IP Library Granted Patent US 12,467,704
Granted Patent B2
US 12,467,704 · App. 18/439,451 · Granted Nov 11, 2025

Smart-gun artificial intelligence systems and methods

Inventor: John Hafen (Woodinville, WA)
F41A17/063F41A17/64
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,467,704
App. No.
18/439,451
Granted
Nov 11, 2025
Kind
B2
Abstract

A computer-implemented method of a smart-gun system that includes obtaining a set of smart-gun data, determining one or more states of a smart-gun based at least in part on the set of smart-gun data, determining one or more states of a user based at least in part on the set of smart-gun data, determining to generate a conversation statement based at least in part on the one or more states of the smart-gun and the one or more states of the user, and presenting a conversation statement generated based at least in part on the one or more states of the smart-gun and the one or more states of the user.

Claims (66)

1 . A computer-implemented method of a smart-gun system, the method comprising:

obtaining a set of smart-gun data comprising:

smart-gun orientation data that indicates an orientation of a smart-gun being handled by a user, the smart-gun including a barrel, a safety, and a firing chamber,

smart-gun configuration data that indicates a plurality of configurations of the smart-gun, including safety on/off, firing configuration, and ammunition configuration, and

smart-gun audio data including an audio recording from a microphone of the smart-gun,

determining, using artificial intelligence, one or more states of the smart-gun based at least in part on the set of smart-gun data;

determining, using artificial intelligence, one or more states of the user based at least in part on the set of smart-gun data;

determining a real-time safety level based at least in part on the one or more states of the smart-gun and the one or more states of the user;

determining a smart-gun configuration change based at least in part on the real-time safety level, the one or more states of the smart-gun and the one or more states of the user, the smart-gun configuration change including putting the smart-gun on safety or otherwise making the smart-gun inoperable to fire;

implementing the smart-gun configuration change automatically without human intervention, including by the user handling the smart-gun, the implementing the smart-gun configuration change including actuating the smart-gun to put the smart-gun on safety or otherwise making the smart-gun inoperable to fire;

determining to generate a conversation statement based at least in part on the real-time safety level, the one or more states of the smart-gun and the one or more states of the user;

generating, in response to the determining to generate the conversation statement, an LLM prompt based at least in part on the real-time safety level, the one or more states of the smart-gun and the one or more states of the user;

submitting the LLM prompt to a remote LLM system;

obtaining a conversation statement from the remote LLM system in response to the LLM prompt; and

presenting the conversation statement via an interface of the smart-gun that includes at least a microphone, the conversation statement presented as a synthesized audio speaking voice having a persona or character.

2 . The computer-implemented method of claim 1 , wherein the ammunition configuration includes one or more of:

whether or not the smart-gun is loaded with ammunition;

whether or not a magazine is loaded in the smart-gun;

a number of rounds of ammunition loaded in the smart-gun;

a number of rounds of ammunition loaded in a magazine of the smart-gun; and

a number of rounds of ammunition loaded in the firing chamber of the smart-gun.

3 . The computer-implemented method of claim 1 , wherein the smart-gun orientation data that indicates an orientation of a smart-gun being handled by the user comprises one or more of:

an orientation of the smart-gun about an X-axis defined by the barrel of the smart-gun,

an orientation of the smart-gun about a Y-axis that is perpendicular to the X-axis,

an orientation of the smart-gun about a Z-axis that is perpendicular to the X-axis and Y-axis,

a level status of X-axis,

an orientation of the X-axis relative to a firing line corresponding at least to whether the smart-gun is pointed toward or away from the firing line, and

an orientation of the X-axis relative to a firing line corresponding to an angle that the smart-gun is aligned with or pointed away from perpendicular to the firing line.

4 . The computer-implemented method of claim 1 , wherein the real-time safety level based at least in part on the one or more states of the smart-gun and the one or more states of the user includes a determination of whether safety is above or below a safety threshold.

5 . A computer-implemented method of a smart-gun system, the method comprising:

obtaining a set of smart-gun data comprising:

smart-gun orientation data that indicates an orientation of a smart-gun being handled by a user, the smart-gun including a barrel, a safety, and a firing chamber,

smart-gun configuration data that indicates one or more configurations of the smart-gun, and

smart-gun audio data including an audio recording from a microphone,

determining, using artificial intelligence, one or more states of the smart-gun based at least in part on the set of smart-gun data;

determining, using artificial intelligence, one or more states of the user based at least in part on the set of smart-gun data;

determining a real-time safety level based at least in part on the one or more states of the smart-gun and the one or more states of the user;

determining to generate a conversation statement based at least in part on the real-time safety level, the one or more states of the smart-gun and the one or more states of the user;

generating, in response to the determining to generate the conversation statement, a prompt based at least in part on the real-time safety level, the one or more states of the smart-gun and the one or more states of the user;

obtaining a conversation statement in response to the prompt; and

presenting the conversation statement via an interface that includes at least a microphone, the conversation statement presented as a synthesized audio speaking voice.

6 . The computer-implemented method of claim 5 , further comprising:

determining a smart-gun configuration change based at least in part on the real-time safety level, the one or more states of the smart-gun and the one or more states of the user, the smart-gun configuration change including putting the smart-gun on safety or otherwise making the smart-gun inoperable to fire, and

implementing the smart-gun configuration change automatically without human intervention, including by the user handling the smart-gun, the implementing the smart-gun configuration change including actuating the smart-gun to put the smart-gun on safety or otherwise making the smart-gun inoperable to fire.

7 . The computer-implemented method of claim 5 , wherein the prompt is an LLM prompt, and wherein the LLM prompt is submitted to a remote LLM system.

8 . The computer-implemented method of claim 5 , wherein the interface including the microphone is part of the smart-gun.

9 . A computer-implemented method of a smart-gun system, the method comprising:

obtaining a set of smart-gun data;

determining one or more states of a smart-gun based at least in part on the set of smart-gun data;

determining one or more states of a user based at least in part on the set of smart-gun data;

determining to generate a conversation statement based at least in part on the one or more states of the smart-gun and the one or more states of the user;

generating a prompt based at least in part on the one or more states of the smart-gun and the one or more states of the user, wherein the prompt is an LLM prompt, and wherein the LLM prompt is submitted to a remote LLM system; and

presenting a conversation statement generated based at least in part on the one or more states of the smart-gun and the one or more states of the user.

10 . The computer-implemented method of claim 9 , wherein the set of smart-gun data comprises one or more of:

smart-gun orientation data that indicates an orientation of a smart-gun, and

smart-gun configuration data that indicates at least one configuration of the smart-gun.

11 . The computer-implemented method of claim 9 , further comprising:

determining a safety level based at least in part on the one or more states of the smart-gun and the one or more states of the user.

12 . The computer-implemented method of claim 9 , wherein the prompt is generated further based at least in part on a safety level.

13 . The computer-implemented method of claim 9 , wherein the conversation statement is obtained in response to the prompt.

14 . The computer-implemented method of claim 9 , further comprising:

determining a smart-gun configuration change based at least in part on the one or more states of the smart-gun and the one or more states of the user, and

implementing the smart-gun configuration change automatically without human intervention.

15 . The computer-implemented method of claim 14 , wherein the smart-gun configuration change includes making the smart-gun inoperable to fire.

16 . The computer-implemented method of claim 9 , wherein the conversation statement presented as a synthesized audio speaking voice.

17 . The computer-implemented method of claim 9 , wherein the conversation statement is presented via a microphone that is part of the smart-gun.

Continuity (5)
Continuation In Part 17200072 · Mar 12, 2021
Continuation 16274791 · Feb 13, 2019
Continuation 15430354 · Feb 10, 2017
Provisional Application 62294171 · Feb 11, 2016
Related Publication 20240183632A1 · Jun 6, 2024
References Cited (33)
US 5448847A · Teetzel · 1995 [cited by applicant]
US 5570528A · Teetzel · 1996 [cited by applicant]
US 6223461B1 · Mardirossian · 2001 [cited by applicant]
US 6735897B1 · Schmitter et al. · 2004 [cited by applicant]
US 7600339B2 · Schumacher et al. · 2009 [cited by applicant]
US 8166693B2 · Hughes et al. · 2012 [cited by applicant]
US 9115944B2 · Arif et al. · 2015 [cited by applicant]
US 9189155B2 · Kushler et al. · 2015 [cited by applicant]
US 9222740B1 · Milde, Jr. et al. · 2015 [cited by applicant]
US 9316454B2 · Milde, Jr. · 2016 [cited by applicant]
US 10260830B2 · Hafen · 2019 [cited by applicant]
US 10365057B2 · Black et al. · 2019 [cited by applicant]
US 20030229499A1 · Von Bosse et al. · 2003 [cited by applicant]
US 20140215883A1 · Milde, Jr. · 2014 [cited by applicant]
US 20140250753A1 · Karmanov Kotliarov et al. · 2014 [cited by applicant]
US 20140259841A1 · Carlson · 2014 [cited by applicant]
US 20140290109A1 · Stewart et al. · 2014 [cited by applicant]
US 20140290110A1 · Stewart et al. · 2014 [cited by applicant]
US 20140360073A1 · Stewart et al. · 2014 [cited by applicant]
US 20140366420A1 · Hager · 2014 [cited by applicant]
US 20150068093A1 · Milde, Jr. et al. · 2015 [cited by applicant]
US 20150184962A1 · Burdine · 2015 [cited by applicant]
US 20150199547A1 · Fraccaroli · 2015 [cited by applicant]
US 20150286373A1 · Chukwu · 2015 [cited by applicant]
US 20170010062A1 · Black et al. · 2017 [cited by applicant]
US 20170205170A1 · Milde, Jr. · 2017 [cited by applicant]
US 20220390200A1 · Faizan · 2022 [cited by examiner]
CA 2299307A1 · 1999 [cited by applicant]
CN 1190921A · 1998 [cited by applicant]
CN 201397085Y · 2010 [cited by applicant]
EP 1605222A1 · 2005 [cited by applicant]
WO 2014163653A1 · 2014 [cited by applicant]
WO 2015116021A1 · 2015 [cited by applicant]