AR Brand Reliability Detection Using Guideline-Based Visual Cues
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Solution Overview
Problem
Existing machine learning models for detecting infringement and fraud in branding, emails, and websites are computationally intensive and prone to identifying irrelevant features, wasting power and processing resources.
Innovation Solution
Training a model on a set of guidelines rather than a large training set of labeled examples to detect reliability, using augmented reality (AR) devices to analyze logos, colors, fonts, and other features, and provide alerts based on reliability scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing machine learning models are used to detect infringement and fraud in branding, emails, and websites, then detection capability is provided, but computational resources and power are wasted due to the computationally intensive nature and identification of irrelevant features
Solution Approach 1:
The patent extracts and isolates only the most relevant visual features (logos, colors, fonts, images, text) from the complete visual content for analysis. By taking out and focusing only on these key branding elements rather than analyzing all visual data, the system reduces computational load and power consumption while maintaining detection accuracy for infringement and fraud identification.
Solution Approach 2:
The system applies different analysis methods and attention levels to different visual elements based on their importance. Critical branding elements like logos and brand names receive focused analysis using trained models, while less important elements are processed with simpler methods. This local quality approach optimizes resource allocation by concentrating computational power where it matters most for reliable detection.
2Reliability
If existing machine learning models are used to detect infringement and fraud, then detection capability is provided, but processing resources are wasted due to the computationally intensive nature and identification of irrelevant features
Solution Approach 1:
The visual content is segmented into distinct branding elements (logos, colors, fonts, images, text) that are analyzed separately using appropriate methods. This segmentation allows the system to process each element type with optimized algorithms, reducing overall processing resource requirements compared to analyzing the complete visual content as a single unit while maintaining comprehensive detection capability.
Solution Approach 2:
The system changes the analysis parameters and depth based on the type of visual element being examined. Different model configurations and processing intensities are applied to different element types (e.g., logo recognition versus text analysis), optimizing processing resource usage by matching computational parameters to the specific requirements of each feature type rather than using a uniform high-resource approach.
3Reliability
If existing machine learning models are used for detection, then detection capability is provided, but the models are prone to identifying irrelevant features as indicative of unreliability
Solution Approach 1:
The system performs preliminary identification and classification of visual elements before detailed analysis. By pre-categorizing content into specific branding element types and filtering out obviously irrelevant content in advance, the system prevents irrelevant features from being misidentified as indicators of unreliability, improving measurement precision while maintaining detection accuracy for genuine threats.
Data Source
AI summary
In some implementations, an augmented reality (AR) device may receive first images representing a webpage, an email, a product, or a store associated with a first entity. The AR device may detect, within the first images, a logo, a font, and/or a color. The AR device may apply a model, trained on a set of guidelines associated with the first entity, to the logo, the font, and/or the color. Accordingly, the AR device may receive, from the model, a first score associated with the webpage, the email, the product, or the store. The AR device may transmit an alert based on the first score. In some implementations, the AR device may further receive second images and apply the model to receive a second score associated with the webpage, the email, the product, or the store. Accordingly, the AR device may transmit an additional alert based on the second score.