Physical Needs Tool for Weather-Aware Travel Product Recommendations
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Solution Overview
Problem
Individuals often travel unprepared for weather conditions due to lack of awareness about future weather at their destinations, leading to potential harm or danger, as existing technologies do not effectively leverage big data to anticipate and fulfill their physical needs.
Innovation Solution
A physical needs tool that combines travel purchase data with weather forecasts to identify necessary products based on historical purchases and predicted weather conditions, sending recommendations and enabling online purchases for users before they arrive at their destinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individuals rely on their own awareness and planning for travel preparations, then they maintain personal autonomy and decision-making control, but they risk being unaware of critical weather conditions and lacking necessary protective equipment
Solution Approach 1:
The system performs preliminary actions by analyzing travel purchase data before the individual's trip, identifying future locations and dates, obtaining weather forecasts for those locations, and determining necessary protective products in advance. This allows the system to proactively recommend products before the individual encounters adverse weather conditions, resolving the contradiction by providing early weather information and preparation opportunities.
Solution Approach 2:
The system uses feedback from the individual's purchase history to refine product recommendations. By analyzing past purchases of protective equipment and comparing them with forecasted weather conditions at future travel destinations, the system provides personalized feedback about specific products the individual may need, thereby improving readiness while respecting individual decision-making.
2Measurement precision
If the system analyzes extensive travel and purchase data to predict needs, then product recommendation accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the data analysis process into distinct functional modules: a location predictor that analyzes travel purchase data to determine future locations, a product predictor that analyzes purchase history to identify needed products, and a physical needs predictor that combines weather forecasts with product predictions. This segmentation reduces overall system complexity by breaking down the complex data processing task into manageable, specialized components.
Solution Approach 2:
The system introduces intermediary components that facilitate data processing: the location predictor acts as an intermediary between travel purchase data and weather forecast retrieval, while the physical needs predictor serves as an intermediary between weather forecasts and product recommendations. These intermediaries simplify the overall system architecture by creating clear data flow pathways and reducing direct complexity between disparate data sources.
Data Source
AI summary
An apparatus includes a database, memory, and processor. The database stores a purchase history, assigned to a user, that includes records of products and a ticket for travel to a second location on a first date. The memory stores categories, each of which is assigned to weather conditions and includes products for which a positive correlation exists between consumer demand and a presence of one or more weather conditions. The processor determines that the user will be in the second location on a third date and obtains a predicted weather condition at the second location on the third date. The weather condition is assigned to a category including a product. The processor determines that a probability the user has a need for the product is greater than a threshold and sends a product recommendation to the user. The processor receives a purchase request from the user and completes a purchase.