Electronic Workspace Change Summaries Using AI for Multi-User Tracking
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Solution Overview
Problem
Users face challenges in tracking changes made to electronic workspaces by multiple collaborators, particularly in determining what changes have been made over time, especially when returning to a document after a period of inactivity.
Innovation Solution
A system utilizing an AI engine or Large Language Model (LLM) to generate a summary of changes in an electronic workspace, incorporating metadata about who made the changes, when they were made, and the nature of the changes, with the ability to filter and batch summaries for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple users collaborate on an electronic workspace simultaneously, then collaboration efficiency is improved, but tracking and understanding changes becomes more difficult
Solution Approach 1:
The patent introduces an AI engine as an intermediary that processes raw change data from multiple users and generates synthesized summary information. This mediator transforms complex multi-user change data into understandable summaries, resolving the contradiction between enabling multiple users to collaborate and maintaining clear change tracking capability.
Solution Approach 2:
The patent replaces manual change tracking mechanisms with an automated AI-based summarization system. Instead of users manually reviewing and understanding changes, the system automatically generates summaries using natural language processing, substituting mechanical human analysis with intelligent automated analysis.
2Loss of information
If users review all changes manually to understand workspace evolution, then complete understanding is achieved, but time consumption increases significantly
Solution Approach 1:
The patent extracts only the essential and relevant change information from the complete set of changes, presenting it in a summarized form. The AI engine identifies and extracts key changes rather than presenting all raw changes, maintaining understanding completeness while reducing time investment required for review.
Solution Approach 2:
The patent applies partial action by providing a summarized subset of changes rather than the complete set. The AI-generated summaries capture the essential information needed for understanding workspace evolution without requiring users to examine every single change, achieving sufficient understanding with less time investment.
3Loss of information
If detailed change information is provided to users, then completeness of information is improved, but ease of understanding deteriorates
Solution Approach 1:
The patent changes the parameter of information presentation from raw, detailed change data to AI-generated natural language summaries. The AI engine transforms the format and structure of change information, converting technical diff data into human-readable summaries that maintain completeness while improving ease of understanding through natural language expression.
Solution Approach 2:
The patent substitutes manual analysis of detailed change information with AI-based summarization. The AI engine automatically processes detailed change data and generates understandable summaries, replacing the need for users to manually interpret complex change information while preserving the completeness of underlying data.
Data Source
AI summary
A data processing system includes: a processor; a network interface; and a memory comprising programming instructions for execution by the processor to: access an electronic workspace; determine a list of changes to the workspace within a period of time; structure a query to a Large Language Model (LLM) or Artificial Intelligence (AI) engine, the query to generate for a user a summary of the changes; obtain a corresponding summary from the LLM or AI engine; and in a user interface, associate a display of the workspace with the corresponding summary to update a user as to a current state of the workspace.