AI Confidence Scoring for Natural Language Data Clarity

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Solution Overview

Problem

Conventional qualitative assessment techniques for natural language data are inefficient and often unable to effectively flag unclear or incomplete data in real-time, leading to resource wastage and delayed processing.

Innovation Solution

An automated machine learning solution leveraging artificial intelligence that determines confidence scores for natural language data, generates requests for additional information when scores are below a threshold, and transmits data to appropriate destinations once scores exceed the threshold, utilizing models for linguistic analysis and context determination.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional qualitative assessment techniques are used, then data processing can be performed, but the assessment is inefficient and cannot effectively flag unclear or incomplete data in real-time

Engineering Contradiction:

Improvereal-time assessment efficiencyVSAvoidaccuracy of flagging unclear data

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces conventional mechanical/manual data assessment processes with an automated machine learning system that uses AI models to evaluate natural language data in real-time, enabling both high productivity and reliable detection of unclear data

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-assessment of data quality through automated ML models that independently evaluate clarity and completeness without requiring manual intervention, achieving real-time processing with high accuracy

Inventive Principle:

Principle #25Self-service

2Reliability

If extensive processing is performed to convert unstructured data into structured data, then data usability can be ascertained, but resources are wasted on potentially unusable data and real-time processing is prevented

Engineering Contradiction:

Improvedata usability assessmentVSAvoidresource wastage

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary assessment of data clarity and completeness using ML models before initiating extensive processing, thereby avoiding resource wastage on potentially unusable data and enabling real-time decisions about data processing

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides feedback about data quality and usability in real-time, allowing users to understand whether data is suitable for processing before investing resources in conversion processes

Inventive Principle:

Principle #23Feedback

3Productivity

If conventional techniques are used for qualitative assessment, then data can be processed, but the process is not real-time and resources are expended on potentially unusable data

Engineering Contradiction:

Improvedata processing throughputVSAvoidprocessing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces slow conventional data assessment methods with automated machine learning models that process natural language data in real-time, dramatically reducing processing time while maintaining high productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250013833A1Method and system for capturing and storing machine learned quantitative classification of natural language data

Publication Date: 2025.01.09 JPMORGAN CHASE BANK NA

AI summary

A method for facilitating qualitative assessment of natural language data via artificial intelligence is disclosed. The method includes receiving, via an application programming interface, an input from a source, the input including the natural language data; determining, by using a model, a confidence score for the input, the confidence score relating to a clarity level of the natural language data; determining, by using the model, whether the confidence score exceeds a predetermined threshold; generating, by using the model, a request for additional information when the confidence score is below the predetermined threshold, the request including a prompt in a natural language format; and transmitting, via the application programming interface, the request back to the source.