AI Estimation of Excess Non-Sapient Payload Capacity
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Solution Overview
Problem
Existing systems are inaccurate in predicting and utilizing excess non-sapient payload capacity on mixed-payload aeronautic excursions, where sapient and non-sapient payloads are combined, leading to underutilization of storage space.
Innovation Solution
An artificial intelligence system that uses a machine-learning model to estimate excess non-sapient payload capacity by correlating aeronautic excursion parameters with storage quantity outputs, selecting optimal aeronautic paths based on these estimates, and integrating with client-interface and path-selection modules to facilitate efficient payload transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems are used to predict excess non-sapient payload capacity, then the prediction process is simple, but the accuracy of prediction is poor
Solution Approach 1:
The patent replaces traditional mechanical or rule-based prediction systems with an artificial intelligence system that uses machine learning models. The AI system processes aeronautic excursion data to generate accurate predictions of excess non-sapient payload capacity, substituting complex computational intelligence for simpler but less accurate conventional methods.
Solution Approach 2:
The patent changes the parameters of the prediction system by introducing multiple input parameters (aeronautic excursion data) and using machine learning to dynamically adjust prediction outcomes. This allows the system to adapt to varying conditions and improve prediction accuracy compared to static conventional systems.
2Productivity
If excess capacity is not utilized, then the system operation is simple, but the capacity utilization is poor
Solution Approach 1:
The AI system automatically identifies and utilizes excess capacity without requiring complex manual intervention or coordination. The system serves itself by processing data, generating predictions, and enabling payload transfer decisions autonomously, improving capacity utilization through self-directed optimization.
Solution Approach 2:
The patent introduces an intermediary AI prediction system that mediates between aeronautic excursion operations and payload transfer decisions. This intermediary processes information and provides predictions that enable more efficient capacity utilization without directly complicating the core operational systems.
3Productivity
If manual prediction methods are used, then the system complexity is low, but the prediction accuracy and operational efficiency are poor
Solution Approach 1:
The patent replaces manual prediction methods with an automated AI system that uses machine learning models to process aeronautic excursion data. This substitution dramatically improves operational efficiency by automating the prediction process and enabling faster, more accurate decision-making for payload transfers.
Data Source
AI summary
A system for selection of physical asset transfer paths using mixed-payload aeronautic excursions includes a client-interface module operating on at least a server, the client-interface module, configured to receive an initial location, a terminal location, and a description of at least an element of non-sapient payload, a path-selection module operating on the at least a server configured to identify at least an aeronautic path from the initial location to the terminal location and a plurality of aeronautic excursions traversing the at least an aeronautic path and select an aeronautic excursion of the plurality of aeronautic excursions based on a plurality of excess non-sapient payload storage estimations corresponding the plurality of aeronautic excursions, and a capacity estimation artificial intelligence module operating on the at least a server, the capacity estimation artificial intelligence module designed and configured to generate the plurality of excess non-sapient payload storage estimations.