AI Entity Resolution for Cross-Document Data Alignment
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Solution Overview
Problem
Existing systems face challenges in seamlessly integrating and reconciling disparate documents and data formats across various entities in complex workflows like manufacturing and supply chains, leading to data misalignments and privacy risks due to non-standardized identifiers and manual, error-prone transcription methods.
Innovation Solution
Integration of Large Language Model (LLM)/Large Multimodal Model (LMM)-based information comparison to align on a ground truth across documents, using verifiable credential technology for secure data exchange, and employing machine learning techniques to automatically identify and reconcile data discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual transcription methods are used to transfer data between systems, then data can be exchanged between different platforms, but errors increase and productivity decreases
Solution Approach 1:
The patent replaces manual transcription (mechanical human operation) with automated AI-based entity resolution systems that use machine learning models to extract, match, and reconcile data across different platforms and formats, eliminating human error and increasing processing speed
Solution Approach 2:
The patent introduces an intermediary entity resolution system that acts as a mediator between disparate data sources, using standardized data models and reconciliation algorithms to transform and align data from different formats before exchanging it between systems
2Adaptability or versatility
If non-standardized identifiers are used across different entities, then each organization can maintain its own data format, but data misalignment and reconciliation difficulties increase
Solution Approach 1:
The patent implements universal entity resolution capabilities that can handle multiple data formats, schemas, and identification systems simultaneously, allowing the system to adapt to different organizational standards while maintaining the ability to reconcile them through a common framework
Solution Approach 2:
The patent dynamically changes data parameters including identifier mappings, data format transformations, and schema alignments based on the specific source and target systems, allowing flexible adaptation to different standards while automatically managing reconciliation
3Productivity
If data is shared across multiple platforms without verification, then information exchange speed increases, but privacy risks and data security concerns increase
Solution Approach 1:
The patent performs preliminary data verification, validation, and privacy protection measures before data is shared across platforms, including entity resolution, data quality checks, and access control setup, enabling fast subsequent exchanges with reduced risk
Data Source
AI summary
The technology disclosed relates to Artificial Intelligence techniques for sharing and managing information across organizational boundaries. Specific embodiments of the technology disclosed include the integration of LLM/LMM-based information comparison to enable alignment on a ground truth (or an agreed upon data standard) across multiple documents related to a community's data, and communicating that alignment to the users in the community. As a result, the users can update data and make amendments to documents affecting related actions among multiple community members. in order to resolve any misalignment between data and physical reality. Information exchange can be securable through verifiable credential technology.