IP Library Granted Patent US 11,960,262
Granted Patent B2
US 11,960,262 · App. 18/073,379 · Granted Apr 16, 2024

Method for devising a schedule based on user input

Inventor: Ali Ebrahimi Afrouzi (Henderson, NV)
Assignee: AI Incorporated
G05B19/0426A47L11/4011B25J9/161B25J11/0085A47L2201/04G05B2219/25419G05D2201/0203
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Quick Facts
Patent No.
US 11,960,262
App. No.
18/073,379
Granted
Apr 16, 2024
Kind
B2
Abstract

Some aspects include a schedule development method for a robotic floor-cleaning device that recognizes patterns in user input to automatically devise a work schedule.

Claims (49)

1. A tangible, non-transitory, machine-readable medium storing instructions that when executed by a control unit of a robotic floor-cleaning device effectuate operations comprising:

receiving, with the control unit of the robotic floor-cleaning device, a new schedule or an adjustment to an existing schedule from a software application executed on an internet-connected device paired with the robotic floor-cleaning device; and

actuating, by the control unit of the robotic floor-cleaning device, the robotic floor-cleaning device to clean according to the new schedule or the adjustment to the existing schedule and a suggested schedule, wherein the control unit only actuates the robotic floor-cleaning device to clean according to the suggested schedule after approval of the suggested schedule;

wherein the software application executed on the internet-connected device is configured to:

propose the suggested schedule for operating the robotic floor-cleaning device comprising at least one day and time to a user; and

receive at least one input designating the new schedule or the adjustment to the existing schedule and an approval of the suggested schedule.

2. The medium of claim 1 , wherein the suggested schedule is inferred using a machine learning algorithm.

3. The medium of claim 2 , wherein the machine learning algorithm uses at least a plurality of user inputs historically provided to the software application to infer the suggested schedule.

4. The medium of claim 3 , wherein the plurality of user inputs designates at least a plurality of schedules previously executed by the robotic floor-cleaning device at a particular past day and time specified in each of the plurality of schedules.

5. The medium of claim 2 , wherein the machine learning algorithm comprises reinforcement learning.

6. The medium of claim 1 , wherein the operations further comprise:

providing, to the software application executed on the internet-connected device, an adjustment to the suggested schedule;

receiving, by the control unit of the robotic floor-cleaning device, the adjusted suggested schedule from the software application; and

actuating, by the control unit of the robotic floor-cleaning device, the robotic floor-cleaning device to clean according to the adjusted suggested schedule.

7. The medium of claim 1 , wherein the internet-connected device comprises at least one of: a remote control, a smartphone, a computer, and a tablet.

8. The medium of claim 1 , wherein the suggested schedule is based on historical cleaning habits of the user.

9. A robotic floor-cleaning device, comprising:

a chassis;

a set of wheels coupled to the chassis and driven by at least one motor;

a control unit;

a cleaning unit; and

a tangible, non-transitory, machine-readable medium storing instructions that when executed by the control unit of the robotic floor-cleaning device effectuate operations comprising:

receiving, with the control unit of the robotic floor-cleaning device, a new schedule or an adjustment to an existing schedule from a software application executed on an internet-connected device paired with the robotic floor-cleaning device; and

actuating, by the control unit of the robotic floor-cleaning device, the robotic floor-cleaning device to clean according to the new schedule or the adjustment to the existing schedule and a suggested schedule, wherein the control unit only actuates the robotic floor-cleaning device to clean according to the suggested schedule after approval of the suggested schedule;

wherein the software application executed on the internet-connected device is configured to:

propose the suggested schedule for operating the robotic floor-cleaning device comprising at least one day and time to a user; and

receive at least one input designating the new schedule or the adjustment to the existing schedule and approval of the suggested schedule.

10. The robotic-floor cleaning device of claim 9 , wherein the operations further comprise:

providing, to the software application executed on the internet-connected device, an adjustment to the suggested schedule;

receiving, by the control unit of the robotic floor-cleaning device, the adjusted suggested schedule from the software application; and

actuating, by the control unit of the robotic floor-cleaning device, the robotic floor-cleaning device to clean according to the adjusted suggested schedule.

11. The robotic-floor cleaning device of claim 9 , wherein the internet-connected device comprises at least one of: a remote control, a smartphone, a computer, and a tablet.

12. The robotic-floor cleaning device of claim 9 , wherein the suggested schedule is based on historical cleaning habits of the user.

13. A method for scheduling cleaning by a robotic floor-cleaning device, comprising:

providing, to a software application executed on an internet-connected device paired with the robotic floor-cleaning device, at least one input designating a new schedule or an adjustment to an existing schedule for operating the robotic floor-cleaning device;

receiving, with a control unit of the robotic floor-cleaning device, the new schedule or the adjustment to the existing schedule from the software application;

proposing, with the software application executed on the internet-connected device, a suggested schedule for operating the robotic floor-cleaning device comprising at least one day and time to a user; and

actuating, by the control unit of the robotic floor-cleaning device, the robotic floor-cleaning device to clean according to the new schedule or the adjustment to the existing schedule and the suggested schedule, wherein the suggested schedule is based on at least historical cleaning habits of the user.

14. The method of claim 13 , wherein the operations further comprise:

providing, to the software application executed on the internet-connected device, at least one input designating approval of the suggested schedule, wherein the control unit only actuates the robotic floor-cleaning device to clean according to the suggested schedule after approval of the suggested schedule.

15. The method of claim 13 , wherein the suggested schedule is inferred using a machine learning algorithm.

16. The method of claim 15 , wherein the machine learning algorithm uses at least a plurality of user inputs historically provided to the software application to infer the suggested schedule.

17. The method of claim 16 , wherein the plurality of user inputs designates at least a plurality of schedules previously executed by the robotic floor-cleaning device at a particular past day and time specified in each of the plurality of schedules.

18. The method of claim 15 , wherein the machine learning algorithm comprises reinforcement learning.

19. The method of claim 13 , further comprising:

providing, to the software application executed on the internet-connected device, an adjustment to the suggested schedule;

receiving, by the control unit of the robotic floor-cleaning device, the adjusted suggested schedule from the software application; and

actuating, by the control unit of the robotic floor-cleaning device, the robotic floor-cleaning device to clean according to the adjusted suggested schedule.

20. The method of claim 13 , wherein the internet-connected device comprises at least one of: a remote control, a smartphone, a computer, and a tablet.

Continuity (5)
Continuation 17838323 · Jun 13, 2022
Continuation 16667206 · Oct 29, 2019
Continuation 15449660 · Mar 3, 2017
Provisional Application 62302914 · Mar 3, 2016
Related Publication 20230099055A1 · Mar 30, 2023
Cited By (1)
US 12,298,730