Data Intelligence Platform for Real-Time User-Specific Product Recommendations
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Solution Overview
Problem
Current systems lack the ability to integrate and process data from multiple independent sources in real-time, predict user behavior, and offer user-specific digital services or products without user input, leading to inefficiencies and outdated data usage.
Innovation Solution
A data intelligence platform that utilizes machine learning algorithms to combine user activity, household, and collateral data to create a digital asset, applying advanced analytics to identify pre-approved user-specific product offerings and communicate them via various channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data collection from multiple data sources is implemented, then system responsiveness and accuracy are improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments data collection into specialized modules: a data collection module for gathering data from multiple sources, a data integration module for processing and standardizing the data, and a machine learning module for analysis. This segmentation allows each module to handle specific tasks efficiently, reducing overall system complexity while maintaining high data accuracy through specialized processing.
Solution Approach 2:
The patent introduces a data integration module as an intermediary between data sources and the machine learning module. This intermediary layer standardizes, cleans, and prepares data from multiple sources before it reaches the analysis layer, thereby maintaining measurement precision while shielding the complexity of data collection and processing operations.
2Loss of time
If pre-emptive data processing and user behavior prediction are implemented, then service responsiveness is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and processing user data in advance of specific requests. The machine learning module analyzes historical data and predicts user needs before they are explicitly stated, enabling pre-emptive service delivery. This reduces response time by having processing results ready beforehand, while the system optimizes computational resources by processing data in batches rather than continuously.
Solution Approach 2:
The machine learning module operates autonomously to predict user behavior and generate product recommendations without requiring manual intervention. The system self-adjusts its predictions based on new data inputs, continuously improving accuracy while managing computational resources efficiently through automated optimization of processing parameters.
3Loss of information
If multiple data sources are integrated into a unified digital asset, then data completeness is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple data sources including user profile data, activity data, and product information into a single unified digital asset. The data integration module combines these disparate data types through standardized formats and common identifiers, creating a comprehensive view of the user that improves data completeness while managing processing complexity through systematic integration approaches.
Solution Approach 2:
The unified digital asset serves multiple functions simultaneously: it stores user information, enables behavior prediction, supports product recommendation, and facilitates real-time processing. This multi-functionality approach consolidates what would otherwise require separate processing systems into a single versatile data structure, improving data completeness while reducing overall system complexity.
Data Source
AI summary
A system, method and computer-readable storage medium are disclosed herein. The system includes one or more servers, one or more processors and a memory storing computer-readable instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include receiving data and information associated with a user, and executing a first ML algorithm to select which other ML algorithm to execute to determine a value profile associated with a collateral asset. Additional ML algorithms are executed to generate a collateral profile associated with the collateral asset, an activity profile associated with a user, and a household profile associated with the user. Data from among the profiles is combined to generate a digital asset, and advanced analytics are applied to the digital asset, in connection with rules and policies, to identify a user-specific product offering. The user-specific product offering is communicated to the user.