Weld Training Result Sharing via QR-Linked Learning Activities
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional weld training systems lack the ability to efficiently share high-quality weld training results, leading to lower engagement and progression in training, as participants often resort to taking lower resolution pictures for sharing, which do not allow for manipulation or modification of the original training data.
Innovation Solution
A system that enables the upload of high-quality weld training results to a central training system, encoded in machine-readable graphics, allowing participants to access and share them via a networked location, and associates these results with learning management systems for evaluation and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If participants take pictures of weld training results for sharing, then the results can be shared with others, but the resolution quality deteriorates and the original training data cannot be manipulated or modified
Solution Approach 1:
The patent implements digital copying of weld training results by encoding them in machine-readable graphics (QR codes, barcodes) that can be shared electronically. Instead of taking physical pictures that lose quality, the system creates digital copies that maintain full resolution and data integrity while being easily shareable through communication circuits.
Solution Approach 2:
The patent introduces machine-readable graphics as an intermediary between the weld training results and the sharing process. These graphics encode the training data and serve as a mediator that preserves data quality while enabling easy distribution through various channels (email, messaging, social media).
2Loss of information
If weld training results are stored locally only, then data security is maintained, but accessibility and sharing capability are reduced
Solution Approach 1:
The patent segments the storage and access of weld training results by encoding them in machine-readable graphics that can be distributed selectively. The full training data remains secure in the central training system, while encoded segments can be shared with specific users or groups who then have controlled access to view or download results.
Solution Approach 2:
The patent adds a new dimension to data storage by creating a distributed access model through machine-readable graphics. Instead of purely local or purely centralized storage, the system creates encoded representations that exist in a third dimension, allowing secure centralized storage with flexible distributed access capabilities.
3Loss of information
If high-quality weld training results are shared digitally, then information quality is maintained, but system complexity increases
Solution Approach 1:
The patent implements universal machine-readable graphics that can encode and transmit multiple types of weld training data (videos, images, measurements, annotations) through a single standardized format. This multi-functional approach maintains high data quality while reducing system complexity by using one versatile encoding system rather than multiple specialized ones.
Data Source
AI summary
Systems and methods for learning management systems with shared weld training results are described. In some examples, weld training results may be shared with a learning management system and/or associated with a particular learning activity of the learning management system. In some examples, the weld training results (and/or a networked location where the weld training results are accessible) may be encoded in a machine readable graphic (e.g., a one dimensional, two dimensional, and/or matrix barcode). In some examples, the machine readable graphic may be read and/or decoded by a user device to obtain the weld training results. In some examples, a particular learning activity may also be encoded in the machine readable graphic.