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 .
Status of Claims
Claims 1-6 are pending and are examined herein.
Claims 3 and 4 are objected to for minor informalities.
Claims 1-6 are rejected for nonstatutory double patenting.
Claims 1-6 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Claims 1-6 are rejected under 35 USC 103.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/29/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 3 objected to because of the following informalities: the phrase ‘each…corresponds to each season when each of the cycles of the data to be input is a season’ could be misleading and seems redundant. While the Examiner recognizes this to mean the cycles simply in terms of seasons, the reader could believe this to mean the inputs are individual seasons. Appropriate correction is required.
Claim 4 objected to because of the following informalities: the phrase ‘each…corresponds to each time period when each of the cycles of the data to be input is a time period’ could be misleading and seems redundant. While the Examiner recognizes this to mean the cycles simply in terms of time periods, the reader could believe this to mean the inputs are individual time periods. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-6 rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-6 respectively of U.S. Patent App. 17707842. Although the claims at issue are not identical, they are not patentably distinct from each other for the reasons outlined in the table below.
Instant Application
17707842
Claim 1
Claim 4
A deterioration detection method performed by a computer, the deterioration detection method comprising:
A deterioration detection method performed by a computer comprising:
acquiring each – model…that detects a change in an output result of a machine learning model
calculating a first matching result obtained by comparing the first output result and the second output result in a first period; calculating a second matching result obtained by comparing the first output result and the second output result in a second period different from the first period
each detection model, which corresponds to each cycle of data to be input
the detection model of which a model applicability domain that indicates an input data range to be an output same as an output of the trained model is narrowed
acquiring a first output result when data is input to the machine learning model; acquiring each second output result when data is input to each detection model that corresponds to each cycle
acquiring a first output result when input data is input to a trained model; acquiring a second output result when the input data is input to a detection model that detects performance deterioration of the trained model
and detecting a change in an output result of the machine learning model based on each of the second output results and the first output result
outputting a change in accuracy deterioration of the trained model by using the first matching result and the second matching result
Claim 2
Claim 2
calculating a matching rate of each of the second output results and the first output result
calculating the first matching result includes calculating a matching rate between the first output result and the second output result for each output class of the trained model as the first matching result
detecting accuracy deterioration of the machine learning model based on each of the matching rate
Claim 3
the outputting includes outputting an alert that indicates that accuracy of the trained model deteriorates when the matching rate for each class or the average value for each class is less than a threshold in any one of a plurality of periods including the first period and the second period
notifying a user of the detected accuracy deterioration
Claim 3
Claim 3
each of the detection models corresponds to each season when each of the cycles of the data to be input is a season
the outputting includes outputting an alert that indicates that accuracy of the trained model deteriorates when the matching rate for each class or the average value for each class is less than a threshold in any one of a plurality of periods including the first period and the second period (a season can be a period)
calculating a matching rate of the first output result obtained from the machine learning model and each of the second output result obtained from each of the detection models that corresponds to each season
Claim 2
calculating the first matching result includes calculating a matching rate between the first output result and the second output result for each output class of the trained model as the first matching result (each model for each season is covered by last limitation)
detecting, when each of the matching rates that correspond to all the seasons are less than a threshold, accuracy deterioration of the machine learning model
Claim 3
the outputting includes outputting an alert that indicates that accuracy of the trained model deteriorates when the matching rate for each class or the average value for each class is less than a threshold in any one of a plurality of periods including the first period and the second period (a season can be a period)
notifying a user of the accuracy deterioration
Claim 4
Claim 3
each of the detection models corresponds to each time period when each of the cycles of the data to be input is a time period
the outputting includes outputting an alert that indicates that accuracy of the trained model deteriorates when the matching rate for each class or the average value for each class is less than a threshold in any one of a plurality of periods including the first period and the second period (a time period can be referenced by simply a period)
calculating a matching rate of the first output result obtained from the machine learning model and each second output result obtained from each of the detection models that corresponds to each time period
Claim 2
calculating the first matching result includes calculating a matching rate between the first output result and the second output result for each output class of the trained model as the first matching result (each model for each time period is covered by last limitation)
detecting, when each of the matching rates that correspond to all the time periods are less than a threshold, accuracy deterioration of the machine learning model
Claim 3
the outputting includes outputting an alert that indicates that accuracy of the trained model deteriorates when the matching rate for each class or the average value for each class is less than a threshold in any one of a plurality of periods including the first period and the second period (a time period can be referenced by simply a period)
notifying a user of the accuracy deterioration
As illustrated in the table above, every limitation in Claims 1-4 has a corresponding equivalent or more specific limitation in the patent application 17707842 Claims 1-4.
Thus, the patent application 17707842 anticipates these claims.
Independent Claims 5 and 6 recite an analogous storage medium and an analogous system, respectively, as does the independent Claims 5 and 6 of 17707842.
Claim Rejections - 35 USC § 101 - Abstract Idea
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 1-6 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis
According to the first part of the analysis, in the instant case Claims 1-4 are directed to a method, Claim 5 is directed to a non-transitory medium that stores the method, and Claim 6 is directed to an apparatus; consequently, these claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis (Combined Step 2A Prong 1-2 and Step 2B Analysis)
Claim 1 includes the following recitation of an abstract idea:
detecting a change in an output result of the machine learning model based on each of the second output results and the first output result (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 1 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
acquiring each detection model, which corresponds to each cycle of data to be input, that detects a change in an output result of a machine learning model (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
acquiring a first output result when data is input to the machine learning model (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
acquiring each second output result when data is input to each detection model that corresponds to each cycle (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 1 does not reflect an improvement to computer technology or any other technology.
Claim 2 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 2 includes the following recitation of an additional abstract idea:
calculating a matching rate of each of the second output results and the first output result (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
detecting accuracy deterioration of the machine learning model based on each of the matching rate (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 2 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
notifying a user of the detected accuracy deterioration (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 2 does not reflect an improvement to computer technology or any other technology.
Claim 3 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 3 includes the following recitation of an additional abstract idea:
calculating a matching rate of the first output result obtained from the machine learning model and each of the second output result obtained from each of the detection models that corresponds to each season (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
detecting, when each of the matching rates that correspond to all the seasons are less than a threshold, accuracy deterioration of the machine learning model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 3 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
notifying a user of the accuracy deterioration (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 3 does not reflect an improvement to computer technology or any other technology.
Claim 4 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 4 includes the following recitation of an additional abstract idea:
calculating a matching rate of the first output result obtained from the machine learning model and each second output result obtained from each of the detection models that corresponds to each time period (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
detecting, when each of the matching rates that correspond to all the time periods are less than a threshold, accuracy deterioration of the machine learning model (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 4 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
notifying a user of the accuracy deterioration (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
Claim 4 does not reflect an improvement to computer technology or any other technology.
Claim 5 recites at least the abstract idea identified above in Claim 1.
Claim 5 recites the following additional elements aside from those described in Claim 1 which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
A non-transitory computer-readable storage medium storing a deterioration detection program that causes a processor included in a computer to execute a process (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 5 does not reflect an improvement to computer technology or any other technology.
Claim 6 recites the following additional elements aside from those described in Claim 1 which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
An information processing apparatus comprising: a memory; and a processor coupled to the memory and configured (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 6 does not reflect an improvement to computer technology or any other technology.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over “Wang” (US 2020/0210538 Al, “Scalable System and Engine for Forecasting Wind Turbine Failure”) in view of “Tanimoto” (US 2018/0075360 Al, “Accuracy-Estimating-Model Generating System and Accuracy Estimating System”).
Regarding Claim 1, Wang teaches
acquiring each - model, which corresponds to each cycle of data to be input, that detects a change in an output result of a machine learning model (Wang, Abstract recites “…retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows…”)
acquiring a first output result when data is input to the machine learning model (Wang, Abstract recites “…evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy…”)
acquiring each second output result when data is input to each - model that corresponds to each cycle (Wang, Abstract recites “…evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy…”)
detecting a change in an output result of the machine learning model based on each of the second output results and the first output result (Wang, Abstract recites “…comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold…”)
Wang does not appear to explicitly teach
detection model
However, Tanimoto, directed to analogous art (specifically focusing on incorporating an accuracy estimating model), teaches
detection model (Tanimoto, Abstract recites “An accuracy estimation unit 91 estimates accuracy of a predictive model using an accuracy estimating model that is learned using, as an explanatory variable, all or part of one or more contexts each indicating a feature value representing an operation status of the predictive model…”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Tanimoto to have a matching rate related to the accuracy of that model for each of the models. A person of ordinary skill in the art replicating Wang would be lost as to what an appropriate time period is for each model. Said person would look into ways of judging a model’s performance to see if the time period of a select model is long enough to be reliable. More particularly, said person would try to find art that assigns a value of some type to help judge the appropriate time period so that adjustments can be made. Tanimoto, Paragraph [0035] recites “For example, the accuracy estimating model generation unit 30 may calculate the accuracy index using the whole error index after the update try time, or calculate the accuracy index using the error index in a predetermined period ( e.g. three months). The optimization target period is substantially a period up to the next update of the predictive model.” Tanimoto specifically connects calculating an accuracy index to the optimization of a time period. Said person not only finds a solution to the problem of implementing Wang but also has motivation for choosing to incorporate Tanimoto’s accuracy index specifically.
Regarding Claim 2, the rejection of Claim 1 is incorporated herein. Wang does not appear to explicitly teach
notifying a user of the detected accuracy deterioration
calculating a matching rate of each of the second output results and the first output result
detecting accuracy deterioration of the machine learning model based on each of the matching rate
However, Tanimoto, directed to analogous art (specifically focusing on incorporating an accuracy estimating model), teaches
notifying a user of the detected accuracy deterioration (Tanimoto, Paragraph [0054] recites “The accuracy display unit 70 displays the accuracy status of each predictive model. In detail, the accuracy display unit 70 visualizes information specified by at least one of the accuracy of the predictive model before updating and the accuracy of the predictive model after updating. For example, the accuracy display unit 70 may visualize the changes of the accuracy index of each predictive model, or visualize the accuracy index of each predictive model estimated by the accuracy degradation estimation unit 50.”)
calculating a matching rate of each of the second output results and the first output result (Tanimoto, Figure 5, Element S22 is labeled as “Apply calculated context to accuracy estimating model to calculate accuracy index.” Examiner is interpreting the accuracy index as a matching rate. Examiner interprets “applying calculated context to accuracy estimating model” as comparing the values associated with each, the first value being the context and the other being the model’s outputs.)
detecting accuracy deterioration of the machine learning model based on each of the matching rate (Tanimoto, Paragraph [0035] recites “The accuracy degradation estimation unit 50 applies the calculated context to the accuracy estimating model, to calculate the accuracy index of the predictive model subjected to the accuracy degradation estimation (specifically, the predictive model currently in operation) in the optimization target period.” The accuracy degradation estimation unit is responsible for detecting accuracy deterioration. It does so by calculating an accuracy index. Examiner is interpreting the accuracy index as the matching rate.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Tanimoto as described above with respect to Claim 1.
Regarding Claim 3, the rejection of Claim 1 is incorporated herein. Wang teaches
each of the detection models that corresponds to each season (Wang, Abstract recites “…retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows…”)
Wang does not appear to explicitly teach
notifying a user of the accuracy deterioration
calculating a matching rate of the first output result obtained from the machine learning model and each of the second output result
detecting, when each of the matching rates are less than a threshold, accuracy deterioration of the machine learning model
However, Tanimoto, directed to analogous art (specifically focusing on incorporating an accuracy estimating model), teaches
notifying a user of the accuracy deterioration (Tanimoto, Paragraph [0054] recites “The accuracy display unit 70 displays the accuracy status of each predictive model. In detail, the accuracy display unit 70 visualizes information specified by at least one of the accuracy of the predictive model before updating and the accuracy of the predictive model after updating. For example, the accuracy display unit 70 may visualize the changes of the accuracy index of each predictive model, or visualize the accuracy index of each predictive model estimated by the accuracy degradation estimation unit 50.”)
calculating a matching rate of the first output result obtained from the machine learning model and each of the second output result (Tanimoto, Figure 5, Element S22 is labeled as “Apply calculated context to accuracy estimating model to calculate accuracy index.” Examiner is interpreting the accuracy index as a matching rate. Examiner interprets “applying calculated context to accuracy estimating model” as comparing the values associated with each, the first value being the context and the other being the model’s outputs.)
detecting, when each of the matching rates are less than a threshold, accuracy deterioration of the machine learning model (Tanimoto, Paragraph [0035] recites “The accuracy degradation estimation unit 50 applies the calculated context to the accuracy estimating model, to calculate the accuracy index of the predictive model subjected to the accuracy degradation estimation (specifically, the predictive model currently in operation) in the optimization target period.” The accuracy degradation estimation unit is responsible for detecting accuracy deterioration. It does so by calculating an accuracy index. Examiner is interpreting the accuracy index as the matching rate. Examiner believes the most obvious way to replicate the accuracy estimation unit is through setting a threshold on how high the accuracy index of a select model has to be in order to be accurate.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Tanimoto as described above with respect to Claim 1.
Regarding Claim 4, the rejection of Claim 1 is incorporated herein. Wang teaches
each of the detection models that corresponds to each time period (Wang, Abstract recites “…retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows…”)
Wang does not appear to explicitly teach
notifying a user of the accuracy deterioration
calculating a matching rate of the first output result obtained from the machine learning model and each of the second output result
detecting, when each of the matching rates are less than a threshold, accuracy deterioration of the machine learning model
However, Tanimoto, directed to analogous art (specifically focusing on incorporating an accuracy estimating model), teaches
notifying a user of the accuracy deterioration (Tanimoto, Paragraph [0054] recites “The accuracy display unit 70 displays the accuracy status of each predictive model. In detail, the accuracy display unit 70 visualizes information specified by at least one of the accuracy of the predictive model before updating and the accuracy of the predictive model after updating. For example, the accuracy display unit 70 may visualize the changes of the accuracy index of each predictive model, or visualize the accuracy index of each predictive model estimated by the accuracy degradation estimation unit 50.”)
calculating a matching rate of the first output result obtained from the machine learning model and each of the second output result (Tanimoto, Figure 5, Element S22 is labeled as “Apply calculated context to accuracy estimating model to calculate accuracy index.” Examiner is interpreting the accuracy index as a matching rate. Examiner interprets “applying calculated context to accuracy estimating model” as comparing the values associated with each, the first value being the context and the other being the model’s outputs.)
detecting, when each of the matching rates are less than a threshold, accuracy deterioration of the machine learning model (Tanimoto, Paragraph [0035] recites “The accuracy degradation estimation unit 50 applies the calculated context to the accuracy estimating model, to calculate the accuracy index of the predictive model subjected to the accuracy degradation estimation (specifically, the predictive model currently in operation) in the optimization target period.” The accuracy degradation estimation unit is responsible for detecting accuracy deterioration. It does so by calculating an accuracy index. Examiner is interpreting the accuracy index as the matching rate. Examiner believes the most obvious way to replicate the accuracy estimation unit is through setting a threshold on how high the accuracy index of a select model has to be in order to be accurate.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Tanimoto as described above with respect to Claim 1.
Claim 5 recites a non-transitory medium which stores substantially similar as listed by the method in Claim 1, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Wang teaches
A non-transitory computer-readable storage medium storing a deterioration detection program that causes a processor included in a computer to execute a process (Wang, Abstract recites “An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s).” Wang, Figure 5 shows various functions like Element 534 “Model Training and Testing”. Examiner believes that the word ‘system’ is referring to generic computer equipment that include a processor and attached memory in order to carry out the tasks described. The instructions for this method would be have to be stored on a non-transitory medium in order to carry out each step with appropriate action.)
Claim 6 recites a system which performs substantially similar steps as listed by the method in Claim 1, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Wang teaches
An information processing apparatus comprising: a memory; and a processor coupled to the memory and configured (Wang, Abstract recites “An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s).” Wang, Figure 5 shows various functions like Element 534 “Model Training and Testing”. Examiner believes that the word ‘system’ is referring to generic computer equipment that include a processor and attached memory in order to carry out the tasks described.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art disclosed during the Patent Cooperation Treaty (PCT) application that was claimed for domestic benefit. Specifically, JP 2009237832 A.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN C PRESLEY whose telephone number is (571)272-2682. The examiner can normally be reached Monday-Friday: 9:00 am - 4:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUSTIN C PRESLEY/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121