Natural Language Processing in Mobile Telematics via Context Profiles
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Solution Overview
Problem
Conventional telematic systems face challenges in creating a natural language speech interface suitable for mobile environments, where commands and requests from diverse users need to be processed in noisy conditions, requiring robustness to partial failure and context awareness to provide natural and rapid responses.
Innovation Solution
A system that uses probabilistic and fuzzy reasoning to process natural language inputs, incorporating context, prior information, and user profiles, with modules for input parsing, text-to-speech conversion, network interfacing, and multimodal interaction, enabling robust and natural query-response interactions in mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition is used in mobile environments, then user interaction becomes more natural, but recognition accuracy deteriorates due to noisy conditions
Solution Approach 1:
The system segments the speech processing task into multiple stages: acoustic feature extraction, phoneme recognition, word recognition, and natural language parsing. This segmentation allows each stage to be optimized independently, improving overall recognition accuracy in noisy mobile environments while maintaining natural interaction capabilities.
Solution Approach 2:
The patent introduces context models and language models as intermediary layers between speech recognition and natural language processing. These intermediaries help disambiguate recognized speech by considering contextual information, user profiles, and domain knowledge, thereby improving accuracy without requiring perfect speech recognition in noisy conditions.
2Ease of operation
If the system processes complete natural language requests, then user interaction becomes more natural, but processing complexity increases due to incomplete and ambiguous definitions
Solution Approach 1:
The system performs preliminary actions by pre-processing natural language requests through parsing and interpretation stages that identify intent, extract entities, and resolve ambiguities before full processing. User profiles and context information are pre-loaded to facilitate faster processing of subsequent requests, reducing overall system complexity.
Solution Approach 2:
The patent implements partial processing where the system handles the most critical aspects of natural language requests (intent recognition, key entity extraction) with high accuracy while using probabilistic methods for less critical components. This approach achieves sufficient natural language processing capability without the full complexity of complete semantic analysis.
3Measurement precision
If multiple data sources are queried to ensure complete results, then response accuracy improves, but response time increases
Solution Approach 1:
The system performs preliminary actions by pre-fetching and caching data from frequently accessed data sources based on user profiles and historical interaction patterns. This allows the system to quickly retrieve information without querying multiple sources in real-time, maintaining high response accuracy while reducing response time.
Solution Approach 2:
The patent implements partial querying where the system queries only the most relevant data sources based on the specific request and user context, rather than exhaustively querying all available sources. This selective approach maintains sufficient response accuracy while significantly reducing the time required to gather information.
4Measurement precision
If context and user profiles are incorporated into processing, then natural language understanding improves, but processing complexity increases
Solution Approach 1:
The system applies local quality by incorporating context and user profile information selectively based on the specific processing needs of each request. Rather than uniformly processing all requests with full context analysis, the system adjusts the depth of context utilization according to the request type, improving language understanding where needed while reducing unnecessary processing complexity.
Solution Approach 2:
The patent creates a universal context management system that handles multiple functions: storing user profiles, maintaining conversation context, managing domain knowledge, and supporting various natural language processing tasks. This multi-functional approach consolidates complexity into a single framework rather than requiring separate mechanisms for each function.
Data Source
AI summary
A mobile system is provided that includes speech-based and non-speech-based interfaces for telematics applications. The mobile system identifies and uses context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for users that submit requests and/or commands in multiple domains. The invention creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context and presenting the expected results for a particular question or command. The invention may organize domain specific behavior and information into agents, that are distributable or updateable over a wide area network.