At Hyperthink, we are at the forefront of leveraging AI to bridge the gap between academic research and commercial opportunities. Our latest project, "Fieldstudy Autonomous Commercialisation," is a bespoke autonomous agent and set of language models designed to analyse vast amounts of research papers and pre-prints, matching them to potential commercialisation projects. This innovative solution not only accelerates the path from research to market but also enhances the efficiency of identifying and capitalising on valuable intellectual property (IP).
Fieldstudy is an advanced AI-driven platform that uses a combination of bespoke ensemble and ranking algorithms to process and evaluate research papers. By employing a "panel of experts" approach, developed with generative AI, the system reads, ranks, and sorts vast amounts of research documents, identifying potential IP for commercialisation. This project has garnered significant support from the academic community and has expanded to include additional IP data sources such as trademarks and patents, utilising graph analytics to explore and analyse adjacent ideas.
We had an early MVP version which we demoed and you can see the original demo here.
The Fieldstudy project employs a rigorous research methodology to ensure the effectiveness and accuracy of the AI-driven platform. The process begins with data collection, where vast amounts of research papers and pre-prints are aggregated from multiple sources. This dataset forms the foundation for training the autonomous agent.
Advanced NLP techniques are used to develop the bespoke ensemble and ranking algorithms. The "panel of experts" approach, powered by generative AI, simulates the evaluation process of human experts, providing nuanced and contextually relevant assessments of each research paper. This ensures that the system can accurately rank and sort research projects based on their commercial potential.
The integration of additional IP data, such as trademarks and patents, involves the use of sophisticated graph analytics. This allows the system to identify and analyse adjacent ideas, uncovering potential collaborations and innovation opportunities. The methodology includes continuous validation and testing, with feedback loops from the academic community to refine and improve the system's accuracy and relevance.
The initial success of the Fieldstudy project has paved the way for several future extensions. One significant area of expansion is the incorporation of more diverse data sources, including global research databases and industry-specific publications. This will enhance the system’s ability to identify commercial opportunities across various sectors.
Another extension involves improving the system's capabilities for patent infringement detection. By leveraging advanced analytics and machine learning techniques, the platform can provide early warnings of potential patent infringements, helping organisations protect their intellectual property more effectively.
The project will also explore the integration of real-time data feeds, allowing the autonomous agent to continuously update its assessments based on the latest research developments. This dynamic approach ensures that the system remains current and highly relevant in fast-evolving fields.
The impact of the Fieldstudy project is profound, offering significant benefits to both academic institutions and commercial entities. One of the primary advantages is the acceleration of the commercialisation process. By automating the evaluation and matching of research projects to commercial opportunities, the system significantly reduces the time required to bring innovations to market.
Enhanced accuracy and efficiency are other major benefits. The generative AI-driven "panel of experts" provides reliable and nuanced assessments, ensuring that high-potential research projects are prioritised. This reduces the risk of valuable innovations being overlooked and increases the likelihood of successful commercialisation.
The integration of IP data and graph analytics offers additional advantages, such as improved patent development and innovation tracking. By identifying and analysing adjacent ideas, the system helps organisations uncover new opportunities for collaboration and innovation, driving growth and competitive advantage.
Cost-effectiveness is another key benefit. By optimising the process of research evaluation and commercialisation, the platform reduces the resources required for these activities. This allows academic institutions and commercial entities to allocate their resources more efficiently, maximising their return on investment.
Overall, the Fieldstudy project represents a significant advancement in the application of AI to research and commercialisation. It offers a powerful, efficient, and highly accurate solution that bridges the gap between academic research and market opportunities, driving innovation and economic growth. For more information about Fieldstudy and how it can benefit your organisation, contact Hyperthink today.
