Upcore Empowered Efficient IP Protection Through Advanced Image Analysis

The implementation of the trademark similarity search and product recognition engine by Upcore Technologies has revolutionized the client's intellectual property protection services. By leveraging advanced computer vision and deep learning techniques, the client can now accurately and efficiently detect potential brand plagiarisms, streamline workflows, and reduce the workload on employees. This case study exemplifies the transformative power of AI and computer vision in protecting intellectual property and ensuring legal compliance. By harnessing the capabilities of advanced algorithms and cutting-edge technologies, businesses can gain a competitive edge, enhance customer satisfaction, and safeguard their valuable assets.

Client:

A Trademark-Related Services and Legal Compliance Consulting Company

The client provides trademark-related services and legal compliance consulting, offering various solutions to protect the intellectual property of products for their customers. To validate logo appearance, they run multiple checks against existing trademarks in the market.

The client was seeking experts in visual search to streamline the process of detecting and verifying trademark image similarity and plagiarism using technology. They required a robust solution to decrease the need for human labor in the workplace, enabling more efficient and scalable operations.

Challenges:

The client regularly works with trademark databases, and manual trademark search and recognition are exhausting and time-consuming tasks. To address this challenge, the client needed an efficient solution to automate processes where possible and eliminate the need for manual searches.

To solve the client's problem, the Upcore team studied the ability of machine learning algorithms (ML) and available trademark data to detect logo plagiarism between each other, providing a ranked list of potential brand plagiarisms based on probability.

To accomplish this task, our engineers leveraged computer vision algorithms based on embeddings learning, a technique that allows the representation of images in a common vector space, enabling the measurement of similarity between them.

Solution:

The Upcore team, experienced in computer vision and image analysis software development, initiated the project with a formalization phase, studying use cases, analyzing related solutions, and, most importantly, examining trademark data sources. This first investigation phase was followed by the model development and experimental stages for idea testing.

The team started by building and testing a neural network model to predict encoded vectors for images in a common vector space, enabling the measurement of distance between them. With unlabeled data sources from the client and open sources, the team worked on obtaining relevant information about trademark images to be used in the solution.

Success was achieved by solving indirect and implicit tasks of visual classification and adapting it to learn confusing plagiarism of trademarks. The final step involved applying various computer vision techniques, including high- and low-level graphical processing (including OCR), to enhance the model's ability to find plagiarisms. The combination of the best attempts became the solution to the client's problem.

Implementation

The implementation of the trademark similarity search and product recognition engine involved several key steps:

1. Data Preprocessing and Augmentation:

- Gathering and cleaning of relevant trademark image data from the client and open sources.

- Data augmentation techniques, such as rotation, scaling, and color transformations, were applied to increase the diversity of the training data and improve model robustness.

2. Model Development and Training:

- Upcore's team experimented with various deep learning architectures, including convolutional neural networks (CNNs) and transformer models, to develop an effective image embedding model.

- The selected model was trained on the preprocessed data, incorporating techniques such as transfer learning and fine-tuning to leverage pre-trained models and improve performance.

- Continuous monitoring and retraining of the model were implemented to ensure accurate predictions as new data became available.

3. Similarity Search and Ranking:

- A similarity search engine was developed to compare the embeddings of new trademark images against the existing database of embeddings.

- Distance metrics, such as cosine similarity or Euclidean distance, were used to rank the potential matches based on their similarity scores.

- Techniques like nearest neighbor search and approximate nearest neighbor algorithms were employed to optimize search performance and scalability.

4. User Interface and Integration:

- A user-friendly interface was developed to allow the client's team to upload new trademark images and retrieve ranked results of potential plagiarisms.

- APIs and data exchange protocols were implemented to enable seamless integration with the client's existing systems and workflows.

Results:

Upcore Technologies, a computer vision technology provider, has aided the client with an innovative solution to automate trademark similarity and brand plagiarism search and detection.

The client has benefited from working with our company in the following aspects:

1. Automated Trademark Similarity Search:

- The developed solution automated the process of searching for similar trademarks, significantly reducing the time and effort required for manual searches.

2. Brand Plagiarism Detection:

- The advanced computer vision algorithms enabled accurate detection of potential brand plagiarisms, ensuring intellectual property protection for the client's customers.

3. Speed Up in Workplace Processes:

- By automating time-consuming tasks, the solution accelerated the client's workflow, enabling faster turnaround times and improved efficiency.

4. Reduced Workload on Employees:

- The automated trademark similarity search and plagiarism detection capabilities reduced the workload on employees, allowing them to focus on higher-value tasks and strategic initiatives.

Technologies Stack:

The development of the trademark similarity search and product recognition engine involved the integration of various cutting-edge technologies:

1. Deep Learning Frameworks: Popular frameworks such as TensorFlow, PyTorch, or Keras were utilized for model development, training, and deployment.

2. Computer Vision Libraries: Libraries like OpenCV, Pillow, and scikit-image were employed for image preprocessing, augmentation, and manipulation.

3. Similarity Search Engines: Specialized engines like Elasticsearch or Faiss were used for efficient similarity search and approximate nearest neighbor algorithms.

4. Cloud Computing Platforms: Cloud-based platforms like Amazon Web Services (AWS) or Google Cloud Platform (GCP) were leveraged for scalable and efficient data processing and model training.

5. User Interface Development: Web development frameworks like React, Angular, or Vue.js were used to create a user-friendly interface for uploading images and retrieving search results.

6. API Development and Integration: REST APIs or gRPC were implemented to enable seamless integration with the client's existing systems and workflows.

7. Security and Privacy Protocols: Robust security measures, such as data encryption and access controls, were implemented to ensure data privacy and compliance with relevant regulations.