CodeFest on Green Plastics: Finalists announced
The European Patent Office (EPO) has announced six finalists for its first ever public CodeFest. Towards the end of 2022, some 60 individuals and teams took up the challenge: to write an AI code that improves access to patent information on green plastics, helping to rid the planet of plastic waste.
Each finalist will present their creative code at an online award ceremony on Thursday 23 February 2023 at 14.00 hrs CET, where the winners will be revealed. The ceremony will also highlight how the finalists' AI solutions could contribute to the United Nations Sustainable Development Goals (SDGs), particularly to responsible consumption and production (SDG 12).
Meet the finalists
The winner will receive a EUR 20 000 cash prize, with the second and third place finalists each receiving EUR 15 000 and EUR 10 000 respectively. With internal competitors from the EPO, external entrants from across Europe and several mixed teams, which solution will take a coveted spot on the podium?
The solutions use various techniques, including neural networks, gradient boosting machines, transformer models, fine-tuned language models, and a sequence-to-sequence approach. Here are the six finalists; a full list of team members and full summary of each solution is available on the official event page, where you can also register to attend the award ceremony (see below).
AI4EPO, Greece and Netherlands
This team's model uses state-of-the-art AI pipelines and large language models from OpenAI for zero-shot, few-shot and other approaches to arrive at a custom MLP neural network for binary and multi-label classification.
Green Hands, Netherlands
As there is currently no classification scheme or labelled data available in this field, Green Hands proposed a new classification scheme, and developed a strategy to automatically assign labels to patents in order to create a labelled training dataset.
Using a gradient boosting machine, Thomas Eißfeller focussed on high sample efficiency, unbiased validation metrics and maximising specificity.
Multimodal Patent Document Classification, Germany
This team created a deep learning architecture to classify patent documents by fusing features from figures and text, thus exploiting the multimodal nature of patents.
Nikolaos Gialitsis, Greece
Nikolaos developed a machine learning model that incorporates both semantic and lexical features, and that was trained on a dataset of patents and scientific publications.
Patent Variables, France,
Germany and Switzerland
The team converted the problem into a sequence-to-sequence challenge, asking the user to define green plastics and then testing any patent claim against that definition.
About the code challenge
An extremely high level of submissions was received in response to the code challenge, which was: To develop creative and reliable artificial intelligence (AI) models for automating the identification of patents related to green plastics.
The competition, open to anyone aged 18 or over and resident in one of the EPO member states, included over 30 participating teams representing diverse nationalities. The finalists were selected by a jury consisting of senior specialists from across the EPO working in sustainability, IT, data science and AI, as well as patent information and analysis.