Per-User Interface Adaptation for Pharmacy Resource Allocation
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Solution Overview
Problem
Current user interfaces in high-volume pharmacies are not customized to individual users or user populations, leading to inefficient resource allocation and lack of personalized experiences for both users and support representatives.
Innovation Solution
A computer system that uses data store mining and machine learning models to determine per-user metrics, transforming the user interface based on expected resource intake and retention values, allowing for personalized experiences and optimized resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single standardized user interface is provided to all users, then device complexity is reduced and ease of manufacture is improved, but user retention and resource utilization deteriorate due to lack of personalization
Solution Approach 1:
The user interface dynamically adapts its configuration based on real-time analysis of user behavior data, transaction history, and engagement metrics. The system automatically reconfigures interface elements, widget arrangements, and content prioritization without requiring manual customization, thus maintaining ease of manufacture while improving user retention through personalization.
Solution Approach 2:
The system changes multiple interface parameters simultaneously including layout configuration, content types, widget visibility, and interaction patterns based on user profiles derived from data analysis. This allows the interface to adapt to different user segments while maintaining a single standardized codebase that can be dynamically configured.
2Reliability
If personalized user interfaces are created for each user, then user retention and resource utilization are improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The user base is segmented into distinct groups based on analyzed behavior patterns, transaction histories, and engagement metrics. Each segment receives a tailored interface configuration optimized for their specific needs and behaviors. This segmentation approach reduces the complexity of creating entirely unique interfaces for each individual user while still providing personalized experiences.
Solution Approach 2:
A single standardized interface framework serves multiple user segments simultaneously through dynamic configuration. The same base interface codebase is universally applied but automatically adjusted to serve different user types, eliminating the need for separate interface developments for each user group while maintaining personalization benefits.
3Measurement precision
If comprehensive data analysis is performed for each user, then accuracy of resource allocation improves, but processing time and computational resources worsen
Solution Approach 1:
User profiles and behavioral patterns are pre-analyzed and stored as structured data during user interactions. This preliminary data preparation enables rapid retrieval and application of personalization parameters without requiring extensive real-time computation during interface generation, thus maintaining high allocation accuracy while reducing processing time.
Solution Approach 2:
Successfully analyzed user profiles and interface configurations are copied and applied to similar users or user segments. Once a user's interface is optimized through data analysis, this configuration serves as a template that can be replicated for users with similar behaviors, significantly reducing the need for repeated comprehensive analysis while maintaining precision.
Data Source
AI summary
A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.