AgenticAI
Scientific literature & patent analysis
Use Case Family:
NLP, Agentic AI, Graph & Relationship Analytics
Business Domain:
R&D
Processes:
Knowledge retrieval
Challenge:
Researchers must analyze vast amounts of scientific literature, technical documents, and patents, making it challenging to stay updated on the latest advancements. Manual reviews are time-consuming and prone to oversight, limiting the speed of innovation and competitive advantage.
Solution:
Using Natural Language Processing (NLP) and Knowledge Graphs to extract key insights from scientific papers, patents, and technical reports.
Multi-agent systems:
- NLP Agents: Summarize large documents, extract trends, and compare findings across sources.
- Patent Analysis Agents: Detect overlaps, find prior art, and assess innovation potential.
- Knowledge Graphs: Connect related research, patents, and emerging technologies for a structured view of trends.
Benefits:
- This AI-driven approach reduces research time, prevents redundant work, and accelerates innovation.
- Researchers gain structured insights, enabling them to focus on problem-solving instead of document reviews.
- AI also helps with early IP risk detection, ensuring R&D efforts align with patent landscapes.
Target Group:
Potential Industries:
Limited Risk systems
Art. 50
with transparency obligations
Disclaimer
The information provided regarding the risk assessment is without guarantee. The complete classification of a use case according to the EU AI Act depends on numerous regulatory and company-specific factors. Therefore, the risk assessment is always case-specific. The risk assessment logic of Casebase is used for this purpose.