Computer Vision Label Registration for Inventory Change Detection

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Solution Overview

Problem

Existing systems require manual intervention for label detection and registration on objects, leading to inefficiencies and potential errors in inventory and asset management.

Innovation Solution

A computer-vision-based system and method that automatically detects and reads labels on objects using machine learning, associates them with object locations, and employs change detection algorithms to manage inventory and assets, with human intervention as a fallback for errors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual intervention is used for label detection and registration, then system complexity is reduced, but productivity and accuracy deteriorate

Engineering Contradiction:

Improvelabel detection and registration speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical label detection and registration processes with an automated computer vision system using cameras, image processing algorithms, and machine learning models. This substitution eliminates human intervention in the core detection and registration tasks, thereby increasing productivity while managing system complexity through software-based solutions rather than mechanical complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically detecting labels, recognizing objects, and registering information without requiring continuous human operation. The automated image processing and machine learning algorithms enable the system to independently complete label detection and registration tasks, improving productivity while reducing the need for manual intervention.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated computer vision system is implemented, then productivity is improved, but measurement precision and reliability may deteriorate due to technical challenges

Engineering Contradiction:

Improveautomated label detection speedVSAvoidlabel detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the label detection process into multiple independent stages: image capture, object detection, label detection, and registration. Each stage can be optimized separately, with image processing handling basic detection, machine learning models providing recognition, and registration ensuring accurate positioning. This segmentation allows the system to maintain high productivity while improving measurement precision through specialized processing at each stage.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system incorporates feedback mechanisms where detected labels and objects are verified and registered, with the ability to correct errors and improve future detections. The machine learning models learn from feedback loops, continuously improving their accuracy based on previous performance, thereby maintaining high measurement precision while preserving the productivity benefits of automation.

Inventive Principle:

Principle #23Feedback

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables efficient, automated label detection and registration, reducing the need for manual input and improving accuracy and speed in inventory and asset management.

Implementation Method 1

a camera capturing images of a support surface

Methodology Applied

Scientific EffectLight reflection: Reflection

Implementation Method 2

the processor detects objects and labels in the captured images

Methodology Applied

Scientific EffectImage processing: Image Processing

Implementation Method 3

the processor automatically reads the label information

Methodology Applied

Scientific EffectOptical character recognition:

Data Source

PatentUS20260037759A1Computer vision system and method of label detection, reading, and registration of labels on objects

Publication Date: 2026.02.05 POSITION IMAGING INC

AI summary

A computer vision system for automatic identification, tracking and management of inventory and/or assets, wherein: the computer vision system is programmed with predetermined and configurable image data conditions that identify and capture certain types of movement; the computer vision system is programmed to constantly capture and process image data from one or more sets of sensors to detect the image data conditions; the computer vision system is programmed to trigger the process of identifying all objects and extracting label information from objects present in a field of view of the system if the image data conditions are fulfilled; the computer vision system processes the image data with one or more object detection algorithms and label detection algorithms and generate correspondences between objects and labels identified; and the computer vision system running change detection algorithms and semantic analysis to detect if any object was moved, removed, occluded and if any new objects were added in to the system.