DETAILED ACTION
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 .
This action is in response to communications filed on 04/15/2024. Claims 1-19 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was filed on 04/15/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted was filed on 07/24/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “learner to generate… performs” in claim 5, and “learner to generate” and “anomaly indication detector” in claim 15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof (e.g. paragraph 45 and figure 7).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 5 and 8 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claims purport to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements (note that the claim only recites a single element/means: “A learner”) as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claims recite a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claims.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
The limitations “subtract… generate…” as recited in claim 1 are each a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018)) but for the recitation of generic computer components. That is, other than reciting “preprocessing circuitry”, the limitation “subtract, from a value at each of points in one cycle of normal data, an average of values at corresponding points in the normal data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2, the normal data being data from a normal state” in the context of the claim encompasses the user making calculations. Other than reciting “first waveform analysis circuitry”, the limitation “generate, through waveform similarity analysis using the difference values with respect to the preceding N cycles, normal waveforms and a normality determination threshold to be used in determining whether or not there is normality as the learned data” in the context of the claim encompasses the user making evaluations and calculations. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements. The claim recites “preprocessing circuitry” and “first waveform analysis circuitry”. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are no more than a generic computer component. Therefore, the claims are not patent eligible.
Regarding claim 2, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further recites performing smoothing, which amounts to a mental step (encompassing a user performing calculations; note: a first-order lag filter broadly includes an equation) and does not include any additional elements.
Regarding claim 3, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes generating waveforms and a threshold, which amounts to a mental step (encompassing a user performing calculations) and the second waveform analysis circuitry is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
Regarding claim 4, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the normal data, which is part of the mental steps, and computing first order difference values, which amounts to a mental step (encompassing a user performing calculations) and the difference analysis circuitry is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
The limitation “performs…” as recited in claim 5 is a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018)) but for the recitation of generic computer components. That is, other than reciting “learner”, the limitation “performs normal waveform learning using normal data and anomaly indication waveform learning using anomaly indication data in generating the learned data, the normal data being data from a normal state, the anomaly indication data being data with anomaly indications” in the context of the claim encompasses the user evaluations. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim recites “learner”. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are no more than a generic computer component. Therefore, the claim is not patent eligible.
Regarding claim 6, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes performing learning, computing, and generating waveforms and thresholds, which amounts to a mental step (encompassing a user performing calculations) and the various circuitry are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
Regarding claim 7, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes performing learning, computing, and generating waveforms and thresholds, which amounts to a mental step (encompassing a user performing calculations) and the various circuitry are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
Regarding claim 8, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes that the measured data is part of a power distribution system, which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (e.g. see MPEP 2106.05(h)).
Claim 9 recites similar claim language as claim 1, and thus have the same issues. It is noted, with respect to claim 9, that the claim similarly recites various “circuitry” to perform the method. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
Regarding claim 10, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes how data is generated and various determinations, which amounts to a mental step (encompassing a user performing evaluations and calculations), by the various circuitry which are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
Regarding claim 11, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes computing and determining, which amounts to mental steps (encompassing a user performing evaluations and calculations), by the various circuitry which are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
Regarding claim 12, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes computing and determining, which amounts to mental steps (encompassing a user performing evaluations and calculations), by the various circuitry are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)).
Regarding claim 13, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes that detector and the measured data is part of a power distribution system, which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (e.g. see MPEP 2106.05(h)).
Claim 14 recites similar claim language as claim 1, and thus have the same issues (subtracting, generating, and determining are mental steps encompassing a user performing evaluations and calculations). It is noted, with respect to claim 9, that the claim similarly recites “learner” and “an anomaly indication detector” including various “circuitry” to perform the method. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
The limitations “generate… perform… performs…” as recited in claim 15 is a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018)) but for the recitation of generic computer components. That is, other than reciting “learner”, the limitation “generate learned data to be used for anomaly indication detection” in the context of the claim encompasses the user evaluations. Other than reciting “an anomaly indication detector”, the limitation “perform anomaly indication detection using the learned data” in the context of the claim encompasses the user evaluations. The limitation “performs normal waveform learning using normal data and anomaly indication waveform learning using anomaly indication data in generating the learned data, the normal data being data from a normal state, the anomaly indication data being data with anomaly indications” in the context of the claim encompasses the user evaluations. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim recites “learner” and “an anomaly indication detector”. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are no more than a generic computer component. Therefore, the claim is not patent eligible.
Claim 16 recites similar claim language as claim 1, and thus have the same issues (subtracting and generating are mental steps encompassing a user performing evaluations and calculations). It is noted, with respect to claim 16, that the claim similarly recites “learner” to perform the method. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
Claims 17 and 19 recite similar claim language as claim 15, and thus have the same issues. It is noted, with respect to claim 17, that the claim recites “a learner” to perform the method. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). It is noted, with respect to claim 19, that the claim further recites “non-transitory computer-readable medium”. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
Claim 18 recites similar claim language as claim 1, and thus have the same issues (subtracting and generating are mental steps encompassing a user performing evaluations and calculations). It is noted, with respect to claim 18, that the claim similarly recites “non-transitory computer-readable storage medium havinq a program stored therein, the program to cause a computer system” to perform the method. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
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)(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 5, 7, 15, 17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kawachi et al. (US 20200401943 A1).
As per independent claim 5, Kawachi teaches a learner to generate learned data to be used for anomaly indication detection (e.g. in paragraphs 40, 42, and 59, “model learning device… generates learning data from observed data… enables learning of a model which performs classification…for a binary classification model into normality and abnormality”, i.e. anomaly indication detection, and figure 2), wherein the learner performs normal waveform learning using normal data (e.g. in paragraphs 42, 46, and 54, “preprocessing unit 110 generates learning data from observed data… waveform of normal operation… UC value is calculated from a difference between the abnormality degree of the normal data and the abnormality degree of the abnormal data… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b” and figures 2 and 4) and anomaly indication waveform learning using anomaly indication data in generating the learned data, the normal data being data from a normal state, the anomaly indication data being data with anomaly indications (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… waveform of normal operation…abnormal operation… generates learning data from observed data… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a (or greater than the threshold a)”).
As per claim 7, the rejection of claim 5 is incorporated and the combination further teaches a normal waveform analysis circuitry to perform the normal waveform learning and an anomaly indication waveform analysis circuitry to perform the anomaly indication waveform learning (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a (or greater than the threshold a)… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”), wherein the normal waveform analysis circuitry generates normal waveforms and a normality determination threshold to be used in determination of normality as normal learned data through waveform similarity analysis using normal data (e.g. in paragraphs 42, 46, and 54, “preprocessing unit 110 generates learning data from observed data… waveform of normal operation… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”) and using detection target data and the normal learned data, extracts candidates for data with anomaly indications as candidate data from the detection target data, the normal data being data from a normal state, the detection target data being data including anomaly indication waveforms (e.g. in paragraphs 42, 46, and 54, “preprocessing unit 110 generates learning data from observed data… waveform of normal operation… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a (or greater than the threshold a)”), and the anomaly indication waveform analysis circuitry generates, for inclusion in the learned data, anomaly indication waveforms and an anomaly indication determination threshold to be used in determining whether or not there is an anomaly indication through waveform similarity analysis using the candidate data (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of abnormal operation… AUC value is calculated from a difference… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… a determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a (or greater than the threshold a)… determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”).
As per independent claim 15, Kawachi teaches an anomaly indication detection system comprising: a learner to generate learned data to be used for anomaly indication detection (e.g. in paragraphs 40, 42, and 59, “model learning device… generates learning data from observed data… enables learning of a model which performs classification…for a binary classification model into normality and abnormality”, i.e. anomaly indication detection, and figure 2) and an anomaly indication detector to perform anomaly indication detection using the learned data, wherein the learner performs normal waveform learning using normal data (e.g. in paragraphs 42, 46, and 54, “generates learning data from observed data… waveform of normal operation… UC value is calculated from a difference between the abnormality degree of the normal data and the abnormality degree of the abnormal data… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b” and figures 2 and 4) and anomaly indication waveform learning using anomaly indication data in generating the learned data, the normal data being data from a normal state, the anomaly indication data being data with anomaly indications (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of normal operation…abnormal operation… generates learning data from observed data… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a (or greater than the threshold a)”).
Claim 17 is the method claim corresponding to system claim 15 and is rejected under the same reasons set forth, and Kawachi further teaches wherein the learned data is generated through the normal waveform learning and the anomaly indication waveform learning (e.g. in paragraphs 40, 42, and 59, “model learning device… generates learning data from observed data… waveform of normal operation…abnormal operation… enables learning of a model which performs classification…for a binary classification model into normality and abnormality”).
Claim 19 is the medium claim corresponding to system claim 17 and is rejected under the same reasons set forth, and Kawachi further teaches a non-transitory computer-readable storage medium havinq a program stored therein, the program to cause a computer system to execute (e.g. in paragraphs 61-62 and 65-67, “program may be stored in, for example, a ROM… executing this program on a computer”).
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.
Claims 1, 3, 9, 10, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kawachi et al. (US 20200401943 A1) in view of Gunji (US 20080077012 A1).
As per independent claim 1, Kawachi teaches a learner to generate learned data to be used for anomaly indication detection (e.g. in paragraphs 40, 42, and 59, “model learning device… generates learning data from observed data… enables learning of a model which performs classification…for a binary classification model into normality and abnormality”, i.e. anomaly indication detection, and figure 2), the learner comprising:
a preprocessing circuitry to subtract associated with normal data in computing difference values, the normal data being data from a normal state (e.g. in paragraphs 42 and 46, “preprocessing unit 110 generates learning data from observed data… waveform of normal operation…abnormal operation… AUC value is calculated from a difference between the abnormality degree of the normal data and the abnormality degree of the abnormal data” and figures 2 and 4); and
a first waveform analysis circuitry to generate, through waveform similarity analysis using the difference values, normal waveforms and a normality determination threshold to be used in determining whether or not there is normality as the learned data (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… AUC value is calculated from a difference… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”),
but does not specifically teach subtract, from a value at each of points in one cycle of normal data, an average of values at corresponding points in the normal data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2 and the difference values with respect to the preceding N cycles.
However, Gunji teaches subtract, from a value at each of points in one cycle of normal data, an average of values at corresponding points in the normal data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2 and waveform similarity analysis using the difference values with respect to the preceding N cycles (e.g. in paragraphs 33, 35, and 44, “standard waveform generator 62 takes an average for cycles between a previous cycle for the latest cycle and a cycle before the range for calculating the average, in order to calculate the standard waveform of the biological motion that is to be targeted for detecting abnormal motion… the last four electrocardiographic waveforms counted from the previous cycle of the latest electrocardiographic waveform… calculates a weighted average for those four electrocardiographic waveforms, and uses it as a standard waveform… detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”). 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 teachings of Kawachi to include the teachings of Gunji because one of ordinary skill in the art would have recognized the benefit of further facilitating determination of normal and abnormal.
As per claim 3, the rejection of claim 2 is incorporated and the combination further teaches a second waveform analysis unit circuitry to generate, as additions to the learned data, anomaly indication waveforms and an anomaly indication determination threshold to be used in determining whether or not there is an anomaly indication through waveform similarity analysis using anomaly indication data, the anomaly indication data being data with anomaly indications (e.g. Kawachi, in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of abnormal operation… AUC value is calculated from a difference… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… a determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a (or greater than the threshold a)”),
As per independent claim 9, Kawachi teaches an anomaly indication detector to perform anomaly indication detection using learned data (e.g. in paragraphs 40, 42, and 59, “model learning device… generates learning data from observed data… enables learning of a model which performs classification…for a binary classification model into normality and abnormality”, i.e. anomaly indication detection, and figure 2), the anomaly indication detector comprising:
a preprocessing circuitry to subtract associated with detection target data in computing difference values, the detection target data being data as an anomaly indication detection target (e.g. in paragraphs 42, 46, and 61-62, “preprocessing unit 110 generates learning data from observed data… waveform of normal operation…abnormal operation… AUC value is calculated from a difference between the abnormality degree of the normal data and the abnormality degree of the abnormal data” and figures 2 and 4); and
a first waveform analysis circuitry to determine whether or not there is an anomaly indication through waveform similarity analysis using the difference values and the learned data, wherein the learned data includes normal waveforms and a normality determination threshold that have been generated through waveform similarity analysis using difference values based on normal data that is data from a normal state, the normality determination threshold being used in determination of normality (e.g. in paragraphs 22, 42, 46, 54-55, 58-59, and 61-62, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… AUC value is calculated from a difference… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”),
but does not specifically teach subtract, from a value at each of points in one cycle of detection target data, an average of values at corresponding points in the detection target data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2 and the difference values with respect to the preceding N cycles.
However, Gunji teaches subtract, from a value at each of points in one cycle of target data, an average of values at corresponding points in the target data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2 and waveform similarity analysis using the difference values with respect to the preceding N cycles (e.g. in paragraphs 33, 35, and 44, “standard waveform generator 62 takes an average for cycles between a previous cycle for the latest cycle and a cycle before the range for calculating the average, in order to calculate the standard waveform of the biological motion that is to be targeted for detecting abnormal motion… the last four electrocardiographic waveforms counted from the previous cycle of the latest electrocardiographic waveform… calculates a weighted average for those four electrocardiographic waveforms, and uses it as a standard waveform… detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”). 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 teachings of Kawachi to include the teachings of Gunji because one of ordinary skill in the art would have recognized the benefit of further facilitating determination of normal and abnormal.
As per claim 10, the rejection of claim 9 is incorporated and the combination further teaches a second waveform analysis unit circuitry, wherein the learned data includes anomaly indication waveforms and an anomaly indication determination threshold that have been generated through waveform similarity analysis using anomaly indication data, the anomaly indication determination threshold being used in determining whether or not there is an anomaly indication, the anomaly indication data being data with anomaly indications (e.g. Kawachi, in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of…abnormal operation… abnormality degree calculation unit… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”; Gunji, in paragraphs 33, 35, and 44, “detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”), and when the first waveform analysis unit circuitry determines that an anomaly indication is present, the second waveform analysis unit circuitry determines whether or not an anomaly indication is present, using the detection target data, the anomaly indication waveforms, and the anomaly indication determination threshold (e.g. Kawachi, in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation… abnormal operation… abnormality determination unit 230 generates… a determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a”; Gunji, in paragraphs 33, 35, and 44, “detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”).
Claim 14 is the system claim corresponding to claims 1 and 9, and is rejected under the same reasons set forth.
Claim 16 is the method claim corresponding to system claim 1 and is rejected under the same reasons set forth.
Claim 18 is the medium claim corresponding to system claim 1 and is rejected under the same reasons set forth and the combination further teaches a non-transitory computer-readable storage medium having a program stored therein, the program to cause a computer system to execute (e.g. Kawachi, in paragraphs 61-62 and 65-67, “program may be stored in, for example, a ROM… executing this program on a computer”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kawachi et al. (US 20200401943 A1) in view of Gunji (US 20080077012 A1) and further in view of Ueda (US 20160020721 A1).
As per claim 2, the rejection of claim 1 is incorporated, but the combination does not specifically teach wherein the preprocessing circuitry performs smoothing using a first-order lag filter on the normal data and computes the difference values with respect to the preceding N cycles, using the normal data smoothed. However, Ueda teaches performing smoothing using a first-order lag filter on data and computes using the data smoothed (e.g. in paragraphs 57 and 75, “regenerative power Q is smoothed by using a first-order lag filter, a moving average filter, or other filters to calculate the smoothed regenerative power Q′ in order to identify average regenerative power”). 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 teachings of the combination to include the teachings of Ueda because one of ordinary skill in the art would have recognized the benefit of performing relevant calculations for determining average.
Claims 4, 6, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kawachi et al. (US 20200401943 A1) in view of Gunji (US 20080077012 A1) and further in view of Hirao et al. (US 20220356962 A1).
As per claim 4, the rejection of claim 1 is incorporated and the combination further teaches wherein the normal data is measured instantaneous value data of at least one of voltage or current as a measurement target (e.g. Gunji, in paragraphs 35 and 39, “waveform when the electrical potential for the electrocardiographic waveform 0.1 mV… measuring the voltage”), and the learner comprises a difference analysis circuitry to compute difference values for normal measured value data of the measurement target and generate, as an addition to the learning data, a threshold to be used in determining whether or not there is normality, using difference values computed (e.g. Gunji, in paragraphs 33, 35, and 44, “detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”), but does specifically teach wherein the difference values comprise first-order difference values and the normal measured value data include root-mean-square value data. However, Hirao teaches difference values comprising first-order difference values and measured value data including root-mean-square value data (e.g. in paragraphs 14 and 75, “determination operation of the abnormality diagnosis unit when detecting a stepwise change in a control command value for controlling the position of the movable portion at an intermediate position between an upper limit position and a lower limit position of the position of the movable portion… state amount is a square root of a square average [i.e. root-mean-square] of the first order difference value of the displacement of the movable portion”). 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 teachings of the combination to include the teachings of Hirao because one of ordinary skill in the art would have recognized the benefit of performing relevant calculations for assessing a state (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 6, the rejection of claim 5 is incorporated and Kawachi further teaches a difference analysis unit circuitry to perform the normal waveform learning and an anomaly indication waveform analysis unit circuitry to perform the anomaly indication waveform learning, wherein the difference analysis unit circuitry computes difference values for measured value data as a measurement target and using difference values computed, learns generates a threshold that is to be used in determination of normality and included in the learned data (e.g. in paragraphs 22, 42, 46, 54-55, 58-59, and 61-62, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… AUC value is calculated from a difference… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification”), and the anomaly indication waveform analysis unit circuitry generates, for inclusion in the learned data, anomaly indication waveforms and an anomaly indication determination threshold to be used in determining whether or not there is an anomaly indication through waveform similarity analysis using anomaly indication data of the measurement target, the anomaly indication data being measured instantaneous value data with anomaly indications (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of…abnormal operation… abnormality degree calculation unit… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b)”),
but does specifically teach wherein the measured value data is of at least one of voltage or current and wherein the difference values comprise first-order difference values and the measured value data include root-mean-square value data.
However, Gunji teaches measured value data is of at least one of voltage or current (e.g. in paragraphs 33, 35, 39, and 44, “standard waveform generator 62 takes an average for cycles between a previous cycle for the latest cycle and a cycle before the range for calculating the average, in order to calculate the standard waveform of the biological motion that is to be targeted for detecting abnormal motion… the last four electrocardiographic waveforms counted from the previous cycle of the latest electrocardiographic waveform… calculates a weighted average for those four electrocardiographic waveforms, and uses it as a standard waveform… detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… waveform when the electrical potential for the electrocardiographic waveform 0.1 mV… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… measuring the voltage… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”). 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 teachings of Kawachi to include the teachings of Gunji because one of ordinary skill in the art would have recognized the benefit of further facilitating determination of normal and abnormal and/or incorporating well-known measured data (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]),
but does specifically teach wherein the difference values comprise first-order difference values and the measured value data include root-mean-square value data.
However, Hirao teaches difference values comprising first-order difference values and measured value data including root-mean-square value data (e.g. in paragraphs 14 and 75, “determination operation of the abnormality diagnosis unit when detecting a stepwise change in a control command value for controlling the position of the movable portion at an intermediate position between an upper limit position and a lower limit position of the position of the movable portion… state amount is a square root of a square average [i.e. root-mean-square] of the first order difference value of the displacement of the movable portion”). 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 teachings of Kawachi to include the teachings of Hirao because one of ordinary skill in the art would have recognized the benefit of performing relevant calculations for assessing a state (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 11, the rejection of claim 9 is incorporated and the combination further teaches a difference analysis circuitry, wherein the normal data is measured instantaneous value data of at least one of voltage or current as a measurement target, the learned data includes a threshold computed using difference values for normal measured value data of the measurement target, the threshold being used in determining whether or not there is normality, the difference analysis circuitry computes a difference value between measured value data of the measurement target that are detection targets and using a r difference value computed and the threshold, determines whether or not an anomaly indication is present (e.g. Gunji, in paragraphs 33, 35, 39, and 44, “detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… waveform when the electrical potential for the electrocardiographic waveform 0.1 mV… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… measuring the voltage… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”), and when the difference analysis unit circuitry determines that an anomaly indication is present, the first waveform analysis circuitry determines whether or not an anomaly indication is present through waveform similarity analysis using the difference values with respect to the preceding N cycles, the normal waveforms, and the normality determination threshold (e.g. Gunji, in paragraphs 33, 35, and 44, “standard waveform generator 62 takes an average for cycles between a previous cycle for the latest cycle and a cycle before the range for calculating the average, in order to calculate the standard waveform of the biological motion that is to be targeted for detecting abnormal motion… the last four electrocardiographic waveforms counted from the previous cycle of the latest electrocardiographic waveform… calculates a weighted average for those four electrocardiographic waveforms, and uses it as a standard waveform… detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”), but does specifically teach wherein the difference values comprise first-order difference values and the measured value data include root-mean-square value data. However, Hirao teaches difference values comprising first-order difference values and measured value data including root-mean-square value data (e.g. in paragraphs 14 and 75, “determination operation of the abnormality diagnosis unit when detecting a stepwise change in a control command value for controlling the position of the movable portion at an intermediate position between an upper limit position and a lower limit position of the position of the movable portion… state amount is a square root of a square average [i.e. root-mean-square] of the first order difference value of the displacement of the movable portion”). 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 teachings of the combination to include the teachings of Hirao because one of ordinary skill in the art would have recognized the benefit of performing relevant calculations for assessing a state (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per independent claim 12, Kawachi teaches an anomaly indication detection device detector to perform anomaly indication detection using learned data (e.g. in paragraphs 40, 42, and 59, “model learning device… generates learning data from observed data… enables learning of a model which performs classification…for a binary classification model into normality and abnormality”, i.e. anomaly indication detection, and figure 2), the anomaly indication detection device detector comprising:
a difference analysis unit circuitry to compute a difference value between measured value data as a measurement target and determine whether or not there is an anomaly indication, using a difference value computed and a threshold (e.g. in paragraphs 22, 42, 46, 54-55, 58-59, and 61-62, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… AUC value is calculated from a difference… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”); and
an anomaly indication waveform analysis unit circuitry to determine whether or not an anomaly indication is present, using measured instantaneous value data of the measurement target, anomaly indication waveforms, and an anomaly indication determination threshold when the difference analysis unit circuitry determines that an anomaly indication is present (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of…abnormal operation… abnormality degree calculation unit… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b)”), wherein the threshold is included in the learned data and is based on computed using difference values for normal measured value data of the measurement target and the anomaly indication waveforms and the anomaly indication determination threshold are included in the learned data and have been generated through waveform similarity analysis using anomaly indication data of the measurement target, the anomaly indication data being measured instantaneous value data with anomaly indications (e.g. in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… generates learning data from observed data… waveform of…abnormal operation… abnormality degree calculation unit… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is…abnormal… determination result indicating abnormality is generated when the abnormality degree is equal to or greater than the threshold a… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b)… in order to determine the thresholds for three-valued classification, it is possible to prepare small amounts of data of three kinds, which are normal, indistinguishable, and abnormal, and determine the two thresholds so as to increase discrimination performance among them (such as an F1 value for multi-valued classification)”),
but does specifically teach wherein the measured value data is of at least one of voltage or current and wherein the difference values comprise first-order difference values and the measured value data include root-mean-square value data.
However, Gunji teaches measured value data is of at least one of voltage or current (e.g. in paragraphs 33, 35, 39, and 44, “standard waveform generator 62 takes an average for cycles between a previous cycle for the latest cycle and a cycle before the range for calculating the average, in order to calculate the standard waveform of the biological motion that is to be targeted for detecting abnormal motion… the last four electrocardiographic waveforms counted from the previous cycle of the latest electrocardiographic waveform… calculates a weighted average for those four electrocardiographic waveforms, and uses it as a standard waveform… detect the abnormality when the difference between the latest electrocardiographic waveform and the most preceding electrocardiographic waveform is large… waveform when the electrical potential for the electrocardiographic waveform 0.1 mV… the absolute value of the difference amount is used as a threshold, but this may be a relative amount and represented as a ratio (%) based on the standard waveform… measuring the voltage… updating the standard waveform 201, the latest electrocardiographic waveform 202, and the threshold range 203, whenever the latest electrocardiographic waveform 202 is updated, with the biological motion in the normal condition”). 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 teachings of Kawachi to include the teachings of Gunji because one of ordinary skill in the art would have recognized the benefit of further facilitating determination of normal and abnormal and/or incorporating well-known measured data (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
but does specifically teach wherein the difference values comprise first-order difference values and the measured value data include root-mean-square value data.
However, Hirao teaches difference values comprising first-order difference values and measured value data including root-mean-square value data (e.g. in paragraphs 14 and 75, “determination operation of the abnormality diagnosis unit when detecting a stepwise change in a control command value for controlling the position of the movable portion at an intermediate position between an upper limit position and a lower limit position of the position of the movable portion… state amount is a square root of a square average [i.e. root-mean-square] of the first order difference value of the displacement of the movable portion”). 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 teachings of the combination to include the teachings of Hirao because one of ordinary skill in the art would have recognized the benefit of performing relevant calculations for assessing a state (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Claims 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kawachi et al. (US 20200401943 A1) in view of Gunji (US 20080077012 A1) and further in view of Nishijima et al. (US 5574611 A).
As per claim 8, the rejection of claim 1 is incorporated and the combination further teaches wherein the anomaly indication detection using the learned data is performed on measured data (e.g. Kawachi, in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b”), but does not specifically teach the measured data of at least one of zero-phase voltage or zero-phase current of a power distribution system that is measured at a slave station that controls a switcher sectionalizing the power distribution system. However, Nishijima teaches data of at least one of zero-phase voltage or zero-phase current of a power distribution system that is measured at a slave station that controls a switcher sectionalizing the power distribution system (e.g. in column 1 lines 6-13 and column 3 lines 3-17, “service interruption minimizing system for a power distribution line… A signal from each slave station is transmitted to the master station M… configuration of a slave station K and a switchgear Sw of one aspect of the invention. Of these, the switchgear Sw includes a contactor CB, various detectors (zero-phase sequence current transformer ZCT, zero-phase sequence potential detector ZPD) for detecting the occurrence of a fault” and figures 1-2). 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 teachings of the combination to include the teachings of Nishijima because one of ordinary skill in the art would have recognized the benefit of incorporating well-known environments where there are anomalies/faults (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 13, the rejection of claim 9 is incorporated and the combination further teaches wherein the anomaly indication detector performs the anomaly indication detection using the learned data on measured data (e.g. Kawachi, in paragraphs 22, 42, 46, 54-55 and 58-59, “abnormality degree of abnormal data… the abnormality degree of normal data… generates learning data from observed data… waveform of normal operation…abnormal operation… determination result indicating whether the observed data, which is input and targeted for abnormality detection, is normal, abnormal, or indistinguishable… a determination result indicating normality is generated when the abnormality degree is equal to or less than the threshold b”), but does not specifically teach the anomaly indication detector is a slave station to control a switcher sectionalizing a power distribution system and the measured data of at least one of zero-phase voltage or zero-phase current of the power distribution system. However, Hirao teaches data of at least one of zero-phase voltage or zero-phase current of a power distribution system that is measured at a device that is slave station that controls a switcher sectionalizing the power distribution system (e.g. in column 1 lines 6-13 and column 3 lines 3-17, “service interruption minimizing system for a power distribution line… A signal from each slave station is transmitted to the master station M… configuration of a slave station K and a switchgear Sw of one aspect of the invention. Of these, the switchgear Sw includes a contactor CB, various detectors (zero-phase sequence current transformer ZCT, zero-phase sequence potential detector ZPD) for detecting the occurrence of a fault” and figures 1-2). 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 teachings of the combination to include the teachings of Nishijima because one of ordinary skill in the art would have recognized the benefit of incorporating well-known environments where there are anomalies/faults (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Joglekar et al. (US 20210374027 A1) teaches ““anomaly score” may include a score that indicates how normal or anomalous something is. For example, an anomaly score can indicate how anomalous a set of input data is. Classification data produced by a machine learning model may be an anomaly score… An anomaly score can be compared against a threshold in order to make a decision. For example, if an anomaly score exceeds a threshold” (e.g. in paragraph 38).
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/W.W/Examiner, Art Unit 2144 09/26/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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