Ambient Sensor Tokenization for Privacy-Aware Query Context
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Solution Overview
Problem
Existing machine-learning systems struggle to accurately determine user intent from queries lacking contextual information, particularly when using ambient sensors that raise privacy concerns and lack spatial information.
Innovation Solution
A machine-learning system that processes ambient sensor data, such as from inertial measurement units and photoplethysmogram sensors, to generate tokenized data for a sequence processing model, providing contextual information to enhance query understanding and privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ambient sensor data is used to provide contextual information, then query understanding is improved, but privacy concerns increase
Solution Approach 1:
The patent extracts only the necessary contextual features from ambient sensor data (such as presence detection, activity state, environmental conditions) while excluding personally identifiable information. This extraction approach provides the LLM with contextual information needed for query understanding without compromising user privacy.
Solution Approach 2:
The system introduces an intermediary processing layer between the ambient sensors and the LLM that transforms raw sensor data into anonymized contextual representations. This intermediary layer filters out sensitive information while preserving the contextual meaning, thus mediating between information needs and privacy protection.
2Measurement precision
If ambient sensor data is tokenized and processed by LLM, then query fulfillment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the processing pipeline into distinct stages: sensor data acquisition, feature extraction, tokenization, and LLM processing. By dividing the complex task into manageable segments, the system can optimize each stage independently and reduce overall computational complexity while maintaining accuracy.
Solution Approach 2:
The system applies partial action by selectively tokenizing and processing only the relevant portions of sensor data that contribute to query understanding. Not all sensor data is processed at full detail - only the portions necessary for contextual understanding are transformed into tokens for LLM processing.
Data Source
AI summary
Aspects of the disclosed technology include machine-learning systems and methods for processing queries using contextual information that is derived from ambient sensors and/or auxiliary sensors. A machine-learning system is configured to tokenize ambient sensor data and/or auxiliary sensor data into representations for processing by a sequence processing model. The sensor data can be processed by the sequence processing model to provide contextual information that can aid in fulfilling the intent of a user query. The contextual information can assist the sequence processing model with reasoning and processing of the user query. Ambient sensor data contains little, if any, personally identifiable information, making it suitable for providing contextual information while maintaining user privacy. As such, embodiments of the present disclosure provide the ability for machine-learning systems to process sensor data with text-based user queries in order to provide contextualized query results.