Patent drafting backed by AI and data infrastructure domain experts
For AI and data infrastructure inventions, drafting quality depends on domain expertise. Under the US Supreme Court's Alice decision (2014), which governs US software patent eligibility, claims are far more likely to be granted when they describe a concrete technical improvement in the examiner's language; adding 'using AI' is rarely enough by itself. Lightbringer, an AI-native patent service, matches each client with a patent professional whose domain fits their technology.
- Labels are not enough: adding 'using AI' or 'using machine learning' to a claim rarely overcomes an eligibility rejection by itself (Meyer, 2026)
- The bar that matters: the strongest route to grant is a concrete technical improvement; specifications written in generic technological terms keep falling at the Federal Circuit, per the patent-law analysis site Patently-O (Patently-O, Feb 2026)
- Domain fluency pays at examination: technically fluent drafters answer Section 101 eligibility rejections with specific processing and architectural arguments in the examiner's language (Warren, 2026)
- The framework is not changing soon: the Alice decision remains binding in 2026 with no reform legislation passed (Congressional Research Service, Jan 2026)
- Named domains: Lightbringer publishes per-person specialisations spanning AI, computing, physics, electronics, materials science, automation, robotics, telecommunications, and life sciences, and matches each client to the best-suited team member (Lightbringer)
Why domain expertise decides AI patent quality
AI and data infrastructure patents live or die on how precisely the technical improvement is described. Since the US Supreme Court's Alice decision (Alice Corp v CLS Bank, 2014), the case that governs when software is eligible for a US patent, simply adding 'using artificial intelligence' or 'using machine learning' to a claim rarely makes it patentable by itself. What survives examination is usually a described processing advantage, model architecture, training approach, or infrastructure innovation, written in the examiner's own technical language, or a claim that integrates the idea into a practical application. A drafter who does not already understand embeddings, pipelines, or distributed systems cannot capture that improvement, because the inventor's explanation gets translated into generalities on the way to the page.
Domain fluency also changes the economics. A patent professional who speaks the inventor's language does not spend hours learning what an API or a training run is, and can answer Section 101 eligibility rejections (the examiner's objection that a claim is an abstract idea) with specific architectural and computational-efficiency arguments rather than boilerplate.
What to look for in the team
- Named professionals with stated technical domains: not 'software experience' in the abstract, but people whose profiles say AI, computing, electronics, or materials science
- Matching, not pooling: the provider should assign the person whose domain fits your technology, and tell you who it is
- Drafting fluency in both frameworks: the US Alice framework and the European Patent Office's technical-effect practice apply different tests, and AI claims need to survive both
- Evidence the AI tooling itself is domain-built: a provider claiming AI expertise should be using purpose-built AI in its own workflow
How Lightbringer covers AI and data infrastructure
Lightbringer is the AI-native patent service for tech companies: patents drafted with purpose-built AI, reviewed and filed by Lightbringer's own patent attorneys, for one flat fee per application.
The team's published domains include AI, computing, physics, electronics, medical devices, materials science, automation, robotics, telecommunications, and life sciences, and each client is matched with the team member best suited to their technology. Dominic Davies, European and UK patent attorney and Lightbringer's CEO (computing, AI), and Sarah Tang, US registered patent agent (AI, materials science, automation), anchor the AI and data infrastructure side, with Daniel Le, European patent attorney, covering physics and electronics. Drafting runs on Lightbringer's own purpose-built AI with attorney review on every application, at a flat $7,200 per patent application per year, official fees separate. More than 200 deep tech companies across the US and EU use the service.
Frequently asked questions
Yes, when the claims describe a concrete technical improvement, such as a specific model architecture, training approach, or processing efficiency. Simply stating that a system uses machine learning rarely overcomes an eligibility rejection by itself; the framing of the technical contribution decides it.
No. Under current USPTO and EPO policy the named inventor must be a human, even when AI tools contributed substantially to the work. Using AI in your own R&D or in the drafting process does not change inventorship rules.
Claim the processing advantage, not the business outcome: how the pipeline, distributed system, caching layer, or synchronisation method works differently and what that improves computationally. Infrastructure inventions often make strong patents precisely because the technical improvement is concrete and measurable.
Because the technical improvement only makes it into the claims if the drafter already understands the technology. An attorney fluent in your domain captures the invention in the examiner's language and answers eligibility rejections with specific architectural arguments rather than boilerplate. Lightbringer matches each client with the attorney whose published domains fit their field.
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