In addition to the provisional patent application, VisionWave has filed a U.S. trademark application for SDNN™ as part of its broader strategy to protect the intellectual-property foundation and brand identity associated with the Company's emerging artificial intelligence architecture. The trademark application remains subject to USPTO examination, and registration is not guaranteed.

The US patent application covers the Company's SDNN™ - Symbiotic Deep Neural Network architecture. Internally, the Company has used the project code name "Mother" to refer to the central core layer of this architecture. SDNN™ is being developed as a proprietary AI framework intended to operate as a central reasoning and coordination layer for networks of distributed intelligent systems. The filing encompasses represents one of VisionWave's most comprehensive intellectual property filings to date. The filing of a provisional patent application does not guarantee the issuance of a patent or any particular scope of claims.

SDNN™ is intended to support the fusion of data from heterogeneous sensors, unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), satellite or external data feeds, relay nodes, and software agents. The architecture described in the filing is designed to support adaptive reasoning, confidence evaluation, coordinated tasking, and human-governed decision workflows across distributed operational networks. The Company believes SDNN™ may represent an important step in the development of multi-domain AI command-and-control and intelligent-system coordination architectures.

Key Technical Innovations Described in the Filing

The provisional application describes a system operating as a closed intelligence loop — Intent → Reason → Task → Execute → Feedback → Adapt → Repeat — with the following core technology areas:

Multi-source data fusion — integration of RF, radar, EO/IR, thermal, and software-agent data streams into a continuously updated operational state.
qSpeed™ reasoning engine, a proprietary reasoning-acceleration framework intended to improve decision-cycle speed by prioritizing the most mission-critical computations first, scoring candidate reasoning tasks across dimensions such as decision relevance, urgency, risk/consequence, information gain, confidence impact, and resource cost.
Trust quarantine architecture, trust scoring, peer-consistency checking, anomaly detection, re-attestation workflows, audit trails, and human-notification processes for distributed network nodes.
Human-in-command governance, policy-enforced approval workflows intended to preserve human authority over consequential actions while enabling autonomous execution within pre-approved operational parameters.
The Cube™ hardware root of trust, a compact secure hardware module with embedded encrypted software/firmware, designed to physically activate and authenticate the SDNN™ system through biometric authentication, cryptographic processing, secure memory, secure boot validation, hardware random number generation, and tamper-detection mechanisms.
Degraded-mode resilience, adaptation protocols intended to support continuity of operation during node loss, communications degradation, or system faults.