Artificial intelligence (AI) is assuming a progressively vital role in driving innovation, with systems like IBM’s Watson and Google’s DeepMind being deployed to solve some of the most significant technical, scientific, and medical challenges of our time. As AI-generated inventions become prevalent, questions arise about how the patent system will protect them and whether it should accommodate non-human inventors. This blog post will show the current state of AI and Patent Strategies and the implications for the future of patentability.
The risks of trademark infringement are real. Fraudsters use government or utility provider trademarks to convince hapless victims to part with billions of dollars yearly.
Brand-imitating products are big business for criminals, with a 2020 Library of Congress report describing counterfeiting as “the largest criminal enterprise in the world” with a global value of between $1.7 and $4.5 TRILLION.
There are estimates that intellectual property theft costs the US economy between $225 and $600 billion annually.
Tools such as Image Recognition can assess brand risk across a vast digital landscape with high levels of precision. The World Intellectual Property Organization (WIPO) launched an image detection AI as early as 2019.
Below, we explain why AI is the secret weapon to counter these threats and how best to use it.
Select the Right AI Tools
With a host of AI tools available in the market, pinpointing the right one can differentiate between seamless trademark enforcement and strategies fraught with loopholes. Here are three factors to consider when selecting your AI trademark protection tool:
Scalability: Choose solutions to handle increased workloads and adapt to growing business needs. Growing businesses and new brands can launch multiple trademarked products at once, all of which will require protection. Your solution must handle this volume and scale. And with markets increasingly globalized, your protection must cover global marketplaces, too.
Accuracy: The precision with which an AI tool identifies potential violations is crucial. Look for tools that reduce false positives and false negatives. False positives (things identified as violations that aren’t) waste time and effort, while false negatives (the opposite) miss revenue-reducing incidences.
Compatibility: Validate that you are using relevant tools for relevant products. Ensure that the AI solution aligns well with your specific enforcement needs and can integrate with other systems you use. You’ve probably chosen the wrong method if you must engineer complex integrations and workarounds. Ensure the solution is future-proofed and allows for avenues of innovation you’ll want to explore.
Data Gathering, Preparation, and Continuous Monitoring:
Data fuels AI. Effective trademark monitoring requires data from various online channels, including e-commerce platforms, social media sites, and websites.
Regularly update your data sources to ensure your AI tool is always equipped with the latest information. Make sure your data analytics can handle unstructured and structured data and can ideally look for trademark infringement in images and textual mentions.
Features to Look For in your AI Trademark Enforcement Solution:
Here are the top three features to look for:
Automated Systems and Alerts: Setting up automation can significantly boost proactive enforcement. Choose a system that offers real-time alerts for potential violations. Ensure a logical way to aggregate infringements and make them readily viewable.
Periodic scans of specified online channels: You should be able to set the parameters of regular scans to develop a manageable workflow around identified violations.
Analysis reports: Ensure that there are intuitive dashboards and sharable reports that pinpoint the most significant threats.
Customization and Training:
No two brands are the same. Some are minimal – a font choice and two essential colours. Others have complex logos and imagery.
Customizing algorithms to recognize your brand’s unique trademarks and elements ensures higher accuracy in detection. A good AI tool can identify shapes, color schemes, text strings, and proprietary fonts. Make sure you add all the elements you can to your search parameters.
Training is a vital aspect of using AI to monitor trademark infringements. Your training datasets need to be appropriate, as large as possible, ranging across multiple media, and kept up to date.
Use diverse data sets that encompass different types of violations—for instance, appropriation of a logo, borrowing of product taglines, and use of a proprietary font.
Regularly update the training set to include new infringement patterns. Remember that violations will now occur in virtual reality spaces, among social media influencers, and on mobile sites. There are trends and innovations within cybercrime, with fraudsters using email scams, WhatsApp messages, fake websites, and deepfaked audio and video content.
Opt for supervised learning to begin within, where the model’s predictions are checked and corrected. Once you know it works well, you can move to unsupervised deep learning and a continuous feedback loop to improve. However, you’ll still want to audit your tool’s effectiveness periodically.
Integration with Legal Expertise:
AI insights can significantly guide legal action. By highlighting potential violations, legal teams can strategize more effectively, ensuring:
- Faster response times.
- The prioritization of significant threats.
- Detailed evidence collection for legal proceedings.
Dupe Killer: Deloitte developed an IP tool to help international fashion brands detect counterfeit products being sold online. When demonstrating their product to Vogue, a search for a particular revealed 312 sites selling duplicate products across 84.8 million scanned e-commerce sites.
Huski: This start-up has created an AI-powered image recognition system designed for brand IP lawyers to monitor e-commerce sites for visual and textual similarities to legitimate brand sites.
GLTR: IBM Watson and Harvard University have been developing an AI tool to identify AI-generated text, which could help identify fake sites, scams, and communications. Systems like Chat-GPT generate text by predicting the statistically next most likely word. GLTR (giant language model test room) spots text that adheres to those parameters too closely.
AI is an Ally, not the Enemy
Although bad actors will continue to use GenAI systems to create fakes of all kinds, innovators are creating ever more sophisticated tools to identify and flag such infringements.
In a swelling arms race of deepfakes and knock-offs, it’s well worth investing in these powerful weapons to defend that most vital of assets: your trademarked IP.
Read more in our recent publication.