SELF-LEARNING AUGMENTED REALITY FOR INDUSTRIAL OPERATIONS

    公开(公告)号:US20190057548A1

    公开(公告)日:2019-02-21

    申请号:US15678654

    申请日:2017-08-16

    Abstract: The example embodiments are directed to a system for self-learning augmented reality for use with industrial operations (e.g., manufacturing, assembly, repair, cleaning, inspection, etc.) performed by a user. For example, the method may include receiving data captured of the industrial operation being performed, identifying a current state of the manual industrial operation based on the received data, determining a future state of the manual industrial operation that will be performed by the user based on the current state, and generating one or more augmented reality (AR) display components based on the future state of the manual industrial operation, and outputting the one or more AR display components to an AR device of the user for display based on a scene of the manual industrial operation. The augmented reality display components can identify a future path of the manual industrial operation for the user.

    AUTONOMOUS REASONING AND EXPERIMENTATION AGENT FOR MOLECULAR DISCOVERY

    公开(公告)号:US20200227142A1

    公开(公告)日:2020-07-16

    申请号:US16739239

    申请日:2020-01-10

    Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a Hypothesis Generation Engine (HGE) to receive one or more property target values for a material; a memory for storing program instructions; an HGE processor, coupled to the memory, and in communication with the HGE, and operative to execute program instructions to: receive the one or more property target values for the material; analyze the one or more property target values as compared to one or more known values in a knowledge base; generate, based on the analysis, an initial set of hypothetical structures, wherein each hypothetical structure includes at least one property target value; execute a likelihood model for each candidate material to generate a likelihood probability for each hypothetical structure, wherein the likelihood probability is a measure of the likelihood that the hypothetical structure will have the target property value; convert each hypothetical structure into a natural language representation; execute an abduction kernel on the natural language representation with the at least one likelihood probability, to output at least one proposed structure that satisfies a likelihood threshold for having the property target value; and receive the output of the executed abduction kernel at a testing module to determine whether the output satisfies the property target values. Numerous other aspects are provided.

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