Inbox Message Triage Using Eligibility-Based TLDR Filtering
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Solution Overview
Problem
Existing electronic messaging systems fail to efficiently distill essential information from lengthy or complex messages, leading to inefficiencies in user comprehension and responsiveness.
Innovation Solution
A decision intelligence-based framework that utilizes AI and ML to analyze messages for eligibility in TLDR processing, summarizing key information and providing actionable modifications before delivery, using AI/ML models to classify and triage messages based on content, metadata, and generate concise summaries or quick actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If TLDR processing is applied to all messages, then user comprehension efficiency is improved, but system processing complexity increases
Solution Approach 1:
The system segments messages into different processing categories based on eligibility criteria. Only messages meeting specific criteria (length thresholds, content type, sender-recipient relationships) undergo TLDR processing, while others are handled through standard pathways. This segmentation prevents unnecessary processing of ineligible messages, maintaining system efficiency while improving user comprehension for relevant messages.
Solution Approach 2:
The system dynamically adjusts processing parameters based on message characteristics. Eligibility determination involves evaluating multiple parameters (message length, content type, participant relationships, timing) and applying different processing intensities accordingly. This parameter-based approach optimizes the balance between processing thoroughness and system complexity.
2Measurement precision
If AI/ML analysis is performed on every message, then summarization accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary eligibility screening using lighter-weight criteria (message length, content type, participant relationships) before committing to full AI/ML summarization. This preliminary action filters out messages that don't meet basic thresholds, ensuring that computationally intensive summarization is only applied to messages where it will provide the most value, thereby reducing overall processing time while maintaining accuracy for eligible messages.
3Measurement precision
If comprehensive message analysis is conducted, then filtering precision is improved, but computational resources consumed increase
Solution Approach 1:
The system applies different levels of analysis quality to different messages based on their eligibility characteristics. Eligible messages receive comprehensive AI/ML analysis with high filtering precision, while ineligible messages receive minimal or no processing. This local quality approach ensures computational resources are concentrated on messages where precise filtering will yield the greatest benefit, rather than uniformly applying high-resource processing to all messages.
Data Source
AI summary
Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for determining and implementing the mechanisms for processing messages pursuant to their eligibility for TLDR processing at the server and/or upon delivery to a recipient inbox. The disclosed framework provides novel mechanisms for triaging messaging, in that mechanism for how they are intra-, post- and/or in real-time processed by the server and/or recipient devices pursuant to providing actionable operations on such messages to ease their consumption by the recipient. The framework operates to determine which messages are eligible candidates for specific forms of triaging, then queues such messages for such types of triaging prior to and/or upon their delivery. Such intra-and/or post-delivery filtering and message management can effectuate an improved messaging experience as it relates to how messages are handled by inboxes and/or displayed for interaction by recipient users.