# US 2026/0064720 A1 · Method and system for selecting diversified data from a dataset > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0064720 A1 - Document ID: US20260064720A1 - Status: Published application - Kind: A1 - Application number: 18/823,716 - Filed: 2024-09-04 - Published: 2026-03-05 - Earliest claimed: 2024-09-04 - Assignees: Honeywell International Inc. - Applicants: Honeywell International Inc. - Inventors: Ambarish Pathak; Benz Paulraj - Web page: https://patentgrep.com/patent/US20260064720A1 ## CPC - G06F 16/285: {Clustering or classification} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The present disclosure relates to a method for selecting diversified data from a dataset. The method comprises determining numerical data and categorical data from the dataset and formulating a correlation between the numerical data and categorical data. A subset of data comprising uncorrelated numerical data and the categorical data is then prepared and allocated into one or more risk groups based on predefined risk factors, including at least one of geographical location, user identity, and number of identical transactions. Samples of data from a risk group of the one or more risk groups are chosen by first selecting an initial data point of the risk group and then iteratively selecting a subsequent data point of the risk group based on angular and euclidean distances of the initial data point from the subsequent data point of the risk group. A final dataset is generated and stored for training an AI model. ## Claims ### Claim 1 A computer-implemented method for selecting diversified data from a dataset, executing, by a processor, operations comprising: determining numerical data and categorical data from the dataset, wherein the numerical data and the categorical data are continuously monitored and adjusted to ensure compliance with policies and regulations; identifying, by the processor, a subset of uncorrelated numerical data based on correlations within the numerical data; removing, by the processor, the numerical data that exceed a predefined correlation threshold; prioritizing, by the processor, the remaining numerical data based on a variability score for each numerical data feature; computing, by the processor, an importance score for each of categorical data feature from one or more categorical data features based on an entropy; arranging, by the processor, the one or more categorical data features based on a respective importance score; selecting a subset of high-importance categorical data features from the arranged one or more categorical data features; formulating, by the processor, a correlation between the selected numerical data and the categorical data feature; preparing, by the processor, a subset of data comprising the uncorrelated numerical data and the categorical data; allocating, by the processor, the subset of data into one or more risk groups based on predefined risk factors including at least one of geographical location, user identity, and number of identical transactions; assigning, by the processor, a sample size to the one or more risk groups; choosing samples of data from a risk group of the one or more risk groups by selecting an initial data point of the risk group and iteratively selecting a subsequent data point of the risk group based on angular and Euclidean distances of the initial data point from the subsequent data point of the risk group; generating, by the processor, a final dataset corresponding to the initial data point of the risk group and the subsequent data point of the risk group representing diversified variations with high coverage of both the numerical data and the categorical data; and storing, by the processor, the final dataset to train an AI model for detecting deviations in the dataset. ### Claim 2 (depends on 1) The method as claimed in claim 1, further comprising: selecting a predetermined number of data from each risk group of the one or more risk groups to maximize variability by arranging data from each risk group in descending order of their variance values; and selecting the data based on the descending order and discarding the remaining data. ### Claim 3 (depends on 1) The method as claimed in claim 1, wherein assigning the sample size to the one or more risk groups can either be of an equal size sample or an unequal size sample. ### Claim 4 (depends on 3) The method as claimed in claim 3, wherein the unequal size sample means either a left skewed sample or a right skewed sample. ### Claim 5 (depends on 1) The method as claimed in claim 1, further comprising: choosing samples of data from each risk group of the one or more risk groups by selecting an initial data point for each risk group and iteratively selecting a subsequent data point for each risk group based on the angular and Euclidean distances of the initial data point from the subsequent data point for each risk group; and generating a final dataset for each risk group corresponding to the initial data point for each risk group and the subsequent data point for each risk group representing diversified variations with high coverage of both the numerical and the categorical data, wherein a risk score is computed for each of the initial data point based on plurality of features, wherein each feature from the plurality of features is determined by an expert and is associated with at least one predefined risk factor, wherein the risk score associated with the plurality of features is configured to quantify contribution to risk assessment based on predefined criteria. ### Claim 6 (depends on 1) The method as claimed in claim 1, wherein preparing the subset of data further comprises a numerical data dominant dataset or a categorical data dominant dataset. ### Claim 7 (depends on 1) The method as claimed in claim 1, further comprising scheming data with high entropy value comprises data with maximum variability in values. ### Claim 8 (depends on 1) The method as claimed in claim 1, wherein the predefined risk factors include factors essential for business transaction. ### Claim 9 A system, comprising: a memory; and a processor configured to: determine numerical data and categorical data from the dataset, wherein the numerical data and the categorical data are continuously monitored and adjusted to ensure compliance with policies and regulations; identify, by the processor, a subset of uncorrelated numerical data based on correlations within the numerical data; remove, by the processor, the numerical data that exceed a predefined correlation threshold: prioritize, by the processor, the remaining numerical data based on a variability score for each numerical data feature; compute, by the processor, an importance score for each of categorical data feature from one or more categorical data features based on an entropy; arrange, by the processor, the one or more categorical data features based on a respective importance score; select a subset of high-importance categorical data features from the arranged one or more categorical data features; formulate a correlation between the selected numerical data and the categorical data feature; prepare a subset of data comprising the uncorrelated numerical data and the categorical data; allocate the subset of data into one or more risk groups based on predefined risk factors including at least one of geographical location, user identity, and number of identical transactions; assign a sample size to the one or more risk groups; choose samples of data from a risk group of the one or more risk groups by selecting an initial data point of the risk group and iteratively selecting a subsequent data point of the risk group based on angular and Euclidean distances of the initial data point from the subsequent data point of the risk group; generate a final dataset corresponding to the initial data point of the risk group and the subsequent data point of the risk group representing diversified variations with high coverage of both the numerical data and the categorical data; and store the final dataset to train an AI model for detecting deviations in the dataset. ### Claim 10 (depends on 9) The system as claimed in claim 9, wherein the processor further configured to: select a predetermined number of data from each risk group of the one or more risk groups to maximize variability by arranging data from each risk group in descending order of their variance values; and select the data based on the descending order and discard the remaining data. ### Claim 11 (depends on 9) The system as claimed in claim 9, wherein assigning the sample size to the one or more risk groups can either be of an equal size sample or an unequal size sample. ### Claim 12 (depends on 11) The system as claimed in claim 11, wherein the unequal size sample means either a left skewed sample or a right skewed sample. ### Claim 13 (depends on 9) The system as claimed in claim 9, wherein the processor further configured to: choose samples of data from each risk group of the one or more risk groups by selecting an initial data point for each risk group and iteratively selecting a subsequent data point for each risk group based on the angular and Euclidean distances of the initial data point from the subsequent data point for each risk group; and generate a final dataset for each risk group corresponding to the initial data point for each risk group and the subsequent data point for each risk group representing diversified variations with high coverage of both the numerical and the categorical data, wherein a risk score is computed for each of the initial data point based on plurality of features, wherein each feature from the plurality of features is determined by an expert and is associated with at least one predefined risk factor, wherein the risk score associated with the plurality of features is configured to quantify contribution to risk assessment based on predefined criteria. ### Claim 14 (depends on 9) The system as claimed in claim 9, wherein preparing the subset of data further comprises a numerical data dominant dataset or a categorical data dominant dataset. ### Claim 15 (depends on 9) The system as claimed in claim 9, wherein the processor further configured to scheme data with high entropy value comprises data with maximum variability in values. ### Claim 16 (depends on 9) The system as claimed in claim 9, wherein the predefined risk factors include factors essential for business transaction. ### Claim 17 A non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor, cause the processor to execute a method for selecting diversified data from a dataset, comprising: determining numerical data and categorical data from the dataset, wherein the numerical data and the categorical data are continuously monitored and adjusted to ensure compliance with policies and regulations; identifying, by the processor, a subset of uncorrelated numerical data based on correlations within the numerical data; removing, by the processor, the numerical data that exceed a predefined correlation threshold; prioritizing, by the processor, the remaining numerical data based on a variability score for each numerical data feature; computing, by the processor, an importance score for each of categorical data feature from one or more categorical data features based on an entropy; arranging, by the processor, the one or more categorical data features based on a respective importance score; selecting a subset of high-importance categorical data features from the arranged one or more categorical data features; formulating a correlation between the selected numerical data and the categorical data feature; preparing a subset of data comprising the uncorrelated numerical data and the categorical data; allocating the subset of data into one or more risk groups based on predefined risk factors including at least one of geographical location, user identity, and number of identical transactions; assigning a sample size to the one or more risk groups; choosing samples of data from a risk group of the one or more risk groups by selecting an initial data point of the risk group and iteratively selecting a subsequent data point of the risk group based on angular and Euclidean distances of the initial data point from the subsequent data point of the risk group; generating a final dataset corresponding to the initial data point of the risk group and the subsequent data point of the risk group representing diversified variations with high coverage of both the numerical data and the categorical data; storing the final dataset to train an AI model for detecting deviations in the dataset. ### Claim 18 (depends on 17) The computer-readable medium as claimed in claim 17, wherein computer-readable instructions, when executed by the processor, causes the processor to: select a predetermined number of data from each risk group of the one or more risk groups to maximize variability by arranging data from each risk group in descending order of their variance values; and select the data based on the descending order and discard the remaining data. ### Claim 19 (depends on 17) The computer-readable medium as claimed in claim 17, wherein computer-readable instructions, when executed by the processor, causes the processor to scheme data with high entropy value comprises data with maximum variability in values. ### Claim 20 (depends on 17) The computer-readable medium as claimed in claim 17, wherein computer-readable instructions, when executed by the processor, causes the processor to prepare the subset of data comprising a numerical data dominant dataset or a categorical data dominant dataset.