IP Library › Granted Patent US 12,213,796
Granted Patent B2
US 12,213,796 · App. 17/843,442 · Granted Feb 4, 2025

Aroma training using virtual reality

Inventors: Derrick Anthony Martin, II (Brooklyn, NY); Alex M. Kass (Palo Alto, CA); Marc Carrel-Billiard (Vence, FR); Alexandria Emily Pabst (Merced, CA)
Assignee: Accenture Global Solutions Limited
A61B5/4011G06F3/015G06F3/04842
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Quick Facts
Patent No.
US 12,213,796
App. No.
17/843,442
Granted
Feb 4, 2025
Kind
B2
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an olfaction training program by an online service executing on a server. A first object and an aroma of the first object is transmitted to the client device of a user that presents the first object in a virtual reality environment and disperses the first aroma. In response, the user interacts with the vital reality environment. The user response is transmitted back to the online service. The user response is stored in a database along with the first data and the first aroma. The online service determines a second object and an aroma of the second object based on the user response. The second object and the aroma of the second object is transmitted to the client device.

Claims (72)

1. A computer-implemented method for aroma training, comprising:

generating training data sets for training a machine learning model to identify one of a plurality of objects to present after initially presenting another one of the plurality of objects to incrementally trigger increased user perception of aromas by users that are suffering from olfactory disfunction, the training data sets each including data indicating (i) another one of the plurality of objects that was initially presented to one of the users, (ii) one of the plurality of objects that were presented to the one of the users after initially presenting the another one of the plurality of objects, (iii) and electrical brain activity data of the one of the users that reflects an extent to which presentation of the aromas of the one of the plurality of objects and the another one of the plurality of objects created fluctuations in electrical activity of a brain of the one of the users when the one of the plurality of objects and the another one of the plurality of object were presented;

training the machine learning model in such a way that the aromas of the another one of the plurality of objects is followed in sequence by the aromas of the one of the plurality of objects, to predict objects from a plurality of objects based on the aromas;

transmitting, to a client device, a first data indicating a first object from the plurality of objects and instructions to disperse a first aroma associated with the first object for presentation to a user of the client device;

receiving, from the client device, a user response that was provided by the user in response to the presentation of the first object and the first aroma, the user response comprising electrical brain activity data of the user that reflects an extent to which an aroma of the first object was perceived;

storing the user response in a database as an instance of a training dataset for the user;

identifying, based on the user response, a second object from the plurality of objects using the trained machine learning model;

determining the aroma of the second object based on the electrical brain activity data of the one of the users; and

transmitting, to the client device, the second data indicating the second object and instructions to disperse the second aroma for presentation to the user of the client device.

2. The computer-implemented method of claim 1 , wherein presenting the first and the second data by the client device comprises:

displaying the respective object on a display screen of a client device wherein the display screen can support virtual reality (VR);

dispersing the respective aroma associated to the respective object and displayed on the display screen of the client device using a aroma dispenser of the client device.

3. The computer-implemented method of claim 1 , comprising:

displaying a plurality of objects in the VR space generated by the client device;

dispersing the aroma associated to a particular object at an intensity level, wherein the intensity level is based on the user position with respect to the VR space and the distance of the user position in the VR space to the particular object in the VR space; and

changing the intensity level of the aroma based on the change in the user position in the VR space and change in the distance of the user position in the VR space to the particular object in the VR space;

receiving a response from the user indicating a user selection of an object in the VR space;

revealing the particular object to the user in the VR space and an indication of whether the object selected by the user is the same as the particular object.

4. The computer-implemented method of claim 3 , comprising:

displaying to the user in the VR space the plurality of objects, receiving a response from the user indicating a selection of an object from the plurality of objects, wherein, in response to the user selection of the object, dispersing the aroma associated to the object; and

receiving a response from the user indicating a selection of two objects from the plurality of objects; and in response to selecting two objects from the plurality of objects, displaying to the user whether the two selected obj ects have the same aroma.

5. The computer-implemented method of claim 4 , wherein the user response provided by the user in response to the presentation of the first object and the first aroma is collected using one or more electrodes of the client device affixed to the scalp of the user wherein the electrical activity of the brain identifies brain activity in response to the user smelling the first object.

6. The computer-implemented method of claim 1 , wherein the user response provided by the user in response to the presentation of the first object and the first aroma based on the user experience comprises (1) an indication whether the user was able to perceive the first aroma, (2) an indication whether the user is able to associate the first object and the first aroma, and (3) a score provided by the user indicating the level of confidence the user has on the association of the first object and first aroma.

7. A system for aroma training, comprising one or more processors and one or more non-transitory computer readable media that store instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

generating training data sets for training a machine learning model to identify one of a plurality of objects to present after initially presenting another one of the plurality of objects to incrementally trigger increased user perception of aromas by users that are suffering from olfactory disfunction, the training data sets each including data indicating (i) another one of the plurality of objects that was initially presented to one of the users, (ii) one of the plurality of objects that were presented to the one of the users after initially presenting the another one of the plurality of objects, (iii) and electrical brain activity data of the one of the users that reflects an extent to which presentation of the aromas of the one object and the another one of the objects created fluctuations in electrical activity of a brain of the one of the users when the one of the plurality of object and the another one of the plurality of objects were presented;

training the machine learning model in such a way that the aromas of the another one of the plurality of objects is followed in sequence by the aromas of the one of the plurality of objects, to predict objects from the plurality of objects based on the aromas;

transmitting, to a client device, a first data indicating a first object from the plurality of objects and instructions to disperse a first aroma associated with the first object for presentation to a user of the client device;

receiving, from the client device, the user response that was provided by the user in response to the presentation of the first object and the first aroma, the user response comprising electrical brain activity data of the user that reflects an extent to which an aroma of the first object was perceived;

storing the user response in a database as an instance of a training dataset for the user;

identifying, based on the user response, a second object from the plurality of objects using the trained machine learning model;

determining a second aroma that is associated with the second object based on the electrical brain activity data of the one of the users;

transmitting, to the client device, the second data indicating the second object and instructions to disperse the second aroma for presentation to the user of the client device.

8. The system of claim 7 , wherein presenting the first and the second data by the client device comprises:

displaying the respective object on a display screen of a client device wherein the display screen can support virtual reality (VR); and

dispersing the respective aroma associated to the respective object and displayed on the display screen of the client device using an aroma dispenser of the client device.

9. The system of claim 7 , wherein the operations comprise:

displaying a plurality of objects in the VR space generated by the client device;

dispersing the aroma associated to a particular object at an intensity level, wherein the intensity level is based on the user position with respect to the VR space and the distance of the user position in the VR space to the particular object in the VR space;

changing the intensity level of the aroma based on the change in the user position in the VR space and change in the distance of the user position in the VR space to the particular object in the VR space;

receiving a response from the user indicating a user selection of an object in the VR space; and

revealing the particular object to the user in the VR space and an indication of whether the object selected by the user is the same as the particular object.

10. The system of claim 9 , wherein the operations comprise:

displaying to the user in the VR space the plurality of objects;

receiving a response from the user indicating a selection of an object from the plurality of objects,

wherein, in response to the user selection of an object, dispersing the aroma associated to the object;

receiving a response from the user indicating a selection of two objects from the plurality of objects; and

in response to selecting two objects from the plurality of objects, displaying to the user whether the two selected objects have the same aroma.

11. The system of claim 10 , wherein the user response provided by the user in response to the presentation of the first object and the first aroma is collected using one or more electrodes of the client device affixed to the scalp of the user wherein the electrical activity of the brain identifies brain activity in response to the user smelling the first object.

12. The system of claim 7 , wherein the user response provided by the user in response to the presentation of the first object and the first aroma based on the user experience comprises (1) an indication whether the user was able to perceive the first aroma, (2) an indication whether the user is able to associate the first object and the first aroma, and (3) a score provided by the user indicating the level of confidence the user has on the association of the first object and first aroma.

13. A non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising:

generating training data sets for training a machine learning model to identify one of a plurality of objects to present after initially presenting another one of the plurality of objects to incrementally trigger increased user perception of aromas by users that are suffering from olfactory disfunction, the training data sets each including data indicating (i) another one of the plurality of objects that was initially presented to one of the users, (ii) one of the objects that were presented to the one of the users after initially presenting the another one of the plurality of objects, (iii) and electrical brain activity data of the one of the users that reflects an extent to which presentation of the aromas of the one of the plurality of objects and the another one of the objects created fluctuations in electrical activity of a brain of the one of the users when the one object and the another object were presented;

training the machine learning model in such a way that the aromas of the another one of the plurality of objects is followed in sequence by the aromas of the one of the objects, to predict objects from a plurality of objects based on the aromas:

transmitting, to a client device, a first data indicating a first object from the plurality of objects and instructions to disperse a first aroma associated with the first object for presentation to a user of the client device;

receiving, from the client device, a user response that was provided by the user in response to the presentation of the first object and the first aroma, the user response comprising electrical brain activity data of the user that reflects an extent to which an aroma of the first object was perceived;

storing the user response in a database as an instance of a training dataset for the user;

identifying, based on the user response, a second object from the plurality of objects using the trained machine learning model;

determining a second aroma that is associated with the second object based on the electrical brain activity data of the one of the users; transmitting, to the client device, the second data indicating the second object and instructions to disperse the second aroma for presentation to the user of the client device.

14. The non-transitory computer readable medium of claim 13 , wherein presenting the first and the second data by the client device comprises:

displaying the respective object on a display screen of a client device wherein the display screen can support virtual reality (VR); and

dispersing the respective aroma associated to the respective object and displayed on the display screen of the client device using an aroma dispenser of the client device.

15. The non-transitory computer readable medium of claim 13 , wherein the operations comprise:

displaying a plurality of objects in the VR space generated by the client device;

dispersing the aroma associated to a particular object at an intensity level, wherein the intensity level is based on the user position with respect to the VR space and the distance of the user position in the VR space to the particular object in the VR space;

changing the intensity level of the aroma based on the change in the user position in the VR space and change in the distance of the user position in the VR space to the particular object in the VR space;

receiving a response from the user indicating a user selection of an object in the VR space;

revealing the particular object to the user in the VR space and an indication of whether the object selected by the user is the same as the particular object.

16. The non-transitory computer readable medium of claim 15 , wherein the operations comprise:

displaying to the user in the VR space the plurality of objects;

receiving a response from the user indicating a selection of an object from the plurality of objects,

wherein, in response to the user selection of an object, dispersing the aroma associated to the object;

receiving a response from the user indicating a selection of two objects from the plurality of objects; and

in response to selecting two objects from the plurality of objects, displaying to the user whether the two selected objects have the same aroma.

Assignments (2)
CORRECTIVE ASSIGNMENT TO CORRECT THE FIRST ASSIGNOR'S SUFFIX PREVIOUSLY RECORDED ON REEL 060550 FRAME 0053. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Aug 10, 2022
From: MARTIN, DERRICK ANTHONY, II; KASS, ALEX M.; CARREL-BILLIARD, MARC; PABST, ALEXANDRIA EMILY
To: ACCENTURE GLOBAL SOLUTIONS LIMITED
Reel/Frame 061143/0414 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 19, 2022
From: MARTIN, DERRICK ANTHONY; KASS, ALEX M.; CARREL-BILLIARD, MARC; PABST, ALEXANDRIA EMILY
To: ACCENTURE GLOBAL SOLUTIONS LIMITED
Reel/Frame 060550/0053 →
Continuity (2)
Provisional Application 63286744 · Dec 7, 2021
Related Publication 20230172524A1 · Jun 8, 2023
References Cited (54)
US 8321797B2 · Perkins · 2012 [cited by examiner]
US 9717454B2 · Mills · 2017 [cited by examiner]
US 9907876B2 · Jin · 2018 [cited by examiner]
US 9925458B2 · Fateh · 2018 [cited by examiner]
US 10437266B2 · Hasenoehrl · 2019 [cited by examiner]
US 10688389B2 · Flego · 2020 [cited by examiner]
US 11216068B2 · Dong · 2022 [cited by examiner]
US 11351450B2 · Flego · 2022 [cited by examiner]
US 20100163027A1 · Hyde · 2010 [cited by examiner]
US 20100168525A1 · Hyde · 2010 [cited by examiner]
US 20100233662A1 · Casper · 2010 [cited by examiner]
US 20140282105A1 · Nordstrom · 2014 [cited by examiner]
US 20140377130A1 · Edwards · 2014 [cited by examiner]
US 20180050171A1 · Tabert · 2018 [cited by examiner]
US 20180280556A1 · Fateh · 2018 [cited by examiner]
US 20190142326A1 · Mills · 2019 [cited by examiner]
US 20210106910A1 · Jain · 2021 [cited by examiner]
US 20220203225A1 · Jain · 2022 [cited by examiner]
US 20230172524A1 · Martin, II · 2023 [cited by examiner]
CN 107015660A · 2017 [cited by examiner]
KR 1020200107395 · 2020 [cited by applicant]
WO WO2016164917A1 · 2016 [cited by examiner]
WO WO2019164737A1 · 2019 [cited by examiner]
Bioinspired Smell and Taste Sensors, 1st ed., Wang et al. (eds.), 2015, 330 pages. [cited by applicant]
ClevelandClinic.org [online], “Treating Smell Loss in COVID-19 Patients,” Mar. 17, 2021, retrieved on Dec. 9, 2022, retrieved from URL<https://consultqd.clevelandclinic.org/treating-smell-loss-in-covid-19-patients/>, 5 … [cited by applicant]
ClinicalTrials.gov [online], “Visual-OLfactory Training in Participants With COVID-19 Resultant Loss of Smell (VOLT),” NCT04710394, last updated Jun. 21, 2022, retrieved on Dec. 9, 2022, retrieved from URL<https://clini… [cited by applicant]
Ezzatdoost et al., “Decoding olfactory stimuli in EEG data using nonlinear features: A pilot study,” Journal of Neuroscience Methods, May 16, 2020, 341:108780, 10 pages. [cited by applicant]
Gellrich et al., “Brain volume changes in hyposmic patients before and after olfactory training,” The Laryngoscope, Dec. 14, 2017, 128(7):1531-1536. [cited by applicant]
Guducu et al., “Separating Normosmic and Anosmic Patients Based on Entropy Evaluation of Olfactory Event-Related Potentials,” Brain Research, Dec. 8, 2018, 1708:78-83. [cited by applicant]
Harvard.edu [online], “How COVID-19 Causes Loss of Smell,” Jul. 24, 2020, retrieved on Dec. 9, 2022, retrieved from URL<https://hms.harvard.edu/news/how-covid-19-causes-loss-smell>, 9 pages. [cited by applicant]
Henkin et al., “Improvement in smell and taste dysfunction after repetitive transcranial magnetic stimulation,” American Journal of Otolaryngology, Jan./Feb. 2011, 32(1):38-46. [cited by applicant]
Iravani et al., “Non-invasive recording from the human olfactory bulb,” Nature Communications, Jan. 31, 2020, 11(1):648, 10 pages. [cited by applicant]
Jacquot et al., “Influence of nasal trigeminal stimuli on olfactory sensitivity,” Comptes Rendus Biologies, Apr. 2004, 327(4):305-311. [cited by applicant]
Klemm et al., “Topographical EEG maps of human responses to odors,” Chemical Senses, 1992, 17(3):347-361. [cited by applicant]
Kollndorfer et al., “Olfactory training induces changes in regional functional connectivity in patients with long-term smell loss,” NeuroImage: Clinical, 2015, 9:401-410. [cited by applicant]
Lorig et al., “EEG activity during administration of low-concentration odors,” Bulletin of the Psychonomic Society, 1990, 28(5):405-408. [cited by applicant]
Lorig et al., “Visual event-related potentials during odor labeling,” Chemical Senses, 1993, 18(4):379-387. [cited by applicant]
Masaoka et al., “The neural cascade of olfactory processing: A combined fMRI-EEG study,” Respiratory Physiology & Neurobiology, Dec. 1, 2014, 204:71-77. [cited by applicant]
NYTimes.com [online], “Virtual Reality Therapy Plunges Patients Back Into Trauma. Here Is Why Some Swear by It,” Jun. 3, 2021, retrieved on Dec. 9, 2022, retrieved from URL<https://www.nytimes.com/2021/06/03/well/mind/v… [cited by applicant]
Olofsson et al., “Smell-Based Memory Training: Evidence of Olfactory Learning and Transfer to the Visual Domain,” Chemical Senses, Jul. 9, 2020, 45(7):593-600. [cited by applicant]
Parastarfeizabadi et al., “Advances in closed-loop deep brain stimulation devices,” Journal of NeuroEngineering and Rehabilitation, Aug. 11, 2017, 14(1):79, 20 pages. [cited by applicant]
Pellegrino et al., “Bimodal odor processing with a trigeminal component at sub- and suprathreshold levels,” Neuroscience, Nov. 5, 2017, 363:43-49. [cited by applicant]
RoadToVR.com [online], “Feelreal VR Scent Mask Hits Roadblock Amidst Crackdown on Flavored Vaping Products,” Jan. 2, 2020, retrieved on Dec. 9, 2022, retrieved from URL<https://www.roadtovr.com/feelreal-vr-scent-mask-va… [cited by applicant]
SensoryCots.com [online], “Fire Training and Learning,” available on or before Aug. 4, 2020 via Internet Archive: Wayback Machine URL<https://web.archive.org/web/20200804224954/https://sensorycots.com/fire-training-and-… [cited by applicant]
TechMoneyFit.com [online], “FeelReal VR Scent Mask Banned Temporarily by FDA, Considered Vaping Product,” Jan. 5, 2020, retrieved on Dec. 9, 2022, retrieved from URL<https://web.archive.org/web/20200921201533/https://te… [cited by applicant]
TheConversation.com [online], “COVID killed your sense of smell? Here's how experts train people to get theirs back,” Jan. 26, 2021, retrieved on Dec. 9, 2022, retrieved from URL<https://theconversation.com/covid-killed… [cited by applicant]
Trachtenberg et al., “Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex,” Nature, Dec. 19, 2002, 420(6917):788-794. [cited by applicant]
Viczko et al., “Effects on Mood and EEG States After Meditation in Augmented Reality With and Without Adjunctive Neurofeedback,” Frontiers in Virtual Reality, Mar. 22, 2021, 2:618381, 15 pages. [cited by applicant]
VRGear.com [online], “Feelreal VR Scent Mask on Temporary Ban, Considered ‘Flavored Vaping Product’,” Jan. 2, 2020, retrieved on Dec. 9, 2022, retrieved from URL<https://vrgear.com/news/feelreal-vr-scent-mask-on-tempora… [cited by applicant]
WUSTL.edu [online], “COVID-19, losing one's sense of smell and regaining it,” Dec. 1, 2020, retrieved on Dec. 9, 2022, retrieved from URL<https://oto.wustl.edu/covid-19-losing-one-sense-of-smell-and-regaining-it/>, 3 pa… [cited by applicant]
Amores et al., “Promoting Relaxation Using Virtual Reality, Olfactory Interfaces and Wearable EEG,” Presented at Proceedings of the 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Network… [cited by applicant]
Extended Search Report in European Appln. No. 22208538.3, dated Apr. 19, 2023, 11 pages. [cited by applicant]
Jung et al., “The Impact of Multi-sensory Stimuli on Confidence Levels for Perceptual-cognitive Tasks in VR,” Presented at Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Atlanta,… [cited by applicant]
Tiele et al., “Wine Aroma Sensory Training Game Employing a Thermal Based Olfactory Display,” Presented at Proceedings of the 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Fukuoka, Japan, M… [cited by applicant]