Korea Electric Power has filed a patent for an artificial intelligence (AI) feedback method. The method involves providing input to a script extractor and executor, generating a two-dimensional information table based on an initial condition provided to the AI, adding popularity frequency or ranking values to the table, and performing pseudolinear transformations to derive a unique characteristic table. The patent aims to improve AI learning and knowledge matching. GlobalData’s report on Korea Electric Power gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Korea Electric Power, Blockchain-based energy trading was a key innovation area identified from patents. Korea Electric Power's grant share as of September 2023 was 48%. Grant share is based on the ratio of number of grants to total number of patents.
Ai feedback method using 2d unique characteristic table
A recently filed patent (Publication Number: US20230316130A1) describes an artificial intelligence (AI) feedback method and system. The method involves using an AI feedback system implemented as a computing device to perform various steps.
In the method, input 1, which includes a basic material for learning, and input 2, which includes core contents for learning, are provided to a script extractor and executor. The script extractor and executor then provide an initial condition for a popularity frequency value or a ranking value of information related to input 2 to the AI based on input 1.
The AI generates a two-dimensional (2D) information table in the form of a matrix, with a key word and key information as both axes, using the popularity frequency value or ranking value derived from the initial condition. The popularity frequency value or ranking value is added to corresponding coordinates of the 2D information table based on the interaction between the AI and the script extractor and executor. The AI also matches the knowledge acquired to the corresponding coordinates.
Furthermore, the AI generates a 2D pseudolinear transformation table, which digitizes the ranking value of the 2D information table and randomly turns on or off each coordinate from the 2D information table. This pseudolinear transformation table has the same area as the 2D information table. The AI then performs pseudolinear transformations multiple times on the 2D information table to create a 2D unique characteristic table. This table has a characteristic vector for each coordinate, and the AI derives information of coordinates whose characteristic vector remains unchanged as a deep truth value.
The patent also describes an AI feedback system that includes a script extractor and executor, an AI, and an output unit. The script extractor and executor receive input 1 and input 2 and generate an initial condition for the popularity frequency value or ranking value. The AI, connected to the script extractor and executor, generates the 2D information table, adds the popularity frequency value or ranking value to the table, and matches acquired knowledge to the coordinates. The output unit is connected to the AI and outputs the deep truth value derived from the 2D information table.
Overall, this patent presents an AI feedback method and system that utilizes a combination of information tables, pseudolinear transformations, and characteristic vectors to derive deep truth values based on input materials.
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