Machine Learning Integration of Unrelated Data Structures for User Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in integrating vast amounts of data from multiple unrelated data structures due to incompatibility issues, leading to inefficient processing, excessive resource consumption, and poor user experience.
Innovation Solution
A system utilizing a machine learning model to integrate data from exchange, account, records, interaction, and metaverse databases to predict a user's probability of acquiring a new item, conserving computing resources and reducing delays by processing incompatible data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple unrelated data structures is integrated using traditional methods, then comprehensive user analysis is achieved, but processing efficiency deteriorates and resource consumption increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that receives data from multiple unrelated data structures (exchange data, account data, record data, interaction data, metaverse data) and transforms them into a unified prediction output. This mediator handles the complexity of integrating incompatible data formats, enabling comprehensive user analysis without requiring traditional complex integration logic that would reduce processing efficiency.
2Measurement precision
If comprehensive data from multiple sources is processed, then prediction accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring data from multiple sources before the actual prediction task. The machine learning model is trained in advance on comprehensive data, enabling it to make accurate predictions with optimized resource usage during inference. This preliminary preparation allows the system to handle complex multi-source data integration once, rather than repeatedly during each prediction operation.
3Loss of information
If multiple data structures are integrated using conventional methods, then complete user profile is obtained, but processing time increases
Solution Approach 1:
The patent replaces traditional mechanical data integration methods (complex ETL processes, data mapping, and transformation logic) with a machine learning-based system. The ML model automatically learns patterns and relationships across different data structures without requiring explicit integration rules, significantly reducing processing time while maintaining information completeness from all data sources.
Data Source
AI summary
In some implementations, a device may retrieve one or more of: exchange data, account data, record data, interaction data, or metaverse data. The device may obtain at least one of location data or wireless network data, where the location data indicates a location, of a user device, associated with a first entity, and where the wireless network data indicates a wireless network, to which the user device has connected, associated with a second entity. The device may determine a probability of the user acquiring an item in a future time interval based on at least one of the exchange data, the account data, the record data, the interaction data, or the metaverse data, and at least one of first information relating to the first entity or second information relating to the second entity. The device may transmit information based on the probability of the user acquiring the item.