IP Library Granted Patent US 12,557,956
Granted Patent B2
US 12,557,956 · App. 18/014,658 · Granted Feb 24, 2026

Vacuum cleaner

Inventors: Massimo Camplani (Bristol, GB); Andrew Collingwood Watson (Gloucester, GB); David Alan Millington (Swindon, GB); Nathan Lawson Mclean (Bristol, GB)
Assignee: Dyson Technology Limited
A47L9/2842A47L9/2826A47L9/2831G01C21/16
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Quick Facts
Patent No.
US 12,557,956
App. No.
18/014,658
Granted
Feb 24, 2026
Kind
B2
Abstract

A vacuum cleaner includes: a sensor configured to generate sensor signals based on sensed motion and orientation of the vacuum cleaner; a vacuum motor; and a controller configured to: process the generated sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and in response to determining that the type of cleaning activity includes cleaning a surface at least partially obstructed by an object, decrease the power of the vacuum motor.

Claims (48)

1 . A vacuum cleaner comprising:

a sensor configured to generate sensor signals based on sensed motion and orientation of the vacuum cleaner;

a vacuum motor; and

a controller configured to:

process the generated sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and

in response to determining that the type of cleaning activity comprises cleaning a surface at least partially obstructed by an object, decrease the power of the vacuum motor.

2 . The vacuum cleaner of claim 1 , wherein the surface at least partially obstructed by an object comprises a surface under or behind the object.

3 . The vacuum cleaner of claim 1 , wherein the object comprises an item of furniture or an appliance.

4 . The vacuum cleaner of claim 1 , wherein the controller is configured to decrease the power of the vacuum motor by setting the power of the vacuum motor to a value less than a pre-determined value.

5 . The vacuum cleaner of claim 4 , wherein the pre-determined value corresponds to a default power of the vacuum motor.

6 . The vacuum cleaner of claim 5 , wherein the controller is configured to set the power of the vacuum motor to the default power when the vacuum cleaner is initially switched on.

7 . The vacuum cleaner of claim 1 , wherein the sensor signals are based only on sensed motion of the vacuum cleaner or only on sensed orientation of the vacuum cleaner.

8 . The vacuum cleaner of claim 1 , wherein the sensor comprises an inertial measurement unit, IMU.

9 . The vacuum cleaner of claim 1 , further comprising:

a cleaner head comprising an agitator; and

one or more diagnostic sensors configured to generate further sensor signals based on sensed parameters of the cleaner head,

wherein the controller is configured to process the generated further sensor signals to determine the type of cleaning activity being performed by the user using the vacuum cleaner.

10 . The vacuum cleaner of claim 9 ,

wherein the cleaner head further comprises an agitator motor arranged to rotate the agitator, and

wherein the sensed parameters of the cleaner head comprise the agitator motor current.

11 . The vacuum cleaner of claim 9 , wherein the sensed parameters of the cleaner head comprise the pressure applied to the cleaner head.

12 . The vacuum cleaner of claim 1 , wherein the controller is configured to process the sensor signals by performing a pre-processing step and a classification step.

13 . The vacuum cleaner of claim 12 , wherein the pre-processing step comprises extracting features from time portions of the sensor signals.

14 . The vacuum cleaner of claim 13 , wherein the classification step comprises processing the extracted features using a machine learning Classifier stored as a program on a non-transient media.

15 . The vacuum cleaner of claim 14 , wherein the machine learning classifier, comprising the program stored on the non-transient media, comprises one or more of: an artificial neural network, a random forest and a support- vector machine.

16 . The vacuum cleaner of claim 12 , wherein the pre-processing step comprises filtering the sensor signals.

17 . A vacuum cleaner comprising:

a sensor configured to generate sensor signals based on sensed motion and orientation of the vacuum cleaner;

a vacuum motor; and

a controller configured to:

process the generated sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and

in response to determining that the type of cleaning activity comprises stair cleaning, decrease the power of the vacuum motor.

18 . A method of controlling the power of a vacuum cleaner, the method comprising:

generating sensor signals based on sensed motion and orientation of the vacuum cleaner;

processing the sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and

in response to determining that the type of cleaning activity comprises cleaning a surface at least partially obstructed by an object, decreasing the power of the vacuum cleaner.

19 . A method of controlling the power of a vacuum cleaner, the method comprising:

generating sensor signals based on sensed motion and orientation of the vacuum cleaner;

processing the sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and

in response to determining that the type of cleaning activity comprises stair cleaning, decreasing the power of the vacuum cleaner.

20 . A computer program stored on a non-transient media, the computer program comprising a set of instructions, which, when executed by a computerised device, cause the computerised device to perform a method of controlling the power of a vacuum cleaner, the method comprising:

generating sensor signals based on sensed motion and orientation of the vacuum cleaner;

processing the sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and

in response to determining that the type of cleaning activity comprises cleaning a surface at least partially obstructed by an object, decreasing the power of the vacuum cleaner.

21 . A computer program stored on a non-transient media, the computer program comprising a set of instructions, which, when executed by a computerised device, cause the computerised device to perform a method of controlling the power of a vacuum cleaner, the method comprising:

generating sensor signals based on sensed motion and orientation of the vacuum cleaner;

processing the sensor signals to determine a type of cleaning activity being performed by a user using the vacuum cleaner; and

in response to determining that the type of cleaning activity comprises stair cleaning, decreasing the power of the vacuum cleaner.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 12, 2025
From: CAMPLANI, MASSIMO; WATSON, ANDREW COLLINGWOOD; MILLINGTON, DAVID ALAN; LAWSON MCLEAN, NATHAN
To: DYSON TECHNOLOGY LIMITED
Reel/Frame 070188/0679 →
Priority Claims (1)
GB 2010659 · Jul 10, 2020 · national
Continuity (1)
Related Publication 20230248197A1 · Aug 10, 2023
References Cited (79)
US 5722109A · Delmas et al. · 1998 [cited by applicant]
US 20020083548A1 · Hansen · 2002 [cited by applicant]
US 20020112315A1 · Conrad · 2002 [cited by applicant]
US 20050166354A1 · Naoya · 2005 [cited by applicant]
US 20090271944A1 · Lovelass · 2009 [cited by applicant]
US 20110232687A1 · Stein · 2011 [cited by applicant]
US 20150264121A1 · Van Biljon et al. · 2015 [cited by applicant]
US 20150265121A1 · Kim et al. · 2015 [cited by applicant]
US 20160073844A1 · Park · 2016 [cited by applicant]
US 20160235270A1 · Santini · 2016 [cited by applicant]
US 20180000304A1 · Zhong et al. · 2018 [cited by applicant]
US 20180008112A1 · Ham et al. · 2018 [cited by applicant]
US 20190216282A1 · Pohlman et al. · 2019 [cited by applicant]
US 20190387943A1 · Kim · 2019 [cited by examiner]
US 20200069134A1 · Ebrahimi Afrouzi et al. · 2020 [cited by applicant]
US 20210393097A1 · King et al. · 2021 [cited by applicant]
US 20230248198A1 · Camplani et al. · 2023 [cited by applicant]
US 20230255421A1 · Camplani et al. · 2023 [cited by applicant]
US 20230255422A1 · Camplani et al. · 2023 [cited by applicant]
US 20230255423A1 · Camplani et al. · 2023 [cited by applicant]
US 20230255425A1 · Camplani et al. · 2023 [cited by applicant]
US 20230263347A1 · Camplani et al. · 2023 [cited by applicant]
US 20230263354A1 · Camplani et al. · 2023 [cited by applicant]
US 20230263355A1 · Camplani et al. · 2023 [cited by applicant]
CN 103239191A · 2013 [cited by applicant]
CN 105892457A · 2016 [cited by applicant]
CN 106175599A · 2016 [cited by applicant]
CN 107920705A · 2018 [cited by applicant]
CN 108670119A · 2018 [cited by applicant]
CN 109691931A · 2019 [cited by applicant]
CN 111031874A · 2020 [cited by applicant]
DE 102014113796A1 · 2016 [cited by applicant]
DE 102015108464A1 · 2016 [cited by applicant]
DE 102018200691A1 · 2019 [cited by applicant]
EP 2682034A2 · 2014 [cited by applicant]
EP 3058860A1 · 2016 [cited by applicant]
EP 3162266A1 · 2017 [cited by applicant]
EP 3263004A1 · 2018 [cited by applicant]
EP 3278698A1 · 2018 [cited by applicant]
GB 2490256A · 2012 [cited by applicant]
GB 2572433A · 2019 [cited by applicant]
GB 2578872A · 2020 [cited by applicant]
JP H04250128A · 1992 [cited by applicant]
JP H05130960A · 1993 [cited by applicant]
JP 05269063A · 1993 [cited by applicant]
JP H05253152A · 1993 [cited by applicant]
JP H06154139A · 1994 [cited by applicant]
JP 2009172235A · 2009 [cited by applicant]
JP 2010115360A · 2010 [cited by applicant]
JP 2011188963A · 2011 [cited by applicant]
JP 2012101060A · 2012 [cited by applicant]
JP 2012148011A · 2012 [cited by applicant]
JP 2013198702A · 2013 [cited by applicant]
JP 2013202094A · 2013 [cited by applicant]
JP 2015521945A · 2015 [cited by applicant]
JP 2015156900A · 2015 [cited by applicant]
JP 2015165835A · 2015 [cited by applicant]
JP 2016022378A · 2016 [cited by applicant]
JP 2017080410A · 2017 [cited by applicant]
JP 2018101444A · 2018 [cited by applicant]
JP 2019111321A · 2019 [cited by applicant]
KR 20180082025A · 2018 [cited by applicant]
KR 20190089795A · 2019 [cited by applicant]
KR 20200009680A · 2020 [cited by applicant]
WO 2012077621A1 · 2012 [cited by applicant]
WO 2018202367A1 · 2018 [cited by applicant]
WO 2020106420A1 · 2020 [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051373, mailed on Sep. 10, 2021, 12 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051369, mailed on Aug. 31, 2021, 12 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051370, mailed on Aug. 30, 2021, 10 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051371, mailed on Sep. 9, 2021, 13 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051372, mailed on Aug. 30, 2021, 10 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051374, mailed on Aug. 31, 2021, 12 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051375, mailed on Sep. 1, 2021, 11 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051376, mailed on Sep. 7, 2021, 11 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Patent Application No. PCT/GB2021/051509, mailed on Sep. 17, 2021, 11 pages. [cited by applicant]
Search Report received for GB Application No. 2010656.3, mailed on Jan. 5, 2021, 1 page. [cited by applicant]
Search Report received for GB Application No. 2010659.7, mailed on Jan. 5, 2021, 1 page. [cited by applicant]
Examination Report received for GB Application No. 2305861.3, mailed on Nov. 13, 2023, 1 page. [cited by applicant]