Key information

Title
Headset-Mounted Threat Detection System Using Acoustic and RF Sensors with Machine Learning for Triangulation and Real-Time Alerts
Application number
20250185
Case type
National
Status
18.02.2025 Andre og senere realitetsskriv foreligger
Filed
18.02.2025
Effective date
18.02.2025
Publicly available
19.08.2026
Next annual fee due
28.02.2027
Applicant
VESPER AS (NO)
Inventor
ALEXANDER M RAFAELSEN (NO)

Abstract and drawing

Disclaimer: This text has been machine-scanned and may contain errors – please refer to "Publications" for legally binding content.
The invention is a headset-mounted threat detection system utilizing acoustic and RF sensors for detecting and triangulating UAVs or other defined threats. By analyzing combined sensor data with a machine learning algorithm, the device determines the threat’s direction and possibly distance, providing real-time auditory alerts. The invention supports various headset configurations, including traditional, helmetmounted, and earbud designs, making it a versatile tool for privacy, security, and situational awareness.

Publications

A1
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Documents

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Date
 Date Doc. No. Process Case number In/Out Journal description To/from
 08.10.2026 08-01 Saksbehandling 20250185 UT Substantive examination VESPER AS
 08.07.2026 07-07 Saksbehandling 20250185 INN Replacement drawings correction of formal deficiencies VESPER AS
 08.07.2026 07-06 Saksbehandling 20250185 INN NIPO Respons ATLAS 20250185 Formal Defects VESPER AS
 08.07.2026 07-05 Saksbehandling 20250185 INN Fig 4 VESPER AS
 08.07.2026 07-04 Saksbehandling 20250185 INN Fig 3 VESPER AS
 08.07.2026 07-03 Saksbehandling 20250185 INN Fig 2 VESPER AS
 08.07.2026 07-02 Saksbehandling 20250185 INN Fig 1 VESPER AS
 08.07.2026 07-01 Saksbehandling 20250185 INN Korrespondanse (Hovedbrev inn) VESPER AS
 07.07.2026 06-01 Saksbehandling 20250185 UT Henlagt manglende svar ENG VESPER AS
 19.03.2026 05-01 Saksbehandling 20250185 UT Substantive examination VESPER AS
 08.01.2026 04-05 Saksbehandling 20250185 INN ATLAS_Response_to_Office_Action VESPER AS
 08.01.2026 04-04 Saksbehandling 20250185 INN ATLAS_Description_Amendment_Background VESPER AS
 08.01.2026 04-03 Saksbehandling 20250185 INN ATLAS_Amended_Claims_MarkedUp VESPER AS
 08.01.2026 04-02 Saksbehandling 20250185 INN ATLAS_Amended_Claims_Clean VESPER AS
 08.01.2026 04-01 Saksbehandling 20250185 INN Korrespondanse (Hovedbrev inn) VESPER AS
 29.08.2025 03-02 Saksbehandling 20250185 UT PT report 02:56:29
29.08.2025 03-01 Saksbehandling 20250185 UT Substantive Examination VESPER AS
 19.02.2025 02-01 Saksbehandling 20250185 UT Informasjon til oppfinner ALEXANDER M RAFAELSEN
 18.02.2025 01-06 Saksbehandling 20250185 INN Headset pat 18feb only illustr VESPER AS
 18.02.2025 01-05 Saksbehandling 20250185 INN Headset pat 18feb greyscale VESPER AS
 18.02.2025 01-04 Saksbehandling 20250185 INN Headset pat 18feb greyscale VESPER AS
 18.02.2025 01-03 Saksbehandling 20250185 INN Headset pat 18feb greyscale VESPER AS
 18.02.2025 01-02 Saksbehandling 20250185 INN erklaering_rett_oppfinnelsen 3 VESPER AS
 18.02.2025 01-01 Saksbehandling 20250185 INN Søknadsskjema Patent VESPER AS

Description, claims and drawings

Disclaimer: This text has been machine-scanned and may contain errors – please refer to "Publications" for legally binding content. Description
(Title:)Headset-Mounted Threat Detection System Using Acoustic and RF Sensors with Machine Learning for Triangulation and Real-Time Alerts1. Field of the Invention:The present invention relates to wearable technology for the detection and triangulation of threats, specifically unmanned aerial vehicles (UAVs) and similar hazards. The invention employs a headset-mounted configuration equipped with acoustic and radio frequency sensors, a machine learning algorithm for threat analysis, and a warning system for real-time alerts.2. Background of the Invention:The proliferation of UAVs, commonly referred to as drones, in recreational, commercial, and potential malicious activities has highlighted the need for precise, portable detection systems. Current systems often lack portability, directionality, or real-time functionality and are unsuitable for personal use.There exists a need for a compact, wearable solution capable of detecting, triangulating, and alerting users to potential UAV threats or other defined hazards, providing directional and possibly distance information in real time.Sensor placement on the sides of the head secures optimal signal reception in an elevated position. 3. Summary of the Invention:This invention provides a headset-mounted device for detecting and triangulating threats such as UAVs. The system integrates acoustic and radio frequency (RF) sensors, leveraging the physical spacing between the sensors and the directional properties of antennas to perform triangulation.Data from these sensors is processed by an onboard machine learning algorithm, which analyzes signal patterns to identify and locate threats. When a threat is detected, the device provides an alert via an auditory signal delivered through the headset. The invention is suitable for use in traditional headsets, helmet-mounted configurations, and compact earbud designs.Key features include:o Triangulation: Utilizes the spatial separation between sensors and antennaefor precise threat localization and ranging.o Positioning is augmented by the directionality of the rf antennaeo Machine Learning: Embedded algorithms process acoustic and RF data to distinguish threats from background signals and determine the position of the threat.o Warning System: Alerts users through audio signals, ensuring immediate awareness. o Adaptability: Supports various headset configurations for versatile applications in civilian and professional contexts.o Signature database: Builds and refines libraries of threat signatures. Synchronization or exchange with commercial or military signature sets is possible.4. Brief Description of the Drawings:o Figure 1: Illustration of the headset device in traditional and earbud configurations, showing sensor placement. -Page 8o Figure 2: Block diagram depicting data flow from sensors to the machine learning algorithm, triangulation processing and output to the warning system. -Page 9 o Figure 3: Schematic of triangulation methodology, illustrating sensor spacing and antenna directionality. -Page 10o Figure 4: Flowchart of the machine learning algorithm used for threat detection and triangulation. -Page 11 5. Detailed Description of the Invention:5a: Device Components:o Headset/Helmet/Earbud Design:• The device can be integrated into traditional over-ear headsets, helmet mounts, or compact earbuds, ensuring versatility.o Sensors:• Acoustic Sensors: Detect characteristic sound signatures emitted by UAVs or other threats (e.g., rotor noise).• Radio Frequency Sensors: Detect RF signals from drone communication systems or other specified sources.• Antennae: Omnidirectional and/or directional antennae give safe, early warning as well as higher directional gain for positioning.o Triangulation Module:• Leverages the spatial separation between acoustic sensors and RF sensors.• Uses directional antennas to enhance localization accuracy.o Processor with Machine Learning Algorithm:• Analyzes incoming data to classify threats based on known patterns and heuristics.• Determines the direction and approximate distance of detected threats using triangulation techniques and doppler analysis.o Warning System:• Provides real-time auditory alerts via the headset or earbuds.• Allows customizable alert tones to differentiate between types of threats, range and direction.o Power Source:• Rechargeable battery optimized for low-power operation.• Optional wireless charging capability.o Communication Port:• USB-C for charging and firmware updates. firmware updates, profiling signature data upload and download, and device charging.(Specified communication/power interface and protocol may change as universally accepted standards evolve) 5b: Operation:o When powered on, the device continuously collects data from acoustic and RF sensors.o The sensors capture environmental signals, which are filtered and pre-processed. o Pre-processed data is analyzed by the onboard machine learning algorithm, trained to recognize UAV-specific acoustic and RF patterns.o The device uses triangulation to determine the threat’s direction and approximate distance based on sensor spacing and antenna data.o Upon identifying a threat within a predefined threshold, the system generates an auditory alert in the headset or earbuds.5c: Machine Learning Algorithm:o The machine learning algorithm operates on a lightweight framework optimized for embedded systems, ensuring minimal power consumption.• Training: Pre-trained on diverse datasets of UAV audio and RF signals to ensure high recognition accuracy.• Real-Time Analysis: Classifies signals in real-time to distinguish threats from background noise.• Adaptive Learning: Continuously improves accuracy using user-provided feedback.• Triangulation Support: Analyzes spatial and directional data to enhance threat localization.5d: Applications:o Civilian Use: For individuals concerned about privacy or safety in high-risk environments.o Professional Use: Beneficial for security personnel, law enforcement and military operators requiring precise UAV detection and localization.o Emergency Services: Enhances situational awareness for first responders and humanitarian aid workers in high-risk areas.
Claims
6. Claims:Claim 1:A headset-mounted threat detection device, comprising:o At least one acoustic sensor and one radio frequency sensor, mounted with a fixed spatial separation;o A processor with an embedded machine learning algorithm trained to detect and classify threats based on acoustic and RF data;o A triangulation module using sensor spacing and antenna directionality to localize threats;o A warning system providing auditory alerts via the headset upon detecting a threat.Claim 2:The device of Claim 1, wherein the machine learning algorithm is optimized for low-power consumption.Claim 3:The device of Claim 1, wherein the system provides directional and distance information about detected threats.Claim 4:The device of Claim 1, further comprising adaptive learning mechanisms for improving detection accuracy over time.Claim 5:The device of Claim 1, wherein the acoustic sensors are tuned to detect UAV-specific noise, including rotor and propeller sounds.Claim 6:The device of Claim 1, wherein the warning system supports customizable auditory signals to differentiate between threat types, range and direction finding.Claim 7: The device of Claim 1, wherein the system is retrofitted to existing headsets utilizing extra sensors and/or incorporating existing system hardware i.e. but not limited to, acoustic sensors / microphones, power supply and speakers already present.Claim 8:The device of Claim 1, wherein phased arrays are utilized in the reception, for enhanced directionality, better off-axis noise suppression and more precise source triangulation.Claim 9:The device of Claim 1, wherein “Chip Antennae”, also known as “virtual antennae” / “antenna boosters” are utilized to improve or facilitate multiband reception and reduce physical size and cost of reception unit.
Drawings

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Patent classes

IPC classesG01S 13/06G01S 11/02G01S 3/04G01S 17/48G02B 27/01G06N 20/00G06F 1/16CPC classesG01S 13/06G01S 11/02G01S 3/04G01S 3/046G01S 17/48G02B 27/017G02B 2027/014G06N 20/00G06N 3/08G06F 1/163

Citations

US 2023168951 A1 (A1)US 2022165165 A1 (A1)US 2022036741 A1 (A1)US 2023401943 A1 (A1)

Applicants, owners and inventors

Applicants
VESPER AS
VESPER AS

Org. number: 934376587

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Ekornveien 7B 1444 DRØBAK NO (FROGN Municipality, Akershus county)Org. number: 934376587Inventors
ALEXANDER M RAFAELSEN
Glosliveien 14 1443 DRØBAK NO (FROGN Municipality, Akershus county)

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