Ohio Patent of the Month – January 2026

Strategic Patent Overview

The convergence of high-fidelity medical imaging, computational fluid dynamics (CFD), and artificial intelligence (AI) has precipitated a paradigm shift in structural heart intervention planning. This report provides an exhaustive technical and economic analysis of United States Patent No. 12,527,627, formally titled “Generative computational predictive model for soft tissue repair planning.” Applied for on January 31, 2024, and officially awarded on January 20, 2026, to DasiSimulations, LLC, this intellectual property has been distinguished as the Ohio Patent of the Month for February 2026. The invention, credited to innovators Taylor Nicole Becker, Lakshmi Prasad Dasi, and Shelley Chee-Mei Gooden, introduces a novel generative framework that transcends the computational limitations of traditional Finite Element Analysis (FEA). By synthesizing three-dimensional imaging data with physics-based predictive algorithms, the technology enables near real-time simulation of complex soft tissue interactions, specifically revolutionizing preoperative planning for Transcatheter Aortic Valve Replacement (TAVR). This report benchmarks the technology against incumbent competitors like FEops, analyzes its profound clinical and economic impacts—including recent Medicare reimbursement milestones—and provides a detailed roadmap for maximizing the Research and Development (R&D) Tax Credit using the rigorous compliance frameworks provided by Swanson Reed.

Introduction: The Convergence of AI and Structural Heart Intervention

The field of interventional cardiology is currently undergoing a digital transformation, moving from static anatomical assessment to dynamic functional prediction. The issuance of US Patent 12,527,627 marks a critical milestone in this evolution.

Patent Metadata and Recognition

The patent, “Generative computational predictive model for soft tissue repair planning,” addresses a persistent bottleneck in medical engineering: the inability to accurately and rapidly predict how biological soft tissue will deform under the mechanical stress of a rigid implant. The recognition of this patent as the Ohio Patent of the Month highlights the state’s growing role as a hub for biomedical innovation, supported by institutions like The Ohio State University and grants from the Ohio Department of Development, which have previously supported DasiSimulations’ work.

The selection of this patent for such an accolade is not merely a reflection of its novelty but of its verified utility. Unlike purely theoretical patents, US Patent 12,527,627 describes a system that has already achieved FDA clearance (under the trade name PrecisionTAVI) and secured CMS reimbursement, bridging the “Valley of Death” that often traps medical startups.

The Clinical Imperative

Transcatheter Aortic Valve Replacement (TAVR) has become the standard of care for patients with severe symptomatic aortic stenosis across all risk profiles. However, the procedure is not without peril. Complications such as paravalvular leak (PVL), coronary artery obstruction, and annular rupture continue to plague outcomes, often necessitating emergency open-heart surgery or resulting in mortality. These complications largely stem from the complex interaction between the rigid stent frame of the transcatheter heart valve (THV) and the patient’s unique, often calcified, aortic root anatomy.

Traditional planning relies on 2D Computed Tomography (CT) measurements (e.g., perimeter, area) to select valve size. However, the aortic annulus is rarely a perfect circle, and the distribution of calcium is highly irregular. The invention described in Patent 12,527,627 moves beyond these static measurements to a “digital twin” approach, simulating the physical deployment of the device to predict outcomes before the patient ever enters the catheterization lab.

Technological Deep Dive: The Generative Predictive Model

The core innovation of Patent 12,527,627 is its departure from deterministic, computationally expensive simulations toward a generative approach that integrates physics-based constraints with machine learning efficiency.

Limitations of Traditional Finite Element Analysis (FEA)

To understand the breakthrough, one must understand the incumbent technology. Historically, predicting tissue deformation required Finite Element Analysis (FEA). FEA involves dividing the geometry of the heart valve and the stent into millions of tiny elements (the mesh). The system then solves complex differential equations governing solid mechanics (stress, strain, elasticity) for each element over time.

• Computational Cost: A single high-fidelity FEA simulation of a TAVR procedure can take 12 to 24 hours on a high-performance computing cluster.
• Workflow Bottleneck: Because of the compute time, FEA is typically offered as a service (SaaS) where hospitals upload images and wait days for a report. This precludes real-time “what-if” scenario planning during clinical conferences.

The Generative Architecture

The DasiSimulations patent introduces a methodology that drastically reduces this computational burden without sacrificing accuracy. The “Generative Computational Predictive Model” likely employs a Reduced Order Model (ROM) or a physics-informed neural network architecture.

1. Automated Segmentation: The system first ingests 3D imaging (CT scans) and uses computer vision to automatically segment the aortic root, identifying the leaflets, calcific nodules, coronary ostia, and left ventricular outflow tract (LVOT).
2. Physics-Based Generative Simulation: Instead of iteratively solving the full equations of motion for every millisecond of deployment, the generative model predicts the final equilibrium state of the tissue-device interaction. It creates a “deformed analytical model” by synthesizing the mechanical properties of the stent (radial force) with the patient-specific tissue stiffness.
3. Probabilistic Outcome Mapping: The model generates a probabilistic map of complications. For instance, it calculates the “threat distance” between the displaced native leaflet and the coronary ostium to predict obstruction risk.

Handling Calcific Nodules and Soft Tissue Anisotropy

A critical differentiator of this patent is its sophisticated handling of calcific nodules. In many simplified models, calcium is treated as a rigid block or ignored. However, the DasiSimulations model specifically parameterizes the size and location of these nodules. This is vital because a calcific nodule acts as a fulcrum; when the stent expands, the nodule can puncture the aortic annulus or be pushed into the coronary artery, causing fatal obstruction. The generative model predicts these specific interactions, accounting for the viscoelastic nature of the surrounding soft tissue which relaxes over time.

Comparative Analysis: Benchmarking Against the Status Quo

To fully appreciate the commercial and technical superiority of Patent 12,527,627, it is necessary to benchmark it against both the current standard of care and primary market competitors. The landscape is currently defined by three tiers of technology:

1. Standard of Care: 2D CT Analysis (e.g., 3mensio).
2. Legacy Simulation: Cloud-based Finite Element Analysis (e.g., FEops).
3. Next-Generation Generative Simulation: DasiSimulations (PrecisionTAVI).

The Competitor: FEops (HEARTguide)

FEops (Belgium) is the established player in TAVR simulation. Their product, HEARTguide, utilizes traditional FEA.

• Methodology: Users upload CT scans to a secure server. FEops engineers manually assist in mesh generation and run simulations on high-performance clusters.
• Turnaround: The process typically takes days. While accurate, this latency disconnects the simulation from the immediate decision-making loop of the Heart Team.
• Focus: FEops has historically focused on structural deformation and has only recently integrated Computational Fluid Dynamics (CFD) for flow analysis.

The Innovation: DasiSimulations (PrecisionTAVI)

DasiSimulations disrupts this model by moving from a “Service” to a “Tool.”

• Methodology: The generative model runs automated algorithms that do not require manual engineering intervention for every case.
• Turnaround: Simulations are completed in minutes. This allows for real-time iteration. If a surgeon wants to see what happens if they implant a 26mm valve instead of a 23mm valve, or if they place it 3mm deeper, they can see the result instantly during the planning meeting.
• Accuracy: Clinical validation studies cited in FDA clearances show that the PrecisionTAVI platform achieves 97% agreement in eccentricity and 99% agreement in stent apposition compared to post-operative reality.

Comparative Data Analysis

The following table synthesizes data from clinical studies and technical specifications to highlight the operational differences between the methodologies.

Feature	Standard of Care (2D CT)	FEops (HEARTguide)	DasiSimulations (Patent 12,527,627)
Primary Technology	Geometric Measurement	Finite Element Analysis (FEA)	Generative Computational Predictive Model
Turnaround Time	Immediate (Manual)	Days (24-48+ hours)	Minutes (Real-time)
User Interaction	Static Measurement	Static Report	Dynamic / Interactive Co-Pilot
Calcific Nodule Modeling	Visual Estimation	High Fidelity Mesh	Physics-Informed Generative Parametrization
Coronary Obstruction Risk	Based on geometric height	FEA Deformation Prediction	Probabilistic Threat Distance
Clinical Validation	High Inter-observer variability	High Accuracy	99% Stent Apposition Agreement
Reimbursement Status	Standard Radiology Codes	Specific CPT/Payment Codes	CMS NTAP / New Tech Payment

Insight: The “Minute” vs. “Day” distinction is not just a convenience factor; it is a clinical safety factor. The ability to iterate allows for optimization rather than just validation. A surgeon using FEops might validate that “Plan A works.” A surgeon using DasiSimulations can explore Plans A, B, and C to find which one works best, minimizing gradient and leak risks simultaneously.

Real-World Impact: Clinical and Economic Dimensions

The awarding of the Ohio Patent of the Month recognizes that the impact of Patent 12,527,627 extends far beyond the computer screen. It directly influences patient survival and hospital economics.

Clinical Impact: Reducing Catastrophic Complications

TAVR complications are often binary: a successful procedure results in a 2-day discharge, while a complication can lead to death or lifelong disability.

• Coronary Obstruction Prevention: By predicting the exact displacement of the native leaflets, the generative model prevents the “nightmare scenario” of TAVR—blocking blood flow to the heart muscle itself. The model provides a “safety margin” that 2D imaging cannot.
• Paravalvular Leak (PVL) Mitigation: PVL is linked to higher long-term mortality. The model simulates the seal at the skirt of the valve. If a significant leak is predicted, the surgeon can choose a valve with an external sealing skirt or plan for post-dilation ballooning before the patient is even anesthetized.
• Conduction Disturbance: The model helps visualize the landing zone relative to the membranous septum. Avoiding pressure on this area reduces the need for permanent pacemaker implantation, a complication that affects 10-20% of TAVR patients.

Economic Impact: The Reimbursement Breakthrough

Innovation without reimbursement often fails in healthcare. DasiSimulations has secured a critical economic advantage: Medicare Reimbursement.

• New Technology Add-On Payment (NTAP) / CPT Codes: The system has been approved for reimbursement codes, allowing hospitals to bill for the simulation service. This changes the software from a “cost center” (overhead) to a “revenue center” or a reimbursable quality assurance step.
• Cost Avoidance: The cost of a single TAVR complication can exceed $50,000 to $100,000 in extended ICU stays and interventions. By preventing even a fraction of these events, the technology offers a massive Return on Investment (ROI) for hospital systems operating under value-based care models (e.g., BPCI bundles).

Future Potentials: Lifelong Planning and Pediatric Applications

The patent title, “Soft Tissue Repair Planning,” is intentionally broad. While the immediate application is TAVR, the underlying generative technology has vast potential across the spectrum of structural heart disease.

Lifelong Planning for Structural Heart Disease

As TAVR moves to younger, lower-risk patients, the durability of the valve becomes paramount. A 60-year-old patient will likely outlive their first TAVR valve and require a second procedure (Valve-in-Valve or TAVR-in-TAVR).

• Sequential Modeling: The generative model can simulate not just the immediate procedure, but the next one. It can predict if placing a certain valve today will make it impossible to access the coronary arteries or implant a second valve 10 years from now. This concept of “Lifelong Planning” is a key strategic pillar for DasiSimulations.

Pediatric and Congenital Heart Defects

Children with congenital heart defects (e.g., Tetralogy of Fallot) often undergo multiple surgeries as they grow.

• Growth Simulation: The generative capabilities could theoretically be trained to simulate tissue growth and remodeling over time. This would allow surgeons to plan interventions that accommodate the somatic growth of the child, potentially reducing the total number of open-heart surgeries a child must endure.
• Complex Anatomies: Congenital anatomies are highly variable and do not fit standard models. The “patient-specific” nature of the segmentation algorithm is perfectly suited for these unique geometries, offering a level of personalized medicine currently unavailable in pediatric cardiology.

The Digital Operating Room and Augmented Reality

The future vision involves moving the simulation from the desktop to the sterile field.

• Real-Time Overlay: By integrating with fluoroscopy systems (C-arms), the generative model could project the predicted “safe zone” directly onto the live X-ray video during the procedure. This “Augmented Reality Co-Pilot” would guide the cardiologist’s hand in real-time, warning them if the catheter moves into a high-risk trajectory.

R&D Tax Credit Analysis: The Four-Part Test

For high-growth medical technology companies like DasiSimulations, the Research and Experimentation (R&D) Tax Credit (IRC Section 41) is a critical source of non-dilutive capital. However, the IRS scrutinizes software claims heavily. To qualify, a project must strictly satisfy the Four-Part Test. The following analysis demonstrates how a project utilizing the technology in Patent 12,527,627 meets these requirements.

Part 1: Permitted Purpose

The Requirement: The activity must relate to a new or improved business component (product, process, software, technique, formula, or invention) with the intent to improve functionality, performance, reliability, or quality. Analysis: The development of the PrecisionTAVI platform meets this test unequivocally.

• Business Component: The software platform itself, sold to hospitals.
• Improvement: The project aimed to improve the reliability of TAVR outcomes (reducing complications) and the performance of the planning process (reducing time from days to minutes). This is not a cosmetic change; it is a fundamental functional advancement in medical planning.

Part 2: Technological in Nature

The Requirement: The research must fundamentally rely on principles of the physical or biological sciences, engineering, or computer science. Analysis: The patent disclosure reveals a heavy reliance on “hard sciences.”

• Computer Science: Implementation of machine learning algorithms (e.g., convolutional neural networks for segmentation).
• Biomedical Engineering: Modeling the viscoelastic properties of the aortic root and the radial force of the nitinol stent frame.
• Fluid Dynamics: Simulating blood flow to predict paravalvular leak. The project did not rely on soft sciences (like market research) but on rigorous engineering principles.

Part 3: Elimination of Uncertainty

The Requirement: The activity must be intended to discover information to eliminate uncertainty concerning the capability or method for developing or improving the business component, or the appropriate design of the business component. Analysis: At the outset of the development of Patent 12,527,627, significant technical uncertainties existed:

• Capability Uncertainty: “Is it possible to generate physics-accurate deformations in real-time without full FEA?” This was an unknown in the field.
• Methodological Uncertainty: “How do we accurately parameterize calcific nodules so that the AI recognizes them as solid obstacles rather than soft tissue?”
• Design Uncertainty: “What is the optimal user interface to display probabilistic risk (e.g., a heat map vs. a binary warning) to a surgeon?”

The elimination of these specific technical uncertainties is the hallmark of qualified research.

Part 4: Process of Experimentation

The Requirement: Substantially all of the activities must constitute elements of a process of experimentation. This involves the identification of uncertainty, the identification of one or more alternatives, and the evaluation of those alternatives (e.g., through modeling, simulation, or trial and error). Analysis: The development process for a medical device algorithm is inherently experimental.

• Hypothesis: “A generative model trained on X dataset can predict coronary obstruction within 1mm accuracy.”
• Testing: The team likely ran thousands of retrospective simulations using historical patient data (Training Set).
• Evaluation: They compared the model’s predictions against the actual clinical outcomes of those patients (Validation Set).
• Refinement: When discrepancies were found (e.g., the model missed a calcium spike), the algorithms were re-weighted and re-trained. This iterative cycle constitutes a classic process of experimentation.

The Swanson Reed Advantage: Compliance and Defense

Navigating the R&D tax credit landscape requires more than just meeting the four-part test; it requires rigorous substantiation to withstand IRS scrutiny. Swanson Reed, a specialized R&D tax advisory firm, employs specific methodologies to assist innovators like DasiSimulations.

The Mandatory “6-Eye Review”

To ensure maximum audit defensibility, Swanson Reed subjects every claim to a “6-Eye Review” process. This involves three distinct layers of expert validation:

1. Eye Pair 1: The Qualified Engineer/Scientist.
   • Role: They review the technical narrative. For Patent 12,527,627, a biomedical engineer would verify that the “uncertainties” claimed (e.g., soft tissue anisotropy) are scientifically valid and not merely routine debugging. They ensure the “Technological in Nature” test is met with precision.
2. Eye Pair 2: The Tax Attorney.
   • Role: They focus on legal eligibility and case law compliance. They would analyze the “Internal Use Software” exclusion rules to ensure the software (which is sold to hospitals) is correctly classified. They also review contracts to ensure the research was not “funded” by grants in a way that would disqualify the expenses.
3. Eye Pair 3: The CPA (Certified Public Accountant).
   • Role: They perform the financial allocation. They ensure that the Qualified Research Expenses (QREs)—including the wages of the data scientists, cloud computing costs for simulation training, and supply costs—are accurately calculated and substantiated by payroll records.

AI-Powered Substantiation: TaxTrex

Documentation is the most common reason for claim denial. The IRS requires “contemporaneous documentation”—records created while the work was being done, not retroactively.

• The Tool: Swanson Reed utilizes TaxTrex, a proprietary AI-driven platform.
• The Application: For a complex software project, TaxTrex integrates with the development environment (e.g., Jira, GitHub). It uses AI to identify and “tag” potentially qualifying activities in real-time. For DasiSimulations, it would log the hours spent by engineers on “optimizing the mesh generation algorithm” and link those hours directly to the “Methodological Uncertainty” regarding calcium segmentation. This creates an unassailable audit trail.

Risk Management and ISO Certification

Swanson Reed is one of the few firms with ISO 31000:2009 Risk Management certification. This operational standard ensures that the claim preparation process is consistent, transparent, and secure. For a company handling sensitive patient data (HIPAA compliant) and valuable IP (Patent 12,527,627), this level of data security and process integrity is non-negotiable.

Final Thoughts

US Patent 12,527,627 represents a watershed moment in the digitization of cardiovascular care. By solving the complex physics of soft tissue repair through a novel generative approach, DasiSimulations has provided the medical community with a tool that is not only faster and more accurate than its predecessors but also economically viable through CMS reimbursement. The recognition as Ohio Patent of the Month for February 2026 underscores the technology’s readiness to save lives and reduce costs.

For the innovators driving this progress, the R&D Tax Credit remains a vital mechanism for reinvestment. By adhering to the Four-Part Test and utilizing rigorous compliance frameworks like Swanson Reed’s 6-Eye Review and TaxTrex system, companies can confidently claim these incentives. This financial support ensures that the cycle of innovation—from the lab bench in Ohio to the catheterization labs of the world—continues to accelerate, ultimately benefiting the patient on the table.