Source: https://patents.google.com/patent/US20160261268A1
Timestamp: 2019-07-19 12:45:08
Document Index: 105199637

Matched Legal Cases: ['§120', '§119', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 62', '§119', 'Application No. 62', 'Application No. 62', 'Application No. 62']

US20160261268A1 - Processing Electromagnetic Interference Signal Using Machine Learning - Google Patents
Processing Electromagnetic Interference Signal Using Machine Learning Download PDF
US20160261268A1
US20160261268A1 US15/153,573 US201615153573A US2016261268A1 US 20160261268 A1 US20160261268 A1 US 20160261268A1 US 201615153573 A US201615153573 A US 201615153573A US 2016261268 A1 US2016261268 A1 US 2016261268A1
US15/153,573
US10141929B2 (en
2013-08-13 Priority to US201361865448P priority Critical
2014-01-07 Priority to US201461924625P priority
2014-01-07 Priority to US201461924637P priority
2014-01-07 Priority to US201461924558P priority
2014-01-07 Priority to US201461924604P priority
2014-03-24 Priority to US201461969612P priority
2014-03-24 Priority to US201461969590P priority
2014-03-24 Priority to US201461969544P priority
2014-03-24 Priority to US201461969558P priority
2014-05-19 Priority to US201462000429P priority
2014-08-12 Priority to US14/458,110 priority patent/US9569055B2/en
2015-09-16 Priority to US201562219635P priority
2015-11-25 Priority to US201562260247P priority
2015-11-25 Priority to US201562260244P priority
2016-05-12 Priority to US15/153,573 priority patent/US10141929B2/en
2016-05-13 Assigned to SAMSUNG ELECTRONICS COMPANY, LTD. reassignment SAMSUNG ELECTRONICS COMPANY, LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SALVADOR, MARTIN, DIGMAN, MIKE, BAU, OLIVIER, PACAREU, SERGI CONSUL, RAKOVA, BOGDANA, SCHOESSLER, PHILIPP
2016-09-08 Publication of US20160261268A1 publication Critical patent/US20160261268A1/en
2016-11-21 Priority claimed from EP16868842.2A external-priority patent/EP3335317A4/en
2018-11-27 Publication of US10141929B2 publication Critical patent/US10141929B2/en
In one embodiment, a method includes receiving, by an electrode of a device, a signal from a user's body. The received signal is based on an electromagnetic interference signal generated by an object that is external to the device. The method further includes determining, using machine learning applied to the received signal, one or more of the following: an identity of the object, an interaction between the user and the object, or a context surrounding the device.
This application claims the benefit, under 35 U.S.C. §120, of U.S. patent application Ser. No. 14/458,110, filed on 12 Aug. 2014, which claims the benefit under 35 U.S.C. §119(e), of each of the following: U.S. Provisional Patent Application No. 61/865448, filed 13 Aug. 2013; U.S. Provisional Patent Application No. 61/924558, filed 7 Jan. 2014; U.S. Provisional Patent Application No. 61/924604, filed 7 Jan. 2014; U.S. Provisional Patent Application No. 61/924625, filed 7 Jan. 2014; U.S. Provisional Patent Application No. 61/924637, filed 7 Jan. 2014; U.S. Provisional Patent Application No. 61/969544, filed 24 Mar. 2014; U.S. Provisional Patent Application No. 61/969558, filed 24 Mar. 2014; U.S. Provisional Patent Application No. 61/969590, filed 24 Mar. 2014; U.S. Provisional Patent Application No. 61/969612, filed 24 Mar. 2014; and U.S. Provisional Patent Application No. 62/000429, filed 19 May 2014. This application also claims the benefit, under 35 U.S.C. §119(e), of each of the following: U.S. Provisional Patent Application No. 62/219,635, filed 16 Sep. 2015; U.S. Provisional Patent Application No. 62/260,244, filed 25 Nov. 2015; and U.S. Provisional Patent Application No. 62/260,247, filed 25 Nov. 2015. Each of these applications is incorporated herein by reference.
A first device may identify a second device using a communcations channel set up between the two devices. For example, the second device may encode identification information in a signal, such as a Bluetooth signal or Wi-Fi signal, and transmit the encoded signal to the first device. The first device may detect the signal and decode it in order to access the encoded identification information of the second device. However, device identification may not occur if one or both devices are not capable of decoding, encoding, transmitting, and/or receiving the signal. In addition, noise or other interference in the communication channel may prevent device identification.
In particular embodiments, the identity of a user associated with a touch event may be used to customize a user experience, for example by loading or executing user-specific data when the user is identified. Such data may include specific settings, applications, and content.
For example, a user's password keychain may be accessed when the associated user is identified. As another example, applications a user has purchased or installed on a device may be loaded onto the touched device. As another example, user-specific data within an application, such as the user's emails, may be loaded or accessed when the associated user is identified. As another example, user identification may automatically log the identified user into the device, and may do using a profile or other data specific to the user. As another example, a user's preferences such as a background image, screen brightness, volume setting, etc. may be loaded. As another example, a content filter may be used, for example when a user is identified as a child. As another example, touch events may be cancelled if they result from a user that is not authorized to interact with the device or to access the requested data or perform the requested functionality associated with the touch event. In particular embodiments, detecting an unauthorized user may result in an unlocked device becoming locked.
In particular embodiments, two or more users may be interacting with a device at the same time. Each user interacting with a device may be identified using the example described above, so long as each user has a second device, such as device 230, associated with that user. More generally, N users may be identified as long as N-1 users have an associated second device.
FIG. 7 illustrates example identifications of several users simultaneous interacting with a touch-sensitive device. As illustrated in FIG. 7, each of users 720A, 720B, and 720C are generating touch events on a shared device such as touch-sensitive device 710. Each user is wearing an example device, wristwatch 730A, 730B, and 730C, respectively. Each user's touch interactions as determined by device 710 is associated with context data such as a timestamp indicating, e.g., a time of first contact, last contact, a duration of contacts, or the like. At the same time, each of devices 730A, B, and C are recording contact events and associating context data such as timestamps with the contact events. As illustrated in FIG. 7, a timestamp associated with contact 750A determined by device 730A for user 720A corresponds to a timestamp associated with contact 740A determined by device 710 for the touch event generated by user 720A. Timestamps associated with contact 750B and 750C likewise correspond to timestamps associated with contact 740B and 740C, respectively. Thus, device 710, devices 720A-C, or any other suitable device may identify particular touch events with particular users based on the correspondence between timestamps for contacts 750A-C and timestamps for contacts 740A-C. In particular embodiments, device 710 may provide different output or functionality based on the user's identity. For example, device 710 may assign each user an input color, and a user's touch input on a display of device 710 may be displayed using that user's assigned color. This disclosure contemplates that the touch and time measurements may take any suitable form, including but not limited to the touch and time measurements illustrated in FIG. 7. While the example of FIG. 7 illustrates touch events occurring sequentially in time, this disclosure contemplates that touch events occurring simultaneous - or touch events that include a combination of sequential and simultaneous touch events - may be differentiated using the techniques described herein.
receiving, by an electrode of a device, a signal from a body of a user, the signal based on an electromagnetic interference signal generated by an object that is external to the device; and
determining, by a computing device, one or more of the following based on machine learning applied to the received signal:
an identity of the object;
an interaction between the user and the object; or
a context surrounding the device.
2. The method of claim 1, wherein identifying the object comprises:
accessing information in a data store associating the object with an electromagnetic interference signal or an aspect of an electromagnetic interference signal;
comparing the received signal with the information;
determining, based on the comparison, a similarity between the received signal and the data store; and
in response to the determined similarity, identifying the object associated with the information.
3. The method of claim 2, further comprising using one or more machine learning algorithms and the received signal to update the information in the data store.
4. The method of claim 1, wherein the electrode is part of a wearable device.
5. The method of claim 1, further comprising identifying, based on the signal, a contact between the user and the object.
6. The method of claim 1, wherein the object comprises an electronic device.
7. The method of claim 6, wherein identifying, based on the signal, the device further comprises identifying an operating mode of the device.
8. The method of claim 1, further comprising providing, based on the identified object, a notification to the user.
9. The method of claim 1, wherein the machine learning applied to the received signal comprises a classification model identifying a plurality of fingerprints.
10. The method of claim 9, wherein each fingerprint identifies one or more of:
a particular device;
a particular context of a particular device; or
a particular action of a particular device.
11. The method of claim 1, further comprising optimizing, based on the received signal, a classification model identifying a plurality of fingerprints.
12. The method of claim 11, wherein optimizing the classification model comprises:
identifying a fingerprint corresponding to the received signal;
presenting to the user an identification of the fingerprint corresponding to the received signal;
receiving from the user input identifying whether the fingerprint corresponds to the received signal; and
updating the classification model based on the input.
13. The method of claim 1, further comprising determining, based on the received signal, one or more boundaries for one or more fingerprints of a classification model.
one or more processors coupled to the electrode and configured to:
determine that a signal received by an electrode of a device from a body of a user is based on an electromagnetic interference signal generated by an object that is external to the device; and
determine one or more of the following based on machine learning applied to the received signal:
15. The apparatus of claim 14, wherein the apparatus further comprises a wearable device.
16. The apparatus of claim 14, wherein the machine learning applied to the received signal comprises a classification model identifying a plurality of fingerprints.
17. The apparatus of claim 16, wherein each fingerprint identifies one or more of:
18. The apparatus of claim 14, wherein the processors are further configured to optimize, based on the received signal, a classification model identifying a plurality of fingerprints.
19. The apparatus of claim 18, wherein the processors are further configured to:
identify a fingerprint corresponding to the received signal;
present to the user an identification of the fingerprint corresponding to the received signal;
receive from the user input identifying whether the fingerprint corresponds to the received signal; and
update the classification model based on the input.
20. One or more non-transitory computer-readable storage media embodying logic that is operable when executed by one or more processors to:
US15/153,573 2013-08-13 2016-05-12 Processing electromagnetic interference signal using machine learning Active 2035-05-03 US10141929B2 (en)
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US14/458,110 US9569055B2 (en) 2013-08-13 2014-08-12 Interaction sensing
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US15/153,573 US10141929B2 (en) 2013-08-13 2016-05-12 Processing electromagnetic interference signal using machine learning
EP16868842.2A EP3335317A4 (en) 2015-11-25 2016-11-21 Processing electromagnetic interference signal using machine learning
KR1020187017888A KR20180077292A (en) 2015-11-25 2016-11-21 Treatment of electro-magnetic interference signal using the machine learning
PCT/KR2016/013408 WO2017090945A1 (en) 2015-11-25 2016-11-21 Processing electromagnetic interference signal using machine learning
US14/458,110 Continuation-In-Part US9569055B2 (en) 2013-08-13 2014-08-12 Interaction sensing
US20160261268A1 true US20160261268A1 (en) 2016-09-08
US10141929B2 US10141929B2 (en) 2018-11-27
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US15/153,573 Active 2035-05-03 US10141929B2 (en) 2013-08-13 2016-05-12 Processing electromagnetic interference signal using machine learning
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Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:RAKOVA, BOGDANA;DIGMAN, MIKE;SCHOESSLER, PHILIPP;AND OTHERS;SIGNING DATES FROM 20160506 TO 20160508;REEL/FRAME:038594/0338