Proximity-Based Fee Computation for Dynamic Pricing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional fee assessment methods for accessing points of interest, such as galleries and events, often require pre-payment and do not accurately reflect the user's experience, as they do not account for varying proximity or viewing quality, leading to inefficiencies and potential overpayment.
Innovation Solution
A system that uses electronic sensors to track a user's proximity and viewing experience in real-time, determining a fee based on their position and view, allowing for dynamic fee adjustments and electronic transactions to settle the charges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-paid fees are charged for access to points of interest, then the venue operator can secure revenue in advance, but the fee structure does not accurately reflect the user's actual viewing experience or proximity to the point of interest
Solution Approach 1:
The system performs preliminary actions by establishing electronic monitoring infrastructure and user tracking mechanisms before the actual viewing event occurs. Sensors and computing devices are positioned in advance to capture proximity data, and user profiles are pre-configured for automatic fee calculation based on real-time location tracking.
Solution Approach 2:
The fee structure transitions from static pre-paid pricing to dynamic pricing that adjusts in real-time based on user proximity to points of interest. The system continuously monitors location data and recalculates fees as users move closer to or farther from featured content, ensuring the final charge accurately reflects the actual viewing experience provided.
2Adaptability or versatility
If real-time proximity tracking is implemented, then fees can be accurately based on user viewing experience, but the system complexity and computational requirements increase
Solution Approach 1:
The monitoring system is segmented into distributed components: multiple sensors positioned throughout the venue, individual user tracking devices, and distributed computing nodes that process proximity calculations. This segmentation allows the system to handle real-time data from multiple users simultaneously without requiring a single complex centralized processing unit.
Solution Approach 2:
User devices autonomously perform proximity calculations and fee assessments based on their own location data and the known positions of points of interest. The system enables users to self-monitor their proximity to featured content and self-assess their viewing experience, reducing the computational burden on central servers while maintaining accurate fee determination.
Data Source
AI summary
In an example, a method includes determining, by a computing device, a proximity of a user to a point of interest. The method also includes determining, by the computing device, a fee to the user based on the proximity of the user to the point of interest, where the fee varies based on the proximity of the user to the point of interest. The method also includes initiating, by the computing device, an electronic transaction based on the fee.