Medical Devices

AI inventions often combine advances in model architectures, training techniques, software, and hardware, making it increasingly difficult to establish novelty and define patentable technical improvements.

Organizations must navigate evolving prior art, patent eligibility considerations, and a rapidly changing state of the art when building AI patent portfolios.

• Strengthen patentability assessments. Conduct comprehensive prior art searches across patents, scientific literature, open-source resources to support patentability assessments or design-arounds.
• Stay ahead of the state of the art. Monitor emerging AI innovations, competitors, and research trends that may impact patent strategy.
• Navigate AI patent eligibility.
  Support claim drafting with patent agent expertise to emphasize technical improvements and patent-eligible subject matter.
• Improve prosecution outcomes. Provide technical support for office action responses, examiner interviews, and patent prosecution.

AI patent disputes often require technical analysis extending beyond patents to research publications, open-source software, product documentation, APIs, and cloud-based implementations.

Our multidisciplinary research supports patent litigation and enforcement involving machine learning, generative AI, computer vision, and AI infrastructure.

• Build stronger invalidity positions. Conduct comprehensive prior art investigations across patents, scientific literature, open-source software, firmware, hardware documentation, and historical AI research.
• Uncover foundational AI prior art. Identify early machine learning research, legacy AI systems, embedded technologies, and non-patent literature that may challenge patent validity.
• Support patent enforcement.
  Develop technical analyses, claim charts, and Evidence of Use through software analysis, hardware teardowns, firmware analysis, and reverse engineering.
• Extend your litigation team. Provide patent search and technical research support across district court, PTAB, and ITC proceedings.

Whether licensing AI technologies or responding to licensing demands, understanding patent strength and commercial implementation is essential.

Because AI systems often span software, hardware, firmware, cloud infrastructure, and machine learning models, building credible technical evidence is essential for successful licensing and commercialization.

• Identify licensing opportunities. Identify where patented AI technologies are implemented across commercial products and platforms.
• Develop technical evidence. Uncover evidence through product teardowns, hardware analysis, firmware extraction, and reverse engineering.
• Connect patents to products.
  Map patented features to real-world AI implementations using technical documentation and product analysis.
• Strengthen licensing positions. Develop technical evidence that supports licensing discussions, portfolio valuation, and commercialization efforts.

The AI landscape evolves rapidly across startups, academia, and major technology companies. Monitoring patent activity alongside research publications provides valuable insight into emerging technologies and competitor strategies.

• Understand the AI landscape. Analyze patent activity, research publications, and technology trends across AI domains.
• Benchmark against competitors. Compare AI patent portfolios to identify strengths, gaps, and strategic positioning.
• Spot emerging opportunities. Identify whitespace, technology shifts, and innovation trends that influence patent strategy.
• Guide portfolio investments. Deliver technical intelligence to support R&D planning, portfolio development, and investment decisions.

Whether preparing for commercialization, evaluating an acquisition, or entering a licensing agreement, understanding the strength and risks of AI-related patents is essential. We provide technical patent research that helps organizations assess portfolios, identify potential obstacles, and make informed strategic decisions.

• De-risk AI product launches. Assess freedom to operate before bringing AI-enabled products to market.
• Identify licensing and commercialization risks. Evaluate third-party patents that may impact product development or require licensing.
• Validate AI patent portfolios. Evaluate the strength, relevance, and technical significance of AI patent portfolios during investments and acquisitions.
• Support informed business decisions. Deliver technical research that strengthens IP due diligence, portfolio evaluation, and licensing strategies.