Key Image Recognition Using Generic Singularities for Fast Duplication
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Solution Overview
Problem
Existing key duplication methods are inefficient and resource-intensive, particularly in the European market where there is a vast variety of key models, requiring significant computational resources and time to identify and reproduce keys without property cards or engraved numbers, and are not universally applicable.
Innovation Solution
A method involving a preliminary phase of generic singularity analysis to efficiently search a database for key models, using characteristic points and correlation techniques to quickly identify keys, allowing for universal key reproduction across diverse models, and a subsequent specific analysis to determine unique coding, enabling efficient key recognition and duplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a database cataloging all key forms with their variants and codings is created, then key identification accuracy is improved, but computer resources and search time increase significantly
Solution Approach 1:
The patent segments the key identification process into two distinct phases: a preliminary phase that extracts generic singularities (basic key characteristics) and a second phase that uses these singularities to guide a targeted search in the database. This segmentation avoids the need to compare every key image against all possible key models, thereby reducing computational resources while maintaining identification accuracy.
Solution Approach 2:
The patent performs preliminary analysis to extract generic singularities (such as key shape, size, and basic features) before conducting the actual database search. This preliminary action filters and pre-processes the key image data, enabling the search engine to focus only on relevant key models and significantly reducing the search space and computational requirements.
2Measurement precision
If a database cataloging all key forms with their variants and codings is created, then key identification accuracy is improved, but search time increases significantly
Solution Approach 1:
The patent divides the identification process into a preliminary phase for extracting generic singularities and a second phase for targeted database searching. This segmentation reduces search time by avoiding comprehensive comparisons across the entire database, while still achieving accurate key identification through the structured two-phase approach.
Solution Approach 2:
The patent performs preliminary extraction of generic singularities before the database search. This preliminary action creates a filtered representation of the key that guides the search engine to relevant entries more quickly, significantly reducing the time required to identify the key model while maintaining high accuracy.
3Ease of manufacture
If crude comparison between key photographs and database headings is used, then implementation simplicity is improved, but key identification efficiency deteriorates
Solution Approach 1:
The patent introduces a preliminary phase that extracts generic singularities from key images before performing database comparisons. While this adds a step to the process, it significantly improves identification efficiency by reducing the search space and enabling more targeted comparisons, thereby achieving better productivity despite increased implementation complexity.
Solution Approach 2:
The patent introduces generic singularities as an intermediary representation between the raw key image and the database search. This intermediary structure facilitates more efficient matching by providing a filtered, structured representation that bridges the gap between simple image comparison and complex database querying, improving overall identification efficiency.
Data Source
Figure 1
AI summary
A user, using personal equipment (10), takes front and back views of a key to be duplicated (12). The image file is sent to a remote site containing databases (20-1, 20-2 ... 20-n) listing all possible key patterns. A generic analysis module (18) identifies the key family (toothed or holed) and a plurality of generic singularities of this key. A search engine (18) determines the key pattern by querying the databases (20-1, 20-2 ... 20-n) based on the identified generic singularities. A plurality of specific analysis modules (26, 36), each corresponding to a key family, identify specific singularities of the key and determine its encoding based on these specific singularities. The key is then duplicated (32, 42) based on the information thus determined from the views taken by the user.