Task Entry N-gram Association via Message Trail Analysis
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Solution Overview
Problem
Existing technologies lack an efficient method to associate user interests with task entries based on message trails, leading to incomplete or outdated task information.
Innovation Solution
The method involves identifying n-grams from message trails and determining similarity scores to associate them with task entries, allowing for dynamic updating and suggestion of task information fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual task creation and updating is used, then task information accuracy depends on user input effort, but task information becomes outdated and incomplete
Solution Approach 1:
The system automatically extracts n-grams from message trails and updates task entries without requiring manual user intervention. The task management system serves itself by autonomously identifying relevant terms from communications and updating corresponding task information fields, eliminating the need for users to manually refresh or complete task details.
Solution Approach 2:
The system establishes a feedback loop where message trails continuously feed information back to task entries. By monitoring new messages and extracting relevant n-grams, the system automatically detects changes and updates task information, creating a self-correcting mechanism that keeps task data current based on ongoing communications.
2Measurement precision
If task entries are manually updated, then user control over task information is maintained, but task information accuracy lags behind current communications
Solution Approach 1:
The system performs preliminary extraction of n-grams from message trails and prepares update candidates before user confirmation is needed. By pre-processing message content and identifying relevant terms in advance, the system readyes accurate task information updates that can be quickly applied, bridging the gap between communication speed and task update accuracy.
Solution Approach 2:
The patent replaces manual mechanical updating processes with automated computational text processing. Instead of users manually reading messages and updating tasks, the system uses algorithms to extract n-grams, calculate similarity scores, and automatically update task entries, dramatically increasing update speed while maintaining or improving accuracy through systematic analysis.
3Adaptability or versatility
If n-gram extraction and similarity scoring is implemented, then task information relevance to user communications is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of keeping tasks updated by breaking it into distinct modular components: message trail identification, n-gram extraction, similarity score calculation, and task entry updating. Each component handles a specific aspect of the process independently, making the overall complex system manageable through functional segmentation of the information processing pipeline.
Data Source
AI summary
Methods and apparatus related to determining an association between a message trail and a task entry of a user and associating an n-gram with the task entry, wherein the n-gram is based on one or more messages of the message trail. A similarity score between the n-gram and one or more aspects of the associated task entry may be determined. The similarity score may be utilized, for example, to determine when to associate the n-gram with the task entry and/or how to utilize the associated n-gram with the task entry.