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Natural language processing via a two-dimensional symbol having multiple ideograms contained therein

  • US 10,102,453 B1
  • Filed: 09/01/2017
  • Issued: 10/16/2018
  • Est. Priority Date: 08/03/2017
  • Status: Active Grant
First Claim
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1. A method of machine learning of written natural languages comprising:

  • receiving a string of natural language texts in a first computing system having at least one application module installed thereon;

    forming, with the at least one application module in the first computing system, a multi-layer two-dimensional (2-D) symbol from the received string of natural language texts based on a set of rules, the 2-D symbol being a matrix of N×

    N pixels of data that contains a super-character, the matrix being divided into M×

    M sub-matrices with each of the sub-matrices containing (N/M)×

    (N/M) pixels, said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing a meaning formed from a specific combination of a plurality of ideograms, where N and M are positive integers, and N is a multiple of M; and

    learning the meaning of the super-character in a second computing system by using an image processing technique to classify the 2-D symbol, which is formed with the at least one application module in the first computing system and transmitted to the second computing system.

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