User Intent Extraction Framework for Multi-Source Sensor Reasoning
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Solution Overview
Problem
Existing systems lack the ability to effectively analyze user-generated content from various capture points to determine user intent, which is crucial for optimizing products and services, preventing crime, and enhancing lifestyle optimization.
Innovation Solution
An intelligent systems framework utilizing an ensemble of ontologies to analyze data from multiple sources, including physical and physiological sensors and communicatively connected devices, to extract strategic and tactical user intents through active reasoning and in-transit data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple capture points and sensors is collected to improve user intent determination accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the complex analysis task into multiple processing stages: data collection from capture points, feature extraction, intent classification, and action generation. Each stage handles specific aspects of the data, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw sensor data and capture point information into structured features before intent determination. This intermediary layer simplifies the relationship between diverse input data and the intent classification system.
2Speed
If real-time data analysis is performed to improve responsiveness, then speed improves, but use of energy increases
Solution Approach 1:
The system performs partial analysis by focusing computational resources on extracting only the most relevant features for intent determination rather than processing all available data in full detail. This selective processing reduces energy consumption while maintaining real-time responsiveness.
Solution Approach 2:
The system performs preliminary feature extraction and data filtering before main intent analysis, preparing data in advance to reduce the computational burden during real-time decision-making, thereby lowering peak energy consumption.
3Measurement precision
If comprehensive features are extracted to improve intent extraction accuracy, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system extracts only the most discriminative and relevant features from the available data, removing unnecessary information that would consume processing time. This selective extraction maintains intent determination accuracy while reducing processing time.
Solution Approach 2:
The system applies different processing depths to different data sources based on their relevance and reliability. High-priority data sources undergo more thorough analysis, while lower-priority sources receive lighter processing, optimizing the balance between accuracy and time consumption.
Data Source
AI summary
Embodiments of the present systems and methods may provide an intelligent systems framework for analysis of user-generated content from various capture points to determine user intent. For example, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise receiving, at the computer system, data relating to a plurality of aspects of at least one person, including data from at least one of physical or physiological sensors and communicatively connected devices, extracting, at the computer system, from the received data, features relevant to events relating to at least one person, extracting, at the computer system, at least one intent of at least one event relating to at least one person, and performing, at the computer system, an action based on the extracted at least one intent.