AI Pipeline Input Validation for Reliable Runtime Execution

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Solution Overview

Problem

Existing AI pipelines often process invalid or incomplete input data, leading to erroneous outputs and wastage of computational resources due to inefficient data management.

Innovation Solution

A unified framework with real-time data validation mechanisms that monitor input data against predefined models, pause the pipeline upon detecting anomalies, and retrieve or generate corrective data to ensure valid input for resumed processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the AI pipeline processes all input data without validation, then productivity is maintained, but reliability deteriorates due to erroneous outputs from invalid data

Engineering Contradiction:

Improveoutput reliabilityVSAvoiddata processing throughput

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary validation of input data against the training data model before processing. This preliminary action identifies invalid data characteristics (such as unexpected column names, data types, or formats) and prevents them from entering the AI processing pipeline, thereby ensuring output reliability without significantly impacting productivity

Inventive Principle:

Principle #10Preliminary action

2Reliability

If the AI pipeline validates input data against training content, then reliability improves, but loss of time increases due to validation overhead

Engineering Contradiction:

Improvedata validityVSAvoidvalidation processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces complex, time-consuming manual validation processes with an automated validation mechanism that compares input data characteristics against the stored training data model. This substitution enables rapid validation by checking data structure, column names, and formats against predefined expectations, minimizing time loss while ensuring data validity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If the AI pipeline processes invalid data, then productivity is maintained, but loss of energy increases due to wastage on erroneous processing

Engineering Contradiction:

Improveprocessing accuracyVSAvoidcomputational resource waste

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system extracts and removes invalid data characteristics from the input data before processing. By comparing input data against the training data model and identifying deviations (such as incorrect column names, data types, or formats), the system extracts only the valid portions or rejects invalid data entirely, preventing wasteful processing and reducing energy loss

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260056821A1Enhancing reliability of artificial intelligence operational pipelines

Publication Date: 2026.02.26 THE TORONTO DOMINION BANK

AI summary

An example operation may include one or more of integrating a trained AI model into an AI pipeline via a software application, receiving input data via the software application and starting execution of the AI pipeline on the input data, determining, during runtime of the AI pipeline, that content included in the input data is not valid based on a comparison of the content included in the input data to training content included in training data, stopping execution of the AI pipeline on the input data based on the input data not being valid, and presenting a notification via a graphical user interface (GUI) of the software application which indicates the input data is not valid. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and/or is associated with an AI model.