Document User Activity Classification System
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Solution Overview
Problem
Current content creation applications lack mechanisms to accurately track and differentiate between users' relationships with a document, leading to unreliable and noisy edit signals that do not provide sufficient information about users' contributions, resulting in processor load and network bandwidth issues.
Innovation Solution
A data processing system that identifies user activity categories by analyzing user interactions with a document within a specific time period, using machine learning models to determine roles such as creators, authors, moderators, and readers, and transmits this information for storage with the document, reducing noise and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If continuous transmission of edit signals is implemented, then information completeness is improved, but processor load and network bandwidth usage increase
Solution Approach 1:
The system transmits user activity category signals at specific intervals rather than continuously, reducing processor load and network bandwidth usage while still providing timely updates about document history and user contributions
Solution Approach 2:
The system extracts only the essential user activity category information (creator, author, moderator, reader) from the full edit signal data, transmitting only this categorized information rather than complete raw data, thereby reducing transmission overhead while maintaining information utility
2Measurement precision
If detailed edit signals are stored, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediaries that automatically analyze user interactions and assign activity categories, replacing complex manual analysis processes and simplifying the overall system architecture while maintaining high measurement precision
Solution Approach 2:
The system transforms raw edit signal data into standardized user activity category parameters (creator, author, moderator, reader), changing the data representation from detailed but complex raw signals to simplified categorical parameters that are easier to store and process
Data Source
AI summary
A method and system for receiving data relating to one or more activities performed by a user on a document within a specific time period, the one or more activities being performed by using an application, analyzing the data to identify a category of user activity based at least on the type of activity performed on the document, and transmitting a signal to a device for storage in association with the document, the signal including the identified category.