Response Generation Device Using Contextual History
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Solution Overview
Problem
Conventional interactive systems struggle to generate robust responses to users who are not viewing content, as they rely solely on input information and fail to handle incomplete or unclear user utterances effectively.
Innovation Solution
A response generation device that acquires input information and uses related information to generate responses when the input is incomplete, employing voice recognition, natural language understanding, and interaction history to determine user intentions and provide appropriate outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If response generation relies solely on input information, then the system maintains simplicity in processing, but it cannot generate robust responses to users who are not viewing content or provide incomplete utterances
Solution Approach 1:
The system pre-acquires and stores related information (content information, user profile information, interaction history) before it is needed for response generation. This allows the system to quickly retrieve and utilize this pre-prepared information when generating responses to incomplete or context-dependent user inputs, improving response robustness without adding significant processing complexity during interaction.
Solution Approach 2:
The system introduces related information (content information, user profile information, interaction history) as an intermediary element that bridges the gap between incomplete input information and appropriate response generation. This intermediary information provides the necessary context and background to generate robust responses even when user input is incomplete or the user is not actively viewing content.
2Adaptability or versatility
If the system uses only input information for response generation, then processing is straightforward, but it fails to handle cases where user intent cannot be determined from input alone
Solution Approach 1:
The system employs multiple types of related information (content information, user profile information, interaction history) that serve multiple functions: providing context for incomplete utterances, personalizing responses based on user preferences, and maintaining conversation continuity. This multi-functional information framework enables the system to handle diverse interaction scenarios without requiring separate processing mechanisms for each case.
Solution Approach 2:
The system pre-acquires and stores related information (content information, user profile information, interaction history) before it is needed for response generation. This allows the system to quickly retrieve and utilize this pre-prepared information when generating responses to incomplete or context-dependent user inputs, improving response robustness without adding significant processing complexity during interaction.
3Adaptability or versatility
If the system generates responses based on content information only, then response timing can be determined accurately, but it cannot implement robust interactive processing for various user utterances outside content viewing
Solution Approach 1:
The system introduces related information (content information, user profile information, interaction history) as an intermediary element that bridges the gap between incomplete input information and appropriate response generation. This intermediary information provides the necessary context and background to generate robust responses even when user input is incomplete or the user is not actively viewing content.
Solution Approach 2:
The system transitions from a single-dimension approach (relying only on current input information) to a multi-dimensional approach by incorporating related information from multiple dimensions: content information (what the user might be referring to), user profile information (user preferences and characteristics), and interaction history (previous context). This dimensional expansion enables robust interactive processing across diverse scenarios without losing contextual information.
Data Source
AI summary
A response generation device according to the present disclosure includes: an acquisition unit that acquires input information serving as a trigger for generating a response to a user; and a response generation unit that generates the response to the user by using related information related to the input information in a case where it is determined that the response to the user is not able to be generated on the basis of only the input information. For example, the acquisition unit acquires voice information uttered by the user as the input information.


