Queue Position Exchange Using ML-Based Time and Price Prediction
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Solution Overview
Problem
There are no known mechanisms or technological solutions for managing queue preferences and facilitating transactions between devices exchanging positions, particularly for time-sensitive and less time-sensitive individuals, as autonomous vehicles become more prevalent, leading to unbalanced time spent in queues or traffic.
Innovation Solution
A queue exchange machine learning model determines potential changes in time spent in a queue and corresponding exchange amounts, allowing queuing devices to trade positions through transactions, which can be physical or electronic, using user application data and digital wallet services for settlement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users join queues to travel to destinations or access services, then queue position is obtained, but time is wasted waiting in the queue
Solution Approach 1:
The system performs preliminary actions by predicting future queue times and pre-calculating optimal exchange amounts before the user actually needs to reduce wait time. The machine learning model analyzes historical queue data, traffic patterns, and user preferences in advance to prepare exchange recommendations, allowing users to make informed decisions about position trading before entering or while in the queue.
Solution Approach 2:
The patent introduces an intermediary queue management system that facilitates transactions between users. This intermediary system includes a machine learning model that acts as a mediator to calculate fair exchange amounts, match buyers and sellers of queue positions, and process transactions automatically. The intermediary handles the complexity of time valuation and transaction coordination, enabling users to trade queue positions without direct negotiation.
2Adaptability or versatility
If queue position exchange is facilitated, then time flexibility is improved, but system complexity increases
Solution Approach 1:
The system changes parameters by dynamically adjusting exchange amounts based on multiple variables including predicted queue time, user time sensitivity, transaction costs, and market conditions. The machine learning model continuously updates these parameters in real-time, allowing the system to adapt to changing queue conditions and user preferences without requiring complex manual intervention for each transaction.
Solution Approach 2:
The queue management system implements self-service by enabling users to autonomously trade queue positions based on their own time valuations and preferences. Users can set their desired queue positions, time constraints, and maximum exchange amounts, and the system automatically matches them with suitable trading partners. This self-service approach reduces the need for centralized coordination while maintaining system functionality.
3Measurement precision
If machine learning models predict queue times and exchange amounts, then transaction accuracy improves, but computational requirements increase
Solution Approach 1:
The system applies partial action by using machine learning models selectively rather than for all queue transactions. The ML model is deployed to predict queue times and calculate exchange amounts only when necessary to improve transaction accuracy, such as in complex market conditions or for high-value transactions. For simpler, routine transactions, the system may use predefined rules or simpler algorithms, reducing overall computational energy consumption while maintaining precision when it matters most.
Data Source
AI summary
Disclosed herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for altering the time spent in a queue for a respective exchange amount. This may include using user application data to predict and/or identify potential preferences for exchanging queue positions and/or determining potential changes in time spent in the queue. A queue exchange machine learning model may determine one or more potential changes in time and/or respective exchange amounts for each potential change in time. The exchange of queue positions may include vehicles traveling on a roadway and exchanging positions in traffic, exchanging positions between mobile device users standing in a line, and/or exchanging positions in an electronic queue. Monetary funding may be transferred between the queuing devices when exchanging positions in the queue.