Multi-Part Identifier for Transparent URL Redirection
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Solution Overview
Problem
Users are frustrated with traditional QR codes because they lack transparency about the URL they redirect to, making users less likely to use them for URL redirection.
Innovation Solution
A system that captures images with multi-part identifiers, combining graphical and human-recognizable textual content, uses machine learning models like convolutional neural networks to identify digital destinations, and performs actions based on these identifiers, providing users with insight into the intended action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional QR codes are used for URL redirection, then the redirection function is achieved, but the user cannot see which URL will be redirected to, causing frustration and reduced usage
Solution Approach 1:
The identifier is segmented into two distinct parts: graphical content (such as logos or icons) and human-recognizable textual content. The graphical content identifies the domain (e.g., a social network logo), while the textual content identifies the specific sub-part (e.g., username or page name). This segmentation allows users to understand both the service and the specific destination before redirection occurs.
Solution Approach 2:
Different parts of the identifier serve different functions with different visual qualities. The graphical content provides visual recognition and brand identification, while the textual content provides explicit, readable information about the specific destination. This local differentiation of quality ensures that each part contributes optimally to user understanding.
2Quantity of substance
If QR codes use only graphical patterns, then encoding capacity is maximized, but human readability and understanding are lost
Solution Approach 1:
The patent merges two types of content representation: graphical content (logos, icons, or other visual identifiers) and human-recognizable textual content (readable text strings). This combination allows the identifier to maintain high encoding capacity through the graphical component while simultaneously providing human readability through the textual component, resolving the contradiction between these two requirements.
3Measurement precision
If a machine learning model is used to identify graphical content, then domain identification accuracy is improved, but system complexity increases
Solution Approach 1:
A machine learning model serves as an intermediary component that bridges the gap between graphical content recognition and domain identification. The model takes graphical content as input and outputs domain identification results, enabling accurate recognition without requiring complex manual analysis. This intermediary approach manages system complexity by encapsulating the complexity within a dedicated, reusable component.
Data Source
AI summary
One general aspect includes a method, including: capturing an image of an object having a multi-part identifier displayed thereon, the multi-part identifier including a first portion and a second portion, the first portion including graphical content and the second portion including human-recognizable textual content. The method also includes based on the captured image, identifying a domain associated with the graphical content. The method also includes based on the captured image, identifying a sub-part of the domain associated with the textual content. The method also includes identifying a digital destination based on the identified domain and the identified sub-part. The method also includes performing an action based on the digital destination. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.