/,/
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`.—%
`
`WIPO
`WORLD
`INTELLECTUAL PROPERTY
`ORGANIZATION
`
`
`DOCUMENT MADE AVAILABLE UNDER THE
`
`PATENT COOPERATION TREATY (PCT)
`International application number:
`PCT/USZO14/070170
`
`International filing date:
`
`12 December 2014 (12.12.2014)
`
`Document type:
`
`Document details:
`
`Certified copy of priority document
`
`Country/Office:
`Number:
`Filing date:
`
`US
`62/014,516
`19 June 2014 (19.06.2014)
`
`Date of receipt at the International Bureau:
`
`05 January 2015 (05.01.2015)
`
`Remark: Priority document submitted or transmitted to the International Bureau in compliance with Rule
`17.1(a),(b) or (b-bis)
`
`34. cremin (lee Colombettes
`12l l Geneva 20, Switzerland
`
`www.wipo.int
`
`1
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`APPLE1030
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`APPLE 1030
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`1
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`L551 1TH. D Slflh‘v DIE I-‘AR’EEV‘! E N'I' U P C()i\~1i\"II‘ZRCI{
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`United States Patent and "l‘rudcmnrk Office
`
`January 04, 2015
`
`APPLICATION THAT MET THE REQUIREMENTS TO BE GRANTED A
`FILING DATE.
`
`APPLICATION NUMBER: 62/014,516
`FILING DATE: June 19, 2014
`RELATED PCT APPLICATION NUMBER: PCT/US14/70170
`
`THE COUNTRY CODE AND NUMBER OF YOUR PRIORITY
`
`APPLICATION, TO BE USED FOR FILING ABROAD UNDER THE PARIS
`CONVENTION, IS US62/014,516
`
`
`
`.1332733137?
`
`7273344..
`
`THIS IS TO CERTIFY THAT ANNEXED HERETO IS A TRUE COPY FROM
`THE RECORDS OF THE UNITED STATES PATENT AND TRADEMARK
`OFFICE OF THOSE PAPERS OF THE BELOW IDENTIFIED PATENT
`
`Patent and Trademark ()ffiw
`
`Certified by
`
`Under Secretary (fi't'fnmmtrcc
`for Intcflcctual I’mpcrfy
`and Director 0? the United States
`
`
`
`
`
`..~,,.,14:""/’.4~’.y’53..&2<«2/2»?1333?.
`
`2
`
`

`

`ABSTRACT OF THE DISCLOSURE
`
`[0038] Systems and methods of monitoring the heart health of a user are provided. A sensor may
`
`continuously monitor the heart rate of the user. The measured heart rate information may be used to
`
`determine other parameters such as heart rate variability, heart rate turbulence, and the number or
`
`frequency of premature heart beats. The measured heart rate information may also be used to
`
`identify and detect arrhythmias. These parameters may be combined to generate a heart health
`
`score. Machine learning methods may also be provided to analyze the measured heart rate
`
`information to determine heart health and identify atrial fibrillation and/or other heart conditions.
`
`645617071.DOC
`
`-l6-
`
`WSGR Docket No. MISS-725.101
`
`3
`
`

`

`Electronic Acknowledgement Receipt
`
`“—
`
`——
`
`Title of Invention:
`
`Systems and Methods for Determining Heart Health by Continuous
`.
`.
`Monitoring
`
`
`
`——
`
`——
`
`Payment information:
`
`yes
`Submitted with Payment
`
`Payment Type
`Deposit Account
`
`$130
`Payment was successfully received in RAM
`
`RAM confirmation Number 4303
`
`Part /.zip (if appl.)
`
`Deposit Account
`
`232415
`
`File Listing:
`
`Document
`Number
`
`Document Descri
`
`tion
`
`P
`
`File Size(Bytes)/
`Message Digest
`
`Multi
`
`Pages
`
`4
`
`

`

`Transmittal ofNew Application
`
`.
`.
`AliveCOR41188725101Transmit
`taLpdf
`
`214877
`
`ioia99000cb9de19f44 s77bdi 7ec1 8376811 5
`Sad4
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`Information:
`
`1561104
`
`Application Data Sheet
`
`AliveCOR41188725101ADS_DJ
`CH_Signed.pdf
`
`464b0c9b945fl89a41ch3750e'fcb98eb901 5
`604b
`
`Information:
`
`..
`.
`AliveCOR41 1 88725101 Specific
`ation.pdf
`
`236519
`
`e60ea34083b12e98a9451 e94ei (11391 4861;)
`208291
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`Multipart Description/PDF files in .zip description
`
`
`
`
`
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`
`Document Description
`
`Specification
`
`Warnings:
`
`Information:
`
`Information:
`
`Drawings-only black and white line
`drawings
`
`AliveCOR41 1887251 01 Drawing
`s.pdf
`
`Fee Worksheet (SB06)
`
`fee-info.pdf
`
`715444
`
`34261 1 5d(/|Uf5 /B( 126b35W1289deaae4b2
`9fef
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`Information:
`
`Total Files Size (in bytes)
`
`2757747
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`5
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`

`

`This Acknowledgement Receipt evidences receipt on the noted date by the USPTO of the indicated documents,
`characterized by the applicant, and including page counts, where applicable. It serves as evidence of receipt similar to a
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`6
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`

`PTOIAIN14 (12-13)
`Approved for use through 01/31/2014. OMB 0651-0032
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`Title of Invention
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
`
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`4683 Clarendon Drive
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`San Jose
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`CA
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`m———m
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`30 Maiden Ln.
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`94108
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`_— WANG
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`PTOIAIN14 (12-13)
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
`
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`City I San Francisco I StateIProvince AI Country of ResidenceII
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`Application Information:
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
`
`Attorney Docket Number 41188-725101
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
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`Title of Invention
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
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`1
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
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`CFR 1.14, as a routine use, to the public if the record was filed in an application which became abandoned or in which the proceedings were
`terminated and which application is referenced by either a published application, an application open to public inspections or an issued
`patent.
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`A record from this system of records may be disclosed, as a routine use, to a Federal, State, or local law enforcement agency, if the
`USPTO becomes aware of a violation or potential violation of law or regulation.
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`EFS Web 2.2.10
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`WSGR Docket No. 41188—725101
`
`PROVISIONAL PATENT APPLICATION
`
`SYSTEMS AND METHODS FOR DETERMINING HEART HEALTH
`BY CONTINUOUS MONITORING
`
`Inventor(s):
`
`Ravi GOPALAKRISHNAN,
`Citizen of The United States, Residing at
`4683 Clarendon Drive
`
`San Jose, CA 95129
`
`Lev KORZlNOV,
`
`Citizen of Russia, Residing at
`30 Maiden Ln.
`
`San Francisco, CA 94108
`
`Fei WANG,
`Citizen of The United States, Residing at
`30 Maiden Ln.
`
`San Francisco, CA 94108
`
`Assignee:
`
`AliveCOR, Inc.
`30 Maiden Ln.
`
`San Francisco, CA 94108
`
`a Delaware Corporation
`
`Entity:
`
`Small business concern
`
`Wm
`
`Wilson Sonsini Goodrich 56 Roseiti
`PR 01:11)? S ION A 1,.
`(T O R P O R HT 1 O?"
`
`650 Page Mill Road
`Palo Alto, CA 94304
`(650) 493—9300 (Main)
`(650) 493-6811 (Facsimile)
`
`ELECTRONICALLY FILED ON JUNE 19, 2014
`
`645617071.D0C
`
`WSGR Docket No. 41188-721101
`
`14
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`SYSTEMS AND METHODS FOR DETERMINING HEART HEALTH
`BY CONTINUOUS MONITORING
`
`CROSS-REFERENCE
`
`[0001] The subj eet matter is application is related to the following co—pending patent applications:
`
`US. Patent Applications Serial Nos. 61/982,002 (filed 4/21/2014), 61/953,616 (filed 3/14/2014),
`
`61/915,113 (filed 12/12/2013), 61/872,555 (filed 8/30/2013), 61/725,422 (filed 11/12/2012),
`
`14/217,032 (filed 3/17/2014), 13/969,446 (filed 8/16/2013), 13/964,490 (filed 8/12/2013),
`
`13/420,520 (filed 3/14/2013), 13/108,738 (filed 5/16/2011), 12/796,188 (filed 6/8/2010), the
`
`contents of which are incorporated herein by reference.
`
`BACKGROUND
`
`[0002] The present disclosure relates to systems, devices, and methods for monitoring and man aging
`
`health and disease.
`
`In particular, the present disclosure relates to systems, devices, and methods for
`
`monitoring and managing cardiac health with portable computing devices such as tablet computers,
`
`smartphones, smart watches or arm bands, or other wearable computing devices.
`
`SUMMARY
`
`[0003] A portable computing device or an accessory thereof may be configured to continuously
`
`measure one or more physiological signals of a user. The heart rate of the user may be continuously
`
`measured. The continuously measurement may be made with a wrist or arm band or a patch in
`
`communication with the portable computing device. The portable computing device may have
`
`loaded onto (e. g., onto a non-transitory computer readable medium of the computing device) and
`
`executing thereon (e. g.. by a processor of the computing device) an application for one or more of
`
`receiving the continuously measured physiological signal(s), analyzing the physiological signal(s),
`
`sending the physiological signal(s) to a remote computer for further analysis and storage, and
`
`displaying to the user analysis of the physiological signal(s). The heart rate may be measured by one
`
`or more electrodes provided on the computing device or accessory, a motion sensor provided on the
`
`computing device or accessory, or by imaging and lighting sources provided on the computing
`
`device or accessory. In response to the continuous measurement and recordation of the heart rate of
`
`the user, parameters such as heart rate (HR), heart rate variability (R-R variability or HRV), and
`
`heart rate turbulence (HRT) may be determined. These parameters and fiirther parameters may be
`
`analyzed to detect and/or predict one or more of atrial fibrillation (AFIB), tachycardia, bradyeardia,
`
`bigeminy, trigeminy, or other cardiac conditions. A quantitative heart health score may also be
`
`generated from the determined parameters. One or more of the heart health score, detected heart
`
`645617071.DOC
`
`VVSGR Docket No. 41 DES-725.101
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`-2-
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`conditions, or recommended user action items based on the heart health score may be displayed to
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`the user through a display of the portable computing device.
`
`[0004] The analysis applied may be through one or more of the generation of a heart health score or
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`the application of one or more machine learning algorithms. The machine learning algorithms may
`
`be trained using population data of heart rate. The population data may be collected from a plurality
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`of the heart rate monitoring enabled portable computing devices or accessories provided to a
`
`plurality of users. The training population of users may have been previously identified as either
`
`having AFIB or not having AFIB prior to the generation of data for continuously measured heart
`
`rate. The data may be used to train the machine learning algorithm to extract one or more features
`
`from any continuously measured heart rate data and identify AFIB or other conditions therefrom.
`
`After the machine learning algorithm has been trained, the machine learning algorithm may
`
`recognize AFIB from the continuously measured heart rate data of a new user who has not yet been
`
`identified as having AFIB or other heart conditions. One or more of training population data or the
`
`trained machine learning algorithm may be provided on a central computing device (e. g., be stored
`
`on a non-transitory computer readable medium of a server) which is in communication with the local
`
`computing devices of the users and the application executed thereon (e. g, through an Internet or an
`
`intranet connection.)
`
`[0005] Other physiological signals or parameters such as physical activity, heart sounds, blood
`
`pressure, blood oxygenation, blood glucose, temperature, activity, breath composition, weight,
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`hydration levels, an electroencephalograph (EEG), an electromyography (EMG), a
`
`mechanomyogram (MMG), an electroculograpm (EOG), etc. may also be monitored. The user may
`
`also input user-related health data such as age, height, weight, body mass index (BMI), diet, sleep
`
`levels, rest levels, or stress levels. One or more of these physiological signals and/or parameters
`
`may be combined with the heart rate data to detect AFIB or other conditions. The machine learning
`
`algorithm may be configured to identify AFIB or other conditions in response to heart rate data in
`
`combination with one or more of the other physiological signals and/or parameters for instance.
`
`Triggers or alerts may be provided to the user in response to the measured physiological signals
`
`an d/or parameters. Such triggers or alerts may notify the user to take corrective steps to improve
`
`their health or monitor other vital signs or physiological parameters. The application loaded onto
`
`and executed on the portable computing device may provide a health dash board integrating and
`
`displaying heart rate information, heart health parameters determined in response to the heart rate
`
`information, other physiological parameters and trends thereof, and recommended user action items
`
`or steps to improve health.
`
`645617071.DOC
`
`VVSGR Docket No. MISS-725.101
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`-3-
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`INCORPORATION BY REFERENCE
`
`[0006] All publications, patents, and patent applications mentioned in this specification are herein
`
`incorporated by reference to the same extent as if each individual publication, patent, or patent
`
`application was specifically and individually indicated to be incorporated by reference.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`[0007] The novel features of the disclosure are set forth with particularity in the appended claims. A
`
`better understanding of the features and advantages of the present disclosure will be obtained by
`
`reference to the following detailed description that sets forth illustrative embodiments, in which the
`
`principles of the disclosure are utilized, and the accompanying drawings of which:
`
`[0008] FIG. 1A shows a flow chart of a method for predicting AFIB from R—R interval
`
`measurements;
`
`[0009] FIG. 1B shows a flow chart of a method used for predicting AFIB from raw heart rate data;
`
`[0010] FIG. 2A shows a flow chart of a method to generate a heart health score from heart rate data;
`
`[0011] F1G. ZB shows a flow chart of a method to generate a heart health score from heart rate and
`
`other physiological data;
`
`[0012] FIG. 3A shows a schematic diagram of an exemplary mobile application for tracking hear
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`health being unlocked to provide further features; and
`
`[0013] FIG. 3B shows screenshots of the mobile application of FIG. 3A being unlocked to provide
`
`further features.
`
`[0014] Atrial Fibrillation Detection/Prediction
`
`DETAILED DESCRIPTION
`
`[0015] Aspects of the present disclosure provide systems and methods for detecting and/or
`
`predicting atrial fibrillation (AFIB) or other arrhythmias of a user by applying one or more machine
`
`leaming-based algorithms. A portable computing device (or an accessory usable