IP Library Granted Patent US 10,136,619
Granted Patent B2
US 10,136,619 · App. 15/568,175 · Granted Nov 27, 2018

Intelligent pet monitoring system

Inventors: Anantha Pradeep (Berkeley, CA); Ratnakar Dev (Berkeley, CA); Thomas Robbins (Berkeley, CA)
Assignee: SMILABLES INC.
A01K29/005A01K27/001
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Quick Facts
Patent No.
US 10,136,619
App. No.
15/568,175
Granted
Nov 27, 2018
Kind
B2
Abstract

Provided are mechanisms and processes for more effectively monitoring pets to enhance caregiving and pet development. In one example, a system includes a pet monitoring device having a plurality of sensors that gather measurement data, such as motion and arousal, from a pet. The system also includes a monitoring hub that receives the measurement data from the pet monitoring device and analyzes the measurement data in relation to a learning receptivity model obtained from a remote platform. The measurement data is analyzed to predict a time and duration when a pet associated with the pet monitoring device will be receptive to learning.

Claims (30)

1. A system comprising:

a pet monitoring device having a plurality of sensors configured to gather measurement data from a pet, the measurement data including motion and arousal; and

a monitoring hub configured to receive the measurement data from the pet monitoring device and analyze the measurement data,

wherein aggregated measurement data is obtained from a remote platform, the remote platform configured to receive information from a plurality of monitoring hubs associated with a plurality of pet monitoring devices,

wherein the aggregated measurement data is analyzed using machine learning techniques to generate a learning receptivity model for predicting times and durations when pets will be receptive to learning, and

wherein the measurement data from the pet monitoring device is analyzed based on the learning receptivity model to predict a time and duration when a pet associated with the pet monitoring device will be receptive to learning.

2. The system of claim 1 , wherein the pet monitoring device is wearable as a collar.

3. The system of claim 1 , wherein the pet monitoring device is wearable as an anklet.

4. The system of claim 1 , wherein the monitoring hub is further configured to suggest learning content for the pet,

wherein the learning content is customized to a developmental stage associated with the pet, and wherein the developmental stage reflects past learning experiences.

5. The system of claim 1 , further comprising a camera and audio sensor, wherein the monitoring hub receives video and audio data from the camera and audio sensor.

6. The system of claim 1 , wherein learning receptivity model is periodically refined based on additional sensor data and measurement data received from the plurality of pet monitoring systems.

7. A method comprising:

receiving measurement data at a monitoring hub, the measurement data obtained from a plurality of sensors associated with a pet monitoring device;

obtaining aggregated measurement data from a remote platform, the remote platform configured to receive information from a plurality of monitoring hubs associated with a plurality of pet monitoring devices,

analyzing the aggregated measurement data using machine learning techniques to generate a learning receptivity model for predicting times and durations when pets will be receptive to learning, and

analyzing the measurement data from the pet monitoring device based on the learning receptivity model to predict a time and duration when a pet associated with the pet monitoring device will be receptive to learning.

8. The method of claim 7 , wherein the measurement data includes position and movement of the pet.

9. The method of claim 8 , wherein the measurement data further includes arousal.

10. The method of claim 7 , wherein the pet monitoring device is wearable as a collar or anklet.

11. The method of claim 1 , further comprising presenting learning content customized to a developmental stage associated with the pet.

12. The method of claim 11 , wherein the customized learning content includes informational material for an owner associated with the pet.

13. The method of claim 11 , wherein the customized learning content includes suggestions for an owner associated with the pet.

14. The method of claim 1 , further comprising determining a developmental stage associated with the pet based on the measurement data.

15. The method of claim 14 , wherein predicting a time and duration is based on the developmental stage.

16. The method of claim 7 , wherein analyzing the measurement data comprises processing the data measurement into an observation about the pet and comparing the observation to the learning receptivity model.

17. The method of claim 16 , wherein the observation includes one of sleep, mobility, stress, position, comfort, health, vigilance, or articulation.

18. The method of claim 7 , wherein analyzing the measurement comprises processing the data measurement into an inference about the pet and comparing the inference to the learning receptivity model.

19. The method of claim 18 , wherein the inference includes one of receptivity to learning, pet well-being, presence of owner, environmental factors; safety of pet or emotional state of pet.

20. The method of claim 7 , further comprising presenting learning content customized based on previous learning associated with the pet.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 21, 2024
From: SMILABLES INC.
To: SENSORI.AI
Reel/Frame 066862/0391 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 21, 2018
From: PRADEEP, ANANTHA; DEV, RATNAKAR; ROBBINS, THOMAS
To: SMILABLES INC.
Reel/Frame 046938/0010 →
Continuity (2)
Continuation 14690512 · Apr 20, 2015
Related Publication 20180132455A1 · May 17, 2018
Cited By (1)
US 12,268,187