Machine Learning Purchase Reminder System for Periodic Events
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Solution Overview
Problem
Consumers face difficulties in remembering to purchase event-related goods and services, such as gifts, due to forgetfulness or lack of time, and struggle to identify appropriate products for upcoming events.
Innovation Solution
A machine learning-based system that analyzes a user's previous interactions and purchase history to predict likelihood of purchasing items for periodic events, sending reminders and recommendations for relevant products before the event, allowing users to interact with merchants and complete purchases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If consumers rely on manual memory and tracking of events and purchases, then they maintain full control over their purchasing decisions, but they experience forgetfulness and difficulty in remembering events and previous purchases
Solution Approach 1:
The system automatically analyzes consumer purchase history and interaction data to identify periodic events and generate purchase reminders without requiring manual input from the consumer. The machine learning model self-trains on available data and autonomously determines when to send reminders, allowing the system to serve itself rather than requiring active consumer management.
Solution Approach 2:
The patent introduces an intermediary machine learning-based system that mediates between the consumer and the complex task of tracking events and purchases. This intermediary automatically processes purchase history, identifies patterns, and generates reminders, reducing the cognitive burden on consumers while maintaining reliable purchase tracking.
2Productivity
If consumers manually track and search for appropriate products for upcoming events, then they can make informed purchasing decisions, but they lack time and experience difficulty in identifying suitable products
Solution Approach 1:
The system performs preliminary analysis of consumer purchase history and interaction data before the actual purchase event occurs. By pre-identifying periodic events and relevant products in advance, the system prepares purchase reminders and product recommendations ahead of time, allowing consumers to make decisions without last-minute searching.
Solution Approach 2:
The system continuously learns from consumer interactions with reminders and product recommendations, using this feedback to improve future predictions. The machine learning model adjusts its predictions based on whether consumers engage with suggested products, refining its understanding of consumer preferences and reducing time spent searching for appropriate products over time.
3Adaptability or versatility
If the system sends purchase reminders based on basic purchase history, then it can provide simple notifications, but it fails to provide personalized product recommendations and interaction-based insights
Solution Approach 1:
The patent introduces a machine learning model as an intermediary layer between raw purchase history data and personalized recommendations. This intermediary processes and analyzes interaction data to extract meaningful patterns, transforming basic purchase information into personalized product suggestions without requiring direct complex analysis by the consumer.
Solution Approach 2:
The system evolves from simple purchase history tracking to sophisticated interaction-based analysis by changing the parameters it processes. Instead of only considering basic purchase data, the model incorporates interaction metrics, engagement patterns, and contextual information, transforming the quality and depth of input parameters to enable personalization.
4Productivity
If consumers rely on traditional reminder systems, then they receive basic notifications, but they miss opportunities for enhanced purchase behavior and merchant engagement
Solution Approach 1:
The system implements a feedback loop where consumer interactions with reminders and product suggestions are continuously analyzed to improve future recommendations. This feedback mechanism ensures that interaction-based insights are captured and utilized, preventing loss of valuable behavioral data while enhancing purchase conversion through increasingly accurate personalization.
Solution Approach 2:
The system performs preliminary analysis of interaction patterns and consumer behavior before purchase events occur, preparing personalized recommendations in advance. By proactively analyzing interaction data and predicting consumer needs beforehand, the system captures valuable insights that would otherwise be lost, enabling targeted interventions that improve purchase conversion.
Data Source
AI summary
According to certain aspects of the disclosure, a computer-implemented method may be used for interaction-driven indications using machine learning. The method may include receiving information associated with previous interactions of a person and associating one or more items with a periodic event. Additionally, determining a likelihood of the person acquiring an item for a next occurrence of the periodic event and transmitting an indication to the person prior to the next occurrence of the periodic event. Additionally, based on interaction with the interactive text or graphics, causing a computing device of a person to navigate to an entity associated with the available items and receiving information related to the items. Additionally, causing display of an interactive interface indicative of at least one of the items, the information related to the one or more available items, or the periodic event.