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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The references listed in the Information Disclosure Statements filed on 02/21/2024 have been considered by the examiner (see attached PTO-1449 forms).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites an operating method of a system for analyzing abnormal data, the operating method comprising: generating an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data; comparing first time-series data input to the anomaly detection model with second time-series data output from the anomaly detection model through an operation on the first time-series data; determining whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data; when the first time-series data is determined to include the abnormal data, comparing a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data; and detecting at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements.,
Claim 9 recites a system for analyzing abnormal data, the system comprising: a detection apparatus configured to generate, in a process, multivariate time-series data via a plurality of sensors; and an analysis apparatus including at least one processor, wherein the processor is configured to: generate an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data; compare first time-series data, which is generated via the detection apparatus and input to the anomaly detection model, with second time-series data output from the anomaly detection model through an operation on the first time-series data; determine whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data; when the first time-series data is determined to include the abnormal data, compare a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data; and detect at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements…
Claim 16 recites an apparatus for analyzing abnormal data, the apparatus comprising: a communicator configured to receive data by establishing communication with outside; and at least one processor, wherein the processor is configured to: generate an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data; input first time-series data received via the communicator to the anomaly detection model, and acquire second time-series data output from the anomaly detection model through an operation on the first time-series data; determine whether or not the first time-series data includes abnormal data, on the basis of comparison between the first time-series data and the second time-series data; when the first time-series data is determined to include the abnormal data, compare a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data; and detect at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements…
and thus grouped as Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
Claim 1 recites an operating method of a system for analyzing abnormal data, the operating method comprising: generating an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data; comparing first time-series data input to the anomaly detection model with second time-series data output from the anomaly detection model through an operation on the first time-series data; determining whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data; when the first time-series data is determined to include the abnormal data, comparing a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data; and detecting at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements,
Claim 9 recites a system for analyzing abnormal data, the system comprising: a detection apparatus configured to generate, in a process, multivariate time-series data via a plurality of sensors; and an analysis apparatus including at least one processor, wherein the processor is configured to: generate an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data; compare first time-series data, which is generated via the detection apparatus and input to the anomaly detection model, with second time-series data output from the anomaly detection model through an operation on the first time-series data; determine whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data; when the first time-series data is determined to include the abnormal data, compare a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data; and detect at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements…
Claim 16 recites an apparatus for analyzing abnormal data, the apparatus comprising: a communicator configured to receive data by establishing communication with outside; and at least one processor, wherein the processor is configured to: generate an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data; input first time-series data received via the communicator to the anomaly detection model, and acquire second time-series data output from the anomaly detection model through an operation on the first time-series data; determine whether or not the first time-series data includes abnormal data, on the basis of comparison between the first time-series data and the second time-series data; when the first time-series data is determined to include the abnormal data, compare a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data; and detect at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements…
and thus grouped as Mental Processes – concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
These judicial exceptions are not integrated into a practical application because the additional elements, the data gathering step, (claim 9) “generate, in a process, multivariate time-series data via a plurality of sensors” and (claim 16) “a communicator configured to receive data by establishing communication with outside” are mere data gathering that do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Furthermore, the additional elements (claim 9) the “an analysis apparatus including at least one processor” and (claim 16) “at least one processor, wherein the processor is configured to” are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions amount to no more than using a computer as a tool to perform an abstract idea. All of which are considered not indicative of integration into a practical application (see MPEP 2106.04(d)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are considered extra-solution activity of pre-solution and post-solution activity which fall under insignificant extra solution activity and deemed insufficient to qualify as “significantly more” - see MPEP 2106.05(g). The additional elements of the processing system are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and deemed insufficient to qualify as “significantly more” see MPEP 2106.05(f).
Dependent claims 2-8, 10-15 and 17-20 when analyzed as a whole are patent ineligible under 35 U.S.C. §101 because the dependent claims fail to establish that the claims are not directed to an abstract idea as they are directed mathematical concepts and/or mental processes and do not add significantly more to the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9-14 and 16-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhatia [US Patent Number 12,298,840 B1].
Regarding claim 1, Bhatia teaches an operating method of a system for analyzing abnormal data (figure 9, detect anomalies – C15L41-50), the operating method comprising:
generating an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data (a trained model … may be obtained - C15L54-57);
comparing first time-series data input to the anomaly detection model with second time-series data output from the anomaly detection model through an operation on the first time-series data (compared at operation 904 and operation 906 - C15L58-60);
determining whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data (if anomaly is detected – C15L60-64);
when the first time-series data is determined to include the abnormal data, comparing a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data (figure 9, continue to loop through operations 906-916 – C15L60-C16L6); and
detecting at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements (figure 9, continue to loop through operations 906-916 – C15L60-C16L6).
Regarding claim 2, Bhatia teaches the first plurality of data elements and the second plurality of data elements each include a plurality of time-series data respectively acquired by a plurality of sensors in a process (data from one or more sensors – C15L50-55).
Regarding claim 3, Bhatia teaches calculating, on the basis of a database related to the process, an abnormality cause possibility of at least one of a process element and an equipment element corresponding to the detected at least one data element (combing of values of at least two variables – C16L7-15).
Regarding claim 4, Bhatia teaches the database includes at least one of an average defect rate, an average maintenance period, an average exchange period, and average sensitivity to temperature and humidity for each of a plurality of process operations (average aisle temperature - C13L31-35).
Regarding claim 5, Bhatia teaches calculating the abnormality cause probability for the detected at least one data element by applying a weight to the database (first weight, second weight – C16L7-15).
Regarding claim 6, Bhatia teaches generating and outputting feedback data regarding the detected at least one data element when the calculated abnormality cause probability is greater than or equal to a reference value (compared to a different threshold – C16L3-6).
Regarding claim 7, Bhatia teaches the feedback data includes at least one of whether or not at least one of the process element and the equipment element corresponding to the detected at least one data element is abnormal and management information regarding at least one of the process element and the equipment element (figure 9, continue to loop through operations 906-916 – C15L60-C16L6).
Regarding claim 9, Bhatia teaches a system for analyzing abnormal data (figure 9, detect anomalies – C15L41-50), the system comprising: a detection apparatus configured to generate, in a process, multivariate time-series data via a plurality of sensors ( data from one or more sensors or other data source – C15L51-54); and an analysis apparatus including at least one processor (anomaly detection service – C3L48-58), wherein the processor is configured to:
generate an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data (a trained model … may be obtained - C15L54-57);
compare first time-series data, which is generated via the detection apparatus and input to the anomaly detection model, with second time-series data output from the anomaly detection model through an operation on the first time-series data (compared at operation 904 and operation 906 - C15L58-60);
determine whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data (if anomaly is detected – C15L60-64);
when the first time-series data is determined to include the abnormal data, compare a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data (figure 9, continue to loop through operations 906-916 – C15L60-C16L6); and
detect at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements (figure 9, continue to loop through operations 906-916 – C15L60-C16L6).
Regarding claim 10, Bhatia teaches the first plurality of data elements and the second plurality of data elements include a plurality of time-series data respectively acquired by a plurality of sensors of the detection apparatus in a process (data from one or more sensors – C15L50-55).
Regarding claim 11, Bhatia teaches the processor is further configured to calculate, on the basis of a database related to the process, an abnormality cause possibility of at least one of a process element and an equipment element corresponding to the detected at least one data element (combing of values of at least two variables – C16L7-15).
Regarding claim 12, Bhatia teaches the database includes at least one of an average defect rate, an average maintenance period, an average exchange period, and average sensitivity to temperature and humidity for each of a plurality of process operations (average aisle temperature - C13L31-35).
Regarding claim 13, Bhatia teaches the processor is further configured to calculate the abnormality cause probability for the detected at least one data element by applying a weight to the database (first weight, second weight – C16L7-15).
Regarding claim 14, Bhatia teaches the processor is further configured to generate and output feedback data regarding the detected at least one data element when the calculated abnormality cause probability is greater than or equal to a reference value (compared to a different threshold – C16L3-6).
Regarding claim 16, Bhatia teaches an apparatus for analyzing abnormal data, the apparatus comprising: a communicator configured to receive data by establishing communication with outside (communicating via such a network - C17L10-13); and at least one processor, wherein the processor is configured to: generate an anomaly detection model by using a training data set including a plurality of pieces of multivariate time-series data (a trained model … may be obtained - C15L54-57);
compare first time-series data, which is generated via the detection apparatus and input to the anomaly detection model, with second time-series data output from the anomaly detection model through an operation on the first time-series data (compared at operation 904 and operation 906 - C15L58-60);
determine whether or not the first time-series data includes abnormal data, on the basis of the comparison between the first time-series data and the second time-series data (if anomaly is detected – C15L60-64);
when the first time-series data is determined to include the abnormal data, compare a first plurality of data elements included in the first time-series data with a second plurality of data elements included in the second time-series data (figure 9, continue to loop through operations 906-916 – C15L60-C16L6); and
detect at least one data element on the basis of a result of comparing the first plurality of data elements with the second plurality of data elements (figure 9, continue to loop through operations 906-916 – C15L60-C16L6).
Regarding claim 17, Bhatia teaches the processor is further configured to calculate, on the basis of a database related to a process, an abnormality cause possibility of at least one of a process element and an equipment element corresponding to the detected at least one data element (combing of values of at least two variables – C16L7-15).
Regarding claim 18, Bhatia teaches the database includes at least one of an average defect rate, an average maintenance period, an average exchange period, and average sensitivity to temperature and humidity for each of a plurality of process operations (average aisle temperature - C13L31-35).
Regarding claim 19, Bhatia teaches the processor is further configured to generate and output feedback data regarding the detected at least one data element when the calculated abnormality cause probability is greater than or equal to a reference value (compared to a different threshold – C16L3-6).
Allowable Subject Matter
Claims 8, 15 and 20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Relevant Prior Art / Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mozaffari et al. (US Patent Number 12,174,689 B1) discloses a system and method for online multivariate anomaly detection and localization;
FITZGERALD et al. (US Patent Application Publication 2023/0134620 A1) discloses a method and system for real-time analytic of time series data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICKY GO whose telephone number is (571)270-3340. The examiner can normally be reached on Monday through Friday from 9:00 a.m. to 5:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached on (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RICKY GO/Primary Examiner, Art Unit 2857
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