Digital Identity Selection Model for Purchase Partitioning
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Solution Overview
Problem
Existing cost saving applications rely heavily on active user engagement and manual comparison, leading to potential oversight in optimizing purchases and failing to utilize diverse digital identities for enhanced cost savings.
Innovation Solution
An item partitioning benefit optimization system that dynamically determines and utilizes multiple digital identities for individual purchase items, dissecting transactions into multiple components to enhance transactional efficiency and introduce granularity in the purchase process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual comparison and selection methods are used, then user engagement is maintained, but cost saving optimization is insufficient
Solution Approach 1:
The system enables self-service by automatically selecting optimal digital identities and partitioning items without requiring user intervention. The machine learning model autonomously processes user attributes, digital identities, and item data to generate optimized purchase recommendations, eliminating the need for manual comparison while maintaining user benefit.
Solution Approach 2:
The patent replaces manual mechanical comparison processes with automated machine learning algorithms. The digital identity selection model uses computational methods to analyze multiple digital identities and determine optimal assignments, substituting human cognitive processing with automated AI-based decision-making.
2Productivity
If single digital identity is used, then system complexity is reduced, but cost saving benefits are limited
Solution Approach 1:
The system segments the purchase process into multiple components by assigning different digital identities to different items within a single transaction. Each item can be independently associated with the most beneficial digital identity, allowing granular optimization without requiring complete system redesign.
Solution Approach 2:
The patent implements dynamic digital identity assignment where the system adapts its recommendations based on real-time analysis of user attributes, item characteristics, and available digital identities. The machine learning model continuously optimizes assignments based on current data, making the system flexible and responsive to changing conditions.
3Productivity
If automated digital identity selection is implemented, then purchase optimization is improved, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single machine learning model to handle multiple tasks: analyzing user attributes, evaluating digital identities, determining optimal assignments, and generating purchase recommendations. This universal approach consolidates what would otherwise require multiple separate systems into one integrated solution.
Solution Approach 2:
The patent leverages parameter changes by transforming raw user attributes and item data into optimized digital identity assignments through machine learning. The system processes input parameters (user attributes, item data) and transforms them into optimized output parameters (digital identity assignments, purchase recommendations), enabling automated optimization.
4Measurement precision
If comprehensive digital identity analysis is performed, then cost saving accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and storing user attribute data, digital identity information, and item characteristics before the actual purchase decision is needed. The machine learning model is trained in advance on historical data, enabling rapid inference during actual transactions without requiring time-consuming analysis at the moment of purchase.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for optimizing an item partitioning benefit. An example method includes detecting, by detection circuitry, a user visit event at an establishment that is associated with a user attribute set. The example method further includes determining, by a multimodal engine and based on the user attribute set, a user digital identity wallet comprising one or more user digital identities associated with a benefit set. The example method further includes, identifying, by a selection engine, one or more candidate items, and determining for each candidate item, by the selection engine and using a digital identity selection model, an optimal user digital identity indicative of an optimal item partitioning benefit. The example method further includes generating, by the selection engine and using the digital identity selection model, a candidate item partitioning set, and outputting, by communications hardware, a verification request.