DOMA TECHNOLOGY LLC has secured a major milestone in real estate technology with a newly patented machine learning framework. This innovation focuses on U.S. Patent titled Predictive machine learning models for parcels of real property. The patent describes an advanced predictive analysis system designed to evaluate and classify risk parameters for land parcels.

Optimizing Real Estate Risk Classification

Patent Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a collection of data associated with a specified parcel of real property, wherein the collection of data includes one or more parameters of interest; using a machine learning model to generate a prediction from the input collection of data for each of the one or more parameters of interest, wherein the prediction for each parameter of interest comprises a likelihood value that the parameter satisfies a particular condition, and wherein the machine learning model is trained using a training set comprising a collection of data associated with a labeled set of data, the labels indicating the existence of particular parameters on the parcels; and based on the prediction, classifying each of the one or more parameters of interest.

Swanson Reed Patent of the Month Recognition

DOMA TECHNOLOGY LLC has achieved a significant milestone in the property sector by securing the Swanson Reed patent of the month for May 2026. This prestigious recognition highlights the groundbreaking predictive assessment system within the Real Estate Development industry. By allowing operators to evaluate specific parcels of land using machine learning models, this invention solves a critical limitation in traditional risk valuation workflows.

The technical excellence of this patent lies in its sophisticated classification pipeline and automated predictive engine. Instead of relying on manual title searches and archaic property records, the system ingests a comprehensive collection of data related to a specific parcel. The underlying machine learning model generates an accurate prediction for various parameters of interest, assigning precise likelihood values to classify conditions and streamline property assessments without human bias.

This invention represents an outstanding advancement because it accelerates the commercial viability of digital property transactions. By converting unstructured property history data into actionable insights, the technology minimizes transaction delays and dramatically reduces underwriting overhead. The recognition from Swanson Reed underlines how this unique application of machine learning sets a new benchmark for operational efficiency and predictive intelligence in the real estate development landscape.

United States Research and Development Tax Credit Compliance

The technological advancements achieved by DOMA TECHNOLOGY LLC align perfectly with the guidelines established for the federal research and development tax credit in the United States. To qualify for this incentive, a project must satisfy a rigorous four part test defined under Section 41 of the Internal Revenue Code. This automated guidance technology satisfies each prong through its systemic resolution of engineering uncertainties.

• Permitted Purpose: The project involves developing a new or improved business component, specifically an advanced machine learning platform for predictive real estate analysis to optimize risk classification.
• Elimination of Uncertainty: The engineering team faced substantial technical uncertainty regarding how to build predictive models that maintain accuracy when handling highly non-uniform and sparse geospatial historical datasets.
• Process of Experimentation: The developers engaged in a systematic process of experimentation, evaluating alternative neural network architectures, testing feature selection mechanisms, and measuring error thresholds through iterative validation cycles.
• Technological in Nature: The research and development activities rely fundamentally on core hard sciences and engineering principles, including computer science, machine learning theory, and statistical data analytics.

Practical Research and Development Tax Credit Applications

In the context of American innovation incentives, specific engineering workflows qualify as research expenditures. The following three scenarios demonstrate how this patent translates into qualified research activities:

1. Developing Predictive Feature Engineering Algorithms: Programing and evaluating software routines that structure raw regional property data into clean input vectors suitable for advanced model training.
2. Optimizing Neural Network Architecture for Property Valuation: Designing and testing alternative machine learning model structures to improve the precision of calculated likelihood values for adverse property conditions.
3. Engineering Automated Labeled Training Pipelines: Building and refining scalable data ingestion workflows that programmatically clean and label historical real estate records to update the underlying predictive systems.