Keunbong (KB) Do

Both in-house teams and outside counsel can let a purpose-built platform carry the shared foundation for their patent work. They can then focus their limited time on the standards and judgment that set their work apart. AI has made this division of labor more valuable by raising the payoff for turning a practice into templates, instructions, and review criteria that run at scale. Let’s call that codification. The platform can encode a great deal of best practice out of the box, and a team can add its own custom templates and instructions on top. The real question is how much to add and how much to leave to the platform. There is no fixed formula because the right balance changes as the team’s practice, the law, and the technology evolve.

‍Key takeaways

• AI has raised the payoff for codifying patent practice. Codification now guides AI-assisted drafting and review directly, not just junior training.

• A purpose-built patent platform can encode a large body of best practice out of the box, so a team can begin with that foundation rather than write its own.

• Solve Intelligence maintains its shared foundation across more than 700 IP teams, keeping it current far more efficiently than any single team could alone.

• The strategic choice is how much of your own practice to codify on top; scarce expert time should go to the standards and judgment that differentiate your work.

• A named professional still signs off on every filing, so accountability for the result never shifts to the AI.

The most effective patent practitioners are already using AI patent drafting to draft faster, catch claim inconsistencies earlier, and free up hours for the strategic work that actually wins allowances.

Key takeaways

• AI patent drafting tools can reduce application drafting time by up to 80 percent, with Solve Intelligence customers consistently reporting 50 percent or greater efficiency gains across drafting and prosecution 
• Roughly 9 out of 10 utility patent applications receive at least one office action rejection, so prosecution efficiency matters as much as drafting speed 
• Solo attorneys use AI to match larger law firms on turnaround speed and client capacity
• The strongest reported results come from iterative AI–attorney collaboration, with practitioners directing the process and owning the final work product

A freedom to operate (FTO) analysis is a claim-by-claim assessment of whether a commercial activity would infringe any third-party patents in the markets where it will take place. AI speeds it up by handling the parts that scale badly by hand: surfacing relevant patents, mapping product features against claims element by element, pulling legal status by jurisdiction, and producing a cited, structured draft for the attorney to review and refine.

Key takeaways

• FTO analysis determines whether a product infringes third-party patents.

• A valid patent on your own invention doesn’t guarantee freedom to operate.

• AI compresses the slowest stages of FTO, including search, triage, and element-by-element claim mapping, while the attorney retains the legal judgment and owns the opinion.

• Generalist AI and purpose-built patent tools share a surface format; but FTO reliability depends on integration, not interface.