LLM-Driven NPC Dialogue Handling in Mixed Reality
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Solution Overview
Problem
Current virtual reality (VR) and augmented reality (AR) systems face challenges in handling out-of-domain (OOD) requests from non-playable characters (NPCs), leading to poor user immersion due to repetitive and unengaging responses.
Innovation Solution
Implementing AI-driven dialog systems within MR environments, which utilize natural language understanding (NLU) and large language models (LLM) to generate context-aware and engaging OOD responses for NPCs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional dialogue tree systems are used for NPCs in VR/AR environments, then the system structure is simple and easy to implement, but the responses become repetitive and unengaging, reducing user immersion
Solution Approach 1:
The patent introduces an intermediary AI assistant system that mediates between the user and the NPC dialogue tree. This intermediary processes user inputs, determines appropriate dialogue nodes, and generates responses dynamically, thereby increasing response diversity without requiring complete redesign of the underlying dialogue tree structure.
Solution Approach 2:
The system enables self-service through automated AI-driven dialogue management. The AI assistant autonomously handles dialogue routing, node selection, and response generation based on user inputs and dialogue state, eliminating the need for manual configuration of multiple dialogue paths while maintaining high response variety.
2Reliability
If comprehensive dialogue trees are designed to handle out-of-domain requests, then response quality improves, but the time and resources needed for dialogue tree design increase significantly
Solution Approach 1:
The patent employs copying by creating virtual representations of dialogue nodes and transitions that can be dynamically instantiated and modified by the AI system. Instead of designing exhaustive static dialogue trees, the system copies and adapts existing dialogue patterns to handle out-of-domain requests, significantly reducing design time while maintaining reliability.
Solution Approach 2:
The system dynamically changes dialogue parameters such as node selection criteria, transition probabilities, and response generation settings based on the detected dialogue state and user input. This allows the same dialogue tree structure to adapt to various out-of-domain scenarios through parameter adjustment rather than requiring separate dialogue paths for each scenario.
3Ease of operation
If static dialogue responses are used for NPCs, then the implementation is straightforward and resource-efficient, but user immersion deteriorates due to repetitive interactions
Solution Approach 1:
The patent transforms static dialogue responses into dynamic interactions by introducing AI-driven dialogue state tracking and adaptive response generation. The system continuously updates dialogue state based on user inputs and dynamically selects or generates appropriate responses, maintaining ease of implementation through modular architecture while significantly improving interaction engagement.
Data Source
AI summary
In one embodiment, a method includes receiving, by a mixed reality (MR) display device, an audio input from a first user of the MR display device, where the MR display device is associated with an MR environment including several MR objects, processing, using a natural language understanding (NLU) model, the audio input to identify one or more intents and one or more slots associated with the audio input, identifying a first MR object from several MR objects that is in an active listening state, where the first MR object is associated with a first set of intents and a first set of slots, determining that either the first set of intents or the first set of slots do not include the one or more identified intents or the one or more identified slots associated with the audio input, and generating, using a large language model (LLM), an out-of-domain (OOD) response.