PRODUCING CALIBRATED CONFIDENCE ESTIMATES FOR OPEN-ENDED ANSWERS BY GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

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United States of America

APP PUB NO 20250077778A1
SERIAL NO

18382331

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Abstract

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A confidence estimation tool uses a calibrated confidence mapping model to estimate confidence for a model-generated candidate root cause. The tool uses a generative artificial intelligence (“AI”) model to determine, based on a description of a current event, a candidate root cause of the current event. The tool determines a description-based confidence score using the description of the current event and descriptions of a set of relevant historical events in a target domain. The tool also determines a cause-based confidence score using the candidate root cause of the current event and root causes of the set of relevant historical events. Finally, the tool determines a final confidence score using the description-based and cause-based confidence scores. Even if the generative AI model is configured for general-domain applications, by referencing relevant historical events, the tool can accurately estimate confidence for a model-generated candidate root cause within the target domain.

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Patent Owner(s)

Patent OwnerAddress
MICROSOFT TECHNOLOGY LICENSING LLCONE MICROSOFT WAY REDMOND WA 98052

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
BANSAL, Chetan Seattle, US 23 115
FONSECA, Rodrigo Lopes Cancado Bothell, US 3 22
LAS-CASAS, Pedro Henrique Bragioni Belo Horizonte, BR 1 0
RAJMOHAN, Saravanakumar Redmond, US 3 48
ZHANG, Shizhuo Champaign, US 1 0
ZHANG, Xuchao Sammamish, US 25 21

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