Message Augmentation Using Topic-Based Recipient and Attachment Suggestions
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Solution Overview
Problem
Existing messaging systems face inefficiencies in addressing messages to groups, leading to uncertainty about who should respond, and attachments are often unsuitable due to manual selection and device-specific suggestions.
Innovation Solution
A system that analyzes message data to automatically suggest relevant users for @mentions and attachments by pre-indexing user and attachment correlations, using machine learning to enhance accuracy and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual selection of recipients and attachments is used, then user control over message content is maintained, but message efficiency and productivity decrease due to uncertainty about who should respond and time-consuming manual selection
Solution Approach 1:
The system pre-indices user correlations and attachment associations with message topics before actual message composition occurs. This preliminary indexing enables rapid retrieval of suggested recipients and attachments during message writing, eliminating the need for manual selection and significantly improving message efficiency while reducing time loss.
2Reliability
If device-specific suggestions are used for attachments, then suggestions are readily available, but suitability and reliability of attachments decrease due to lack of context awareness
Solution Approach 1:
The system analyzes the actual message content and topic to generate context-aware attachment suggestions, rather than relying on device-specific or generic suggestions. This feedback mechanism ensures that suggested attachments are suitable and relevant to the specific message being composed, significantly improving attachment suitability while the indexing infrastructure manages the system complexity.
3Ease of operation
If generic messages are sent to groups, then ease of operation is improved, but loss of information increases due to uncertainty about who should respond
Solution Approach 1:
The system acts as an intermediary between the user's intent and the message delivery by automatically identifying and suggesting specific recipients based on message topic analysis. This intermediary function maintains ease of operation by allowing users to send to groups while simultaneously preserving information clarity by highlighting the most relevant individuals who should respond, eliminating uncertainty about message intent.
Data Source
AI summary
An augmentation service receives message information about a message being authored by a message sender. The augmentation service calls a topic extraction service to extract a topic from the message information and then accesses inferences, based upon the topics, to identify a suggested augmentation to the message. The suggested augmentation is surfaced for the sender of the message. Similarly, a messaging system can process the message prior to sending it to a recipient and insert suggested augmentations into the message so that the recipient sees a message which has additional content over that which was sent by the sender.