Key Blank Image Matching for Fast Part Number Identification
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Solution Overview
Problem
The existing methods for identifying key blank part numbers are time-consuming, inefficient, and prone to errors, especially with the increasing variety of key blank styles and the reliance on outdated resources, leading to significant overhead costs and customer dissatisfaction for lock and key businesses.
Innovation Solution
A method and system using machine learning models to identify key blank part numbers by training algorithms to match images of cut keys with corresponding key blanks, incorporating user feedback for model refinement, and providing a user interface for efficient key blank selection and purchase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional printed key blank manufacturing books are used for identification, then key blank part numbers can be identified, but the process is time-consuming and requires specialized industry knowledge
Solution Approach 1:
The patent replaces the manual mechanical process of flipping through printed books with an automated image recognition system using machine learning models. The system captures an image of the key blank and automatically identifies the part number, eliminating the need for manual searching and specialized knowledge while maintaining high accuracy.
Solution Approach 2:
The patent creates a digital copy (image) of the physical key blank and compares it against a database of digital key blank images. This allows for rapid visual matching and identification without handling physical books or requiring manual comparison of printed images.
2Measurement precision
If key reading machines are used for identification, then accurate key blank identification can be achieved, but the equipment is expensive and bulky
Solution Approach 1:
The patent replaces expensive, durable key reading machines with a software-based solution that runs on common mobile devices. Instead of investing in specialized hardware, the system uses readily available smartphones or tablets with camera capabilities, significantly reducing equipment costs and complexity.
Solution Approach 2:
The patent makes the identification system universally accessible by implementing it as a mobile application that can run on various smartphone and tablet devices. This allows any lock and key business to use the system without purchasing proprietary equipment, as the solution works across multiple device platforms.
3Adaptability or versatility
If memorization of every key is attempted, then complete key blank knowledge can be achieved, but it is beyond human capability
Solution Approach 1:
The patent enables the system to automatically acquire and add new key blank images to its database through user feedback. When a user encounters a key blank not in the database, they can submit an image, and the system automatically adds it after verification, allowing the database to grow and adapt without manual curation effort.
Solution Approach 2:
The patent incorporates a feedback mechanism where users can confirm or correct identification results. This feedback is used to continuously improve the machine learning model's accuracy and expand the database coverage, enabling the system to handle increasingly diverse key blank styles while maintaining ease of use.
4Adaptability or versatility
If multiple key blank manufacturing books are consulted, then comprehensive key blank identification can be achieved, but the process becomes more complex and time-consuming
Solution Approach 1:
The patent merges multiple key blank manufacturing book databases into a single unified digital database accessible through the mobile application. This consolidation allows the system to search across all key blank styles simultaneously, eliminating the need to consult multiple separate books while maintaining comprehensive coverage.
Solution Approach 2:
The patent transitions from a two-dimensional printed book format to a multi-dimensional digital database structure that can store, organize, and search images from numerous manufacturers and styles. This dimensional transformation enables efficient querying and comparison across the entire key blank catalog without physical constraints.
Data Source
AI summary
One or more machine learning models are trained to search an image database to find images that are visually similar to images of cut keys, and a key blank identification system, which utilizes the trained machine learning models, is provided to a user. The user uploads one or more images of a cut key, the trained machine learning models identify one or more key blank part numbers that match with the cut key images, and the results are presented to the user. The user provides feedback relating to the accuracy of the results, and the feedback data is incorporated into the trained machine learning model to refine the model.