DETAILED ACTION
The following NON-FINAL office action is in response to application 18/222267 filed on 07/14/2023. This communication is the first action on the merits.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Status of Claims
Claims 1-10 are currently pending and have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/14/23 complies with the provisions of 37 CFR 1.97 and is being considered.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, Claim 1 recites:
An analysis apparatus comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
classify a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance; and
identify a sensor having detected information corresponding to a cause of the classified anomaly based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance.
The claim limitations in the abstract idea have been underlined above; the remaining limitations are “additional elements.”
Step 1:
Under Step 1 of the analysis, Claim 1 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, Claim 1 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “classify a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance” and “identify a sensor having detected information corresponding to a cause of the classified anomaly based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance” are mental processes to classify data and identify the source of the classified data. They are merely data observations, evaluations, and/or judgements performed to classify anomalies and are capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Claim 1 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to instructions to apply the “classify a type of an anomaly having occurred…” and “identify a sensor having detected...” limitations on a general use computer.
In addition to the abstract ideas recited in Claim 1, the claimed method recites additional elements including: “An analysis apparatus comprising at least one memory configured to store instructions,” and “at least one processor configured to execute the instructions,” The memory, processor, and the apparatus that they comprise are merely computer components recited at a high level of generality with instructions to “apply” the judicial exception, and do not integrate the abstract idea into a practical application. See MPEP 2106.05(f). Furthermore, the time-series sensing data, plurality of sensors, learned model, information corresponding to the cause of the anomaly, sensing data, and past sensing data are within the abstract idea because they are all either data or computer algorithms that are neither acquired nor implemented via the anomaly apparatus.
Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 1 amounts to significantly more than the abstract idea.
The analysis apparatus and processor as they appear in Claims 2-8 are subject to the same analysis and conclusions as described for the 2-Prong analysis described for Claim 1
Claim 2 recites:
The analysis apparatus according to Claim 1, wherein:
the past data includes information corresponding to past sensing data in normality; and
the at least one processor is configured to execute the instructions to check a state of deviation from the information corresponding to the past sensing data in normality of the information corresponding to the sensing data, and thereby identify the sensor having detected the information corresponding to the cause of the anomaly.
Step 1:
Under Step 1 of the analysis, Claim 2 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 2 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “past data includes information corresponding to past sensing data in normality,” “check a state of deviation from the information corresponding to the past sensing data in normality of the information corresponding to the sensing data,” and “identify the sensor having detected the information corresponding to the cause of the anomaly” are mental processes to make comparisons and perform data observation. They are merely data observations, evaluations, and/or judgements performed to identify anomalies and are capable of being performed mentally and/or with the aid of pen and paper. Additionally, the limitation, “check a state of deviation…” is a mathematical calculation, assuming the method of checking the deviation involves some sort of mathematical process or operation.
Step 2A – Prong 2:
Claim 2 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 2 the claimed method recites additional elements including “The analysis apparatus according to Claim 1” and “the at least one processor is configured to execute the instructions.” The analysis apparatus and processor have been addressed in the Claim 1 analysis and are found to be general use computer components. The “information corresponding to past sensing data in normality” is a further limitation placed on the past data while the “information corresponding to the cause of the anomaly” is a further limitation placed on the anomaly recited in Claim 1. Furthermore, the sensing data, sensors, and information corresponding to the cause of the anomaly, sensing data, and past sensing data are within the abstract idea because they are all either data or data acquiring instruments that are neither acquired nor implemented via the anomaly apparatus.
Under Step 2B, Claim 2 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amounts to instructions to apply the judicial exceptions on a general use computer. Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 2 amounts to significantly more than the abstract idea.
Claim 3 recites:
The analysis apparatus according to Claim 1, wherein:
the past data includes information corresponding to past sensing data in occurrence of anomaly; and
the at least one processor is configured to execute the instructions to check a state of similarity of the information corresponding to the sensing data to the information corresponding to the past sensing data in occurrence of anomaly,
and thereby identify the sensor having detected the information corresponding to the cause of the anomaly.
Step 1:
Under Step 1 of the analysis, Claim 3 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 3 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations, “the past data includes information corresponding to past sensing data in occurrence of anomaly” and “identify the sensor having detected the information corresponding to the cause of the anomaly,” are both data selection, observation, and judgements while the limitation, “check a state of similarity…,” is both a mental process and mathematical calculation that involves the comparison of data sets, one in normality and one in the occurrence of an anomaly. This is merely data observation, evaluation, and/or judgement capable of being performed mentally and/or with the aid of pen and paper. This check can also be performed numerically or with the use of metrics/statistical methods and as such is a mathematical calculation.
Step 2A – Prong 2:
Claim 3 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Step 2B:
In addition to the abstract ideas recited in Claim 3 the claimed method recites an additional element: “The analysis apparatus according to Claim 1, wherein: the past data includes information corresponding to past sensing data in occurrence of anomaly; and the at least one processor is configured to execute the instructions.” The analysis apparatus and processor are, once again, general use computer components with directions to “apply” the judicial exception. The sensing data and anomaly are merely data/data observations that are not acquired or performed by the analysis apparatus and are merely aspects of the abstract idea.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as they merely amount to instructions to apply the judicial exceptions on a general use computer.
Claim 4 recites:
The analysis apparatus according to Claim 1, wherein
the at least one processor is configured to execute the instructions to compare the sensing data with the past sensing data, and thereby identify the sensor having detected the information corresponding to the cause of the classified anomaly.
Step 1:
Under Step 1 of the analysis, Claim 4 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 4 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “compare the sensing data with the past sensing data” and “identify the sensor having detected the information corresponding to the cause of the classified anomaly” are mental processes to evaluate the presence and source of an anomaly in a dataset. They are merely data observations, evaluations, and/or judgements and are capable of being performed mentally and/or with the aid of pen and paper. Additionally, the comparing limitation may be performed using metrics or statistical methods and is a mathematical calculation as well.
Step 2A – Prong 2:
Claim 4 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 4 the claimed method recites an additional element: “The analysis apparatus according to Claim 1, wherein the at least one processor is configured to execute the instructions” which has been found to be no more than applying the judicial exception on a generic use computer.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2 and in the subject matter eligibility analysis for the similar elements in Claim 1.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 4 amounts to significantly more than the abstract idea.
Claim 5 recites:
The analysis apparatus according to Claim 1, wherein
the at least one processor is configured to execute the instructions to compare a feature value obtained by conversion of the sensing data with a normality feature value obtained by conversion of the past sensing data in normality, and thereby identify the sensor having detected the information corresponding to the cause of the classified anomaly.
Step 1:
Under Step 1 of the analysis, Claim 5 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 5 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitation, “compare a feature value obtained by conversion of the sensing data with a normality feature value obtained by conversion of the past sensing data in normality” includes the comparison of feature values obtained from different data sets and the conversion of data sets into the feature values, making the limitation both a mental process and mathematical calculation. Mathematical operations can be used to make a comparison, and the comparison is also a data evaluation, observation, and judgement that can be performed mentally or with the aid of pen and paper, making it a mental process. As the feature values in the instant application are likely numerical and their conversion certainly is, mathematical operations are involved in the conversion of the sensing data as well as the comparison of the feature values. The limitation, “identify the sensor having detected the information corresponding to the cause of the classified anomaly” is a mental process, as it is also performed via data observations, evaluations, and/or judgements and is capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Claim 5 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 5 the claimed method recites an additional element: “The analysis apparatus according to Claim 1, wherein the at least one processor is configured to execute the instructions” which amounts to no more than instructions to apply the judicial exception on a general use computer.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2 and the 2-Prong Analysis for Claim 1.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 5 amounts to significantly more than the abstract idea.
Claim 6 recites:
The analysis apparatus according to Claim 1, wherein the at least one processor is configured to:
execute the instructions to classify the type of the anomaly having occurred, and also
identify information indicating the sensor having detected the information corresponding to the cause of the anomaly; and
compare the identified sensor with a sensor indicated by the identified information, and output information corresponding to a result of the comparison.
Step 1:
Under Step 1 of the analysis, Claim 6 belongs to a statutory category, namely it is an apparatus and method claim.
Step 2A – Prong 1:
In the instant case, Claim 6 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “classify the type of the anomaly having occurred,” “identify information indicating the sensor having detected the information corresponding to the cause of the anomaly” and “compare the identified sensor with a sensor indicated by the identified information” are mental processes to classify data, identify a data source, and compare the data sources determined via differing methods. They are merely data observations, evaluations, and are capable of being performed mentally and/or with the aid of pen and paper. Metrics/numerical values/statistical methods may be used for the classification, identification, and comparison, making them mathematical calculations as well.
Step 2A – Prong 2:
Claim 6 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 6 the claimed method recites additional elements including “the analysis apparatus according to Claim 1, wherein the at least one processor is configured to: execute the instructions” and “output information corresponding to a result of the comparison.” The “analysis apparatus…” element merely amounts to instructions to apply the judicial exception on a general-purpose computer. The “output information…” step is a data output step recited at a high level of generality, and thus amounts to “insignificant extra-solution” activity. See MPEP 2106.05(g) “Insignificant Extra-Solution Activity.”
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount instructions to apply the judicial exception as well as data gathering that generally links the classification of the anomaly and identification and comparison of the sensors to the technological environment of sensors and anomaly detection, as well as insignificant extra-solution activity. Such insignificant extra-solution activity (“output information corresponding to a result of the comparison”), e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 6 amounts to significantly more than the abstract idea.
Claim 7 recites:
The analysis apparatus according to Claim 6, wherein the at least one processor is configured to
assign a new label to the time-series sensing data in occurrence of anomaly in a case where the identified sensor is different from the sensor indicated by the identified information.
Step 1:
Under Step 1 of the analysis, Claim 7 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 7 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitation “assign a new label to the time-series sensing data in occurrence of anomaly in a case where the identified sensor is different from the sensor indicated by the identified information” is a mental process to group the timeseries data according to its classification. This is merely an extension of the data observation, evaluation, and/or judgements performed to categorize the data and is capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Claim 7 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 7 the claimed method recites an additional element: “the analysis apparatus according to Claim 6, wherein the at least one processor is configured to.” The “analysis apparatus…” element merely amounts to instructions to apply the judicial exception on a general-use computer.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to instructions to apply the judicial exception.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 7 amounts to significantly more than the abstract idea.
Claim 8 recites:
The analysis apparatus according to Claim 1, wherein the at least one processor is configured to:
classify an operating condition based on the time-series sensing data in occurrence of anomaly received from the plurality of sensors; and
classify the type of the anomaly having occurred based on a result of the classification and a learned model learned for each operating condition.
Step 1:
Under Step 1 of the analysis, Claim 8 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 8 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “classify an operating condition based on the time-series sensing data in occurrence of anomaly received from the plurality of sensors” and “classify the type of the anomaly having occurred based on a result of the classification and a learned model learned for each operating condition” are mental processes and mathematical calculations to classify the anomaly and further characterize it. They are merely data observations, evaluations, and/or judgements performed to improve anomaly detection, classification, and sensor identification and are capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Claim 8 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 8 the claimed method recites an additional element: “the analysis apparatus according to Claim 1, wherein the at least one processor is configured,” which amounts to no more than instructions to apply the judicial exception on a generic-use computer. Note that neither the “plurality of sensors” nor the “operating conditions” are part of or observed by the claimed apparatus and thus are part of the recited “classification,” making them part of the abstract idea.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, are merely instructions to apply the judicial exception on a general-use computer.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 8 amounts to significantly more than the abstract idea.
Claim 9 recites:
A non-transitory computer-readable recording medium having a program recorded thereon, the program comprising instructions for causing an information processing apparatus to realize processes to:
classify a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance; and
identify a sensor having detected information corresponding to a cause of the classified anomaly based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance.
Step 1:
Under Step 1 of the analysis, Claim 9 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 9 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “classify a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance” and “identify a sensor having detected information corresponding to a cause of the classified anomaly based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance” are mental processes to classify an anomaly and its source. These are merely data observations, evaluations, and/or judgements performed to and are capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Claim 9 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”. The “non-transitory computer-readable recording medium having a program recorded thereon, the program comprising instructions for causing an information processing apparatus to realize processes” amounts to no more than generically recited computer components and instructions to “apply” the judicial exception, and does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. For instance, as recited in the claims, any non-transitory computer-readable recording medium capable of executing the instructions would be capable of identifying and classifying any anomaly, as well as any sensor that detected the anomaly.
Step 2B:
In addition to the abstract ideas recited in Claim 9 the claimed method recites the additional element “a non-transitory computer-readable recording medium having a program recorded thereon, the program comprising instructions for causing an information processing apparatus to realize processes,” which merely recites computer components at a high level of generality with instructions to “apply” the judicial exception, and does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2 are merely instructions to “apply” the judicial exception, as evidenced by MPEP 2106.05(f).
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 9 amounts to significantly more than the abstract idea.
Claim 10 recites:
An analysis method executed by an information processing apparatus, the analysis method comprising:
classifying a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance; and
identifying a sensor having detected information corresponding to a cause of the classified anomaly based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance.
Step 1:
Under Step 1 of the analysis, Claim 10 belongs to a statutory category, namely it is an apparatus and method claim.
Step 2A – Prong 1:
In the instant case, Claim 10 is found to recite at least one judicial exception (i.e. abstract idea). The claim limitations “classifying a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance” and “identifying a sensor having detected information corresponding to a cause of the classified anomaly based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance” are mental processes to classify an anomaly and identify the sensor that detected it. They are merely data observations, evaluations, and/or judgements performed to *and are capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Claim 10 is very broad in scope and not limited to any particular technological context or machine. Further, performing the abstract idea would not result in any improvement to any technology and/or machine.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
In addition to the abstract ideas recited in Claim 10 the claimed method recites the additional element “An analysis method executed by an information processing apparatus” which amounts to no more than generically recited computer components and instructions to “apply” the judicial exception, and does not integrate the abstract idea into a practical application. See MPEP 2106.05(f). Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, apply the “analysis method” on a generically recited “information processing apparatus.”
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 10 amounts to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,2, 4 and 8-10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Harpale et. al (US 20170284896 A1).
Regarding Claim 1, Harpale discloses an analysis apparatus comprising: at least one memory configured to [Paragraph [0043] – “The processor 410 also communicates with a storage device 430. The storage device 430 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, and/or semiconductor memory devices. The storage device 430 may store programs 412 and 414 for controlling the processor 410. – see also Fig. [4]] store instructions and at least one processor configured to [Paragraph [0043] – “The processor 410 performs instructions of the programs 412, 414, and thereby operates in accordance with any of the embodiments described herein.” – see also Fig. [4]] execute the instructions to classify a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly [Paragraph [0059] – “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.” – classification occurs when determining known or unknown fault] received from a plurality of sensors [Paragraph [0023] - “…In some embodiments, the detector 140 may comprise a plurality of detectors. In some embodiments, the scorer 150 may output an anomaly score…” – see also Fig. [1]] and a learned model learned in advance [Paragraph [0021] - “…As used herein, the term “model” may refer to, for example, a structured model that includes information about various items, and relationships between those items, and may be used to represent and understand a piece of machinery. By way of example, the model might relate to a learned model of specific types of: jet engines, gas turbines, wind turbines, etc.”];
identifying a sensor having detected information corresponding to a cause of the classified anomaly [Paragraph [0022] – an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”] corresponding to sensing data and information corresponding to past data that is past sensing data stored in advance [Paragraph [0022],[0047], [0059] – “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.” – past sensing data; “In some embodiments (such as shown in FIG. 4), the storage device 430 stores fault database 460. The fault database 460 may store a plurality of known faults and fault characteristic data associated with each known fault.” – stored in advance; “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.” – fault/fault states are information corresponding to past data that is sensing data].
Regarding Claim 2, Harpale discloses the analysis apparatus according to Claim 1, wherein: the past data includes information corresponding to past sensing data in normality [Paragraph [0022] – “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”];
and the at least one processor is configured to execute the instructions to [Paragraph [0043] – “The processor 410 performs instructions of the programs 412, 414, and thereby operates in accordance with any of the embodiments described herein.” – see also Fig. [4]] check a state of deviation from the information corresponding to the past sensing data in normality of the information corresponding to the sensing data [Paragraph [0022]- “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”],
and thereby identifying the sensor having detected the information [Paragraph [0022] – “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”].
Regarding Claim 4, Harpale discloses the analysis apparatus according to Claim 1, wherein the at least one processor is configured to execute the instructions to [Paragraph [0043] – “The processor 410 performs instructions of the programs 412, 414, and thereby operates in accordance with any of the embodiments described herein.” – see also Fig. [4]] compare the sensing data with the past sensing data, and thereby identify the sensor having detected the information corresponding to the cause of the classified anomaly [Paragraph [0022] – “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”].
Regarding Claim 8, Harpale discloses the analysis apparatus according to Claim 1 wherein at least one processor is configured [Paragraph [0043] – “The processor 410 also communicates with a storage device 430. The storage device 430 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, and/or semiconductor memory devices. The storage device 430 may store programs 412 and 414 for controlling the processor 410. The processor 410 performs instructions of the programs 412, 414, and thereby operates in accordance with any of the embodiments described herein.” – see also Fig. [4]] to: classify an operating condition based on the time-series sensing data in occurrence of anomaly received from the plurality of sensors [Paragraph [0044], [0047]- “The processor 410 may automatically determine an anomaly associated with the piece of machinery by comparing received time-series data with a normal engine profile model associated with the piece of machinery. The normal engine profile model may be based on the piece of machinery with all related sensor values in a predicted range (i.e., a healthy state of the machinery). Furthermore, the processor 410 may automatically determine that the anomaly is not a known fault based on performing a lookup of known failure modes. Known failure modes may be determined based on fault characteristic data that is stored in a database.” – this is the time series data in occurrence of anomaly; “In some embodiments (such as shown in FIG. 4), the storage device 430 stores fault database 460. The fault database 460 may store a plurality of known faults and fault characteristic data associated with each known fault. The fault characteristic data may comprise data such as, but not limited to, temperatures, currents, resistances, etc. that may be used to identify known faults. Note that the database described herein is only one example, and additional and/or different information may be stored therein. Moreover, various databases might be split or combined in accordance with any of the embodiments described herein.” – the failure modes described in Harpale correspond to the classified operating conditions listed in the instant specification];
and classifying the type of the anomaly having occurred based on a result of the classification and a learned model learned for each operating condition [Paragraph [0021], [0044] – “As used herein, the term “model” may refer to, for example, a structured model that includes information about various items, and relationships between those items, and may be used to represent and understand a piece of machinery. By way of example, the model might relate to a learned model of specific types of: jet engines, gas turbines, wind turbines, etc. Note that any of the models described herein may include relationships between sensors within the piece of machinery or phases of the machinery.” – the learned model; “The processor 410 may automatically determine an anomaly associated with the piece of machinery by comparing received time-series data with a normal engine profile model associated with the piece of machinery. The normal engine profile model may be based on the piece of machinery with all related sensor values in a predicted range (i.e., a healthy state of the machinery) …Known failure modes may be determined based on fault characteristic data that is stored in a database. The fault characteristic data may comprise data such as, but not limited to, temperatures, currents, resistances, etc. that is associated with and may be used to identify known faults. For example, a specific failure mode may be associated with a component that has a specific temperature range over a period of time and exhibits a high resistance.”
Regarding Claim 9, Harpale discloses a non-transitory computer-readable recording medium having a program recorded thereon, the program comprising instructions for causing an information processing apparatus to realize [Paragraph [0043] “The processor 410 also communicates with a storage device 430. The storage device 430 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, and/or semiconductor memory devices. The storage device 430 may store programs 412 and 414 for controlling the processor 410.” – see Fig. [4]], the processes to classify a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly [Paragraph [0059] – “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.” – classification occurs when determining known or unknown fault] received from a plurality of sensors [Paragraph [0023] - “…In some embodiments, the detector 140 may comprise a plurality of detectors. In some embodiments, the scorer 150 may output an anomaly score. In some embodiments, and as illustrated in FIG. 1, the scorer 150 may receive labeled fault data...” – see also Fig. [1]] and a learned model learned in advance [Paragraph [0021] - “…As used herein, the term “model” may refer to, for example, a structured model that includes information about various items, and relationships between those items, and may be used to represent and understand a piece of machinery. By way of example, the model might relate to a learned model of specific types of: jet engines, gas turbines, wind turbines, etc.”];
and identifying a sensor having detected information corresponding to a cause of the classified anomaly [Paragraph [0022] – “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”] corresponding to sensing data and information corresponding to past data that is past sensing data stored in advance [Paragraph [0059] – “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.”].
Regarding Claim 10, Harpale discloses an analysis method executed by an information processing apparatus [Paragraph [0043] – “The processor 410 performs instructions of the programs 412, 414, and thereby operates in accordance with any of the embodiments described herein.” – see also Fig. [4]], the analysis method comprising: classifying a type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly [Paragraph [0059] – “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.” – classification occurs when determining known or unknown fault] received from a plurality of sensors [Paragraph [0023] - “…In some embodiments, the detector 140 may comprise a plurality of detectors. In some embodiments, the scorer 150 may output an anomaly score. In some embodiments, and as illustrated in FIG. 1, the scorer 150 may receive labeled fault data...” – see also Fig. [1]]] and a learned model learned in advance [Paragraph [0021] - “…As used herein, the term “model” may refer to, for example, a structured model that includes information about various items, and relationships between those items, and may be used to represent and understand a piece of machinery. By way of example, the model might relate to a learned model of specific types of: jet engines, gas turbines, wind turbines, etc.”];
and identifying a sensor having detected information corresponding to a cause of the classified anomaly corresponding to sensing data and information [[Paragraph [0022] – “Therefore, by comparing how a normal engine profile model deviates from normal operation for a particular phase, an operator may be presented with anomalies that serve as indicators which may point to a cause of the anomalies as well as the sensors/drivers that are behind the anomalies.”] corresponding to past data that is past sensing data stored in advance [Paragraph [0059] – “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.”].
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Harpale et. al. in view of Kodeswaran et. al. (US 20170067763 A1).
Regarding Claim 3, Harpale discloses the analysis apparatus according to Claim 1, wherein: the past data includes information corresponding to past sensing data in occurrence of anomaly [Paragraph [0059] - “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460).”];
checking a state of similarity of the information corresponding to the sensing data to the information corresponding to the past sensing data in occurrence of anomaly [Paragraph [0059] – Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault.”];
Harpale does not disclose identifying the sensor having detected the information corresponding to the cause of the anomaly.
However, Kodeswaran discloses identifying the sensor having detected the information corresponding to the cause of the anomaly [Paragraph [0003] – “In summary, one aspect of the invention provides a method of identifying failed sensors in a system of interconnected devices, said method comprising: utilizing at least one processor to execute computer code that performs the steps of: receiving data from a first plurality of sensors, each sensor being operatively coupled to a device within a system of interconnected devices; associating the data received with an activity; comparing the data received with previously stored data associated with the activity; detecting, based on the comparing, an anomaly associated with at least one of the first plurality of sensors, wherein the anomaly indicates a problem with the at least one of the first plurality of sensors; and recording, at a data storage location, the anomaly, wherein the data storage location stores data associated with previously identified anomalies.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to additionally compare past and current information in anomaly, as disclosed by Kodeswaran in the anomaly detecting apparatus disclosed by Harpale as another method of identifying the sensor associated with the anomaly.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Harpale et. al. in view of Shibuya et. al. (US 20120290879 A1).
Regarding Claim 5, Harpale discloses the analysis apparatus according to Claim 1,
Harpale does not disclose the comparison of a feature value obtained by conversion of the sensing data with a normality feature value obtained by conversion of the past sensing data in normality and thereby identifying the sensor having detected the information corresponding to the cause of the classified anomaly.
However, Shibuya discloses the comparison [Paragraph [0070] – “The mode dividing information from the mode dividing unit 104 and the feature vector information from the feature amount extraction unit 105 are input into the anomaly-measurement computation unit 107 to classify the feature vector for each mode and are compared with the stored normal model created by the normal-model creation unit 106 in learning to compute the anomaly measurement (S115).” – See Fig. [1C] block S116] of a feature value obtained by conversion [Paragraph [0064] – “The sensor signal analysis unit is configured to include a feature amount extraction unit 105 that receives a sensor signal 102 output from facility 101 to perform feature selection, feature amount extraction, and feature conversion of the signal and acquire a feature vector…” – see Fig.[1A], [1B]; Paragraph [0090] – “In the feature conversion, various techniques including principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), projection to latent structure (PLS), canonical correlation analysis (CCA), and the like are used and any technique may be used or the techniques may be combined and used,” – see Fig. 1C block S114] of the sensing data [Paragraph [0070] – “…Meanwhile, the feature amount extraction unit 105 inputs the sensor signal 102 output from the facility 101 (S113) and performs feature selection, feature amount extraction, and feature conversion, and acquires the feature vector (S114).”] with a normality feature value obtained by conversion [Paragraph [0064]] of the past sensing data [Paragraph [0065] – “The operation of the system includes two phases of `learning` that creates a model used for anomaly pre-detection or diagnosis in advance and `evaluation` that actually performs anomaly pre-detection or diagnosis based on the model and an input signal.”] in normality [Paragraph [0068] – The mode dividing information from the mode dividing unit 104 and the feature vector information from the feature amount extraction unit 105 are input into the normal-model creation unit 106 to select the learning data from the feature vector (S105), perform learning for each mode by using the selected learning data, and create the normal model (S106). The created normal model is input into the anomaly-measurement computation unit 107 together with the feature vector information from the feature amount extraction unit 105 to compute the anomaly measurement (S107).”];
and thereby identifying the sensor having detected the information corresponding to the cause of the classified anomaly [Paragraph [0117] – “In the diagnosis, it is important to present the cause event to be easily appreciated. That is, it needs to describe which state the sensor signal has the anomaly. To do so, a normal signal and an actual signal may be displayed overlapping with each other with respect to the predetermined prior and post time. For example, in the case where the anomaly measurement is computed by the projection distance method or the local sub-space classifier, coordinates (FIG. 6 and b of FIG. 7) below a vertical line in the affine partial space are displayed as the normal signal from the evaluation data. The signal is displayed as the time-series information to easily verify that the anomaly measurement is deviated from the normal state. Further, since it is considered that a signal having a large deviation when the anomaly occurs contributes to the anomaly judgment, when the signals are displayed in the order of the large deviation from the top, it is easily verified which sensor signal has the anomaly.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the anomaly apparatus disclosed in Harpale with the comparison of anomalous feature values with past feature values in normality as disclosed by Shibuya in order to accurately identify the sensor that detected information corresponding to the anomaly.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Harpale et. al. in view of Patil et. al (US 20180210942 A1).
Regarding Claim 6, Harpale discloses the analysis apparatus according to Claim 1, wherein the at least one processor is configured to: execute [Paragraph [0043] – “The processor 410 performs instructions of the programs 412, 414, and thereby operates in accordance with any of the embodiments described herein.” – see also Fig. [4]] the instructions to classify the type of the anomaly having occurred [Paragraph [0059] – “Referring back to FIG. 3, the anomaly is automatically determined to be associated with an unknown fault at S330. For example, the data associated with an anomaly may be automatically looked up in a database of known fault states (e.g. the fault database 460). If the data corresponds to a known fault then an operator may be alerted to that known fault. However, if the fault is not a known fault then an unknown fault may be indicated and an alert associated with an unknown failure mode may be transmitted at S340 to an operator, an alert system, or other device.” – classification occurs when determining known or unknown fault.]
Harpale does not disclose identifying information indicating the sensor having detected the information corresponding to the cause of the anomaly, comparing the identified sensor with a sensor indicated by the identified information, or outputting information corresponding to a result of the comparison.
However, Patil discloses identifying information indicating the sensor having detected the information corresponding to the cause of the anomaly [Paragraph [0013] – “For example, in a case that the time-series data is associated with an engine, the engine may comprise six cylinders and six different time-series data streams (e.g., one per cylinder) may be received at the detector 110. In this embodiment, the detector 110 may determine (1) if the time-series data associated with a cylinder comprises anomalous data and (2) for time-series data that is determined to be anomalous, the detector may also provide a closeness indicator that specifies how close or far away the anomalous time-series data is from time-series data that was determined to be normal.”].
and comparing the identified sensor with a sensor indicated by the identified information [Paragraph [0013] – “For example, if time-series data associated with cylinder number 3 is determined to be anomalous, the detector 110 may compare the time-series data associated with cylinder number 3 to the time-series data associated with the remaining five cylinders.” – sensor time-series data associated with the identified anomalous sensor is compared to sensor time-series data associated with non-anomalous sensors as identified information to indicate the anomaly],
and outputting information corresponding to a result of the comparison [Paragraph [0016] – “At S210, anomalous time-series data may be received from a detector. In some embodiments, the detector may transmit both the anomalous time-series data as well as a closeness indicator.” – closeness indicator is the result of the comparison].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the anomaly apparatus disclosed in Harpale with the comparison of sensor data associated with the anomaly to sensor data in normality disclosed in Patil in order to accurately identify the sensor that detected information corresponding to the anomaly.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Harpale et. al. in view of Patil et. al (US 20180210942 A1) in further view of Hanaoka et. al. (US 20130238619 A1).
Regarding Claim 7, Harpale discloses the analysis apparatus according to Claim 6.
Patil discloses that the identified sensor is different from the sensor indicated by the identified information [Paragraph [0013] – “For example, if time-series data associated with cylinder number 3 is determined to be anomalous, the detector 110 may compare the time-series data associated with cylinder number 3 to the time-series data associated with the remaining five cylinders.” – the good information is used to tell which sensor is producing bad information]
Harpale and Patil do not disclose the assignment of a new label to the time-series sensing data in occurrence of anomaly in such a case.
However, Hanaoka discloses the assignment of a new label to time-series sensing data [Paragraph [0088] – “FIG. 15 is a flow chart illustrating that the feature quantity adding processing by the non-similarity determination carried out by the additional feature quantity writing unit 602. The processing adds the separate label by referring to the feature quantity table 116 when there is a difference in appearance frequency of the feature quantity for the separate feature quantity calculation method in a section having the same feature quantity for any feature quantity calculation method.” – see Fig. [15], S1503].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update the anomaly identification and comparison methods disclosed by the combination of Harpale and Patil, with new labels as disclosed by Hanaoka.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10318553 B2, Baum, M., Identification of Systems with Anomalous Behavior Using Events Derived from Machine Data Produced by those Systems, 2019.
US 20220198264 A1, Guo, S., Time Series Anomaly Ranking, 2022
US 20230140482 A1, Hashimoto, A., Abnormality Information Estimation System, Operation Analysis System, Motor Control Device, Abnormality Information Estimation Method, and Program, 2023.
US 11620539 B2, Platini, M., Method and Device for Monitoring a Process of Generating Metric Data for Predicting Anomalies, 2023.
US 20150363925A1, Shibuya, H., Anomaly Diagnosis Method and Apparatus, 2015
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANELLE A HOLMES whose telephone number is (571)272-4336. The examiner can normally be reached Monday - Friday 8:00 m - 5 pm.
Arleen M Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.A.H./Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857