Prioritized Local Shopping Service with Real-Time Busyness Data
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Solution Overview
Problem
Current systems lack an efficient method to prioritize shopping stops based on real-time data such as store busyness, inventory levels, and sales, leading to suboptimal shopping experiences in terms of time and savings.
Innovation Solution
A network-based prioritized local shopping service that utilizes location-aware mobile devices to aggregate data from various sources like user check-ins, geo-located tweets, and security cameras to provide users with optimized shopping routes and visualization tools, integrating payment capabilities for seamless transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional shopping methods are used without real-time data, then system complexity is low, but shopping time is excessive and efficiency is poor
Solution Approach 1:
The system segments shopping information into multiple data sources including user check-ins, geo-located tweets, security camera feeds, and sensor grid data. Each data source is processed independently and then integrated to provide comprehensive store busyness information, allowing the system to manage complexity through modular data collection and processing
Solution Approach 2:
The patent introduces a centralized server as an intermediary that aggregates data from multiple sources, processes it to determine store busyness levels, and provides recommendations to users. This intermediary layer manages the complexity of integrating multiple data sources while presenting simplified information to end users
2Loss of time
If real-time data aggregation from multiple sources is implemented, then shopping route optimization is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary data aggregation and processing by continuously collecting and analyzing data from multiple sources before users need shopping recommendations. The server maintains updated information about store busyness levels, inventory, and sales in advance, so when a user requests routing information, the data is already prepared and can be quickly returned
Solution Approach 2:
The system implements feedback loops where user check-ins and shopping behavior data are continuously collected and used to update store busyness information. This real-time feedback allows the system to adapt to changing conditions and provide increasingly accurate routing recommendations, reducing waiting time through iterative optimization
3Productivity
If comprehensive shopping data is collected and analyzed, then shopping route optimization is improved, but information processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from comprehensive shopping data, such as store busyness levels, inventory status, and sales performance. Rather than processing all raw data, the server identifies and extracts key metrics that directly impact routing decisions, reducing information processing load while maintaining shopping optimization effectiveness
Data Source
AI summary
Systems and methods to provide a prioritized shopping system are discussed. For example, a method can include receiving a list of target items, receiving busyness data for a plurality of local merchants, developing a prioritized shopping plan, and communicating the prioritized shopping plan to a mobile device. Each target item in the list of target items can represents a product or service that a user has indicated an interest in purchasing. The prioritized shopping plan is based at least in part on the busyness data and the list of target items. Busyness data provides an indication of traffic levels within at least a portion of the plurality of local merchants.