Role of Artificial Intelligence in Trademark Search

Over the years, digital technologies have impacted industries worldwide. Among these technologies, artificial intelligence (AI) is especially significant as it enables machines to learn from experience, adapt to new inputs and perform tasks like humans. Essentially, it is the latest digital frontier that is anticipated to have a substantial impact on human lives by changing the way humans live and work.

The IP industry is not unaffected by the growing impact of these digital technologies. Several digital technologies are currently in use in the industry, while numerous others like AI are still being researched for future use. For instance, advanced AI tools and software are anticipated to transform the way trademark searches will be performed in the future. One of the major areas that AI could improve substantially is the trademark ecosystem. According to a recent research, the trademark examinations could be significantly improved by using AI for faster and more accurate trademark search results. 

Current IP scenario and Role of Artificial Intelligence in Trademark Search

Granting an intellectual property (IP) right to a product/service depends on the examination of the applications. Hence, an examination is crucial to ensure the exclusivity for said product/service. At present, the trademark examination process at the national IP offices is performed manually using human trademark examiners who are needed to conduct an exhaustive search in a large unordered database. Furthermore, they need to decide if there is any similarity between the mark submitted through application and the existing approved trademarks using conventional methods like the Vienna classification. This complexity around trademark search is a big IP challenge. Combining the expertise of a trademark examiner with automation using AI technology can offer the best solution for trademark search.

At present, there are already some systems which offer a good solution for the problem of similarity in images (such as the Google’s Reverse Image Search). However, it is important to understand that the trademark similarity search is more complex than it seems since it involves searching for dissimilar images rather than the conventional approach of searching similar (or identical) images. Further, spotting differences between trademarks is more complicated as it is difficult to find pairs of similar trademarks. Also, there is no official definition of similar trademarks, as trademarks are classified as similar if they give a misleading impression of similarity.

There are several questions that need to be answered such as: what should one do if one comes across two trademarks with extremely similar figures but different text? Similarly, what should one do if it is the vice versa? In the first case, one is bound to get bad results if only text similarity is considered. In the second case, however, one cannot overlook the text since text may be inscribed in the image itself. Furthermore, there can be a case where trademarks are not visually similar yet have the same content. All these scenarios lead us to the view that it is crucial to categorize the similarity questions as they require different approaches. Following are the popular metrics that can be used to quantify similarities between trademarks:

  1. Visual similarity: to check if two trademarks appear visually similar.
  2. Semantic similarity: to check if the two trademarks have the same semantic content.
  3. Text similarity: to check if the two trademarks have similar text.

To improve the trademark search process, marks must be examined based on a range of similarity metrics. Furthermore, AI can be used to identify as well as distinguish between a novel trademark and those existing in the trademarks database. The combination of similarity metrics and AI can provide the best and the most accurate trademark search results.

Way Forward

With the advent of several digital technologies like AI that can simulate and even perform better than humans, searching information on trademarks can be simplified. As of now, the use of this technology in the IP domain is still in its nascent stage. However, it is rapidly picking pace like in many other industries. The sooner the IP offices and government organizations start utilizing AI-driven trademark searches, the better it will be for both trademark examiners and customers. To watch a webinar on the subject, click here.

-Gopal Singh Rawat (Trademark) and Rahul Rana (InBound Sales) and the Editorial Team

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