USPTO's 2024 AI Guidance: Claim Strategy, Portfolios, and Fundraising (Part 2)

In Part 1, we looked at the foundations: eligibility under §101 and the rules on human inventorship for AI-assisted inventions. Now we turn to how you use that guidance to build strong claims, a real portfolio, and a better story for investors.

Section 4: Claim Strategy for AI Inventions – How To Get Through the Gate

Good claims turn your messy technical reality into a legal asset. For AI inventions, the 2024 guidance makes one thing clear:

The "technical improvement" focus

Comparing two claims:

Claim checklist for AI founders

When working with your patent team, make sure your claims:

• Describe implementation: data sources, model placement (cloud vs edge), training vs inference, hardware roles.
• State the technical problem in context: too much latency, poor accuracy under noise, high power usage, etc.
• Show improvement: metrics or clear qualitative upgrades to the way the system works.
• Avoid pure business language: if the claim sounds like a business pitch with "AI" sprinkled in, it is in danger.
• Use dependent claims to capture variants: method, system, computer-readable medium, device, etc.

A simple "AI Patent Readiness Matrix"

You can run each feature through this sequence:

1. Problem definition – what technical pain are you fixing?
2. Model and architecture – what makes your setup non-trivial?
3. Data flow and hardware integration – how data moves, and where the model sits.
4. Improvement metric – what got better, and by how much?
5. Claim mapping – where each of the above shows up in the actual claims.

Section 5: IP Portfolio Strategy – Thinking Beyond One Patent

You are not just filing a single patent. You are building an IP asset that should support fundraising, partnerships, and exit.

Core vs peripheral patents

A healthy AI portfolio often has:

Trade secrets and patents together

Many AI teams keep some elements as trade secrets, such as:

• Model weights
• Training data pipelines
• Data cleaning or augmentation methods
• Edge deployment scripts

Patents require disclosure, so you will not want to patent every trick. A common setup:

• Patent the system and key mechanisms.
• Keep parameters, datasets, and some tooling under trade secret protection.

Global strategy

Patent rules differ across the US, EU, China, and others, especially for software and AI.

Typical pattern:

• File in the US first.
• Use that as a base for PCT and regional filings in the EU and key markets.
• Align global filings with where you expect customers, partners, or acquirers.

Why investors care

Investors know AI moves fast. What they want to see is:

• You are not easy to clone.
• Your IP has been drafted with modern guidance in mind.
• You are not stuck with claims that will crumble at the first eligibility or inventorship challenge.

A small number of well-structured families can often say more than a stack of weak filings.

Section 6: AI + Business Methods – The High-Risk Zone

If your AI sits in fintech, AdTech, e-commerce, or other business-method heavy fields, you have extra risk on §101.

Why AI + business methods are tricky

Historically, business method patents are more likely to be rejected as abstract ideas. Adding AI does not fix that by itself.

Examples of technical angles:

• Moving fraud detection to the edge to cut settlement latency.
• Using specialized model updates that reduce network bandwidth.
• Packet-level control flows to gateways or routers.

Strategy if you are in fintech or similar fields

• Center your claims on system engineering, not business concepts.
• Make sure the claims show hardware or network behavior changing because of your approach.
• Consider alternative claim sets: If high-level business claims struggle, have system or device claims ready that focus on the tech deployment.

Section 7: Future-Proofing Your AI IP – 5–10 Year View

The rules for AI IP will not stand still. Over the next decade, founders should watch several trends.

Likely developments:

• More focus on training data and model disclosure in some contexts.
• Global alignment on AI inventorship rules (all human, AI not listed).
• More patents on AI as part of devices: edge learning, self-updating systems, etc.
• Hybrid strategies: patents for system-level designs, trade secrets for models and data.

For you, this means:

• Keeping long-term logs of how AI systems are trained and updated.
• Staying aware of new USPTO guidance and court cases.
• Planning ahead for global filing choices, not just US.

Section 8: Case Studies – How AI Startups Can Apply the Guidance

Case A: "EdgeHealth" – Med-tech AI on edge devices

• Problem: Real-time anomaly detection in patient vitals on bedside devices.
• Human work: Sensor selection, model design for drift, power/latency tradeoffs, on-device deployment.
• Claim focus: A system with sensors, an edge processor running a dynamic model, secure alerts to the cloud, and lower false positives vs static models.

This aligns well with the 2024 guidance: clear technical setting, identifiable human inventors, documented performance improvement.

Case B: "FinAI Flow" – Fraud detection in mobile payments

• Problem: Real-time fraud detection without slowing down settlement.
• Human work: Choosing a hybrid transformer + rule engine, designing a secure gateway, tuning for latency and false positives.
• Claim focus: Transaction packets, model-in-the-loop at a secure gateway, final block/allow decisions, reduced latency.

Because it is fintech, the team must push hard on the technical story: packet-level control, network behavior, gateway integration. This is how they avoid the "just a business method with AI" trap.

Section 9: Risk Management & Enforcement – Avoid Nasty Surprises

AI patents carry several specific risks:

• Eligibility risk: rejected as abstract, especially if claims are too high-level.
• Inventorship risk: challenges if AI did most of the work and human roles are unclear.
• Novelty/obviousness risk: crowded AI field, lots of prior art.
• Global gaps: strong US patents but weak or no coverage abroad.
• Trade secret leaks: loss of model or data advantages if staff leave or systems are exposed.

How to manage these risks

• Run eligibility and inventorship checklists before filing.
• Log human vs AI work throughout the R&D cycle.
• Do basic prior art searches early.
• Keep deployment logs and performance benchmarks to support enforcement later.
• Treat IP as part of your risk function, not just a formality.

Section 10: Building an IP-Ready R&D Culture

Strong AI IP comes from how your team works day to day.

Good patterns:

• R&D + IP syncs: regular sessions between engineers and counsel.
• Invention logs: short writeups when a non-obvious technique or architecture emerges.
• Claim-ready summaries: engineers write 1–2 page system summaries with problem, design, and metrics.
• Patent readiness gates: you do not file until you have: a working prototype, measurable improvement, clear human contribution notes.

Training your engineers on basic eligibility and inventorship rules pays off. They will start flagging "this is patentable" events instead of waiting for management.

Section 11: Fundraising & Exit – IP as a Valuation Lever

When you talk to investors or acquirers, your AI IP is part of your story of defensibility and upside.

Expect questions like:

• Do you have patents filed that reflect the 2024 AI guidance?
• Are human inventors clearly identified and documented?
• Do your claims describe technical improvements, not just business outcomes?
• How broad is your coverage – only US, or key markets too?
• How do patents and trade secrets work together in your plan?

IP readiness levels

The last category lets you argue for a stronger multiple. You can point to: filing dates, performance metrics in the spec, coverage of key system pieces, clean inventorship and assignment chains.

Section 12: Final Takeaways for Founders

Bringing it all together:

• Do not ignore the 2024 USPTO AI guidance. Treat it as part of your design brief for both product and patent.
• Show technical improvement in your claims. "AI does X" is not enough; show how the system works better because of it.
• Keep inventorship clean. Only humans can be inventors, and you must be able to show who did what.
• Think portfolio, not single filing. Mix patents and trade secrets, and plan for global coverage where it matters.
• Use IP as a story in fundraising. It is part of your moat, not just a line item.