# US 2021/0241029 A1 · Form of artificial intelligence and training method thereof

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- Number: US 2021/0241029 A1
- Document ID: US20210241029A1
- Status: Published application
- Kind: A1
- Application number: 16/781,657
- Filed: 2020-02-04
- Published: 2021-08-05
- Earliest claimed: 2020-02-04
- Inventors: Andrew Polar; Michael Poluektov
- Web page: https://patentgrep.com/patent/US20210241029A1

## CPC

- G06N 20/00: Machine learning
- G06F 9/3001: {Arithmetic instructions}
- G06F 18/2135: based on approximation criteria, e.g. principal component analysis
- G06F 18/2178: {based on feedback of a supervisor}
- G06F 18/22: Matching criteria, e.g. proximity measures
- G06F 18/24323: {Tree-organised classifiers}
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method for identification of multiple discrete Urysohn operators, arranged in a tree and connected in both parallel and sequential ways, capable of replacing adequately any continuous multivariate function, which may be considered as a generic tool for mapping an ordered data into a scalar, and used as training process for artificial intelligence.

## Claims

### Claim 1

A method of constructing a tree of the discrete Urysohn operators capable of mapping provided ordered data sets into provided scalars by accomplishing multiple steps for each individual data set including but not limited to:
(a) provided a model approximation and a data to be modeled, computing a difference between a model predicted scalar and an actual value,
(b) identifying a direction for an incrementing of all inputs for a root operator of said tree needed for reduction of the said difference between said model predicted scalar and said actual value,
(c) having all these directions for all said inputs of said root operator, update all branch operators which deliver these said inputs to said root operator in such a way that the branch outputs, which are the inputs of said root operator, become incremented into said identified directions and therefore reduce said absolute difference between the updated model and the provided data set compared to said difference before execution of this update step.