DETAILED ACTION
This Office action is in response to application filed on 4/12/2023.
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 .
Information Disclosure Statement
The information Disclosure Statements (IDSs) filed 3/25/2026, 3/31/2025, 3/26/2025, 3/21/2024, and 4/12/2023 have been considered.
Abstract Objection
The abstract filed on 4/12/2023 is objected to because of the following informalities:
The abstract is a brief narrative of the disclosure as a whole, as concise as the disclosure permits, in a single paragraph preferably not exceeding 150 words, or 15 lines, commencing on a separate sheet. See 37 CFR 1.72 (b) and MPEP § 608.01(b).
Claim Objections
Claims 1-3 and 7 are objected to because of the following informalities:
Claims 1 and 7 recite “the vicinity”, “the operation” should read “a vicinity”, “an operation”
Claim 2 recites “the generated sample segments” should read “generated sample segments”
Claim 3 if recites “second processing circuitry”, should claim 1 consider “a first processing circuitry?”
Claims 1 recites “set parameter data of the target device” and claims 4 and 6 recite “set parameter data of the monitor target device”, they should be consistent?
In addition, claim 1 recites “target device” same or similar to a monitor target device”, it should be consistent.
Appropriate correction is required.
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 1-7 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 claim 1, 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 claim is to a machine/manufacture, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test.
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, to generate a segment set containing the training segments,” under the broadest reasonable 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”, that is concepts performed in the human mind, such as observation, evaluation, judgment, opinion).
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,” under the broadest reasonable 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).
The limitation “to generate a sample segment showing a normal region of the
operation of the target device from the training segments contained in the at least one similar segment set,” under the broadest reasonable interpretation, covers performance of the limitation using mental process (i.e., generating a sample segment showing a normal region, similar segment set).
Similarly, independent claim 7 is directed to a judicial exception (abstract idea) without significantly more 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; and determining whether or not the monitor target device is defective on a basis of the calculated degree of normality” that 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 process (i.e., determining whether or not the monitor target device is defective on a basis of the calculated degree of normality).
Therefore, the claims 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 claim:
“to collect both training time-series data acquired by a sensor mounted on a
target device same with or similar to a monitor target device or disposed at in the vicinity of the target device, and either set parameter data of the target device or environment data concerning the target device, 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 the vicinity of the monitor target device” (claim 7), are insignificantly extra-solution activities (i.e., collecting data), recited at a high level generality, and computer tools used to facilitate the application of the judicial exception, 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)).
Accordingly, these additional elements, when considered individually and 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 specified at a high level of generality (i.e., mere data gathering by collecting data acquired by sensor), 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); and
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).
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), 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), 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 § 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 3 is 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.
The recitation in claim 3, “a monitor target device which is a target” lacks antecedent basis. It is unclear whether the “a monitor target device” refers to “a monitor target device” as recited in claim 1 since claim 3 depends on claim 1. Also, it is not clear whether “a target” refers to “the target device” recited in claim 1 or a different element.
For purpose of examination, the claim language is interpreted as “[[a]]the monitor target device which is [[a]]the target device” as recited in claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(1) the claimed invention was patented, described in a printed publication, or in public use,
on sale or otherwise available to the public before the effective filing date of the claimed invention
Claims 1-2 and 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated over US 2018/0217812 of Nakamura et al., hereinafter “Nakamura” (IDS 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 a target device same with or similar to a monitor target device or disposed at in the vicinity of the target device, and either set parameter data of the target device or environment data concerning the target device (Fig 1, 110 - acquires training time-series data, see [0053], [0003]), while associating the training time-series data with the set parameter data or the environment data (Fig 1, 111, [0052] );
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 (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 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] ).
As per Claim 2, Nakamura teaches the learning device according to claim 1, 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 the generated sample segments (Fig 3 steps S110-S140).
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 same with or similar to a monitor target device or disposed at in the vicinity of the target device, and either set parameter data of the target device or environment data concerning the target device (Fig 1, 110 - acquires training time-series data, see [0053], [0003], [0005] ), while associating the training time-series data with the set parameter data or the environment data (Fig 1, 111, [0052] );
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 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] );
collecting 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 ([0003], [0053]);
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); and
determining whether or not the monitor target device is defective on a basis of the calculated degree of normality (see [0005], [0044]. Fig 7, if distance d less than the search result distance Z[i] at step S153 considered defective, or if distance between two points is greater than a condition radius ɛ/2, is considered “anomaly”.)
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 3-6 are rejected under AIA 35 U.S.C. 103 as being obvious over US Nakamura in view of Tora et al., hereinafter Tora, US 2021/0397938.
As per Claim 3, Nakamura teaches a defect detection for detecting whether or not a monitor target device which is a target to be monitored is defective, the defect detection device comprising:
second processing circuitry (Fig 2, processors 901, 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 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 teaches an abnormality is detected through the detection, see [0005], but Nakamura does not explicitly teach a defect detection device.
Tora teaches a defect detection device (Fig 1 shows a detection device, detects an anomaly, see [0038]). 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 teaches the defect detection according to claim 3, 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).
Nakamura teaches an abnormality is detected through the detection, see [0005], but Nakamura does not explicitly teach a defect detection device.
Tora teaches a defect detection device (Fig 1 shows a detection device, detects an anomaly, see [0038]). 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 5, Nakamura teaches a defect detection for detecting whether or not a monitor target device which is a target to be monitored is defective, the defect detection device comprising:
second processing circuitry (Fig 2, processors 901, 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 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 teaches an abnormality is detected through the detection, see [0005], but Nakamura does not explicitly teach a defect detection device.
Tora teaches a defect detection device (Fig 1 shows a detection device, detects an anomaly, see [0038]). 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 teaches the defect detection according to claim 5, 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).
Nakamura teaches an abnormality is detected through the detection, see [0005], but Nakamura does not explicitly teach a defect detection device.
Tora teaches a defect detection device (Fig 1 shows a detection device, detects an anomaly, see [0038]). 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]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2019/0108422 of Cantwell (Fault detection classification).
US 2019/0205786 of Shen et al (Method and system for classifying time-series).
US 2016/0148103 of Sarrafzadeh et al. (Fast Behavior and abnormality detection).
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to LYNDA DINH whose telephone number is (571) 270-
7150. The examiner can normally be reached on M-F 10 AM - 6 PM ET.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857