ML Customized Notification Generation and Authentication
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Solution Overview
Problem
Current notification systems for users in brick-and-mortar stores are limited, as they often rely on pre-programmed, non-location-specific offers that are not tailored to individual users' interests or shopping habits, leading to wasteful marketing efforts and low engagement.
Innovation Solution
A system that uses machine learning to generate customized notifications based on users' location, purchase history, and preferences, providing real-time offers that are relevant to their interests while they are in proximity to relevant products or services, and includes a unique security key for authentication and redemption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-programmed, non-location-specific offers are used, then the notification system is simple to implement, but the relevance and effectiveness of notifications deteriorates
Solution Approach 1:
The notification system transitions from static pre-programmed offers to dynamic real-time offers that automatically adapt to user location, purchase history, and preferences. The system continuously updates notifications based on current user context, making each notification personally relevant and timely.
Solution Approach 2:
The system changes multiple parameters including location-based context, temporal relevance, product specificity, and offer customization based on user profile data. By adjusting these parameters dynamically, the system generates highly relevant notifications without requiring complex manual programming for each scenario.
2Reliability
If location-based real-time customized notifications are generated, then user engagement and notification effectiveness improve, but device complexity and data processing requirements worsen
Solution Approach 1:
A centralized notification processing system acts as an intermediary between user data sources, location services, and delivery channels. This intermediary layer consolidates complex data processing and ML model execution, allowing individual client devices to remain relatively simple while still receiving highly customized notifications.
Solution Approach 2:
Manual programming of notification logic is replaced with automated machine learning models that infer user preferences and generate appropriate notifications. This substitution reduces the need for complex rule-based systems while maintaining high engagement through intelligent, adaptive notification generation.
3Reliability
If comprehensive user data monitoring and analysis is implemented, then notification personalization improves, but data collection and processing requirements worsen
Solution Approach 1:
Instead of processing all available user data, the system selectively processes only the necessary data parameters relevant to generating effective notifications. By identifying and focusing on key data points such as location, purchase history, and preferences, the system achieves high personalization while reducing overall data processing volume.
Solution Approach 2:
The system transforms raw data into meaningful parameters and features that drive notification generation. By aggregating and transforming data into relevant user profiles and contextual parameters, the system reduces the complexity of processing while maintaining comprehensive personalization capabilities.
Data Source
AI summary
A system for employing machine learning to generate customized notifications for a user is provided. The system may identify a landmark in proximity to a user's mobile device and may obtain information associated with products or services offered by the landmark. While the mobile device is within a predetermined distance of the landmark, a machine learning model may be employed to generate a customized notification, such as an offer—e.g., a discount or a special financing offer for one of the identified products—generated specifically for use by the user based on the user's spending and/or financing history. The customized notification may be transmitted to the mobile device with a security key for accessing the offer. In response to receiving an indication that the security key was selected and the product purchased, the user may be authenticated and the offer may be applied to an account associated with the user.