Mixed Reality Device Task Location Data Association

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Solution Overview

Problem

Existing technologies lack the ability to automatically associate data related to a task location with time-series data detected by a tool during screw-turning tasks, making it difficult to discriminate which parts of the time-series data correspond to specific screws.

Innovation Solution

A processing device that estimates a task location using images and extracts relevant time-series data from a tool, associating this data with task location information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual annotation is used to associate task location data with time-series data, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improveassociation precisionVSAvoidtime for manual annotation

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically associates task location data with time-series data through image processing and data matching algorithms, eliminating the need for manual annotation. The processing device autonomously performs the association by extracting feature information from images and matching it with tool detection data, thereby reducing time loss while maintaining precision.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual annotation processes with automated computational methods. Image processing algorithms and data matching mechanisms substitute for human annotators, enabling automatic association of task location information with time-series data from tools, thus eliminating time-consuming manual intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated data association is implemented, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improvedata association efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system divides the data association task into separate functional modules: image processing unit for extracting feature information, tool detection unit for acquiring time-series data, and data association unit for matching the two data types. This segmentation reduces overall system complexity by making each component independent and easier to implement.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces feature information extraction as an intermediary step between image data and tool detection data. This intermediary mechanism simplifies the association process by first extracting key features from images and then using these features as keys to match with corresponding tool data, reducing the complexity of direct data association.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250124559A1Processing device, training device, mixed reality device, processing method, and storage medium

Publication Date: 2025.04.17 KK TOSHIBA

AI summary

According to one embodiment, a processing device is configured to estimate a first task location by using a first image, a screw being turned at the first task location. The processing device is configured to extract first time-series data from time-series data acquired by a tool turning the screw, the first time-series data being time-series data when the screw is being turned at the first task location. The processing device is configured to associate the first time-series data with data related to the first task location.