ML-based Furniture Layout Generation in Virtual Spaces

USPTO published patent application US20260091318A1 on April 2, 2026, disclosing machine learning techniques for generating recommended furniture layouts in virtual spaces within online games. The system processes space configurations, user criteria such as furniture style and budget, and characteristics of proximate spaces to output AI-generated furnished layouts. Inventors include Anushka Nair, Nitish Victor, Caedmon Somers, Ashwin Nathan, Han Liu, Jesse Hans Stokes Harder, and Marjan Moodi.

Interactive Driving Environment Stories via Learning Model

USPTO published patent application US20260091321A1 disclosing a system and method for generating interactive stories about driving environments using a learning model. The invention acquires sensor data from vehicles, contextual cues about occupants, and their preferences to generate and dynamically adjust interactive virtual driving stories displayed within the vehicle. Application No. 18902256 was filed on September 30, 2024.

Methods and Systems of Predicting Total Loss Events

USPTO published patent application US20260091748A1 assigned to Cambridge Mobile Telematics Inc., covering machine learning methods for predicting total loss events. The system uses mobile device sensors to detect crash events and generates confidence scores for total loss predictions using multiple ML models. Application was filed December 9, 2025 and published April 2, 2026.

Machine Learning System for Modifying ADAS Behavior for Optimum Vehicle Trajectory

USPTO published patent application US20260091794A1 by inventors Nakamura and Kim for a machine learning system that modifies Advanced Driver Assistance Systems (ADAS) behavior to achieve optimum vehicle trajectory. The system uses V2X communication to transmit vehicle-specific customized data and receives individualized optimum behavior data, enabling connected vehicles to implement region-wide optimum behaviors while satisfying individual vehicle needs.

System and Method for Identification of Archaeological Features Using Remotely Sensed Data

The USPTO published patent application US20260092777A1 for an AI-driven system designed to identify gravesites and archaeological features through remote sensing and machine learning. Invented by Nicholas A. Kuncewicz, the technology integrates multimodal data including spectral imagery and LiDAR to detect and classify features. No compliance obligations arise from this patent application publication.

Artificial Intelligence Aiding for Gyrocompassing

Honeywell International Inc. filed patent application US20260092782A1 for an AI-assisted gyrocompassing system. The invention uses a trained machine learning model to predict vehicle attitude, heading, or latitude from inertial measurement unit data, reducing initial alignment time during gyrocompass unit start-up. The application was filed October 1, 2024, and published April 2, 2026.

Automated High Throughput Protorheology

USPTO published patent application US20260092850A1 disclosing methods and systems for automatic, high-throughput estimation of rheological properties of fluidic materials using videography and neural-network processing. The invention enables parallel testing to achieve economical property predictions without expensive rheometric equipment.

Method and Apparatus for Determining Physical State of Object via Neural Network

USPTO published patent application US20260093961A1 for a computer-implemented method using neural networks to determine physical states of objects. The method divides object shapes into sub-shapes, computes local solutions via a neural network model, and derives global solutions representing the object's physical state. Invented by Jianing Huang, Youjia Wu, Kaixuan Zhang, and Ze Cheng. This is an informational publication with no regulatory obligations.

Stealthx Transport LLC - FMCSA Procedural Order

The Federal Motor Carrier Safety Administration (FMCSA) issued a procedural order against Stealthx Transport LLC, a motor carrier. The document, filed as docket FMCSA-2026-0662-0003, appears to address procedural matters related to the carrier's regulatory compliance. Specific details of the order are not available in the filing.

Road Dawg Petition for FMCSA Rulemaking

The Federal Motor Carrier Safety Administration received a petition from Road Dawg requesting regulatory action on commercial vehicle safety and compliance standards. The petition (FMCSA-2023-0257-7006) is filed as part of an ongoing rulemaking docket. No specific compliance requirements or deadlines have been established as FMCSA has not yet acted on the petition.