Capital One Services, LLC

Devrev, Inc.

Anindya Misra

Harshit Krishna

Eckow Fred Ayison

Sripal Togaru

Cindy Liu

Milovan Milovanovic

Nimar Singh Arora

Jan Olderdissen

Ken Chen

Brian Byrne

Steven Poitras

Bhavana Thudi

Manoj Agarwal

A method may include a obtaining a first data model instance comprising an identifier string and. a set of attributes associated with a set of attribute name strings. The method may include obtaining an ontology graph that includes a first label, a second label, and an association between them. The method may include using a prediction model to select the first label based on the first data model instance and determining the second label based on the relationship. The method may include determining a selected set of labels that includes the first label and the second label to associate with the first data model instance. The method may include associating the selected set of labels with the first data model instance in a dataset that includes a plurality of records, where each record is associated with a different data model instance.

Multi-layer graph-based categorization

Disclosed is an improved approach to process data and events from disparate ecosystems pertaining to products. An approach is provided to automatically cluster events from various ecosystems into noteworthy incidents and to correlate them with entities extracted from each system. Incidents are correlated between ecosystems to classify the type of incidents and to give a coherent converged picture of the event streams coming from the various ecosystems. Noteworthy incidents are automatically converted into tickets and their severity is ascertained from the associated incidents. Tickets that reference underlying defects with the product or service are converted into issues.

Method and system for integrated analysis of data from multiple source environments

A device may log update information associated with an operation, wherein the update information identifies one or more changes made to the dataset during the operation and a source of the operation. The device may identify, based on identification information associated with the dataset, a lineage record identifier associated with the dataset. The device may generate a lineage record associated with the operation, wherein the lineage record includes the lineage record identifier and the update information. The device may send a request for validation to an authorized node to validate the lineage record, wherein the authorized node is authorized to access the dataset. The device may store, based on the lineage record being validated by the authorized node, the lineage record in a distributed ledger, wherein the distributed ledger is accessible to a plurality of nodes to enable the plurality of nodes to access the update information.

Data lineage management

A method includes receiving a data capture event affecting personal data of a user stored in at least one storage device of a computing system and mapped in a privacy graph database. Personal data of the user may be identified in the data capture event and classified into the data categories. In response to the data capture event, a mapping of user-centric nodes associated with the at least one user associated with other users in the privacy graph database is automatically updated using the classified personal data in the data capture event. A request by a requester for personal data of at least one specific user stored in the at least one storage device is received. The privacy graph database is queried to provide the requested personal data and locations of the requested personal data of the at least one specific user in the request stored in the computing system.

Computer-based methods and systems for building and managing privacy graph databases

A method may include a obtaining a first data model instance comprising an identifier string and a set of attributes associated with a set of attribute name strings. The method may include obtaining an ontology graph that includes a first label, a second label, and an association between them. The method may include using a prediction model to select the first label based on the first data model instance and determining the second label based on the relationship. The method may include determining a selected set of labels that includes the first label and the second label to associate with the first data model instance. The method may include associating the selected set of labels with the first data model instance in a dataset that includes a plurality of records, where each record is associated with a different data model instance.

Computer-based methods and systems for managing private data of users

Growing community of inventors

Arlington, VA, United States of America

Anindya Misra