Air Conditioner Learning Model for Adaptive Temperature Control

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Solution Overview

Problem

Conventional air conditioning systems lack the ability to automatically adjust temperatures based on user preferences and environmental conditions, leading to suboptimal comfort and energy efficiency.

Innovation Solution

A data learning server is employed to generate a learning model using set and current temperatures, along with external environmental data, to recommend optimal temperature settings for an air conditioner, which can be communicated to the air conditioner for automatic adjustment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional air conditioning systems are used with fixed temperature settings, then the system structure remains simple, but user comfort and energy efficiency deteriorate due to inability to adapt to changing conditions

Engineering Contradiction:

Improvetemperature adaptation capabilityVSAvoidsystem structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

A separate learning server is introduced as an intermediary component that processes temperature data and generates learning models. This mediator handles the complex adaptive functions externally, allowing the air conditioner itself to remain relatively simple while gaining sophisticated temperature adaptation capabilities through the learning model.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary learning by collecting temperature data and generating learning models in advance. The learning server continuously updates the learning model based on historical data, so when temperature control decisions are needed, the system already has pre-computed optimal settings ready to be applied immediately.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If manual temperature adjustment is used, then the control system remains simple, but user comfort deteriorates due to lack of personalization and automatic optimization

Engineering Contradiction:

Improveautomatic temperature controlVSAvoidlearning model system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system provides self-service by automatically learning user preferences and environmental patterns, then autonomously determining optimal temperature settings without requiring manual user input. The learning server continuously improves the learning model using accumulated data, enabling the system to serve itself and make intelligent temperature control decisions independently.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms where temperature setting results are continuously monitored and fed back to the learning server. This feedback loop allows the learning model to be continuously refined and updated based on actual performance and user responses, creating a closed-loop system that automatically improves over time.

Inventive Principle:

Principle #23Feedback

3Loss of energy

If standard temperature settings are applied without learning, then the system operates simply, but energy efficiency deteriorates due to inability to optimize based on real-time conditions

Engineering Contradiction:

Improveenergy consumptionVSAvoiddata collection and processing system

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The patent replaces mechanical trial-and-error temperature adjustment with an intelligent learning system. Instead of relying on fixed mechanical control logic, the system uses data-driven learning models that process temperature information and automatically determine optimal settings, substituting mechanical simplicity with intelligent optimization that reduces energy consumption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically changes temperature parameters based on learned patterns and real-time conditions. The learning model continuously adjusts temperature settings by analyzing multiple parameters including historical data, environmental conditions, and user preferences, enabling flexible parameter optimization that adapts to changing circumstances and minimizes energy waste.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12013134B2Data learning server and method for generating and using learning model thereof

Publication Date: 2024.06.18 SAMSUNG ELECTRONICS CO LTD

AI summary

An apparatus and a method for a data learning server is provided. The apparatus of the disclosure includes a communicator configured to communicate with an external device, at least one processor configured to acquire a set temperature set in an air conditioner and a current temperature of the air conditioner at the time of setting the temperature via the communicator, and a generate or renew a learning model using the set temperature and the current temperature, and a storage configured to store the generated or renewed learning model to provide a recommended temperature to be set in the air conditioner as a result of generating or renewing the learning model. For example, the data learning server of the disclosure may generate a learned learning model to provide a recommended temperature using a neural network algorithm, a deep learning algorithm, a linear regression algorithm, or the like as an artificial intelligence algorithm.