Data learning server and method for generating and using learning model thereof

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing air conditioning systems lack an efficient method for automatically recommending optimal temperature settings based on user preferences and environmental conditions, leading to suboptimal comfort and energy usage.

Innovation Solution

A data learning server is employed to generate and update a learning model using set and current air conditioner temperatures, along with external environmental data, to provide a recommended temperature setting, which is then communicated to the air conditioner for implementation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a data learning server generates and uses a learning model to automatically recommend temperature settings, then user comfort and energy efficiency are improved, but system complexity increases

Engineering Contradiction:

Improveautomatic temperature recommendationVSAvoidsystem structure

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

A data learning server is introduced as an intermediary component between the air conditioner and the user. This server collects temperature data, generates learning models, and provides recommended temperature settings, thereby automating the temperature control process while managing system complexity through modular architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service by automatically collecting temperature data from the air conditioner, generating learning models through machine learning algorithms, and providing recommended temperature settings without requiring manual user intervention in the model generation process

Inventive Principle:

Principle #25Self-service

2Measurement precision

If a learning model is generated using set temperature and current temperature data, then temperature recommendation accuracy is improved, but data processing requirements increase

Engineering Contradiction:

Improvetemperature recommendation accuracyVSAvoiddata processing load

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The data learning server extracts only the essential features needed for temperature recommendation - specifically set temperature and current temperature data - from the air conditioner. This selective extraction reduces data processing requirements while maintaining recommendation accuracy by focusing on the most relevant parameters

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentEP4071416A1Data learning server and method for generating and using learning model thereof

Publication Date: 2022.10.12 SAMSUNG ELECTRONICS CO LTD

Figure 1A
Figure 1B
Figure 2A~2B

AI summary

The present invention provides an air conditioner, comprising a blowing fan configured to discharge cooling air to an outside; a temperature sensor configured to sense a current temperature around the air conditioner; a display; and at least one processor configured to: obtain a recommended temperature, which is a result obtained by applying a set temperature to a learning model, control the display to display the received recommended temperature, and set the received recommended temperature in the air conditioner, wherein the learning model is a learning model learned using a plurality of set temperatures previously set in the air conditioner.