This Office action is in response to amendment filed on 6/25/2026.
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 .
Response to Amendments
The amendments filed on 6/25/2026 to the abstract and the claims are entered.
Claims 1-3, 5, and 7 have been amended.
Claim 8 has been added.
Claims 1-8 have been examined.
Response to Arguments
Applicant’s arguments filed on 6/25/2026 have been fully considered.
The abstract has been amended. Thus, the abstract objection is withdrawn.
Claims 1-3 and 7 have been amended. Thus, the claim objections are withdrawn.
Claims 3 and 5 have been amended. Thus, the 112(b) rejections have been withdrawn.
Applicant’s argument regarding the 101 rejection have been considered, but the claim amendment does not overcome the 101 rejection. Thus, the 101 rejection is maintained.
Applicant’s arguments regarding the 102/103 rejections are moot in view of new ground of rejection as necessitated by the amendments.
In response to Applicant’s argument that Nakamura is silent as to “environment data concerning the target device, including an environment data of a space, where the target device is placed”, the Examiner respectfully disagrees and submits that as indicated in the previous Office action, paragraph [0048] of Nakamura describes: “The operation of the appliance is tightly correlated with an activity of a human or a climatic phenomenon” which is considered an appliance that reacts to climatic phenomena or human activity operating in an environmental control context, with the collected data being interpreted as “environment data”. In addition, Nakamura discloses: “Most of measurement values for a physical phenomenon such as a temperature change continuously”, see [0047], meaning continuous measurements of physical phenomena like temperature are interpreted as environment data.
Claim Objections
Claims 1, 3, 5, and 7 are objected to because of the following informalities:
Claim 1 recites “a sensor mounted on or disposed in/at a vicinity of a target device”; Claims 3 and 5 recite “a sensor mounted on the monitor target device or disposed at in the vicinity of the monitor target device”; Claim 7 recites “a sensor mounted on a target device or disposed in a vicinity of the target device” (page 6) and “a sensor mounted on the monitor target device or disposed at in a vicinity of the monitor target device” (page 7), which are unclear because the word “disposed” means placed, or positioned in a specific location. In contrast, “in the vicinity” of the target device means the sensor is located near/nearby the target device.
It is suggested to rewrite, e.g., claim 1: “a sensor mounted on or near a target device”; Claims 3 and 5: “a sensor mounted on or near the monitoring target or near a target device
In addition, “a monitor target device” should be amended as “a/the monitoring target device” in all claims for consistency.
Claims 3 and 5 (line 4) recite extra language “according to claim 1/ according to claim 2”, which should be deleted, see line 13.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1-8 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1, line 18 recites “a monitor target device to be monitored” is unclear because claim 1, line 4 recites “a target device.” In addition, the specification, in ¶ 13 discloses “a target device is a device to be monitored”. Therefore, “a monitor target device” is “a target device” because both devices are used for monitoring.
For purpose of examination, “a monitor target device” is interpreted as “a target device”, or “a target device” is interpreted as “a monitoring target device”.
Examiner note: It is suggested to amend claim 1, lines 3-4: “to collect both training time-series data acquired by a sensor mounted on or near monitoring target device”, and all “target device” should be corrected as “monitoring target device”.
Similarly, claim 7 should be amended in the same manner as claim 1.
Dependent claims are rejected for the same reason as respective parent claim.
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 1-8 are rejected under 35 U.S.C. 101 as 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.
Regarding claims 1 and 7, the examiner submits that under Step 1 of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) for evaluating claims for eligibility under 35 U.S.C. 101, the claims are to a device/machine and a method, which are one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test.
Regarding claim 1 (see italic text for judicial exception):
The limitation “to divide the training time-series data into training segments which are pieces of partial time-series data showing an operation state containing both a rise from a first value to a second value and a fall from the second value to the first value in a waveform represented by the training timeseries data”, under broadest interpretation, covers performance of the limitation using mathematical concepts (i.e., divide the training time-series) and mental processes (i.e., showing an operation state containing both a rise from a first value to a second value and a fall from the second value to the first value in a waveform represented by the training timeseries), which are performed in the human mind, such as observation, evaluation, judgment, opinion, see MPEP 2106.04(a).III.
The limitation “to classify the training segments contained in the generated segment set into at least one similar segment set by grouping similar training segments, using either the set parameter data or the environment data, and to generate a sample segment showing a normal region of an operation of the target device from the training segments contained in the at least one similar segment set”, under broadest interpretation, covers performance of the limitation using mathematical concepts (i.e., applied algorithms of machine learning to classify the training segments) and mental processes (i.e., grouping similar training segments, and generating a sample segment showing a normal region).
The limitation “wherein, maintenance on a monitor target device to be monitored for a defect is performed to improve in a yield of products manufactured by the monitor target device when the monitor target device is determined to be defective based on the generated sample segment” falls into the grouping of mental processes.
Similarly, independent claim 7 is directed to a judicial exception (abstract idea) as explained above with regards to claim 1. In addition, claim 7 also recites “calculating a degree of normality of the test segment by referring to the generated sample segment, … when the monitor target device is determined to be defective on a basis of the calculated degree of normality” which falls in the groupings of mathematical concepts (i.e., calculating a degree of normality of the test segment by referring to the generated sample segment) and mental processes (when the monitor target device is determined to be defective on a basis of the calculated degree of normality).
Therefore, claims 1 and 7 recite a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, this judicial exception is not
integrated into a practical application. In particular, the additional elements recited in the claims:
“to collect both training time-series data acquired by a sensor mounted on or disposed in a vicinity of a target device, and either set parameter data of the target device, set to cause the target device to operate, or environment data concerning the target device, including an environment data of a space where the target device is placed, while associating the training time-series data with the set parameter data or the environment data” (claims 1 and 7), and “collecting test time-series data acquired by a sensor mounted on the monitor target device or disposed at in a vicinity of the monitor target device” (claim 7), are insignificantly extra-solution activity (i.e., data gathering); and “to generate a segment set containing the training segments”; and “to generate a sample segment showing a normal region of an operation of the target device from the training segments contained in the at least one similar segment set,” are recited at a high level generality, and merely invoked as tools, i.e., using a processing circuitry to perform processing data. Simply implementing the abstract idea on a computer is not a practical application of the abstract idea (see MPEP 2106.05(f)).
“wherein, maintenance on a monitor target device to be monitored for a defect is performed to improve in a yield of products manufactured by the monitor target device when the monitor target device is determined to be defective based on the generated sample segment” appends a transformation at a high level of generality (see MPEP 2106.05(c)).
Accordingly, these additional elements, when considered individually or in
combination, do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considering the claim as a whole. The claims are directed to a judicial exception under Step 2A of the test.
Additionally, under Step 2B of the test, the claims do not include additional elements that, when considered individually and in combination, are sufficient to amount to significantly more than the judicial exception because the additional elements:
recite extra-solution activities using elements (i.e., mere data gathering by collecting data acquired by a sensor) specified at a high level of generality, which as indicated in the MPEP: "Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step) would not provide significantly more" (see MPEP 2106.05(b), section III);
append generic computer components (i.e., a processing circuitry) used to facilitate the application of the abstract idea (i.e., mere computer implementation), which as indicated in the MPEP: "Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more" (see MPEP 2106.05(f), item 2); and
append transformations at a high level of generality, which as indicated in the MPEP: “A transformation applied to a generically recited article or to any and all articles would likely not provide significantly more than the judicial exception” (see MPEP 2106.05(c)).
The claims, when considered as a whole, do not provide significantly more under Step 2B of the test. Based on the analysis, the claims are not patent eligible.
With regards to the dependent claims, they are also directed to the non-statutory subject matter because:
they just extend the abstract idea of the independent claims by additional limitations (claims 2-6, 8), that under the broadest interpretation in light of the specification, cover performance of the limitations using mental processes and/or mathematical concepts, and
the additional elements recited in the dependent claims, when considered individually and in combination, refer to extra-solution activities (e.g., mere data gathering) and/or generic computer components (i.e., a first/second processing circuitry) used to facilitate the application of the abstract idea (Claims 2-6, 8), which as indicated in the Office's guidance does not integrate the judicial exception into a practical application (Step 2A -Prong Two) and/or does not provide significantly more (Step 2B).
Claim Rejections - 35 USC § 103
The following is a quotation under AIA of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action.
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-8 are rejected under AIA 35 U.S.C. 103 as being obvious over Nakamura et al., hereinafter “Nakamura” (US 2018/0217812 -of record), in view of Tora et al., hereinafter Tora ( US 2021/0397938 - of record ).
As per Claim 1, Nakamura teaches a learning device (Fig 1- time-series data search device 100 considered learning device) comprising:
first processing circuitry (Fig 2, processor 901 considered processing circuitry)
to collect both training time-series data acquired by a sensor mounted on or disposed in a vicinity of a target device, and either set parameter data of the target device, set to cause the target device to operate, or environment data concerning the target device ( time-series data obtained by a sensor installed at “mounted on” the “appliance” considered “monitoring/target device”, or the device to be monitored, Fig 1, 110 - acquires training time-series data, see [0053] ), including an environment data of a space where the target device is placed, while associating the training time-series data with the set parameter data or the environment data (“The operation of the appliance is tightly correlated with an activity of a human or a climatic phenomenon”, see [0048] considered an appliance that reacts to climatic phenomena or human activity operates in an environmental control context, and the data is collected considered “environmental data”. In addition, “the measurement values for a physical phenomenon such as a temperature change continuously”, see [0047], meaning continuous measurements of physical phenomena like temperature are considered “environmental data” );
to divide the training time-series data into training segments (divides into segment sets, see [0334], [0023], [0061], [0220] ) which are pieces of partial time-series data showing an operation state containing both a rise from a first value to a second value and a fall from the second value to the first value in a waveform represented by the training time-series data, to generate a segment set containing the training segments (Fig 3, S110: generate initial segment set F from training time series data S. It is noted time-series data showing up and down movements in variations in values overtime. Thus, time series data” includes upward and downward signals. Fig 4, S112: Training segment Si when i=1, initial segment set. Thus, a segment set containing training segments, see [0111]-[0116]);
to classify the training segments contained in the generated segment set into at least one similar segment set by grouping similar training segments, using either the set parameter data or the environment data (sorts initial segments by order of feature quantities. It is noted “sorting” is considered a form of classifying, see Abstract, [0070], [0131], when segments are positioned in a close distance from each other, the similar segments can be located [0128], [0135]-[0136] ); and
to generate a sample segment (see [0062], [0099] ) showing a normal region of the operation of the target device from the training segments contained in the at least one similar segment set (similar search of time-series data, finding the distance between partial time-series data of training time-series data, i.e., distance between two points [0042]-[0043], if equal to or less than a condition radius ɛ/2, is considered “a normal region of the operation, see [0058]-[0059], [0128], [0165]-[0166] ),
Nakamura does not explicitly teach wherein, maintenance on a monitor target device to be monitored for a defect is performed to improve in a yield of products manufactured by the monitor target device when the monitor target device is determined to be defective based on the generated sample segment.
Tora teaches wherein, maintenance on a monitor target device to be monitored for a defect is performed to improve in a yield of products manufactured by the monitor target device when the monitor target device is determined to be defective based on the generated sample segment (Fig 15, step S207: “detect anomaly on basis of degree anomaly” considered evaluating a sample segment using a degree of anomaly score is a standard way to decide if a device is defective, see [0005]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Nakamura to implement maintenance on monitoring target device to detecting anomaly based on the degree of anomaly as taught by Tora that would detect anomaly in the detection object data on the basis of the degree of anomaly (Tora, [0038]).
As per Claim 2, Nakamura in view of Tora teaches the learning device according to claim 1, Nakamura teaches wherein the at least one similar segment set comprises two or more similar segment sets, the first processing circuitry generates a sample segment for each of the two or more similar segment sets, and the first processing circuitry is further configured to sort generated sample segments (Fig 3 steps S110-S140).
As per Claim 3, Nakamura teaches a defect detection for detecting whether or not the monitor target device to be monitored is defective (the training time series data is “detected as a singularity” considered for defect detection, see [0005], i.e., where a singularity refers to a point where an abrupt change, discontinuity, or irregularity, see [0044] ), and the learning device further comprising:
second processing circuitry (Fig 2, processors 901, also considered a second processing circuitry, see [0088]) to collect test time-series data acquired by a sensor mounted on the monitor target device or disposed at in the vicinity of the monitor target device (Fig 1, 110 - acquires test time-series data, see [0053], [0056], [0071]);
to generate a test segment from the test time-series data (see [0056], [0071]), the test segment being partial time-series data showing an operation state containing both a rise from a first value to a second value and a fall from the second value to the first value in a waveform represented by the test time-series data (similar patterns appear repeatedly in the time-series data, see [0048], [0005], i.e., using “two feature quantities (features)” considered first and second values derived from time-series data considered patterns or “waveform of training time-series”, see [0324]-[0325]. It is noted time-series data showing up and down movements in variations in values overtime);
to refer to a related sample segment from the one or more sample segments generated by the learning device according to claim 1 (when segments are positioned in a close distance from each other, the similar segments can be located [0128], [0135]-[0136]. It is noted that a similar segment is considered related sample segment), and
to calculate a degree of normality showing the degree to which the generated test segment is contained in the normal region of the sample segment which is referred to (Fig 7 step S152, Fig 3, steps S130-S160); and
to determine whether or not the monitor target device is defective on a basis of the calculated degree of normality (Fig 7, if distance d less than the search result distance Z[i] at step S153 considered defective).
Nakamura does not explicitly teach the defect detection device comprising: the learning device including the first processing circuitry according to claim 1.
Tora teaches the defect detection device (Fig 1 shows a detection device 10, detects an anomaly, see [0038] ) comprising: the learning device (Fig 1, detection unit 133. The detection unit 133 can perform detection using only feature values with large/ little change in the training data considered “learning device”, see [0054]-[0055] ) including the first processing circuitry (control unit 13 ) according to claim 1.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Nakamura using a defect detection device as taught by Tora that would detect anomaly in the detection object data on the basis of the degree of anomaly (Tora, [0038]).
As per Claim 4, Nakamura in view of Tora teaches the defect detection device according to claim 3, Nakamura teaches wherein the second processing circuitry collects the test time-series data while associating the test time-series data with either set parameter data of the monitor target device or environment data concerning the monitor target device, and the related sample segment is generated from the training segment associated with either the same set parameter data as that associated with the test time-series data or the same environment data as that associated with the test time-series data (when segments are positioned in a close distance from each other, the similar segments can be located [0128], [0135]-[0136]. It is noted a similar segment is considered related sample segment).
As per Claim 5, Nakamura teaches a defect detection for detecting whether or not the monitor target device to be monitored is defective (the training time series data is “detected as a singularity” considered for defect detection, see [0005], i.e., where a singularity refers to a point where an abrupt change, discontinuity, or irregularity, see [0044] ), and the learning device further comprising:
second processing circuitry (Fig 2, processors 901, also considered a second processing circuitry, see [0088]) to collect test time-series data acquired by a sensor mounted on the monitor target device or disposed at in the vicinity of the monitor target device (Fig 1, 110 - acquires test time-series data, see [0053], [0056], [0071]); to generate a test segment from the test time-series data, the test segment being partial time-series data showing an operation state containing both a rise from a first value to a second value and a fall from the second value to the first value in a waveform represented by the test time-series data (similar patterns appear repeatedly in the time-series data, see [0048], [0005], i.e., using “two feature quantities (features)” considered first and second values derived from time-series data considered patterns or “waveform of training time-series”, see [0324]-[0325]. It is noted time-series data showing up and down movements in variations in values overtime);
to refer to a related sample segment from the one or more sample segments generated by the learning device according to claim 2 (when segments are positioned in a close distance from each other, the similar segments can be located [0128], [0135]-[0136]. It is noted that a similar segment is considered related sample segment), and
to calculate a degree of normality showing the degree to which the generated test segment is contained in the normal region of the sample segment which is referred to (Fig 7 step S152, Fig 3, steps S130-S160); and
to determine whether or not the monitor target device is defective on a basis of the calculated degree of normality (Fig 7, if distance d less than the search result distance Z[i] at step S153 considered defective).
Nakamura does not explicitly teach the defect detection device comprising: the learning device including the first processing circuitry according to claim 2
Tora teaches a defect detection device (Fig 1 shows a detection device 10, detects an anomaly, see [0038] ), comprising: the learning device (Fig 1, detection unit 133. The detection unit 133 can perform detection using only feature values with large/ little change in the training data considered “learning device”, see [0054]-[0055] ) including the first processing circuitry (control unit 13 ) according to claim 2.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Nakamura using a defect detection device as taught by Tora that would detect anomaly in the detection object data on the basis of the degree of anomaly (Tora, [0038]).
As per Claim 6, Nakamura in view of Tora teaches the defect detection device according to claim 5, Nakamura teaches wherein the second processing circuitry collects the test time-series data while associating the test time-series data with either set parameter data of the monitor target device or environment data concerning the monitor target device, and the related sample segment is generated from the training segment associated with either the same set parameter data as that associated with the test time-series data or the same environment data as that associated with the test time-series data (when segments are positioned in a close distance from each other, the similar segments can be located [0128], [0135]-[0136]. It is noted a similar segment is considered related sample segment).
As per Claim 7, Nakamura teaches a defect detection method comprising:
collecting both training time-series data acquired by a sensor mounted on a target device or disposed in a vicinity of the target device, and either set parameter data of the target device, set to cause the target device to operate or environment data concerning the target device ( time-series data obtained by a sensor installed at “mounted on” the appliance, see [0003], “appliance” considered “monitoring/target device”, or the device to be monitored, Fig 1, 110 - acquires training time-series data, see [0053] ), including an environment data of a space where the target device is placed, while associating the training time-series data with the set parameter data or the environment data (“The operation of the appliance is tightly correlated with an activity of a human or a climatic phenomenon”, see [0048] considered an appliance that reacts to climatic phenomena or human activity operates in an environmental control context, and the data is collected considered “environmental data”. In addition, “the measurement values for a physical phenomenon such as a temperature change continuously”, see [0047], meaning continuous measurements of physical phenomena like temperature are considered “environmental data”);
dividing the training time-series data into training segments (divides into segment sets, see [0334], [0023], [0061], [0220] ) which are pieces of partial time-series data showing an operation state containing both a rise from a first value to a second value and a fall from the second value to the first value in a waveform represented by the training time-series data, to generate a segment set containing the training segments (similar patterns appear repeatedly in the time-series data, see [0048], [0005], i.e., using “two feature quantities (features)” considered first and second values derived from time-series data considered patterns or “waveform of training time-series”, see [0324]-[0325]. It is noted time-series data showing up and down movements in variations in values overtime);
classifying the training segments contained in the generated segment set into at least one similar segment set by grouping similar training segments, using either the set parameter data or the environment data (sorts initial segments by order of feature quantities. It is noted “sorting” is considered a form of classifying, see Abstract, [0070], [0131], when segments are positioned in a close distance from each other, the similar segments can be located [0128], [0135]-[0136] );
generating a sample segment showing a normal region of an operation of the target device from the training segments contained in the at least one similar segment set (similar search of time-series data, finding the distance between partial time-series data of training time-series data, i.e., distance between two points [0042]-[0043], if equal to or less than a condition radius ɛ/2, is considered “a normal region of the operation, see [0058]-[0059], [0128], [0165]-[0166] );
collecting test time-series data acquired by a sensor mounted on the monitor target device or disposed at in a vicinity of the monitor target device ([0003], [0053]); and generating a test segment from the test time-series data, the test segment being partial time-series data showing the operation state (see [0056], [0071]), and calculating a degree of normality of the test segment by referring to the generated sample segment (Fig 7 step S152, Fig 3, steps S130-S160),
Nakamura does not explicitly teach wherein, maintenance of a monitor target device to be monitored for a defect is performed to improve in a yield of products manufactured by the monitor target device when the monitor target device is determined to be defective on a basis of the calculated degree of normality.
Tora teaches wherein, maintenance of a monitor target device to be monitored for a defect is performed to improve in a yield of products manufactured by the monitor target device when the monitor target device is determined to be defective on a basis of the calculated degree of normality (Fig 15, step S207: “detect anomaly on basis of degree anomaly” considered evaluating a sample segment using a degree of anomaly score is a standard way to decide if a device is defective, see [0005]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the present claimed invention, to modify the teaching of Nakamura to implement maintenance on monitoring target device to detecting anomaly based on the degree of anomaly as taught by Tora that would detect anomaly in the detection object data on the basis of the degree of anomaly (Tora, [0038]).
As per Claim 8, Nakamura teaches the learning device according to claim 1, wherein both the rise from the first value to the second value and the fall from the second value to the first value are included in a single training segment (Fig 2, S110: training time series data S, where “time series data” includes upward and downward signals; Fig 4, S112: training segment Si, where i=1 considered “a single training segment”. Thus, a single both upward and downward signals can be included on a single training segment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857
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