Context-Aware Natural Language Disambiguation
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Solution Overview
Problem
Conventional speech recognition technologies accurately identify user voice inputs but struggle to interpret ambiguous commands, leading to unclear actions due to lack of context, especially when processing natural language inputs in dynamic environments.
Innovation Solution
The method involves identifying ambiguous elements in user inputs and leveraging context data from various sources, including the device and external devices, to disambiguate the inputs, forming altered inputs that accurately reflect user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition technology is used to accurately identify user voice inputs, then word recognition accuracy is improved, but the ability to interpret ambiguous commands and understand user intentions deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing context data from multiple sources (device state, application context, user profile, environmental data) before the user input is fully processed. This pre-prepared context information is then quickly retrieved and applied to disambiguate the user's command, resolving the contradiction between accurate word recognition and meaningful interpretation.
Solution Approach 2:
The patent introduces context data as an intermediary element between the user's ambiguous command and the system's interpretation. This intermediary layer of contextual information (from device state, applications, user profile, etc.) mediates the translation of ambiguous speech into precise user intentions, allowing the system to maintain both accurate word recognition and effective command interpretation.
2Adaptability or versatility
If multiple context data sources are accessed and combined to disambiguate user inputs, then understanding of user intentions is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the context data collection process into distinct, independent modules: device state context, application context, user profile context, and environmental context. Each module independently collects and processes its specific type of data, then the results are combined. This segmentation reduces overall system complexity by making each component manageable and independently optimizable while still achieving comprehensive context understanding.
Solution Approach 2:
The patent creates a universal context collection framework that can gather information from multiple diverse sources (device sensors, applications, user profiles, environmental data) through a unified interface. This multi-functional system handles different types of context data using consistent processing methods, reducing complexity compared to having separate specialized systems for each data source.
3Measurement precision
If context data from external devices is integrated, then accuracy of processing natural language inputs is improved, but loss of information and data security risks increase
Solution Approach 1:
The system implements feedback mechanisms where context data from external devices is continuously validated against device state and user profile information. The system monitors the quality and relevance of incoming context data, providing feedback to external sources about data accuracy and requesting corrections when inconsistencies are detected. This feedback loop maintains high processing accuracy while filtering out potentially insecure or inaccurate information.
Data Source
AI summary
An embodiment provides a method, including: receiving, at a device, user input; identifying, using a processor, elements included in the user input; determining, using a processor, that at least one of the identified elements renders the user input ambiguous; identifying, using a processor, a source of context data; accessing, using a processor, context data associated with the user input from the source of context data; disambiguating, using a processor, the user input based on the context data associated with the user input; and forming, using a processor, an altered input based on the disambiguating. Other embodiments are described and claimed.