Task Image Analysis Using Start-End Similarity Candidates

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Solution Overview

Problem

Existing methods struggle to accurately identify the start and end points of repetitive tasks in image data, leading to inaccuracies in task period estimation.

Innovation Solution

A method involving setting reference start and end images, extracting candidates based on similarity, and evaluating task periods using a combination of candidates to improve accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If arbitrary motion is extracted from image data, then task analysis can be performed, but accuracy deteriorates due to erroneous extraction

Engineering Contradiction:

Improvetask analysis capabilityVSAvoidtask period identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the task identification process into distinct phases: extracting multiple candidate start/end points from image data, generating multiple candidate task periods from these candidates, and evaluating each candidate period independently. This segmentation allows systematic evaluation of multiple possibilities rather than relying on single arbitrary extraction, thereby improving accuracy while maintaining task analysis capability.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If multiple candidate combinations are evaluated, then accuracy improves, but device complexity increases

Engineering Contradiction:

Improvetask period identification accuracyVSAvoidevaluation system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary extraction of multiple candidate start points and end points from image data before generating task period candidates. By preparing these candidate pools in advance and establishing evaluation criteria beforehand, the system structures the complexity into manageable preprocessing and evaluation stages, making the overall process more controllable and systematic.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements an evaluation mechanism that assesses multiple candidate task periods and selects the most appropriate one based on evaluation results. This feedback-based selection process allows the system to handle complexity by systematically comparing candidates against established criteria and choosing the optimal solution, thereby improving accuracy without requiring overly complex real-time processing.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260024307A1Task analysis method, information processing system, and storage medium

Publication Date: 2026.01.22 KONICA MINOLTA INC

AI summary

A task analysis method includes, presetting a captured image of an operation of an operator at a start of a predetermined task as a reference start image, and presetting the captured image of the operation of the operator at an end of the task as a reference end image; acquiring imaging data of the predetermined task repeated a plurality of times; extracting, from the acquired imaging data, a first candidate for the operation at the start based on a first similarity to the reference start image and a second candidate for the operation at the end based on a second similarity to the reference end image; and specifying a task period by evaluating validity of a third candidate of the task period defined by a combination of the extracted first candidate and second candidate.