AUTOMATED TRAINING AND SELECTION OF MODELS FOR DOCUMENT ANALYSIS

Patent №

US 10,936,974

Granted

2021-03-02

Filed 2018

Owner

ICERTIS, INC.

Lab

AI components

6

ml · nlp · vision · kr · planning · hardware

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

16231842

Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Machine learningNatural languageVisionKnowledge representationPlanningAI hardwareG06N 20/20G06F 18/23G06N 20/00G06V 10/762G06V 30/416G06N 5/01G06N 5/041

AI classification

Machine learning1.00
Natural language1.00
Planning1.00
Knowledge representation0.99
AI hardware0.98
Vision0.94
Speech0.00
Evolutionary computation0.00

Ownership

ICERTIS, INC.

assignment · 478490592

Assignors

CHAUDHARI, DHRUV, SHAH, HARSHIL, JAIN, AMITABH, DARDA, MONISH MANGALKUMAR

On an employer assignment, the assignors are typically the inventors.

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