Virtual Object Valence Profiling for Contextual Health Assessment
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Solution Overview
Problem
Existing healthcare technologies struggle to accurately assess the health state of individuals due to limited contextual resolution, failing to consider the nature of digital activities and objects involved, which is crucial for understanding health implications.
Innovation Solution
Health management platforms monitor digital activities and their associated objects to generate a personalized valence index, estimating health states through contextual data analysis without invasive user input, using classification algorithms to identify deviations from routine patterns and determine the valence of these activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional health assessment methods are used, then the assessment process is simple, but the measurement precision and contextual resolution are insufficient
Solution Approach 1:
The patent introduces a health management platform as an intermediary system that collects, processes, and analyzes contextual data from multiple sources (digital activities, social media, mobile devices) to assess health states. This platform mediates between raw data and health assessment outcomes, enabling precise measurement without requiring direct complex intervention in patient care workflows.
Solution Approach 2:
The system segments health assessment into multiple dimensions by analyzing different types of digital activities (social media usage, messaging, browsing patterns) and contextual data from various sources. Each data source contributes specific aspects of health assessment, allowing comprehensive evaluation through divided analysis components.
2Loss of information
If comprehensive contextual data collection is implemented, then the information completeness improves, but the loss of time and data processing complexity increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing contextual data from digital activities and mobile devices before health assessment is needed. Data is aggregated, cleaned, and organized in advance, so when assessment is required, the processing time is minimized as the foundation work is already completed.
Solution Approach 2:
The health management platform maintains continuous data collection and processing operations, constantly monitoring digital activities and contextual information. This continuous operation ensures information completeness is maintained over time while establishing efficient processing routines that reduce per-assessment time costs.
3Measurement precision
If invasive user input methods are used, then the data accuracy improves, but the ease of operation and user compliance decrease
Solution Approach 1:
The system implements self-service by automatically collecting contextual data from users' digital activities, mobile devices, and social media interactions without requiring active user participation. The health assessment process serves itself by utilizing naturally occurring digital footprints, eliminating the need for users to complete surveys or provide manual input while maintaining high data accuracy.
Solution Approach 2:
The health management platform acts as an intermediary that passively observes and collects data from users' existing digital behaviors. Instead of directly requesting information from users, the system mediates by gathering contextual evidence from third-party sources and digital activity logs, providing accurate assessment without burdening users.
Data Source
AI summary
Introduced here are health management platforms able to infer the health state of a subject based on behavioral changes reflected in the digital activities performed by a subject. A health management platform can initially acquire contextual data pertaining to digital activities performed by the subject. The health management platform can identify the target(s) of the digital activities by parsing the contextual data, and then compile the digital activities and corresponding activities into a personalized valence index. Thereafter, the health management platform can compare digital activities performed by the subject to the entries included in the personalized valence index. If a matching entry is not discovered, the health management platform can identify an instance of behavior change. These instances of behavior change may be indicative of changes in the subject's health state.