Scene-Aware Conversational AI for Multi-Modal Context Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Dialogue systems in vehicles face difficulties in determining context from spoken language, leading to cumbersome interactions where users need to provide multiple utterances to obtain requested information, especially when the context is not explicitly mentioned.
Innovation Solution
The use of scene-aware context systems that incorporate audio data and sensor data, such as gaze and gesture recognition, to identify points of interest and provide additional context to language models, enabling them to generate accurate outputs without requiring explicit context from the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the dialogue system relies solely on spoken language to determine context, then the system structure remains simple, but the user interaction becomes cumbersome requiring multiple utterances
Solution Approach 1:
The patent combines multiple sensing modalities (audio, visual, inertial, barometric) into a unified context determination system. The sensor fusion module integrates data from various sensors including microphones, cameras, gyroscopes, and barometers to comprehensively identify user intent and context, resolving the contradiction by merging simple individual components into a coordinated complex system that enhances ease of operation.
Solution Approach 2:
The system performs preliminary context analysis by continuously monitoring sensor data before the user completes their utterance. The processor analyzes sensor patterns, device orientation, and environmental context in advance to predict user intent, allowing the system to prepare responses proactively rather than requiring complete explicit user input, thus improving interaction efficiency.
2Measurement precision
If the system requests additional explicit context from the user, then the language model can process accurate information, but the interaction time increases
Solution Approach 1:
The system implements feedback loops where sensor data continuously informs and refines context determination. The processor monitors sensor patterns, compares them against known contexts, and adjusts its interpretation of user intent in real-time. This feedback mechanism allows the system to maintain high context accuracy by validating assumptions against multiple data sources while reducing interaction time through iterative refinement rather than sequential clarification questions.
Solution Approach 2:
The system performs preliminary context analysis by continuously monitoring sensor data before the user completes their utterance. The processor analyzes sensor patterns, device orientation, and environmental context in advance to predict user intent, allowing the system to prepare responses proactively rather than requiring complete explicit user input, thus improving interaction efficiency.
3Measurement precision
If the system uses multi-modal sensor data to determine context, then context identification accuracy improves, but the processing complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct functional modules: audio processing module, visual processing module, inertial processing module, barometric processing module, and sensor fusion module. Each module handles specific sensor data types independently, then the fusion module integrates results. This segmentation reduces processing complexity by dividing the overall task into manageable, specialized components while maintaining high context identification accuracy through comprehensive multi-modal analysis.
Data Source
AI summary
In various examples, techniques for using scene-aware context for dialogue systems and applications are described herein. For instance, systems and methods are disclosed that process audio data representing speech in order to determine an intent associated with the speech. Systems and methods are also disclosed that process sensor data representing at least a user in order to determine a point of interest associated with the user. In some examples, the point of interest may include a landmark, a person, and/or any other object within an environment. The systems and methods may then generate a context associated with the point of interest. Additionally, the systems and methods may process the intent and the context using one or more language models. Based on the processing, the language model(s) may output data associated with the speech.