Auto-formatting Data Tables Using Learning Network Feature Extraction
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Solution Overview
Problem
Manual table formatting in electronic documents, such as spreadsheets, is tedious and time-consuming, especially for complex tables in domains like finance and government.
Innovation Solution
A computer-implemented method using a learning network to automatically determine formats for cells in a data table based on the semantic meaning and structure of the data, by extracting feature representations from attribute values and mapping them to appropriate formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual formatting is used for data tables, then users can control formatting details, but the process becomes tedious and time-consuming
Solution Approach 1:
The system performs automatic table formatting by analyzing the data table structure and content, then applying appropriate formats without requiring manual user intervention. The formatting module automatically identifies table regions, determines cell formats based on data characteristics, and applies formatting rules, enabling the system to serve itself in the formatting task.
Solution Approach 2:
The patent replaces the manual mechanical formatting process with an automated computational system. A learning network analyzes attribute values of cells and automatically determines formatting options, substituting human manual operations with an intelligent automated system that processes formatting decisions algorithmically.
2Productivity
If automatic formatting is implemented using a learning network, then formatting time is reduced, but the system complexity increases
Solution Approach 1:
The learning network is designed to handle multiple formatting tasks universally - it can analyze different data types, determine various cell formats (number, text, date, etc.), and apply diverse formatting rules through a single unified system, reducing the need for multiple specialized formatting tools.
Solution Approach 2:
The learning network acts as an intermediary between the raw data table and the formatting output. It receives attribute values from the data table, processes them through feature extraction and analysis, and generates formatting decisions, serving as a mediating layer that bridges data understanding and formatting application.
3Adaptability or versatility
If manual formatting is used, then formatting can be customized for each cell, but the process is difficult for professional and complex tables
Solution Approach 1:
The system applies local quality by analyzing and determining formats for individual cells based on their specific attribute values and characteristics. Each cell's format is determined independently based on its own data properties, allowing customized formatting decisions for each cell while maintaining overall automation.
Solution Approach 2:
The learning network analyzes parameter changes in data attributes (such as data type, length, pattern, and semantic meaning) and automatically adjusts formatting parameters accordingly. By monitoring changes in data characteristics, the system adapts formatting decisions to match the specific requirements of different cells and table structures.
Data Source
AI summary
According to implementations of the present disclosure, there is provided a solution for auto-formatting of a data table. A computer-implemented method comprises obtaining values of at least one attribute for a plurality of cells in a data table, the values of the at least one attribute indicating at least one of a semantic meaning of data filled in the cells or a structure of the data table, the cells being arranged in rows and columns in the data table; extracting a feature representation of the values of the at least one attribute In using a first learning network; and determining respective formats for the cells based on the feature representation using at least the first learning network. In this way, it is possible to implement automatic mapping from attribute settings of cells to cell formats by using a learning network, thereby achieving auto-formatting of the data table.