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US Patent 11783211 Synthetic data generation in computer-based reasoning systems

Patent 11783211 was granted and assigned to Diveplane on October, 2023 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Diveplane
Diveplane
Current Assignee
Diveplane
Diveplane
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11783211
Patent Inventor Names
Christopher James Hazard
Christopher Fusting
Michael Resnick
Date of Patent
October 10, 2023
Patent Application Number
17900502
Date Filed
August 31, 2022
Patent Citations
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US Patent 10098607 Tomographic device and also method and computer program for model-based positioning
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US Patent 10559299 Reconciliation between simulator and speech recognition output using sequence-to-sequence mapping
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US Patent 10459444 Autonomous vehicle fleet model training and testing
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US Patent 7873587 Method and system for creating a program to preform a desired task based on programs learned from other tasks
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US Patent 8911358 Endoscopic vision system
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US Patent 9489635 Methods and systems for vehicle perception feedback to classify data representative of types of objects and to request feedback regarding such classifications
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US Patent 9922286 Detecting and correcting anomalies in computer-based reasoning systems
...
Patent Primary Examiner
‌
Moustafa M Meky
CPC Code
‌
G06N 20/00
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G06N 5/04
Patent abstract

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic training data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, validity of the generated value may be checked based on feature information. In some embodiments, generated synthetic data may be checked against all or a portion of the training data to ensure that it is not overly similar.

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