Electronic Inbox Digest Using LLM Summaries for Non-Uniform Emails
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Solution Overview
Problem
Conventional inbox messaging systems lack functionality that enables users to quickly and efficiently understand and act on received messages due to non-uniformity in email composition and formatting.
Innovation Solution
Implementing a computerized email digest system that utilizes large language models (LLMs) to analyze and summarize incoming messages, providing interactive summaries based on user preferences and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional inbox messaging systems are used to display emails, then users can access their email content, but users cannot quickly and efficiently understand and act on received messages due to non-uniformity in composition and formatting
Solution Approach 1:
The system segments email content into structured components (subject, body, attachments, metadata) and processes each segment separately through the LLM. This segmentation transforms the non-uniform email format into organized, analyzable units that can be efficiently summarized and presented to users, directly resolving the contradiction between maintaining email content integrity and improving user comprehension efficiency
Solution Approach 2:
The LLM acts as an intermediary between the raw email content and the user. It receives non-uniform email inputs, processes them through computational analysis, and outputs standardized summaries and actionable insights. This intermediary layer abstracts away the formatting complexities from the user, enabling quick understanding without losing essential information
2Productivity
If LLM computational analysis is applied to email content, then user engagement and comprehension are enhanced, but system complexity and computational resources increase
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant portions of email content using the LLM, rather than processing every single email in full detail. The LLM generates summaries at varying levels of depth based on email importance and user preferences, reducing computational overhead while maintaining high productivity for critical messages
Solution Approach 2:
The system implements self-service through automated LLM-based summarization and insight generation that operates without manual user intervention. Once configured, the system autonomously processes incoming emails, generates digests, and presents actionable information, reducing the need for complex manual processing infrastructure while maintaining high comprehension speed
Data Source
AI summary
Disclosed embodiments are directed toward a computer-implemented system and method for providing an email digest in association with an interface display of an electronic inbox. The disclosed digest includes non-native inbox functionality related to a summary data structure that includes an interactive portion, whereby upon generation of electronic prompts via a large language model (LLM), the digest can leverage functionality of the LLM to determine how electronic inbox content can be rendered for display.