CTNF 19/251,545 CTNF 93859 DETAILED ACTION 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. Priority Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application is recognized as a continuation of parent Application No. 17/591,535 filed on 12/03/2021 . Information Disclosure Statement The information disclosure statement (IDS) submitted on 9/29/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is/are being considered by the examiner. Specification The current title of the application is as follows: SYSTEMS AND METHODS FOR DETECTION OF CATEGORICAL DRAFT At least FIG. 7B of Applicant’s Drawings makes clear that the system is directed to detecting “ drift ”, The examiner believes the use of the term “DRAFT” in the title of the Invention to be a typographical error. 06-11-01 AIA The following title is suggested: SYSTEMS AND METHODS FOR DETECTION OF CATEGORICAL DRIFT 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 1-4, 6-11, 13-17 and 19-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding independent claim 1, Claim 1 recites the following limitation(s): determining a degree of divergence by providing the first probability distribution and the second probability distribution as input to a distance function; Which recites a step of determining or calculating a metric, e.g. the degree of divergence, using a distance function. The term “ determine ” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may, given a distance function such as a Jaccard distance, Hamming distance, correlation, etc. calculate a degree of divergence by applying the function either mentally or aided by pen & paper. and detecting categorical drift has occurred from the first data sample to the second data sample when the degree of divergence satisfies a threshold comparison with a statistical metric of data points of a training data sample. Which recites a step of comparing a metric (e.g. the degree of divergence) to a threshold and drawing a conclusion based on said comparison. The limitation recites a step of performing an evaluation or judgment (e.g. by determining whether categorical drift has occurred based on whether a threshold is exceeded), which represents a step or process that can be performed in the human mind and/or aided by pen & paper. For example, given a degree of divergence of 0.5 and a threshold of 0.3, one of ordinary skill in the art may compare the metric to the threshold and determine that the degree of divergence has exceeded the threshold. Claim 1 recites the following additional elements: generating a first probability distribution based on the first data sample and a second probability distribution based on the second data sample; which encompasses a step of mere data gathering & outputting (e.g. the first and second probability distributions are outputs of the process of generating), which represents insignificant extra-solution activity as described in MPEP 2106.05(g). a computerized method comprising: parsing received time-series data via a data stream into a plurality of data samples including a first data sample and a second data sample, wherein the first data sample includes a first set of input fields corresponding to a first set of data points, and wherein the second data sample includes a second set of input fields corresponding to a second set of data points; which encompasses a step of mere data gathering & outputting (e.g. parsing received time-series data is a step of data gathering), which represents insignificant extra-solution activity as described in MPEP 2106.05(g). The judicial exception is not integrated into a practical application because the computer system elements are recited at a high level of generality. Which amounts to no more than applying the steps to within a generic computer environment. Accordingly, these elements do not integrate the abstract idea into a practical application because the limitations do not impose any meaningful limits on practicing the abstract idea, see MPEP 2106.06(f). As such, the claim is directed to the abstract idea of a mental process. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept. The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception. The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions: generating a first probability distribution based on the first data sample and a second probability distribution based on the second data sample; MOPUR et al. (US PGPUB No. 2021/0365478; Pub. Date: Nov. 25, 2021) discloses the limitation at issue: See Paragraph [0047], (Disclosing a system for updating data models. The method may identify data drift based on changes in densities of a plurality of clusters over a predefined period of time. Densities of the plurality of clusters are prepared by data clustering unit 210 after formation of the plurality of clusters via a histogram algorithm used to determine the distribution of the representative points present within each cluster, i.e. generating a first probability distribution based on the first data sample and a second probability distribution based on the second data sample (e.g. the plurality of clusters determined from input data).) a computerized method comprising: parsing received time-series data via a data stream into a plurality of data samples including a first data sample and a second data sample, wherein the first data sample includes a first set of input fields corresponding to a first set of data points, and wherein the second data sample includes a second set of input fields corresponding to a second set of data points; WALTERS et al. (US PGPUB No. 2020/0012900; Pub. Date: Jan. 9, 2020) discloses the limitation at issue: See FIG. 5A & Paragraphs [0070]-[0073], (Disclosing a system for detecting data drift. FIG. 5 illustrates a method for generating synthetic data for use in another application comprising step 501 of retrieving actual data. Actual data includes streams of information, i.e. parsing received time-series data via a data stream into a plurality of data samples including a first data sample and a second data sample (e.g. a stream of information comprising a plurality of records). Actual data includes stream of data such as transaction data, human resources data, web log data, web security data, web protocols data, or system logs data. Step 504 of method 500 comprises determining distinct classes of sensitive data portions, i.e. wherein the first data sample includes a first set of input fields corresponding to a first set of data points, and wherein the second data sample includes a second set of input fields corresponding to a second set of data points (e.g. the data stream is parsed into different classes).) Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions. Based on the above, the claim is not patent eligible. Regarding dependent claim 2, Claim 2 depends upon Claim 1, as such claim 2 presents the same abstract idea of a mental process as identified in the discussion above. Claim 2 recites the following limitation(s): generating the statistical metric of the data points of the training data sample, based on a bootstrap process, for detecting the categorical drift, the statistical metric representing a mean value for the data points of the training data sample . Which recites a step of calculating a mean value for data points of a training data sample based on a dataset, the step of generating a statistical metric describes a calculation, which is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may, given a dataset, calculate a mean by adding all the values together and dividing the total number of values of numerical data. Based on the above, the claim is not patent eligible. Regarding dependent claim 3, Claim 3 depends upon Claim 2, as such claim 3 presents the same abstract idea of a mental process as identified in the discussion above. Claim 3 recites the following limitation(s): wherein the bootstrap process includes determining one or more values associated with an input field type included in the data points of the training data sample, Which recites a step of determining a value of a data point, which represents an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may, given a dataset, observe topics or attributes of said data based on the characteristics. For example, one of ordinary skill in the art may be given a dataset comprising names and dates and determine the values present in the dataset that correspond to names and dates either mentally or aided by pen & paper. and determining the mean value for the data points of the training data sample, Which recites a step of calculating a mean value for data points of a training data sample based on a dataset, the step of generating a statistical metric describes a calculation, which is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may, given a dataset, calculate a mean by adding all the values together and dividing the total number of values of numerical data. Based on the above, the claim is not patent eligible. Claim 3 recites the following additional elements: wherein the data points have the input field type. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the input field type indicates a type of data) which represents insignificant extra-solution activity as described in MPEP 2106.05(g). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept. The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception. The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions: wherein the data points have the input field type. WALTERS et al. (US PGPUB No. 2020/0012900; Pub. Date: Jan. 9, 2020) discloses the limitation at issue: See Paragraph [0144], (Process 1400 comprises step 1407 of validating attributes of a synthetic data stream generated from actual data by validating the keys present in the synthetic data stream, i.e. wherein the data points have the input field type.) Based on the above, the claim is not patent eligible. Regarding dependent claim 4, Claim 4 depends upon Claim 1, as such claim 4 presents the same abstract idea of a mental process as identified in the discussion above. Claim 4 recites the following limitation(s): wherein the first set of data points of the first data sample represents are received earlier in time via the data stream than the second set of data points of the second data sample. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the limitation describes a characteristic of the data, specifically a difference between first and second data samples) which represents insignificant extra-solution activity as described in MPEP 2106.05(g). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept. The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception. The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions: wherein the first set of data points of the first data sample represents are received earlier in time via the data stream than the second set of data points of the second data sample. MOPUR et al. (US PGPUB No. 2021/0365478; Pub. Date: Nov. 25, 2021) discloses the limitation at issue: See FIG. 4 & Paragraph [0043], (FIG. 4 illustrates a plurality of clusters generated via clustering techniques 402, 404, 406 reflecting data points over time. The chart of FIG. 4 illustrates the three clusters in a graph representing point of temperature measured over time wherein clusters 402, 404, 406 are located at distinct time windows, i.e. herein the first set of data points of the first data sample represents are received earlier in time via the data stream (e.g. for example cluster 402) than the second set of data points of the second data sample (e.g. for example cluster 404, 406 which are timestamped at a time past the timestamps of cluster 402).) Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions. Based on the above, the claim is not patent eligible. Regarding dependent claim 6, Claim 6 depends upon Claim 1, as such claim 6 presents the same abstract idea of a mental process as identified in the discussion above. Claim 6 recites the following limitation(s): wherein the degree of divergence corresponds to a first input field type, Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the limitation describes a characteristic of a metric, specifically describing the degree of divergence as corresponding to a type of data) which represents insignificant extra-solution activity as described in MPEP 2106.05(g). wherein the first data sample and the second data sample include a plurality of input field types. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the limitation describes data samples as having input field types) which represents insignificant extra-solution activity as described in MPEP 2106.05(g). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept. The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception. The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions: wherein the degree of divergence corresponds to a first input field type, WALTERS et al. (US PGPUB No. 2020/0012900; Pub. Date: Jan. 9, 2020) discloses the limitation at issue: See Paragraph [0191] & [0196], (FIG. 19 comprises step 1908 wherein a baseline data metric of the data profile of the baseline synthetic data is determine wherein the data profile includes a data schema and statistical profile of said synthetic data. Step 1916 comprises detecting data drift based on a difference between a current data metric and a baseline data metric, i.e. wherein the degree of divergence corresponds to a first input field type (e.g. the data metric includes information relating to the schema of the synthetic data, i.e. data fields).) wherein the first data sample and the second data sample include a plurality of input field types . WALTERS et al. (US PGPUB No. 2020/0012900; Pub. Date: Jan. 9, 2020) discloses the limitation at issue: See Paragraph [0144], (Dataset generator 1307 may validate the schema of a synthetic data stream using a JSON validator wherein the schema describes key-value pairs present in a reference data stream. An example schema is described including keys such as "first_name", "last_name" and associated data types, i.e. wherein the first data sample and the second data sample include a plurality of input field types (e.g. the data stream and therefore the distinct classes of data found in said data stream have an associated schema comprising data fields/attributes).) Regarding dependent claim 7, Claim 7 depends upon Claim 1, as such claim 7 presents the same abstract idea of a mental process as identified in the discussion above. Claim 7 recites the following limitation(s): wherein each of the training data sample, the first data sample, and the second data sample are received from a same data source. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the same data source is manipulated by providing data samples such as by retrieving/transmitting a data stream) which represents insignificant extra-solution activity as described in MPEP 2106.05(g). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept. The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception. The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions: wherein each of the training data sample, the first data sample, and the second data sample are received from a same data source. WALTERS et al. (US PGPUB No. 2020/0012900; Pub. Date: Jan. 9, 2020) discloses the limitation at issue: See FIG. 5A & Paragraph [0071], (FIG. 5A depicts process 500 for generating synthetic data from an incoming data stream which is used to train a data model for use in another application by retrieving actual data.) See FIG. 19 & Paragraph [0188], (Process 1900 of FIG. 19 for determining data drift includes receiving model input data which may include actual data, a mix of actual and synthetic data or entirely synthetic data which are derived from an input data stream, i.e. wherein each of the training data sample, the first data sample, and the second data sample are received from a same data source.) Regarding independent claim 8, The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 9, The claim is analogous to the subject matter of dependent claim 2 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 10, The claim is analogous to the subject matter of dependent claim 3 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 11, The claim is analogous to the subject matter of dependent claim 4 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 13, The claim is analogous to the subject matter of dependent claim 6 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 14, The claim is analogous to the subject matter of dependent claim 7 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding independent claim 15, The claim is analogous to the subject matter of independent claim 1 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 16, The claim is analogous to the subject matter of dependent claim 2 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 17, The claim is analogous to the subject matter of dependent claim 3 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 19, The claim is analogous to the subject matter of dependent claim 6 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 20, The claim is analogous to the subject matter of dependent claim 7 directed to a device or apparatus and is rejected under similar rationale. 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, 4, 6-8, 11, 13-15 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WALTERS et al. (US PGPUB No. 2020/0012900; Pub. Date: Jan. 9, 2020) in view of MOPUR et al. (US PGPUB No. 2021/0365478; Pub. Date: Nov. 25, 2021) . Regarding independent claim 1, WALTERS discloses a computerized method comprising: parsing received time-series data via a data stream into a plurality of data samples including a first data sample and a second data sample, See FIG. 5A & Paragraphs [0070]-[0072], (Disclosing a system for detecting data drift. FIG. 5 illustrates a method for generating synthetic data for use in another application comprising step 501 of retrieving actual data. Actual data includes streams of information, i.e. parsing received time-series data via a data stream into a plurality of data samples including a first data sample and a second data sample (e.g. a stream of information comprising a plurality of records).) wherein the first data sample includes a first set of input fields corresponding to a first set of data points, and wherein the second data sample includes a second set of input fields corresponding to a second set of data points; See FIG. 5A& Paragraphs [0070]-[0073], (Actual data includes stream of data such as transaction data, human resources data, web log data, web security data, web protocols data, or system logs data. Step 504 of method 500 comprises determining distinct classes of sensitive data portions, i.e. wherein the first data sample includes a first set of input fields corresponding to a first set of data points, and wherein the second data sample includes a second set of input fields corresponding to a second set of data points (e.g. the data stream is parsed into different classes).) and detecting categorical drift has occurred from the first data sample to the second data sample when the degree of divergence satisfies a threshold comparison with a statistical metric of data points of a training data sample. See FIG. 18 & Paragraph [0184], (Determining a difference between predicted data and event data includes determining whether said difference meets or exceeds a threshold difference.) See FIG. 19 & Paragraph [0200], (FIG. 19 illustrates a process for detecting data drift comprising step 1920 of determining a parameter drift threshold indicating a different between an updated model parameter and a baseline model parameter. The difference includes a difference in at least one of a model weight, a model coefficient, a model offset, or another model parameter, i.e. a statistical metric of data points of a training data sample (e.g. the data drift threshold is determined based on characteristics of the model trained on input data).) WALTERS does not disclose the step of generating a first probability distribution based on the first data sample and a second probability distribution based on the second data sample; determining a degree of divergence by providing the first probability distribution and the second probability distribution as input to a distance function; MOPUR discloses the step of generating a first probability distribution based on the first data sample and a second probability distribution based on the second data sample; See Paragraph [0047], (Disclosing a system for updating data models. The method may identify data drift based on changes in densities of a plurality of clusters over a predefined period of time. Densities of the plurality of clusters are prepared by data clustering unit 210 after formation of the plurality of clusters via a histogram algorithm used to determine the distribution of the representative points present within each cluster, i.e. generating a first probability distribution based on the first data sample and a second probability distribution based on the second data sample (e.g. the plurality of clusters determined from input data).) determining a degree of divergence by providing the first probability distribution and the second probability distribution as input to a distance function; See Paragraph [0047], (A cross-correlation may be performed between two related density sets to identify data drift to determine a degree of correlation indicating a similarity between densities across the clusters which would indicate a consistency of the input data over time, i.e. determining a degree of divergence (e.g. by assessing correlations between densities of clusters) by providing the first probability distribution and the second probability distribution as input to a distance function (e.g. the cross-correlation function is applied to the plurality of densities associated with the clusters);) WALTERS and MOPUR are analogous art because they are in the same field of endeavor, correcting model drift. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of WALTERS to include the method of clustering an input dataset in order to identify data drift based on changes in densities of said clusters as disclosed by MOPUR . Paragraph [0057] of MOPUR discloses that the system may determine, communicate and use only information corresponding to data drift and outliers in order to save time, bandwidth and computing power when updating data models, which represents an improvement in resource utilization. Regarding dependent claim 4, As discussed above with claim 1, WALTERS-MOPUR discloses all of the limitations. MOPUR further discloses the step wherein the first set of data points of the first data sample represents are received earlier in time via the data stream than the second set of data points of the second data sample . See FIG. 4 & Paragraph [0043], (FIG. 4 illustrates a plurality of clusters generated via clustering techniques 402, 404, 406 reflecting data points over time. The chart of FIG. 4 illustrates the three clusters in a graph representing point of temperature measured over time wherein clusters 402, 404, 406 are located at distinct time windows, i.e. herein the first set of data points of the first data sample represents are received earlier in time via the data stream (e.g. for example cluster 402) than the second set of data points of the second data sample (e.g. for example cluster 404, 406 which are timestamped at a time past the timestamps of cluster 402).) Regarding dependent claim 6, As discussed above with claim 1, WALTERS-MOPUR discloses all of the limitations. WALTERS further discloses the step wherein the degree of divergence corresponds to a first input field type, See Paragraph [0191] & [0196], (FIG. 19 comprises step 1908 wherein a baseline data metric of the data profile of the baseline synthetic data is determine wherein the data profile includes a data schema and statistical profile of said synthetic data. Step 1916 comprises detecting data drift based on a difference between a current data metric and a baseline data metric, i.e. wherein the degree of divergence corresponds to a first input field type (e.g. the data metric includes information relating to the schema of the synthetic data, i.e. data fields).) wherein the first data sample and the second data sample include a plurality of input field types . See Paragraph [0144], (Dataset generator 1307 may validate the schema of a synthetic data stream using a JSON validator wherein the schema describes key-value pairs present in a reference data stream. An example schema is described including keys such as "first_name", "last_name" and associated data types, i.e. wherein the first data sample and the second data sample include a plurality of input field types (e.g. the data stream and therefore the distinct classes of data found in said data stream have an associated schema comprising data fields/attributes).) Regarding dependent claim 7, As discussed above with claim 1, WALTERS-MOPUR discloses all of the limitations. WALTERS further discloses the step wherein each of the training data sample, the first data sample, and the second data sample are received from a same data source . See FIG. 5A & Paragraph [0071], (FIG. 5A depicts process 500 for generating synthetic data from an incoming data stream which is used to train a data model for use in another application by retrieving actual data.) See FIG. 19 & Paragraph [0188], (Process 1900 of FIG. 19 for determining data drift includes receiving model input data which may include actual data, a mix of actual and synthetic data or entirely synthetic data which are derived from an input data stream, i.e. wherein each of the training data sample, the first data sample, and the second data sample are received from a same data source.) Regarding independent claim 8, The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 11, The claim is analogous to the subject matter of dependent claim 4 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 13, The claim is analogous to the subject matter of dependent claim 6 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 14, The claim is analogous to the subject matter of dependent claim 7 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding independent claim 15, The claim is analogous to the subject matter of independent claim 1 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 19, The claim is analogous to the subject matter of dependent claim 6 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 20, The claim is analogous to the subject matter of dependent claim 7 directed to a device or apparatus and is rejected under similar rationale . 07-22-aia AIA Claim (s) 2-3, 9-10 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over WALTERS in view of MOPUR as applied to claim 1 above, and further in view of JAW (US PGPUB No. 2021/0357702; Pub. Date: Nov. 18, 2021) . Regarding dependent claim 2, As discussed above with claim 1, WALTERS-MOPUR discloses all of the limitations. WALTERS-MOPUR does not disclose the step of generating the statistical metric of the data points of the training data sample, based on a bootstrap process, for detecting the categorical drift, the statistical metric representing a mean value for the data points of the training data sample . JAW discloses the step of generating the statistical metric of the data points of the training data sample, based on a bootstrap process, for detecting the categorical drift, the statistical metric representing a mean value for the data points of the training data sample . See Paragraphs [0116] & [0118], (Disclosing a system for identifying one or more states of a text string describing an event based on the one or more identified states. The model monitor system is configured to perform data/model integrity checks and detect data drift and accuracy degradation. Data monitored by the model monitor system includes data involved in model training and during production and may comprise statistics that characterize the above datasets including mean, variance and higher order moments of the data sets, i.e. generating the statistical metric of the data points of the training data sample, based on a bootstrap process (e.g. the monitoring process is performed during model training and production), for detecting the categorical drift (e.g. model monitor system may detect data drift), the statistical metric representing a mean value for the data points of the training data sample (e.g. the statistics include mean, variance, etc. of a plurality of datasets).) WALTERS , MOPUR and JAW are analogous art because they are in the same field of endeavor, correcting model drift. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of WALTERS-MOPUR to include the method of monitoring statistical data for a data model as disclosed by JAW . Paragraph [0054] of JAW discloses that the system may use a plurality of trained classifiers operating in parallel in order to reduce overall prediction latency. The parallel model provides flexibility in re-training, updating or managing an individual predictive model without influencing the performance of other predictive models. Regarding dependent claim 3, As discussed above with claim 2, WALTERS-MOPUR-JAW discloses all of the limitations. WALTERS further discloses the step wherein the bootstrap process includes determining one or more values associated with an input field type included in the data points of the training data sample, See FIG. 5A & Paragraphs [0070]-[0072], (FIG. 5 illustrates a method for generating synthetic data for use in another application comprising step 501 of retrieving actual data. Actual data includes streams of information.) See Paragraph [0144], (Dataset generator 1307 may validate the schema of a synthetic data stream using a JSON validator wherein the schema descirbe skey-value pairs present in a reference data stream. An example schema is described including keys such as "first_name", "last_name" and associated data types, i.e. wherein the bootstrap process includes determining one or more values associated with an input field type included in the data points of the training data sample (e.g. actual data comprises data having a data schema comprising attributes having types. Actual data is used to train data models).) wherein the data points have the input field type. See Paragraph [0144], (Process 1400 comprises step 1407 of validating attributes of a synthetic data stream generated from actual data by validating the keys present in the synthetic data stream, i.e. wherein the data points have the input field type.) Additionally, JAW further discloses the step of determining the mean value for the data points of the training data sample, See Paragraphs [0117]-[0118], (Data monitored by model monitor system includes statistics that characterize datasets including a mean metric. The system uses the monitored data to perform a process of data/model integrity checks used to detect data drift and accuracy degradation. The process may begin by detecting data drift in training and prediction data by monitoring differences in distributions of training data, text, validation and prediction data, change in distributions of training data, test, validation and prediction data over time, covariates that are causing changes in the prediction output, etc., i.e. determining the mean value for the data points of the training data sample.) Regarding dependent claim 9, The claim is analogous to the subject matter of dependent claim 2 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 10, The claim is analogous to the subject matter of dependent claim 3 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 16, The claim is analogous to the subject matter of dependent claim 2 directed to a device or apparatus and is rejected under similar rationale. Regarding dependent claim 17, The claim is analogous to the subject matter of dependent claim 3 directed to a device or apparatus and is rejected under similar rationale . 07-22-aia AIA Claim s 5 , 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over WALTERS in view of MOPUR as applied to claim 1 above, and further in view of JAW (US PGPUB No. 2021/0357702; Pub. Date: Nov. 18, 2021) . Regarding dependent claim 5, As discussed above with claim 1, WALTERS-MOPUR discloses all of the limitations. WALTERS-MOPUR does not disclose the step wherein generating at least one of the first probability distribution and the second probability distribution includes generation by a machine-learning model based on a predetermined probability distribution function . Basel discloses a step wherein generating at least one of the first probability distribution and the second probability distribution includes generation by a machine-learning model based on a predetermined probability distribution function . See Paragraph [0034], (Disclosing a system for performing clustering operations using machine learning techniques. Machine learning classifier 134 is configured to indicate one or more user-defined labels for a plurality of clusters representing real-time time-series data, i.e. wherein generating at least one of the first probability distribution and the second probability distribution includes generation by a machine-learning model based on a predetermined probability distribution function. Machine learning classifier 134 is trained to predict the operating state of a device based on real-time time -series data. Note [0055] wherein the machine learning classifier may identify and compensate for sensor drift via a second clustering operation 220, i.e. wherein generating at least one of the first probability distribution and the second probability distribution includes generation by a machine-learning model (e.g. clusters are generated via machine learning classifier 134) based on a predetermined probability distribution function (e.g. machine learning classifier 134 may perform predictive analysis over the real-time, time-series data).) WALTERS , MOPUR and Basel are analogous art because they are in the same field of endeavor, correcting data drift. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of WALTERS-MOPUR to include the method of clustering and classifying data using machine learning techniques as disclosed by Basel . Paragraphs [0006] & [0055]-[0056] of Basel disclose that the system may perform subsequent clustering operations to correct for data drift by updating the first machine learning classifier using cluster and label mapping techniques that may reduce the time, expense and labor associated with updating the machine learning classifier. Regarding dependent claim 12, The claim is analogous to the subject matter of dependent claim 5 directed to a non-transitory, computer readable medium and is rejected under similar rationale. Regarding dependent claim 18, The claim is analogous to the subject matter of dependent claim 5 directed to a device or apparatus and is rejected under similar rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm. 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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. /FMMV/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159 Application/Control Number: 19/251,545 Page 2 Art Unit: 2159 Application/Control Number: 19/251,545 Page 3 Art Unit: 2159 Application/Control Number: 19/251,545 Page 4 Art Unit: 2159 Application/Control Number: 19/251,545 Page 5 Art Unit: 2159 Application/Control Number: 19/251,545 Page 6 Art Unit: 2159 Application/Control Number: 19/251,545 Page 7 Art Unit: 2159 Application/Control Number: 19/251,545 Page 8 Art Unit: 2159 Application/Control Number: 19/251,545 Page 9 Art Unit: 2159 Application/Control Number: 19/251,545 Page 10 Art Unit: 2159 Application/Control Number: 19/251,545 Page 11 Art Unit: 2159 Application/Control Number: 19/251,545 Page 12 Art Unit: 2159 Application/Control Number: 19/251,545 Page 13 Art Unit: 2159 Application/Control Number: 19/251,545 Page 14 Art Unit: 2159 Application/Control Number: 19/251,545 Page 15 Art Unit: 2159 Application/Control Number: 19/251,545 Page 16 Art Unit: 2159 Application/Control Number: 19/251,545 Page 17 Art Unit: 2159 Application/Control Number: 19/251,545 Page 18 Art Unit: 2159 Application/Control Number: 19/251,545 Page 19 Art Unit: 2159 Application/Control Number: 19/251,545 Page 20 Art Unit: 2159 Application/Control Number: 19/251,545 Page 21 Art Unit: 2159 Application/Control Number: 19/251,545 Page 22 Art Unit: 2159 Application/Control Number: 19/251,545 Page 23 Art Unit: 2159 Application/Control Number: 19/251,545 Page 24 Art Unit: 2159 Application/Control Number: 19/251,545 Page 25 Art Unit: 2159 Application/Control Number: 19/251,545 Page 26 Art Unit: 2159 Application/Control Number: 19/251,545 Page 27 Art Unit: 2159 Application/Control Number: 19/251,545 Page 29 Art Unit: 2159 Application/Control Number: 19/251,545 Page 30 Art Unit: 2159
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