Dynamic Message Content Adaptation Based on User Emotion
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Solution Overview
Problem
Existing electronic messaging systems fail to adapt message content dynamically based on the perceived emotional state of users, particularly in response to geo-location-specific events or weather conditions, leading to irrelevant or insensitive messaging.
Innovation Solution
A system that determines a user's geo-location and analyzes news feeds to identify major events or weather conditions, then forms and delivers messages tailored to the perceived emotional state, using services for IP address queries, profile data, and third-party databases to assess emotional responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If message content is standardized and sent to all users, then system complexity is reduced and message delivery is simplified, but message relevance and user engagement deteriorate
Solution Approach 1:
The system dynamically adjusts message content based on real-time detection of user emotional states and contextual factors (geo-location, weather, news events). Instead of using static standardized messages, the system continuously adapts message parameters such as tone, content selection, and timing to match the user's current emotional state and situation, thereby improving relevance without requiring complete system redesign.
Solution Approach 2:
The system changes message parameters (content selection, tone, timing) based on detected emotional states and contextual parameters. By mapping emotional states to appropriate message templates and adjusting message characteristics according to detected conditions like weather or news events, the system achieves personalized relevance while maintaining a structured approach to message generation.
2Adaptability or versatility
If message content is personalized based on user emotions and context, then message relevance and user engagement are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the message generation process into distinct modules: emotional state detection, contextual analysis (geo-location, weather, news), message template selection, and content generation. By dividing the complex task into separate functional segments that can be processed independently and combined, the system manages processing complexity while achieving high message adaptability.
Solution Approach 2:
The system introduces intermediary services and components that specialize in specific tasks: emotional state detection services, geo-location services, weather services, and news aggregation services. These intermediaries handle complex data collection and analysis separately, allowing the main message generation system to focus on content creation while reducing overall processing complexity through service specialization.
3Adaptability or versatility
If the system continuously monitors user emotions and context, then message relevance is improved, but energy consumption and operational overhead increase
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic checks at strategic intervals or triggered by specific events (weather changes, news events, time-based schedules). Emotional state detection and contextual updates occur periodically rather than continuously, reducing energy consumption while maintaining sufficient real-time adaptation for effective message personalization.
Solution Approach 2:
The system leverages freely available public data sources for geo-location, weather, and news information without requiring active user participation or additional sensor data collection. By using self-service approaches with publicly available APIs and data feeds, the system reduces operational overhead and energy consumption while still achieving comprehensive contextual awareness for message adaptation.
Data Source
AI summary
One embodiment of the present invention provides a system for dynamically forming the content of a message to a user based on a perceived emotion state of the user. During operation, the system determines a geo-location of a user. Next, the system analyzes a news feed associated with the geo-location of the user to determine a perceived emotion state of the user. The system then forms a content for a message to the user based on the perceived emotional state of the user. Finally, the system delivers the message.