Email Message Prioritization Using Context-Aware LLM Summaries
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Solution Overview
Problem
Existing email applications present message information in a static format, failing to provide dynamic and time-sensitive updates, leading to inefficient user interaction and resource consumption.
Innovation Solution
Implementing an event-based assistant (EBA) that utilizes a Large Language Model (LLM) to analyze email content, prioritize messages, and provide dynamic summaries and actionable insights directly in the message list view, personalized based on user context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If email applications present message information in a static format, then the system structure is simple, but user interaction efficiency deteriorates and users must open multiple emails to access detailed information
Solution Approach 1:
The patent introduces an event-based assistant as an intermediary component that sits between the email system and the user. This assistant uses LLMs to process email content, generate summaries, and provide prioritization recommendations, thereby mediating the interaction and improving efficiency without requiring fundamental changes to the core email system architecture
Solution Approach 2:
The system performs preliminary processing of email content by generating summaries and priority assessments before the user needs to read the full messages. This advance preparation allows users to quickly identify important emails and reduces the time spent reviewing detailed content, directly addressing the productivity improvement goal
2Loss of time
If users manually search through messages to find relevant content, then information completeness is maintained, but time consumption increases and computing resources are wasted
Solution Approach 1:
The event-based assistant provides self-service functionality by automatically analyzing incoming emails, generating summaries, and prioritizing messages without requiring user intervention. The system serves itself by processing content and presenting only the most relevant information, eliminating the need for users to manually search through entire message threads
Solution Approach 2:
The system incorporates feedback mechanisms where users can rate the accuracy of generated summaries and prioritization suggestions. This feedback loop allows the system to continuously improve its performance, becoming more accurate over time and reducing the need for manual verification by users
3Loss of information
If dynamic summaries and prioritization are generated for each message, then information relevance and timeliness improve, but computing and network resource consumption increases
Solution Approach 1:
Instead of processing every detail of every email message, the system applies partial action by generating only the necessary summary information and key prioritization indicators. This selective processing approach maintains information relevance while significantly reducing the computational burden compared to full message analysis for every email
Solution Approach 2:
The system dynamically adjusts processing parameters based on message characteristics and user context. By changing parameters such as summary length, analysis depth, and prioritization granularity according to the specific email content and user profile, the system optimizes the balance between information quality and resource consumption
Data Source
AI summary
A representation of an email application includes a message view comprising a plurality of email messages. In response to receiving an indication that a new email message has been received, a prompt is generated for input to a large language model (LLM). The prompt is usable to cause the LLM to analyze content of the new email message and determine a priority of the new email message. The priority is localized to a context based at least in part by a recipient and sender of the new email message.