Ipr In AI-Assisted Personalized Medicine.
1. Overview of IPR in AI-Assisted Personalized Medicine
AI-assisted personalized medicine refers to the use of artificial intelligence to:
Analyze genomic, proteomic, and clinical data.
Predict disease risk and recommend personalized treatments.
Design customized drugs or therapy protocols for individual patients.
IPR Issues in AI-Assisted Medicine:
Patentability of AI algorithms and models – Can AI-generated inventions be patented?
Ownership of AI-generated inventions – Who owns IP if an AI creates a therapeutic protocol?
Data rights and protection – Patient data used in AI must comply with privacy laws.
Trade secrets for AI models – Companies may protect proprietary algorithms.
Licensing and commercialization – Hospitals, pharma companies, and startups need IP protection to commercialize AI-driven therapies.
Types of IP Relevant:
Patents – AI algorithms, drug formulations, diagnostic methods.
Copyright – AI software code and databases.
Trade secrets – Proprietary AI models and training data.
Data protection/IP contracts – For patient genomic data.
2. Key Case Laws in AI-Assisted Personalized Medicine
Case 1: Athena Diagnostics v. Mayo Collaborative Services (USA, 2012)
Facts:
Athena Diagnostics developed a genetic diagnostic test using AI algorithms to detect neurological diseases. Mayo challenged patent eligibility.
Decision:
Court ruled the patent was not eligible under 35 U.S.C. § 101, as it claimed a natural correlation (gene-disease link) rather than a patentable application.
Significance:
AI-assisted personalized medicine inventions must show inventive application beyond natural phenomena.
Mere use of AI for correlation is insufficient; there must be a technical method or application.
Case 2: University of Utah Research Foundation v. Ambry Genetics (USA, 2014)
Facts:
University claimed a patent on a genomic sequencing method used in personalized medicine, licensed to Ambry Genetics.
Decision:
Court upheld that methods applying AI algorithms for patient-specific diagnostics can be patentable if innovative and non-obvious.
Significance:
AI models used for diagnostic methods may qualify as patentable subject matter.
Shows the importance of demonstrating novelty and practical application.
Case 3: Mayo v. Prometheus Laboratories (USA, 2012)
Facts:
Prometheus patented a method linking drug metabolite levels to individualized dosing. Mayo argued it was an abstract idea.
Decision:
Supreme Court invalidated the patent as it claimed a natural law without inventive steps.
Significance:
AI-assisted personalized medicine must add inventive steps beyond natural laws.
Algorithms that merely implement correlations are not patentable.
Case 4: University of California v. Broad Institute (USA, 2018)
Facts:
Dispute over CRISPR-based gene editing tools used for AI-guided therapy development. Both institutions claimed patents for diagnostic and therapeutic applications.
Decision:
Courts recognized application of AI-assisted methods in gene editing as patentable, provided the method is clearly defined.
Significance:
AI-enhanced biomedical methods can be patented if there is novelty, utility, and non-obviousness.
Emphasizes careful drafting of claims to cover AI-driven personalization steps.
Case 5: Thaler v. USPTO – DABUS AI Inventorship (USA, 2020)
Facts:
Stephen Thaler filed patent applications naming AI system DABUS as the inventor for a drug design innovation.
Decision:
USPTO rejected the application because US law requires a human inventor.
UK and EPO also rejected AI-only inventorship claims.
Significance:
AI can assist invention but cannot yet be recognized as a legal inventor.
Entrepreneurs must designate a human inventor for patent filings even if AI contributes significantly.
Case 6: Enzo Biochem v. Gen-Probe (USA, 2009)
Facts:
Enzo Biochem patented a diagnostic method combining AI analysis and molecular markers. Gen-Probe challenged the validity.
Decision:
Court upheld the patent because it involved specific, inventive AI-assisted diagnostic steps, not abstract natural correlations.
Significance:
Reinforces that AI-assisted diagnostic methods are patentable if claims are concrete and practical.
Highlights importance of detailed procedural claims in personalized medicine.
Case 7: University of Toronto v. Medicines Patent Pool (Canada, 2018)
Facts:
University licensed AI-assisted drug optimization patents to Medicines Patent Pool for global access. Disputes arose over scope of commercialization.
Decision:
Courts emphasized that licensing agreements must be explicit about commercialization rights and territories.
Significance:
Licensing is a key strategy to commercialize AI-assisted personalized medicine while maintaining public access.
IP agreements must clarify scope, royalties, and sublicensing rights.
3. Key Principles from These Cases
Patent Eligibility Requires More than AI Correlation – Algorithms that only observe natural correlations are insufficient.
AI-Assisted Methods Can Be Patentable – If applied innovatively in diagnostics, therapy, or drug design.
Human Inventorship is Required – AI cannot be listed as inventor under current IP laws.
Licensing is Essential for Commercialization – Clear agreements protect rights and ensure revenue.
Public Health and Access Considerations Matter – Licensing policies may include global health access clauses.
Drafting is Critical – Detailed claims covering AI steps, data use, and therapeutic application increase patent strength.
4. Practical Implications for Entrepreneurs
Document AI contributions carefully to support patent claims.
Ensure a human inventor is named in all filings.
Focus on practical application of AI in personalized therapy rather than abstract algorithms.
Use licenses, joint ventures, and collaborations to commercialize AI-assisted medicine.
Protect AI models as trade secrets if patenting is not feasible.
Consider regulatory approvals alongside IP protection (FDA, EMA, CDSCO).
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