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US Patent 10083523 Methods and apparatus for autonomous robotic control

Patent 10083523 was granted and assigned to Neurala on September, 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
Neurala
Neurala
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Current Assignee
Neurala
Neurala
0
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
100835230
Patent Inventor Names
Anatoly Gorshechnikov0
Massimiliano Versace0
Date of Patent
September 25, 2018
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Patent Application Number
152626370
Date Filed
September 12, 2016
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Patent Citations Received
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US Patent 12000958 Reinforcement learning engine for a radar system
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US Patent 10974389 Methods and apparatus for early sensory integration and robust acquisition of real world knowledge
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US Patent 11003956 System and method for training a neural network for visual localization based upon learning objects-of-interest dense match regression
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US Patent 11070623 Methods and apparatus for iterative nonspecific distributed runtime architecture and its application to cloud intelligence
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US Patent RE49461 Graphic processor based accelerator system and method
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US Patent 11327156 Reinforcement learning engine for a radar system
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US Patent 11527077 Advanced driver assist system, method of calibrating the same, and method of detecting object in the same
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US Patent 10665251 Multi-modal anomaly detection
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Patent Primary Examiner
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Li Liu
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Patent abstract

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on “stovepiped,” or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.

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