AI Tools for Patent Attorneys

Skip to the map if you want the short version. Each row is a layer of real patent work, the tools worth evaluating there, and the one thing to check before you trust the output.

Search is its own discipline. The job is to find references, map them to your claims, and leave a trail an attorney can defend. A model that can summarize a patent is not the same thing as a system that can find one. This is the layer for purpose-built tools: PatSnap for patent analytics and landscape analysis, IPRally for semantic patent search and reference discovery, and Solve Intelligence, which folds prior-art analysis into its patent platform. The patent office is moving on this too. In late 2025 the USPTO opened its Automated Search Pilot, which can provide participating applicants with AI-assisted search results before examination even begins. ChatGPT and Claude can help you brainstorm search strings or digest a reference you already have, but neither should ever be the database of record behind an opinion or a filing decision.

This is where purpose-built patent AI has pulled clear of the generalists, and two names lead it.

DeepIP, publicly reported as founded in 2024, lives inside Microsoft Word and goes straight at drafting time. Public reporting says it raised a $15M Series A in 2025. DeepIP says it has helped draft more than 8,500 applications, counts firms like Schwegman among its users, and reports cutting drafting time by roughly half. Solve Intelligence takes the platform route: Solve says more than 500 IP teams use its platform and that its product covers drafting, prosecution, claim charts, and litigation. Its Series B and Palito.ai acquisition have been publicly reported. Both make the same case for buying vertical over general: patent work needs technical and jurisdictional depth that a broad legal tool doesn't reach.

Their core is text: disclosures, claims, specifications, office-action responses. Both have since added figure features too. DeepIP generates line-art figures inside Word and keeps reference numerals synced across the spec, claims, and drawings; Solve auto-labels figures and preps them for export. So the real question isn't whether they touch drawings, it's whether a figures feature bolted onto a drafting suite is enough, or whether you want a dedicated drawing tool you can run on its own. The PatentDrawingAI vs DeepIP and PatentDrawingAI vs Solve Intelligence comparisons go deeper on the drawing-specific tradeoffs.

Harvey

Harvey is the biggest name in legal AI. It hit roughly an $11 billion valuation in 2026, with recent rounds led by Sequoia and GIC and OpenAI's Startup Fund as an early backer, and is used by tens of thousands of lawyers. It started general and has built real IP muscle. In early 2026 it shipped patent and IP-litigation workflow templates for infringement claim charts, invalidity contentions, an office-action analyzer, license agreements, and filing documents like application data sheets and declarations. If your firm already runs Harvey, extending it into patent prosecution is an easy call.

What Harvey's public materials reviewed for this article do not describe is a dedicated patent drawing workflow. Harvey's IP output is documents and analysis, so the figure slot stays open in any Harvey-based stack. The Harvey patent drawings guide takes that question on directly.

Legora

Legora, formerly Leya and based in Stockholm, is the firmwide-workspace play. It reached about $5.6 billion after a $600 million Series D and is built around legal research, drafting, and large-scale document review. Its Tabular Review feature turns an entire document set into a structured grid, and firms like Bryan Cave Leighton Paisner, with 1,200 lawyers, and Goodwin have deployed it firmwide. It's edging toward IP, and practitioners already use it to spot claim amendments, but for now it's a generalist research-and-review layer with patent capability growing at the margins.

Like Harvey, Legora's public materials reviewed for this article do not describe a dedicated patent drawing workflow. The Legora patent drawings guide breaks down that gap.

ChatGPT and Claude are the utility players. Use them at the edges of a matter. They'll help you rough out claim angles, widen a search query, write inventor-interview questions, or summarize something you paste in. None of that makes either one a patent search engine, a drafting platform, or a drawing tool.

Two cautions. First, decide before anyone uploads unfiled invention details what your data settings and client obligations actually permit; consumer terms are not enterprise terms. Second, treat a generated image as a sketch of an idea, not a deliverable. It still needs every drawing-specific control that 37 CFR 1.84 demands. The ChatGPT patent drawings guide walks through the general-assistant case, and the Nano Banana patent drawings guide covers the stronger image-model version of the same issue.

The generalists skip this layer, and the drafting suites bolt a figures feature onto a much bigger product. PatentDrawingAI is the dedicated, standalone version of it. It starts from what inventors and patent teams already have on hand: a sketch, a product photo, a screenshot, a slide diagram, or a CAD render exported as a PNG, JPG, or WebP. It returns a patent drawing in about one to three minutes per figure.

And it doesn't stop at the raw AI image, which is the whole point. You refine the figure with plain-English edits such as "add thread detail to the screw," "remove the side extrusion," or "lighten the top surface." Auto-Label finds the components and drops in numbered reference labels with leader lines. Manual tools handle hatching, drawing marks, and whiteout. Drawing Set assembly lays the figures onto formal sheets, with the margins, sheet numbering, and figure labels formatted to meet 37 CFR 1.84. You export filing-ready PDF, PNG, or SVG, with DXF linework where a CAD workflow needs it.

For firms, Matter Management keeps it organized by client, matter, docket number, responsible attorney, and drawing type, with CSV import and usage exports for billing. Compare it against the field in the best patent drawing software guide.

The output is complete on its own. You review the set and file it, the same way you'd check any drawings before they go to the USPTO. PatentDrawingAI isn't a law firm and doesn't give legal advice. It produces and formats the drawings. The filing judgment stays with you.

PatentDrawingAI drawing editor

Two patterns jump out. Most of the patent-specific and enterprise tools hide pricing behind a sales call, so treat "contact vendor" as a real cost, and get supported inputs, export formats, and review workflow in writing before you commit. And only one row is a dedicated, self-serve drawing tool with public pricing; the rest either skip figures or fold them into a larger platform you buy through sales.

Start with your bottleneck, not a vendor list. Losing hours finding references? Look at search. Drowning in claims, specs, and office actions? Look at DeepIP or Solve. Need firmwide research and review? That's Harvey or Legora. Figures holding up filings? Go straight at the drawing layer instead of hoping a text tool covers it. It won't.

PatentDrawingAI is built around those six questions, and it sits beside whatever else you run: PatSnap, IPRally, DeepIP, Solve, Harvey, Legora, ChatGPT, Claude, or a plain manual workflow. It's the one piece you can pick up on its own, from $19 a month, purpose-built for that drawing checklist, whether or not the rest of your stack touches figures.

For the broader decision, compare this with the best patent drawing software comparison, PatentDrawingAI vs DeepIP, and PatentDrawingAI vs Solve Intelligence. Those pages go deeper on the drawing-specific tradeoffs this stack guide summarizes.