CTFR 18/344,836 CTFR 98833 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. Response to Arguments In the previous Office Action issued February 11, 2026 (hereinafter “the previous Office Action”), claims 1-20 were pending. This action is in response to the amendment and remarks filed May 11, 2026. In the amendment, claims 1 and 3-20 were amended, no claims were canceled, and no claims were added. Thus, claims 1-20 are pending. The objections to the drawings, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks. The objections of claims 10-15 and 17-20, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks. Claim Rejections - 35 USC § 101 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-8 are directed to a computer program product [machine]. Claims 9-15 are directed to a method [process]. Claims 16-20 are directed to a computer system [machine]. Regarding Claim 1: Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion). segment the data captured from the multivariate process into a plurality of zone intervals of a time series compute, from the segmented data, a contrastive metric for each variable of the plurality of variables during each zone interval of the plurality of zone intervals compare, for each zone interval of the plurality of zone intervals, the computed contrastive metric each variable of the plurality of variables to the contrastive metric of each of remaining variables of the plurality of variables, wherein the comparison is performed to define representationally relevant zone intervals of the plurality of zone intervals for each variable of the plurality of variables …derive zone-based feature vectors for each variable of the plurality of variables during corresponding zone intervals of the representationally relevant zone intervals wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval concatenate the zone-based feature vectors into a representation vector that represents the multivariate process during the time series As drafted, under their broadest reasonable interpretation (BRI), in view of the specification, the above limitations cover concepts performed in the human mind (observation, evaluation, judgement, or opinion). Given a sufficiently small set of data, nothing in the claim prohibits this process from being performed mentally or with pen and paper. Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application. A computer program product for monitoring a multivariate process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause a computing device to: apply representation learning to… The following additional elements are directed to insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)]. capture, via a plurality of sensors, data associated with a plurality of variables Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception. A computer program product for monitoring a multivariate process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause a computing device to: apply representation learning to… The following additional elements are directed to receiving or transmitting data over a network. The courts (as per Intellectual Ventures v. Symantec , 838 F.3d 1307, 1321; 120 USPQ2d 1353, 1362 (Fed. Cir. 2016))/(as per Symantec , 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner ( e.g. , at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) II.]. capture, via a plurality of sensors, data associated with a plurality of variables Regarding Claim 2: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1. Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The following additional element is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application. model the multivariate process by the representation vector based on machine learning Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The following additional element is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception. model the multivariate process by the representation vector based on machine learning Regarding Claim 3: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 2. The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). removal of a second zone-based feature vector from the zone-based feature vectors, the second zone-based feature vector being least relevant of the representationally relevant zone intervals of the plurality of zone intervals based on the comparison of the computed contrastive metric concatenating of the remaining zone-based feature vectors into a modified representation vector, wherein the remaining zone-based feature vectors include the zone-based feature vectors and excludes the second zone-based feature vector Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The following additional element is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application. adjust the model via:… training of the model on the modified representation vector Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The following additional element is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception. adjust the model via:… training of the model on the modified representation vector Regarding Claim 4: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). rank order the computed contrastive metric for each variable of the plurality of variables during each zone interval Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 5: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). compute the contrastive metric for each variable of the plurality of variables by computing a variance of each variable of the plurality of variables at a predetermined time across the time series Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 6: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 5. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). aggregate the computed variance of each variable and apply a probabilistic function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 7: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 5. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). apply a zone density clustering function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 8: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). compute the contrastive metric for each variable of the plurality of variables by a first derivative of a variance of each variable of the plurality of variables at a predetermined time across the time series Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claims 9-15: Claims 9-15 correspond to claims 1-6 and 8. In particular, 9:1, 10:2, 11:3, 12:4, 13:5, 14:6, 15:8. Step 2A, Prong 1: The claim recites the same abstract ideas as in claims 1-6 and 8. Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claims 9-15 at this step mirror that of claims 1-6 and 8. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claims 9-15 at this step mirror that of claims 1-6 and 8. Regarding Claim 16: Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion). segmenting the data captured from the multivariate process into a time series of snapshot intervals, each snapshot interval further segmented into a plurality of zone intervals computing, from the segmented data, a contrastive metric for each variable of the plurality of variables during each zone interval of the plurality of zone intervals comparing, from each zone interval of the plurality of zone intervals the computed contrastive metric of each variable of the plurality of variables to one or more predetermined threshold values, wherein the comparison is performed to define representationally relevant zone intervals of the plurality of zone intervals for each variable of the plurality of variables …derive zone-based feature vectors for each variable of the plurality of variables during corresponding relevant zone intervals wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval concatenating the zone-based feature vectors into a representation vector for the multivariate process during the time series of snapshots As drafted, under their broadest reasonable interpretation (BRI), in view of the specification, the above limitations cover concepts performed in the human mind (observation, evaluation, judgement, or opinion). Given a sufficiently small set of data, nothing in the claim prohibits this process from being performed mentally or with pen and paper. Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application. A computer system for monitoring a multivariate process, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more computer-readable tangible storage devices for execution by the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising: apply representation learning to… The following additional elements are directed to insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)]. capturing, via a plurality of sensors, data associated with a plurality of variables Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to amount to significantly more than the judicial exception. A computer system for monitoring a multivariate process, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more computer-readable tangible storage devices for execution by the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising: apply representation learning to… The following additional elements are directed to receiving or transmitting data over a network. The courts (as per Intellectual Ventures v. Symantec , 838 F.3d 1307, 1321; 120 USPQ2d 1353, 1362 (Fed. Cir. 2016))/(as per Symantec , 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner ( e.g. , at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) II.]. capturing, via a plurality of sensors, data associated with a plurality of variables Regarding Claim 17: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 16. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). computing the contrastive metric for each variable of the plurality of variables by computing a variance of each variable of the plurality of variables at a predetermined time across all snapshot intervals Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 18: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 17. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). aggregating the computed variance of each variable of the plurality of variables and applying a probabilistic function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 19: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 17. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). applying a probabilistic zone density clustering function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Regarding Claim 20: Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 16. The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgement, or opinion). computing the contrastive metric for each variable of the plurality of variables by a first derivative of a variance of each variable of the plurality of variables at a predetermined time across all snapshot intervals Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim s 1-2, 4, 9-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (US 20190034497), hereinafter Song, in view of Natsumeda et al. (US 11675641), hereinafter Natsumeda, and further in view of Dalli et al. (US 11948083), hereinafter Dalli . Regarding Claim 1: Song discloses: A computer program product for monitoring a multivariate process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause a computing device to: Song, [0096], “aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.” [0101], “Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention…These computer program instructions may be provided to a processor…such that the instructions, which execute via the processor…” [0018], “methods and devices are presented for representing multivariate time series data and retrieving time series segments in historical data. The exemplary embodiments of the present invention employ two deep learning approaches…” In para. 96, Song discloses a computer program product embodied in a computer readable medium having computer readable program code, and para. 101 specifies the computer program code is to be executed by a processor. Lastly, para. 18 states Song is directed to using deep learning for time series representation and retrieval [monitoring a multivariate process] capture, via a plurality of sensors, data associated with a plurality of variables Song, [0075], “At block 401 , multivariate time series segments are retrieved from a plurality of sensors.” Song discloses retrieving multivariate time series segments [capture…data associated with a plurality of variables] from a plurality of sensors [via a plurality of sensors]. segment the data captured from the multivariate process into a plurality of zone intervals of a time series Song, [0032], “At block 124 , a multivariate time series segment is generated by a sliding window (e.g., window size can be 90, 180, 360, etc.) over a raw time series.” Song discloses generating multivariate time series segments [segment the data captured from the multivariate process] by using a sliding window over a raw time series [into a plurality of zone intervals of a time series]. compute, from the segmented data, a contrastive metric for each variable of the plurality of variables during each zone interval of the plurality of zone intervals Song, [0034] “At block 128 , hash codes are obtained by utilizing tanh( ) and sign( )function.” [0068], “In a first step, input attention based LSTM/GRU is employed to extract a best representation for multivariate time series segments. In a second step, pairwise loss is used as the objective function to ensure that similar pair should produce similar hash codes and dissimilar pair should produce dissimilar hash codes.” [0077], “At block 405 , an input attention based recurrent neural network is applied to extract real value features and corresponding hash codes.” In para. 34, Song discloses obtaining hash codes using tanh and sign [compute a contrastive metric]. Para. 68 and 77 further specify that the hash codes correspond to the features of the multivariate time series segments [from the segmented data…for each variable of the plurality of variables during each zone interval of the plurality of zones]. compare, for each zone of the plurality of zone intervals, the computed contrastive metric of each variable of the plurality of variables to the contrastive metric of each remaining variables of the plurality of variables, wherein the comparison is performed to define representationally relevant zone intervals for each variable Song, [0035], “At block 130 , similarity measurements of a query index (hash codes) are determined.” [0068], “pairwise loss is used as the objective function to ensure that similar pair should produce similar hash codes and dissimilar pair should produce dissimilar hash codes.” In para. 35, Song discloses using similarity measurements on the hash codes [compare, for each zone of the plurality of zone intervals], and para. 68 states the comparison is performed using pairwise loss [the computed contrastive metric of each variable of the plurality of variables to the contrastive metric of each remaining variables of the plurality of variables]. [0036], “At block 132 , indexes are stored in a database (e.g., an index database).” [0020], “The method can provide effective and compact (higher quality) representations of multivariate time series segments, can generate discriminative binary codes (more effective) for indexing multivariate time series segments, and, given a query time series segment, can obtain the relevant time series segments with higher accuracy and efficiency.” After the similarity measurements are determined, in view of para. 36, the indexes are stored. Para. 20 further states that indexing the multivariate time series segments allows the method/system to obtain relevant time series segments based on queries [wherein the comparison is performed to define representationally relevant zone intervals for each variable]. Song does not explicitly disclose: apply representation learning to derive zone-based feature vectors for each variable of the plurality of variables during corresponding zone intervals of the representationally relevant zone intervals wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval concatenate the zone-based feature vectors into a representation vector that represents the multivariate process during the time series However, in the same field, analogous art Natsumeda teaches: apply representation learning to derive zone-based feature vectors for each variable of the plurality of variables during corresponding zone intervals… Natsumeda, [24], “The feature extractor 511 includes subsequence generators 511 A and Long Short-Term Memory (LSTM) models 511 B. The feature extractor 511 generates subsequences of given multi-variate time series with sliding window and then convert each of the subsequences into a feature vector. The feature extractor 511 can include LSTM models to convert a subsequence of multi-variate time series into a feature vector.” [49], “At block 915 A, extract, by a feature extractor, feature values from individual attributes of the multi-variate time series and concatenating the feature values into the feature vectors.” In paras. 24 and 49, Natsumeda teaches using a feature extractor – an LSTM model – to extract features from subsequences of multi-variate time series data and create feature vectors [apply representation learning to derive zone-based feature vectors for each variable during corresponding zone intervals…] wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals As cited above in paras. 24 and 49, Natsumeda teaches extracting subsequences of multi-variate time series data to create feature vectors [a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals]. the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables As cited above in paras. 24 and 49, Natsumeda teaches extracting subsequences of multi-variate time series data to create feature vectors, where a subsequence includes variables in the subsequence and excludes other variables outside of the subsequence [the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables]. concatenate the zone-based feature vectors into a representation vector that represents the multivariate process during the time series Natsumeda, [51], “At block 915 C, convert, by a feature converter, multiple ones of the feature vectors into a new feature vector.” Natsumeda discloses converting multiple ones of the feature vectors [concatenate the zone-based feature vectors] into a new feature vector [into a representation vector that represents the multivariate process during the time series]. The new feature vector is a representation of the multivariate process during the time series because it is all the feature vectors that were made by extracting features from the multi-variate time series segments. Song, Natsumeda, and the instant application are analogous art because they are all directed to processing multi-variate time-series data. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song with Natsumeda in order to increase interpretability of the time-series data. “As a further advantage, the present invention gives high interpretability of the results since it provides the importance of subsequences and also the importance of the attributes” (Natsumeda, [58]). Natsumeda discloses that subsequence importance and importance of the features/attributes allows for high interpretability because the expert user will be able to quickly discern importance. Song in view of Natsumeda do not explicitly disclose: the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval However, in the same field, analogous art Dalli teaches: the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval Dalli, [144], “a feature importance vector I may represent the feature importance in a global manner such that I={β 1 ,β 2 +β 10 ,β 3 +β 5 ,β 7 ,β 8 }, corresponding to the features {x,y,xy,x 2 ,y 2 }. The vector I may be sorted in descending order such that the most prominent feature is placed in the beginning of the vector.” Dalli discloses a feature importance vector in which features that have higher importance are placed at the beginning of the vector. This corresponds to the claimed language because higher importance/higher activity features are sorted from each other. Song, Natsumeda, Dalli and the instant application are analogous art because they are all directed to feature vectors. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song and Natsumeda with Dalli in order to increase feature interpretability. “Through the white-box nature of the rule-based XAI model, a user may then be able to inspect for any potential bias by inspecting the contents of the sorted feature importance vector F.sub.s whereby F 0 and M 0 may contain the feature with the highest bias. A normalization may also be applied on the resulting feature importance vector or matrix. In an exemplary embodiment, the F and M vectors may be used to create appropriate reports and analyses of bias” (Dalli, [141]). Regarding Claim 2: As discussed above, Song and Natsumeda in view of Dalli teach [ the ] computer program product of claim 1 , and Natsumeda further teaches: wherein the program instructions further cause the computing device to model the multivariate process by the representation vector based on machine learning Natsumeda, [47], “At block 915 , generate, by a model-based signature generator, feature vectors from input multi-variate time series data from which a failure prediction is to be made.” Natsumeda discloses that block 915, as discussed above in claim 1 [to model the multivariate process by the representation vector], uses a machine learning model [based on machine learning]. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Natsumeda, and Dalli, further with Natsumeda to use machine learning in order to increase the robustness of the system. “A description will now be given regarding various advantages of the present invention, in accordance with one or more embodiments of the present invention. As a first advantage, the present invention is based on machine learning. This is more general and easier to apply than rule-based methods” (Natsumeda, [53]-[54]). Regarding Claim 4: As discussed above, Song and Natsumeda in view of Dalli teach [ the ] computer program product of claim 1 , and Song further teaches: wherein the program instructions further cause the computing device to rank order the computed contrastive metric for each variable of the plurality of variables during each zone interval Song, [0034]-[0037] “At block 128 , hash codes are obtained by utilizing tanh( ) and sign( ) function. At block 130 , similarity measurements of a query index (hash codes) are determined. At block 132 , indexes are stored in a database (e.g., an index database). At block 134 , an output can be top ranked time series segments retrieved from the historical data (e.g., history database).” Song discloses obtaining the hash codes [the computed contrastive metrics for each variable during each zone interval] which are indexed into a database and can be retrieved and outputted in top ranking [rank order]. Regarding Claim 9: Claim 9 is a computer-implemented method claim corresponding to computer program product claim 1 and is rejected for at least the same reasons as given in the rejection of claim 1, with the exception of the following limitations. Song discloses: A computer-implemented method comprising: Song, [0101], “Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention…These computer program instructions may be provided to a processor…such that the instructions, which execute via the processor…” Regarding Claim 10: Claim 10 is a computer-implemented method claim corresponding to computer program product claim 2 and is rejected for at least the same reasons as given in the rejection of claim 2. Regarding Claim 12: Claim 12 is a computer-implemented method claim corresponding to computer program product claim 4 and is rejected for at least the same reasons as given in the rejection of claim 4 . 07-21-aia AIA Claim s 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Song and Natsumeda in view of Dalli, and further in view of Hamilton et al. (US 20230046601), hereinafter Hamilton . Regarding Claim 3: As discussed above, Song and Natsumeda in view of Dalli teach [ the ] computer program product of claim 2 , but do not explicitly disclose: wherein the program instructions further cause the computing device to adjust the model via: removal of a second zone-based feature vector from the zone-based feature vectors, the second zone-based feature vector being least relevant of the representationally relevant zone intervals of the plurality of zone intervals based on the comparison of the computed contrastive metric concatenating of remaining zone-based feature vectors into a modified representation vector, wherein the remaining zone-based feature vectors including the zone-based feature vectors and excludes the second zone-based feature vector training of the model on the modified representation vector However, in the same field, analogous art Hamilton teaches: wherein the program instructions further cause the computing device to adjust the model via: Hamilton, [0025], “In the refining stage of the training, the risk prediction model is updated to remove filters from the feature learning model. To do so, influencing scores are calculated for the filters of the feature learning model. In some examples, the filters of the feature learning model can include filters with different window sizes and can be organized as blocks of filters with each block containing filters of the same window size…The block of filters having an influencing score or a metric calculated based on the influencing score lower than a threshold can be removed from the feature learning model…The risk prediction model with the updated feature learning model may be retrained again using the training data to obtain the trained risk prediction model.” Hamilton teaches retraining and updating their risk prediction model and feature learning model [adjust the model]. removal of a second zone-based feature vector from the zone-based feature vectors, the second zone-based feature vector being least relevant of the representationally relevant zone intervals of the plurality of zone intervals based on the comparison of the computed contrastive metric Hamilton, [0077], “In some examples, L-1 norms can be introduced in the loss function to drive weights corresponding to less or unimportant features to zero and then remove the irrelevant features in the feature vector 802 as described above.” As cited above in para. 25, Hamilton teaches assigning generated influencing scores to filter blocks [zone-based feature vectors], the filter blocks with scores below a threshold are removed [removal of a second zone-based feature vector]. Further in view of para. 77, Hamilton teaches that the removed features are those that are unimportant/irrelevant [the second zone-based feature vector being least relevant of the representationally relevant zone intervals]. Furthermore, the removed filter blocks are decided by comparing their influential scores to a threshold [based on the comparison of the computed contrastive metric]. concatenating of remaining zone-based feature vectors into a modified representation vector, wherein the remaining zone-based feature vectors including the zone-based feature vectors and excludes the second zone-based feature vector Hamilton, [0076], “The feature vector 802 can be determined by the feature learning model 128 , and the feature vector 802 may be a concatenation or other suitable combination of output vectors from different filters included in an original set of filters of the risk prediction model 120 .” In para. 76, Hamilton teaches concatenating suitable filters that were determined by the feature learning model. In view of para. 25, cited above, and para. 75, Hamilton’s disclosure of removing unimportant filter blocks from the feature learning model and the feature learning concatenating suitable filters corresponds to concatenating the remaining zone-based feature vectors into a modified representation vector. training of the model on the modified representation vector As cited above in para. 25, Hamilton teaches retraining the risk prediction model with the updated feature learning model, which has the filters with the removed irrelevant filters. Song, Natsumeda, Dalli, Hamilton, and the instant application are analogous art because they are all directed to time series data feature learning. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Natsumeda, and Dalli with Hamilton in order to decrease computational resource cost. “[R]emoving less influencing filters from the feature learning model based on the model parameters learned from the data itself allows the dimensionality of the features to be reduced without sacrificing the predictiveness of the model. As a result, the risk prediction model can provide a more accurate prediction while using less computational resources, such as CPU time and memory usage” (Hamilton, [0027]). Regarding Claim 11: Claim 11 is a computer-implemented method claim corresponding to computer program product claim 3 and is rejected for at least the same reasons as given in the rejection of claim 3 . 07-21-aia AIA Claim s 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Song and Natsumeda in view of Dalli, and further in view of Seow (US 20220121942) . Regarding Claim 5: As discussed above, Song and Natsumeda in view of Dalli teach [ the ] computer program product of claim 1 , but do not explicitly disclose: wherein the program instructions further cause the computing device to compute the contrastive metric for each variable of the plurality of variables by computing a variance of each variable of the plurality of variables at a predetermined time across the time series However, in the same field, analogous art Seow teaches: wherein the program instructions further cause the computing device to compute the contrastive metric for each variable of the plurality of variables by computing a variance of each variable of the plurality of variables at a predetermined time across the time series Seow, [0064], “assuming that the input data corresponds to video data, features may include location, velocity, acceleration etc. The symbolic analysis component 318 may generate separate sets of probabilistic clusters for each of these features. Feature symbols (e.g., alpha symbols) are generated that correspond to each statistically relevant probabilistic cluster…the symbolic analysis component 318 may determine a statistical distribution (e.g., mean, variance, and standard deviation) of data in each probabilistic cluster…” [0102], “the metadata can include information such as a number of objects in the video (e.g., for a given time period or for a given frame or series of frames)” In para. 64, Seow discloses determining statistical significance [computing the contrastive metrics] by calculating statistical distribution, such as the variance, of each cluster of features [computing a variance of each variable...], and para. 102 states the data can be for a given time period, a given frame, or a series of frames [at a predetermined time across the time series]. Song, Natsumeda, Dalli, Seow, and the instant application are analogous art because they are all directed to time series data feature learning. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Natsumeda, and Dalli with Seow to use statistical distributions in order to identify statistical significances. “The symbolic analysis component 318 may further assign a set of alpha symbols to probabilistic clusters having statistical significance. Each probabilistic cluster may be associated with a statistical significance score that increases as more data that maps to the probabilistic cluster is received. The symbolic analysis component 318 may assign alpha symbols to probabilistic clusters whose statistical significance score exceeds a threshold” (Seow, [0064]). Regarding Claim 13: Claim 13 is a computer-implemented method claim corresponding to computer program product claim 5 and is rejected for at least the same reasons as given in the rejection of claim 5 . 07-21-aia AIA Claim s 6-7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Song, Natsumeda, and Dalli in view of Seow, and further in view of Jackson (US 20190259041) . Regarding Claim 6: As discussed above, Song, Natsumeda, Dalli, and Seow teach [ the ] computer program product of claim 5 , and Seow further teaches: wherein the program instructions further cause the computing device to aggregate the computed variance of each variable and Seow, [0064], “assuming that the input data corresponds to video data, features may include location, velocity, acceleration etc. The symbolic analysis component 318 may generate separate sets of probabilistic clusters for each of these features. Feature symbols (e.g., alpha symbols) are generated that correspond to each statistically relevant probabilistic cluster…the symbolic analysis component 318 may determine a statistical distribution (e.g., mean, variance, and standard deviation) of data in each probabilistic cluster…” Seow discloses calculating the variances for the clusters [aggregate the computed variances]. Song and Natsumeda in view of Dalli, and further in view of Seow do not explicitly disclose: apply a probabilistic function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals However, in the same field, analogous art Jackson teaches: a apply a probabilistic function to select variables from the plurality of variables… Jackson, [0263], “A mixture model assumes that a set of observed objects is a mixture of instances from multiple probabilistic clusters. Conceptually, each observed object is generated independently by first choosing a probabilistic cluster according to the probabilities of the clusters, and then choosing a sample according to the probability density function of the chosen cluster.” Jackson teaches choosing a sample [select variables…] using a probability density function of a cluster [apply a probabilistic function]. Song, Natsumeda, Dalli, Seow, Jackson, and the instant application are analogous art because they are all directed to feature selection. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Natsumeda, Dalli, and Seow with Jackson to cluster data in order to gain insight into the data distribution to be able to select better data. “Cluster analysis can be used as a standalone data mining tool to gain insight into the data distribution, or as a preprocessing step for other data mining algorithms operating on the detected clusters. Clustering is related to unsupervised learning in machine learning. Typical requirements include scalability, the ability to deal with different types of data and attributes, the discovery of clusters in arbitrary shape, minimal requirements for domain knowledge to determine input parameters, the ability to deal with noisy data, incremental clustering, and insensitivity to input order, the capability of clustering high-dimensionality data, constraint-based clustering, as well as interpretability and usability.” Regarding Claim 7: As discussed above, Song, Natsumeda, Dalli, and Seow teach [ the ] computer program product of claim 5 , but do not explicitly disclose: wherein the program instructions further cause the computing device to apply a zone density clustering function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals However, in the same field, analogous art Jackson teaches: wherein the program instructions further cause the computing device to apply a…density clustering function to select variables from the plurality of variables… Jackson, [0263], “A mixture model assumes that a set of observed objects is a mixture of instances from multiple probabilistic clusters. Conceptually, each observed object is generated independently by first choosing a probabilistic cluster according to the probabilities of the clusters, and then choosing a sample according to the probability density function of the chosen cluster.” Jackson teaches choosing a sample [select variables…] using a probability density function of a cluster [apply a density clustering function]. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Natsumeda, Dalli, and Seow with Jackson to cluster data in order to gain insight into the data distribution to be able to select better data. “Cluster analysis can be used as a standalone data mining tool to gain insight into the data distribution, or as a preprocessing step for other data mining algorithms operating on the detected clusters. Clustering is related to unsupervised learning in machine learning. Typical requirements include scalability, the ability to deal with different types of data and attributes, the discovery of clusters in arbitrary shape, minimal requirements for domain knowledge to determine input parameters, the ability to deal with noisy data, incremental clustering, and insensitivity to input order, the capability of clustering high-dimensionality data, constraint-based clustering, as well as interpretability and usability.” Regarding Claim 14: Claim 14 is a computer-implemented method claim corresponding to computer program product claim 6 and is rejected for at least the same reasons as given in the rejection of claim 6 . 07-21-aia AIA Claim 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Song and Natsumeda in view of Dalli, and further in view of Nolan et al. (US 20200027008), hereinafter Nolan . Regarding Claim 8: As discussed above, Song and Natsumeda in view of Dalli teach [ the ] computer program product of claim 1 , but do not explicitly disclose: wherein the program instructions further cause the computing device to compute the contrastive metric for each variable of the plurality of variables by a first derivative of a variance of each variable of the plurality of variables at a predetermined time across the time series However, in the same field, analogous art Nolan teaches: wherein the program instructions further cause the computing device to compute the contrastive metric for each variable of the plurality of variables by a first derivative of a variance of each variable of the plurality of variables… Nolan, [0067], “At block 706 , the example system tuner 120 differentiates the variance data model 426 to determine the variance rate of change model 430 , h′(x). For example, the model generator 306 differentiates the variance data model 426 to generate a function that determines the rate of change at each sample within the variance data 416 .” Nolan discloses determining the rate of change [a first derivative] at each sample [of each variable] within the variance data [of a variance]. Song, Natsumeda, Dalli, Nolan and the instant application are analogous art because they are all directed to input data processing. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Natsumeda, and Dalli with Nolan in order to increase the systems interpretability. “The variance rate of change model 430 is used to acquire insight into the topology of the variance data 416 and/or to help illustrate different degrees of transient behavior(s) associated with an environment or system under test/analysis” (Nolan, [0067]) . 07-21-aia AIA Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Song in view of Garvey et al. (US 20170249564), hereinafter Garvey, further in view of Natsumeda, and further in view of Dalli . Regarding Claim 16: Song discloses: A computer system for monitoring a multivariate process, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more computer-readable tangible storage devices for execution by the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising: Song, [0101], “Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention…These computer program instructions may be provided to a processor…such that the instructions, which execute via the processor…” capturing, via a plurality of sensors, data associated with a plurality of variables Song, [0075], “At block 401 , multivariate time series segments are retrieved from a plurality of sensors.” Song discloses retrieving multivariate time series segments [capture…data associated with a plurality of variables] from a plurality of sensors [via a plurality of sensors]. segmenting the data captured from the multivariate process into a time series of snapshot intervals Song, [0032], “At block 124 , a multivariate time series segment is generated by a sliding window (e.g., window size can be 90, 180, 360, etc.) over a raw time series.” Song discloses generating multivariate time series segments [segmenting data captured from the multivariate process] by using a sliding window over a raw time series [into a time series of snapshot intervals]. computing, from the segmented data, a contrastive metric from the segmented data for each variable of the plurality of variables during each zone interval of the plurality of zone intervals Song, [0034] “At block 128 , hash codes are obtained by utilizing tanh( ) and sign( )function.” [0068], “In a first step, input attention based LSTM/GRU is employed to extract a best representation for multivariate time series segments. In a second step, pairwise loss is used as the objective function to ensure that similar pair should produce similar hash codes and dissimilar pair should produce dissimilar hash codes.” [0077], “At block 405 , an input attention based recurrent neural network is applied to extract real value features and corresponding hash codes.” In para. 34, Song discloses obtaining hash codes using tanh and sign [computing a contrastive metric]. Para. 68 and 77 further specify that the hash codes correspond to the features of the multivariate time series segments [from the segmented data for each variable of the plurality of variables during each zone interval of the plurality of zones]. comparing, from each zone interval of the plurality of zone intervals, the computed contrastive metric of each variable of the plurality of variables to one or more predetermined threshold values, wherein the comparison is performed to define representationally relevant zone intervals of the plurality of zone intervals for each variable of the plurality of variables Song, [0035], “At block 130 , similarity measurements of a query index (hash codes) are determined.” [0068]-[0069], “pairwise loss is used as the objective function to ensure that similar pair should produce similar hash codes and dissimilar pair should produce dissimilar hash codes. Specifically, assuming that the method includes query i and sample j , if they are a similar pair S i j = 1 , then p S i j | B = σ Ω i j , where Ω i j is the inner product of the hash codes of query i , e.g., b h i and that of sample j , i.e., b h j .” In para. 35, Song discloses using similarity measurements on the hash codes [comparing the computer contrastive metric], and paras. 68-69 state that the comparison is performed using pairwise loss, and similarity is determined if S i j = 1 [one or more predetermined threshold values]. [0036], “At block 132 , indexes are stored in a database (e.g., an index database).” [0020], “The method can provide effective and compact (higher quality) representations of multivariate time series segments, can generate discriminative binary codes (more effective) for indexing multivariate time series segments, and, given a query time series segment, can obtain the relevant time series segments with higher accuracy and efficiency.” After the similarity measurements are determined, in view of para. 36, the indexes are stored. Para. 20 further states that indexing the multivariate time series segments allows the method/system to obtain relevant time series segments based on queries [to define representationally relevant zone intervals of the plurality of zone intervals for each variable of the plurality of variables]. Song does not explicitly disclose: each snapshot interval further segmented into a plurality of zone intervals applying representation learning to derive zone-based feature vectors for each variable of the plurality of variables during corresponding relevant zone intervals wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval concatenating the zone-based feature vectors into a representation vector for the multivariate process during the time series of snapshots However, in the same field, Garvey teaches: each snapshot interval further segmented into a plurality of zone intervals Garvey, [0067], “At 430 , the process determines whether to stop or continuing segmenting the time-series signal based on the linear approximation that was generated for the selected segment.” [0068], “At 440 , the process divides the selected segment into two or more sub-segments. The break point(s) between the segments may vary depending on the particular implementation. In one or more embodiments, the segment may be broken in half. However, in other embodiments, the break points may be determined based on an analysis of the slopes/trends of the sequence of values that belong to the segments. By analyzing slopes or trends, a more accurate linear approximation may be derived.” In para. 67, Garvey discloses a process of determining whether to continue segmenting a segment of the time-series data. Para. 68 explains the process of further dividing a segment into two or more segments [each snapshot interval further segmented into a plurality of zone intervals]. Song, Garvey, and the instant application are analogous art because they are all directed to . It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song with Garvey in order to obtain more accurate linear approximations of the time-series segments. “In one or more embodiments, the segment may be broken in half. However, in other embodiments, the break points may be determined based on an analysis of the slopes/trends of the sequence of values that belong to the segments. By analyzing slopes or trends, a more accurate linear approximation may be derived. For instance, if breaking a segment in two, a first portion of the segment, which may be more or less than half the segment, may generally trend downward. The second portion of the segment may then slope upward. The break point may be selected in between these two portions of the segment, which allows a better fit to be derived through a linear regression model” (Garvey, [0068]). Song in view of Garvey do not explicitly disclose: applying representation learning to derive zone-based feature vectors for each variable of the plurality of variables during corresponding relevant zone intervals wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval concatenating the zone-based feature vectors into a representation vector for the multivariate process during the time series of snapshots However, in the same field, analogous art Natsumeda teaches: applying representation learning to derive zone-based feature vectors for each variable of the plurality of variables during corresponding…zone intervals Natsumeda, [24], “The feature extractor 511 includes subsequence generators 511 A and Long Short-Term Memory (LSTM) models 511 B. The feature extractor 511 generates subsequences of given multi-variate time series with sliding window and then convert each of the subsequences into a feature vector. The feature extractor 511 can include LSTM models to convert a subsequence of multi-variate time series into a feature vector.” [49], “At block 915 A, extract, by a feature extractor, feature values from individual attributes of the multi-variate time series and concatenating the feature values into the feature vectors.” In paras. 24 and 49, Natsumeda teaches using a feature extractor – an LSTM model – to extract features from subsequences of multi-variate time series data and create feature vectors [applying representation learning to derive zone-based feature vectors for each variable during corresponding…zone intervals] wherein a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals As cited above in paras. 24 and 49, Natsumeda teaches extracting subsequences of multi-variate time series data to create feature vectors [a first zone-based feature vector of the zone-based feature vectors is derived for a first zone interval of the plurality of zone intervals]. the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables As cited above in paras. 24 and 49, Natsumeda teaches extracting subsequences of multi-variate time series data to create feature vectors, where a subsequence includes variables in the subsequence and excludes other variables outside of the subsequence [the first zone-based feature vector includes values of a first set of variables of the plurality of variables and excludes values of a second set of variables of the plurality of variables]. concatenating the zone-based feature vectors into a representation vector for the multivariate process during the time series of snapshots Natsumeda, [51], “At block 915 C, convert, by a feature converter, multiple ones of the feature vectors into a new feature vector.” Natsumeda discloses converting multiple ones of the feature vectors [concatenating the zone-based feature vectors] into a new feature vector [into a representation vector for the multivariate process during the time series of snapshots]. The new feature vector is a representation of the multivariate process during the time series of snapshots because it is all the feature vectors that were made by extracting features from the multi-variate time series segments. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song and Garvey with Natsumeda in order to increase interpretability of the time-series data. “As a further advantage, the present invention gives high interpretability of the results since it provides the importance of subsequences and also the importance of the attributes” (Natsumeda, [58]). Natsumeda discloses that subsequence importance and importance of the features/attributes allows for high interpretability because the expert user will be able to quickly discern importance. Song in view of Garvey, and further in view of Natsumeda do not explicitly disclose: the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval However, in the same field, analogous art Dalli teaches: the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval Dalli, [144], “a feature importance vector I may represent the feature importance in a global manner such that I={β 1 ,β 2 +β 10 ,β 3 +β 5 ,β 7 ,β 8 }, corresponding to the features {x,y,xy,x 2 ,y 2 }. The vector I may be sorted in descending order such that the most prominent feature is placed in the beginning of the vector.” Dalli discloses a feature importance vector in which features that have higher importance are placed at the beginning of the vector. This corresponds to the claimed language because higher importance/higher activity features are sorted from each other. Song, Garvey, Natsumeda, Dalli and the instant application are analogous art because they are all directed to feature vectors. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Garvey, and Natsumeda with Dalli in order to increase feature interpretability. “Through the white-box nature of the rule-based XAI model, a user may then be able to inspect for any potential bias by inspecting the contents of the sorted feature importance vector F.sub.s whereby F 0 and M 0 may contain the feature with the highest bias. A normalization may also be applied on the resulting feature importance vector or matrix. In an exemplary embodiment, the F and M vectors may be used to create appropriate reports and analyses of bias” (Dalli, [141]) . 07-21-aia AIA Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Song, Garvey, and Natsumeda in view of Dalli, and further in view of Seow . Regarding Claim 17: As discussed above, Song, Garvey, Natsumeda, and Dalli teach [ the ] computer system of claim 16 , but do not explicitly disclose: further capable of performing the method comprising computing the contrastive metric for each variable of the plurality of variables by computing a variance of each variable of the plurality of variables at a predetermined time across all snapshot intervals However, in the same field, analogous art Seow teaches: further capable of performing the method comprising computing the contrastive metric for each variable of the plurality of variables by computing a variance of each variable of the plurality of variables at a predetermined time across all snapshot intervals Seow, [0064], “assuming that the input data corresponds to video data, features may include location, velocity, acceleration etc. The symbolic analysis component 318 may generate separate sets of probabilistic clusters for each of these features. Feature symbols (e.g., alpha symbols) are generated that correspond to each statistically relevant probabilistic cluster…the symbolic analysis component 318 may determine a statistical distribution (e.g., mean, variance, and standard deviation) of data in each probabilistic cluster…” [0102], “the metadata can include information such as a number of objects in the video (e.g., for a given time period or for a given frame or series of frames)” In para. 64, Seow discloses determining statistical significance [computing the contrastive metrics] by calculating statistical distribution, such as the variance, of each cluster of features [computing a variance of each variable], and para. 102 states the data can be for a given time period, a given frame, or a series of frames [at a predetermined time across all snapshot intervals]. Song, Garvey, Natsumeda, Dalli, Seow, and the instant application are analogous art because they are all directed to time series data feature learning. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Garvey, Natsumeda, and Dalli with Seow to use statistical distributions in order to identify statistical significances. “The symbolic analysis component 318 may further assign a set of alpha symbols to probabilistic clusters having statistical significance. Each probabilistic cluster may be associated with a statistical significance score that increases as more data that maps to the probabilistic cluster is received. The symbolic analysis component 318 may assign alpha symbols to probabilistic clusters whose statistical significance score exceeds a threshold” (Seow, [0064]) . 07-21-aia AIA Claim s 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Song, Garvey, Natsumeda, and Dalli in view of Seow, and further in view of Jackson . Regarding Claim 18: As discussed above, Song, Garvey, Natsumeda, Dalli, and Seow teach [ the ] computer system of claim 17 , and Seow further teaches: further capable of performing the method comprising aggregating the computed variance of each variable of the plurality of vairables and Seow, [0064], “assuming that the input data corresponds to video data, features may include location, velocity, acceleration etc. The symbolic analysis component 318 may generate separate sets of probabilistic clusters for each of these features. Feature symbols (e.g., alpha symbols) are generated that correspond to each statistically relevant probabilistic cluster…the symbolic analysis component 318 may determine a statistical distribution (e.g., mean, variance, and standard deviation) of data in each probabilistic cluster…” Seow discloses calculating the variances for the clusters [aggregating the computed variances]. Song, Garvey, Natsumeda, and Dalli in view of Seow do not explicitly disclose: applying a probabilistic function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals However, in the same field, analogous art Jackson teaches: applying a probabilistic function to select variables from the plurality of variables… Jackson, [0263], “A mixture model assumes that a set of observed objects is a mixture of instances from multiple probabilistic clusters. Conceptually, each observed object is generated independently by first choosing a probabilistic cluster according to the probabilities of the clusters, and then choosing a sample according to the probability density function of the chosen cluster.” Jackson teaches choosing a sample [select variables…] using a probability density function of a cluster [applying a probabilistic function]. Song, Garvey, Natsumeda, Dalli, Seow, Jackson, and the instant application are analogous art because they are all directed to feature selection. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Garvey, Natsumeda, Dalli, and Seow with Jackson to cluster data in order to gain insight into the data distribution to be able to select better data. “Cluster analysis can be used as a standalone data mining tool to gain insight into the data distribution, or as a preprocessing step for other data mining algorithms operating on the detected clusters. Clustering is related to unsupervised learning in machine learning. Typical requirements include scalability, the ability to deal with different types of data and attributes, the discovery of clusters in arbitrary shape, minimal requirements for domain knowledge to determine input parameters, the ability to deal with noisy data, incremental clustering, and insensitivity to input order, the capability of clustering high-dimensionality data, constraint-based clustering, as well as interpretability and usability.” Regarding Claim 19: As discussed above, Song, Garvey, Natsumeda, Dalli, and Seow teach [ the ] computer system of claim 17 , and Seow further teaches: further capable of performing the method comprising applying a probabilistic zone density clustering function to select variables from the plurality of variables during each zone interval of the plurality of zone intervals However, in the same field, analogous art Jackson teaches: further capable of performing a method comprising applying a probabilistic…density clustering function to select variables from the plurality of variables… Jackson, [0263], “A mixture model assumes that a set of observed objects is a mixture of instances from multiple probabilistic clusters. Conceptually, each observed object is generated independently by first choosing a probabilistic cluster according to the probabilities of the clusters, and then choosing a sample according to the probability density function of the chosen cluster.” Jackson teaches choosing a sample [select variables…] using a probability density function of a cluster [apply a probabilistic…density clustering function]. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Garvey, Natsumeda, Dalli, and Seow with Jackson to cluster data in order to gain insight into the data distribution to be able to select better data. “Cluster analysis can be used as a standalone data mining tool to gain insight into the data distribution, or as a preprocessing step for other data mining algorithms operating on the detected clusters. Clustering is related to unsupervised learning in machine learning. Typical requirements include scalability, the ability to deal with different types of data and attributes, the discovery of clusters in arbitrary shape, minimal requirements for domain knowledge to determine input parameters, the ability to deal with noisy data, incremental clustering, and insensitivity to input order, the capability of clustering high-dimensionality data, constraint-based clustering, as well as interpretability and usability.” 07-21-aia AIA Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Song, Garvey, and Natsumeda in view of Dalli, and further in view of Nolan . Regarding Claim 20: As discussed above, Song, Garvey, Natsumeda, and Dalli teach [ the ] computer system of claim 16 , but do not explicitly disclose: further capable of performing a method comprising computing the contrastive metric for each variable of the plurality of variables by a first derivative of a variance of each variable of the plurality of variables at a predetermined time across all snapshot intervals However, in the same field, analogous art Nolan teaches: further capable of performing a method comprising computing the contrastive metric for each variable of the plurality of variables by a first derivative of a variance of each variable of the plurality of variables… Nolan, [0067], “At block 706 , the example system tuner 120 differentiates the variance data model 426 to determine the variance rate of change model 430 , h′(x). For example, the model generator 306 differentiates the variance data model 426 to generate a function that determines the rate of change at each sample within the variance data 416 .” Nolan discloses determining the rate of change [a first derivative] at each sample [of each variable] within the variance data [of a variance]. Song, Garvey, Natsumeda, Dalli, Nolan and the instant application are analogous art because they are all directed to input data processing. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Song, Garvey, Natsumeda, and Dalli with Nolan in order to increase the systems interpretability. “The variance rate of change model 430 is used to acquire insight into the topology of the variance data 416 and/or to help illustrate different degrees of transient behavior(s) associated with an environment or system under test/analysis” (Nolan, [0067]) . Response to Arguments 07-37 AIA Applicant's arguments filed May 11, 2025 (“Remarks”) have been fully considered but they are not persuasive. 35 U.S.C. § 101: Remarks, pp. 16-17. Applicant argues with respect to Step 2A, Prong 1, that amended claim 1 recites steps that cannot be performed by the human mind. Examiner respectfully disagrees. Limitations such as computing a contrastive metric and comparing the contrastive metric are mentally performable under broadest reasonable interpretation and given a sufficiently small data set. Remarks, pp. 17-18. Applicant argues the claimed features are tied to computer technology, describing decision trees. Examiner respectfully disagrees. The claim does not recite or reflect any limitations related to decision trees. Remarks, pp. 18-22. Applicant argues with respect tot Step 2A, Prong 2, that amended claim 1 is integrated into a practical application, and cites the specification for improvements. Examiner respectfully disagrees. The claim does not recite or reflect the improvements found in the specification. As an example, on pp. 18-19 of Remarks, the cited specification describes monitoring industrial processes and effectively capturing the structure of their multivariate data. Although the claim recites processing multi-variate data, it does not recite a practical application such monitoring industrial processes. Therefore, the processing of multi-variate data as claimed is mentally performable and not integrated into a practical application. Applicant further argues there are massive volumes of multi-variate time-series data from a large number of monitored variables. Examiner respectfully disagrees. The claim does not recite or reflect massive volumes or data nor a large number of monitored variables. For at least these reasons, the claim does not integrate into a practical application. 35 U.S.C. § 103: Remarks, pp. 22-27. Applicant argues with respect to claim 1 that Natsumeda does not teach or suggest a first zone-based feature vector is derived for a first zone interval of a plurality of zone intervals. Examiner respectfully disagrees. As discussed under section 103, Natsumedia discloses feature vector subsequences of given multi-variable time series. The subsequences are interpreted as zone intervals because both refer to a subset of the entire feature vector. Applicant further argues Natsumeda does not teach that the first zone-based feature vector includes values of a first set of variables of the plurality of variables that excludes values of a second set of variables. Examiner respectfully disagrees. Natsumeda discloses feature vector subsequences of given multi-variate time series. It follows that features in the subsequence are included values and features outside of the subsequence are excluded values. Applicant further argues Natsumeda does not teach that the first set of variables has an activity higher than an activity of the second set of variables during the first zone interval. Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PHUNG whose telephone number is (703) 756-1499. The examiner can normally be reached Monday-Thursday: 9:00AM-4:00PM ET. 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, KAMRAN AFSHAR can be reached at (571) 272-7796. 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. /S.H.P./Examiner, Art Unit 2125 /KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125 Application/Control Number: 18/344,836 Page 2 Art Unit: 2125 Application/Control Number: 18/344,836 Page 3 Art Unit: 2125 Application/Control Number: 18/344,836 Page 4 Art Unit: 2125 Application/Control Number: 18/344,836 Page 5 Art Unit: 2125 Application/Control Number: 18/344,836 Page 6 Art Unit: 2125 Application/Control Number: 18/344,836 Page 7 Art Unit: 2125 Application/Control Number: 18/344,836 Page 8 Art Unit: 2125 Application/Control Number: 18/344,836 Page 9 Art Unit: 2125 Application/Control Number: 18/344,836 Page 10 Art Unit: 2125 Application/Control Number: 18/344,836 Page 11 Art Unit: 2125 Application/Control Number: 18/344,836 Page 12 Art Unit: 2125 Application/Control Number: 18/344,836 Page 13 Art Unit: 2125 Application/Control Number: 18/344,836 Page 14 Art Unit: 2125 Application/Control Number: 18/344,836 Page 15 Art Unit: 2125 Application/Control Number: 18/344,836 Page 16 Art Unit: 2125 Application/Control Number: 18/344,836 Page 17 Art Unit: 2125 Application/Control Number: 18/344,836 Page 18 Art Unit: 2125 Application/Control Number: 18/344,836 Page 19 Art Unit: 2125 Application/Control Number: 18/344,836 Page 20 Art Unit: 2125 Application/Control Number: 18/344,836 Page 21 Art Unit: 2125 Application/Control Number: 18/344,836 Page 22 Art Unit: 2125 Application/Control Number: 18/344,836 Page 23 Art Unit: 2125 Application/Control Number: 18/344,836 Page 24 Art Unit: 2125 Application/Control Number: 18/344,836 Page 25 Art Unit: 2125 Application/Control Number: 18/344,836 Page 26 Art Unit: 2125 Application/Control Number: 18/344,836 Page 27 Art Unit: 2125 Application/Control Number: 18/344,836 Page 28 Art Unit: 2125 Application/Control Number: 18/344,836 Page 29 Art Unit: 2125 Application/Control Number: 18/344,836 Page 30 Art Unit: 2125 Application/Control Number: 18/344,836 Page 31 Art Unit: 2125 Application/Control Number: 18/344,836 Page 32 Art Unit: 2125 Application/Control Number: 18/344,836 Page 33 Art Unit: 2125 Application/Control Number: 18/344,836 Page 34 Art Unit: 2125 Application/Control Number: 18/344,836 Page 35 Art Unit: 2125 Application/Control Number: 18/344,836 Page 36 Art Unit: 2125 Application/Control Number: 18/344,836 Page 37 Art Unit: 2125 Application/Control Number: 18/344,836 Page 38 Art Unit: 2125 Application/Control Number: 18/344,836 Page 39 Art Unit: 2125 Application/Control Number: 18/344,836 Page 40 Art Unit: 2125 Application/Control Number: 18/344,836 Page 41 Art Unit: 2125 Application/Control Number: 18/344,836 Page 42 Art Unit: 2125 Application/Control Number: 18/344,836 Page 43 Art Unit: 2125 Application/Control Number: 18/344,836 Page 44 Art Unit: 2125 Application/Control Number: 18/344,836 Page 45 Art Unit: 2125 Application/Control Number: 18/344,836 Page 46 Art Unit: 2125 Application/Control Number: 18/344,836 Page 47 Art Unit: 2125 Application/Control Number: 18/344,836 Page 48 Art Unit: 2125 Application/Control Number: 18/344,836 Page 49 Art Unit: 2125 Application/Control Number: 18/344,836 Page 50 Art Unit: 2125 Application/Control Number: 18/344,836 Page 51 Art Unit: 2125 Application/Control Number: 18/344,836 Page 52 Art Unit: 2125 Application/Control Number: 18/344,836 Page 53 Art Unit: 2125
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