User Intent Extraction Using Ontology-Based Reasoning
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Solution Overview
Problem
Existing systems lack the capability 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 define concepts and relationships for user intentions, supported by active reasoning and in-transit data analysis, leveraging a range of sensors and devices to extract strategic and tactical intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an intelligent systems framework with ensemble of ontologies is implemented to determine user intent, then user intent determination accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex intent determination task into multiple ontology modules (user ontology, product ontology, context ontology, etc.), each handling specific aspects of user intent analysis. This modular segmentation allows the system to manage complexity while maintaining high determination accuracy through specialized ontological reasoning in each domain.
Solution Approach 2:
The patent introduces an intermediary reasoning engine that mediates between raw sensor data and final intent determination. This intermediary layer processes data through multiple ontologies sequentially, transforming complex multi-source data into structured intent conclusions, thereby reducing the direct complexity burden on the overall system architecture.
2Measurement precision
If data from multiple capture points and sensors is collected to analyze user intent, then determination accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments data processing by creating dedicated ontology modules for different data sources (user behavior data, product interaction data, environmental sensor data). Each ontology processes specific data types independently before integration, reducing the complexity of handling multi-source data while improving determination accuracy through specialized processing.
Solution Approach 2:
The patent implements dynamic ontology instantiation that adapts the processing pipeline based on the specific capture point and sensor type. The system dynamically selects and configures relevant ontologies based on incoming data characteristics, optimizing processing complexity for each specific data scenario while maintaining comprehensive analysis capability.
3Speed
If real-time data analysis is performed to extract user intent, then responsiveness is improved, but computational energy consumption increases
Solution Approach 1:
The patent performs preliminary ontology compilation and rule preparation during system initialization or idle periods. By pre-processing and structuring ontological knowledge bases in advance, the system reduces real-time computational requirements during actual intent extraction, achieving fast responsive performance while managing energy consumption through shifted computational workload.
Solution Approach 2:
The patent implements periodic ontology updates and incremental learning mechanisms that refresh the knowledge base at scheduled intervals rather than continuously. This periodic approach maintains real-time responsiveness for intent extraction while significantly reducing computational energy consumption by avoiding continuous full-system reprocessing.
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.