Context-Aware Dialogue Control for Streaming AI Characters
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Solution Overview
Problem
Current systems for generating dialogue in applications struggle to provide relevant and human-like responses to user queries, especially when the query requires knowledge of the current context associated with the application, such as previous tasks or user interactions.
Innovation Solution
Systems and methods that utilize various sources of contextual information, including vector databases and embeddings, to generate responses by processing textual inputs and additional contextual information, thereby enhancing relevance and accuracy of responses and making characters seem more human-like.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current systems use sets of pre-defined responses for different queries, then the system can provide basic dialogue functionality, but the responses become less relevant when the query requires knowledge of current context such as previous tasks or user interactions
Solution Approach 1:
The system performs preliminary actions by storing contextual information (previous tasks, user interactions, application state) in databases before the dialogue occurs. This pre-prepared contextual data is then retrieved and used to generate relevant responses, allowing the system to adapt to current context without requiring real-time data processing during dialogue.
Solution Approach 2:
The patent introduces an intermediary mechanism using language models and vector databases as mediators between the user query and the response generation. The language model processes both the query and retrieved contextual information to generate appropriate responses, while vector databases serve as intermediaries for storing and retrieving relevant contextual data, enabling context-aware dialogue.
2Ease of operation
If current systems merely select responses from pre-defined sets, then the system structure remains simple, but the characters appear less human-like and interactive to users
Solution Approach 1:
The system merges multiple components including language models, vector databases, contextual information storage, and response generation mechanisms into a unified dialogue system. This integration allows the system to naturally combine pre-defined response templates with dynamically retrieved contextual information, creating human-like conversations while managing system complexity through modular architecture.
Data Source
AI summary
In various examples, controlling dialogue using contextual information for conversational artificial intelligence (AI) systems and applications is described herein. Systems and methods are disclosed that use various sources of contextual information, along with textual inputs (e.g., queries), to generate textual outputs (e.g., responses) associated with a dialogue between a user (e.g., a user's character) and another character (e.g., a non-playable character) of an application. For instance, the contextual information may be stored in one or more databases, such as one or more vector databases, and/or in a specific form, such as embeddings that represent the contextual information. One or more language models may then process a textual input and/or at least a portion of the stored contextual information in order to generate a textual output. This textual output may then be used to generate speech that is output by the other character.