AI Table Column Splitting via Semantic Entity Recognition

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Solution Overview

Problem

Existing table processing software requires manual adjustments and is inefficient when users provide data with different column-splitting symbols, leading to inaccurate column-splitting results due to the reliance on single rules for separating text data.

Innovation Solution

A method and apparatus that utilize AI technology for automatic column-splitting by performing information extraction and alignment of entity information, allowing for the classification of text data into columns based on semantic meaning rather than simple separators, thereby improving accuracy and efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional column-splitting function uses a specified rule (separator) to split text data, then the splitting process is simple and fast, but the accuracy deteriorates when different users use different column-splitting symbols

Engineering Contradiction:

Improvecolumn-splitting efficiencyVSAvoidcolumn-splitting accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces the traditional mechanical separator-based column-splitting system with an AI-based semantic recognition system. Instead of relying on fixed delimiters like commas or spaces, the system uses natural language processing to understand the semantic structure of text data and automatically identify column boundaries based on meaning, thereby resolving the contradiction between simple splitting processes and accurate results when different symbols are used.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameter of column-splitting from relying on fixed separator characters to using dynamic semantic analysis. The system adapts its splitting strategy based on the content of each row, identifying delimiters through AI recognition of data patterns and semantic relationships rather than predetermined rules, thus maintaining high accuracy across diverse data formats.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If manual adjustment is performed to achieve accurate column-splitting results, then the accuracy improves, but the time consumption and operation complexity increase significantly

Engineering Contradiction:

Improvecolumn-splitting accuracyVSAvoidmanual adjustment time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements a self-service column-splitting system where the AI automatically performs semantic analysis, identifies data structures, and executes column-splitting operations without requiring manual intervention. The system self-adjusts to different data formats and automatically corrects splitting errors, eliminating the need for time-consuming manual adjustments while maintaining high accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent performs preliminary semantic analysis and data structure recognition before executing the column-splitting operation. By pre-processing the text data to identify potential delimiters and data patterns using AI, the system prepares optimal splitting strategies in advance, ensuring accurate results are achieved automatically without requiring subsequent manual adjustments.

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If a single column-splitting rule is applied to all rows, then the operation is simple and consistent, but the adaptability deteriorates when data from multiple users with different formats is aggregated

Engineering Contradiction:

Improvesplitting operation simplicityVSAvoiddata format compatibility

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent transforms the static, fixed rule-based column-splitting approach into a dynamic, adaptive system. The AI continuously analyzes each row of data to identify the most appropriate splitting strategy, adjusting its approach based on the specific format, delimiters, and semantic structure of the input data. This dynamic adaptation maintains operational simplicity while achieving high versatility across different user data formats.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent creates a universal column-splitting system that can handle multiple data formats, delimiters, and structures through a single AI-powered interface. The system performs multi-functionality by simultaneously supporting various text formats, recognizing different types of delimiters, and adapting to diverse data patterns, thereby maintaining ease of operation while achieving broad adaptability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11487935B2Method and apparatus for automatically splitting table content into columns, computer device, and storage medium

Publication Date: 2022.11.01 TENCENT TECHNOLOGY (SHENZHEN) CO LTD

AI summary

A method for automatically splitting row-based table content into columns is provided, including: receiving first table content sent by a client, the first table content including one or more rows of text data to be split into columns; performing information extraction on the one or more rows of text data in the first table content to obtain an information tag in the one or more rows of text data; performing column splitting on the one or more rows of text data according to the information tag to obtain second table content, the second table content comprising one or more columns of text data after the column splitting; and transmitting the second table content to the client.