Machine Learning Video Analysis for Process Delay Factors

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Solution Overview

Problem

Existing methods for identifying causes of delays in processes are time-consuming, resource-intensive, and prone to human error, relying heavily on human expertise that may be limited and outdated.

Innovation Solution

A system utilizing machine learning models to analyze video data from camera devices to identify candidate delay factors, predict their correlation with delays, and implement corrective actions to mitigate them, thereby automating the process improvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If human expertise is used to identify delay factors, then the identification process can be performed with simple tools, but the process becomes time-consuming and resource-intensive

Engineering Contradiction:

Improvesimplicity of identification processVSAvoidtime to identify delay factors

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The patent replaces manual human analysis with an automated computer vision system that uses machine learning models to analyze video data and identify delay factors. This substitution of mechanical human effort with automated computational systems directly resolves the contradiction by eliminating time-consuming manual processes while maintaining identification accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the automated platform to independently identify, analyze, and prioritize delay factors without requiring continuous human intervention. The machine learning models automatically process video feeds, detect delays, and generate insights, making the system self-sufficient and eliminating the time loss associated with human expertise requirements.

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If human expertise is used to identify delay factors, then existing simple tools can be utilized, but human error increases and reliability decreases

Engineering Contradiction:

Improvesimplicity of tools requiredVSAvoidaccuracy of delay factor identification

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent replaces fallible human judgment with reliable automated machine learning models that consistently analyze video data according to trained parameters. This substitution eliminates human error while maintaining tool simplicity, as the automated system uses the same accessible video recording infrastructure but applies sophisticated algorithmic analysis to achieve high reliability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the machine learning models continuously learn from analyzed delay instances, improving their accuracy over time. The platform provides feedback loops that refine delay factor identification based on historical data and correction patterns, thereby increasing reliability while maintaining the simplicity of the underlying video-based toolset.

Inventive Principle:

Principle #23Feedback

3Ease of manufacture

If traditional methods are used to improve processes, then implementation can be straightforward, but the methods become outdated and less effective

Engineering Contradiction:

Improveease of implementationVSAvoidcurrency of improvement methods

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic adaptability through machine learning models that continuously evolve and update their delay factor identification capabilities. The system adapts to new delay patterns, processes, and environments automatically, ensuring currency and effectiveness while maintaining ease of implementation through the same basic video recording infrastructure. The dynamic nature of the algorithms allows the system to stay current without requiring frequent manual updates.

Inventive Principle:

Principle #15Dynamics

4Ease of operation

If manual analysis of video data is performed, then the system remains simple and easy to operate, but productivity decreases

Engineering Contradiction:

Improvesimplicity of system operationVSAvoidspeed of delay identification

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces slow manual video analysis with automated machine learning processing that operates at computational speeds. The system maintains ease of operation by using the same simple video recording inputs but achieves dramatically improved productivity through automated algorithmic analysis that processes footage instantly rather than requiring time-consuming manual review.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250317551A1System and method for pre-identifying delay factors to mitigate process delays

Publication Date: 2025.10.09 DISNEY ENTERPRISES INC

AI summary

A process improvement platform may receive an indication of a delay in a process and information identifying one or more candidate delay factors associated with the delay; and analyze current video data of the process using a machine learning model trained to identify different candidate delay factors that cause different delays in different processes. The platform may detect, based on analyzing the current video data, the delay and a candidate delay factor of the one or more candidate delay factors and analyze historical video data of the process to determine whether a correlation exists between the candidate delay factor and the delay. The platform may predict, based on analyzing the historical video data, that the candidate delay factor causes delays in the process; and determine, based on predicting that the candidate delay factor causes the delays, an action to be performed to mitigate subsequent delays.