Neurolaw Patent Audits In AI-Assisted Cognitive Therapy Firms

31 Jan 2026 --
0 Comments

🧠 What Is Neurolaw in the Context of AI‑Assisted Cognitive Therapy?

Neurolaw is the intersection of neuroscience, law, and technology — especially where legal rights are affected by neurological data and technologies that interpret or influence cognition.

In AI‑Assisted Cognitive Therapy, neurolaw covers legal questions around:

Patent eligibility of AI that interprets brain/behavioral data.

Ownership of data and models derived from patient cognitive patterns.

Liability when AI influences therapeutic decisions.

Regulatory compliance (FDA, data protection, medical device law).

Ethical constraints on modifying human cognition.

A patent audit here is a review of a company’s entire patent portfolio as it applies to these technologies — checking for validity, enforceability, freedom‑to‑operate, overlapping IP, and legal risk.

🧾 Key Legal Themes in Neurolaw Patent Audits

Patent audit issues often center on:

Patent eligibility under Section 101 (Patentable subject matter).

Novelty and non‑obviousness (Sections 102/103).

Enablement and written description (Section 112).

Interplay between AI algorithm and neural data — is it “abstract”?

Ownership of patient‑derived algorithms vs. data.

Cross‑licensing and standards essential patents (SEPs).

Regulatory impact (FDA approval can affect patent enforceability).

📚 Case Studies in Neurolaw Patent Disputes

Below are six detailed case examples — drawn from real litigation and adapted to illustrate issues AI cognitive therapy firms might face.

✅ Case 1: Mayo Collaborative Services v. Prometheus Laboratories, 566 U.S. 66 (2012)

Issue: Patent eligibility for medical diagnostic methods.
Summary:
A method for optimizing drug dosage based on metabolite levels was held patent‑ineligible because the claims were essentially natural laws with routine, conventional steps added.

Key Point for AI Cognitive Therapy:
If an AI model claims to diagnose or treat cognitive disorders by applying neural markers and machine learning, courts may treat this as an “abstract idea” unless the claim shows a significant inventive concept beyond routine data collection and algorithmic processing.

Takeaway for Audits:
Patents must emphasize novel technical improvements in how data is processed or therapy is delivered, not just data collection plus generic AI.

✅ Case 2: Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)

Issue: Patentability of computer‑implemented inventions.
Summary:
Software that performed escrow services via generic computer functions was ruled ineligible because it merely automated a fundamental economic practice.

Relevance:
AI cognitive therapy can risk rejection if the patent describes broad AI steps (“train neural network on patient data” + “output recommendation”) without specific technical innovation.

Audit Insight:
Look for patents that merely state AI processes; these are vulnerability points in audits.

✅ Case 3: DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014)

Issue: Patent eligibility for an internet‑based invention.
Summary:
The Federal Circuit upheld a patent that solved “a problem unique to computer technology” — retaining users on merchant sites.

Application:
An AI therapy patent that solves a technical problem unique to neural data integration systems (e.g., latency issues between EEG signal processing and therapy adaptation) can be stronger.

Audit Tip:
Identify and document specific technical challenges addressed; these strengthen eligibility.

✅ Case 4: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016)

Issue: Eligibility of self‑referential data structures in software.
Summary:
The court upheld a database structure innovation because it improved computer functionality.

Implication:
If an AI cognitive therapy algorithm introduces a new neural signal encoding structure, proving technical improvement in data processing helps survive eligibility challenges.

Audit Focus:
Highlight patents that describe technical improvements to hardware/processing, not just cognitive outcomes.

✅ Case 5: In re BRCA1‑ and BRCA2‑Based Hereditary Cancer Test Patent Litigation (Myriad), 569 U.S. 576 (2013)

Issue: Natural phenomena and isolated DNA.
Summary:
Naturally occurring DNA sequences aren’t patentable simply because isolated.

Relevance:
In AI cognitive therapy, claims that cover natural neural patterns or biomarkers alone are weak. Patents must tie to innovative processing or therapeutic application.

Audit Check:
Ensure patent claims are not simply stating discovery of neural marker correlations.

✅ Case 6: American Axle & Manufacturing, Inc. v. Neapco Holdings LLC, 599 U.S. 23 (2023)

Issue: Eligibility of manufacturing technique claims with mathematical concepts.
Summary:
The Supreme Court held that adding conventional steps to a mathematical formula isn’t enough.

Parallel:
Claims that center around an algorithm for processing EEG data using conventional hardware also risk invalidation.

Audit Recommendation:
Flag patents relying on mathematical formulas without inventive integration.

🔎 Deeper Audit Issues in AI Cognitive Therapy

Here’s how these cases apply at a practical level:

✅ Patent Eligibility Risk — AI Algorithms

AI diagnostics and personalized therapy recommendations often look like:

Collect neural patterns

Apply standard AI model

Output therapy suggestion

As shown in Alice and Mayo, this can be seen as abstract unless technical improvements are defined (e.g., real‑time neural signal calibration systems, reduced computational latency).

✅ Novelty & Non‑Obviousness — Stacking Patents

In fast‑moving AI fields, patents that differ only in training dataset size or generic AI tweaks are often invalidated.

Audit Action:
Check for:

Prior publications on similar neural biomarkers

Open source models trained on similar tasks

Incremental tweaks that don’t change technical outcomes

✅ Ownership of Learned Models

Patient data usage raises:

Who owns derivative AI models?

Does training on patient neural data violate rights?

Licenses needed for datasets?

Audit Query:
Review data‑use agreements, AI model ownership assignment clauses.

✅ Enablement and Disclosure

Patents must allow others skilled in the art to reproduce the invention.

Red Flag:
Vague AI training procedures without hyperparameters, datasets, or architecture details.

✅ Regulatory & Compliance

Even valid patents can suffer enforcement difficulty if:

The technology must get medical device clearance (e.g., FDA/Ayush).

Claims exceed regulatory approval scope.

⚖️ Case Summary Table

Case	Core Principle	Relevance to AI Cognitive Therapy
Mayo v. Prometheus	Natural laws + routine steps not patentable	Algorithms interpreting neural data risk being abstract
Alice v. CLS Bank	Generic computer automation not patentable	AI processing steps alone aren’t enough
DDR Holdings	Technical problem unique to computing is patentable	Patent stronger if solving unique neural processing challenge
Enfish	Improved data structures can be patentable	Novel neural data encoding systems are stronger claims
Myriad	Natural phenomena not patentable	Neural biomarkers alone can't be patented
American Axle	Conventional steps around math concepts fail	Standard AI formulas not patentable without inventive integration

🧩 Practical Steps for Firms

📌 During an Audit, Evaluate:

Patent claim language – technical improvements vs. abstract outcomes.

Comparison with prior art – same neural pattern analytics?

Enablement quality – detailed procedures?

Ownership & data rights – who owns the trained models?

Regulatory alignment – do patent claims exceed approved use?

Freedom to Operate – any third‑party patents blocking commercialization?

📍 Final Insights

AI cognitive therapy patents live in a highly contested patent eligibility space.

Neurolaw trends show courts will scrutinize abstract claims, especially where human cognition is involved.

Detailed, technical patent drafting and solid documentation of improvements are essential.

Patent audits must combine legal analysis, neuroscience expertise, and AI technical review.