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US Patent 10268749 Clustering sparse high dimensional data using sketches

Patent 10268749 was granted and assigned to Amazon on April, 2019 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Amazon
Amazon
Current Assignee
Amazon
Amazon
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10268749
Patent Inventor Names
Aswin Natarajan8
Amit Chandak8
Bharath Venkatesh8
Gourav Roy8
Gowda Dayananda Anjaneyapura Range8
Jagannathan Srinivasan8
Prateek Gupta8
Sathish Kumar Palanisamy8
...
Date of Patent
April 23, 2019
Patent Application Number
14990161
Date Filed
January 7, 2016
Patent Citations Received
‌
US Patent 11973782 Computer-implemented method, device, and computer program product
1
‌
US Patent 11860848 Encoder-decoder transformer for table generation
2
‌
US Patent 11934401 Scalable count based interpretability for database artificial intelligence (AI)
3
‌
US Patent 11782991 Accelerated large-scale similarity calculation
‌
US Patent 11379535 Accelerated large-scale similarity calculation
‌
US Patent 11546417 Method for managing artificial intelligence application, device, and program product
6
‌
US Patent 11610061 Modifying text according to a specified attribute
7
Patent Primary Examiner
‌
Jonathan Han
Patent abstract

An approximate data structure to represent clusters of observation records of a data set is identified. A hierarchical representation of a plurality of clusters, including the targeted number of clusters among which the observation records are to be distributed, is generated. Each node of the hierarchy comprises an instance of the approximate data structure. Until a set of termination criteria are met, iterations of a selected clustering methodology are run. In a given iteration, distances of observation records from the cluster representatives of a current version of the model are computed using the hierarchical representation, and a new version of the model with modified cluster representatives is generated.

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