DETAILED ACTION
This Office Action is in response to Amendment filed on 24 August 2025.
Claims 1-4, 6-8, 10-13, 15-16, 19-22, 24-25, and 29-32 are pending. Claims 5, 9, 14, 17, 18, 23, 26, 27, 28 and 33 are cancelled. The pending claims have been considered and 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 .
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 1-4, 6-8, 10-13, 15-16, 19-22, 24-25, and 29-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims fall within at least one of the four categories of patent eligible subject matter. However, the claimed invention is directed to mental processes and mathematical concepts without significantly more.
The following is an analysis of the claims regarding subject matter eligibility in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG):
Subject Matter Eligibility Analysis
Step 1: Do the Claims Specify a Statutory Category?
Claims 1-4,6-8, and 29-32 are directed to a system, claims 10-13, and 15-16 are directed to method/process, and claims 19-22, 24-25 is directed to a computer program product comprising at least one non-transitory computer-readable storage medium, therefore satisfying Step 1 of the analysis.
Step 2 Analysis for Claims 1-4, 6-8, and 29-32
Step 2A – Prong 1: Is a Judicial Exception Recited?
Independent claim 1, recites the limitations “generate… one or more feature based at least on sensor data collected from a plurality of sensors associated with an asset, wherein the one or more features are indicative of at least one interdependency between a first sensor signal of a first sensor in the plurality of sensors and a second sensor signal of a second sensor in the plurality of sensors;” (Mental process), “ wherein the at least one interdependency between the first sensor signal and the second sensor signal is identified by performing a correlational analysis between the first sensor signal and the second sensor signal, wherein the correlation analysis is performed to generate a correlation matrix, and the correlation matrix indicates relationship between the first sensor signal and the second sensor signal” (Mental process/Mathematical concept), “evaluate prediction accuracy of the trained model by determining whether the trained model distinguishes the sensor data from the normal operation period and anomalous operation period”, (Mental process), “determine an anomaly score for the data stream based at least on the one or more feature” (Mental process/Mathematical concept), “generate fault data indicative of the potential fault based on the interdependencies between the first sensor signal and the second sensor signal” (Mental process). The limitations cover concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III) and mathematical concepts (See Specification paragraph 0073) (see MPEP § 2106.04(a)(2), subsection I).
The limitations cite processes that, under their broadest reasonable interpretation, covers performance of the limitations in the human mind and mathematical concepts but for the recitation of generic computer components (i.e., use of a processor or a generic computer). That is, nothing in the claim elements preclude the steps from practically being performed in the mind or mathematical concepts. The limitations involve generating data, determining data, thereby describing an observation, evaluation, and/or opinion of data. Such an observation, evaluation, and/or opinion of data can be performed by a human and recites a mental process. The limitations involve determining an anomaly score (See Specification, paragraph 0023 and 0080), thereby describing mathematical concepts.
If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind or mathematical concepts but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping or “Mathematical Concept” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claim recites an abstract idea.
Claims 2-4, 6-8, and 29-32 cite further details pertaining to “generate… one or more features…”, “determine an anomaly score…”, “generate fault data…”. specified in claim 1; and additionally, “determine a second anomaly...”, “generate fault data...”, “an advanced pattern recognition model, a moving-mean principal component analysis (MMPCA) model, or an autoencoder model”, “evaluate prediction accuracy”, “average sensor value”, “performing a correlational analysis”, filter the sensor data”, “generate the at least one corrective action”. Each of the limitations in these dependent claims describes processes that, under their broadest reasonable interpretation, contain mental processes or mathematical concepts directed to performing the abstract idea identified in claim 1.
If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind or mathematical concepts but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping or “Mathematical Concepts” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, claims 2011 each recite an abstract idea.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
Claim 1, indicates the system comprising “a processor and a memory that stores executable instructions that, when executed by the processor, cause the processor to”, “executed by the processor” and “via input to a user interface”. Even if the described systems are implemented on a computer, there is no indication that the combination of elements in the claim solves any particular technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the identified judicial exceptions. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component is not a practical application of the abstract idea(s). The computer system cited in the claim is described at a high level of generality such that it represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). This limitation can also be viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Claim 1 also cites “the correlation matrix indicates relationship between the first sensor signal and the second sensor signal;”. This is selecting a particular data source or type of data to be manipulated, thus insignificant extra-solution activity (See MPEP 2106.05(g)).
Claim 1 also recites “a trained model, …, the trained model configured to characterize patterns associated with a normal operation period of the asset and anomalous operation period of the asset to detect deviations” and “wherein the trained model is configured to be trained until the evaluated prediction accuracy satisfies a prediction accuracy requirement”. The claim recites the use of a trained model without any specification of details pertaining to how the associated model is trained. Such details would include description of specific algorithms used in training the model. As currently written, the limitations in the claims describe merely certain data inputted to the trained model. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component is not a practical application of the abstract idea(s).
Claim 1 also cites “based on the interdependencies between the first sensor signal and the second sensor signal;”, “based on a correlation of the sensor data collected from the plurality of sensors associated with the asset”, and “provide the one or more features and historical data associated with the asset as input”. This is mere data gathering and selecting a particular data source or type of data to be manipulated, thus insignificant extra-solution activity (See MPEP 2106.05(g)).
Claim 1 also cites “receive a real-time data stream comprising data associated with the asset”. This is mere data gathering and selecting a particular data source or type of data to be manipulated, thus insignificant extra-solution activity (See MPEP 2106.05(g)).
Claim 1 also cites “wherein the one or more features comprising an average sensor value from the plurality of sensor values associated with the asses and a plurality of deltas from the average sensor value”. This is mere data gathering and selecting a particular data source or type of data to be manipulated, thus insignificant extra-solution activity (See MPEP 2106.05(g)).
Claim 1, also cites “initiate performance of predictive maintenance associated with the asset based at least in part on the fault data, wherein the predictive maintenance comprising trigging one or more prioritized maintenance actions for the asset based on the anomaly score”. This limitation describes insignificant extra-solution activity pertaining to generically applying a resolution to an identified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken. As such, these limitations do not integrate the abstract idea(s) into a practical application.
Claims 2-4,6-8, and 29-32, recite the use of a trained model without any specification of details pertaining to how the associated model is trained. Such details would include description of specific algorithms used in training the model. As currently written, the limitations in the claims describe merely certain data inputted to the trained model. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component is not a practical application of the abstract idea(s).
Claims 2-4, 6-8, and 29-32, recite the particular component and a furnace. The specification cites the system is generic and be linked to general applications of different types of enterprises, thus no specific problem for a specific system (See specification 0063). The limitations are viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of component and furnace (see MPEP 2106.05(h)). As such, these limitations do not integrate the abstract idea(s) into a practical application.
Claim 4, recites “the anomaly score being determined by processing the data stream in accordance with a trained model, wherein the data stream comprises data collected during operation of the asset, wherein the anomaly score is high when the asset operation is irregular, and the anomaly score is inversely proportional to a health index of the asset indicative of safety and health of the asset”. This is mere data gathering and selecting a particular data source or type of data to be manipulated, thus insignificant extra-solution activity (See MPEP 2106.05(g)).
Claims 2-4, 6-8, and 29-32 describe further details regarding the to “generate… one or more features…”, “determine an anomaly score…”, “generate fault data…”. These claims contain no additional elements which would integrate the abstract idea(s) into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the identified abstract idea(s).
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
In the instant case, as detailed in the analysis for Step 2A-Prong 2, claim 1 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. The computer system recited in the claim describe a generic computer processor and/or computer components at a high level and do not represent “significantly more” than the judicial exception.
Claim 1 cites the limitation pertaining to “cause presentation of the fault data and an indication of the one or more features considered in the determination of the anomaly score via the user interface” describe insignificant extra-solution activity. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing displays and user interfaces in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply displaying the results of the abstract idea(s) on a general-purpose display and user interface does not describe an inventive concept.
Claim 1 also cites “initiate performance of predictive maintenance associated with the asset based at least in part on the fault data, wherein the predictive maintenance comprising trigging one or more prioritized maintenance actions for the asset based on the anomaly score”. This limitation describes insignificant extra-solution activity pertaining to generically applying a resolution to an identified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken. As such, these limitations do not describe an inventive concept.
Claims 2-4, 6-8, and 29-32 recite limitations regarding the use of a trained model. As discussed above in the Step 2A - Prong 2 analysis regarding integration of the abstract idea into a practical application, the limitations, as currently written, describe merely certain data inputted to the trained model. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component, or utilizing generic artificial intelligence technology to apply the identified judicial exception, does not describe an inventive concept.
Claims 2-4, 6-8, and 29-32, recite the particular component and a furnace. As discussed above in the Step 2A - Prong 2 analysis regarding integration of the abstract idea into a practical application, the limitations, as currently written, describe a particular component and a furnace, while the specification cites the system is generic and be linked to general applications of different types of enterprises, thus no specific problem for a specific system (See specification 0063), and do not represent “significantly more” than the identified judicial exception.
Conclusion
In light of the above, the limitations in claims 1-4, 609 and 29-32 recite and are directed to abstract ideas and recite no additional elements that would amount to significantly more than the identified abstract idea(s). Claims 1-4, 6-8, and 29-32 are therefore not patent eligible.
Step 2 Analysis for Claims 11-19
Claims 10-13 and 15-16, contain limitations for a system which are similar to the limitations for the methods specified in claims 1-4 and 6-7, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 10-13 and 15-16 is similar to that presented above for claims 1-4 and 6-7.
Conclusion
In light of the above, the limitations in claims 10-13 and 15-16 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claims 10-13 and 15-16 are therefore not patent eligible.
Step 2 Analysis for Claims 19-22 and 24-25
Claims 19-22 and 24-25, contains limitations for a computer program product comprising at least one non-transitory computer-readable medium which are similar to the limitations for the methods specified in claim 1-4 and 6-7, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 19-22 and 24-25 are similar to that presented above for claims 1-4 and 6-7.
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
Claim 19-22 and 24-25 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea.
Claim 19-22 and 24-25 recites the additional elements of a “a computer program product comprising at least one non-transitory computer-readable medium having computer-executable program code portions stored therein, the computer-executable program code portions comprising program code instructions configured to:”. The computer-readable medium and processors cited in the claim describe generic computer components at a high level and do not represent “significantly more” than the identified judicial exception. The executing of instructions on the processors recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception.
Conclusion
In light of the above, the limitations in claim 19-22 and 24-25 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claim 19-22 and 24-25 is therefore not patent eligible.
Response to Arguments
Applicant's arguments filed 24 September 2025 have been fully considered but they are not persuasive.
Applicant argues the limitations “cause presentation of the fault data and an indication of the one or more features considered in the determination of the anomaly score via the user interface, wherein the one or more features comprising an average sensor value from the plurality of sensors associated with the asset and a plurality of deltas from the average sensor value; and initiate performance of predictive maintenance associated with the asset based at least in part on the fault data, wherein the predictive maintenance comprises triggering one or more prioritized maintenance actions for the asset based on the anomaly score using a trained model, cannot be performed mentally by a human.”
The Examiner did not argue these limitations were abstract idea, but rather Claim 1 cites the limitation pertaining to “cause presentation of the fault data and an indication of the one or more features considered in the determination of the anomaly score via the user interface” describe insignificant extra-solution activity. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing displays and user interfaces in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply displaying the results of the abstract idea(s) on a general-purpose display and user interface does not describe an inventive concept. Claim 1 also cites “initiate performance of predictive maintenance associated with the asset based at least in part on the fault data, wherein the predictive maintenance comprising trigging one or more prioritized maintenance actions for the asset based on the anomaly score”. This limitation describes insignificant extra-solution activity pertaining to generically applying a resolution to an identified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken. As such, these limitations do not describe an inventive concept. The claim recites the use of a trained model without any specification of details pertaining to how the associated model is trained. Such details would include description of specific algorithms used in training the model. As currently written, the limitations in the claims describe merely certain data inputted to the trained model. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general-purpose processor or other generic computer component is not a practical application of the abstract idea(s).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the identified abstract ideas and do not represent “significantly more” than the identified judicial exception.
Applicant argues “the system generates one or more features, such as average values and deviation deltas from correlated sensor pairs, and feeds these into a trained machine learning model”. This is mere data gathering and selecting a particular data source or type of data to be manipulated, thus insignificant extra-solution activity (See MPEP 2106.05(g)). The trained machine learning model is used merely as a tool to implement the abstract idea.
Applicant argues the independent claims are not directed to an abstract idea, but rather a technological improvement. The Examiner respectfully disagrees. The claims do recite various limitations that can be considered abstract idea of mental processes, see above rejection Step 2A-prong 1 analysis. Concerning the technological improvement, the Examiner also disagrees, the limitation describes insignificant extra-solution activity pertaining to generically applying a resolution to an identified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH D MANOSKEY whose telephone number is (571)272-3648. The examiner can normally be reached M-F 7:30am to 3:30pm.
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/JOSEPH D MANOSKEY/Primary Examiner, Art Unit 2113 November 18, 2025