Neural Database Training for Accurate Supplier Entity Matching
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Solution Overview
Problem
Discovering raw material suppliers for business-to-business transactions is challenging due to the inefficiency and inaccuracies in traditional methods, leading to outdated supplier databases and missed opportunities for better suppliers.
Innovation Solution
A neural database is trained using structured entity data and metadata generated by a machine learning model, such as GPT-4, to accurately match entities with relevant suppliers based on their goods and services, adapting to user preferences and historical data through reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to find suppliers, then the process is simple and straightforward, but the accuracy and efficiency of supplier discovery is poor
Solution Approach 1:
The patent introduces a neural database as an intermediary system between the user query and the supplier entities. This neural database, trained on structured entity data and metadata, acts as a mediator that processes queries and returns accurate supplier recommendations, resolving the contradiction by providing high accuracy without requiring complex manual search processes
Solution Approach 2:
The patent replaces traditional mechanical search methods with a neural database system that uses machine learning models. Instead of manual database querying or simple search algorithms, the system uses neural networks processed structured entity data and metadata to generate accurate supplier predictions, substituting complex mechanical search processes with intelligent automated systems
2Productivity
If manual database management is performed, then data can be maintained, but the process is time-consuming and prone to errors
Solution Approach 1:
The neural database system performs self-service by automatically processing queries and returning supplier recommendations without requiring manual database management. The system maintains itself through automated training on structured entity data and metadata, eliminating the need for manual updates and reducing search time significantly
Solution Approach 2:
The system performs preliminary action by pre-training the neural database on structured entity data and metadata before actual queries are processed. This preliminary training enables the system to quickly and efficiently answer supplier discovery queries without requiring manual database management during operation, thereby reducing both time loss and improving productivity
3Reliability
If traditional supplier databases are used, then existing supplier information is available, but the information becomes outdated and misses better supplier opportunities
Solution Approach 1:
The neural database system incorporates feedback mechanisms through continuous training on updated structured entity data and metadata. This feedback loop ensures the system learns from new and updated supplier information, maintaining reliable up-to-date knowledge and preventing loss of information about better supplier opportunities that may arise over time
Data Source
AI summary
Certain aspects of the disclosure provide a method of training a neural database for entity matching. In examples, a method may include: extracting, from an electronic data repository, entity data related to a first entity that provides a good or a service; transforming the entity data into structured entity data configured to be processed by a machine learning model; processing the structured entity data with the machine learning model to generate metadata associated with the structured entity data; augmenting the structured entity data with the metadata associated with the structured entity data; and training the neural database based on the augmented structured entity data to predict one or more second entities that supply materials for the first entity and associated with the good or the service.