Granted Patent B2

US 12,575,546 · App. 18/901,782 · Granted Mar 17, 2026

Inventors: Kenneth Scott Ehrman (Upper Saddle River, NJ); Michael Ehrman (Plano, TX)

Assignee: Protect Animals With Satellites, LLC

A01K29/005A01K15/02A01K27/001A01K27/009H04W4/021

Abstract

Claims (50)

1 . A collar comprising:

a strap configured to be worn by an animal;

a power source;

memory storing instructions;

one or more processors in communication with the memory;

a movement sensor configured to detect a movement of the animal wearing the collar and output movement data; and

a geolocation sensor configured to output location data corresponding to a location of the collar,

wherein the instructions, when executed by the one or more processors, are configured to cause the collar to:

receive the movement data from the movement sensor;

receive the location data from the geolocation sensor;

receive geo-fence data indicative of a boundary defining a predetermined geographical area in which it is permissible for the collar to be located;

determine whether the animal wearing the collar is within a predetermined distance of the boundary; and

in response to determining that the animal wearing the collar is within the predetermined distance of the boundary and based at least in part on the movement data, cause the collar to output a corrective action to deter the animal wearing the collar from approaching the boundary,

wherein causing the collar to output the corrective action is performed at least in part using a machine learning algorithm.

2 . The collar of claim 1 , wherein the instructions, when executed by the one or more processors, are further configured to determine, based at least in part on the movement data and using the machine learning algorithm, an activity level of the animal wearing the collar.

3 . The collar of claim 2 , wherein outputting the corrective action is based at least in part on the activity level of the animal wearing the collar.

4 . The collar of claim 3 , wherein a severity of the corrective action is based at least in part on the activity level of the animal wearing the collar.

5 . The collar of claim 1 , wherein the machine learning algorithm is trained using feedback from a user.

6 . The collar of claim 1 , wherein the instructions, when executed by the one or more processors, are further configured to determine, based at least in part on the movement data and using the machine learning algorithm, an activity type of the animal wearing the collar.

7 . The collar of claim 6 , wherein the instructions, when executed by the one or more processors, are further configured to cause the collar to operate in a predetermined mode of a plurality of modes based at least in part on the activity type.

8 . The collar of claim 6 , wherein the instructions, when executed by the one or more processors, are further configured to cause the collar to change a severity level of the corrective action based at least in part on the activity type.

9 . The collar of claim 1 further comprising a magnetometer configured to determine a direction in which the collar is oriented, the direction in which the collar is oriented being indicative of a direction in which the animal wearing the collar is facing.

10 . The collar of claim 9 , wherein the instructions, when executed by the one or more processors, are further configured to cause the collar to prevent an output of the corrective action responsive to determining that the animal wearing the collar is located outside the predetermined geographical area and is facing toward the predetermined geographical area.

11 . The collar of claim 9 , wherein the instructions, when executed by the one or more processors, are further configured to cause the collar to output the corrective action responsive to determining that the animal wearing the collar is located outside the predetermined geographical area and is facing away from the predetermined geographical area.

12 . The collar of claim 9 , wherein the instructions, when executed by the one or more processors, are further configured to cause the collar to:

determine whether the animal wearing the collar is moving toward the boundary; and

wherein outputting the corrective action further comprises determining that the animal wearing the collar is moving toward the boundary.

13 . The collar of claim 1 , wherein the movement sensor comprises an accelerometer.

14 . The collar of claim 1 , wherein the instructions, when executed by the one or more processors, are further configured to adjust the predetermined distance based at least in part on a determined accuracy of the geolocation sensor.

15 . The collar of claim 1 , wherein the corrective action is a first corrective action and the instructions, when executed by the one or more processors, are further configured to cause the collar to:

determine whether the collar is outside of the predetermined geographical area; and

in response to determining that the animal wearing the collar is outside of the predetermined geographical area, output a second corrective action, the second corrective action being more severe than the first correction.

16 . A collar comprising:

a strap configured to be worn by an animal;

a power source;

memory storing instructions;

one or more processors in communication with the memory;

a movement sensor configured to detect a movement of the animal wearing the collar and output movement data; and

a geolocation sensor configured to output location data corresponding to a location of the collar,

wherein the instructions, when executed by the one or more processors, are configured to cause the collar to:

receive the movement data from the movement sensor;

receive the location data from the geolocation sensor;

receive geo-fence data indicative of a boundary defining a predetermined geographical area in which it is permissible for the collar to be located;

determine, based at least in part on the movement data and using a machine learning algorithm, an activity of the animal;

determine whether the animal wearing the collar is within a predetermined distance of the boundary; and

in response to determining that the animal wearing the collar is within the predetermined distance of the boundary and based at least in part on the activity of the animal, output a corrective action to deter the animal wearing the collar from approaching the boundary.

17 . The collar of claim 16 , wherein the instructions, when executed by the one or more processors, are further configured to determine, based at least in part on the movement data and using the machine learning algorithm, an activity level of the animal wearing the collar.

18 . The collar of claim 17 , wherein outputting the corrective action is based at least in part on the activity level of the animal wearing the collar.

19 . The collar of claim 18 , wherein a severity of the corrective action is based at least in part on the activity level of the animal wearing the collar.

20 . The collar of claim 16 , wherein the instructions, when executed by the one or more processors, are further configured to determine, based at least in part on the movement data and using the machine learning algorithm, an activity type of the animal wearing the collar and cause the collar to operate in a predetermined mode of a plurality of modes based at least in part on the activity type.

Assignments (2)

Continuity (6)

References Cited (215)