Leveraging Image Feature Extraction for Intellectual Property Retrieval

Based on Patent Research | CN-115203380-A (2024)

Traditional intellectual property search systems often fail to understand complex requests involving both text and pictures. This gap causes poor search results that lack precision for intangible assets like trademarks. Image feature extraction identifies visual patterns to convert graphics into searchable data. This method allows systems to combine different data types into one clear view. Using this approach ensures more relevant search results and improves how firms categorize their intellectual property.

SOLUTION OVERVIEW

Manual Retrieval Upgraded with AI Analysis

Image feature extraction technology solves retrieval hurdles for intangible assets by translating visual concepts into mathematical data. The process begins when a system receives a trademark image or a design schematic. Complex algorithms then scan the graphic to identify shapes, colors, and spatial patterns. These unique visual signatures are converted into searchable digital codes. By comparing these codes against vast databases, the system identifies similar assets that traditional text-only searches would overlook, providing a holistic view of the intellectual property landscape.

This technology integrates seamlessly with existing legal databases to automate the screening of new trademark filings. It acts like a digital fingerprint scanner for logos, quickly identifying visual similarities that might lead to legal disputes. This automation reduces the heavy reliance on manual cross-referencing and speeds up the clearance process for new brands. Such capabilities support more informed decision-making for IP managers and legal teams. Enhanced visual intelligence ensures that firms can protect their non-financial assets with greater confidence while streamlining their daily research workflows.

HOW IT WORKS

Finding Semantic Matches Through Images

Processing Visual Asset Inputs

The system begins by receiving digital files of trademark logos, designs, or patent figures. It prepares these visual assets for analysis by normalizing the graphics to ensure consistent evaluation across different formats. This step transforms raw images into a standardized format ready for deep scanning.

Analyzing Complex Visual Patterns

Advanced algorithms scan the graphic to identify specific elements like shapes, color distributions, and spatial arrangements. This process identifies the unique characteristics that define the visual identity of an intangible asset. These identified patterns serve as the basis for creating a distinct digital signature.

Generating Searchable Mathematical Signatures

The system converts the detected visual patterns into complex mathematical codes known as feature vectors. These digital fingerprints translate abstract visual concepts into a language that databases can understand and sort. The resulting codes allow the system to handle visual information with the same precision as text data.

Cross-referencing Against Global Databases

The final stage involves comparing the new mathematical signatures against millions of existing records in intellectual property databases. By measuring the similarity between these digital codes, the system identifies potential conflicts or overlaps that traditional text searches might miss. This automated comparison provides legal teams with a comprehensive view of the IP landscape.

Potential Benefits

Enhanced Visual Search Accuracy

By converting logos and design schematics into digital signatures, the system identifies similarities that text-only searches miss. This precise feature extraction ensures that professionals find highly relevant assets that were previously hidden.

Automated Brand Protection Workflows

The technology automates the screening of new trademark filings by instantly comparing them against existing visual databases. This digital fingerprinting reduces manual cross-referencing and speeds up the clearance process for new intellectual property.

Improved Risk Mitigation Strategies

Early detection of visual similarities helps legal teams identify potential disputes before they escalate into costly litigation. Such proactive analysis allows firms to manage their intangible assets with greater confidence and legal certainty.

Streamlined Data Management Efficiency

Integrating visual intelligence with traditional legal databases creates a unified view of the intellectual property landscape. This holistic approach saves time for IP managers by simplifying complex research tasks into automated digital workflows.

Implementation

1 Establish Database Connectivity. Connect the feature extraction system to existing trademark and design databases to allow for seamless data exchange and retrieval.

2 Configure Extraction Parameters. Set specific algorithms for identifying shapes, colors, and spatial patterns within visual assets to ensure high precision during scanning.

3 Standardize Image Inputs. Implement normalization protocols to convert various graphic formats into a uniform digital state ready for mathematical signature generation.

4 Integrate Search Workflows. Embed the automated comparison tool into the legal team's daily research workflow to speed up the brand clearance process.

5 Monitor System Accuracy. Regularly review visual similarity results against manual findings to refine the detection of potential legal disputes and overlaps.

Source: Analysis based on Patent CN-115203380-A "Text processing system and method based on multi-mode data fusion" (Filed: August 2024).