AI Data Interpolation Platform for Volatile Pricing Projections
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Solution Overview
Problem
Existing systems struggle with generating accurate and reliable data projections when faced with limited, outdated, or unreliable data, particularly in volatile environments, and lack real-time updating capabilities and integration with other tools.
Innovation Solution
A data interpolation platform utilizing AI engines to collect, preprocess, and normalize data, deploy AI models for predictive pricing, and update graphical user interfaces in real-time to reflect changing conditions, integrating with various data sources and tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems use traditional data modeling technology to generate data projections, then they can produce projections, but the accuracy and reliability of projections deteriorate when limited, outdated, or volatile data is available
Solution Approach 1:
The patent replaces traditional mechanical data modeling systems with AI-driven models that can dynamically adapt to limited, outdated, or volatile data conditions. The AI engine automatically selects and adjusts modeling approaches based on data quality and characteristics, eliminating the need for manual model selection and improving both reliability and accuracy of projections.
Solution Approach 2:
The system dynamically changes modeling parameters and data weighting based on the quality, recency, and volatility of available data. The AI engine adjusts parameters such as data decay rates, model complexity, and confidence intervals in real-time, allowing accurate projections even when data conditions deteriorate.
2Measurement precision
If existing systems require large corpora of historical datasets to generate accurate data projections, then projection accuracy improves, but the system becomes unable to operate when limited historical data is available
Solution Approach 1:
The patent implements dynamic data requirements where the AI engine automatically adjusts the amount and type of historical data needed based on current data availability and volatility. When limited data is available, the system adapts by using alternative data sources, adjusting time windows, or switching to simpler models that require fewer historical observations, maintaining operational versatility while preserving accuracy.
Solution Approach 2:
The AI-driven platform performs multiple functions including data collection, quality assessment, model selection, parameter optimization, and projection generation within a single unified system. This multi-functionality allows the system to handle diverse data conditions and requirements, adapting to both data-rich and data-poor environments without requiring separate systems.
3Adaptability or versatility
If existing systems use antiquated modeling technology, then system complexity remains low, but the system becomes unable to account for data volatility, diverse data types, and multiple data sources
Solution Approach 1:
The AI engine performs self-service by automatically assessing data characteristics, selecting appropriate models, and optimizing parameters without requiring complex manual configuration. The system self-adapts to diverse data types and sources by automatically detecting data patterns and adjusting its processing approach, reducing the apparent complexity for users while handling sophisticated multi-source, multi-type data integration.
Solution Approach 2:
The patent introduces an AI intermediary layer that sits between diverse data sources and the projection generation process. This intermediary automatically standardizes, validates, and integrates data from multiple sources and formats, managing the complexity of handling diverse data types while presenting a simplified interface to users and downstream systems.
4Productivity
If existing systems lack real-time updating capabilities, then system simplicity is maintained, but the system becomes unable to automatically re-calculate and adjust data projections with changing conditions
Solution Approach 1:
The patent implements continuous data monitoring and projection updating where the AI engine continuously ingests new data, reassesses conditions, and recalculates projections in real-time. This continuous operation maintains up-to-date projections automatically, with the system continuously adapting to changing conditions without requiring manual intervention or complex batch processing schedules.
Solution Approach 2:
The system incorporates feedback loops where projection results and new incoming data are continuously fed back to the AI engine, which automatically adjusts models and recalculates projections. This feedback mechanism enables automatic adaptation to changing conditions, with the system learning from and responding to new information in real-time, managing complexity through automated closed-loop control.
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.