AI Learning Engine Predicts Optimal Resource Timing
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Solution Overview
Problem
Conventional methods struggle to accurately predict when resource properties, such as airline ticket prices, are at an optimum state, leading to suboptimal utilization or access, often resulting in using resources too early or late.
Innovation Solution
An artificial intelligence system employing trained learning engines to predict the time frame when resource attributes will have a minimum value, allowing users to reserve and utilize resources at the predicted optimum time, with optional fail-safe mechanisms and flexible payment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used for resource properties, then the system is simple and easy to implement, but the prediction accuracy is low leading to suboptimal resource utilization
Solution Approach 1:
The patent introduces trained learning engines as intermediary components between the user and the resource booking system. These learning engines analyze historical data and predict optimal booking times, acting as mediators that translate complex patterns into actionable predictions. This resolves the contradiction by providing high prediction accuracy through specialized intermediary systems without requiring the entire system to become complex.
Solution Approach 2:
The system performs preliminary actions by training learning engines in advance with historical resource property data. This preliminary training enables the system to make accurate predictions without complex real-time analysis, resolving the contradiction between prediction accuracy and system complexity by preparing predictive capabilities beforehand.
2Productivity
If resources are accessed too early or too late without accurate prediction, then the system operation is simple, but the resource utilization efficiency deteriorates
Solution Approach 1:
The patent replaces mechanical timing methods (simple calendars or manual tracking) with intelligent learning engine predictions. The learning engines analyze patterns in resource property variations and predict optimal access times, substituting mechanical time-based approaches with data-driven predictive systems. This resolves the contradiction by significantly reducing timing errors and improving resource utilization efficiency.
Solution Approach 2:
The system implements feedback mechanisms where actual resource property outcomes are fed back into the learning engines to continuously improve prediction accuracy. This feedback loop reduces timing errors over time and improves resource utilization efficiency, resolving the contradiction between these two parameters.
3Measurement precision
If AI learning engines are deployed to predict optimal resource access times, then prediction accuracy improves, but computational resource requirements increase
Solution Approach 1:
The patent performs computationally intensive activities in advance by training learning engines offline with historical data. Once trained, these models can make predictions with high accuracy using minimal computational resources during actual operation. This resolves the contradiction by shifting computational burden from runtime to training time, improving prediction accuracy while reducing ongoing computational resource consumption.
Solution Approach 2:
The system creates simplified predictive models (copies of the complex training process) that can operate efficiently during runtime. These trained learning engine models are lightweight versions that capture the essence of complex patterns without requiring the full computational power needed for training, resolving the contradiction between prediction accuracy and computational resource usage during operation.
Data Source
AI summary
An artificial intelligence system is configured to receive over a network a search request for a transportation instrument associated with a first date. Matching scheduled transportation events are identified. Matching scheduled transportation events categorized by a first trained learning engine are presented by a user device. A selection of a first of the matching scheduled transportation events is received from the user device. A second trained learning engine is used to predict a time frame when the first matching scheduled transportation event will be associated with a minimum attribute value. A reservation instruction is transmitted to a remote system to reserve utilization of the first matching scheduled transportation event during the time frame predicted by the second trained learning engine. A user is enabled to utilize the first matching scheduled transportation event for transport.