# US 2019/0318254 A1 · Method and apparatus for automated decision making

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- Number: US 2019/0318254 A1
- Document ID: US20190318254A1
- Status: Published application
- Kind: A1
- Application number: 16/468,597
- Filed: 2017-12-14
- Published: 2019-10-17
- Earliest claimed: 2016-12-15
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Daniel Ansorregui LOBETE; Karthikeyan Palavedu SARAVANAN
- Web page: https://patentgrep.com/patent/US20190318254A1

## CPC

- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 5/045: Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
- G06N 3/092: Reinforcement learning
- G06N 20/00: Machine learning
- G06N 3/08: Learning methods
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method for a first electronic device comprises generating a decision-making data structure using a machine learning data structure; transmitting, to a second electronic device, the decision-making data structure; receiving, from the electronic device, result data regarding a result of performing a selected action selected from the decision-making data structure; and updating the machine learning data structure using the result data.

## Claims

### Claim 1

A method for operating a first electronic device, the method comprising:
generating a decision-making data structure using a machine learning data structure;
transmitting, to a second electronic device, the decision-making data structure;
receiving, from the electronic device, result data regarding a result of performing a selected action selected from the decision-making data structure; and
updating the machine learning data structure using the result data.

### Claim 2 (depends on 1)

The method of claim 1,
wherein the machine learning data structure comprises data regarding a plurality of states, and data regarding a plurality of possible actions for each of the plurality of states, and
wherein the decision-making data structure comprises data regarding the plurality of states, and data regarding one action for each of the plurality of states.

### Claim 3 (depends on 2)

The method of claim 2,
wherein the machine learning data structure further comprises a rating value associated with each of the plurality of possible actions for each of the plurality of states, and
wherein the one action for each of the plurality of states in the decision-making data structure corresponds to an action with a highest rating value among the plurality of possible actions for each of the plurality of states in the machine learning data structure.

### Claim 4 (depends on 3)

The method of claim 3,
wherein the machine learning data structure further comprises a Q-table regarding Q-value of each of the plurality of possible actions for each of the plurality of states,
wherein the one action for each of the plurality of states in the decision-making data structure corresponds to an action with a highest Q-value for each of the plurality of states in the Q-table.

### Claim 5 (depends on 4)

The method of claim 4,
wherein the decision-making data structure further comprises a look-up table regarding each of the plurality of states and the action with the highest Q-value.

### Claim 6

An apparatus of a first electronic device, the apparatus comprising:
a memory storing a machine learning data structure;
a transceiver; and
at least one processor,
wherein the at least one processor is configured to:
generate a decision-making data structure using the machine learning data structure,
control the transceiver to transmit, to a second electronic device, the decision-making data structure,
control the transceiver to receive, from the second electronic device, result data regarding a result of performing a selected action selected from the decision-making data structure, and
update the machine learning data structure stored in the memory using the result data.

### Claim 7 (depends on 6)

The apparatus of claim 6,
wherein the machine learning data structure comprises data regarding a plurality of states, and data regarding a plurality of possible actions for each of the plurality of states, and
wherein the decision-making data structure comprises data regarding the plurality of states, and data regarding one action for each of the plurality of states.

### Claim 8 (depends on 7)

The apparatus of claim 7,
wherein the machine learning data structure further comprises a rating value associated with each of the plurality of possible actions for each of the plurality of states, and
wherein the one action for each of the plurality of states in the decision-making data structure corresponds to an action with a highest rating value among the plurality of possible actions for each of the plurality of states in the machine learning data structure.

### Claim 9 (depends on 8)

The apparatus of claim 8,
wherein the machine learning data structure further comprises a Q-table regarding Q-value of each of the plurality of possible actions for each of the plurality of states,
wherein the one action for each of the plurality of states in the decision-making data structure corresponds to an action with a highest Q-value for each of the plurality of states in the Q-table.

### Claim 10 (depends on 9)

The apparatus of claim 9,
wherein the decision-making data structure further comprises a look-up table regarding each of the plurality of states and the action with the highest Q-value.

### Claim 11

An apparatus of a second electronic device, the apparatus comprising:
a memory;
at least one sensor;
a transceiver; and
at least one processor,
wherein the at least one processor is configured to:
control the transceiver to receive, from a first electronic device, a decision-making data structure,
control the at least one sensor to obtain state information on a current state of the second electronic device,
select an action from the decision-making data structure using the state information,
perform the selected action, and
control the transceiver to transmit, to the first electronic device, result data regarding a result of the performing the selected action,
wherein the decision-making data structure is generated using a machine learning data structure.

### Claim 12 (depends on 11)

The apparatus of claim 11,
wherein the machine learning data structure comprises data regarding a plurality of states, and data regarding a plurality of possible actions for each of the plurality of states, and
wherein the decision-making data structure comprises data regarding the plurality of states, and data regarding one action for each of the plurality of states.

### Claim 13 (depends on 12)

The apparatus of claim 12,
wherein the machine learning data structure further comprises a rating value associated with each of the plurality of possible actions for each of the plurality of states, and
wherein the one action for each of the plurality of states in the decision-making data structure corresponds to an action with a highest rating value among the plurality of possible actions for each of the plurality of states in the machine learning data structure.

### Claim 14 (depends on 13)

The apparatus of claim 13,
wherein the machine learning data structure further comprises a Q-table regarding Q-value of each of the plurality of possible actions for each of the plurality of states,
wherein the one action for each of the plurality of states in the decision-making data structure corresponds to an action with a highest Q-value for each of the plurality of states in the Q-table.

### Claim 15 (depends on 14)

The apparatus of claim 14,
wherein the decision-making data structure further comprises a look-up table regarding each of the plurality of states and the action with the highest Q-value.