Service Purchase Value Forecasting With Bayesian Pricing Models
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Solution Overview
Problem
Existing systems struggle to determine optimal times for resource transfers of temporal resources like services due to their variable and irregular value fluctuations, making it difficult for recipient entities to predict future pricing and make informed acquisition decisions.
Innovation Solution
A system utilizing a Bayesian network and machine learning (ML) data system component to analyze historical data, predict future resource values, and construct dynamic resource values and service level parameters, enabling better decision-making by comparing current and future prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If recipient entities attempt to acquire services at optimal times, then cost savings improve, but the ability to predict future pricing deteriorates due to irregular value fluctuations
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing historical service purchase data, pricing information, and service level parameters before acquisition decisions are needed. This pre-processing of data enables future predictions without requiring complex real-time analysis, thus improving prediction capability while maintaining cost optimization ability
Solution Approach 2:
The patent introduces an intermediary predictive analytics system that mediates between the fluctuating service values and the recipient entity's acquisition decisions. This intermediary processes historical data, identifies patterns in irregular fluctuations, and provides predicted future values, thereby improving prediction accuracy without requiring the recipient entity to directly analyze complex market variations
2Reliability
If historical data is collected and analyzed to predict future prices, then decision-making quality improves, but system complexity increases
Solution Approach 1:
The system segments the complex predictive analytics functionality into distinct modular components: data collection modules that gather historical service purchase information, data storage modules that organize pricing and service level parameters, and analysis modules that generate predictions. This segmentation improves decision-making quality through comprehensive analysis while managing system complexity through modular architecture that allows independent development and maintenance of each component
Solution Approach 2:
The patent creates a universal predictive analytics platform that handles multiple service types and procurement scenarios through a single integrated system. The system universally processes diverse historical data, applies consistent analytical methods across different service categories, and provides predictions for various acquisition decisions, thereby improving overall decision-making quality while avoiding the complexity of separate specialized systems
Data Source
AI summary
The present invention relates to system, method and computer program product for customized processing temporal resources and constructing resource values. The system comprises a computer-executable platform comprising a resource value construction module that is structured to access a data storage module and determine a resource value offer of the resource. The system further comprises a Bayesian network connected to the computer-executable platform. Moreover, the system comprises a user interface connected to the Bayesian network, the user interface comprising: a selection module that is structured to receive a user section of an indication of resource; and a management module that allows the user to manage the information of the resources via the user interface.