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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/19/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 06/24/2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Responses to Amendments and Arguments
The amendments filed 08/19/2026 have been entered. Claims 1, 3, 6 and 8 are amended, and claims 2 and 9 are canceled. Claims 1, 3-4, 6 and 8 remain pending in the application.
Applicant's amendments filed 08/19/2026 with respect to the rejection of
Claims 1-4 and 6 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first Paragraph have been fully considered and are persuasive. Thus, the rejection of claims 1-4 and 6 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first Paragraph has been withdrawn.
Applicant's argument and amendments filed 08/19/2026 with respect to
the rejection of claims 1-4, 6 and 8-9 directed to a judicial exception under 35 U.S.C. 101 have been fully considered but are not persuasive. (See the detailed response presented below).
On pages 9-12 of Applicant’s response, Applicant alleges that Amended claim 1 ( and similarly claim 8) adds three features beyond the previously presented claims that together define how the abnormality determination models are generated. … recites that the abnormality determination model generating unit is configured to "configure a data set in which variables indicating the operation state of the facility are arranged in a horizontal direction and time-series signals for each facility are arranged in a vertical direction." … Second, amended claim 1 recites that "the first abnormality determination model and the second abnormality determination model are generated by extracting data in the vertical direction from the data set … Third, amended claim 1 recites that the abnormality determination model generating unit is configured to "calculate, for the data of the KL-dimensional vectors, a variance value for K pieces of data of each of the L variables of the L-dimensional vectors" and … However, eligibility of amended claim 1 (and similarly claim 8) does not rest on the average, variance, correlation, or principal component analysis operations in isolation. The ordered combination defines when each abnormality determination model is generated, from which data in the data set it is generated, and how the generation path is selected automatically based on objective signal characteristics. These are specific model generation rules that improve the process of generating abnormality determination models in the technical field of facility abnormality monitoring, not mere instructions to perform mathematical calculations on a generic computer. Examiner respectfully disagrees.
The limitations of “configure a data set in which variables indicating the operation state of the facility are arranged in a horizontal direction and time-series signals for each facility are arranged in a vertical direction” may encompass mathematical calculations by manually calculating or inferring data set using the clipped signals of the time series signals, where the configured data set are a mathematical concept to be used for calculating the first to third abnormality determination models (i.e., mathematical software or algorithm) which are used to determine an abnormality of a facility. (See paragraphs 0060-0069).
The limitations of “the first abnormality determination model and the second abnormality determination model are generated by extracting data in the vertical direction from the data set, and the third abnormality determination model is generated by extracting data in the horizontal direction from the data set” may encompass mathematical calculations for generating mathematical software or algorithms (i.e., the first to third abnormality determination models) which are used to determine an abnormality of a facility. (See paragraphs 0065-0066 and 0088).
The limitations of "calculate, for the data of the KL-dimensional vectors, a variance value for K pieces of data of each of the L variables of the L-dimensional vectors" and to "determine that the time-series signals of the L-dimensional vector clipped out by the second time-series signal clipping unit do not consist of signals indicating the same operation in a case where a minimum value of the calculated variance values exceeds a predetermined value" may encompass mathematically calculating the variance value of the L variables of the L-dimensional vectors related to the time-series signals and to thereby determine/estimate the time-series signal of the L-dimensional vector. (See paragraphs 0060-0069). Claim 1 does not present a technical solution to a technical problem by providing an improvement to the functioning of computer (e.g., a display device), or to any other technology or technical field related to, for example, generating the first to third abnormality determination models, calculating the variance value for K pieces of data of each of the L variables of the L-dimensional vectors, estimating/determining signals using the time-series signals of the L-dimensional vector notifying the determined result with specific features and/or structure of tangible/physical element. (See MPEP 2106.04(d)). Therefore, the Examiner maintains the claims are ineligible.
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: “a first time-series signal clipping unit,” “an abnormality determination unit,” “a second time-series signal clipping unit,” “an abnormality determination model generating unit,” “a time-series signal collecting unit,” “a trigger condition determination model generating unit”, and “an abnormality determination unit” in claims 1, 3 and 6, which are interpreted as described in Fig. 1 and its related descriptions (paragraphs 0022-0025).
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.
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 § 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.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether any abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
The 35 USC 101 analysis between each element of claims and its combination is presented in the table below
Claim number and elements
Judicial exception (Step 2A Prong one)
Practical application (Step 2A Prong two)/ Significantly more (Step 2B)
Claim 1
Step 1: Yes, statutory class
Step 2A Prong two: No / Step 2B: No
A system including:
an abnormality determination model generation device; and
an abnormality determination device for determining an abnormality of a facility performing a predetermined operation, the abnormality determination device comprising:
Step2A Prong one: Yes
“an abnormality determination model generation device” and “an abnormality determination device” are high level of generalities to perform a generic computer function of a generic computer component and to collect data.
a first time-series signal clipping unit configured to clip out a time-series signal for abnormality determination from one or more time-series signals indicating an operation state of the facility; and
an abnormality determination unit configured to:
determine an abnormality of the facility from the time-series signal for abnormality determination using any one of a first abnormality determination model, a second abnormality determination model and a third abnormality determination model that are generated by the abnormality determination model generation device, and
determine necessity of repair of the facility based on a number of times the facility has been determined to be abnormal in a predetermined period, and notify a result of the determination via a display device configured to display the result of the determination,
“clip out a time-series signal for abnormality determination” is an insignificant extra-solution activity to collect routine data (i.e., clipped-out signals). (para 0044, 0048, 0057, 0076, 0096-0098, 0060-0064).
“determine an abnormality of the facility from the time-series signal … determine necessity of repair of the facility …” is a math or mental process.
“notify a result of the determination via a display device configured to display the result of the determination” is an insignificant extra-solution activity to perform a generic computer function of a generic computer.
“a first abnormality determination model, a second abnormality determination model and a third abnormality determination model” are indicative of mathematical algorithms/concepts. (para 0065-0066, 0088).
“a time-series signal clipping out unit” and “an abnormality determination unit” are high level of generalities to perform a generic computer function of a generic computer component and to collect data (i.e., clipped-out signals).
wherein
the abnormality determination model generation device includes:
a second time-series signal clipping unit configured to clip K times from the time-series signals indicating the operation state of the facility during normal operation of the facility; and
abstract idea
mental process or mathematical concept
“a time-series signal clipping unit” is a high level of generality to perform a generic computer function of a generic computer component to collect data (i.e., clipped signals).
“clip K times from one or more time-series signals ~” is a math process to collect data (i.e., clipped signals) used for mathematical calculations. (para 0025, 0035-0037, 0060-0064).
an abnormality determination model generating unit configured to generate the abnormality determination model from the time-series signals during the normal operation clipped out by the second time-series signal clipping unit,
wherein the abnormality determination model generating unit is configured to:
“an abnormality determination model generating unit” is high level of generality to perform a generic computer function of a generic computer component.
“abnormality determination model” is mathematical/software algorithm to perform mathematical process.
“generate the abnormality determination model~” is a mathematical process.
clip L items per one time of clipping from the time-series signals during the normal operation clipped by the second time-series signal clipping unit and configure an L-dimensional vector including L variables;
configure a data set in which variables indicating the operation state of the facility are arranged in a horizontal direction and time-series signals for each facility are arranged in a vertical direction;
generate the first abnormality determination model by calculating an average and a variance of each of the L variables in a case where a maximum value of a correlation between the L variables is less than a predetermined value for K L-dimensional vectors in an L-dimensional variable space;
generate the second abnormality determination model by performing principal component analysis and calculating a transformation coefficient of a principal component in a case where the maximum value of the correlation between the L variables is greater than or equal to a predetermined value for the K L-dimensional vectors in the L- dimensional variable space; and
configure an M-dimensional vector including M types of variables at a same time and generate the third abnormality determination model by performing principal component analysis on a plurality of M-dimensional vectors in an M-dimensional variable space and calculating a transformation coefficient of a principal component in a case where the time-series signal during the normal operation clipped out by the second time-series signal clipping unit has M types (M > 2), and
“clip L items … configures an L-dimensional vector” is a mathematical calculation or process to collect mathematical results (L-dimensional vector including L variables) based on the time-series signals. (Para 0060-0064).
“configure a data set …” a mathematical calculation or process. (Para 0060-0064).
“generate a first abnormality determination model by calculating an average and a variance ~” is a math process.
“generate a second abnormality determination model by performing principal component analysis and calculating a transformation coefficient ~” is a math process.
“first, second and third abnormality determination models” are mathematical concepts which are calculated or processed based on the data. (Para 0065-0066).
“configure an M-dimensional vector including M types of variables … generate a third abnormality determination model by performing principal component analysis … calculating a transformation coefficient ~ ” is a math process.
wherein the first abnormality determination model and the second abnormality determination model are generated by extracting data in the vertical direction from the data set, and the third abnormality determination model is generated by extracting data in the horizontal direction from the data set, the first abnormality determination model and the second abnormality determination model are abnormality determination models generated in a case where a time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit consists of signals indicating the same operation ,the third abnormality determination model is an abnormality determination model generated in a case where the time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit does not consist of signals indicating the same operation and there are two or more types (M types) of signals, and the abnormality determination model generating unit is configured to calculate, for the data of the K L-dimensional vectors, a variance value for K pieces of data of each of the L variables of the L-dimensional vectors and determine that the time-series signals of the L- dimensional vector clipped out by the second time-series signal clipping unit do not consist of signals indicating the same operation in a case where a minimum value of the calculated variance values exceeds a predetermined value.
The first abnormality determination model, the second abnormality determination model the third abnormality determination model are mathematical concept which are generated by performing mathematical calculations.
“wherein the first abnormality determination model and the second abnormality determination model are generated by … ” is math processes. (Para 0060-0069).
Claims 1, 3-4, 6 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 3-4, 6 and 8 are directed to an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as addressed below and presented in the above table.
Step 2A: Prong One
Regarding Claim 1, the limitations recited in Claim 1, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mathematical calculations and/or the mind, as presented in the above table. Nothing in the claim elements precludes the step from practically being performed in the mind and/or the mathematical calculations. For example, the limitations of “determine an abnormality of the facility from the time-series signal for abnormality determination using any one of a first abnormality determination model, a second abnormality determination model and a third abnormality determination model that are generated by the abnormality determination model generation device, and determine necessity of repair of the facility based on a number of times the facility has been determined to be abnormal in a predetermined period” in the context of this claim may encompass manually calculating or inferring abnormality of the facility based on routine data (i.e., the clipped-out time-series signal) and necessity of repair of the facility using a mathematical or software algorithm (i.e., the first to third abnormality determination models). (See paragraphs 0025, 0035-0037, 0060-0064, 0088, 0071-0093). Similarly, the limitations of “clip K times from the time-series signals indicating the operation state of the facility during normal operation of the facility” and “generate the abnormality determination model from the time-series signals during the normal operation clipped out by the second time-series signal clipping unit” in the context of this claim may encompass mathematical calculations by manually calculating or inferring the clipped signals of the time series signals to determine an abnormality of a facility using a mathematical or software algorithm (i.e., the abnormality determination model). (See paragraphs 0025, 0035-0037, 0060-0064). Similarly, the limitations of “clip L items per one time of clipping from the time-series signals during the normal operation clipped by the second time-series signal clipping unit and configure an L-dimensional vector including L variables; configure a data set in which variables indicating the operation state of the facility are arranged in a horizontal direction and time-series signals for each facility are arranged in a vertical direction; generate the first abnormality determination model by calculating an average and a variance of each of the L variables in a case where a maximum value of a correlation between the L variables is less than a predetermined value for K L-dimensional vectors in an L-dimensional variable space; generate the second abnormality determination model by performing principal component analysis and calculating a transformation coefficient of a principal component in a case where the maximum value of the correlation between the L variables is greater than or equal to a predetermined value for the K L-dimensional vectors in the L-dimensional variable space; and configure an M-dimensional vector including M types of variables at a same time and generate the third abnormality determination model by performing principal component analysis on a plurality of M-dimensional vectors in an M-dimensional variable space and calculating a transformation coefficient of a principal component in a case where the time-series signal during the normal operation clipped out by the time-series signal clipping unit has M types (M > 2)” in the context of this claim may encompass mathematical calculations by manually calculating or inferring the clipped signals of the time series signals to determine an abnormality of a facility using a mathematical or software algorithm (i.e., the abnormality determination model). (See paragraphs 0060-0064). The first, second and third abnormality determination models are mathematical concepts which are calculated or processed based on the data (the time-series signals). (See paragraphs 0065-0066 and 0088).
Similarly, the limitations of “wherein the first abnormality determination model and the second abnormality determination model are generated by extracting data in the vertical direction from the data set, and the third abnormality determination model is generated by extracting data in the horizontal direction from the data set, the first abnormality determination model and the second abnormality determination model are abnormality determination models generated in a case where a time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit consists of signals indicating the same operation ,the third abnormality determination model is an abnormality determination model generated in a case where the time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit does not consist of signals indicating the same operation and there are two or more types (M types) of signals, and the abnormality determination model generating unit is configured to calculate, for the data of the K L-dimensional vectors, a variance value for K pieces of data of each of the L variables of the L-dimensional vectors and determine that the time-series signals of the L- dimensional vector clipped out by the second time-series signal clipping unit do not consist of signals indicating the same operation in a case where a minimum value of the calculated variance values exceeds a predetermined value” in the context of this claim may encompass mathematical calculations to generate the first, second and third abnormality determination models which are mathematical concepts which are calculated or processed based on the data (the time-series signals) and calculate a variance value for K pieces of data of each of the L variables of the L-dimensional vectors to determine/estimate the time-series signal of the L-dimensional vector. (See paragraphs 0060-0069).
Step 2A: Prong Two
This judicial exception is abstract ideal itself and not integrated into a practical application. In particular, the specification details use of a processor of the arithmetic unit to perform mathematical calculations of “clip K times from the time-series signals indicating the operation state of the facility during normal operation of the facility”, “generate the abnormality determination model from the time-series signals during the normal operation clipped out by the second time-series signal clipping unit”, “clip L items per one time of clipping from the time-series signals during the normal operation clipped by the second time-series signal clipping unit and configure an L-dimensional vector including L variables; configure a data set in which variables indicating the operation state of the facility are arranged in a horizontal direction and time-series signals for each facility are arranged in a vertical direction; generate the first abnormality determination model by calculating an average and a variance of each of the L variables in a case where a maximum value of a correlation between the L variables is less than a predetermined value for K L-dimensional vectors in an L-dimensional variable space; generate the second abnormality determination model by performing principal component analysis and calculating a transformation coefficient of a principal component in a case where the maximum value of the correlation between the L variables is greater than or equal to a predetermined value for the K L-dimensional vectors in the L-dimensional variable space; and configure an M-dimensional vector including M types of variables at a same time and generate the third abnormality determination model by performing principal component analysis on a plurality of M-dimensional vectors in an M-dimensional variable space and calculating a transformation coefficient of a principal component in a case where the time-series signal during the normal operation clipped out by the time-series signal clipping unit has M types (M > 2)”, and “wherein the first abnormality determination model and the second abnormality determination model are generated by extracting data in the vertical direction from the data set, and the third abnormality determination model is generated by extracting data in the horizontal direction from the data set, the first abnormality determination model and the second abnormality determination model are abnormality determination models generated in a case where a time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit consists of signals indicating the same operation ,the third abnormality determination model is an abnormality determination model generated in a case where the time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit does not consist of signals indicating the same operation and there are two or more types (M types) of signals, and the abnormality determination model generating unit is configured to calculate, for the data of the K L-dimensional vectors, a variance value for K pieces of data of each of the L variables of the L-dimensional vectors and determine that the time-series signals of the L- dimensional vector clipped out by the second time-series signal clipping unit do not consist of signals indicating the same operation in a case where a minimum value of the calculated variance values exceeds a predetermined value”. The abnormality determination model generation device, the an abnormality determination device, time-series signal clipping out unit, the time-series signal clipping unit, the abnormality determination unit, and the abnormality determination model generating unit which are operated by the processor are recited at high-level of generalities to perform arithmetic calculations and generic computer functions of generic computer components without their specific features/structure configured to determine the abnormality such that it amounts no more than mere instructions to apply the exception using a generic computer component, as presented in the table above.
The limitation of “clip out a time-series signal for abnormality determination from one or more time-series signals indicating an operation state of the facility” is an insignificant extra-solution activity to merely collect routine data (i.e., the clipped-out time-series signal which may be performed by a generic computer function of a generic computer. The limitation of “notify a result of the determination via a display device configured to display the result of the determination” is an insignificant extra-solution activity to merely display the result on a display which may be performed by a generic computer function of a generic computer, where the display is a high level of generality to perform a generic computer function of a generic computer. (See at least MPEP 2106.05.a.II. iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; MPEP 2106.05.h. vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). The additional elements of the abnormality determination model generation device, and the abnormality determination device are recited at high-level of generalities and operated by the processor to perform arithmetic calculations and generic computer functions of generic computer components without their specific features/structure configured to determine the abnormality. There is no showing of integration into a practical application such as an improvement to the functioning of a computer, or to any other technology or technical field, or use of a particular machine.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “clip out a time-series signal for abnormality determination from one or more time-series signals indicating an operation state of the facility” is an insignificant extra-solution activity to merely collect routine data (i.e., the clipped-out time-series signal which may be performed by a generic computer function of a generic computer. The limitation of “notify a result of the determination via a display device configured to display the result of the determination” is an insignificant extra-solution activity to merely display the result on a display which may be performed by a generic computer function of a generic computer, where the display is a high level of generality to perform a generic computer function of a generic computer. As discussed above, with respect to integration of the abstract idea into a practical application, using the processor to perform “clip K times from the time-series signals indicating the operation state of the facility during normal operation of the facility”, “generate the abnormality determination model from the time-series signals during the normal operation clipped out by the second time-series signal clipping unit”, “clip L items per one time of clipping from the time-series signals during the normal operation clipped by the second time-series signal clipping unit and configure an L-dimensional vector including L variables; configure a data set in which variables indicating the operation state of the facility are arranged in a horizontal direction and time-series signals for each facility are arranged in a vertical direction; generate the first abnormality determination model by calculating an average and a variance of each of the L variables in a case where a maximum value of a correlation between the L variables is less than a predetermined value for K L-dimensional vectors in an L-dimensional variable space; generate the second abnormality determination model by performing principal component analysis and calculating a transformation coefficient of a principal component in a case where the maximum value of the correlation between the L variables is greater than or equal to a predetermined value for the K L-dimensional vectors in the L-dimensional variable space; and configure an M-dimensional vector including M types of variables at a same time and generate the third abnormality determination model by performing principal component analysis on a plurality of M-dimensional vectors in an M-dimensional variable space and calculating a transformation coefficient of a principal component in a case where the time-series signal during the normal operation clipped out by the time-series signal clipping unit has M types (M > 2)”, and “wherein the first abnormality determination model and the second abnormality determination model are generated by extracting data in the vertical direction from the data set, and the third abnormality determination model is generated by extracting data in the horizontal direction from the data set, the first abnormality determination model and the second abnormality determination model are abnormality determination models generated in a case where a time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit consists of signals indicating the same operation ,the third abnormality determination model is an abnormality determination model generated in a case where the time-series signal of the L-dimensional vector clipped out by the second time-series signal clipping unit does not consist of signals indicating the same operation and there are two or more types (M types) of signals, and the abnormality determination model generating unit is configured to calculate, for the data of the K L-dimensional vectors, a variance value for K pieces of data of each of the L variables of the L-dimensional vectors and determine that the time-series signals of the L- dimensional vector clipped out by the second time-series signal clipping unit do not consist of signals indicating the same operation in a case where a minimum value of the calculated variance values exceeds a predetermined value” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Claim 1 is not patent eligible.
Regarding Claims 3-4 and 6, the limitations are further directed to an abstract idea of further mathematical calculation, as described in claim 1. The limitation of “collect a time-series signal indicating an operation state of the facility and a time-series signal as a trigger candidate for determining a condition for clipping the time-series signal indicating the operation state from a predetermined monitoring target section” and “clip the time-series signal for abnormality determination by inputting a value of each time of the time-series signal as the trigger candidate to the trigger condition determination model and clipping out L pieces of data for one or more of the time-series signals indicating the operation state of the facility in a predetermined period starting from a time point when output of the trigger condition determination model is turned on” in Claim 6 are insignificant extra-solution activities to merely gather routine data (i.e., “the time-series signal”) used for performing abstract idea. The additional elements of the time-series signal collecting unit and the trigger condition determination model generating unit in claim 3 which are operated by the processor are recited at high-level of generalities to perform arithmetic calculations and generic computer functions of generic computer components without their specific features/structure configured to determine the abnormality such that it amounts no more than mere instructions to apply the exception using a generic computer component, as presented in the table above. The limitation of the machine learning model including a decision tree in claim 4 is indicative of a mathematical concept and/or software program itself used to perform abstract idea. For the reasons described above with respect to Claim 1, 3-4 and 6, the judicial exceptions are not meaningfully integrated into a practical application, or amount to significantly more than the abstract idea.
Regarding Claim 8, it is a method type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HIRATA et al. (JP 2012128840 A in IDS filed 06/05/2023) teaches an abnormality monitoring system that is applicable to general purposes without being aware of the kind of an object to be monitored, and can detect abnormality thereof with high precision. HIRATA teaches “a statistical monitoring indexing processing part 275 acquires M×L kinds of variables from time-series data, previously obtained from the object to be monitored in normal operation, in a section in which M kinds (M≥1) of predetermined operation are carried out, and calculates the degree of dispersion of one normal pattern while regarding points in an M×L-dimensional space determined by the M×L kinds of variables as the one normal pattern. A statistical monitoring indexing processing part 375, on the other hand, acquires M×L kinds of variables from M kinds of time-series data obtained from constant operation facilities in operation, regards points in an M×L-dimensional space determined by the M×L kinds of variables as an in-operation pattern, and calculates the degree of deviation of the in-operation pattern from the distribution of the normal pattern based upon the degree of dispersion of the normal pattern. A determination processing part 377 determines abnormality of the object to be monitored based upon the degree of deviation.”
MAEDA et al. (JP 2010092355 A) teaches detecting an abnormality in a plant or equipment at an early stage, where (1) a locus is divided into clusters in chase of time by paying attention on behavior of temporal data. (2) Modeling of a divided cluster group is carried out in a subspace and an outlier is computed as an abnormality candidate. (3) A state transition by change with time, environmental variation, maintenance (parts replacement), and a working condition is obtained by using the learning data as a reference (compare, reference, or the like). (4) Modeling shall be based on a subspace method, such as a regression analysis method with omission of N piece of data (N=0, 1, 2, and so on) or a projection distance method (for example, when N=1, modeling is carried out by excluding this, assuming that one abnormal data is mixed) or a local subspace method. Meanwhile applying of a straight line in a regression analysis method corresponds to the regression analysis of the lowest degree.
Conclusion
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/BYUNG RO LEE/Examiner, Art Unit 2858
/CHRISTOPHER P MCANDREW/Primary Examiner, Art Unit 2858
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