VisionWave Holdings Seeks U.S. Patent for SDNN™ AI Architecture Targeting Autonomous Defense and Intelligent Systems

VisionWave Holdings (Nasdaq: VWAV), a technology company specializing in advanced sensing, artificial intelligence, imaging, and autonomous technologies, announced on June 15, 2026, the filing of a U.S. provisional patent application for its Symbiotic Deep Neural Network (SDNN™) architecture. The filing covers a proprietary neural-network framework intended to support real-time multi-source data fusion, adaptive reasoning, and coordinated control of distributed intelligent platforms across multiple domains including defense, security, counter-unmanned aerial systems (UAS), robotics, and civil infrastructure.

Alongside the provisional patent application, VisionWave has also submitted a U.S. trademark application for SDNN™ to protect the intellectual property foundation and brand identity associated with this emerging artificial intelligence architecture. The trademark application is currently under examination by the U.S. Patent and Trademark Office (USPTO), with registration not yet guaranteed.

Internally codenamed "Mother," the SDNN™ architecture is envisioned as a central reasoning and coordination layer for networks of distributed intelligent systems. The provisional patent application represents one of VisionWave’s most comprehensive intellectual property filings to date, although the filing of a provisional application does not ensure patent issuance or define claim scope.

The SDNN™ system is designed to integrate heterogeneous sensor data streams from sources such as unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), satellite feeds, relay nodes, and software agents. It aims to facilitate adaptive reasoning, confidence evaluation, coordinated tasking, and human-governed decision workflows within distributed operational networks. VisionWave believes SDNN™ could be a significant advancement in multi-domain AI command-and-control and intelligent system coordination architectures.

The provisional application details a closed intelligence loop operating through the sequence: Intent → Reason → Task → Execute → Feedback → Adapt → Repeat. Core technology components described include:

- Multi-source data fusion integrating radio frequency (RF), radar, electro-optical/infrared (EO/IR), thermal, and software-agent data into a continuously updated operational state.

- The qSpeed™ reasoning engine, a proprietary acceleration framework prioritizing mission-critical computations by scoring candidate reasoning tasks based on decision relevance, urgency, risk, information gain, confidence impact, and resource cost.

- Trust quarantine architecture featuring trust scoring, peer-consistency checks, anomaly detection, re-attestation workflows, audit trails, and human-notification processes for distributed network nodes.

- Human-in-command governance with policy-enforced approval workflows to maintain human authority over consequential actions while enabling autonomous execution within pre-approved parameters.

- The Cube™ hardware root of trust, a compact secure hardware module embedding encrypted software and firmware, incorporating biometric authentication, cryptographic processing, secure memory, secure boot validation, hardware random number generation, and tamper detection.

- Degraded-mode resilience protocols designed to ensure operational continuity during node loss, communication degradation, or system faults.

VisionWave’s provisional application outlines six primary use case categories for SDNN™:

1. Counter-UAS and anti-drone defense, leveraging fusion of RF direction-finding, surveillance radar, EO/IR, and thermal sensor data to support detection, classification, tracking, and operator decision workflows against hostile or unidentified unmanned aerial systems.

2. Missile detection and interception decision-support, providing multi-sensor threat fusion and prioritized coordination for low-altitude cruise and ballistic missile threats.

3. UGV-based ground confirmation, coordinating unmanned ground vehicles to corroborate uncertain detections and dynamically update situational confidence.

4. Multi-robot industrial coordination, enabling assignment, monitoring, and adaptive re-tasking of autonomous robotic systems across inspection, logistics, and manufacturing environments.