CTNF 19/057,521 CTNF 99353 DETAILED ACTION This action is in response to the filing 02/19/2025. Claims 1-20 are pending and have been fully examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Status of the Claims Claims 12-19 are rejected under 35 U.S.C. 112(b). Claims 1-20 are rejected under 35 U.S.C. 101. Claims 12, 13, 15, and 18 are rejected under 35 U.S.C. 102. Claims 1-8, 10-11, and 19 are rejected under 35 U.S.C. 103 Claim Rejections - 35 USC § 112 07-30-02 AIA 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 12-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 12, and similarly Claim 19 , recite “to calculate, using … , at least one of statistics of the plurality of the pieces of learning data on a basis of the plurality of the pieces of learning data, and learn an estimated structure.” It is not clear of what encompasses and is meant by the above limitation. As recited, the use of at least one of … appears to set forth calculating at least one of: statistics and learn and estimated structure , reciting both the “statistics” and the “learn an estimated structure” (itself necessitating the statistic calculated) as being members of the “at least one.” A review of the disclosure sets forth the act of calculating at least one statistic [0085] and the Examiner can find no recitation similar to “to calculate … learn an estimated structure.” The Examiner recommends that the Applicant amend the limitations to recite, “to calculate, using … , at least one [[of]] statistic[[s]] of the plurality of the pieces of learning data on a basis of the plurality of the pieces of learning data, and learn an estimated structure…” The claims will be interpreted as such for the purposes of examination. Claims 13-18 depend from Claim 12. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim 1 and similarly Claim 20, Step 1: Claim 1 recites an apparatus and Claim 20 recites a method. Step 2A Prong 1: Abstract idea Claim 1, and similarly Claim 20, recite, to estimate an anomaly detection order in which occurrence of the anomaly is detected for the plurality of the anomaly detection sensors…; This limitation describes performing an estimation, or forming a judgement, based on gathered data. The human mind is equipped to evaluate time-series data to determine an order. Therefore this claim recites a mental process in the form of a judgement that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III). to estimate an anomaly propagation order in which the anomaly has propagated …; This limitation describes performing an estimation, or forming a judgement, based on gathered and known data. The human mind is equipped to know dependency information between sensors [spec; 0144]. Therefore this claim recites a mental process in the form of a judgement that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III). and to estimate a factor of the anomaly … . The step of estimating a factor is performed on known data and described by the specification, par. 0052, as being estimated via a calculation. This limitation is a process that recites a mathematical calculation. Therefore, the claim recites a mathematical concept, see MPEP 2106.04(a)(2)(1)(C). Step 2A Prong 2: Additional Elements Claim 1, and similarly Claim 20, additionally recite, to acquire a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a plurality of facility components constituting a target facility; This limitation is a step that merely obtains data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). … a basis of a plurality of the pieces of sensor data acquired; … on a basis of a detection time at which the process has detected the plurality of the anomaly detection sensors; … on a basis of anomaly detection sensor information regarding the plurality of the anomaly detection sensors detected and an estimated structure indicating a dependence relationship between the facility components; …. on a basis of the anomaly detection order estimated and the anomaly propagation order estimated. These limitations merely describe data used for the abstract ideas and additional elements. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 1, and similarly Claim 20 as applies, further recite, a processor; and a memory storing a program …; This limitation describes mere instructions to apply an exception in the form of general-purpose computer components. See MPEP 2106.05(f)(2). to detect a plurality of anomaly detection sensors in which an anomaly has occurred among the plurality of the sensors … ; This limitation describes mere instructions to apply an exception in the form of applying a commonplace algorithm, an anomaly detection method recited at a high level of generality, being applied on a general purpose computer. See 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Step 2B: Significantly More Claim 1, and similarly Claim 20, additionally recite, to acquire a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a plurality of facility components constituting a target facility; This limitation is a step that merely obtains data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). … a basis of a plurality of the pieces of sensor data acquired; … on a basis of a detection time at which the process has detected the plurality of the anomaly detection sensors; … on a basis of anomaly detection sensor information regarding the plurality of the anomaly detection sensors detected and an estimated structure indicating a dependence relationship between the facility components; …. on a basis of the anomaly detection order estimated and the anomaly propagation order estimated. These limitations merely describe data used for the abstract ideas and additional elements. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 1, and similarly Claim 20 as applies, further recite, a processor; and a memory storing a program …; This limitation describes mere instructions to apply an exception in the form of general-purpose computer components. See MPEP 2106.05(f)(2). to detect a plurality of anomaly detection sensors in which an anomaly has occurred among the plurality of the sensors … ; This limitation describes mere instructions to apply an exception in the form of applying a commonplace algorithm, an anomaly detection method recited at a high level of generality, being applied on a general purpose computer. See 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 2 recites, wherein the estimated structure is represented by a matrix. This limitation is a step that merely describes data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claims, see MPEP 2106.05(g)(3). Claim 3 recites, to output information regarding an estimation result of the factor of the anomaly. This limitation is a step that merely outputs data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claims, see MPEP 2106.05(g)(3). Claim 4 recites, wherein the process detects the anomaly detection sensors using a univariate type anomaly detecting method. This limitation describes mere instructions to apply an exception in the form of applying a computer algorithm, in the form of a generally recited univariate method, being performed on a general-purpose computer. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 5 recites, wherein the process detects the anomaly detection sensor using a multivariate type anomaly detecting method. This limitation describes mere instructions to apply an exception in the form of applying a computer algorithm, in the form of a generally recited multivariate method, being performed on a general-purpose computer. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 6 recites, wherein the process detects the anomaly detection sensor using a univariate type anomaly detecting method and a multivariate type anomaly detecting method. This limitation describes mere instructions to apply an exception in the form of applying a computer algorithm, in the form of a generally recited univariate and a generally recited multivariate method, being performed on a general-purpose computer. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 7 recites, wherein the process estimates the anomaly propagation order on a basis of the anomaly detection sensor information, facility operation state information indicating an operation state of the target facility, and the estimated structure indicating a dependence relationship between the facility components depending on the operation state of the target facility. This limitation is a step that merely describes data used in a previous step, the step of "estimate an anomaly propagation order." Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 8 recites, to correct a dependence relationship among the pieces of sensor data for the estimated structure on a basis of dependent pair information related to a pair of the sensors having a dependence relationship among the plurality of the sensors and non- dependent pair information related to a pair of the sensors having no dependence relationship. This limitation is a step that covers performance of this limitation in the mind in the form of making an making an evaluation regarding the dependance between components, which the human mind is equipped to do, see specification [0144]. Therefore, this limitation recites a mental process. See MPEP 2106.04(a)(2)(III). Claims can recite a mental process even if they are claimed as being performed on a computer or in a computer environment, see MPEP 2106.04(a)(2)(III)(C). Claim 9 recites, to compare the estimated structure with the estimated structure at a time of occurrence of the anomaly … This limitation recites a step of comparing known information. This limitation is a step that covers performance of this limitation in the mind in the form of making an making an observation. Therefore, this limitation recites a mental process. See MPEP 2106.04(a)(2)(III). Claims can recite a mental process even if they are claimed as being performed on a computer or in a computer environment, see MPEP 2106.04(a)(2)(III)(C). Claim 9 further recites, on a basis of the estimated structure, the estimated structure at a time of occurrence of the anomaly, and the anomaly detection sensor information and estimate a change in a relationship among the pieces of sensor data, wherein the process estimates a factor of the anomaly in consideration of a change in a relationship among the pieces of sensor data estimated on a basis of the anomaly detection order estimated and the anomaly propagation order estimated. These limitations merely describe the further feature of data used for the abstract ideas and additional elements. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 10 recites, to estimate, on a basis of device-attached sensor information in which a device provided in the target facility and the sensor provided in the device are associated with each other, the anomaly detection order estimated, and the anomaly propagation order estimated, a factor of the anomaly in units of the device. This limitation merely describes the data generated at an earlier limitation ("in units of the device") and the data used. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 11 recites, to output related structure graph display information for displaying a graph in which … This limitation is a step that merely outputs data in the form of display. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 19 , and similarly independent Claim 12 from which Claims 13-18 depend Step 1 : Claim 19 recites a system comprising the apparatus of Claim 1, and Claim 12 recites an apparatus Step 2A Prong 1: Abstract idea Claim 19, and similarly Claim 12, recite, and to calculate, using a plurality of pieces of the learning data candidates acquired as a plurality of pieces of learning data, at least one of statistics of the plurality of the pieces of learning data … This limitation is a process that recites a mathematical calculation. Therefore, the claim recites a mathematical concept, see MPEP 2106.04(a)(2)(1)(C). and learn an estimated structure indicating a dependence relationship between the facility components on a basis of the statistics calculated. This limitation is a step that covers performance of this limitation in the mind in the form of making an making an observation in the form of learning, see specification [0144] reciting that an operator is equipped to know the dependance relationships between components. Therefore, this limitation recites a mental process. See MPEP 2106.04(a)(2)(III). Claims can recite a mental process even if they are claimed as being performed on a computer or in a computer environment, see MPEP 2106.04(a)(2)(III)(C). Step 2A Prong 2: Additional Elements Claim 19, and similarly Claim 12, recite, to acquire, as learning data candidates, a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a target facility during a time of normal operation of the target facility; This limitation is a step that merely obtains data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 19, and similarly Claim 12, further recite, a processor; and a memory storing a program,… This limitation describes mere instructions to apply an exception in the form of general-purpose computer components. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Step 2B: Significantly More Claim 19, and similarly Claim 12, recite, to acquire, as learning data candidates, a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a target facility during a time of normal operation of the target facility; This limitation is a step that merely obtains data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 19, and similarly Claim 12, further recite, a processor; and a memory storing a program,… This limitation describes mere instructions to apply an exception in the form of general-purpose computer components. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 13 recites, to acquire the plurality of the pieces of learning data to be used for learning on a basis of the plurality of the learning data candidates acquired, wherein the process calculates at least one of the statistics among the plurality of the pieces of learning data on a basis of the learning data acquired, and learns the estimated structure on a basis of the statistics calculated. This limitation is a step that merely describes limiting data from obtained data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim 14 recites, wherein the process selects the plurality of the learning data candidates whose variance is less than a selection threshold among the plurality of the learning data candidates acquired, and acquires a plurality of the selected learning data candidates as the plurality of the pieces of learning data. The claim recites the step of comparing collected information to a predefined threshold, which is an act of evaluating information that can be practically performed in the human mind, see MPEP 2106.04(a)(2)(III). Claim 15 recites, wherein the process calculates the statistics using a waveform based statistical index. This limitation describes mere instructions to apply an exception in the form of applying a computer algorithm, in the form of a generally recited waveform-based statistical index, being performed on a general-purpose computer. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 16 recites, wherein the process calculates the statistics using a distribution-based statistical index. This limitation describes mere instructions to apply an exception in the form of applying a computer algorithm, in the form of a generally recited distribution-based statistical index, being performed on a general-purpose computer. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 17 recites, wherein the process calculates the statistics using a waveform based statistical index and a distribution-based statistical index. This limitation describes mere instructions to apply an exception in the form of applying a computer algorithm, in the form of a generally recited distribution-based statistical index and a generally recited waveform-based statistical index, being performed on a general-purpose computer. See MPEP 2106.05(f)(2). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(d) and 2106.05(f)(2). The claim does not contain significantly more than the judicial exception. Claim 18 recites, to generate a pair of the sensors from among the plurality of the sensors on a basis of a connection relationship among a plurality of devices constituting the target facility and facility design information in which the plurality of the sensors provided in a plurality of the devices is defined, wherein the process acquires the learning data on a basis of the pair of the sensors generated and learns the estimated structure. This limitation is a step that merely describes limiting data from obtained data. Therefore, this step is a mere data gathering, extra solution activity that is understood to be merely nominal. See MPEP 2106.05(g)(3). The combination of these additional elements are no more than mere data gathering in conjunction with the abstract idea in order to provide data for the abstract ideas to be applied to. Therefore, this does not meaningfully limit the claim, see MPEP 2106.05(g)(3). Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (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 12-13, 15, and 18 are rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being unpatentable in view of Fujitsuka (U.S. PGPub No. 20200192342). Regarding Claim 12 , Fujitsuka teaches, A learning device comprising: a processor; and a memory storing a program, upon executed by the processor (see CPU 101 and memory 102 of causal learning device 100 [Fig. 2]) , to perform a process: to acquire, as learning data candidates, a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a target facility during a time of normal operation of the target facility (acquisition unit 110 acquires time-series data of the sensor values acquired from the sensors [0065]; the learning process is performed on sensor values from sensors in a normal state in advance [0039]) ; and to calculate, using a plurality of pieces of the learning data candidates acquired as a plurality of pieces of learning data, at least one of statistics of the plurality of the pieces of learning data on a basis of the plurality of the pieces of learning data, and learn an estimated structure indicating a dependence relationship between the facility components on a basis of the statistics calculated (the correlation determination unit (120) selects a pair of sensors from which sensor values are acquired ("learning data") [0066]; the correlation determination unit (120) calculates an error ("statistic") from the acquired values indicating a high or low correlation [0066]; after determining this statistic, a low correlation causal relationship estimation unit (130) using CCM is applied to the data to determine further causal relationships [0067]; and after determining the error statistic, a high correlation casual relationship estimation unit (140) is applied using bidirectional polynomials to determine a causal relationship [0068; after identifying all causal relationships ("estimated structure") the causal relationship for the entire sensors is stored [0070]; The Examiner additionally points to Fig. 5 as showing the learning method performed as a flowchart) . Regarding Claim 13 , Fujitsuka teaches, The learning device according to claim 12, the process comprising: to acquire the plurality of the pieces of learning data to be used for learning on a basis of the plurality of the learning data candidates acquired, wherein the process calculates at least one of the statistics among the plurality of the pieces of learning data on a basis of the learning data acquired, and learns the estimated structure on a basis of the statistics calculated (the correlation determination unit (120) selects a pair of sensors from which sensor values are acquired ("learning data") [0066]; the correlation determination unit (120) calculates an error ("statistic") from the acquired values indicating a high or low correlation [0066]; after determining this statistic, a low correlation causal relationship estimation unit (130) using CCM is applied to the data to determine further causal relationships [0067]; and after determining the error statistic, a high correlation causal relationship estimation unit (140) is applied using bidirectional polynomials to determine a causal relationship [0068; after identifying all causal relationships ("estemated structure") the causal relationship for the entire sensors is stored [0070]; The Examiner additionally points to Fig. 5 as showing the learning method performed as a flowchart) . Regarding Claim 15 , Fujitsuka teaches, The learning device according to claim 12, wherein the process calculates the statistics using a waveform based statistical index (convergent cross mapping (CCM) may be applied to determine low-correlation causal relationships [0067]) . Regarding Claim 18 , Fujitsuka teaches, The learning device according to claim 12, the process comprising: to generate a pair of the sensors from among the plurality of the sensors on a basis of a connection relationship among a plurality of devices constituting the target facility and facility design information in which the plurality of the sensors provided in a plurality of the devices is defined, wherein the process acquires the learning data on a basis of the pair of the sensors generated and learns the estimated structure (where pairs of sensors are evaluated by the correlation determination unit (120) [0026]; where the sensors provide information on a facility or an environment of a factory and the sensors are determined in accordance with the installation place thereof ("facility design information") [0025]; further see Fig. 5 and the corresponding rejection for Claim 12 as to why [0065-0070] teach learning the estemated structure) . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-3, 7-8, 10-11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fujitsuka (U.S. PGPub No. 20200192342) in view of Kageyama (U.S. PGPub No. 20200278670) . Regarding Claim 1 , Fujitsuka teaches, An anomaly factor estimating device comprising: a processor; and a memory storing a program, upon executed by the processor (causal relationship learning device and anomaly analysis device comprising processors 101 and 201 with memories 102 and 202 [Fig. 2]) , to perform a process: to acquire a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a plurality of facility components constituting a target facility (where sensor(s) are of a facility [0025]; sensor values are acquired [0073]) ; to detect a plurality of anomaly detection sensors in which an anomaly has occurred among the plurality of the sensors on a basis of a plurality of the pieces of sensor data acquired (an anomaly detection unit is applied to the acquired sensor values [0074]) ; to estimate an anomaly propagation order in which the anomaly has propagated on a basis of anomaly detection sensor information regarding the plurality of the anomaly detection sensors detected and an estimated structure indicating a dependence relationship between the facility components (the sensor corresponding to the identified cause of the anomaly ("propagation order") is identified via causal relationship storage ("estemated structure") [0076]; and where already-identified anomalous sensors are employed to identify causal relationships indicative of the anomaly's identified cause ("information regarding the plurality of the anomaly detection sensors") [0075-76]) ; While Fujitsuka teaches if an anomaly is detected, the anomaly detection unit extracts anomaly information indicating the sensor from which the anomaly is detected and the occurrence time of the anomaly [0075] and where information indicating the sensor corresponding to the identified cause is output [0076] , Fujitsuka does not appear to explicitly teach and Kageyama teaches, to estimate an anomaly detection order in which occurrence of the anomaly is detected for the plurality of the anomaly detection sensors on a basis of a detection time at which the process has detected the plurality of the anomaly detection sensors (time of state-change (anomaly detection time) is acquired [0046]) ; and to estimate a factor of the anomaly on a basis of the anomaly detection order estimated and the anomaly propagation order estimated (where a cause-estimation means (4) estimates the region being the cause of an anomaly (propagation order) and acquires the time of state-change (detection order) from state-change detection means (2) [0046]; where a score is assigned to each region as being indicative of the state change ("abnormality") based on propagation relationships and detection time ("order") [0049]) . It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly detection and propagation estimation method of Fujitsuka to incorporate a numerical result of the estimation that also explicitly considers detected event time of Kageyama. The resulting combination provides an additional consideration as to why a particular region may be the cause of an anomaly, where "the earlier the state change is detected, the more highly the region is likely to be the cause" [Kageyama; 0053] and allows for easily distinguished expression of a particular region being the cause [of the anomaly] [Kageyama; 0015]. Regarding Claim 2 , Fujitsuka teaches, The anomaly factor estimating device according to claim 1, wherein the estimated structure is represented by a matrix (causal relationships between two sensor outputs (X and Y, [0049]) are shown exemplary in three-dimensional space as points, but may further be represented in any dimension [0043]) . Regarding Claim 3 , Fujitsuka does not appear to disclose and Kageyama teaches, The anomaly factor estimating device according to claim 1, the process comprising: to output information regarding an estimation result of the factor of the anomaly (where a cause-estimation means (4) estimates the region being the cause of an anomaly (propagation order) and acquires the time of state-change (detection order) from state-change detection means (2) [0046]; where a score is assigned to each region as being indicative of the state change ("abnormality") based on propagation relationships and detection time ("order") [0049]; the score is output [0067 and Figs. 6-7]) . The same motivation for Claim 1 also applies to Claim 3. Regarding Claim 7 , Fujitsuka does not appear to disclose and Kageyama teaches, The anomaly factor estimating device according to claim 1, wherein the process estimates the anomaly propagation order on a basis of the anomaly detection sensor information, facility operation state information indicating an operation state of the target facility, and the estimated structure indicating a dependence relationship between the facility components depending on the operation state of the target facility (where a cause-estimation means (4) estimates the region being the cause of an anomaly (propagation order) and acquires the time of state-change (detection order) from state-change detection means (2) and stored propagation relationship information [0046]) . The same motivation for Claim 1 also applies to Claim 7. Regarding Claim 8 , Fujitsuka teaches, The anomaly factor estimating device according to claim 1, the process comprising: to correct a dependence relationship among the pieces of sensor data for the estimated structure on a basis of dependent pair information related to a pair of the sensors having a dependence relationship among the plurality of the sensors and non- dependent pair information related to a pair of the sensors having no dependence relationship (the sensor corresponding to the identified cause of the anomaly ("propagation order") is identified via causal relationship storage ("estemated structure") [0076]; where the causal relationship is learned via sensors in a normal state in advance [0039] but may be reconstructed ("corrected") [0042]) . Regarding Claim 10 , Fujitsuka does not appear to disclose and Kageyama teaches, The anomaly factor estimating device according to claim 1, the process comprising: to estimate, on a basis of device-attached sensor information in which a device provided in the target facility and the sensor provided in the device are associated with each other, the anomaly detection order estimated, and the anomaly propagation order estimated, a factor of the anomaly in units of the device (where a cause-estimation means (4) estimates the region being the cause of an anomaly (propagation order) and acquires the time of state-change (detection order) from state-change detection means (2) [0046]; where a score is assigned to each region as being indicative of the state change ("abnormality") based on propagation relationships and detection time ("order") [0049]; a "possibility" factor is displayed as a ratio of each respective unit's score to the total summation of the scores ("in units of the device") [0067 and Fig. 6]) . The same motivation for Claim 1 also applies to Claim 10. Regarding Claim 11 , Fujitsuka does not appear to disclose and Kageyama teaches, The anomaly factor estimating device according to claim 1, the process comprising: to output related structure graph display information for displaying a graph in which the estimated structure, the anomaly detection sensor, and an estimation result of a factor of the anomaly are associated with each other on a basis of the estimated structure, the anomaly detection sensor information, and information regarding the estimation result of the factor of the anomaly estimated (the display output may include: an inter-detection unit relationship information ("estemated structure") [0059, Fig. 5], the labeled regions ("the anomaly detection sensor") [Fig. 5-7], detection time [Fig. 5], and possibility representative of a respective device's score indicative of causing the abnormality [0049, 0067, and Figs. 6-7]) . The same motivation for Claim 1 also applies to Claim 11. Regarding Claim 19 , Fujitsuka teaches, A precise diagnostic system comprising: the anomaly factor estimating device according to claim 1; and a learning device comprising: a processor; and a memory storing a program, upon executed by the processor (see CPU 101 and memory 102 of causal learning device 100 [Fig. 2]) , to perform a process: to acquire, as learning data candidates, a plurality of pieces of time-series sensor data collected by a plurality of sensors provided in a target facility during a time of normal operation of the target facility (acquisition unit 110 acquires time-series data of the sensor values acquired from the sensors [0065]; the learning process is performed on sensor values from sensors in a normal state in advance [0039]) ; and to calculate, using a plurality of pieces of the learning data candidates acquired as a plurality of pieces of learning data, at least one of statistics of the plurality of the pieces of learning data on a basis of the plurality of the pieces of learning data, and learn an estimated structure indicating a dependence relationship between the facility components on a basis of the statistics calculated (the correlation determination unit (120) selects a pair of sensors from which sensor values are acquired ("learning data") [0066]; the correlation determination unit (120) calculates an error ("statistic") from the acquired values indicating a high or low correlation [0066]; after determining this statistic, a low correlation causal relationship estimation unit (130) using CCM is applied to the data to determine further causal relationships [0067]; and after determining the error statistic, a high correlation causal relationship estimation unit (140) is applied using bidirectional polynomials to determine a causal relationship [0068; after identifying all causal relationships ("estemated structure") the causal relationship for the entire sensors is stored [0070]; The Examiner additionally points to Fig. 5 as showing the learning method performed as a flowchart) . 07-21-aia AIA Claim s 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Fujitsuka in view of Kageyama, further in view of Oka et al. (U.S. PGPub No. 20230229136) . Regarding Claim 4 , while Fujitsuka discloses that the step of detecting an anomaly may be performed by a well-known anomaly detection method [0074], Fujjitsuka does not explicitly disclose and Oka teaches, The anomaly factor estimating device according to claim 1, wherein the process detects the anomaly detection sensors using a univariate type anomaly detecting method (an abnormality degree may be performed via the Hotelling method (univariate) [0051], where abnormality degree is performed on data associated with sensor data [0046]) . It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly detection to detect anomalous sensors as disclosed by Fujitsuka to incorporate the particular abnormality detection technique of Oka. The resulting combination allows for detection that is not limited to one-to-one relationships [Oka; 0051], and the particular use of the Hotelling method allows for detection of outliers from predetermined criterion [Oka; 0055] the Examiner notes, therefore improving reliability. Regarding Claim 5 , while Fujitsuka discloses that the step of detecting an anomaly may be performed by a well-known anomaly detection method [0074], Fujjitsuka does not explicitly disclose and Oka teaches, The anomaly factor estimating device according to claim 1, wherein the process detects the anomaly detection sensor using a multivariate type anomaly detecting method (an abnormality degree may be performed via the graphical lasso method (multivariate) [0051], where abnormality degree is performed on data associated with sensor data [0046]) . It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly detection to detect anomalous sensors as disclosed by Fujitsuka to incorporate the particular abnormality detection technique of Oka. The resulting combination allows for detection that is not limited to one-to-one relationships [Oka; 0051]. The particular use of the graphical lasso method allows for consideration of data from a plurality of sensors in continuous or batch processing [Oka; 0062], the Examiner notes that therefore, efficiency is improved. The particular use of the Hotelling method allows for detection of outliers from predetermined criterion [Oka; 0055] the Examiner notes, therefore improving reliability. Regarding Claim 6 , while Fujitsuka discloses that the step of detecting an anomaly may be performed by a well-known anomaly detection method [0074], Fujjitsuka does not explicitly disclose and Oka teaches, The anomaly factor estimating device according to claim 1, wherein the process detects the anomaly detection sensor using a univariate type anomaly detecting method and a multivariate type anomaly detecting method (an abnormality degree may be performed via the graphical lasso method (multivariate) and the Hotelling method (univariate) [0051], where abnormality degree is performed on data associated with sensor data [0046]; it can be shown exemplary on Fig. 9 that both Hotelling method (for the "flow rate sensor;" "H") and graphical lasso (for the "temperature 2 sensor;" "glasso") may be applied) . It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly detection to detect anomalous sensors as disclosed by Fujitsuka to incorporate the particular abnormality detection technique of Oka. The resulting combination allows for detection that is not limited to one-to-one relationships [Oka; 0051], and the particular use of the graphical lasso method allows for consideration of data from a plurality of sensors in continuous or batch processing [Oka; 0062], the Examiner notes that therefore, efficiency is improved . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Kulkarni et al. (U.S. PGPub No. 20200336499) discloses use of both multivariate and univariate datasets for determining anomaly [0021], a matrix used to compute similarities between assets (“devices”) [0035], and use of relationship dependency [0039]. Maeda et al. (U.S. PGPub No. 20140195184) discloses an anomaly detection method for use in a plant (“facility”) where operating information such as time and sensor information, in conjunction with logs, are used to make reference equipment record while classifying and presenting anomalies requiring action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AUDREY E WHITESELL whose telephone number is (703)756-4767. The examiner can normally be reached 8:30am - 5:00pm MST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at 5712723655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.E.W./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113 Application/Control Number: 19/057,521 Page 2 Art Unit: 2113 Application/Control Number: 19/057,521 Page 3 Art Unit: 2113 Application/Control Number: 19/057,521 Page 4 Art Unit: 2113 Application/Control Number: 19/057,521 Page 5 Art Unit: 2113 Application/Control Number: 19/057,521 Page 6 Art Unit: 2113 Application/Control Number: 19/057,521 Page 7 Art Unit: 2113 Application/Control Number: 19/057,521 Page 8 Art Unit: 2113 Application/Control Number: 19/057,521 Page 9 Art Unit: 2113 Application/Control Number: 19/057,521 Page 10 Art Unit: 2113 Application/Control Number: 19/057,521 Page 11 Art Unit: 2113 Application/Control Number: 19/057,521 Page 12 Art Unit: 2113 Application/Control Number: 19/057,521 Page 13 Art Unit: 2113 Application/Control Number: 19/057,521 Page 14 Art Unit: 2113 Application/Control Number: 19/057,521 Page 15 Art Unit: 2113 Application/Control Number: 19/057,521 Page 16 Art Unit: 2113 Application/Control Number: 19/057,521 Page 17 Art Unit: 2113 Application/Control Number: 19/057,521 Page 19 Art Unit: 2113 Application/Control Number: 19/057,521 Page 20 Art Unit: 2113 Application/Control Number: 19/057,521 Page 21 Art Unit: 2113 Application/Control Number: 19/057,521 Page 22 Art Unit: 2113 Application/Control Number: 19/057,521 Page 23 Art Unit: 2113 Application/Control Number: 19/057,521 Page 24 Art Unit: 2113 Application/Control Number: 19/057,521 Page 25 Art Unit: 2113 Application/Control Number: 19/057,521 Page 26 Art Unit: 2113 Application/Control Number: 19/057,521 Page 27 Art Unit: 2113
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