DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 8-18 are pending.
Claims 1-7 are cancelled.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for Application No. EP21163887 filed on 03/22/2021.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on 09/21/2023 have been considered by the examiner.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 15, line 21, replace “g)” with “f)”
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) 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.
Claims 8-18 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 8 recites the limitation “c) comparing the simulated error-free operating state for each of the plurality of individually occurring causes of an anomaly with the simulated operating state when one of the occurring causes of an anomaly is present, each qualitative symptom being derived from the comparison which describes a qualitative deviation of the simulated operating state from the error-free operating state”. It is unclear what Applicant means by the limitation. More specifics are as follows:
There is insufficient antecedent basis for the elements “the simulated error-free operating state” and “the simulated operating state” in the claim.
It is unclear how “the simulated error-free operating state” can be “for … individually occurring causes of an anomaly”. In other words, it is unclear how during the occurring of anomaly, the error-free (no anomaly) operating state can be obtained or aquired.
It is unclear what Applicant means by “each qualitative symptom” as the claim does not establish qualitative symptoms. Once qualitative symptoms are established in the recitation of the claim, “each qualitative symptom” (emphasis added) may be clearer for construing the claim.
It is unclear what Applicant means by the element “a qualitative deviation”. If Applicant means a statistical deviation by the element “a qualitative deviation,” Examiner respectfully requests the claim recites the element accordingly. Further, it is unclear how the comparison of the simulated operating state and the error-free operating state can have deviation, if both states are based on the anomaly occurring as discussed on section 8.b above (both states are the same).
For purposes of examination, the limitation will be interpreted as deriving a deviation by comparing a state of the simulated error-free operation and a state of the simulated operation with the anomaly. Appropriate clarification through claim amendment is respectfully requested.
Further, the claim recites the limitation “f) comparing the symptom …”. There is insufficient antecedent basis for the elements “the symptom” and “the identified symptom”. The claim recites elements “each qualitative symptom”, “a qualitative symptom”, “the symptom”, “each symptom”, “derived symptoms” and “the identified symptom”. Appropriate clarification through claim amendment is respectfully requested.
Independent claims 14 and 15 are rejected under 35 U.S.C. 112(b) for similar reason as that of independent claim 8 as discussed above.
Claims 9-13 are dependent claims of claim 8. The claim 8 is rejected under 35 U.S.C. 112(b), and therefore, claims 9-13 are rejected under 35 U.S.C. 112(b).
Claims 16-18 are dependent claims of claim 15. The claim 15 is rejected under 35 U.S.C. 112(b), and therefore, claims 16-18 are rejected under 35 U.S.C. 112(b).
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim is directed to a software. Computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs, are not physical “things.” They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program’s functionality to be realized. In contrast, a claimed non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program’s functionality to be realized, and is thus statutory. See Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035.
Claims 8-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 8:
(Step 2A, Prong One)
The claim recites, “a) simulating an error-free operation of the technical plant via a computer-implemented simulation tool; b) simulating a plurality of individual causes of an anomaly occurring during operation of the technical plant; e) determining a qualitative symptom of the anomaly if an anomaly occurs during real operation of the technical plant; f) comparing the symptom determined during real operation of the technical plant with each symptom previously derived during simulations of the operating states when one of the occurring causes of an anomaly is present; and g) identifying those derived symptoms having a determined degree of similarity to the symptom determined during real operation of the technical plant”.
Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the Specification, such as paragraphs [0067], [0072] and [0074]-[0082], for instance, the c) comparing, e) determining, f) comparing and g) identifying functions are mental processes that can be performed through observation, evaluation and judgement based on simulated operational data and real operational data. That is, other than reciting an “a computer-implemented simulation tool” (a generic electronic device or generic computer component), a person may perform, through observation, evaluation and judgement, the features enunciated above.
Under their broadest reasonable interpretation and based on the description provided in the published Specification, such as paragraphs [0064]-[0066] and [0075]-[0082], for instance, the limitation of the a) simulating and b) simulating, as claimed, are processes that entails purely mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Accordingly, the claim recites an abstract idea.
(Step 2A, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional limitations of, “d) operating the technical plant; g) … at least one of storing causes of the anomaly associated with the identified symptoms in a data memory of the technical plant and displaying the causes of the anomaly associated with the identified symptoms.”
The additional limitation “operating the technical plant” as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to determine, compare and identify as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application. see MPEP 2106.05(f)
The additional limitation of “g) … at least one of storing causes of the anomaly associated with the identified symptoms in a data memory of the technical plant and displaying the causes of the anomaly associated with the identified symptoms.” is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering or data output. (i.e., all uses of the recited judicial exception require such data gathering or data output). The claim does not recite an improvement in a technology as set forth in MPEP 2106.04(d) and MPEP 2106.05(a). Accordingly, the additional limitations recited in the claim do not integrate the abstract idea into a practical application.
In view of the foregoing, the additional limitations are not sufficient to demonstrate integration of a judicial exception into a practical application.
(Step 2B)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional limitation “d) operating the technical plant”, as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to determine, compare and identify as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more.
The additional limitation “g) … storing … or … displaying …”, as recited represents a function that is recognized as well-understood, routine, and conventional, for instance, as demonstrated in Ramu et al. (US 2024/0102242 A1) paragraph [0040] (“In an embodiment, a computing unit (103) such as a personal computer, a laptop, a server, or any other computing device may be used to perform the off-line analysis and the on-line analysis. In an embodiment, the computing unit (103) can be a cloud environment and the computing unit (103) is connected to the paper plant via a network. The computing unit (103) comprises one or more hardware processors and a memory. The off-line analysis is performed by the computing unit (103) to label the parameters related to the paper machine (101) to comprise normal patterns and abnormal patterns. The on-line analysis is performed to predict the break in the paper web using the labelled parameters. Further, the predicted break in the paper web is provided to a notification unit (104) to alert an operator in the paper plant. Also, a root cause for the break, an estimated time to break and one or more actions to be performed to avoid a break in the paper web and notify the one or more actions on the notification unit (104). In an embodiment, the notification unit (104) may include, but are not limited to a display unit, a hooter, a light notification and a combination thereof.”), Kale (US 2022/0026879 A1) paragraph [0033] (“During a period of normal operation, the SNN is trained to recognize normal data patterns. Subsequently, the SNN detects anomaly that deviates from the normal patterns. Based on the detection, a predictive maintenance is scheduled. The SNN can be trained to predict common troubles by inducing/reproducing the troubles in the system. In some cases, the SNN can be used in conjunction with a black box data recorder that stores a recent set of data such that when an operational or other problem occurs, the black box data can be retrieved to train the SNN to recognize the indications to the trouble ahead of time. Subsequently, the SNN can suggest pre-emptive maintenance operations to avoid the foregoing troubles.”), and Hollender et al. (US 2020/0012270 A1) paragraph [0049] (“The anomaly detection 120 then outputs 1400 the anomaly alert AA via the interface 110 to enable deactivating of the advanced process controller APC in case of an anomaly detection for the industrial process system. This includes in one embodiment to prompt an operator 10 of the industrial process system with the anomaly alert AA so that the operator takes corrective action 11 in deactivating the APC. In another embodiment, it includes to send the anomaly alert AA directly to the industrial process system in a machine readable format which can be executed by a computer-implemented control unit of the industrial process system 300 to automatically deactivate the APC in response to the anomaly alert AA.”).
Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Claim 9 recitation “wherein during said identifying those symptoms which are identical to the symptom determined during actual operation of the technical plant are identifiable and at least of stored in the memory and displayed” simply add more detail to or are cumulative to the abstract idea and the insignificant extra-solution activity of claim 8. The claim is not patent eligible.
Claim 10 recitation “wherein during said simulating a plurality of individual causes of an anomaly and said comparing the simulated error-free operating state, an intensity of the cause of the anomaly is varied within a specific range for each possible cause of an anomaly, and wherein upon variation a plurality of symptoms are derivable, which, if applicable, are utilized to identify the cause of the anomaly during said identifying” simply add more detail to or are cumulative to the abstract idea of claim 8. The claim is not patent eligible.
Claim 11 recitation “wherein during said simulating a plurality of individual causes of an anomaly and said comparing the simulated error-free operating state, an intensity of the cause of the anomaly is varied within a specific range for each possible cause of an anomaly, and wherein upon variation a plurality of symptoms are derivable, which, if applicable, are utilized to identify the cause of the anomaly during said identifying” simply add more detail to or are cumulative to the abstract idea of claim 8. The claim is not patent eligible.
Claim 12 recitation “wherein the technical plant comprises a production or process plant” simply add more detail to or are cumulative to the preamble of claim 8. The claim is not patent eligible.
Claim 13 recitation “A computer program including program code instructions which, when executed by a computer, implement the method as claimed in claim 8” may be tools that are used to determine, compare and identify as recited in claim 8, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not sufficient to demonstrate integration of a judicial exception into a practical application, and does not amount to significantly more. The claim is not patent eligible.
Independent claims 14 and 15 are not patent eligible for similar reasons as discussed above for claim 8.
Claim 16 recitation “wherein the server comprises an operator station server” may be tools that are used to determine, compare and identify as recited in claim 8, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not sufficient to demonstrate integration of a judicial exception into a practical application, and does not amount to significantly more. The claim recitation “the technical plant comprises a production or process plant” simply add more detail to or are cumulative to the preamble of claim 15. The claim is not patent eligible.
Claim 17 recitation “A control system for a technical plant, comprising: at least the server as claimed in claim 15; and a client” may be tools that are used to determine, compare and identify as recited in claim 8, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not sufficient to demonstrate integration of a judicial exception into a practical application, and does not amount to significantly more. The claim is not patent eligible.
Claim 18 recitation “wherein the server comprises an operator station server and the client comprises an operator station client” may be tools that are used to determine, compare and identify as recited in claim 8, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using a generic electronic or computer component. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not sufficient to demonstrate integration of a judicial exception into a practical application, and does not amount to significantly more. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8-9 and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ramu et al. (US 2024/0102242 A1) (“Ramu”), in view of Hollender et al. (US 2020/0012270 A1) (“Hollender”).
Regarding independent claim 8, Ramu teaches:
A method for determining a cause of an anomaly during operation of a technical plant, the method comprising: (Ramu: Abstract “A system and method for labelling normal and abnormal regions in data related to a paper machine for web break prediction and labelling individual parameters for root cause analysis, using machine learning models, includes using the machine learning models in real-time to predict breaks in the paper web, analyzing root cause for the breaks in the paper web, and estimating a time to break. An auto-data-labeling framework helps in adaptive learning for autonomous model improvement of the deployed model, transfer learning, shortlisting parameters and automating feasibility study.”) (Ramu: [0040] “In an embodiment, a computing unit (103) such as a personal computer, a laptop, a server, or any other computing device may be used to perform the off-line analysis and the on-line analysis. In an embodiment, the computing unit (103) can be a cloud environment and the computing unit (103) is connected to the paper plant via a network. The computing unit (103) comprises one or more hardware processors and a memory. …”) [The root cause reads on “a cause”. The paper web break reads on “an anomaly during operation”. The paper machine reads on “a technical plant”.]
a) simulating an error-free operation of the technical plant via a computer-implemented simulation tool; b) simulating a plurality of individual causes of an anomaly occurring during operation of the technical plant; (Ramu: [0007] “In an embodiment, the simulation parameters are generated using historical parameters of the paper plant. Further, the plurality of machine learning models is configured to label the simulated parameters into normal patterns and abnormal patterns. In an embodiment, the abnormal patterns are proximate to a timestamp of the break in the paper web. Further, an output from each of the plurality of machine learning models is received, where the output is indicative of labels comprising the normal patterns and the abnormal patterns. Furthermore, a model from the plurality of machine learning models is selected based on one or more performance metrics and the output of the plurality of machine learning models and storing one or more model parameters of the selected model in a memory of the computing unit. Thereafter, an auto-labeller is provided with historical parameters related to the paper machine, comprising at least one of the normal patterns and the abnormal patterns, and the plurality of details of the selected model, to label the historical parameters into the normal patterns and the abnormal patterns. The labels generated by the auto-labeller are stored as labelled data in a database, where the labelled data is used for predicting a break in the paper web in real-time and root causes for the break in the paper web.”) [Outputting the simulated parameters of normal patterns based on the historical parameters that are not proximate to the timestamp of the break read on “simulating an error-free operation …”, and outputting the simulated parameters of abnormal patterns based on the historical parameters that are proximate to the timestamp of the break read on “simulating a plurality of individual causes of an anomaly occurring during operation …”.]
d) operating the technical plant; (Ramu: [0028] “In an embodiment, the present disclosure relates to a method of predicting a break in a paper web in a paper machine. A plurality of sensors is used to monitor parameters related to the paper machine, where the parameters comprise a plurality of process parameters and a plurality of operating parameters. During manufacturing of paper, a paper web is provided/formed in the paper machine. The method comprises receiving the parameters from the plurality of sensors. Further, a pattern in a variation of each parameter, with time is determined. Furthermore, each of the determined pattern Is compared with corresponding expected pattern. Thereafter, a break in the paper web is predicted based on the comparison.”) [Monitoring the paper machine during manufacturing of paper reads on “operating the technical plant”.]
e) determining a qualitative symptom of the anomaly if an anomaly occurs during real operation of the technical plant; (Ramu: [0027] “In an embodiment, the present disclosure relates to training a plurality of machine learning models to predict a break in a paper web. Training the plurality of machine learning models comprises providing labelled data comprising normal patterns and abnormal patterns of parameters related to a paper machine, to the plurality of machine learning models. In an embodiment, a timestamp is associated with the abnormal value, and the timestamp of the abnormal values is proximate to a timestamp of the break in the paper web. The labelled data is generated using an auto-labeller using simulated parameters and historical parameters related to the paper machine. Further, the plurality of machine learning models is configured to detect patterns in the parameters and determine the detected patterns into at least one of normal patterns and abnormal patterns. Furthermore, the plurality of machine learning models is configured to generate an expected pattern for each parameter based on the determination, where each expected pattern comprises at least one of, normal patterns and abnormal patterns. Furthermore, an output from each of the plurality of machine learning models is received, where the output is indicative of a prediction of the break in the paper web based on a comparison of the labelled data with corresponding expected patterns. Thereafter, a machine learning model is selected from the plurality of machine learning models based on the output of the plurality machine learning models.”) (Ramu: [0028] “In an embodiment, the present disclosure relates to a method of predicting a break in a paper web in a paper machine. A plurality of sensors is used to monitor parameters related to the paper machine, where the parameters comprise a plurality of process parameters and a plurality of operating parameters. During manufacturing of paper, a paper web is provided/formed in the paper machine. The method comprises receiving the parameters from the plurality of sensors. Further, a pattern in a variation of each parameter, with time is determined. Furthermore, each of the determined pattern Is compared with corresponding expected pattern. Thereafter, a break in the paper web is predicted based on the comparison.”) [The predicting the breakage based on comparing the determined pattern with the expected pattern reads on “determining a qualitative symptom of the anomaly if an anomaly occurs during real operation …”, where the expected pattern of the predicted breakage reads on “a qualitative symptom” and the determined pattern reads on “an anomaly … during real operation”.]
f) comparing the symptom determined during real operation of the technical plant with each symptom previously derived during simulations of the operating states when one of the occurring causes of an anomaly is present; and (Ramu: [0028] as discussed above) (Ramu: [0029] “In an embodiment, the plurality of models is further trained to estimate a time to break the paper web and determine a root cause that causes a break in the paper web based on abnormal patterns in the parameters and the labelled data. Also, the plurality of models is further trained to generate an association between the abnormal patterns and the estimation of time to break of the paper web, and an association between the abnormal patterns and the root cause for the break in the paper web.”) [Associating the root cause for the break with the abnormal pattern upon the comparing reads on “comparing … when one of the occurring causes of an anomaly is present”.]
g) identifying those derived symptoms having a determined degree of similarity to the symptom determined during real operation of the technical plant, and (Ramu: [0026] “Each batch is labelled to comprise the normal patterns and the abnormal patterns based on one of, independently analyzing each batch or by providing similar batches to the auto-labeller.”) (Ramu: [0054] “In an embodiment, each batch of parameters is associated with a grade and each grade comprises specific operating conditions. Examples of grade may include, but are not limited to, bond or writing grade, book grade, text grade. In an embodiment, the grade may be determined based on basis weight of the paper. In an embodiment, independent batches may be analyzed and labelled as normal regions and abnormal regions. For example, the auto-labeller (304) considers Independent batch analysis for a given batch to label normal and abnormal regions. But if the grade and operating conditions of a batch are known then, it is compared with all other batches within the same grade and operating condition to make the labeling more robust. For example, each batch may be associated with a grade and each grade may be associated with one or more operating conditions. For example, while manufacturing a bond paper, the paper machine (101) may be operated at 1500 m/min, whereas, while manufacturing a text grade paper, the paper machine (101) may be operated at 2000 m/min. Likewise, each grade may be associated with different operating conditions and also may include different process conditions. Hence, when a batch associated with a text grade and the paper machine (101) is operated at 2000 m/min, is provided to the auto-labeller (304) for labelling, the auto-labeller (304) may compare the batch with similar historical batches belonging to the text grade and which were operated at 2000 m/min. Based on the comparison with the similar historical batches, the auto-labeller (304) labels the batch.”) at least one of storing causes of the anomaly associated with the identified symptoms in a data memory of the technical plant and displaying the causes of the anomaly associated with the identified symptoms. (Ramu: [0030] “In an embodiment, the selected model identifies the abnormal patterns in the parameters received from the one or more sensors based on the comparison of each of the determined pattern with corresponding expected pattern. Further, the selected model determines a root cause for the break in the paper web and an estimate of time to break in the paper web based on an association between the abnormal patterns and the root cause for the break in the paper web and an association between the abnormal patterns and the estimation of time to break of the paper web.”) (Ramu: [0031] “In an embodiment, the parameters are received after each break in the paper web. Further, the parameters are provided to an auto-labeller for generating labels comprising the normal patterns and abnormal patterns. Furthermore, the generated labels are stored as labelled data in a database.”)(Ramu: [0040] “In an embodiment, a computing unit (103) such as a personal computer, a laptop, a server, or any other computing device may be used to perform the off-line analysis and the on-line analysis. In an embodiment, the computing unit (103) can be a cloud environment and the computing unit (103) is connected to the paper plant via a network. The computing unit (103) comprises one or more hardware processors and a memory. The off-line analysis is performed by the computing unit (103) to label the parameters related to the paper machine (101) to comprise normal patterns and abnormal patterns. The on-line analysis is performed to predict the break in the paper web using the labelled parameters. Further, the predicted break in the paper web is provided to a notification unit (104) to alert an operator in the paper plant. Also, a root cause for the break, an estimated time to break and one or more actions to be performed to avoid a break in the paper web and notify the one or more actions on the notification unit (104). In an embodiment, the notification unit (104) may include, but are not limited to a display unit, a hooter, a light notification and a combination thereof.”)
Ramu does not expressly teach: c) comparing the simulated error-free operating state for each of the plurality of individually occurring causes of an anomaly with the simulated operating state when one of the occurring causes of an anomaly is present, each qualitative symptom being derived from the comparison which describes a qualitative deviation of the simulated operating state from the error-free operating state.
Hollender teaches:
c) comparing the simulated error-free operating state for each of the plurality of individually occurring causes of an anomaly with the simulated operating state when one of the occurring causes of an anomaly is present, (Hollender: [0042] “In an embodiment, a computer program product includes instructions that when loaded into a memory of the computer system and executed by at least one processor of the computer system cause the computer system to execute the steps of the computer-implemented method and to perform the functions of the computer system as disclosed.”) (Hollender: [0064] “The post-processor module can overcome this disadvantage by combining the capabilities of high-sensitivity anomaly detection with high granularity in root cause identification. FIGS. 4A to 4C illustrate different embodiments of the post-processor function. FIG. 4A shows an embodiment for determining one or more root causes amongst the received technical status data using density based screening. Thereby, technical status data samples showing anomalies are compared with samples showing no anomalies to identify the most deviating dimensions as root cause signals. In the example, two signals A, B are sampled over multiple time intervals. For example, a signal value for each of the signals may be sampled every hour. In the example, for both signals the last three samples are analyzed. For signal A the last three samples are aA1, aA2 and aA3 showing a potential anomaly. For signal B the last three samples are aB1, aB2 and aB3 showing a potential anomaly. The post-processor knows corresponding normal series of samples for signals A and B: nA1, nA2, n A3 and nB1, nB2, nB3. The normal samples may correspond to historic data reflecting normal operation of the industrial process system in the past or they may be computed based on a model of the industrial process system which allows to simulate normal operation of the industrial process system. Such normal samples serve as reference samples for the comparison of potentially abnormal samples.”) (Hollender: [0065]-[0067] “[0065] The post-processor now computes the distances dA1, dA2, dA3 between the normal and abnormal samples in the following way: [0066] [dA1 dA2 dA3]=[aA1 aA2aA3]−[nA1 nA2 nA3] [0067] [dB1 dB2 dB3]=[aB1 aB2 aB3]−[nB1 nB2 nB3]”) each qualitative symptom being derived from the comparison which describes a qualitative deviation of the simulated operating state from the error-free operating state. (Hollender: [0033] “In an embodiment, density based screening may be used where sensor data samples showing anomalies are compared with samples showing no anomalies to identify the most deviating dimensions as root cause signals. In an alternative embodiment, scoring anomaly detection applied to groups of signals and/or single signals can be used to generate a list of most likely root cause signals.”) (Hollender: [0075] “FIG. 4B illustrates root cause identification by using anomaly scoring at signal group level. In this embodiment, an anomaly alert vector AAv was already generated as described in FIG. 3B. The post-processor now performs a scoring function on the vector elements (group anomaly indicators). Such a scoring function can be, for example, to determine the maximum value of the vector elements Max(AA1, AA2, AA3) or to sort the vector elements Sorte List (AA1, AA2, AA3) to provide to the operator a prioritized list of with regards to the signal groups which most likely caused the anomaly. FIG. 4C illustrates that the same anomaly scoring can be applied to the embodiment where anomaly detection is performed at the single signal level as disclosed in FIG. 3C. In this case the length of the anomaly alert vector AAv1 corresponds to the number of signals because for each signal a corresponding Machine Learning Model MLMa to MLMi was used to compute a respective anomaly indicator. That is, the scoring function is applied to AAv1 which provides an improved granularity to the operator for fast identification of the root cause.”) [The prioritized list with regards to the signal groups or the root cause which most likely caused the anomaly based in determined deviating dimensions reads on “qualitative symptom being derived from the comparison which describes a qualitative deviation …”.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Ramu and Hollender before them, to modify associating the root cause for the break/anomaly, to incorporate determining the root cause which most likely caused the break/anomaly by providing prioritized list of the possible root causes using machine learning models.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for faster identification of the root cause by providing improved granularity to the operator. (Hollender: [0013], [0014] and [0075])
Regarding claim 9, Ramu and Hollender teach all the claimed features of claim 8. Ramu further teaches:
wherein during said identifying those symptoms which are identical to the symptom determined during actual operation of the technical plant are identifiable and at least of stored in the memory and displayed. (Ramu: [0030], [0031] and [0040] as discussed in claim 8) [Identifying and labeling abnormal patterns that lead to the predicted breakage reads on “identifying those symptoms which are identical”.]
Regarding claim 12, Ramu and Hollender teach all the claimed features of claim 8. Ramu further teaches:
wherein the technical plant comprises a production or process plant. (Ramu: [0003] “Paper is consumed in multiple forms in our everyday activities like packaging paper, writing paper, printing paper, paperboard, cardboard, tissue paper, books, magazines, documents, newspaper etc. In recent times, paper has gained momentum in the packaging sector for both industrial and consumer products. Paper making process involves a paper machine, which is an industrial machine that converts pulp into paper in large quantities at high speed. Pulp parameters or properties are different for different grades (for example paper basis weight for tissue paper is different from board paper or writing paper). Pulp is input to the paper machine and the pulp is mixed with water in the paper machine. The paper machine creates different grades of paper depending on application using the relevant pulp.”)
Regarding claim 13, Ramu and Hollender teach all the claimed features of claim 8. Hollender further teaches:
A computer program including program code instructions which, when executed by a computer, implement the method as claimed in claim 8. (Hollender: [0042] “In an embodiment, a computer program product includes instructions that when loaded into a memory of the computer system and executed by at least one processor of the computer system cause the computer system to execute the steps of the computer-implemented method and to perform the functions of the computer system as disclosed.”)
The motivation to combine Ramu and Hollender as described in claim 8 is incorporated herein.
Regarding independent claim 14:
The claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
Regarding independent claim 15:
The claim recites similar limitations as corresponding claim 8 and is rejected using the same teachings and rationale.
Regarding claim 16, Ramu and Hollender teach all the claimed features of claim 15. Ramu further teaches:
wherein the server comprises an operator station server (Ramu: [0040] “In an embodiment, a computing unit (103) such as a personal computer, a laptop, a server, or any other computing device may be used to perform the off-line analysis and the on-line analysis. In an embodiment, the computing unit (103) can be a cloud environment and the computing unit (103) is connected to the paper plant via a network. The computing unit (103) comprises one or more hardware processors and a memory. …”) [The personal computer or the laptop reads on “an operator station server”.] and the technical plant comprises a production or process plant. (Ramu: [0003] “Paper is consumed in multiple forms in our everyday activities like packaging paper, writing paper, printing paper, paperboard, cardboard, tissue paper, books, magazines, documents, newspaper etc. In recent times, paper has gained momentum in the packaging sector for both industrial and consumer products. Paper making process involves a paper machine, which is an industrial machine that converts pulp into paper in large quantities at high speed. Pulp parameters or properties are different for different grades (for example paper basis weight for tissue paper is different from board paper or writing paper). Pulp is input to the paper machine and the pulp is mixed with water in the paper machine. The paper machine creates different grades of paper depending on application using the relevant pulp.”)
Regarding claim 17, Ramu and Hollender teach all the claimed features of claim 15. Ramu further teaches:
A control system for a technical plant, comprising: at least the server as claimed in claim 15; and a client. (Ramu: [0040] “In an embodiment, a computing unit (103) such as a personal computer, a laptop, a server, or any other computing device may be used to perform the off-line analysis and the on-line analysis. In an embodiment, the computing unit (103) can be a cloud environment and the computing unit (103) is connected to the paper plant via a network. The computing unit (103) comprises one or more hardware processors and a memory. The off-line analysis is performed by the computing unit (103) to label the parameters related to the paper machine (101) to comprise normal patterns and abnormal patterns. The on-line analysis is performed to predict the break in the paper web using the labelled parameters. Further, the predicted break in the paper web is provided to a notification unit (104) to alert an operator in the paper plant. Also, a root cause for the break, an estimated time to break and one or more actions to be performed to avoid a break in the paper web and notify the one or more actions on the notification unit (104). In an embodiment, the notification unit (104) may include, but are not limited to a display unit, a hooter, a light notification and a combination thereof.”) [The notification unit reads on “a client”.]
Regarding claim 18, Ramu and Hollender teach all the claimed features of claims 15 and 17. Hollender further teaches:
wherein the server comprises an operator station server and the client comprises an operator station client. (Hollender: [0093] “The systems and techniques described here can be implemented in a computing device that includes a backend component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wireless local area network (“WLAN”), a wide area network (“WAN”), and the Internet.”) (Hollender: [0094] “The computing device can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.”)
The motivation to combine Ramu and Hollender as described in claim 15 is incorporated herein.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Ramu, in view of Hollender, further in view of Kale (US 2022/0026879 A1) (“Kale”).
Regarding claim 10, Ramu and Hollender teach all the claimed features of claim 8. Ramu and Hollender do not expressly teach the recitations of claim 10.
Kale teaches:
wherein during said simulating a plurality of individual causes of an anomaly and said comparing the simulated error-free operating state, an intensity of the cause of the anomaly is varied within a specific range for each possible cause of an anomaly, and wherein upon variation a plurality of symptoms are derivable, which, if applicable, are utilized to identify the cause of the anomaly during said identifying. (Kale: [0108] “In one embodiment, the computing device is further configured to: cause an event (e.g., simulated artificial disturbance outside normal operating range by a predetermined deviation, such as more than 25% or more than 2-3 sigma standard deviation of a normal range or value of an operating parameter) of a first type that affects the operation of the at least one machine; collect first data during the event; and train the ANN using the first data. The predicted maintenance service is associated with preventing a future event of the first type.”) (Kale: [0113] “In one embodiment, the method further comprises: identifying an occurrence of a predetermined type (e.g., types stored in a database in memory); in response to identifying the occurrence, retrieving a first portion of the sensor data stored in the memory device, wherein the first portion corresponds to a predetermined period of time prior to identifying the occurrence; and training the ANN using the first portion of the sensor data. Predicting the maintenance service comprises identifying a preemptive action to perform for the at least one machine.”) (Kale: [0114] “In one embodiment, the occurrence of the predetermined type is an event that causes physical damage to the at least one machine, or a product manufactured using the at least one machine.”) (Kale: [0262] “The residual error threshold can be set for each operational region to prevent false anomaly detection in sparsely trained regions. A lower predictability (i.e. by higher nonlinearity within a region) will indicate a less predictable region, and will have a looser threshold. Therefore, a large variation from the normal operational behavior would be required for an anomaly to be detected. Conversely, a higher predictability will result in a lower threshold. In such cases, the residual error would be expected to be tighter in that operational region, so a smaller deviation from normal operational behavior would be detected as an anomaly.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Ramu, Hollender and Kale before them, to modify the simulation settings to associate the root causes with the break/anomaly, to incorporate using artificial disturbance settings outside normal operating range of the operating parameter to collect data upon the artificial disturbance settings.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for using the collected data to train the ANN to predict break/anomaly, and preemptively perform actions to mitigate the break/anomaly. (Kale: [0108], [0113]-[0114] and [0262])
Regarding claim 11, Ramu and Hollender teach all the claimed features of claims 8-9. Ramu and Hollender do not expressly teach the recitations of claim 11.
Kale teaches:
wherein during said simulating a plurality of individual causes of an anomaly and said comparing the simulated error-free operating state, an intensity of the cause of the anomaly is varied within a specific range for each possible cause of an anomaly, and wherein upon variation a plurality of symptoms are derivable, which, if applicable, are utilized to identify the cause of the anomaly during said identifying. (Kale: [0108] “In one embodiment, the computing device is further configured to: cause an event (e.g., simulated artificial disturbance outside normal operating range by a predetermined deviation, such as more than 25% or more than 2-3 sigma standard deviation of a normal range or value of an operating parameter) of a first type that affects the operation of the at least one machine; collect first data during the event; and train the ANN using the first data. The predicted maintenance service is associated with preventing a future event of the first type.”) (Kale: [0113] “In one embodiment, the method further comprises: identifying an occurrence of a predetermined type (e.g., types stored in a database in memory); in response to identifying the occurrence, retrieving a first portion of the sensor data stored in the memory device, wherein the first portion corresponds to a predetermined period of time prior to identifying the occurrence; and training the ANN using the first portion of the sensor data. Predicting the maintenance service comprises identifying a preemptive action to perform for the at least one machine.”) (Kale: [0114] “In one embodiment, the occurrence of the predetermined type is an event that causes physical damage to the at least one machine, or a product manufactured using the at least one machine.”) (Kale: [0262] “The residual error threshold can be set for each operational region to prevent false anomaly detection in sparsely trained regions. A lower predictability (i.e. by higher nonlinearity within a region) will indicate a less predictable region, and will have a looser threshold. Therefore, a large variation from the normal operational behavior would be required for an anomaly to be detected. Conversely, a higher predictability will result in a lower threshold. In such cases, the residual error would be expected to be tighter in that operational region, so a smaller deviation from normal operational behavior would be detected as an anomaly.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Ramu, Hollender and Kale before them, to modify the simulation settings to associate the root causes with the break/anomaly, to incorporate using artificial disturbance settings outside normal operating range of the operating parameter to collect data upon the artificial disturbance settings.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for using the collected data to train the ANN to predict break/anomaly, and preemptively perform actions to mitigate the break/anomaly. (Kale: [0108], [0113]-[0114] and [0262])
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MICHAEL W CHOI/ Primary Examiner, Art Unit 2116