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US Patent 10510022 Machine learning model feature contribution analytic system

Patent 10510022 was granted and assigned to Sas (company) on December, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Sas (company)
Sas (company)
0
Current Assignee
Sas (company)
Sas (company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
105100220
Patent Inventor Names
Ralph Walter Abbey0
Ricky Dee Tharrington, Jr.0
Xin Jiang Hunt0
Date of Patent
December 17, 2019
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Patent Application Number
164512280
Date Filed
June 25, 2019
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Patent Citations
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US Patent 10133811 Non-transitory computer-readable recording medium, data arrangement method, and data arrangement apparatus
Patent Citations Received
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US Patent 11514369 Systems and methods for machine learning model interpretation
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US Patent 11568286 Providing insights about a dynamic machine learning model
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US Patent 11568187 Managing missing values in datasets for machine learning models
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US Patent 11875239 Managing missing values in datasets for machine learning models
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US Patent 11983172 Generation of a predictive model for selection of batch sizes in performing data format conversion
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US Patent 11989667 Interpretation of machine leaning results using feature analysis
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US Patent 12124925 Dynamic analysis and monitoring of machine learning processes
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US Patent 11551818 Computer system and method of presenting information related to basis of predicted value output by predictor
Patent Primary Examiner
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George Giroux
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Patent abstract

Systems and methods for machine learning, models, and related explainability and interpretability are provided. A computing device determines a contribution of a feature to a predicted value. A feature computation dataset is defined based on a selected next selection vector. A prediction value is computed for each observation vector included in the feature computation dataset using a trained predictive model. An expected value is computed for the selected next selection vector based on the prediction values. The feature computation dataset is at least a partial copy of a training dataset with each variable value replaced in each observation vector included in the feature computation dataset based on the selected next selection vector. Each replaced variable value is replaced with a value included in a predefined query for a respective variable. A Shapley estimate value is computed for each variable.

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