Cognitive Evaluation from Social Interaction Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cognitive evaluations face challenges in measuring social interactions effectively, making it difficult to identify cognitive issues in individuals.
Innovation Solution
A computer-implemented method and system that builds a patient model using social network data to analyze changes in social interactions, generating a patient metric score to detect cognitive impairments by employing machine learning and AI techniques with IoT devices and sensors for monitoring and data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If social interactions are measured using traditional methods, then the measurement process is simple, but the measurement precision is insufficient to detect cognitive issues
Solution Approach 1:
The patent segments social interaction measurement into multiple dimensions including frequency of interactions, duration of interactions, types of interactions, and emotional sentiment. This segmentation allows for more precise measurement of cognitive changes by analyzing specific aspects of social behavior rather than using a single crude metric.
Solution Approach 2:
The patent introduces intermediary technologies such as sensors, cameras, microphones, and machine learning algorithms that mediate between the social interactions and the measurement process. These intermediaries enable precise detection of subtle changes in social behavior that would be imperceptible through traditional observation methods.
2Reliability
If traditional cognitive evaluation methods are used, then the ease of operation is high, but the reliability of detection is low
Solution Approach 1:
The system enables self-service monitoring where individuals passively generate data through their natural social interactions without requiring active participation in evaluation tasks. Sensors and devices automatically collect data on communication patterns, social media activity, and interpersonal interactions, eliminating the need for structured testing while improving reliability through continuous real-world observation.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors social interaction data, compares it against baseline patterns, and provides alerts when significant deviations occur. This automated feedback loop improves reliability by maintaining continuous surveillance without requiring manual intervention, while the system learns and adapts to individual patterns over time.
3Difficulty of detecting and measuring
If continuous monitoring of social interactions is implemented, then the detection capability is improved, but the loss of information due to data volume becomes significant
Solution Approach 1:
The patent extracts only the most relevant features from the vast amount of social interaction data, such as frequency of contacts, duration of interactions, diversity of social network, and sentiment analysis. By extracting these key indicators rather than processing all raw data, the system maintains high detection capability while avoiding information overload and privacy concerns.
Solution Approach 2:
The system transforms raw social interaction data into meaningful parameters and metrics that capture cognitive status. By changing the parameters from raw data points to aggregated meaningful metrics (such as social engagement score, interaction diversity index), the system reduces data volume while preserving essential information for detection.
Data Source
AI summary
A computer-implemented method, system, and computer program product are provided for determining cognitive issues. The method includes building, by a processor device with social network data, a patient model for social interactions between a patient and other people. The method also includes computing, by the processor device, changes between the patient model and new social network data. The method additionally includes evaluating, by the processor device, the changes between the patient model and new social network data to generate evaluated changes. The method further includes determining, by the processor device, a patient metric score responsive the evaluated changes. The method also includes controlling an operation of a processor-controlled device responsive to the patient metric score.