Multimodal Assistant Context Extraction for Ambiguous User Requests
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Solution Overview
Problem
Intelligent automated assistants struggle to efficiently resolve ambiguities in user requests due to insufficient utilization of context data, leading to inefficient device operation and increased power consumption.
Innovation Solution
The method involves determining ambiguous portions of user requests, extracting metadata from context data, and generating responses based on this metadata to enhance the assistant's understanding and reduce power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the digital assistant processes all context data to resolve ambiguities, then the accuracy of understanding user requests is improved, but the power consumption and processing time increase
Solution Approach 1:
The patent extracts only the relevant metadata from context data that is necessary to resolve ambiguities in user requests. Instead of processing all context data, the system identifies and extracts specific metadata elements (such as application metadata, device state metadata, or screen content metadata) that are directly relevant to disambiguation, thereby reducing power consumption while maintaining accuracy.
Solution Approach 2:
The system performs partial processing of context data by selectively analyzing only the portions of context data that contain metadata relevant to the ambiguous user request. This partial action approach avoids the excessive processing of all context data, reducing computational load and power consumption while still achieving sufficient disambiguation accuracy.
2Reliability
If the digital assistant retrieves and processes comprehensive context data, then the reliability of response is improved, but the device operation efficiency decreases
Solution Approach 1:
The patent extracts only the essential metadata from context data that is necessary to reliably resolve ambiguities. By extracting specific metadata elements rather than processing comprehensive context data, the system maintains response reliability while improving device operation efficiency through reduced processing time.
Solution Approach 2:
The system performs preliminary extraction of metadata from context data before using it to resolve ambiguities. This preliminary action prepares the data in advance in a condensed format, making the subsequent disambiguation process more efficient while maintaining reliability through the use of pre-processed, relevant information.
3Use of energy by moving object
If the digital assistant uses selective context data extraction, then the power consumption is reduced, but the complexity of the processing system increases
Solution Approach 1:
The patent introduces metadata as an intermediary layer between the raw context data and the disambiguation process. This metadata intermediary captures essential information from context data in a structured, simplified format, reducing the complexity of processing while enabling selective extraction that lowers power consumption.
Solution Approach 2:
The system changes the parameter representation of context data by extracting metadata with specific characteristics (such as application identifiers, device state parameters, or screen content features). This parameter transformation converts complex context data into a more manageable format that reduces processing complexity while enabling energy-efficient selective processing.
Data Source
AI summary
Systems and processes for operating an intelligent automated assistant are provided. An example process includes receiving an utterance including a user request and determining whether at least a portion of the user request is ambiguous. If at least the portion of the user request is ambiguous then a set of context data based on the ambiguous portion of the user request is determined, metadata is extracted from the context data and a response to the user request is determined based on the extracted metadata.