LLM-Powered Intelligent Fields for Accurate Tabular Data Generation

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Solution Overview

Problem

Current techniques for generating data for data structures are inefficient and prone to errors, leading to poor performance and increased resource usage, especially in tabular data structures like spreadsheets, due to the need for repeated human interaction and complex automated processes.

Innovation Solution

Integrate a large language model with a tabular data structure database platform to automatically generate contextually relevant data in intelligent data fields, reducing human interaction and computational resources by leveraging relationships between data fields.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If current techniques for generating data are used, then data can be generated for tabular data structures, but the process is inefficient and prone to errors requiring repeated human interaction

Engineering Contradiction:

Improvedata generation efficiencyVSAvoiddata generation accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system enables self-service by allowing the tabular data structure to automatically generate its own data through embedded AI models. The AI model processes contextual information from existing data fields and autonomously populates new fields without requiring human intervention, thereby improving both efficiency and accuracy of data generation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical data entry and traditional automated scripting with an AI-based system. The AI model uses natural language processing and contextual understanding to generate data, substituting the mechanical processes of manual input and rule-based automation with intelligent, adaptive data generation capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If complex automated processes are used for data generation, then data can be generated, but resource usage increases and performance decreases

Engineering Contradiction:

Improvedata generation speedVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies local quality by deploying AI models specifically within the tabular data structure where they are needed, rather than using a centralized complex processing system. Each AI model operates locally on specific data fields, processing only the relevant contextual information required for that field, thereby reducing overall computational resource consumption while maintaining high data generation speed.

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If repeated human interaction is required for data generation, then data can be updated, but time consumption increases

Engineering Contradiction:

Improvedata update flexibilityVSAvoidtime for human interaction

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system eliminates time loss by implementing self-service data generation where the AI model continuously monitors and updates data fields based on changing contextual relationships. When new data is introduced or relationships change, the AI automatically adjusts and generates appropriate data without requiring human review or intervention, maintaining adaptability while eliminating time consumption.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250328728A1Intelligent generation of data for a tabular data structure via a large language model

Publication Date: 2025.10.23 ZIRA TECHNOLOGIES INC

AI summary

Embodiments provide for generating data for a data structure via artificial intelligence and/or machine learning and for intelligently generating prompts associated with the same.