AI Message Summarization and Priority Routing for Inbox Overload
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Solution Overview
Problem
Existing electronic messaging systems are time-intensive and resource-consuming due to users reading and interacting with numerous messages to determine relevance, leading to reduced productivity and excessive computing resource usage.
Innovation Solution
An electronic messaging system uses a generative AI model to categorize and prioritize incoming activity items, generating digest, importance, and content summaries, allowing users to navigate through summarized information based on priority, reducing the need for individual message retrieval and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users read and interact with numerous messages individually to determine relevance, then message review completeness is improved, but productivity deteriorates and computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by generating summaries of messages and activity items before users review them. The generative AI model creates digest summaries, importance summaries, and content summaries in advance, allowing users to quickly assess relevance without reading entire messages, thus maintaining review completeness while improving productivity
Solution Approach 2:
The patent introduces an intermediary mechanism - the AI-generated summary - that mediates between the full message content and user review. These summaries act as intermediaries that preserve the essential information needed for relevance determination while reducing the time and cognitive resources required for review
2Measurement precision
If users read and interact with numerous messages individually, then message review completeness is improved, but computing resource consumption increases
Solution Approach 1:
The system extracts the essential information from full messages by generating condensed summaries using AI models. Instead of processing and displaying complete messages, the system takes out only the critical content elements needed for relevance assessment, reducing computing resource consumption while maintaining review effectiveness
Solution Approach 2:
The patent changes the parameter of message representation from full-text format to summarized format with priority ratings. This parameter transformation reduces the computational burden of message processing while preserving the information necessary for complete review, as summaries capture key points and priority indicators
3Quantity of substance
If the system provides all new messages to users without prioritization, then information completeness is improved, but information overload increases and usability deteriorates
Solution Approach 1:
The system segments the information presentation by dividing messages into categorized summaries with different priority levels (digest summaries, importance summaries, content summaries). This segmentation organizes information completeness into manageable segments that users can review systematically, reducing overload while maintaining completeness
Solution Approach 2:
The patent applies local quality by providing different levels of summary detail and priority emphasis for different messages based on their importance. High-priority messages receive more prominent presentation with importance summaries, while lower-priority messages are presented with standard digest summaries, making the interface more usable by adapting information presentation to local needs
4Loss of information
If the system generates comprehensive summaries for all messages, then information quality is improved, but processing time and resource consumption increase
Solution Approach 1:
The system applies partial action by generating different types of summaries selectively rather than comprehensive summaries for all messages. The AI model generates digest summaries for all new activity items, importance summaries for high-priority items, and content summaries as needed, providing information quality where most needed while reducing processing time for lower-priority content
Data Source
AI summary
An electronic message computing system tracks new activity items that reflect activities that have not yet been seen by the user. A. generative artificial intelligence (AI) model generates a digest summary that is provided to the user the next time the user accesses the electronic message system. The digest summary summarizes new activity. The generative AI model also generates importance summaries that summarize the importance of a particular activity to the user, and content summaries that summarize the content of an activity item (such as an electronic mail message). The electronic messaging system also assigns a priority to each new activity item and provides the summaries, along with a priority, to a client computing system. The client computing system conducts a user experience, navigating the user through the new activity items, based upon the priority assigned by the electronic message computing system.