Trademark infringement is a serious concern for businesses. It can lead to loss of revenue, damage to reputation, and legal disputes that are costly and time-consuming to resolve. AI-powered tools can help lawyers identify potential trademark infringement. By monitoring online platforms, e-commerce websites, and social media, these systems can flag instances of unauthorized use of trademarks.
Trademark infringement occurs when one party uses a trademark that is confusingly similar to another party’s trademark, potentially causing confusion among consumers. Detecting and preventing trademark infringement is crucial for protecting intellectual property and maintaining brand integrity. Here’s how machine learning can be applied to this task:
1. AI-Powered Search Tools
Unlike human search engines that return endless lists of articles that require manual sorting through to find what you want, AI-powered search tools understand intent so results are relevant to the query. They also take into account user interactions, content types and hidden semantic relationships to continually learn and improve (or “get smarter”) over time.
As a result, search engines have become more than just tools for finding information — they now help shape the way we think and act. This is reflected in a host of new technologies that make use of machine learning to perform tasks traditionally performed by humans, including legal research, contract analysis and management, intellectual property searching and trademark clearance, risk assessment, and more.
Many attorneys see a future where AI will fundamentally change the nature of their work. Companies such as Corsearch are already using AI to instantly comb the PTO’s trademark registry for existing marks that might conflict with a client’s idea and prioritize the list by degree of risk, based on the similarity of the marks and covered products. This saves attorneys considerable time and effort while enabling them to focus on the most promising opportunities.
Other tools, such as the Global Brand Database, an image-based trademark search engine created by WIPO, use machine learning to identify similarities between a new trademark application and previously registered trademarks, providing narrower and more precise search results. This helps businesses avoid costly trademark infringement lawsuits based on image similarity and reduces expenses linked to the protection of distinctive signs.
A growing number of businesses have opted to develop their own generative AI platforms for creating brands, logos, and other visual assets. As a result, there is now a real risk that some of this newly generated branding will infringe upon third-party intellectual property rights, such as copyrights and trademarks. Fortunately, business owners can reduce this risk by evaluating their transaction terms and demanding that any generative AI platform they engage provide proof of proper licensure for the training data it uses or provide broad indemnification for intellectual property infringement caused by the platform’s unauthorized branding.
2. AI-Powered Analysis
Trademark infringement is a real concern for businesses that invest in building their brand and reputation. Fortunately, trademark attorneys have well-established procedures for responding to unauthorized branding—ranging from sending strongly worded cease-and-desist notices and licensing demand letters to filing trademark infringement lawsuits. However, identifying potential trademark infringement is a time-consuming process. One way to streamline this process is through trademark journal analysis—an AI-based solution that automatically scans trademark journals for potential infringement.
AI is used for many types of data analysis, but it’s especially useful in analyzing large amounts of data with complex structures and relationships. This type of analysis would be extremely difficult and time-consuming for humans to perform, but AI can quickly turn massive datasets into meaningful analyses that reveal hidden patterns and trends.
In addition, AI can analyze and compare vast amounts of intellectual property data to identify potential patent infringements, evaluate patent ability, conduct due diligence, and enhance technology licensing and transfer. Using this information, AI can help patent owners maximize their IP strategies and increase revenue opportunities.
The intersection of AI and IP law is a rapidly developing area. For instance, AI-generated content raises several new legal issues, including who owns the copyright to such works and how they can be protected from infringement.
AI can also improve the efficiency of patent searches. It can be particularly helpful in identifying and assessing prior art, which can help identify potential patent infringement.
In fact, the Patent and Trademark Office (PTO) has already begun to use AI for some patent searching tasks. However, this approach is controversial because it could reduce the number of human examiners and, in turn, raise costs and delay prosecution of applications. In addition, the unions that represent PTO examiners may be resistant to AI because of concerns about job losses and reduced working hours. However, the PTO is working to address these concerns through training and education programs.
3. AI powered Classification Models
In order to train classification models that can identify trademark infringement, it is common for a combination of image and text analysis (for images and logos) to be used. These are some of the most commonly used algorithms and techniques in supervised classification for this task.
Logistic Regression
The binary classification algorithm of logistic regression can be used to analyze trademark infringements based on text. It is interpretable and serves as a baseline.
Decision trees and random forest are algorithms that can be used to handle text or image data. They are able to capture complex relationships and are fairly interpretable.
Support Vector Machines (SVM)
SVMs work well for text classification as well as image classification. They find a hyperplane which separates two classes the most. Kernel SVMs are useful for non-linear decisions boundaries.
Deep learning models such as convolutional networks (CNNs), recurrent networks (RNNs), or transformer-based models, e.g. BERT, for text, are able to capture intricate patterns within data. They can handle unstructured data, and they are well-known for their high performance on complex tasks.
AdaBoost, Gradient Boosting or XGBoost are ensemble methods that can improve classification accuracy. They combine weak classifiers and decision trees. These methods are robust and used often in trademark infringement analyses.
Transfer Learning
You can use deep learning models such as VGG, ResNet or Inception for image analysis and refine them using your trademark dataset. Transfer learning reduces the amount of labeled training data.
You may need to combine image and text information in some situations. You can do this by creating a neural network multi-modal that simultaneously processes text and images. This allows the model to learn how they interact. Consider engineering domain-specific features, such as keywords or visual elements, that may be relevant for trademark analysis.
Imbalanced data handling
Consider the possibility of class imbalance, since there may be more trademarks that are not infringing than those that are. Oversampling or under sampling can be used to handle datasets with imbalances. To optimize classification performance, experiment with different hyper parameters and architectures.
Validate the performance of your model using techniques such as cross-validation, metrics such as accuracy, precision and recall, F1 score, and ROC AUC.
Interpretability
It may be necessary to ensure the model’s decisions are interpretable, depending on the context and the legal requirements. Techniques such as feature importance analysis and SHAP (SHapley Additive ExPlanations) may help to provide insight into model decisions.
Update and retrain classification models regularly to keep up with changes in trademark data, and to reflect evolving legal standards.
Consider that your choice of classification model or technique will depend on the complexity and nature of the trademark infringement problem. It is important to consult with legal experts during the model development and evaluation process. This will ensure that models are aligned with legal standards.
4. AI-Powered Insights
As AI-enabled tools continue to evolve, they can be used to provide insights that help businesses identify potential trademark infringement. For example, MikeTM Watch, an automated trademark journal analysis tool powered by AI, can alert a business to any unauthorized branding that appears in the marketplace — and provides valuable trademark analytics to make informed decisions regarding trademark enforcement.
The increasing use of AI in infringement analysis is raising a number of important questions about how trademark law and policy will be affected. For instance, how will trademark infringement and the likelihood of confusion analyses be altered when traditional concepts such as imperfect recollection and average consumer decision-making tendencies are replaced by AI-learned decision-making tendencies?
In addition, the use of generative AI (AI that creates and learns from data sets to perform tasks without human intervention) is creating legal issues in copyright and trademark law. These include copyright infringement and rights of use issues, questions about the ownership of AI-generated works, and whether users should be able to prompt these tools with direct reference to other creators’ copyrighted and trademarked works by name without their permission.
Some companies that offer generative AI software are focused on addressing these concerns by requiring the use of properly licensure training data for their AI, and by offering broad indemnification to protect their clients against potential intellectual property infringement caused by failures of AI-generated works to meet the requirements of existing copyright law. However, this type of protection is still in its early stages, and it will be interesting to see how this develops over time.
The evolving use of AI-powered insight tools has the potential to transform how businesses approach strategic planning and brand building. However, successful implementation requires a clear understanding of how to incorporate these technologies into your business and a strong commitment to ensuring that they are aligned with your business goals. Moreover, insights are only as good as the data and categorization list, or taxonomy, that you feed the AI – so it is critical to invest in a robust solution that can grow with your business.
5. AI-Powered Branding
A trademark is a word, symbol, phrase, sound, color, or design that distinguishes the goods and services of a brand from those of others. AI can be used to identify and monitor trademark infringements in online marketplaces, websites, social media platforms, and other online channels. It can also aid in identifying patterns and providing data to assist with taking legal action.
However, it is important to note that the use of AI in trademark infringement analysis may not be a seamless transition. The legal standards and rules around intellectual property are complex and difficult for computers to evaluate, and attorneys, judges, and businesses must be cognizant that AI is not a replacement for human analysis and critical thinking.
For instance, determining whether a mark is likely to cause confusion among consumers requires context-dependent factors and legal standards that are difficult for AI to evaluate. Similarly, determining liability for secondary trademark infringement under traditional law must be evaluated by humans based on whether the AI agent knew or should have known that its recommendation was infringing.
Furthermore, existing copyright laws must be reviewed and applied to the creation of AI-created content to ensure that trademark infringement issues do not arise. For example, if AI-created content such as an image or video is similar to a Nike “swoosh” design mark, then the creator of the AI could be held liable for copyright infringement or vicarious and contributory trademark infringement.
Additionally, the accuracy of AI systems depends on the data they are trained on. Thus, a large and varied dataset is critical to an accurate and robust search and evaluation process. However, the sheer volume of data can be overwhelming for legal professionals, and it is often time-consuming to manually review and analyze all of the data produced by AI systems. Therefore, it is important that companies and legal professionals consider using an AI-based tool that combines data from multiple sources to provide more comprehensive results. This can help reduce the number of errors and save attorneys’ time.