IoT Pet Chatbot Emotion Detection via Voice and Activity Analysis
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Solution Overview
Problem
Current pet products that aim to understand companion animal emotions and states rely on unilateral data detection, resulting in low accuracy and inability to determine real conversations, as they only calculate result values when a user operates the device.
Innovation Solution
A device that uses an IoT system with a microphone and behavior sensor to generate big data on companion animal behavior, calculating basic emotion, situation, and behavior pattern variables to provide accurate intention determination and conversational responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a device is based on first stored data and calculates result values only when a user operates the device, then the device structure is simple and ease of operation is maintained, but measurement precision and reliability of emotion detection deteriorate
Solution Approach 1:
The system is divided into multiple functional modules: data collection module (voice and activity sensors), data processing module (emotion analysis algorithms), and interaction module (chatbot response). This segmentation allows complex functions to be distributed across separate components, improving detection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
A server acts as an intermediary between the companion animal's IOT device and the user terminal. The server collects, stores, and processes big data from multiple sources, then provides processed emotion information to the user terminal. This intermediary handles the complexity of data processing centrally, allowing the user terminal to remain relatively simple while achieving high measurement precision through sophisticated server-side analysis.
2Adaptability or versatility
If a device uses unilateral motion data detection, then the device complexity is reduced, but the ability to determine real conversations and intention deteriorates
Solution Approach 1:
The system merges multiple data sources including voice information, activity amount information, and contextual information from various IOT devices. By combining these diverse data streams through big data processing, the system achieves comprehensive conversation understanding and intention determination that surpasses unilateral detection methods.
Solution Approach 2:
The system transitions from analyzing single-dimension motion data to multi-dimensional analysis by incorporating voice data, activity levels, environmental context, and historical interaction patterns. This dimensional expansion enables the system to understand companion animal intentions and conversations with significantly higher accuracy.
3Measurement precision
If big data processing is implemented to analyze companion animal behavior in various situations, then measurement precision and adaptability improve, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing data in the background through IOT devices and servers. Emotion variables, situation variables, and behavior pattern variables are calculated and stored in advance, so when a user queries the companion animal's state, the information is already prepared and can be retrieved quickly, reducing perceived processing time while maintaining high accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the chatbot provides responses based on analyzed emotion and behavior data, and user interactions further refine the understanding. This feedback loop allows the system to learn from interactions and improve intention determination accuracy over time without requiring increased processing time for each individual query.
Data Source
AI summary
A method of operating a chatbot based on a companion animal's emotion by using a user terminal, according to an embodiment of the present disclosure includes receiving voice information and activity amount information from an TOT device when receiving a chatting value from a user; calculating a basic emotion variable, a situation variable, and a behavior pattern variable based on the voice information and the activity amount information; and searching for an answer value corresponding to the chatting value under conditions of the basic emotion variable, the situation variable, and the behavior pattern variable to output the searched answer value, wherein the TOT device includes a microphone and a behavior sensor and generates the voice information and the activity amount information by detecting a crying sound and a behavior of the companion animal wearing the TOT device.