IP Library › Granted Patent US 11,511,757
Granted Patent B2
US 11,511,757 · App. 16/852,638 · Granted Nov 29, 2022

Vehicle manipulation with crowdsourcing

Inventors: Gabriele Zijderveld (Somerville, MA); Rana el Kaliouby (Milton, MA); Abdelrahman N. Mahmoud (Somerville, MA); Seyedmohammad Mavadati (Watertown, MA)
Assignee: Affectiva, Inc.
B60W40/08G05D1/0291G06V20/59G06V40/172G10L17/00G06F16/9536
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Quick Facts
Patent No.
US 11,511,757
App. No.
16/852,638
Granted
Nov 29, 2022
Kind
B2
Abstract

Vehicle manipulation is performed using crowdsourced data. A camera within a vehicle is used to collect cognitive state data, including facial data, on a plurality of occupants in a plurality of vehicles. A first computing device is used to learn a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data. The cognitive state profiles include information on an absolute time or a trip duration time. Voice data is collected and is used to augment the cognitive state data. A second computing device is used to capture further cognitive state data on an individual occupant in an individual vehicle. A third computing device is used to compare the further cognitive state data with the cognitive state profiles that were learned. The individual vehicle is manipulated based on the comparing of the further cognitive state data.

Claims (45)

1. A computer-implemented method for vehicle manipulation comprising:

collecting, using a camera within a vehicle, cognitive state data including facial data, on a plurality of occupants in a plurality of vehicles;

learning, on a first computing device, a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data;

capturing, on a second computing device, further cognitive state data on an individual occupant in an individual vehicle;

comparing, on a third computing device, the further cognitive state data with the cognitive state profiles that were learned; and

manipulating the individual vehicle based on the comparing of the further cognitive state data, wherein the individual vehicle is an autonomous or semi-autonomous vehicle.

2. The method of claim 1 wherein the cognitive state profiles include information on an absolute time or a trip duration time.

3. The method of claim 2 wherein the absolute time includes time of day, day of week, day of month, or time of year information.

4. The method of claim 1 further comprising collecting voice data and augmenting the cognitive state data with the voice data.

5. The method of claim 4 further comprising performing voice recognition on the individual occupant, using the voice data.

6. The method of claim 1 wherein the individual occupant is a passenger within the vehicle.

7. The method of claim 1 further comprising performing facial recognition on the individual occupant.

8. The method of claim 7 further comprising using the cognitive state profiles across a fleet of vehicles.

9. The method of claim 1 wherein the manipulating the individual vehicle includes a locking out operation; recommending a break for an occupant; recommending a different route; recommending how far to drive; responding to traffic; adjusting seats, mirrors, climate control, lighting, music, audio stimuli, or interior temperature; brake activation; or steering control.

10. The method of claim 1 wherein the manipulating the individual vehicle is based on one or more of a make for the individual vehicle, a vehicle class for the individual vehicle, tires for the individual vehicle, a weather pattern for the individual vehicle, and a traffic pattern for the individual vehicle.

11. The method of claim 1 wherein the cognitive state profiles are based on cognitive state event temporal signatures.

12. The method of claim 1 wherein the cognitive state data is used in detection of one or more of drowsiness, fatigue, distraction, sadness, stress, happiness, anger, frustration, confusion, disappointment, hesitation, cognitive overload, focusing, engagement, attention, boredom, exploration, confidence, trust, delight, disgust, skepticism, doubt, satisfaction, excitement, laughter, calmness, curiosity, humor, depression, envy, sympathy, embarrassment, poignancy, or mirth.

13. A computer-implemented method for vehicle manipulation comprising:

collecting, using a camera within a vehicle, cognitive state data including facial data, on an occupant of the vehicle;

accessing analysis of aggregated cognitive state data collected from a plurality of people within a plurality of vehicles, wherein the aggregated cognitive state data is analyzed using machine learning;

comparing the cognitive state data with the analysis of aggregated cognitive state data; and

manipulating the vehicle based on the comparing of the cognitive state data with the analysis of aggregated cognitive state data, wherein the vehicle is an autonomous or semi-autonomous vehicle.

14. The method of claim 13 wherein the aggregated cognitive state data is collected using crowdsourcing.

15. The method of claim 14 wherein the crowdsourcing is performed in near-real-time.

16. The method of claim 14 wherein the crowdsourcing is performed over a geographically constrained area.

17. The method of claim 16 wherein the geographically constrained area comprises a vehicle route.

18. The method of claim 13 further comprising learning a cognitive state profile for the occupant of the vehicle.

19. The method of claim 18 further comprising basing the manipulating the vehicle on the cognitive state profile for the occupant of the vehicle.

20. The method of claim 13 further comprising capturing further cognitive state data on the occupant of the vehicle.

21. The method of claim 20 further comprising modifying the manipulating the vehicle, based on the further cognitive state data.

22. The method of claim 13 further comprising capturing additional cognitive state data on a plurality of occupants of the vehicle.

23. A computer program product embodied in a non-transitory computer readable medium for vehicle manipulation, the computer program product comprising code which causes one or more processors to perform operations of:

collecting, using a camera within a vehicle, cognitive state data including facial data, on a plurality of occupants in a plurality of vehicles;

learning, on a first computing device, a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data;

capturing, on a second computing device, further cognitive state data on an individual occupant in an individual vehicle;

comparing, on a third computing device, the further cognitive state data with the cognitive state profiles that were learned; and

manipulating the individual vehicle based on the comparing of the further cognitive state data, wherein the individual vehicle is an autonomous or semi-autonomous vehicle.

24. A computer system for vehicle manipulation comprising:

a memory which stores instructions;

one or more processors coupled to the memory wherein the one or more processors, when executing the instructions which are stored, are configured to:

collect, using a camera within a vehicle, cognitive state data including facial data, on a plurality of occupants in a plurality of vehicles;

learn, on a first computing device, a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data;

capture, on a second computing device, further cognitive state data on an individual occupant in an individual vehicle;

compare, on a third computing device, the further cognitive state data with the cognitive state profiles that were learned; and

manipulate the individual vehicle based on the comparing of the further cognitive state data, wherein the individual vehicle is an autonomous or semi-autonomous vehicle.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 20, 2020
From: ZIJDERVELD, GABRIELE; EL KALIOUBY, RANA; MAHMOUD, ABDELRAHMAN N.; MAVADATI, SEYEDMOHAMMAD
To: AFFECTIVA, INC.
Reel/Frame 052437/0482 →
Continuity (43)
Continuation In Part 15875644 · Jan 19, 2018
Continuation In Part 15273765 · Sep 23, 2016
Continuation In Part 14796419 · Jul 10, 2015
Continuation In Part 14460915 · Aug 15, 2014
Continuation In Part 13153745 · Jun 6, 2011
Provisional Application 62955493 · Dec 31, 2019
Provisional Application 62954819 · Dec 30, 2019
Provisional Application 62954833 · Dec 30, 2019
Provisional Application 62926009 · Oct 25, 2019
Provisional Application 62925990 · Oct 25, 2019
Provisional Application 62893298 · Aug 29, 2019
Provisional Application 62611780 · Dec 29, 2017
Provisional Application 62593440 · Dec 1, 2017
Provisional Application 62593449 · Dec 1, 2017
Provisional Application 62557460 · Sep 12, 2017
Provisional Application 62541847 · Aug 7, 2017
Provisional Application 62524606 · Jun 25, 2017
Provisional Application 62503485 · May 9, 2017
Provisional Application 62469591 · Mar 10, 2017
Provisional Application 62448448 · Jan 20, 2017
Provisional Application 62370421 · Aug 3, 2016
Provisional Application 62301558 · Feb 29, 2016
Provisional Application 62273896 · Dec 31, 2015
Provisional Application 62265937 · Dec 10, 2015
Provisional Application 62222518 · Sep 23, 2015
Provisional Application 62128974 · Mar 5, 2015
Provisional Application 62082579 · Nov 20, 2014
Provisional Application 62047508 · Sep 8, 2014
Provisional Application 62023800 · Jul 11, 2014
Provisional Application 61972314 · Mar 30, 2014
Provisional Application 61953878 · Mar 16, 2014
Provisional Application 61927481 · Jan 15, 2014
Provisional Application 61924252 · Jan 7, 2014
Provisional Application 61916190 · Dec 14, 2013
Provisional Application 61867007 · Aug 16, 2013
Provisional Application 61467209 · Mar 24, 2011
Provisional Application 61447464 · Feb 28, 2011
Provisional Application 61447089 · Feb 27, 2011
Provisional Application 61439913 · Feb 6, 2011
Provisional Application 61414451 · Nov 17, 2010
Provisional Application 61388002 · Sep 30, 2010
Provisional Application 61352166 · Jun 7, 2010
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