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US Patent 11062792 Discovering genomes to use in machine learning techniques

Patent 11062792 was granted and assigned to Analytics for Life, Inc. on July, 2021 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Current Assignee
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Analytics for Life, Inc.
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
110627920
Patent Inventor Names
Timothy Burton0
Ian Shadforth0
Paul Grouchy0
Sunny Gupta0
Abhinav Doomra0
Ali Khosousi0
Date of Patent
July 13, 2021
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Patent Application Number
156534410
Date Filed
July 18, 2017
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Patent Citations
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US Patent 10127214 Methods for generating natural language processing systems
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US Patent 10405219 Network reconfiguration using genetic algorithm-based predictive models
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US Patent 10417523 Dimension grouping and reduction for model generation, testing, and documentation
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US Patent 10366346 Systems and techniques for determining the predictive value of a feature
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Patent Citations Received
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US Patent 11521115 Method and system of detecting data imbalance in a dataset used in machine-learning
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US Patent 11526701 Method and system of performing data imbalance detection and correction in training a machine-learning model
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US Patent 11537941 Remote validation of machine-learning models for data imbalance
Patent Primary Examiner
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Daniel C Puentes
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Patent abstract

A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.

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