Machine Learning Object Location With AR Guidance

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Solution Overview

Problem

Locating objects in large or complex spaces, such as laboratories or storage areas, is challenging due to non-sensical layouts and the absence of consistent check-in/check-out procedures, leading to time wastage and inefficiency in finding desired items.

Innovation Solution

Utilizing a machine learning model integrated with augmented reality (AR) to process images, recognize object features, and determine their locations, allowing for dynamic object addition/removal without manual logging, and providing AR guidance for object location.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual search methods are used in large or complex spaces, then the system complexity remains low, but the search time and efficiency deteriorate significantly

Engineering Contradiction:

Improvesearch efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical search methods with an automated computer vision system that uses cameras, machine learning models, and image processing algorithms to locate objects, thereby significantly improving search efficiency while managing system complexity through automation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service object location by automatically capturing images, processing them through machine learning models, and providing location information without requiring manual intervention or complex user interaction, thus improving productivity while keeping the user interface simple

Inventive Principle:

Principle #25Self-service

2Measurement precision

If traditional object location methods are used, then the system remains simple to operate, but the accuracy and speed of object location deteriorate

Engineering Contradiction:

Improveobject location accuracyVSAvoidoperational simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent introduces an intermediary computer vision system that bridges the user and the physical environment, automatically processing visual information and providing accurate object location data without requiring complex user operations or manual scanning procedures

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual visual search with automated image capture and processing mechanisms, achieving high measurement precision through machine learning models while maintaining ease of operation through automatic execution without user intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If dynamic object addition and removal are allowed without manual logging, then the system adaptability improves, but the complexity of tracking and locating objects increases

Engineering Contradiction:

Improvedynamic object handling capabilityVSAvoidtracking system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements continuous image capture and processing that operates uninterrupted during dynamic object addition and removal, allowing the machine learning model to continuously track and locate objects without manual logging interruptions, thereby improving adaptability while managing tracking complexity through continuous automated operation

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system performs self-service tracking by automatically detecting and locating objects as they are added or removed from the environment, eliminating the need for manual logging while maintaining accurate tracking through continuous machine learning processing

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12430788B2Object location determination

Publication Date: 2025.09.30 MICRON TECHNOLOGY INC

AI summary

Methods, apparatuses, and non-transitory machine-readable media associated with determining a location of an object are described. An object location determination can include receiving a user request associated with an object, receiving first signaling from a first image source, and receiving second signaling from a second image source. The object location determination can include writing data that is based at least in part on a combination of the user request, the first signaling, and the second signaling and determining a confidence level of identification of the object associated with the user request based on the user request, the first signaling, and the second signaling. The object location determination can include identifying output data representative of a location of the object based on the confidence level and transmitting the output data representative of the location of the object via third signaling.