Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application is a continuation of PCT Application No. PCT/JP2021/048035, filed on December 12, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/11/2024 and 02/03/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Regarding claim 1, in Step 1 of the 101 analyses set forth in MPEP 2106, the claim recites An information processing system comprising. A system is one of the four statutory categories.
In Step 2a Prong 1 of the 101 analyses set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a [ mental process/mathematical concept] but for recitation of generic computer components:
extracts features that depend on sequence from a plurality of pieces of partial time-series data obtained by dividing the time-series data along a time axis, (a person can mentally extract features from a plurality of partial time series data by a process of simply evaluating the time-series data and making a judgement on what the features of the data are. (MPEP 2106))
and generates a plurality of first vectors in which the extracted features are embedded, each of the first vectors corresponding to each of the pieces of the partial time-series data one to one; (A person can mentally generate a plurality of first vectors in which features are embedded by a process of simply evaluating the features and making a judgement on how they should be embedded in the vector. (MPEP 2106)))
that generates a second vector in which the first vectors are embedded; (A person can mentally generate a plurality of first vectors in which features are embedded by a process of simply evaluating the features and making a judgement on how they should be embedded in the vector. (MPEP 2106)))
that extracts features that depend on sequence from the first vectors, and generates a third vector in which the extracted features are embedded; (A person can mentally generate a plurality of first vectors in which features are embedded by a process of simply evaluating the features and making a judgement on how they should be embedded in the vector. (MPEP 2106)))
that generates a fourth vector in which the second vector and the third vector are embedded; (A person can mentally generate a plurality of first vectors in which features are embedded by a process of simply evaluating the features and making a judgement on how they should be embedded in the vector. (MPEP 2106)))
that transforms the fourth vector into a first value that represents a condition of the device. (A person can mentally transform a vector into a value that is representative of the condition of a device by a process of simply evaluating the vector and making a judgement on the value of the condition of the device. (MPEP 2106)))
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a [ mental process/mathematical concept] but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analyses set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
a memory containing program instructions; (Adding insignificant extra-solution activity (mere data storage) to the judicial exception (MPEP 2106.05(g))).
and a processor coupled to the memory, wherein the processor is configured to execute the program instructions to: (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
generate a trained model that predicts a condition of a device from time-series data acquired from the device, and the trained model includes: (Merely training a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f))).
a first component that (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
a second component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
a third component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
a fourth component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and a fifth component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analyses set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a memory containing program instructions; (Adding insignificant extra-solution activity (mere data storage) to the judicial exception (MPEP 2106.05(g) )), Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).).
and a processor coupled to the memory, wherein the processor is configured to execute the program instructions to: (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
generate a trained model that predicts a condition of a device from time-series data acquired from the device, and the trained model includes: (Merely training a generic machine learning model constitutes “applying” the machine learning model (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
a first component that (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
a second component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
a third component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
a fourth component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
and a fifth component (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)) Furthermore, the additional element is directed to application of a computer tool (machine learning model), which is not indicative of significantly more (MPEP 2106.05(f)).)
Claims 11 and 13 are rejected on the same grounds as Claim 1.
Regarding claim 2 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites wherein the trained model further includes: a sixth component that generates a plurality of fifth vectors each obtained by calculating, for each of the first vectors, a difference between each of the first vectors and an average vector of the first vectors; (Calculating a difference between vectors is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106). Further In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally calculate the difference between two vectors by a process of simply evaluating the vectors ana making a judgement on what the difference is between them. (MPEP 2106).))
a seventh component that generates a sixth vector in which the fifth vectors are embedded; (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a vector by embedding the values of another vectorby a process of simply evaluating the vectors ana making a judgement on how they should be embedded. (MPEP 2106).))
and an eighth component that extracts features that depend on sequence from the fifth vectors, (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally extract features from a vector series data by a process of simply evaluating the time-series data and making a judgement on what the features of the data are. (MPEP 2106)) and generates a seventh vector in which the extracted features are embedded, (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a vector by embedding the values of another vector by a process of simply evaluating the vectors ana making a judgement on how they should be embedded. (MPEP 2106).))
and the fourth component generates the fourth vector in which the sixth vector and the seventh vector are further embedded. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a vector by embedding the values of another vector by a process of simply evaluating the vectors ana making a judgement on how they should be embedded. (MPEP 2106).))
Regarding claim 3 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites wherein the trained model further includes: a sixth component that divides the first vectors into a plurality of groups, and for each of the groups, (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally divide vectors into groups by a process of simply evaluating the vectors ana making a judgement on how they should be grouped. (MPEP 2106).))
generates a plurality of fifth vectors each obtained by calculating a difference between each of the first vectors belonging to the group and an average vector of the first vectors belonging to the group; (Calculating a difference between vectors is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106). Further In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally calculate the difference between two vectors by a process of simply evaluating the vectors ana making a judgement on what the difference is between them. (MPEP 2106).))
a seventh component that generates a sixth vector in which the fifth vectors are embedded; (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a vector by embedding the values of another vector by a process of simply evaluating the vectors ana making a judgement on how they should be embedded. (MPEP 2106).))
and an eighth component that, for each of the groups, extracts features that depend on sequence from the fifth vectors belonging to the group, (In step 2A, prong 1, this recites a mental process without significantly more. a person can mentally extract features from a vector series data by a process of simply evaluating the time-series data and making a judgement on what the features of the data are. (MPEP 2106))
and generates a seventh vector in which the extracted features are embedded, (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a vector by embedding the values of another vector by a process of simply evaluating the vectors ana making a judgement on how they should be embedded. (MPEP 2106).))
and the fourth component generates the fourth vector in which the sixth vector and the seventh vector are further embedded. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a vector by embedding the values of another vector by a process of simply evaluating the vectors ana making a judgement on how they should be embedded. (MPEP 2106).))
Regarding claim 4 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites wherein the trained model further includes: a ninth component that includes the first component, the second component, the third component, and the fourth component, the ninth component inputting, into the ninth component, a plurality of pieces of partial time-series data constituting time-series data representing execution data up to an observed condition,
and generating and outputting a plurality of the fourth vectors corresponding to the input pieces of time-series data one to one; (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
a tenth component that inputs, into the tenth component, the fourth vectors output from the ninth component, and calculates a change point of a health index; (Calculating a change point of a health index is directed to a mathematical calculation and thus is not patent eligible (MPEP 2106). Further In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally calculate a health index by a process of simply evaluating the vector and making a judgement on what the health index should be. (MPEP 2106).))
and an eleventh component that generates and outputs a second value serving as a teacher of the first value, on a basis of the change point of the health index. (In step 2A, prong 1, this recites a mental process without significantly more. A person can mentally generate a teacher value by a process of simply evaluating the vectors and making a judgement on what the teacher value should be. (MPEP 2106).))
Regarding claim 5 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites wherein the first value is a value representing remaining useful life of the device. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 6 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites wherein the first value is a value representing presence or absence of abnormality in the device, presence or absence of a failure, or a deterioration state. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 7 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites wherein the processor is further configured to execute the instructions to issue an alarm in response to the first value. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Regarding claim 8 it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 8 recites wherein the processor is further configured to execute the instructions to execute a coping method defined in advance with respect to the device, in response to the first value. (In step 2A, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))). In step 2B, generally linking the use of the judicial exception to a particular technological environment is not indicative of significantly more.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Natsumeda et al. Pub No.: US 20220004182 A1, Narwariya et al. Pub. No.: US 20210406603 A1, Shalaby et al. Pub No.: US 20220187819 A1, Dixit Patent No.: US 10964130 B1, Li et al. Pub No.: US 20120143564 A1, Bonissone et al. Pub No.: US 20080140361 A1, All teach methods of Remaining Useful Life prediction.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7:20am-5:20pm; F: Out of Office.
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/THOMAS BERNARD LANE/ Examiner, Art Unit 2142 /HAIMEI JIANG/Primary Examiner, Art Unit 2142
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