Text Management System Using Cluster-Based Formatting
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Solution Overview
Problem
Current text transmission systems require manual modification of text attributes like font, size, and color, which is tedious and time-consuming, and do not automatically adapt to user preferences or text history.
Innovation Solution
A method and system that extract key elements from input text, map users to existing or create clusters based on text modification schemes, and automatically modify text according to these schemes, reducing user intervention and enhancing usability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual text modification is used to change font, size, and color attributes, then text formatting can be achieved, but the process becomes tedious and time-consuming
Solution Approach 1:
The system automatically modifies text attributes by analyzing user preferences and text history without requiring manual intervention. The text modification scheme is applied autonomously based on extracted key elements and clustered user patterns, eliminating the need for users to manually format each text element.
Solution Approach 2:
The system pre-processes text by extracting key elements and determining the appropriate text modification scheme before actual text transmission or display. User preferences and historical data are analyzed in advance to establish the formatting rules that will be automatically applied.
2Productivity
If automatic text modification based on user clusters is implemented, then text formatting efficiency is improved, but system complexity increases due to clustering and mapping mechanisms
Solution Approach 1:
The system divides the text modification process into distinct functional modules: key element extraction, user cluster determination, text modification scheme selection, and actual text modification. This segmentation allows each module to handle a specific task independently, managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediate structures including key element extraction results, user cluster classifications, and structured table mappings as mediators between user input and final text output. These intermediaries organize and structure the data flow, making the overall system more manageable despite its automated capabilities.
3Stability of the object's composition
If text modification schemes are stored in structured tables with key element mappings, then consistent formatting is achieved, but information storage requirements increase
Solution Approach 1:
The system applies different text modification schemes to different key elements within the text based on their specific characteristics. Each key element type (e.g., names, dates, locations) can have its own specific formatting rules in the structured table, allowing localized customization without uniform application across all text.
Solution Approach 2:
The structured table mapping mechanism serves multiple functions: it stores user preference profiles, defines text modification schemes, and provides the lookup logic for automatic formatting. This multi-functional design reduces the need for separate storage structures for each function.
Data Source
AI summary
Aspects of the present disclosure relate to input text management. Input text can be received from a user. A set of key elements can be extracted from the input text. A determination can be made whether the user is mapped to an existing cluster. In response to determining that the user is mapped to an existing cluster, a structured table mapping key elements to text modifications can be referenced. The input text can be modified according to the structured table mapping key elements to text modifications, wherein the existing cluster is associated with a text modification scheme within the structured table, wherein the text modification scheme includes a first mapping of key elements to text modifications.