AI Robot Language Setting Using Local Language Distribution
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Solution Overview
Problem
Artificial intelligence robots deployed in airports and multiplexes often use pre-set languages without considering the languages spoken by nearby users, leading to ineffective communication.
Innovation Solution
An AI server that receives voice data and event information to generate language distribution information, determines major output languages based on actual and expected user languages, and transmits control signals to set the appropriate language for robots, ensuring they communicate in languages frequently used by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots use pre-set languages in idle state, then robot operation is simple and stable, but communication effectiveness with users deteriorates
Solution Approach 1:
The robot's output language is changed from a static pre-set state to a dynamic state that adapts based on real-time language distribution detection. The robot continuously monitors the control area, analyzes language usage patterns of surrounding users, and adjusts its output language accordingly, making the system flexible and adaptive to changing environmental conditions.
Solution Approach 2:
The system implements a feedback mechanism where the robot detects languages used by users in the control area, analyzes language distribution information, and uses this feedback to adjust its output language. This closed-loop control ensures the robot's communication aligns with user preferences, improving interaction effectiveness.
2Adaptability or versatility
If robots sequentially output multiple pre-set languages, then language coverage is comprehensive, but user experience deteriorates due to irrelevant language output
Solution Approach 1:
Instead of uniformly outputting all pre-set languages throughout the control area, the system applies local quality by detecting language distribution in specific regions and adjusting the robot's output language according to the local user population. Each area receives language output tailored to its specific user demographics, improving relevance and user experience.
Solution Approach 2:
The system changes the parameter of output language selection from a fixed sequence to a dynamically adjusted parameter based on detected language distribution. By analyzing the proportion of different languages used by users in the control area, the system optimizes the output language parameter to match user preferences, maintaining comprehensive language capability while improving user experience.
3Device complexity
If robots use fixed language setting, then device complexity is low, but adaptability to different user groups deteriorates
Solution Approach 1:
The system introduces an intermediary language distribution analysis mechanism between the robot and users. This intermediary detects and analyzes the language usage patterns of users in the control area, then translates this information into appropriate language selection for the robot. This adds adaptability without requiring complex direct interaction between the robot and diverse user groups.
Data Source
AI summary
An artificial intelligence server for setting a language of a robot includes a communication unit and a processor. The communication unit is configured to communicate with the robot. The processor is configured to receive voice data for a control area from the robot, generate first language distribution information using the received voice data, receive event information for the control area, generate second language distribution information using the received event information, determine at least one major output language for the robot based on the generated first language distribution information and the generated second language distribution information, and transmit a control signal for setting the determined major output language to the robot.