]). Except for the recitation of the generic computer elements (i.e., the at least one data processor) used to facilitate the application of the judicial exception (e.g., mere computer implementation), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to filter data.
the limitation “grouping, by the at least one data processor, related measurement values within the cleaned training data using a correlation algorithm” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data for grouping purposes (e.g., using a correlation algorithm to group related measurement values; see specification at [0042]). Except for the recitation of the generic computer elements (i.e., the at least one data processor) used to facilitate the application of the judicial exception (e.g., mere computer implementation), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to manipulate data for grouping purposes.
the limitation “training, by the at least one data processor, a machine learning model using the grouped cleaned training data, wherein the machine learning model is configured to generate predicted parameters associated with normal operation of the industrial asset” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data (e.g., predict information based on data; see specification at [0043]-[0044]). Except for the recitation of the generic computer elements (i.e., the at least one data processor) and the generic computer implementation (e.g., training a machine learning model using data, see specification at [0019], [0043]) used to facilitate the application of the judicial exception, the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to transform data.
the limitation “determining, by the at least one data processor, deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with normal operation of the industrial asset over time” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to compare data and obtain a result (e.g., comparing data for determining differences; see specification at [0045]-[0046]). Except for the recitation of the generic computer elements (i.e., the at least one data processor) used to facilitate the application of the judicial exception (e.g., mere computer implementation), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data and obtain a result (i.e., deviation data).
the limitation “inferring, by the at least one data processor, a severity of an anomaly in the industrial asset based on the deviation data, wherein the severity indicates a likelihood of asset malfunction” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to manipulate data and obtain a result (e.g., using deviation data to infer an anomaly; see specification at [0047]). Except for the recitation of the generic computer elements (i.e., the at least one data processor) used to facilitate the application of the judicial exception (e.g., mere computer implementation) and/or a field of use (e.g., anomaly detection of an industrial asset), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to manipulate data and obtain a result (i.e., a severity of an anomaly).
the limitation “in response to inferring that the severity exceeds a predetermined threshold, transmitting, by the at least one data processor, a control signal to a controller coupled to the industrial asset to modify operation of the industrial asset” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes and/or mathematical concepts to compare data (see specification at [0047]). Except for the recitation of the generic computer elements (i.e., the at least one data processor, a controller) used to facilitate the application of the judicial exception (e.g., mere computer implementation, signal transmission), the transformation recited at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (e.g., modify operation of the industrial asset) and/or a field of use (e.g., anomaly detection of an industrial asset), the limitation in the context of the claim mainly refers to performing a mental evaluation and/or applying mathematical concepts to compare data.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not integrated into a practical application when considering the claim as a whole. In particular, the additional elements recited in the claim (see non-italic text):
“receiving, by at least one data processor, from a sensor configured to monitor an industrial asset, an initial set of measurement data characterizing measurement values acquired by the sensor” add extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) using elements (i.e., a sensor, an industrial asset) recited at a high level of generality (see MPEP 2106.05(g)), and append the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer (i.e., at least one data processor) as a tool to perform an abstract idea (see MPEP 2106.05(f));
“training, by the at least one data processor, a machine learning model using the grouped cleaned training data, wherein the machine learning model is configured to generate predicted parameters associated with normal operation of the industrial asset” append the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer (i.e., at least one data processor) as a tool to perform an abstract idea (e.g., machine learning, see specification at [0019], [0043]) (see MPEP 2106.05(f));
“receiving, by the at least one data processor, from the sensor, a new set of measurement data characterizing measurement values acquired by the sensor during continued operation of the industrial asset” add extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) using elements (i.e., the sensor, the industrial asset) recited at a high level of generality (see MPEP 2106.05(g)), and append the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer (i.e., at least one data processor) as a tool to perform an abstract idea (see MPEP 2106.05(f)); and
“in response to inferring that the severity exceeds a predetermined threshold, transmitting, by the at least one data processor, a control signal to a controller coupled to the industrial asset to modify operation of the industrial asset” append the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., at least one data processor, signal transmission, a controller) (see MPEP 2106.05(f)), and add transformation recited at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (i.e., modify operation of the industrial asset) (see MPEP 2106.05(c)).
Accordingly, these additional elements, when considered individually and in combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claim is directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claim, when considered as a whole, does not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
generally link the use of the judicial exception to a particular technological environment or field of use (e.g., anomaly detection of an industrial asset), which as indicated in the MPEP: “As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible “simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.” Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (see MPEP 2106.05(h))
recite extra-solution activities (i.e., mere data gathering) using elements (i.e., a sensor; an industrial asset) specified at a high level of generality, which as indicated in the MPEP: “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process” (see MPEP 2106.05(g)); and “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more” (see MPEP 2106.05(b), section III);
append generic computer components (i.e., at least one data processor, a controller) used to facilitate the application of the abstract idea (i.e., mere computer implementation) (e.g., signal transmission; training a machine learning model; see specification at [0043]), which as indicated in the MPEP: “As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible ... Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more” (see MPEP 2106.05(f)); and
append transformations at a high level of generality such that substantially all practical applications of the judicial exception(s) are covered (i.e., modify operation of the industrial asset), which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c))
The claim, when considered as a whole, does not provide significantly more under Step 2B of the test.
Based on the analysis, the claim is not patent eligible.
Similarly, independent claim 14 is directed to a judicial exception (abstract idea) without significantly more as explained above with regards to claim 1.
With regards to the dependent claims they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (Claims 5-6, 8-9, 11-13, 18 and 20), that under the broadest reasonable interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering/outputting using a data type or source), generic computer components/implementation and/or field of use (Claims 3-4, 10, 16-17, 19 and 21), which as indicated in the Office’s guidance does not integrate the judicial exception into a practical application (Step 2A – Prong Two) and/or does not provide significantly more (Step 2B) when considering the claimed invention as a whole.
Examiner’s Note
Claims 22-23 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 22, the examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test, claim 22 recites the judicial exception (abstract idea) of independent claim 1 as indicated above.
Furthermore, under Step 2A - Prong Two of the test, the claim recites the additional elements recited in independent claim 1, and “modifying operation of the industrial asset comprises at least one of adjusting a rate of speed of the industrial asset, modifying a frequency of operation of the industrial asset, or terminating operation of the industrial asset to prevent a malfunction”, which when considering the claim as a whole, integrates the judicial exception into a practical application by effecting a particular transformation of an article to a different state or thing (i.e., at least one of adjusting a rate of speed of the industrial asset, modifying a frequency of operation of the industrial asset, or terminating operation of the industrial asset to prevent a malfunction) (see MPEP 2106.05(c)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application. The claim, when considered as a whole, is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance-Revised Step 2A, see also MPEP 2106.04(d)).
Similar analysis applies to claim 23.
Subject Matter Not Rejected Over Prior Art
Claims 1, 3-6, 8-14 and 16-21 are distinguished over the prior art of record for the following reasons:
Regarding claim 1.
Chan (US 20200387818 A1) discloses/teaches:
A method (Fig. 2A, item 100; [0005], [0018], [0081]: a method for building and deploying a model to optimize assets in an industrial process is presented) comprising:
receiving, by at least one data processor, from a sensor configured to monitor an industrial asset, an initial set of measurement data characterizing measurement values acquired by the sensor (Fig. 2A, item 120; Fig. 2C, item 120-1; [0081], [0089]-[0091], [0121], [0149]: process data from assets is obtained using sensors, the method being deployed in computer servers (see [0074]; see also Fig. 1C, [0006], [0038], [0052], [0067], [0102], [0146], [0161]));
generating, by the at least one data processor, cleaned training data by automatically removing outliers from the initial set of measurement data (Fig. 2A, item 120; Fig. 2C, item 120-2; [0081], [0092]-[0102]: data is cleansed (e.g., to remove outliers), with the removal employing different techniques, including short-term outliers and removal of highly noisy values (see also Fig. 1C, [0038], [0053], [0068], [0152]));
grouping, by the at least one data processor, related measurement values within the cleaned training data using a correlation algorithm (Fig. 2A, item 120; Fig. 2C, item 120-4; [0081], [0107]-[0111]: cross-correlation is performed in the cleansed data to incorporate highly correlated inputs (see also Fig. 1C, [0006], [0038], [0054], [0070], [0153]));
training, by the at least one data processor, a machine learning model using the cleaned training data (Fig. 2A, item 130; [0081], [0118]-[0119]: models are build using the processed data (see also Fig. 1C, [0006], [0038], [0057]-[0058], [0070])), wherein the machine learning model is configured to generate predicted parameters associated with normal operation of the industrial asset (Fig. 2A, item 140; [0062]-[0063], [0081], [0123]: model generates online predictions based on normal operation data (see [0071]) for asset failure detection in industrial process (see [0043], [0066], [0077]));
receiving, by the at least one data processor, from the sensor, a new set of measurement data characterizing measurement values acquired by the sensor during continued operation of the industrial asset (Fig. 2F, item 140-1; [0120]-[0121]: real-time input measurements from plant sensors are received for online monitoring of industrial process (see also [0071]-[0072]));
determining, by the at least one data processor, deviation data ([0123]: models compare current real-time data to pre-defined performance criterions from historical data);
inferring, by the at least one data processor, a severity of an anomaly in the industrial asset based on the deviation data ([0123]: based on the comparison, degradation in performance conditions is detected); and
in response to inferring that the severity exceeds a predetermined threshold, transmitting, by the at least one data processor, a control signal to a controller coupled to the industrial asset to modify operation of the industrial asset ([0062], [0072]: solutions are determined for real-time process control and optimization implementation (see also [0003], [0039], [0043], [0064], [0077])).
Regarding “removing outliers from the initial set of measurement data based on at least one standard deviation range”, Jones (US 20180329865 A1) teaches:
“A standard procedure, commonly used to remove outliers, is to compute the standard deviation (σ) of the data set and simply define all data outside a 2σ interval of the mean, for example, as outliers” ([0074]: outliers are removed based on standard deviation range (see also [0075] regarding using percentile operation for calculating relative and absolute error for determining threshold values)).
Regarding “removing outliers from the initial set of measurement data based on at least one dynamic threshold for at least one portion of the initial set of measurement data”, Sepe (US 20230106311 A1) teaches:
“Also, the offline model setup step 6 comprises a preprocessing sub-step 62 of the signals, wherein a filtering and/or a signal decorrelation processing is carried out, to define the window of asset operating conditions, where each signal has to be processed and/or monitored. For instance, there are signals to be observed in a certain speed range or power range, or at not running engine conditions” ([0049]: a window of asset operating conditions is defined during model setup (analogous to selecting a portion of the data for training the model) (see also [0059])); and
“As it can be seen, the anomaly detection is performed dynamically. The threshold of the detection depends on the specific engine condition because it uses the signal reconstruction technique” ([0056]: anomaly detection uses thresholds that depend on specific equipment conditions (analogous to determining one or more dynamic thresholds for the selected portion of the data)).
Regarding “determining, by the at least one data processor, deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with normal operation of the industrial asset over time; and wherein the severity indicates a likelihood of asset malfunction”, Sepe (US 20230106311 A1) further teaches:
“Then, an anomaly detection sub-step 74 is executed, which identifies if a signal or a group of signals received from the gas turbines 21, 22, and 23 of the fleet 2 has an anomaly or not. In some embodiments this methodology can be performed by machine learning algorithms, implementing supervised and/or unsupervised methodologies. Anomaly detection aims at identifying which signals have an anomalous features pattern with respect to the pattern of the healthy reference features. This detection can be done by using signal reconstruction techniques like AAKR (Auto-Associative Kernel Regression), used in this case to reconstruct the expected signal features with respect to the healthy features pattern available as configuration parameters. The comparison between the reconstructed features and the measured ones is performed by using a distance metric or a similarity metric (likelihood) and comparing them with respect to a threshold ...” ([0066]: anomaly detection in industrial assets is performed by comparing model predictions and measurements using a distance/similarity metric (analogous to deviation)); and
“In case of the anomaly classification 75 detects a (or at least one) sensor malfunction, a severity assignment 771 step is executed, where a severity is assigned to the anomalies identified as sensor malfunctions. The severity assignment 771 assigns a severity score to anomalies classified as sensor malfunction basing on anomaly type and time lasting and sensor redundancy … Also, frequency and time lasting of an anomaly will be considered to evaluate the severity” ([0089]: based on results of the anomaly classification, anomalies are assigned severity scores using information such as time lasting of the anomaly).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“training, by the at least one data processor, a machine learning model using the grouped cleaned training data” (examiner submits that the prior art of record groups data for removing redundant inputs for training the model (see Chan [0070]), whereas the current application recites the use of the grouped cleaned training data for training the machine learning model)
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claim 14.
Chan (US 20200387818 A1) discloses/teaches:
A system (Fig. 6, item 50, 60; [0006]: a computer system for executing a method for building and deploying a model to optimize assets in an industrial process is presented (see also [0074], [0124], [0162])) comprising:
a data processor (Fig. 6, item 84 – “central processor unit”; [0006]: computer system includes processor capabilities to execute the method (see also [0162])), and
a memory (Fig. 6, items 90 and 95 – “memory” and “disk storage”) storing non-transitory, computer-readable instructions, which when executed cause the data processor to perform operations ([0006]-[0007]: computer system includes memory capabilities to store instructions to perform the method (see also [0162]-[0163])) comprising:
receiving, from a sensor configured to monitor an industrial asset, an initial set of measurement data characterizing measurement values acquired by the sensor (Fig. 2A, item 120; Fig. 2C, item 120-1; [0081], [0089]-[0091], [0121], [0149]: process data from assets is obtained using sensors, the method being deployed in computer servers (see [0074]; see also Fig. 1C, [0006], [0038], [0052], [0067], [0102], [0146], [0161]));
generating cleaned training data by automatically removing outliers from the initial set of measurement data (Fig. 2A, item 120; Fig. 2C, item 120-2; [0081], [0092]-[0102]: data is cleansed (e.g., to remove outliers), with the removal employing different techniques, including short-term outliers and removal of highly noisy values (see also Fig. 1C, [0038], [0053], [0068], [0152]));
grouping related measurement values within the cleaned training data using a correlation algorithm (Fig. 2A, item 120; Fig. 2C, item 120-4; [0081], [0107]-[0111]: cross-correlation is performed in the cleansed data to incorporate highly correlated inputs (see also Fig. 1C, [0006], [0038], [0054], [0070], [0153]));
training a machine learning model using the cleaned training data (Fig. 2A, item 130; [0081], [0118]-[0119]: models are build using the processed data (see also Fig. 1C, [0006], [0038], [0057]-[0058], [0070])), wherein the machine learning model is configured to generate predicted parameters associated with normal operation of the industrial asset (Fig. 2A, item 140; [0062]-[0063], [0081], [0123]: model generates online predictions based on normal operation data (see [0071]) for asset failure detection in industrial process (see [0043], [0066], [0077]));
receiving, from the sensor, a new set of measurement data characterizing measurement values acquired by the sensor during continued operation of the industrial asset (Fig. 2F, item 140-1; [0120]-[0121]: real-time input measurements from plant sensors are received for online monitoring of industrial process (see also [0071]-[0072]));
determining deviation data ([0123]: models compare current real-time data to pre-defined performance criterions from historical data);
inferring a severity of an anomaly in the industrial asset based on the deviation data ([0123]: based on the comparison, degradation in performance conditions is detected); and
in response to inferring that the severity exceeds a predetermined threshold, transmitting a control signal to a controller coupled to the industrial asset to modify operation of the industrial asset ([0062], [0072]: solutions are determined for real-time process control and optimization implementation (see also [0003], [0039], [0043], [0064], [0077])).
Regarding “removing outliers from the initial set of measurement data based on at least one standard deviation range”, Jones (US 20180329865 A1) teaches:
“A standard procedure, commonly used to remove outliers, is to compute the standard deviation (σ) of the data set and simply define all data outside a 2σ interval of the mean, for example, as outliers” ([0074]: outliers are removed based on standard deviation range (see also [0075] regarding using percentile operation for calculating relative and absolute error for determining threshold values)).
Regarding “removing outliers from the initial set of measurement data based on at least one dynamic threshold for at least one portion of the initial set of measurement data”, Sepe (US 20230106311 A1) teaches:
“Also, the offline model setup step 6 comprises a preprocessing sub-step 62 of the signals, wherein a filtering and/or a signal decorrelation processing is carried out, to define the window of asset operating conditions, where each signal has to be processed and/or monitored. For instance, there are signals to be observed in a certain speed range or power range, or at not running engine conditions” ([0049]: a window of asset operating conditions is defined during model setup (analogous to selecting a portion of the data for training the model) (see also [0059])); and
“As it can be seen, the anomaly detection is performed dynamically. The threshold of the detection depends on the specific engine condition because it uses the signal reconstruction technique” ([0056]: anomaly detection uses thresholds that depend on specific equipment conditions (analogous to determining one or more dynamic thresholds for the selected portion of the data)).
Regarding “determining deviation data characterizing a difference between the new set of measurement data and the predicted parameters associated with normal operation of the industrial asset over time; wherein the severity indicates a likelihood of asset malfunction”, Sepe (US 20230106311 A1) further teaches:
“Then, an anomaly detection sub-step 74 is executed, which identifies if a signal or a group of signals received from the gas turbines 21, 22, and 23 of the fleet 2 has an anomaly or not. In some embodiments this methodology can be performed by machine learning algorithms, implementing supervised and/or unsupervised methodologies. Anomaly detection aims at identifying which signals have an anomalous features pattern with respect to the pattern of the healthy reference features. This detection can be done by using signal reconstruction techniques like AAKR (Auto-Associative Kernel Regression), used in this case to reconstruct the expected signal features with respect to the healthy features pattern available as configuration parameters. The comparison between the reconstructed features and the measured ones is performed by using a distance metric or a similarity metric (likelihood) and comparing them with respect to a threshold ...” ([0066]: anomaly detection in industrial assets is performed by comparing model predictions and measurements using a distance/similarity metric (analogous to deviation)); and
“In case of the anomaly classification 75 detects a (or at least one) sensor malfunction, a severity assignment 771 step is executed, where a severity is assigned to the anomalies identified as sensor malfunctions. The severity assignment 771 assigns a severity score to anomalies classified as sensor malfunction basing on anomaly type and time lasting and sensor redundancy … Also, frequency and time lasting of an anomaly will be considered to evaluate the severity” ([0089]: based on results of the anomaly classification, anomalies are assigned severity scores using information such as time lasting of the anomaly).
The closest prior art of record, taken individually or in combination, fail to teach or suggest (see italic text):
“training a machine learning model using the grouped cleaned training data” (examiner submits that the prior art of record groups data for removing redundant inputs for training the model (see Chan [0070]), whereas the current application recites the use of the grouped cleaned training data for training the machine learning model)
in combination with all other limitations within the claim, as claimed and defined by the applicant.
Regarding claims 3-6, 8-13 and 16-21.
They are also distinguished over the prior art of record due to their dependency.
Allowable Subject Matter
Claim 22-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims (see Claim Rejections - 35 USC § 101 section; see also Claim Rejections - 35 USC § 112 section).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GUPTA; Supriya et al., US 20250067164 A1, FIELD PUMP EQUIPMENT SYSTEM
Reference discloses processing time series data corresponding to pump operation, the processing including machine learning model, to detect performance issues.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
