Sensor Data Collection Control via Natural Language Discourse Patterns
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Solution Overview
Problem
Current sensor data collection methods often fail to match user needs, leading to under- or over-collection of data, and miss opportunities to collect valuable data that could be monetized, due to a lack of identification of user interests in time to implement appropriate collection plans.
Innovation Solution
A method that analyzes natural language interactions to generate a discourse pattern, determining a sensor activation plan based on this pattern, and adjusts data collection parameters accordingly, ensuring that sensor data is collected only when and where it is needed, using techniques such as topic modeling and statistical correlation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data collection is performed at regular intervals regardless of user needs, then data collection is systematic and automated, but resources are wasted through over-collection of unnecessary data
Solution Approach 1:
The system dynamically adjusts sensor data collection parameters based on real-time discourse pattern detection. Instead of fixed regular intervals, the collection frequency and activation are adapted according to detected user interests and contextual patterns in natural language interactions, optimizing resource usage while maintaining data collection efficiency
Solution Approach 2:
The system changes data collection parameters (such as sampling rate, activation state, and collection duration) based on detected discourse patterns. When user interests are identified through natural language analysis, the system modifies collection parameters to match the specific needs revealed in the discourse, preventing both over-collection and under-collection
2Productivity
If sensor data collection is performed at regular intervals, then automated monitoring is maintained, but opportunities to collect valuable user-specific data are missed
Solution Approach 1:
The system uses feedback from natural language interactions to guide sensor data collection. By analyzing discourse patterns and user expressions of interest, the system receives feedback about what data would be valuable to users, then adjusts collection accordingly, enabling both automated operation and user-specific adaptability
Solution Approach 2:
The system automatically identifies user interests through natural language analysis and self-adjusts data collection parameters without manual configuration. The discourse pattern detection and sensor activation planning occur autonomously, allowing the system to serve specific user needs while maintaining automated operation
3Adaptability or versatility
If natural language analysis is used to determine data collection, then user needs are accurately identified, but system complexity increases
Solution Approach 1:
The system uses a multi-functional natural language processing module that serves multiple purposes: detecting user interests, identifying discourse patterns, and determining relevant data collection parameters. This universal module handles various types of natural language inputs and contexts, reducing the need for separate specialized components and managing system complexity
Data Source
AI summary
A discourse pattern is generated by analyzing a set of natural language interactions. A sensor activation plan corresponding to the discourse pattern is determined, the sensor activation plan comprising a data collection parameter corresponding to a first sensor. Within a first natural language interaction, a first pattern having above a threshold similarity to the discourse pattern is detected. Responsive to the detecting, a configuration of the sensor is adjusted according to the sensor activation plan.