Invention Grant
- Patent Title: Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data
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Application No.: US17461982Application Date: 2021-08-30
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Publication No.: US11755602B2Publication Date: 2023-09-12
- Inventor: Shawn Andrew Pardue Smith , Bryon Kristen Jacob
- Applicant: data.world, Inc.
- Applicant Address: US TX Austin
- Assignee: data.world, Inc.
- Current Assignee: data.world, Inc.
- Current Assignee Address: US TX Austin
- Agency: KOKKA & BACKUS, PC
- Main IPC: G06F16/2458
- IPC: G06F16/2458 ; G06F16/25 ; G06N5/04 ; G06F16/215

Abstract:
Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to receive or analyze datasets in parallel by implementing, for example, parallel computing processor systems to correlate subsets of parallelized data from disparately-formatted data sources to identify entity data and to aggregate graph data portions. In some examples, a method may include classifying data parallelized data to identify a class of observation data, constructing one or more content graphs in a graph data format, correlating parallelized data to other subsets of parallelized data associated with a class of observation data; and aggregating observation data to represent an individual entity.
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