Extrapolated Event Indication Generation via Statistical Data Brackets

Solution Overview

Problem

Existing models face challenges in accurately predicting subsequent events with high fidelity and efficiency due to the need for large amounts of data, which increases processing time and reduces accuracy when reducing redundant data inputs.

Innovation Solution

A system utilizing multiple variable statistical algorithms and decision tree analysis to identify and generate determined variables and values that define strong correlations with previous events, allowing for the generation of an extrapolated indication for subsequent events with increased fidelity and reduced redundancy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a large amount of model data is utilized to determine parameters for predicting subsequent events, then the accuracy of the prediction increases, but the processing time and computing power consumption increase

Engineering Contradiction:

Improveprediction accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts and identifies only the most relevant parameters from the large set of model data using statistical algorithms and machine learning techniques. By selecting a subset of critical parameters that have the strongest correlation with subsequent events, the system maintains high prediction accuracy while significantly reducing the amount of data that needs to be processed, thereby decreasing processing time and computing resource consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent dynamically adjusts and optimizes parameter selection based on the specific characteristics of the data and the prediction task. By changing which parameters are included in the model and how they are weighted, the system can achieve high accuracy with fewer parameters, thus resolving the contradiction between accuracy and processing efficiency.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If a large amount of model data is utilized to determine parameters for predicting subsequent events, then the accuracy of the prediction increases, but the computing power consumption increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidcomputing power consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent extracts and identifies only the most relevant parameters from the large set of model data using statistical algorithms and machine learning techniques. By selecting a subset of critical parameters that have the strongest correlation with subsequent events, the system maintains high prediction accuracy while significantly reducing the amount of data that needs to be processed, thereby decreasing processing time and computing resource consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent dynamically adjusts and optimizes parameter selection based on the specific characteristics of the data and the prediction task. By changing which parameters are included in the model and how they are weighted, the system can achieve high accuracy with fewer parameters, thus resolving the contradiction between accuracy and processing efficiency.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If the quantity of model input data is reduced to improve processing efficiency, then the processing time decreases, but the accuracy of the prediction reduces

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidprediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent extracts and identifies only the most relevant parameters from the large set of model data using statistical algorithms and machine learning techniques. By selecting a subset of critical parameters that have the strongest correlation with subsequent events, the system maintains high prediction accuracy while significantly reducing the amount of data that needs to be processed, thereby decreasing processing time and computing resource consumption.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent dynamically adjusts and optimizes parameter selection based on the specific characteristics of the data and the prediction task. By changing which parameters are included in the model and how they are weighted, the system can achieve high accuracy with fewer parameters, thus resolving the contradiction between accuracy and processing efficiency.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240193679A1Integrated generation of a hi-fi extrapolated indication associated with a subsequent event efficiently without redundancy

Publication Date: 2024.06.13 TRUIST BANK

AI summary

A system with a processor configured to execute a front-end variable determination program including steps to receive determination data indicative of previous events; to identify, utilizing a multiple variable statistical algorithm, data brackets, each having interpolated correlation with indications of the previous events; to identify, utilizing bivariate analysis and the data brackets, a determined variable defining a strong interpolated correlation with indications of the previous events; and to generate, utilizing bivariate analysis, an associated determined value that separates ranges of data associated with the determined variable based on the strong interpolated correlation within the determination data. The processor is further configured to execute a back-end indication program including steps to receive the determined variable and each determined value from the front-end determination program; receive input data indicating the determined variable for subsequent event; and generate the extrapolated indication for the subsequent event having increased fidelity or reduced redundancy.