Palantir technologies inc. (20240320227). SYSTEMS AND METHODS FOR AUTOMATIC CLUSTERING AND CANONICAL DESIGNATION OF RELATED DATA IN VARIOUS DATA STRUCTURES simplified abstract

SYSTEMS AND METHODS FOR AUTOMATIC CLUSTERING AND CANONICAL DESIGNATION OF RELATED DATA IN VARIOUS DATA STRUCTURES

Organization Name

palantir technologies inc.

Inventor(s)

Lawrence Manning of New York NY (US)

Rahul Mehta of New York NY (US)

Daniel Erenrich of Mountain View CA (US)

Guillem Palou Visa of London (GB)

Roger Hu of New York NY (US)

Xavier Falco of London (GB)

Rowan Gilmore of London (GB)

Eli Bingham of New York NY (US)

Jason Prestinario of New York NY (US)

Yifei Huang of Jersey City NJ (US)

Daniel Fernandez of New York NY (US)

Jeremy Elser of New York NY (US)

Clayton Sader of San Francisco CA (US)

Rahul Agarwal of San Francisco CA (US)

Matthew Elkherj of Menlo Park CA (US)

Nicholas Latourette of San Francisco CA (US)

Aleksandr Zamoshchin of Aurora CO (US)

SYSTEMS AND METHODS FOR AUTOMATIC CLUSTERING AND CANONICAL DESIGNATION OF RELATED DATA IN VARIOUS DATA STRUCTURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320227 titled 'SYSTEMS AND METHODS FOR AUTOMATIC CLUSTERING AND CANONICAL DESIGNATION OF RELATED DATA IN VARIOUS DATA STRUCTURES

The patent application describes computer-implemented systems and methods for automatically clustering and identifying related data in various data structures.

• The systems and methods involve grouping records into pairs, analyzing the pairs to determine the probability that both members relate to a common entity, and identifying clusters of overlapping pairs to generate a collection of records related to a common entity.
• Clusters can be further analyzed to determine canonical names or other properties for the entities by analyzing record fields and identifying similarities.

Potential Applications:

• Data organization and management in databases
• Entity resolution in large datasets
• Data mining and analysis in various industries

Problems Solved:

• Efficiently identifying and organizing related data
• Streamlining data analysis processes
• Improving data accuracy and consistency

Benefits:

• Enhanced data organization and retrieval
• Increased efficiency in data analysis
• Improved data quality and consistency

Commercial Applications:

• Database management software
• Data analytics platforms
• Customer relationship management systems

Questions about the technology: 1. How does this technology improve data analysis processes? 2. What industries can benefit the most from this data clustering and identification technology?

Frequently Updated Research: Research on improving algorithms for data clustering and entity resolution in large datasets is ongoing. Stay updated on advancements in this field for the latest innovations.

Original Abstract Submitted

computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. data structures may include a plurality of records, wherein each record is associated with a respective entity. in accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.