# US 2023/0267104 A1 · Smart datastore selection for protection engines based on uncertainty quantification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0267104 A1 - Document ID: US20230267104A1 - Status: Published application - Kind: A1 - Application number: 17/652,405 - Filed: 2022-02-24 - Published: 2023-08-24 - Earliest claimed: 2022-02-24 - Assignees: Dell Products L.P. - Applicants: Dell Products L.P. - Inventors: Suraj D. Vithalkar; Rahul Deo Vishwakarma; Parmeshwr Prasad - Web page: https://patentgrep.com/patent/US20230267104A1 ## CPC - G06F 11/1448: {Management of the data involved in backup or backup restore} - G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors - G06F 11/1458: {Management of the backup or restore process} - G06F 16/2365: {Ensuring data consistency and integrity} - G06F 16/285: {Clustering or classification} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract One example method includes collecting respective data for each datastore in a group of datastores, clustering the data, based on the clustering, applying one of two different mutually exclusive labels to each of the datastores, applying an uncertainty quantifier to determine, for each datastore, a confidence level that the label assigned to that datastore is the correct label, ranking the datastores according to their respective label and confidence level, and presenting, to a user, a list that includes at least a datastore with a highest confidence level. ## Claims ### Claim 1 A method, comprising: for each datastore in a group of datastores; collecting data; clustering the data; based on the clustering, applying one of two different mutually exclusive labels to the datastore; applying an uncertainty quantifier to determine, for the datastore, a confidence level that the label assigned to the datastore is correct; ranking the datastores of the group of datastores according to respective label and confidence level; and presenting, to a user, a list that includes whichever of the datastores has a highest confidence level. ### Claim 2 (depends on 1) The method as recited in claim 1, wherein one of the two different mutually exclusive labels is a HIGH label, and the other of the two different mutually exclusive labels is a LOW label, and the HIGH label indicates a healthy datastore, and the LOW label indicates an unhealthy datastore. ### Claim 3 (depends on 1) The method as recited in claim 1, wherein the data that is collected for one of the datastores comprises data indicative of a health of that one datastore. ### Claim 4 (depends on 1) The method as recited in claim 1, wherein the clustering clusters the data into two clusters that collectively contain all the datastores, and a first one of the two different mutually exclusive labels is assigned to datastores in a first cluster of the two clusters, and a second one of the two different mutually exclusive labels is assigned to datastores in a second cluster of the two clusters. ### Claim 5 (depends on 1) The method as recited in claim 1, wherein the clustering is performed using HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). ### Claim 6 (depends on 1) The method as recited in claim 1, wherein the label on the datastore with the highest confidence level is HIGH. ### Claim 7 (depends on 1) The method as recited in claim 1, further comprising receiving a user selection of a datastore with the label HIGH and a highest confidence level of any of the datastores. ### Claim 8 (depends on 1) The method as recited in claim 1, further comprising receiving a user command to restore protected data to a datastore with the label HIGH and a highest confidence level of any of the datastores. ### Claim 9 (depends on 1) The method as recited in claim 1, further comprising receiving a user command to deploy a data protection resource to a datastore with the label HIGH and a highest confidence level of any of the datastores. ### Claim 10 (depends on 1) The method as recited in claim 1, further comprising performing a credibility assessment with respect to the data, and assigning a credibility score to each of the datasets. ### Claim 11 A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: for each datastore in a group of datastores; collecting data; clustering the data; based on the clustering, applying one of two different mutually exclusive labels to the datastore; applying an uncertainty quantifier to determine, for the datastore, a confidence level that the label assigned to the datastore is correct; ranking the datastores of the group of datastores according to respective label and confidence level; and presenting, to a user, a list that includes whichever of the datastores has a highest confidence level. ### Claim 12 (depends on 11) The non-transitory storage medium as recited in claim 11, wherein one of the two different mutually exclusive labels is a HIGH label, and the other of the two different mutually exclusive labels is a LOW label, and the HIGH label indicates a healthy datastore, and the LOW label indicates an unhealthy datastore. ### Claim 13 (depends on 11) The non-transitory storage medium as recited in claim 11, wherein the data that is collected for one of the datastores comprises data indicative of a health of that one datastore. ### Claim 14 (depends on 11) The non-transitory storage medium as recited in claim 11, wherein the clustering clusters the data into two clusters that collectively contain all the datastores, and a first one of the two different mutually exclusive labels is assigned to datastores in a first cluster of the two clusters, and a second one of the two different mutually exclusive labels is assigned to datastores in a second cluster of the two clusters. ### Claim 15 (depends on 11) The non-transitory storage medium as recited in claim 11, wherein the clustering is performed using HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise). ### Claim 16 (depends on 11) The non-transitory storage medium as recited in claim 11, wherein the label on the datastore with the highest confidence level is HIGH. ### Claim 17 (depends on 11) The non-transitory storage medium as recited in claim 11, further comprising receiving a user selection of a datastore with the label HIGH and a highest confidence level of any of the datastores. ### Claim 18 (depends on 11) The non-transitory storage medium as recited in claim 11, further comprising receiving a user command to restore protected data to a datastore with the label HIGH and a highest confidence level of any of the datastores. ### Claim 19 (depends on 11) The non-transitory storage medium as recited in claim 11, further comprising receiving a user command to deploy a data protection resource to a datastore with the label HIGH and a highest confidence level of any of the datastores. ### Claim 20 (depends on 11) The non-transitory storage medium as recited in claim 11, further comprising performing a credibility assessment with respect to the data, and assigning a credibility score to each of the datasets.