AI Agent Data Enrichment for Mixed-Format Data Integration

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Solution Overview

Problem

Conventional methods for enriching and structuring unstructured data are hindered by scalability, adaptability, and integration issues, often requiring extensive rule creation, leading to inconsistencies and errors, and fail to provide a holistic view when combining structured and unstructured data.

Innovation Solution

Utilizing AI agents, computer vision algorithms, and NLP to enrich and structure unstructured data, allowing for customizable output datasets that can be added to decentralized databases, with users rewarded in cryptocurrencies for contributing data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If conventional rule-based methods are used for enriching and structuring unstructured data, then data processing can be automated, but the system requires extensive rule creation and lacks scalability

Engineering Contradiction:

Improveautomation of data enrichment and structuringVSAvoidcomplexity of rule creation and maintenance

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces conventional mechanical rule-based processing systems with an AI agent that uses machine learning models to automatically enrich and structure unstructured data. The AI agent learns from data patterns and autonomously performs enrichment tasks without requiring extensive manual rule creation, thereby reducing system complexity while maintaining automation capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI agent performs self-learning and self-adjustment during the data enrichment process. It automatically adapts to different data types and formats by learning from the data itself rather than requiring pre-programmed rules for each scenario. This self-service capability reduces the need for continuous rule maintenance and system reconfiguration.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual cleaning and labeling of unstructured data is performed, then data quality can be maintained, but the process is time-consuming and prone to errors

Engineering Contradiction:

Improvequality and consistency of dataVSAvoidtime required for data cleaning and labeling

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent substitutes manual data cleaning and labeling operations with an AI agent that automatically performs these tasks. The AI agent uses natural language processing and computer vision capabilities to accurately identify, clean, and label unstructured data without human intervention, thereby maintaining high data quality while significantly reducing the time required compared to manual processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If unstructured data is stored in disparate silos, then data can be collected from multiple sources, but integration and holistic view become difficult

Engineering Contradiction:

Improveability to collect data from diverse sourcesVSAvoidcomplexity of data integration and consolidation

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The AI agent serves as a universal data processing platform that can handle multiple data types and formats from diverse sources. It performs enrichment and structuring operations across different data silos using a single unified approach, thereby maintaining the ability to collect data from varied sources while simplifying the integration process through consistent AI-based processing rather than separate integration systems for each data type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Ease of manufacture

If conventional rule-based solutions are applied to unstructured data, then processing can be standardized, but valuable information may be missed and inconsistencies occur

Engineering Contradiction:

Improvestandardization of data processingVSAvoidmissed valuable information and data inconsistencies

Core Design Contradiction:

Ease of manufactureVSLoss of information

Solution Approach 1:

The patent replaces rigid rule-based processing with flexible AI-based processing that can adapt to nuanced patterns in unstructured data. The AI agent detects subtle relationships and valuable information that rule-based systems miss, while maintaining standardized processing through consistent AI inference. This substitution preserves standardization benefits while eliminating the information loss and inconsistencies caused by overly rigid rules.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250307217A1Computer-implemented methods and computing systems for enriching and structuring data associated with an item

Publication Date: 2025.10.02 WONG CHIEN YAW

AI summary

A computer-implemented method for enriching and structuring data associated with an item includes receiving initial query data associated with the item in structured and/or unstructured form, from a user computing device associated with a user, and generating enriched structured query data, providing the enriched structured query data to an Artificial Intelligence (AI) agent and receiving AI response data associated with the item, in structured and/or unstructured form, from the AI agent, wherein the AI agent is in communication with a plurality of data repositories, adding the received AI response data to the initial query data to generate enriched data associated with the item, rearranging the enriched data into a predefined data structure to generate enriched and structured output data, wherein the enriched and structured output data comprises one or more recommendations pertaining to the item, and transmitting the enriched and structured output data to the user computing device.