AI Research
Helpful Reminders for Patent Eligibility of AI, Machine Learning, and Other Software-Related Inventions | McCarter & English, LLP

Patent applications for artificial intelligence (AI), machine learning, and other software-related inventions are often rejected by the US Patent and Trademark Office (Patent Office) as being too abstract and thus ineligible for patenting under 35 U.S.C. §101, the patent law that provides what categories of inventions are patentable. The Patent Office recently issued a memo offering guidance to patent examiners (and, by extension, to patent applicants) for evaluating subject matter eligibility for software-related inventions. The memo contains important reminders that may help applicants seeking to patent software-related inventions avoid rejection.
As discussed in more detail at the Patent Office’s subject matter eligibility web page, the key inquiry for patent eligibility involves two prongs: 1) whether the patent claim recites a judicial exception to eligibility (abstractness, mental processes, products of nature, etc.), and if so, 2) whether the patent claim integrates the judicial exception into a practical application.
For prong 1, the memo includes the following helpful reminders:
For prong 2, the memo includes the following helpful reminders:
- Consider a claim as a whole rather than individual claim wording in isolation.
- Especially important for AI, machine learning, and other software-related claims, improving the functioning of a computer or improving another technology or technological field is evidence of patent eligibility. The patent application’s specification does not need to discuss the improvement explicitly, but the specification can be consulted in determining whether the claimed invention provides the improvement.
- Avoid oversimplifying the claims. This inquiry includes evaluating whether a claim recites the idea of a solution (patent ineligible) or recites a particular solution to a problem (patent eligible).
The memo also provides a general reminder that a claim should be rejected under Section 101 only when it is more than 50 percent likely that the claim is patent ineligible. Of course, that standard is itself abstract and could provide a patent applicant grounds for arguing that the claimed invention is patentable—or not.
[View source.]
AI Research
Study reveals why humans adapt better than AI

Humans adapt to new situations through abstraction, while AI relies on statistical or rule-based methods, limiting flexibility in unfamiliar scenarios.
A new interdisciplinary study from Bielefeld University and other leading institutions explores why humans excel at adapting to new situations while AI systems often struggle. Researchers found humans generalise through abstraction and concepts, while AI relies on statistical or rule-based methods.
The study proposes a framework to align human and AI reasoning, defining generalisation, how it works, and how it can be assessed. Experts say differences in generalisation limit AI flexibility and stress the need for human-centred design in medicine, transport, and decision-making.
Researchers collaborated across more than 20 institutions, including Bielefeld, Bamberg, Amsterdam, and Oxford, under the SAIL project. The initiative aims to develop AI systems that are sustainable, transparent, and better able to support human values and decision-making.
Interdisciplinary insights may guide the responsible use of AI in human-AI teams, ensuring machines complement rather than disrupt human judgement.
The findings underline the importance of bridging cognitive science and AI research to foster more adaptable, trustworthy, and human-aligned AI systems capable of tackling complex, real-world challenges.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
AI Research
Josh Bersin Company Research Reveals How Talent Acquisition Is Being Revolutionized by AI

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Jobs aren’t disappearing. Through AI, talent acquisition is fast evolving from hand-crafted interviewing and recruiting to a data-driven model that ensures the right talent is hired at the right time, for the right role with unmatched accuracy
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Traditional recruiting isn’t working: in 2024, only 17% of applicants received interviews and 60% abandoned slow application processes
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AI drives 2–3x faster hiring, stronger candidate quality, sharper targeting—and 95% candidate satisfaction at Foundever, from 200,000+ applicants in just six months
OAKLAND, Calif., Sept. 16, 2025 /PRNewswire/ — The Josh Bersin Company, the world’s most trusted HR advisory firm, today released new research showing that jobs aren’t disappearing—they’re being matched with greater intelligence. The research, produced in collaboration with AMS, reveals major advances in talent acquisition (TA) driven by AI-enabled technology, which are yielding 2–3x faster time to hire, stronger candidate-role matches, and unprecedented precision in sourcing.
The global market for recruiting, hiring, and staffing is over $850 billion and is growing at 13% per year, despite the economic slowdown, though signs of strain are evident. This means TA leaders are turning to AI to adapt, as AI transforms jobs, creates the need for new roles, new skills, and AI expertise.
According to the research and advisory firm, even without AI disruption, over 20% of employees consider changing jobs each year, driving demand for a new wave of high-precision, AI-powered tools for assessment, interviewing, selection, and hiring. Companies joining this AI revolution are hiring 200-300% faster, with greater accuracy and efficiency than their peers, despite the job market slowdown.
According to the report, The Talent Acquisition Revolution: How AI is Transforming Recruiting, the TA automation revolution is delivering benefits across the hiring ecosystem: job seekers experience faster recognition and better fit, while employers gain accurate, real-time, and highly scalable recruitment.
This is against a context of failure with current hiring. In 2024, less than one in four (17%) of applicants made it to the interview stage, and 60% of job seekers, due to too-slow hiring portals, abandoned the whole application process.
The research shows how organizations are already realizing benefits such as lower hiring costs, stronger internal mobility, and higher productivity. AI-empowered TA teams are also streamlining operations by shifting large portions of manual, admin-heavy work to specialized vendors.
AI Research
Causaly Introduces First Agentic AI Platform Built for Life Sciences Research and Development
Specialized AI agents automate research workflows and accelerate
drug discovery and development with transparent, evidence-backed insights
LONDON, Sept. 16, 2025 /PRNewswire/ — Causaly today introduced Causaly Agentic Research, an agentic AI breakthrough that delivers the transparency and scientific rigor that life sciences research and development demands. First-of-their-kind, specialized AI agents access, analyze, and synthesize comprehensive internal and external biomedical knowledge and competitive intelligence. Scientists can now automate complex tasks and workflows to scale R&D operations, discover novel insights, and drive faster decisions with confidence, precision, and clarity.
Industry-specific scientific AI agents
Causaly Agentic Research builds on Causaly Deep Research with a conversational interface that lets users interact directly with Causaly AI research agents. Unlike legacy literature review tools and general-purpose AI tools, Causaly Agentic Research uses industry-specific AI agents built for life sciences R&D and securely combines internal and external data to create a single source of truth for research. Causaly AI agents complete multi-step tasks across drug discovery and development, from generating and testing hypotheses to producing structured, transparent results always backed by evidence.
“Agentic AI fundamentally changes how life sciences conducts research,” said Yiannis Kiachopoulos, co-founder and CEO of Causaly. “Causaly Agentic Research emulates the scientific process, automatically analyzing data, finding biological relationships, and reasoning through problems. AI agents work like digital assistants, eliminating manual tasks and dependencies on other teams, so scientists can access more diverse evidence sources, de-risk decision-making, and focus on higher-value work.”
Solving critical research challenges
Research and development teams need access to vast amounts of biomedical data, but manual and siloed processes slow research and create long cycle times for getting treatments to market. Scientists spend weeks analyzing narrow slices of data while critical insights remain hidden. Human biases influence decisions, and the volume of scientific information overwhelms traditional research approaches.
Causaly addresses these challenges as the first agentic AI platform for scientists that combines extensive biomedical information with competitive intelligence and proprietary datasets. With a single, intelligent interface for scientific discovery that fits within scientists’ existing workflows, research and development teams can eliminate silos, improve productivity, and accelerate scientific ideas to market.
Comprehensive agentic AI research platform
As part of the Causaly platform, Causaly Agentic Research provides scientists multiple AI agents that collaborate to:
- Conduct complex analysis and provide answers that move research forward
- Verify quality and accuracy to dramatically reduce time-to-discovery
- Continuously scan the scientific landscape to surface critical signals and emerging evidence in real time
- Deliver fully traceable insights that help teams make confident, evidence-backed decisions while maintaining scientific rigor for regulatory approval
- Connect seamlessly with internal systems, public applications, data sources, and even other AI agents, unifying scientific discovery
Availability
Causaly Agentic Research will be available in October 2025, with a conversational interface and foundational AI agents to accelerate drug discovery and development. Additional specialized AI agents are planned for availability by the end of the year.
Explore how Causaly Agentic Research can redefine your R&D workflows and bring the future of drug development to your organization at causaly.com/products/agentic-research.
About Causaly
Causaly is a leader in AI for the life sciences industry. Leading biopharmaceutical companies use the Causaly AI platform to find, visualize, and interpret biomedical knowledge and automate critical research workflows. To learn how Causaly is accelerating drug discovery through transformative AI technologies and getting critical treatments to patients faster, visit www.causaly.com.
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