Self-learning selection of information-analysis runtimes

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United States of America

PATENT NO 11288601
APP PUB NO 20200302343A1
SERIAL NO

16360118

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Abstract

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A self-learning computer-based system has access to multiple runtime modules that are each capable of performing a particular algorithm. Each runtime module implements the algorithm with different code or runs in a different runtime environment. The system responds to a request to run the algorithm by selecting the runtime module or runtime environment that the system predicts will provide the most desirable results based on parameters like accuracy, performance, cost, resource-efficiency, or policy compliance. The system learns how to make such predictions through training sessions conducted by a machine-learning component. This training teaches the system that previous module selections produced certain types of results in the presence of certain conditions. After determining whether similar conditions currently exist, the system uses rules inferred from the training sessions to select the runtime module most likely to produce desired results.

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Patent Owner(s)

  • INTERNATIONAL BUSINESS MACHINE CORPORATION

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Brokaw, Scott Louis Groton, US 4 5
Gupta, Ritesh Kumar Hyderabad, IN 14 12
Jacobson, Eric Allen Arlington, US 3 5
Kabra, Namit Hyderabad, IN 47 71
Ramos, Jo Arao Grapevine, US 2 249

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