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.
Show notes
๐ Here you can find the post on the ๐IP Business Academy blog by Nouiere Jรคrvinen (Litigence) about the possibilities to circumvent the limitations of AI-supported validity search:
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๐ An overview of patent research can be found here in the Glossary of the digital IP Lexicon ๐dIPlex
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๐ AI-based patent search for synthetic inventing with an video interview with Daniel Holzner (ABP) and Wolfgang Hildesheim (IBM)
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๐ Here you can find an interview with Stefan Brehm (predori) about AI-based patent research on the ๐IP Business Academy:
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๐ 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 ๐dIPlex:
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๐ Please note the current Call for Subject Matter Experts for the IP Business Academy
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๐ The newsletter ๐ฏ๐๐ฃ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐ ๐ฃ๐๐น๐๐ฒ also has an archive of all previous issues in which you can research topics, which you can find here:
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