CTNF 16/827,877 CTNF 95989 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. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/04/2026 has been entered. 12-151 AIA 26-51 12-51 Status of Claims This action is in response to the amendments filed 03/04/2026. Claims 1, 4, 7, 9-10, 13, and 16 have been amended, claims 20-21 have been added. Claims 1, 3-4, 7-10, 12-13, 16-17, and 19-21 are currently pending. Response to Arguments Applicant’s arguments regarding the 101 rejection have been fully considered but they are not persuasive. Applicant argues on page 13 that “the claimed steps of “generating a plurality of attractors from the plurality of pieces of time series data measured by the sensor device” and “generating a persistence diagram by performing persistent homology conversion on each of the generated plurality of attractors involve complex computations and data transformations that cannot be practically performed in the human mind with or without a physical aid like pen and paper” and further argues “the volume and complexity of sensor data and the topological data analysis calculations involved in generating attractors and persistence diagrams are far beyond human mental capacity”. Examiner respectfully disagrees and notes that neither the claims nor Applicant’s disclosure describe the steps directed to generating attractors from time series data or generating a persistence diagram in such a way that either makes clear the technical computer operations required to perform these steps or at least distinguishes them from the way a person could generate attractors from observed time series data and generate a persistence diagram in their mind, potentially assisted by pen and paper. Applicant further argues on pages 13-14 that “the core inventive step of “setting, for each piece of the plurality of pieces of time series data in the persistence diagram, a weight based on a time period of existence. . . the weight being set based on a first criterion and a second criterion” involves intricate logic for dynamically adjusting data significance, which is not a mere mental observation or evaluation. Examiner respectfully disagrees and notes that neither the claims nor Applicant’s disclosure describe the steps directed to setting this weight in such a way that either makes clear the technical computer operations required to perform these steps or at least distinguishes them from the way a person could decide in their mind to set a weight for observed data based on mentally determined criteria and decide in their mind that this weight always has a value greater than zero. Wherein this weight setting is performed “to distinguish data sets having high frequency components” and “to suppress noise” is interpreted in light of section 2103 of the MPEP as the intended use and does not provide additional patentable weight to these limitations. Applicant further argues on pages 14-15 that the claims integrate any claimed judicial exceptions into a practical application that provides an improvement to a technology and that “amended claim 1 describes a specific technological solution to the technical problem of accurately extracting features from volatile time series data containing time and frequency components for anomaly detection”. Examiner respectfully disagrees and notes that the claim limitations directed to feature extraction were interpreted as a mental step, as the claim recites this feature extraction as a method of generating a Betti sequence from the weighted data. Applicant has not shown where in the claims or Applicant’s disclosure the steps directed to extracting these features by generate a Betti sequence are described in such a way that either makes clear the technical computer operations required to perform these steps or at least distinguish them from the way a person could decide in their mind to extracting these features by generating a Betti sequence. Examiner also notes that, with regards to Applicant’s arguments related to the limitations directed to the weight setting criteria, that these limitations were rejected under 35 U.S.C. 112(a) as lacking support from Applicant’s original disclosure. However, Examiner notes that the broadest reasonable interpretation of determining, or setting a weight based on predetermined criteria includes a mental step, as a person could decide in their mind to set weights for data according to predetermined criteria. Section 2106.05(a) of the MPEP states, “the judicial exception alone cannot provide the improvement”. Applicant has not persuasively pointed to claim features directed to additional elements that would provide this argued improvement. Applicant further argues on pages 15-17 that the claimed steps directed to training a neural network model “directly ties the improved feature extraction to a practical application in areas like anomaly detection” and compares the claims to the Ex Parte Desjardins decision in stating that the claims “provide an analogous improvement by enabling more accurate and nuanced machine learning for time series data”. Examiner respectfully disagrees with Applicant’s comparison of the claims to Desjardins , as the claims in Desjardins were interpreted as being directed to an “improvement to how the machine learning model itself operates”. Examiner notes that the Applicant’s claims do not change how the machine learning model itself operates, but rather at most improve the kind of training data used by the model. Using improved training data may result in a better result from a machine learning model, but does not necessarily change or improve the workings of the actual model. Applicant’s claims are more suitably compared to Recentive Analytics, Inc v. Fox Corp., No. 23-2437 (Fed. Cir. 2025), as the claims merely recite use of a machine learning model in a new environment (see page 13 of Recentive ). Page 15 of Recentive further states “the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by a human with greater speed and efficiency than could previously be achieved. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improve computer technique) do not themselves create eligibility”. Similarly, Applicant has argued that the claims reflect an improvement to a task, but Applicant has not shown how the claims reflect an improved machine learning model. The 101 rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended. Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new ground(s) of rejection. Applicant argues that the prior art does not teach the claimed steps directed to “weight setting with values greater than zero”, and Examiner notes that the Adams reference has been brought in to modify the method of weight setting from Petri such that weights are not set to zero (see at least section 4 of Adams). Applicant also argues that the claim limitations related to the “first criterion is used to distinguish data sets having high frequency components and the second criterion being used to suppress noise from a hole equal or earlier than a threshold time” reflect a novel insight into the nature of noise in topological data analysis of time series data. Examiner does not necessarily disagree with Applicant’s characterization of paragraph [0070] of Applicant’s specification; however, Examiner notes that the limitations in question have been rejected under 35 U.S.C. 112(a) as containing new matter not supported by Applicant’s original disclosure. While Applicant’s original disclosure discusses wherein the process of setting weights can be based on multiple criterion related to an existence value and a time of existence in paragraph [0076], the original disclosure does not support wherein these criteria must be “ a range up to a particular distance (see the 112(b) rejection of claim 1 for interpretation of a “particular distance”) from a diagonal line of the persistence diagram” and “being a hole having the appearance time equal to or earlier than the threshold time”. The prior art rejections have been updated in light of the 112 rejections to include the amended limitations and to clarify the reasoning given for the limitations that were not amended. Claim Rejections - 35 USC § 112 07-30-01 AIA The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 1, 3-4, 7-10, 12-13, 16-17, and 19-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitations “the weight being set based on a first criterion and a second criterion, the first criterion used to distinguish data sets having high frequency components and the second criterion being used to suppress noise from a hole equal to or earlier than a threshold time, the first criterion being a range up to a particular distance from a diagonal line of the persistence diagram and the second criterion being a hole having the appearance time equal to or earlier than the threshold time”. Examiner notes that paragraph [0076] of Applicant’s specification recites “As illustrated in FIG. 16, in the information stored in the weight setting DB 15, "criterion 1 (the existence value), criterion 2 (the time of existence), weight" are associated with each other. "Criterion 1" indicates a criterion of the appearance time, "criterion 2" indicates a criterion of the time of existence, and "weight" 10 indicates a weight to be set”. At least paragraph [0068] of Applicant’s specification recites “As illustrated in FIG. 9, in the general denoising, data plotted at a position within a fixed distance from the diagonal line in plotting results on the persistence diagram, that is, data corresponding to an area in which the time of existence is relatively short and that appears close to the diagonal line is deleted” and paragraph [0075] recites “the weight setting unit 23 changes weight between an area (a) that is a range extending up to a particular distance from the diagonal line of the persistence diagram and an area (b) that is a range in which the appearance time is equal to or earlier than a predetermined value”. However, Applicant’s original disclosure does not support “the first criterion used to distinguish data sets having high frequency components and the second criterion being used to suppress noise from a hole equal to or earlier than a threshold time” or “the first criterion being a range up to a particular distance from a diagonal line of the persistence diagram and the second criterion being a hole having the appearance time equal to or earlier than the threshold time”. For purposes of examination, Examiner is interpreting that the weights may be set by criteria related to the existence value and the time of existence, and that data corresponding to a short period of existence based on the distance from a diagonal line is considered as noise and can be given a lower weight. Claims 9 and 10 recite a similar limitation and are rejected for the same reasons. Dependent claims 3-4, 7-8, 12-13, 16-17, and 19-21 are also rejected because they fail to correct the deficiencies of the independent claims on which they depend. Claim 21 is additionally rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 21 recites the limitation “the setting, based on the first criterion, does not set the weights to cause a zero degree of influence when the time period of existence is equal to or less than the first threshold”. As explained in the rejection of claim 1, paragraph [0076] of Applicant’s specification teaches wherein one weight setting criterion can be related to an existence value and a second criterion can be related to a time of existence. However, Applicant’s original disclosure does not support wherein a first criterion does not set a weight to cause a zero degree of influence when a time period of existence is less than or equal to a threshold. For purposes of examination, Examiner is interpreting that a weight can be set to suppress influences of data in which the time of existence is relatively short, as described in at least paragraph [0074] of Applicant’s specification. 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. Claims 1, 3-4, 7-10, 12-13, 16-17, and 19-21 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. The limitation “the first criterion being a range up to a particular distance from a diagonal line of the persistence diagram and the second criterion being a hole having the appearance time equal to or earlier than the threshold time” in claim 1 is a relative term which renders the claim indefinite. The term “particular” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For purposes of examination, Examiner is interpreting that denoising can occur based on a predetermined distance from the diagonal line the persistence diagram, as explained in at least paragraphs [0098]-[0099] of Applicant’s specification. Claims 9 and 10 recite a similar limitation and are rejected for the same reasons. Dependent claims 3-4, 7-8, 12-13, 16-17, and 19-21 are also rejected because they fail to correct the deficiencies of the independent claims on which they depend. 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, 3-4, 7-10, 12-13, 16-17, and 19-21 are rejected under 35 U.S.C. 101. Claims 1, 3-4, 7-8, and 19-21 are directed to a method, claim 9 is directed to a non-transitory computer-readable storage medium, and claims 10, 12-13, and 16-17 are directed to a system; therefore, claims 1, 3-4, 7-10, 12-13, 16-17, and 19-21 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 1, 3-4, 7-10, 12-13, 16-17, and 19-21 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”. Claim 1: Claim 1 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). Claim 1 recites the following abstract ideas: generating a plurality of attractors from the plurality of pieces time series data measured by the sensor device (mental step directed to observation – a person could generate a plurality of attractors (as described by paragraph [0055] of Applicant’s specification as a finite set of points) in their mind, potentially assisted by pen and paper, having observed a plurality of pieces of time series data that were measured by a sensor device. Examiner’s Note: see MPEP 2106.04(a)(2)(III) for discussion of mental processes assisted by pen and paper); generating a persistence diagram by performing persistent homology conversion on each of the generated plurality of attractors (mental step directed to evaluation – a person could generate a persistence diagram for each of the attractors in their mind, potentially assisted by pen and paper, by performing persistent homology conversion. Examiner’s Note: while this limitation is not being interpreted as a mathematical calculation, MPEP 2016.04(a)(2)(A)(ii) provides additional guidance on how to interpret limitations directed to data conversion); acquiring an appearance time and a disappearance time of a hole that occurs by performing the persistent homology conversion on the plurality of pieces of time series data (mental step directed to observation, evaluation, judgement – a person could perform persistent homology conversion in their mind, potentially assisted by pen and paper, to acquire appearance and disappearance times of a hole corresponding to observed time series data. Examiner notes that the broadest reasonable interpretation of acquiring this data could also include receiving data over a network, which would be interpreted under well-understood, routine, conventional activity as described in MPEP 2106.05(d)(II)); setting, for each piece of the plurality of pieces of time series data in the persistence diagram, a weight based on a time period of existence of the hole that occurs between the appearance time and the disappearance time and that corresponds to each piece of the plurality of pieces of time series data (mental step directed to observation, evaluation – a person could set a weight for a particular data item in a persistence diagram at between an observed or mentally determined appearance and disappearance time), the weight being set based on a first criterion and a second criterion, the first criterion used to distinguish data sets having high frequency components and the second criterion being used to suppress noise from a hole equal to or earlier than a threshold time, the first criterion being a range up to a particular distance from a diagonal line of the persistence diagram and the second criterion being a hole having the appearance time equal to or earlier than the threshold time (this limitation is interpreted based on the 112(a) rejection of claim 1 such that weights may be set by criteria related to the existence value and the time of existence, and that data corresponding to a short period of existence based on the distance from a diagonal line is considered as noise and can be deleted. Given this interpretation, Examiner notes that a person could set a weight in their mind based on observed or mentally determined criterion, distinguish observed data sets having high frequency components in their mind, determine a range from a diagonal line in an observed or mentally determined persistence diagram as a criterion in their mind (see the 112(b) rejection of claim 1 for interpretation of a “particular distance”), and compare an observed or mentally determined appearance time to a threshold time in their mind, potentially assisted by pen and paper); the weight being a first value when the time period of existence is more than a first threshold, the weight being a second value greater than zero less than the first value as the time period of existence is equal or less than the first threshold (mental step directed to observation, evaluation, judgement – a person could change the value of a mentally determined weight over time by comparing an observed time period to a threshold in their mind, a person could also set a weight in their mind such that a weight for a data point corresponding to a hole for which the time period of existence is less than or equal to a threshold has a smaller or lesser degree of influence for a future feature extraction procedure, potentially assisted by pen and paper when performing persistent homology conversion); thereby the weight is set such that each piece of the plurality of pieces of time series data corresponding to the hole of which the time period of existence is equal to or less than the first threshold is decreased in a degree of an influence in the extracting features, the decreased degree of influence suppresses noise components caused by the hole with the time period of existence equal to or less than the first threshold (this limitation is interpreted as the outcome or intended result of setting the weight for the time series data and does not further limit the scope or provide additional patentable weight to the claim – see MPEP 2103(I)(C)); extracting, as the Betti sequence, features of the plurality of pieces of time series data from the persistence diagram with the weight set to generate a Betti sequence in which high frequency components are considered and noise is suppressed (mental step directed to observation, evaluation, judgement – a person could decide on, or extract which features are relevant for a plurality of pieces of time series data set having observed the weights of that dataset and generate a Betti sequence to consider observed high frequency components and suppress noise in their mind, potentially assisted by pen and paper. Examiner notes that the broadest reasonable interpretation of extracting features as a Betti sequence also includes a mathematical relationship as described in paragraph [0054] of Applicant’s specification), generating a training dataset by adding, to the generated Betti sequence, a label indicating a state of the object, the training data set including the Betti sequence and the label (mental step directed to observation, evaluation – a person could generate a training dataset in their mind by adding labels to mentally observed or determined Betti sequences, potentially assisted by pen and paper); training a machine learning on a neural network model using the training data set. . . for predicting the label in the training dataset (mental step directed to observation, evaluation, judgement – a person could predict labels for a training dataset in their mind, potentially assisted by pen and paper. As the claims do not require a particular kind of neural network model or particular steps for training a neural network model to predict a label, Examiner is interpreting the neural network model as a generic computer component (see MPEP 2106.05(d). Therefore, using a generic training process to train a generic neural network model to predict labels is interpreted as mere instructions to apply an abstract concept (in this case the mental steps of evaluation and judgement) using a generic computer (see MPEP 2106.05(f)). Claim 1 recites the following additional elements: receiving, from a sensor device mounted on an object, a plurality of pieces of time series data having frequency components; inputting the Betti sequence in the training dataset to the neural network; and outputting a determination result obtained by the neural network model. Receiving time series data with frequency components from a sensor device, inputting the Betti sequence to the neural network, and outputting a determination result obtained by a neural network are interpreted as aspects of the technological environment or field of use in which the abstract ideas are performed and as transmitting and receiving data over a network. These additional elements do not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(d)(II) and MPEP 2106.05(h)). Claim 9 is a non-transitory computer-readable storage medium claim and its limitation is included in claim 1. The only difference is that claim 9 requires a non-transitory computer-readable storage medium, which is interpreted as a generic computer component merely used to apply the claimed judicial exceptions (see MPEP 2106.05(f)). Therefore, claim 9 is rejected for the same reasons as claim 1. Claim 10 is a system claim and its limitation is included in claim 1. The only difference is that claim 10 requires a system comprising a sensor device, a memory, and a processor, which are interpreted as generic computer components merely used to apply the claimed judicial exceptions (see MPEP 2106.05(f)). Therefore, claim 10 is rejected for the same reasons as claim 1. The independent claims are not patent eligible. Dependent claims 3-4, 7-8, 12-13, 16-17, and 19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception. Claim 3 recites the following abstract ideas: generating bar code data from the persistence diagram with the weight set and generating a Betti sequence in accordance with the bar code data (mental step directed to evaluation – a person could generate bar code data from the persistence diagram with the weight set and generate Betti sequence data from the bar code in their mind, potentially assisted by pen and paper (the broadest reasonable interpretation of generating bar code data from the persistence diagram and Betti sequence data from the bar code data is made in light of at least paragraphs [0060] – [0061] of Applicant’s specification). Examiner’s Note: while this limitation is not being interpreted as a mathematical calculation, MPEP 2016.04(a)(2)(A)(ii) provides additional guidance on how to interpret limitations directed to data conversion). Claim 3 does not recite any additional elements and therefore does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas. Claim 4 recites the following abstract ideas: setting a weight less than the first value when the appearance time of the hole is equal to or earlier than a threshold time (mental step directed to observation, evaluation – a person could set a weight for a particular value in the persistence diagram having observed a hole at a particular appearance time and comparing it to a threshold time in their mind, potentially assisted by pen and paper). Claim 4 does not recite any additional elements and therefore does not integrate claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas. Claim 7 recites the following abstract ideas: extracting as the features, from the persistence diagram with the weight set, a total of the time period of existence with respect to the items of data in the persistence diagram with the weight set (mental step directed to observation, evaluation, judgement – a person could decide on which features are relevant for a time series data set having evaluated the persistence diagram over a total time period of existence of the data items in their mind, potentially assisted by pen and paper). Claim 7 does not recite any additional elements and therefore does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas. Claim 8 recites the abstract ideas from claim 1 on which it depends. Claim 8 recites the following additional elements: the plurality of pieces of time series data is obtained, whenever desired, from a sensor that is set by a user, the features of the plurality of pieces of time series data that are obtained whenever desired are displayed, and a change in the features of the plurality of pieces of time series data is detected. Obtaining and displaying time series data from a sensor under specific conditions is interpreted as receiving or transmitting data over a network, which does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d)(II)). Claim 12 is a system claim and its limitation is included in claim 3. Claim 12 is rejected for the same reasons as claim 3. Claim 13 is a system claim and its limitation is included in claim 4. Claim 13 is rejected for the same reasons as claim 4. Claim 16 is a system claim and its limitation is included in claim 7. Claim 16 is rejected for the same reasons as claim 7. Claim 17 is a system claim and its limitation is included in claim 8. Claim 17 is rejected for the same reasons as claim 8. Claim 19 recites the following abstract ideas: scoring the size of the features extracted by the extracting; and the determining determines the anomaly exists when the score crosses a threshold (mental steps directed to observation, evaluation, and judgement – a person could score a size of observed extracted features in their mind and determine whether an anomaly exists by comparing the mentally determined score to a threshold in their mind, potentially assisted by pen and paper). Claim 19 recites the following additional elements: wherein the time series data is acceleration data, and the anomaly being a partial defect causing increased noise in the acceleration data. Wherein the time series data is acceleration data and wherein the anomaly is a partial defect causing noise in the acceleration data are interpreted as aspects of the technological environment or field of use in which the abstract ideas are performed and as selecting a particular type of data to be manipulated. This additional element does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(h) and MPEP 2106.05(g)). Claim 20 recites wherein the state of the object includes at least one of a walk label, a run label, a conveyance label and a sit label (mental step directed to observation, evaluation – a person could add at least a walk label to a generated Betti sequence in their mind, potentially assisted by pen and paper). Examiner notes that determining at least a walk label could also be interpreted as an aspects of the technological environment or field of use in which the abstract ideas are performed, which would not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(h)). Claim 21 recites wherein the setting, based on the first criterion, does not set the weights to cause a zero degree of influence when the time period of existence is equal to or less than the first threshold (this limitation is interpreted based on the 112(a) rejection of claim 21 such that a weight can be set to suppress influences of data in which the time of existence is relatively short. Given this interpretation, Examiner notes that a person could compare an observed or mentally determined time period of existence to a threshold and decide in their mind whether or not to set a weight that would cause a zero degree of influence or suppress an influence of data with a short time of existence compared to a predetermined threshold). Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. 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, 3-4, 7-10, 12-13, 16-17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Umeda* (US 20170147946 A1, herein Umeda 946) in view of Pokorny et al (“Topological Trajectory Clustering with Relative Persistent Homology”, herein Pokorny), in further view of Petri et al (“Topological Strata of Weighted Complex Networks”, herein Petri), in further view of Adams et al (Persistence Images: A Stable Vector Representation of Persistent Homology”, in further view of Umeda** (“Time Series Classification via Topological Data Analysis”, herein Umeda) . *a copy of this document was included with the IDS dated 03/24/2020. **a copy of this document was provided with the action dated 02/09/2023. Regarding claim 1, Umeda 946 teaches a topological data analysis method [for detecting changes corresponding to an anomaly in data] (para. [0083] recites “Here, persistent homology will be explained. First, "homology" is a method for expressing features of an object by the number of holes in m (m2:0) dimensions. A "hole" referred to here is an element in a homology group, and a 0-dimensional hole is a cluster, a I-dimensional hole is a hole (tunnel), a 2-dimensional hole is a void. The number of holes of each dimension is called a Betti number” (i.e. a topological data analysis method for extracting features)) , the method being implemented by a computer, the method comprising: receiving, from a sensor device mounted on an object, a plurality of pieces of time series data having frequency components (para. [0071]-[0072] recite “FIG. 2 is a diagram depicting an example of series data that is stored in the first series data storage unit 101. The series data in FIG. 2 is time series data that indicates change in heart rate, where the vertical axis represents a heart rate (beats per minute) and the horizontal axis represents time. Here, time series data of a heart rate is exemplified as series data, however, the series data is not limited to this kind of time series data. the series data may also be biological data other than heart rate data (time series data of brain waves, pulse, body temperature and the like), wearable sensor data (time series data of a gyro sensor, acceleration sensor, geomagnetic sensor and the like), financial data (time series data of interest, commodity prices, balance of international payments, stock prices and the like), natural environment data (time series data of temperature, humidity, carbon dioxide concentration and the like), social data (data of labor statistics, population statistics and the like)” (i.e., receiving time series data with frequency components, such as heart rate data, from a sensor, such as a heart rate sensor)) ; generating a plurality of attractors from the plurality of pieces of time series data measured by the sensor device (para. [0004] recites “As a method for performing machine learning on series data, there is a known method in which a feature value that is extracted from series data is used as input. The feature value that is used is, for example, (a) a statistical amount such as an average value, a maximum value and a minimum value, (b) a moment of a statistical amount such as a dispersion and kurtosis, and (c) data of frequency that is calculated using Fourier transformation and the like”. Para. [0069] recites “The first generator 103 generates a pseudo attractor in from series data that is stored the first series data storage unit 101, and stores the generated pseudo attractor in the pseudo attractor data storage unit 105”. Fig. 3 and para. [0109] recite “The machine learning unit 115 determines whether there is unprocessed series data (step S11). When there is unprocessed series data (step S11: YES route), the processing returns to step S1. When there is no unprocessed series data (step S11: NO route), the processing ends” (i.e. generating a plurality of attractors for a plurality of time series data, exemplified here by heart rate data received from and measured by a heart rate sensor)) ; generating a persistence diagram by performing persistent homology conversion on each of the generated plurality of attractors (para. [0085] recites “Here, "persistent homology" is a method for characterizing transition of m-dimensional holes in an object (here, a set of points), and it is possible to find features related to arrangement of points by using persistent homology”. Para. [0087] recites “In the calculation processing of persistent homology, a birth radius and a death radius of elements (or in other words, holes) of a homology group are calculated. FIG. 7 is a diagram depicting an example of a persistence diagram that is generated based on a birth radius and a death radius that are found by calculation of the persistent homology”. Para. [0089] recites “FIG. 9 is a diagram depicting an example of data (hereinafter, referred to as barcode data) for generating a persistence diagram and barcode diagram. A value that represents a hole dimension, a birth radius of a hole and a death radius of the hole are included in the example in FIG. 9. In step S3, barcode data is generated for each hole dimension” (i.e. generating a persistence diagram by performing persistent homology conversion for a given attractor of the plurality of attractors as shown by the iterative process in at least fig. 3)) ; acquiring an appearance time and a disappearance time of a hole that occurs by performing the persistent homology conversion on the plurality of pieces of the time series data (para. [0085] recites “Here, "persistent homology" is a method for characterizing transition of m-dimensional holes in an object (here, a set of points), and it is possible to find features related to arrangement of points by using persistent homology. In this method, each point in an object is gradually made to inflate into a sphere, and in that process, a time at which each hole is born (expressed by a radius of a sphere at birth) and a time at which each hole dies (expressed by a radius of a sphere at death) are identified” (i.e., a birth, or appearance time and a death, or disappearance time of a hole can be acquired using persistent homology conversion on the time series data)) ; extracting, as the Betti sequence, features of the plurality of time series data from the persistence diagram [with the weight set] (para. [0083] recites “persistent homology will be explained. First, "homology" is a method for expressing features of an object by the number of holes in m (m >= 0) dimensions. A "hole" referred to here is an element in a homology group, and a 0-dimensional hole is a cluster, a I-dimensional hole is a hole (tunnel), a 2-dimensional hole is a void. The number of holes of each dimension is called a Betti number.” Para. [0090] - [0091] recite “By executing processing such as described above (i.e. in fig. 3 and fig. 9), a similarity relationship between barcode data that is generated by a certain pseudo attractor and barcode data that is generated from another pseudo attractor is equivalent to similarity relationship between pseudo attractors. Therefore, a relationship between a pseudo attractor and barcode data is a one-to-one relationship. In other word, when pseudo attractors are the same, generated barcode data are the same. That is, when rules of change in series data are the same, generated barcode data are the same. On the other hand, when barcode data are the same, pseudo attractors are also the same. Moreover, when pseudo attracters are similar, barcode data are also similar, and thus conditions necessary for machine learning are satisfied. When pseudo attractors are different, barcode data are also different”. Para. [0103] recites “the third generator 111 generates one block of barcode data by combining barcode data of plural hole dimensions. Series data is data that represents a relationship between a radius (in other words, time) of spheres in persistent homology and a Betti number. A relationship between barcode data and generated series data will be explained using FIG. 15. The upper graph is a graph that is generated from barcode data, and the horizontal axis represents a radius. The lower graph is a graph that is generated from series data, and the vertical axis represents a Betti number and the horizontal axis represents time. As described above, the Betti number represents the number of holes; for example, in the upper graph, the number of holes that exist when the radius corresponds to the dashed line is 10, and thus in the lower graph, the Betti number that corresponds to the dashed line is also 10. The Betti number is calculated for each block” (i.e. persistent homology conversion is used to extract features from time series data and convert those features into a Betti sequence, which is a format usable by a machine learning unit – see machine learning unit 115 in fig. 1)) to generate a Betti sequence in which high frequency components are considered and noise is suppressed (this limitation is interpreted as the outcome or intended result of extracting the Betti sequence for the time series data and does not further limit the scope or provide additional patentable weight to the claim (see MPEP 2103). However, Examiner notes that Petri states that its method allows recovering “complete and accurate long range information from noisy redundant network data” in at least paragraph 7 of the introduction and Umeda 946 discusses noise suppression in at least paragraphs [0094] – [0100] and fig. 21A – 22B) ; generating a training dataset by adding, to the generated Betti sequence, a label indicating a state of the object, the training data set including the Betti sequence and the label (para. [0124]-[0125] recite “FIG. 41 is a diagram depicting a graph in which the three graphs illustrated in FIGS. 38 to 40 are overlapped. In FIGS. 38 to 41, the vertical axis represents a Betti number, and the horizontal axis represents time. As illustrated in FIG. 41, for elevator A and elevator B, for which rules for controlling change in original series data are considered to be the same, the shapes of the Betti time series are similar. However, the shape of the Betti time series for the running machine, for which a rule for controlling change in original series data is considered not to be the same, differs from the shapes of the Betti time series for elevator A and the Betti time series for elevator B. Particularly, during the time from 0 to approximately 150, and during the time from approximately 380 to approximately 450, the shapes are remarkably different”. Para. [0165] recites “each of the plural series data sets may be a labeled series data set, and (c2) the performing may include performing the machine learning for a relationship between the Betti numbers for the radius of the N-dimensional sphere and a label. It becomes possible to also handle supervised learning” (i.e., a training data set comprised of a Betti sequences with associated labels based on whether an anomaly has been determined, or a state of an object)) ; training a [neural network] model using the training data set by inputting the Betti sequence in the training dataset to the [neural network] model for predicting the label in the training dataset (para. [0074] recites “Machine learning of this embodiment may be supervised learning or unsupervised learning. In the case of supervised learning, series data that is stored in the first series data storage unit 101 is labeled series data, and parameters of calculation processing are adjusted based on a comparison of output results of machine learning and the label. The label is called teacher data. Supervised learning and unsupervised learning are well-known techniques, and a detailed explanation is omitted here”. Para. [0108] recites “Returning to the explanation of FIG. 3, the machine learning unit 115 executes machine learning in which series data that is stored in the second series data storage unit 113 is used as input (step S9). The machine learning unit 115 stores the machine learning result in the learning result storage unit 117. The machine learning result includes a classification result for series data (in other words, the machine learning output), and may also include parameters when calculating output from input”. Para. [0124]-[0125] recite “FIG. 41 is a diagram depicting a graph in which the three graphs illustrated in FIGS. 38 to 40 are overlapped. In FIGS. 38 to 41, the vertical axis represents a Betti number, and the horizontal axis represents time. As illustrated in FIG. 41, for elevator A and elevator B, for which rules for controlling change in original series data are considered to be the same, the shapes of the Betti time series are similar. However, the shape of the Betti time series for the running machine, for which a rule for controlling change in original series data is considered not to be the same, differs from the shapes of the Betti time series for elevator A and the Betti time series for elevator B. Particularly, during the time from 0 to approximately 150, and during the time from approximately 380 to approximately 450, the shapes are remarkably different” (i.e., training a machine learning model with labeled data to classify, or predict a label based on the Betti sequence in the dataset)) . However, while Umeda 946 teaches performing persistent homology conversion (see at least para. [0083]-[0086]), Umeda 946 does not explicitly teach a topological data analysis method for detecting changes corresponding to an anomaly in data. Pokorny teaches a topological data analysis method for detecting changes corresponding to an anomaly in data (section I recites “In this work, we propose a topological rather than purely geometric approach to clustering trajectories into consistent subsets with potential future applications to anomaly detection and policy learning for robotics and autonomous driving”. Section II recites “The present work studies large datasets of real world GPS traces as a particular source of trajectory data, which, as well as trajectories extracted from video, forms a common real data-source studied for example in anomalous event detection problems” (i.e., a topological data analysis method for anomaly detection)) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by adapting the topological data analysis method from Umeda 946 for a use case such as the anomaly detection from Pokorny. Umeda 946 and Pokorny are both directed to methods of performing persistent homology conversion, but Umeda 946 does not discuss potential use cases. One of ordinary skill in the art would be motivated to adapt the persistent homology method taught by Umeda 946 for a real world use case like the anomaly detection in Pokorny. However, while the combination of Umeda 946 and Pokorny teaches performing persistent homology conversion (see at least para. [0083]-[0086] of Umeda 946), the combination of Umeda 946 and Pokorny does not explicitly teach setting, for each piece of the plurality of pieces of times series data in the persistence diagram, a weight based on a time period of existence and the hole that occurs between the appearance time and the disappearance time and that corresponds to each piece of the plurality of pieces of times series data, the weight being set based on a first criterion and a second criterion, the first criterion used to distinguish data sets having high frequency components and the second criterion being used to suppress noise from a hole equal to or earlier than a threshold time, the first criterion being a range up to a particular distance from a diagonal line of the persistence diagram and the second criterion being a hole having the appearance time equal to or earlier than the threshold time, the weight being a first value when the time period of existence is more than a first threshold, the weight being a second value [greater than zero] and less than the first value as the time period of existence when the time period of existence is equal to or less than the first threshold, thereby the weight is set such that the each piece of the plurality of pieces of time series data corresponding to the hole of which the time period of existence is equal to or less than the first threshold is decreased in a degree of influence in the extracting features , the decreased degree of influence suppresses noise components caused by the hole with the time period of existence equal to or less than the first threshold. Petri teaches setting, for each piece of the plurality of pieces of times series data in the persistence diagram, a weight based on a time period of existence and the hole that occurs between the appearance time and the disappearance time and that corresponds to each piece of the plurality of pieces of times series data (introduction para. 2 recites “given a weighted network G we consider the set of all filtered networks, F(G), ordered by the descending thresholding weight parameter, in the spirit of persistent homology”. Introduction para. 8 recites “Each weighted hole g is characterized by three quantities: its birth index B g , its persistence p g and its length λ g . After ranking links in a descending order according to their weights, the birth index of a hole is the rank t of its weight w. As we proceed adding links to the filtration in ranking order, it is possible that a link with rank t’ > t will appear and cross the hole. We call this closure of the weighted hole, or death δ g . The persistence p g is the interval between the birth and death of g, p g = δ g – B g = t’ – t. Finally, the length λ g is the number of links composing g” (i.e., setting weights for the time series data in the persistence diagram based on the time period of existence between the birth, or appearance time and the death, or disappearance time)) , the weight being set based on a first criterion and a second criterion, the first criterion used to distinguish data sets having high frequency components and the second criterion being used to suppress noise from a hole equal to or earlier than a threshold time, the first criterion being a range up to a particular distance from a diagonal line of the persistence diagram and the second criterion being a hole having the appearance time equal to or earlier than the threshold time (this limitation is interpreted such that weights may be set by criteria related to the existence value and the time of existence, and that data corresponding to a short period of existence based on the distance from a diagonal line is considered as noise and can be deleted (see the 112(a) rejection of claim 1) and that denoising can occur based on a predetermined distance from the diagonal line the persistence diagram, as explained in at least paragraphs [0098]-[0099] of Applicant’s specification (see the 112(b) rejection of claim 1). Wherein the criterion are used “to distinguish data sets” and “to suppress noise” are interpreted as the outcome or intended result of setting the weight for the time series data and does not further limit the scope or provide additional patentable weight to the claim (see MPEP 2103). Examiner notes that Petri states that its method allows recovering “complete and accurate long range information from noisy redundant network data” in at least paragraph 7 of the introduction and Umeda 946 discusses noise suppression in at least paragraphs [0094] – [0100] and fig. 7 and 21A – 22B)) , the weight being a first value when the time period of existence is more than a first threshold, the weight being a second value [greater than zero] and less than the first value as the time period of existence when the time period of existence is equal to or less than the first threshold, (introduction para. 2 recites “given a weighted network G we consider the set of all filtered networks, F(G), ordered by the descending thresholding weight parameter, in the spirit of persistent homology” (i.e., setting weights for data in the persistent homology process). Introduction para. 3 recites “we call the set F(G) graph filtration: considering the set of all filtered networks captures the link weights and connectivity structure over all weight scales, without the need to resort to any assumption on an eventual metric structure underlying the graph structure. The graph filtration of a network Ω is built following these steps: Rank the weights of links from w max to w min : the discrete parameter Et scans the sequence. At each step t of the decreasing edge ranking we consider the thresholded graph G(w ij , ϵ t ), i.e. the subgraph of Ω with links of weight larger than ϵ t ”. Introduction para. 5 recites “A weighted network hole of weight w is a loop composed by n nodes i 0 , i 1 , i 2 , . . . , i n-1 , where all cyclic edges (i l ,i l+1 )(with i 0 ≡ i n ) have weights ≥ w, while all the other possible edges crossing the loop are strictly weaker than w. We focus on this special class of subgraphs, because formally such weighted holes represent the generators of the first homology group, H 1 , of the clique complex of the graph thresholded by weight w”. Introduction para. 8 recites “Each weighted hole g is characterized by three quantities: its birth index B g , its persistence p g and its length λ g . After ranking links in a descending order according to their weights, the birth index of a hole is the rank t of its weight w (i.e., the lower weights are considered a lower rank, or less influential). As we proceed adding links to the filtration in ranking order, it is possible that a link with rank t’ > t will appear and cross the hole. We call this closure of the weighted hole, or death δ g . The persistence p g is the interval between the birth and death of g, p g = δ g – B g = t’ – t. Finally, the length λ g is the number of links composing g” (i.e. the weight of a data item decreases as the time of existence changes with respect to a threshold)) thereby the weight is set such that the each piece of the plurality of pieces of time series data corresponding to the hole of which the time period of existence is equal to or less than the first threshold is decreased in a degree of influence in the extracting features, the decreased degree of influence suppresses noise components caused by the hole with the time period of existence equal to or less than the first threshold (this limitation is interpreted as the outcome or intended result of setting the weight for the time series data and does not further limit the scope or provide additional patentable weight to the claim (see MPEP 2103). However, Examiner notes that Petri states that its method allows recovering “complete and accurate long range information from noisy redundant network data” in at least paragraph 7 of the introduction and Umeda 946 discusses noise suppression in at least paragraphs [0094] – [0100] and fig. 21A – 22B) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by using the persistent homology method of analyzing a weighted network from Petri to augment the persistence homology conversion method from Umeda 946 (as modified by Pokorny). Umeda 946 and Petri are both directed to using persistent homology to analyze time series data, but Umeda does not explicitly teach whether items in the persistence diagram are weighted. One of ordinary skill in the art would understand that Petri is directed to more explicitly explaining the details of the persistent homology conversion method that Umeda implements. However, while Petri teaches setting a weight for the persistence diagram (see at least paragraph 8 of the introduction), the combination of Umeda 946, Pokorny, and Petri does not explicitly setting a weight such that a second value of that weight is greater than zero. Adams teaches setting a weight such that a second value of that weight is greater than zero (section 4 recites “There are three choices the user makes when generating a PI (i.e., persistence image): the resolution, the distribution (and its associated parameters), and the weighting function. In certain applications it may be points of small or medium persistence that perform best for ML tasks, and hence, one may choose to use more general weighting functions. In our experiments in §6, we use a piecewise linear weighting function f : R 2 → R which only depends on the persistence coordinate y. Given b > 0, define w b : R →R via PNG media_image1.png 96 306 media_image1.png Greyscale We use f(x,y) = w b (y), where b is the persistence value of the most persistent feature in all trials of the experiment. In the event that the birth coordinate is zero for all points in the PD (i.e., persistence diagram), as is often the case for H 0 , it is possible to generate a 1-dimensional (instead of 2-dimensional) PI using 1-dimensional distributions. This is the approach we adopt” (i.e., setting a weight such that later values do not reach zero)) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by substituting the known weighting function from Petri (which modifies Umeda 946) for the known weighting function from Adams. Umeda 946, Petri, and Adams are all directed to methods of generating persistence diagrams using persistent homology. One of ordinary skill would be motivated to substitute the weighting method from Petri with the weighting method from Adams, as Adams states in section 4, “In certain applications it may be points of small or medium persistence that perform best for ML tasks, and hence, one may choose to use more general weighting functions”. However, while the combination of Umeda 946, Pokorny, Petri, and Adams teaches a machine learning unit 115, the combination of Umeda 946, Pokorny, Petri, and Adams does not teach using a neural network. Umeda teaches using a neural network (section 2 and fig. 1 recite “Our classification algorithm comprises preprocessing and learning parts. The preprocessing part, in which we convert a time series dataset into a new dataset suitable for application to a machine learning algorithm consists of two steps first, we convert the time series into a quasi-attractor that represents the transition rules of the corresponding system (see Chapter 3), and second, we convert the quasi-attractor into a new form termed a Betti sequence that is generated by extracting the topological information of the quasi-attractor (see Chapter 4). In the learning part, we construct a classifier based on a one-dimensional CNN using the Betti sequence dataset (see Chapter 6)” (i.e., using a neural network)) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by implementing the machine learning classification method from Umeda 946 (as modified by Pokorny, Petri, and Adams) with the neural network from the time series classification system from Umeda. Umeda 946 and Umeda are both directed to methods of using persistent homology for classification; therefore, one of ordinary skill in the art would understand how to apply the known persistent homology method utilizing machine learning from Umeda 946 with the neural network from Umeda to yield predictable results. Regarding claim 3, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein the extracting includes generating bar code data from the persistence diagram with the weight set and generating a Betti sequence in accordance with the bar code data (Umeda 946 para. [0102]-[0103] recite “the third generator 111 reads out barcode data that is stored in the barcode data storage unit 109 (i.e. barcode data for data from the changed persistence diagram generated in paragraphs [0095] – [0101]). Then, the third generator 111 integrates the read out barcode data and generates series data from the integrated barcode data (step S7). The third generator 111 stores the generated series data in the second series data storage unit 113. As described above, barcode data is generated for each hole dimension, and thus the third generator 111 generates one block of barcode data by combining barcode data of plural hole dimensions. Series data is data that represents a relationship between a radius (in other words, time) of spheres in persistent homology and a Betti number. A relationship between barcode data and generated series data will be explained using FIG. 15. The upper graph is a graph that is generated from barcode data, and the horizontal axis represents a radius. The lower graph is a graph that is generated from series data, and the vertical axis represents a Betti number and the horizontal axis represents time. As described above, the Betti number represents the number of holes; for example, in the upper graph, the number of holes that exist when the radius corresponds to the dashed line is 10, and thus in the lower graph, the Betti number that corresponds to the dashed line is also 10. The Betti number is calculated for each block” (i.e. generating a Betti number from the barcode data). Petri introduction para. 2 recites “given a weighted network G we consider the set of all filtered networks, F(G), ordered by the descending thresholding weight parameter, in the spirit of persistent homology” (i.e., the data in the persistent homology process is weighted)) . Regarding claim 4, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein the setting includes setting a weight (Petri introduction para. 2 recites “given a weighted network G we consider the set of all filtered networks, F(G), ordered by the descending thresholding weight parameter, in the spirit of persistent homology.” Petri introduction para. 3 recites “we call the set F(G) graph filtration: considering the set of all filtered networks captures the link weights and connectivity structure over all weight scales, without the need to resort to any assumption on an eventual metric structure underlying the graph structure. The graph filtration of a network Ω is built following these steps: Rank the weights of links from wmax to wmin: the discrete parameter Et scans the sequence. At each step t of the decreasing edge ranking we consider the thresholded graph G(wij, ϵt), i.e. the subgraph of Ω with links of weight larger than ϵt”. Introduction para. 5 recites “A weighted network hole of weight w is a loop composed by n nodes i0, i1, i2, . . . , in-1, where all cyclic edges (il ,il+1)(with i0 ≡ in) have weights ≥ w, while all the other possible edges crossing the loop are strictly weaker than w. We focus on this special class of subgraphs, because formally such weighted holes represent the generators of the first homology group, H1, of the clique complex of the graph thresholded by weight w”. Introduction para. 8 recites “Each weighted hole g is characterized by three quantities: its birth index Bg, its persistence pg and its length λg. After ranking links in a descending order according to their weights, the birth index of a hole is the rank t of its weight w. As we proceed adding links to the filtration in ranking order, it is possible that a link with rank t’ > t will appear and cross the hole. We call this closure of the weighted hole, or death δg. The persistence pg is the interval between the birth and death of g, pg = δg – Bg = t’ – t. Finally, the length λg is the number of links composing g” (i.e. data items in the persistence diagram are assigned weights that can be changed over time)) less than the first value when the appearance time of the hole is equal to or less than a threshold (Umeda 946 para. [0095]-[0101] recite “The effect of noise will be explained using FIGS. 10A to 14B. Values included in series data that corresponds to the pseudo attractor illustrated in FIG. 10A are shifted by noise that occurs at a certain time. As a result, the pseudo attractor illustrated in FIG. 10B is obtained. In FIG. 10B, point b1, point b2 and point b3 are shifted from the original positions. Here, attention will be paid to an effect due to shifting of point b2. As illustrated in FIGS. 11A and 11B, at the instant that the radius of spheres is 0, the number of clusters when there is no noise and when there is noise is 6, and the number of holes is 0. As illustrated in FIGS. 12A and 12B, at the instant when the radius is 5, the number of clusters when there is no noise and when there is noise is 3 and the number of holes is 0. However, the relationship between the sphere for point b2 and spheres around that sphere is different. As illustrated in FIG. 13A, at the instant that the radius of spheres is 6, the number of clusters when there is no noise is 1 and the number of holes is 0. However, when there is noise, the number of clusters is 1 and the number of holes is 1 (FIG. 13B). In this way, when there is noise, a hole is born and the homology group is different. As illustrated in FIGS. 14A and 14B, at the instant when the radius of spheres is 7, the number of clusters when there is no noise and when there is noise is 1, and the number of holes is 0. Therefore, when there is noise, a hole is born in part of a time period from when the radius goes 6 to when the radius goes to 7. As explained using FIGS. 10A to 14B, when noise occurred, there is a case where a one-dimensional or greater hole is born only for a short time. By executing the processing of step S5 (i.e. step S5 from para. [0093] and fig. 3), data that is generated in both cases is nearly the same, and thus the effect of noise is able to be removed. Data of persistent intervals having a length that is less than the predetermined length is deleted, and thus a similarity relationship among barcode data after data is deleted is not strictly equivalent to a similarity relationship among original barcode data. When data is not deleted, the similarity relationships are equivalent” (i.e. data that appears as a hole in the persistence diagram for less than a threshold amount of time is treated as noise and removed)) . Regarding claim 7, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein the extracting includes extracting as the features, from the persistence diagram with the weight set, a total of the time period of existence with respect to the items of data in the persistence diagram with the weight set (Umeda 946 para. [0093] recites “A length of a persistent interval is calculated by subtracting a birth radius from a death radius. The predetermined length is, for example, a length of an amount of time that corresponds to one portion of K equal portions (hereinafter, referred to as blocks) obtained by dividing a time from when a 0-dimensional hole is born until it dies. However, the predetermined length is not limited to a length of one block, and may also be a length of plural blocks.” Umeda 946 para. [0103] recites “A relationship between barcode data and generated series data will be explained using FIG. 15. The upper graph is a graph that is generated from barcode data, and the horizontal axis represents a radius. The lower graph is a graph that is generated from series data, and the vertical axis represents a Betti number and the horizontal axis represents time. As described above, the Betti number represents the number of holes; for example, in the upper graph, the number of holes that exist when the radius corresponds to the dashed line is 10, and thus in the lower graph, the Betti number that corresponds to the dashed line is also 10. The Betti number is calculated for each block” (i.e. the features are extracted for each block which subdivides the total time of existence). Petri introduction para. 2 recites “given a weighted network G we consider the set of all filtered networks, F(G), ordered by the descending thresholding weight parameter, in the spirit of persistent homology” (i.e., the data in the persistent homology process is weighted)) . Regarding claim 8, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein the plurality of pieces of time series data is obtained, whenever desired, from a sensor that is set by a user (Umeda 946 para. [0072] recites “the series data may also be biological data other than heart rate data (time series data of brain waves, pulse, body temperature and the like), wearable sensor data (time series data of a gyro sensor, acceleration sensor, geomagnetic sensor and the like), financial data (time series data of interest, commodity prices, balance of international payments, stock prices and the like), natural environment data (time series data of temperature, humidity, carbon dioxide concentration and the like), social data (data of labor statistics, population statistics and the like”. Umeda 946 para. [0120] recites “FIGS. 25 to 28 are diagrams for series data that will be used in the following explanation. FIG. 25 is a diagram in which three graphs of series data used in the following explanation is overlap. In FIG. 25, the vertical axis represents measurement values from a gyro sensor (hereinafter, referred to as sensor values), and the horizontal axis represents time. Thick solid line is a graph that represents sensor values that are obtained when moving inside an elevator A, the dashed line is a graph that represents sensor values that are obtained when moving inside an elevator B, and the solid line is a graph that represents sensor values that are obtained when exercising on a running machine” (i.e. the time series data can be obtained from sensors)) , the features of the plurality of pieces of time series data that are obtained whenever desired are displayed (Umeda 946 para. [0157] recites “the aforementioned information processing apparatus 1 is computer device as illustrated in FIG. 53. That is, a memory 2501 (storage device), a CPU 2503 (central processing unit) that is a hardware processor, a hard disk drive (HDD) 2505, a display controller 2507 connected to a display device 2509, a drive device 2513 for a removable disk 2511, an input unit 2515, and a communication controller 2517 for connection with a network are connected through a bus 2519 as illustrated in FIG. 53. An operating system (OS) and an application program for carrying out the foregoing processing in the embodiment, are stored in the HDD 2505, and when executed by the [0158] CPU 2503, they are read out from the HDD 2505 to the memory 2501. As the need arises, the CPU 2503 controls the display controller 2507, the communication controller 2517, and the drive device 2513, and causes them to perform predetermined operations (i.e. time series data can be displayed when desired)) , and a change in the features of the plurality of pieces of time series data is detected (Umeda 946 para. [0071] – [0072] recite “FIG. 2 is a diagram depicting an example of series data that is stored in the first series data storage unit 101. The series data in FIG. 2 is time series data that indicates change in heart rate, where the vertical axis represents a heart rate (beats per minute) and the horizontal axis represents time. Here, time series data of a heart rate is exemplified as series data, however, the series data is not limited to this kind of time series data”. Umeda 946 para. [0079] recites “In the generation of a pseudo attractor, an effect of differences in appearance due to the butterfly effect and the like is removed, and a rule of change of original series data is reflected in the pseudo attractor. A similarity relationship among pseudo attractors is equivalent to a similarity relationship among rules. Therefore, that a certain pseudo attractor is similar to a different pseudo attractor means that rules of change in original series data are similar. Pseudo attractors that are similar to each other are generated from series data for which rules of change are the same but phenomena (appearance) are different. Pseudo attractors that are different are generated from series data for which rules of change are different but phenomena are similar” (i.e. persistent homology conversion is performed when changes in the time series data are detected)) . Claim 9 is a non-transitory computer readable medium claim and its limitation is included in claim 1. The only difference is that claim 9 requires a non-transitory computer readable medium (see at least para. [0158] of Umeda 946) . Therefore, claim 9 is rejected for the same reasons as claim 1. Claim 10 is a system claim and its limitation is included in claim 1. The only difference is that claim 10 requires a system (see at least para. [0157] of Umeda 946) . Therefore, claim 10 is rejected for the same reasons as claim 1. Claim 12 is a system claim and its limitation is included in claim 3. Claim 12 is rejected for the same reasons as claim 3. Claim 13 is a system claim and its limitation is included in claim 4. Claim 13 is rejected for the same reasons as claim 4. Claim 16 is a system claim and its limitation is included in claim 7. Claim 16 is rejected for the same reasons as claim 7. Claim 17 is a system claim and its limitation is included in claim 8. Claim 17 is rejected for the same reasons as claim 8. Regarding claim 19, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein the time series data is acceleration data (Umeda 946 para. [0071] recites “the series data may also be biological data other than heart rate data (time series data of brain waves, pulse, body temperature and the like), wearable sensor data (time series data of a gyro sensor, acceleration sensor, geomagnetic sensor and the like), financial data (time series data of interest, commodity prices, balance of international payments, stock prices and the like), natural environment data (time series data of temperature, humidity, carbon dioxide concentration and the like), social data (data of labor statistics, population statistics and the like)” (i.e., the time series data can be acceleration data)) ; and the method further comprises: scoring the size of the features extracted by the extracting (Umeda section 6.2 para. 3 recites “Our similar concept architecture separates multivariate Betti sequences into univariate ones and performs feature learning on each univariate series individually. This architecture extracts the respective features of a multi-channel Betti sequence and then combines their features and calculates a score for each label” (i.e., scoring extracted, or learned features)) ; and the determining determines the anomaly exists when the score crosses a threshold, the anomaly being a partial defect causing increased noise in the acceleration data (Umeda 946 para. [0094] recites “Elements whose time from birth to death is short mostly occur due to noise that is added to a time series. By deleting data of persistent intervals whose lengths are less than the predetermined length, it is possible to lessen an effect of noise, and thus it becomes possible to improve classification performance. However, a target of deletion is taken to be data of persistent intervals whose dimension is 1 or more”. Umeda 946 para. [0163]-[0164] recite “each of the Betti numbers may be a number of holes whose difference between a radius at birth and a radius at death is a predetermined length or more. It becomes possible to remove an effect of noise. The machine learning method may further include: (D) calculating an average of values included in the series data set for each of the plural series data sets. And the performing may include (c1) performing the machine learning, the series data set of Betti numbers and the average being used as input in the machine learning” (i.e., determining that an output, or score, of a machine learning model corresponds to noise, or an anomaly, based on a comparison to a predetermined length, or threshold)) . Regarding claim 20, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein state of the object includes at least one of a walk label, a run label, a conveyance label and a sit label (Umeda 946 para. [0120] recites “The gyro sensor is worn on a right arm of a person. FIG. 28 is a diagram depicting a graph only for a running machine”. Umeda 946 para. [0165] recites “each of the plural series data sets may be a labeled series data set, and (c2) the performing may include performing the machine learning for a relationship between the Betti numbers for the radius of the N-dimensional sphere and a label. It becomes possible to also handle supervised learning”. Pokorny section IV B recites “we illustrate the use of a probabilistic model in conjunction with our approach. Consider the indoor scene in Fig. 7. We would like to model the behavior of pedestrians in this space using a dataset of 197 trajectories with 26029 datapoints X С R2 from [2]. We extract 45 trajectories traversing between the shaded regions, displayed in black on the left. Besides these trajectories, we consider a Gaussian Mixture Model M, modeling the positions of potential collision threats in this space (shown in color) Our classification scheme in this case hence allows us to understand the movement of pedestrians relative to the superlevel sets of a mixture model” (i.e., a data label can be related to transportation trajectories, or conveyance, or a person who can be walking or running)) . Regarding claim 21, the combination of Umeda 946, Pokorny, Petri, Adams, and Umeda teaches the method according to claim 1, wherein the setting, based on the first criterion, does not set the weights to cause a zero degree of influence when the time period of existence is equal to or less than the first threshold (this limitation is interpreted based on the 112(a) rejection of claim 21 such that a weight can be set to suppress influences of data in which the time of existence is relatively short, as described in at least paragraph [0074] of Applicant’s specification. Given this interpretation, Examiner notes that Petri states that its method allows recovering “complete and accurate long range information from noisy redundant network data” in at least paragraph 7 of the introduction and Umeda 946 discusses noise suppression in at least paragraphs [0094] – [0100] and fig. 21A – 22B) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. “Metric Learning to improve the persistent homology-based gait recognition” (Carrazana et al) teaches a method for gait recognition modeled using a persistent-homology-based representation and linear discriminant analysis. “Persistent-homology-based gait recognition” (Lamar-Leon et al) teaches a method for studying the stability of a topological gait structure under small perturbations. “Persistent homology for automatic determination of human-data based cost of bipedal walking” (Vasudevan et al) teaches a method based on persistent homology to process motion capture data to determine when the number of contact points changes during the course of a step. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEAH M FEITL whose telephone number is (571) 272-8350. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /L.M.F./ Examiner, Art Unit 2147 /VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147 Application/Control Number: 16/827,877 Page 2 Art Unit: 2147 Application/Control Number: 16/827,877 Page 3 Art Unit: 2147 Application/Control Number: 16/827,877 Page 4 Art Unit: 2147 Application/Control Number: 16/827,877 Page 5 Art Unit: 2147 Application/Control Number: 16/827,877 Page 6 Art Unit: 2147 Application/Control Number: 16/827,877 Page 7 Art Unit: 2147 Application/Control Number: 16/827,877 Page 8 Art Unit: 2147 Application/Control Number: 16/827,877 Page 9 Art Unit: 2147 Application/Control Number: 16/827,877 Page 10 Art Unit: 2147 Application/Control Number: 16/827,877 Page 11 Art Unit: 2147 Application/Control Number: 16/827,877 Page 12 Art Unit: 2147 Application/Control Number: 16/827,877 Page 13 Art Unit: 2147 Application/Control Number: 16/827,877 Page 14 Art Unit: 2147 Application/Control Number: 16/827,877 Page 15 Art Unit: 2147 Application/Control Number: 16/827,877 Page 16 Art Unit: 2147 Application/Control Number: 16/827,877 Page 17 Art Unit: 2147 Application/Control Number: 16/827,877 Page 18 Art Unit: 2147 Application/Control Number: 16/827,877 Page 19 Art Unit: 2147 Application/Control Number: 16/827,877 Page 20 Art Unit: 2147 Application/Control Number: 16/827,877 Page 21 Art Unit: 2147 Application/Control Number: 16/827,877 Page 22 Art Unit: 2147 Application/Control Number: 16/827,877 Page 23 Art Unit: 2147 Application/Control Number: 16/827,877 Page 24 Art Unit: 2147 Application/Control Number: 16/827,877 Page 25 Art Unit: 2147 Application/Control Number: 16/827,877 Page 26 Art Unit: 2147 Application/Control Number: 16/827,877 Page 27 Art Unit: 2147 Application/Control Number: 16/827,877 Page 28 Art Unit: 2147 Application/Control Number: 16/827,877 Page 29 Art Unit: 2147 Application/Control Number: 16/827,877 Page 30 Art Unit: 2147 Application/Control Number: 16/827,877 Page 31 Art Unit: 2147 Application/Control Number: 16/827,877 Page 32 Art Unit: 2147 Application/Control Number: 16/827,877 Page 33 Art Unit: 2147 Application/Control Number: 16/827,877 Page 34 Art Unit: 2147 Application/Control Number: 16/827,877 Page 35 Art Unit: 2147 Application/Control Number: 16/827,877 Page 36 Art Unit: 2147 Application/Control Number: 16/827,877 Page 37 Art Unit: 2147 Application/Control Number: 16/827,877 Page 38 Art Unit: 2147 Application/Control Number: 16/827,877 Page 39 Art Unit: 2147 Application/Control Number: 16/827,877 Page 40 Art Unit: 2147 Application/Control Number: 16/827,877 Page 41 Art Unit: 2147 Application/Control Number: 16/827,877 Page 42 Art Unit: 2147 Application/Control Number: 16/827,877 Page 43 Art Unit: 2147 Application/Control Number: 16/827,877 Page 44 Art Unit: 2147 Application/Control Number: 16/827,877 Page 45 Art Unit: 2147
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