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US Patent 11762635 Artificial intelligence engine with enhanced computing hardware throughput

Patent 11762635 was granted and assigned to Microsoft on September, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Microsoft
Microsoft
Current Assignee
Microsoft
Microsoft
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11762635
Patent Inventor Names
Matthew Brown
Date of Patent
September 19, 2023
Patent Application Number
16008775
Date Filed
June 14, 2018
Patent Citations
‌
US Patent 8521664 Predictive analytical model matching
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US Patent 9213527 Model elements in software tools as automatons in a software customization system and method
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US Patent 9235653 Discovering entity actions for an entity graph
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US Patent 9390404 Methods, apparatuses, and systems for generating solutions
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US Patent 9443192 Universal artificial intelligence engine for autonomous computing devices and software applications
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US Patent 9460088 Written-domain language modeling with decomposition
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US Patent 9524461 Conceptual computation system using a hierarchical network of modules
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US Patent 9558454 System and method for model-based inventory management of a communications system
...
Patent Citations Received
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US Patent 12093011 Method and system for generating an artificial intelligence model
0
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US Patent 11868896 Interface for working with simulations on premises
0
Patent Primary Examiner
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Abu Zar Ghaffari
Patent abstract

An artificial intelligence (“AI”) engine is disclosed with AI-engine modules and a plurality of learning agents. The AI-engine modules include instructor, learner, and predictor modules. The learner module is configured to train a plurality of AI models in parallel, and the instructor module is configured to coordinate with a plurality of simulators for respectively training the AI models. The learning agents are configured to process training requests from the instructor on data from the simulators for training the AI models. The learner module is further configured to first train the AI models on a first batch of similar data synchronously pooled in a memory of the learner module with a first processor. The learner module is further configured to subsequently train the AI models on a second, different batch of similar data synchronously pooled in the memory of the learner module with the first processor.

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