CodeFest 2026 finalists

​​The European Patent Office (EPO) has announced the finalists of CodeFest 2026, the fourth edition of its open innovation challenge. This year’s competition invited individuals and teams from EPO member states to develop automated solutions for assessing the economic and technological value of patents. Selected from a pool of highly competitive submissions, the finalists demonstrate innovative approaches to analysing patent data and translating it into actionable insights for businesses, investors and innovators. Their projects showcase the potential of data-driven tools to improve the way intellectual property (IP) is understood and leveraged. 

​The winners will be revealed at the prize ceremony held on 16 September during the PATLIB conference in Warsaw, Poland. During the ceremony, the finalists will present their solutions and explain how they contribute to advancing IP valuation methodologies and supporting more informed decision-making across Europe’s innovation ecosystem. 

​Join us to celebrate the finalists and find out who takes home a CodeFest trophy and cash prize. The winner will receive EUR 20 000, with the first and second runners-up receiving EUR 10 000 and EUR 5 000 respectively.  

​Meet the finalists  

​This fourth edition of CodeFest received a total of 23 proposals submitted by individuals and teams from 12 countries. The six finalists were selected after a careful evaluation process conducted by an independent jury of EPO and field experts in IT, data science, AI and patent information and analysis, as well as external members who are recognised experts on the economic and technological value of patents. 

​This edition marked a milestone for women’s engagement in the competition, with record female participation, increased representation across teams and the highest number of female team leads to date. The finalists are presented in alphabetical order. 

​Confused Electrons

​For this year's challenge, the team developed an AI- and machine learning-based patent intelligence platform to address a key question: what makes a patent strategically valuable over time?  

​By assessing a combination of factors such as technological impact, market relevance and legal strength, the platform helps evaluate patent portfolios and identify long-term competitive advantages. A distinctive feature is its ability to link patent analysis with real-world business and technology trends. 

​EUREKA

​The team developed an AI-driven system that mirrors the patent examination process by identifying the closest prior art to assess an invention’s strength.  

​The system uses advanced AI models trained on patent data to go beyond basic keyword matching. By modelling examiner-style reasoning, this solution offers a particularly sophisticated approach to evaluating innovation. 

​Lucas Prieels

​Lucas developed an AI-assisted extension of the EPO’s IPscore methodology, combining a well-established evaluation framework with modern machine learning to enable faster and more data-driven patent portfolio analysis.  

​The system uses AI and natural language processing to assess factors such as citation impact, renewal likelihood and technological relevance, while supporting users in completing qualitative evaluations. Results are integrated into a familiar IPscore-style workflow, making them easy to interpret and apply.  

​Marc Donovici

​Marc developed a machine learning framework that uses citation data, semantic analysis and predictive models to assesses the long-term, economic value of patents based on analysis of their technological influence, legal robustness and commercial potential.  

​The system takes into consideration aspects such as patent renewal patterns and citation impact to identify patents with sustained value. Designed with a strong focus on transparency, the framework provides clear insights into the factors driving patent value, supporting more informed IP management and investment decisions. 

​Red-Cube-Coders

​The team developed an AI and machine learning-based approach to assess the technical strength and commercial value of patents.  

​The solution introduces two key indicators: a “commercialisation score”, estimating their market potential, and a “blocking score”, measuring how patents may constrain future developments. Using machine learning and patent analytics, it analyses relationships between patents and signals of technological and economic relevance. By focusing on real-world impact, the framework supports a more business-oriented understanding of patent portfolio value. 

​Turbo KD  

​The team developed the IP Impact Index, an AI-assisted framework designed to translate complex patent data into clear, business-oriented insights.  

​The platform uses a multi-dimensional approach to assess patents based on factors such as technological significance, market relevance and strategic positioning, providing transparent and explainable results rather than a single opaque score. By combining patent data with structured analytics and intuitive presentation, the solution helps stakeholders better understand and use patent information for decision-making. 

​Join us for the online prize ceremony on 16 September  

​Eager to see who will take the top spot? Register using the link below to attend the online CodeFest 2026 prize ceremony on 16 September.