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US Patent 10157347 Adaptable systems and methods for processing enterprise data

Patent 10157347 was granted and assigned to Predii on December, 2018 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Predii
Predii
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Current Assignee
Predii
Predii
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
101573470
Patent Inventor Names
Tilak B. Kasturi0
Aniket Dalal0
Hieu Ho0
Date of Patent
December 18, 2018
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Patent Application Number
145330850
Date Filed
November 4, 2014
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Patent Citations
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US Patent 10013679 Method and system for generating vehicle service content from metadata representing meaning of vehicle service data
Patent Citations Received
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US Patent 12086729 Architecture for refining mapped human experiences insights within a human experience flow
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US Patent 11501201 Systems, methods, and apparatuses for training, storage, and interaction with machine learning models
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US Patent 11501186 Artificial intelligence (AI) based data processing
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US Patent 11954112 Systems and methods for data processing and enterprise AI applications
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US Patent 11975862 Database query processing for hardware component identification using multi-ordered machine learning models
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US Patent 12001469 Generation of process models in domains with unstructured data
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US Patent 12026087 Automatically testing artificial intelligence models in connection with enterprise-related properties
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0
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Patent Primary Examiner
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Michael B. Holmes
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Patent abstract

A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.

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