Interpolation System Tracking Differential Changes
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Solution Overview
Problem
Conventional modeling platforms face challenges in navigating and selecting preferred series of interpolated data when the necessary inputs are unknown, while a target output is known, as analysts must manually estimate and adjust component values, leading to a ripple effect and lack of insight into dramatic changes in chemical properties.
Innovation Solution
The system allows users to access and analyze series of interpolated data using a navigation dialog to supply differential criteria, generating and exploring marginal variations, and selecting preferred series that meet user-defined criteria by interpolating missing input values to produce target outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual estimation and adjustment of component values is performed, then the target output can be achieved, but the process becomes time-consuming and complex with ripple effects requiring multiple iterations
Solution Approach 1:
The patent replaces manual mechanical adjustment processes with automated computational systems. The differential analysis system automatically calculates component values and their interrelationships, substituting the manual iterative adjustment process with algorithmic computation that rapidly determines optimal component settings without requiring repeated manual iterations.
Solution Approach 2:
The patent introduces differential analysis as an intermediary computational layer between the target output specification and the component value determination. This intermediary system analyzes the differential relationships between components and automatically adjusts values to achieve the target output, eliminating the need for direct manual trial-and-adjustment cycles.
2Adaptability or versatility
If comprehensive interpolation analysis is conducted to generate multiple series of data, then more alternative solutions are available, but the complexity of navigating and selecting preferred series increases
Solution Approach 1:
The patent extracts and highlights only the differentially significant changes between alternative series, separating the essential comparative information from the complete data set. By focusing on differential pathways and significant variations rather than presenting all raw interpolated data, the system reduces navigation complexity while preserving adaptability across multiple alternative series.
Solution Approach 2:
The patent applies differential analysis to identify and emphasize locally significant changes in specific components or parameters within the alternative series. Rather than treating all data uniformly, the system identifies regions of significant differential change and presents those selectively, reducing the overall complexity burden while maintaining versatility in exploring alternatives.
3Loss of information
If analysts manually track changes in component values, then insight into dramatic changes can be gained, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent implements automated feedback mechanisms that continuously calculate and present differential changes between alternative series. The system automatically feeds back information about which components exhibit dramatic changes and how these changes propagate through the system, providing insightful differential analysis without requiring manual tracking and reducing errors associated with human oversight.
Data Source
AI summary
Embodiments relate to systems and methods for tracking differential changes in conformal data input sets. A database can store sets of operational data, such as financial, medical, climate or other information. For given data, a portion of the input data can be known or predetermined, while for a second portion can be unknown and subject to interpolation. The interpolation engine can generate a conformal interpolation function and interpolated input sets that map to a set of target output data. The operator can access a view of known (or interpolated) input data to view one or more series of interpolated input data, and analyze the differential between those interpolated values. The operator can for instance apply a constraint or filter to view only those interpolated series whose maximum marginal difference for any variable is less that a given threshold, such as ten percent, and graphically navigate between different series.