Predictive Aspect Formatting for Communication Contexts
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Solution Overview
Problem
Conventional methods for formatting communications, such as text messages or emails, require manual adjustments by users to emphasize certain aspects, which can be burdensome and inefficient, especially when anticipating future composition based on contextual clues within the communication.
Innovation Solution
A method that identifies communications between users and determines the need for visual formatting changes based on context data, automatically implementing these changes, such as color, size, or typographical adjustments, without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual formatting adjustments are made by users, then message emphasis and conveyance are improved, but user burden and time consumption increase
Solution Approach 1:
The system automatically analyzes context data and applies formatting changes without user intervention. The processor identifies communications and autonomously determines visual changes based on context data, allowing the system to serve itself rather than requiring manual user formatting
Solution Approach 2:
The system performs formatting adjustments in advance based on predicted user intent. By analyzing context data before the user completes composition, the system proactively applies appropriate visual formatting, eliminating the need for later manual adjustments
2Ease of operation
If automatic formatting is implemented based on context data, then user burden is reduced, but system complexity increases
Solution Approach 1:
Context data serves as an intermediary between the communication content and formatting decisions. The processor analyzes this intermediate data layer to determine appropriate visual changes, bridging the gap between raw communication data and formatting actions without requiring complex direct analysis
Solution Approach 2:
The system uses context data as feedback to continuously refine formatting decisions. By monitoring communication patterns and context information, the system adjusts its formatting predictions, creating a self-improving loop that manages complexity through adaptive learning
3Productivity
If visual changes are automatically applied to communications, then formatting efficiency is improved, but manual control is reduced
Solution Approach 1:
The formatting system transitions from static manual adjustment to dynamic automatic application. The processor continuously monitors context data and dynamically adjusts formatting in real-time, allowing the system to adapt to changing communication contexts automatically while maintaining high efficiency
Solution Approach 2:
The system changes formatting parameters (visual properties) based on context data analysis. By automatically adjusting parameters such as text styling, spacing, and visual emphasis based on analyzed context, the system achieves efficient adaptive formatting without manual intervention
Data Source
AI summary
One embodiment provides a method, including: identifying, on an information handling device, a communication between a first user and at least one other individual; determining, based upon an analysis of context data associated with the communication, that an indication exists to implement a visual change to an aspect of the communication; and; implementing, responsive to the determining, the visual change to the aspect. Other aspects are described and claimed.