Personalized Message Generation Using Entity Relationship Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing messaging systems fail to effectively indicate the relevance of content to recipients, leading to ignored or skipped messages due to lack of contextual information.
Innovation Solution
A system that analyzes content items and accompanying metadata to identify entities, determines relationship information among these entities, the sender, and the recipient, and modifies messages or content based on this information to enhance relevance and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a general caption and hyperlink are used to share content, then the message structure remains simple, but the recipient cannot understand the relevance or context of the content
Solution Approach 1:
The system performs preliminary analysis of the content item to identify entities and determine relationships among entities, the sender, and recipient before generating the message. This advance preparation enables the system to include relevant contextual information in the generated message without requiring complex manual structuring by the user.
Solution Approach 2:
The system automatically generates personalized messages by itself, analyzing content items and determining appropriate contextual information to include. This self-service capability eliminates the need for manual message composition while maintaining simplicity for the user.
2Productivity
If personalized content with relationship information is generated, then recipient engagement increases, but the processing complexity and time required increase
Solution Approach 1:
The system segments the message generation process into distinct functional modules: content analysis module to identify entities, relationship determination module to analyze connections among entities, and message generation module to compose the final message. This segmentation allows each module to specialize in a specific task, improving overall processing efficiency despite the increased complexity of generating personalized content.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically analyzes content items and determines relationship information. This intermediary layer acts as a bridge between the raw content and the final personalized message, handling the complex processing tasks automatically without requiring direct user intervention in each step.
3Reliability
If relationship information is analyzed and stored for future reference, then future message personalization improves, but data storage requirements and processing overhead increase
Solution Approach 1:
The system extracts only the essential relationship information from content analysis and stores selectively in the database. Rather than storing all possible data about content items, the system identifies and stores only the relationship information that is relevant for future message personalization, reducing unnecessary data storage while maintaining personalization accuracy.
Data Source
AI summary
The system receives a message having a sender and a recipient. The system identifies entities associated with the message and associated with any content that is associated with the message. The system determines whether to, and how to, modify the message based on relationship information among the sender, the recipient, and any entities identified in the message or components of the message. A relationship between a sender and recipient may be determined using, for example, a database of relationship information. The system modifies, for example, text, images, or video of the message to generate the personalized message. The personalized message include the original message along with context information to help indicate the relevance of the message to the recipient. The context information can include text, images, video, or other information. To illustrate, the context information can include keywords or identifiers that indicate entities associated with the message.