Automated Intervention System for Interpersonal Relationships
Solution Overview
Problem
Current methods for improving interpersonal relationships lack real-time monitoring and personalized feedback, relying heavily on subjective human judgment and are not scalable for large or underprivileged populations, with limited access to effective interventions.
Innovation Solution
A data-driven, automated system integrating expert knowledge and machine learning to monitor and analyze multimodal data from smart devices, providing real-time feedback and personalized interventions to predict and prevent conflicts, enhance positive interactions, and improve relationship dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional therapy interventions are used to improve interpersonal relationships, then therapists can provide personalized guidance, but the interventions cannot provide in-the-moment feedback when problems occur and reach only a fraction of individuals experiencing relationship problems
Solution Approach 1:
The system enables self-service by allowing users to independently monitor their own interactions and receive automated feedback without requiring constant therapist intervention. Users collect data through their smartphone devices and the system automatically processes this data to generate insights and recommendations, making relationship improvement accessible to anyone with a smartphone.
Solution Approach 2:
The patent replaces the mechanical system of human therapist observation and judgment with an automated computational system. Machine learning algorithms process multimodal data (audio, text, metadata) to objectively measure interaction quality, replacing subjective human assessment with scalable automated analysis that can serve unlimited users simultaneously.
2Loss of information
If retrospective self-reports and therapist observations are used to assess relationship functioning, then valuable information about relationship quality is obtained, but the interventions lack real-time monitoring and in-the-moment feedback capability
Solution Approach 1:
The system performs preliminary action by continuously collecting and analyzing interaction data in real-time before problems escalate. The automated system monitors communications as they occur, detects negative patterns early, and provides immediate feedback to prevent conflicts from developing, rather than waiting for retrospective assessment after interactions have concluded.
Solution Approach 2:
The patent implements continuous real-time feedback loops where user interactions are immediately analyzed and insights are returned to users during or right after communications occur. This feedback mechanism provides actionable guidance in-the-moment, allowing users to adjust their behavior during ongoing relationship interactions rather than receiving delayed retrospective feedback.
3Ease of operation
If online interventions with web-based educational materials and questionnaires are used, then accessibility is improved, but the interventions lack detailed real-time monitoring and personalized feedback capability
Solution Approach 1:
The system merges multiple data modalities (audio recordings, text messages, metadata) into a unified analysis framework. By combining these diverse data sources, the system achieves both the accessibility of web-based interventions and the detailed personalization of comprehensive assessment, creating a multi-dimensional view of relationship dynamics that neither modality could provide alone.
Solution Approach 2:
The patent transforms static questionnaire responses into dynamic real-time measurements by continuously analyzing communication parameters such as tone, timing, frequency, and content. This parameter transformation enables the system to provide detailed personalized feedback while maintaining the ease of access characteristic of online interventions.
4Measurement precision
If remote online conference sessions with experts are used, then personalized expert guidance is provided, but the interventions cannot be scaled to large and underprivileged populations due to cost and expert availability
Solution Approach 1:
The system introduces an automated machine learning intermediary that processes interaction data and generates personalized recommendations. This intermediary provides expert-level analysis at scale, replacing the need for expensive human experts to directly evaluate every user interaction, while still delivering high-quality personalized guidance to unlimited users simultaneously.
Solution Approach 2:
The patent creates computational models that copy and replicate expert therapeutic knowledge in the form of machine learning algorithms. These algorithmic copies of expert guidance can be deployed across unlimited devices without additional cost per user, making high-quality personalized intervention accessible to large populations including underprivileged communities.
Data Source
AI summary
A method for promoting interpersonal interactions includes a step of receiving data streams from a plurality of mobile smart devices from a plurality of users, the data streams recording information about users' daily lives. Intervention signals are sent to a user in response to data acquired from two or more individuals and interpreted with respect to user internal states, moods, emotions, predetermined behaviors, and interactions with other users.