Five key challenges are identified: bias in training data, the complexity of patent language, the inability to account for legal changes, limited understanding of patent drawings, and difficulties in evaluating subjective concepts like novelty. Solutions involve hybrid approaches, combining AI with human expertise, using advanced NLP techniques, and incorporating real-time legal updates and litigation data.

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📌 Here you can find the post on the 📝IP Business Academy blog by Nouiere Järvinen (Litigence) about the possibilities to circumvent the limitations of AI-supported validity search:

📌 An overview of patent research can be found here in the Glossary of the digital IP Lexicon 🔗dIPlex

📌 AI-based patent search for synthetic inventing with an video interview with Daniel Holzner (ABP) and Wolfgang Hildesheim (IBM)

📌 Here you can find an interview with Stefan Brehm (predori) about AI-based patent research on the 📝IP Business Academy:

📌 If you are interested in typical AI technologies used in patent research and patent analysis, you can find background information here on the digital IP Lexicon 🔗dIPlex:

📌 Please note the current Call for Subject Matter Experts for the IP Business Academy

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📌 The newsletter 🎯𝗜𝗣 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗣𝘂𝗹𝘀𝗲 also has an archive of all previous issues in which you can research topics, which you can find here: