Learning Device for Unrecognizable User Target Recognition
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Solution Overview
Problem
Conventional techniques struggle with accurately specifying users over networks when using unpronounceable or unreadable identification information, leading to increased effort and difficulty in user interaction.
Innovation Solution
A learning device that acquires user commands, learns recognition information based on system operations and history, automatically setting appropriate calls or targets without manual effort, using voice recognition and association with user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional voice recognition techniques are used to determine user targets, then conversation can proceed smoothly when users have pronounceable nicknames, but users cannot specify other users when identification information contains unpronounceable or unreadable characters
Solution Approach 1:
The patent introduces a learning device as an intermediary between the user's voice input and the information processing system. This mediator captures voice commands, learns the association between unpronounceable identification information and user intents through multiple interactions, and translates them into system-understandable commands, thereby enabling reliable user specification regardless of identification format
Solution Approach 2:
The learning device performs self-learning by automatically analyzing use history and operation patterns without requiring manual programming. It autonomously builds the mapping between diverse identification information formats and corresponding users, allowing the system to adapt to new users and identification styles independently
2Reliability
If users manually search for desired partners from friend lists when voice specification fails, then user specification can be achieved, but time and effort are significantly increased
Solution Approach 1:
The learning device performs preliminary learning during normal system usage by continuously analyzing voice commands and their corresponding outcomes. This preliminary action builds a knowledge base that enables rapid user specification in future interactions, eliminating the need for time-consuming manual searches
Solution Approach 2:
The system implements feedback mechanisms where the learning device monitors whether voice-specified users are correctly identified and adjusts its learning model accordingly. This feedback loop continuously improves the accuracy and speed of user specification, reducing time loss over successive interactions
3Device complexity
If the system requires pronounceable nicknames for voice recognition, then voice command processing is simple, but user flexibility in choosing identification information is restricted
Solution Approach 1:
The learning device dynamically changes the parameters of voice recognition by learning optimal mapping strategies for different types of identification information. Instead of requiring fixed pronounceable formats, the system adapts its recognition parameters based on the specific identification style of each user, maintaining processing simplicity while enabling diverse identification choices
Data Source
AI summary
A learning device (100) according to an aspect of the present disclosure includes: an acquisition unit (131) that acquires a content of a command input by a user to a predetermined information processing system; and a learning unit (132) that in a case where it is determined that the command includes an unrecognizable target, learns recognition information for recognizing the target on the basis of operation of the information processing system by the user or a use history of the information processing system.