AI Pricing Interpolation Platform for Volatile Low-Data Markets
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Solution Overview
Problem
Existing data modeling systems struggle with generating accurate and reliable data projections when faced with limited, outdated, and unreliable data, particularly in volatile environments, and lack real-time updating capabilities.
Innovation Solution
A data interpolation platform utilizing AI engines to collect, preprocess, and normalize data, deploy AI models for predictive pricing, and update projections in real-time, with an interactive GUI for customization and integration with other tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing data modeling systems use traditional modeling technology, then they can process data, but they fail to generate accurate projections when data is limited, outdated, or unreliable
Solution Approach 1:
The system transitions from traditional static modeling parameters to dynamic AI-driven parameters that adapt to data quality and volatility. The AI models adjust their internal parameters and weighting schemes based on the characteristics of available data, enabling accurate projections even when data is limited or outdated.
Solution Approach 2:
The patent replaces traditional mechanical data modeling systems with AI-based models. This substitution enables the system to handle non-linear relationships, volatile data conditions, and limited data scenarios that traditional mechanical systems cannot process effectively, thereby improving both reliability and adaptability.
2Reliability
If existing systems require large corpus of historical datasets, then they can generate projections, but they become inaccurate when such data is unavailable
Solution Approach 1:
The AI models are designed to function effectively with partial data rather than requiring complete historical datasets. The system performs sufficient analysis with available data, applying appropriate weighting and uncertainty modeling to generate reliable projections even when the quantity of historical data is limited.
Solution Approach 2:
The system dynamically adjusts data requirements and modeling parameters based on the quantity and quality of available historical data. When data is abundant, traditional methods may be used; when data is limited, the AI models switch to alternative parameter configurations that are optimized for low-data scenarios.
3Adaptability or versatility
If existing systems use antiquated modeling technology, then they are simple to implement, but they cannot account for data volatility, diverse data types, or multiple data sources
Solution Approach 1:
The AI modeling platform is designed as a universal system that can handle multiple data types (structured, unstructured, time-series), diverse data sources (internal databases, external APIs, market data), and various volatility conditions through a single integrated architecture. This multi-functionality achieves high adaptability without proportionally increasing operational complexity.
Solution Approach 2:
The system introduces AI models as intermediary layers between raw diverse data and final projections. These intermediaries process, normalize, and synthesize data from multiple sources and formats, managing the complexity of handling diverse data types while presenting simplified, accurate results to users.
4Reliability
If existing systems cannot update projections with changing conditions, then they are stable, but they become unreliable when new or updated data is discovered
Solution Approach 1:
The system implements continuous feedback loops where new or updated data is automatically detected, processed, and fed back into the AI models for re-projection. This feedback mechanism ensures that projections remain reliable by continuously incorporating the latest information while maintaining system stability through controlled update cycles.
Solution Approach 2:
The AI models operate continuously, constantly monitoring for new data and updating projections without interruption. This continuous operation ensures that projections remain current and reliable, automatically adapting to changing market conditions and new information as it becomes available.
Data Source
AI summary
A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and/or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.