Time Interval Geolocation Objects With Price-Time Priority Queues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack the ability to dynamically organize and trade time interval units as commodities due to non-transparent, non-substitutable, and non-transferrable market structures, lacking legal and physical calculation mechanics for time interval units, which limits flexibility and transferability, and fails to account for contingencies or value time interval capacity.
Innovation Solution
Implementing price-time priority queues for time interval units, which involve receiving location data, generating routes, determining virtual hubs, and selecting optimized routes based on travel cost and market depth data to facilitate the trading of time interval units as commodities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional market structures are used for time interval units, then systems are closed and non-transparent, but implementing price-time priority queues increases market transparency and flexibility
Solution Approach 1:
The patent implements dynamic price-time priority queues that automatically adjust and reorganize time interval units based on real-time pricing and temporal priorities. This dynamic restructuring transforms static, closed market structures into transparent, adaptive systems where time interval capacity units can be freely traded and reallocated, resolving the contradiction between maintaining system simplicity and achieving market transparency.
Solution Approach 2:
The patent changes key parameters by introducing price tags and time priority attributes to time interval units, transforming them from non-tradable administrative allocations into tradable commodities. This parameter transformation enables market transparency through visible pricing mechanisms while the standardized queue structure prevents excessive system complexity.
2Adaptability or versatility
If time interval units are made non-transferrable, then scheduling flexibility is limited, but making them transferable requires legal and physical calculation mechanics
Solution Approach 1:
The patent substitutes complex legal and physical calculation mechanics with an electronic price-time priority queue system. Time interval units are represented as digital commodities that automatically transfer through standardized electronic transactions, replacing cumbersome legal frameworks with automated computational matching that handles transferability, pricing, and scheduling flexibility simultaneously.
Solution Approach 2:
The patent introduces price-time priority queues as an intermediary mechanism between time interval suppliers and demanders. This intermediary automatically matches and transfers time interval units based on pricing and temporal priorities, providing the necessary calculation mechanics for transferability without requiring complex legal frameworks, thereby enabling scheduling flexibility through automated mediation.
3Quantity of substance
If time interval capacity is not valued, then trading cannot occur, but valuing it requires implementing price-time priority queues
Solution Approach 1:
The patent assigns value to time interval capacity by introducing price parameters and temporal priority attributes, transforming abstract time resources into quantifiable tradable commodities. This parameter assignment enables trading mechanisms to function by providing measurable value metrics, while the standardized queue structure organizes these valued units efficiently without excessive complexity.
Data Source
AI summary
Various implementations directed to price time priority queue routing for time interval object capacity units are provided. The method may also include generating data packet routes based on the origin location data and the destination location data. The method may further include determining virtual hubs along the data packet routes, where the virtual hubs include a first virtual hub based on the origin location data and a second virtual hub based on the destination location data. The method may additionally include receiving travel cost data for the routes for geolocation time interval object exchange units. In addition, the method may include receiving market depth data for a geolocation exchange for the geolocation exchange units based on the data packet routes. The method may also include selecting an optimized route of the routes for the geolocation exchange units based on an objective function.


