ff
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`WIPO
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`International application number:
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`International filing date:
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`THIS IS TO CERTIFY THAT ANNEXED HERETOIS A TRUE COPY FROM
`THE RECORDS OF THE UNITED STATES PATENT AND TRADEMARK
`OFFICE OF THOSE PAPERS OF THE BELOW IDENTIFIED PATENT
`APPLICATION THAT MET THE REQUIREMENTSTO BE GRANTED A
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`UNTFED STATES DEPARTMENT OF COMMERCE
`
`United States Patent and ‘Praderrark CMfice
`
`January 04, 2015
`
`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 THEPARIS
`CONVENTION,IS US62/014,516
`
`Certified by
`
`Under Seeretary of Commerce
`for Intellectual Praperty
`and Director of the United States
`Patient and Yrademark Office
`
`2
`
`

`

`ABSTRACT OF THE DISCLOSURE
`
`[0038] Systems and methods of monitoring the heart health of a user are provided. A sensor may
`
`continuously monitorthe 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.
`
`6456170_1.DOC
`
`-16-
`
`WSGRDocket No. 41188-725.101
`
`3
`
`

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`
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`a s
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`Electronic AcknowledgementReceipt
`
`ition
`
`Title of Invention:
`
`Systems and Methods for Determining Heart Health by Continuous
`ves
`Monitoring
`
`re
`
`4
`
`

`

`Transmittal of New Application
`
`.
`:
`AliveCOR41188725101Transmit
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`AliveCOR41 188725101ADS_DJ
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`
`oc
`.
`AliveCOR41 188725101 Specific
`ation.pdf
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`Specification
`
`Claims
`
`Abstract
`
`Warnings:
`
`Information:
`
`Drawings-only black and whiteline
`drawings
`
`AliveCOR41188725101Drawing
`s.pdf
`
`Fee Worksheet (SB06)
`
`fee-info.pdf
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`Total Files Size (in bytes)
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`Title of Invention|Systems and Methods for Determining Heart Health by Continuous Monitoring
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`Systems and Methods for Determining Heart Health by Continuous Monitoring
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`WSGRDockct No. 41188-725.101
`
`PROVISIONAL PATENT APPLICATION
`
`SYSTEMS AND METHODS FOR DETERMINING HEART HEALTH
`BY CONTINUOUS MONITORING
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`Inventor(s):
`
`Ravi GOPALAKRISHNAN,
`Citizen of The United States, Residing at
`4683 Clarendon Drive
`San Jose, CA 95129
`
`Lev KORZINOV,
`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
`
`WER
`Wilson Sonsini Goodrich & Rosati
`PROFESSIONAL CORPORATION
`
`650 Page Mill Road
`Palo Alto, CA 94304
`(650) 493-9300 (Main)
`(650) 493-6811 (Facsimile)
`
`ELECTRONICALLY FILED ON JUNE 19, 2014
`
`6456170_1.DOC
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`WSGR Docket No. 41188-725.101
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`SYSTEMS AND METHODS FOR DETERMINING HEART HEALTH
`BY CONTINUOUS MONITORING
`
`CROSS-REFERENCE
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`[0001] The subject matter is application is related to the following co-pending patent applications:
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`U.S. Patent Applications Serial Nos. 61/982,002 (filed 4/21/2014), 61/953,616 (filed 3/14/2014),
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`61/915,113 (filed 12/12/2013), 61/872,555 (filed 8/30/2013), 61/725,422 (filed 11/12/2012),
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`14/217,032 (filed 3/17/2014), 13/969,446 (filed 8/16/2013), 13/964,490 (filed 8/12/2013),
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`13/420,520 (filed 3/14/2013), 13/108,738 (filed 5/16/2011), 12/796,188 (filed 6/8/2010), the
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`contents of which are incorporated herein by reference.
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`BACKGROUND
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`[0002] The present disclosure relates to systems, devices, and methods for monitoring and managing
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`health and disease.
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`In particular, the present disclosure relates to systems, devices, and methods for
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`monitoring and managingcardiac health with portable computing devices such as tablet computers,
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`smartphones, smart watches or arm bands, or other wearable computing devices.
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`SUMMARY
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`[0003] A portable computing device or an accessory thereof may be configured to continuously
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`measure one or more physiological signals of a user. The heart rate of the user may be continuously
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`measured. The continuously measurement may be made with a wrist or arm bandor a patch in
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`communication with the portable computing device. The portable computing device may have
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`loaded onto (e.g., onto a non-transitory computer readable medium of the computing device) and
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`executing thereon (e.g., by a processor of the computing device) an application for one or more of
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`receiving the continuously measured physiological signal(s), analyzing the physiological signal(s),
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`sending the physiological signal(s) to a remote computer for further analysis and storage, and
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`displaying to the user analysis of the physiological signal(s). The heart rate may be measured by one
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`or more electrodes provided on the computing device or accessory, a motion sensor provided on the
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`computing device or accessory, or by imaging andlighting sources provided on the computing
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`device or accessory. In response to the continuous measurement and recordation of the heart rate of
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`the user, parameters such as heart rate (HR), heart rate variability (R-R variability or HRV), and
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`heart rate turbulence (HRT) maybe determined. These parameters and further parameters may be
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`analyzed to detect and/or predict onc or moreofatrial fibrillation (AFIB), tachycardia, bradycardia,
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`bigeminy, trigeminy, or other cardiac conditions. A quantitative heart health score mayalso be
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`generated from the determined parameters. One or more of the heart health score, detected heart
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`conditions, or recommendeduser 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.
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`[0004] The analysis applicd may be through one or more of the gencration of a heart health score or
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`the application of one or more machine learning algorithms. The machine learning algorithms may
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`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
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`plurality of users. The training population of users may have been previously identified as either
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`having AFIB or not having AFIB priorto the generation of data for continuously measured heart
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`rate. The data may be used to train the machine learning algorithm to extract one or more features
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`from any continuously measured heart rate data and identify AFIB or other conditions therefrom.
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`After the machine learning algorithm has been trained, the machine learning algorithm may
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`recognize AFIB from the continuously measured heart rate data of a new user who has not yet been
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`identified as having AFIB orother heart conditions. One or more of training population data or the
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`trained machine learning algorithm may be provided on a central computing device (e.g., be stored
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`on a non-transitory computer readable medium ofa server) which is in communication with the local
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`computing devices of the users and the application executed thereon (e.g, through an Internet or an
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`intranet connection.)
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`[0005] Other physiological signals or parameters such as physical activity, heart sounds, blood
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`pressure, blood oxygenation, blood glucose, temperature, activity, breath composition, weight,
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`hydration levels, an electroencephalograph (EEG), an electromyography (EMG), a
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`mechanomyogram (MMG),an electroculograpm (EOG), etc. may also be monitored. The user may
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`also input user-related health data such as age, height, weight, body mass index (BMJ), diet, sleep
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`levels, rest levels, or stress levels. One or more of these physiological signals and/or parameters
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`may be combined with the heart rate data to detect AFIB or other conditions. The machine learning
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`algorithm may be configured to identify AFIB or other conditions in response to heart rate data in
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`combination with one or moreof the other physiological signals and/or parameters for instance.
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`Triggers or alerts maybe provided to the user in response to the measured physiological signals
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`and/or parameters. Such triggers or alerts may notify the user to take corrective steps to improve
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`their health or monitor other vital signs or physiological parameters. The application loaded onto
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`and executed on the portable computing device may provide a health dash board integrating and
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`displaying heart rate information, heart health parameters determined in responseto the heart rate
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`information, other physiological parameters and trends thereof, and recommendeduser action items
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`or steps to improvehealth.
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`INCORPORATION BY REFERENCE
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`[0006] All publications, patents, and patent applications mentioncd in this specification are herein
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`incorporated by reference to the same extent as if cach individual publication, patent, or patent
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`application was specifically and individually indicated to be incorporated by reference.
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`BRIEF DESCRIPTION OF THE DRAWINGS
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`[0007] The novel features of the disclosure are set forth with particularity in the appended claims. A
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`better understanding of the features and advantages ofthe present disclosure will be obtained by
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`reference to the following detailed description that sets forth illustrative embodiments, in which the
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`principles of the disclosure are utilized, and the accompanying drawings of which:
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`[0008] FIG. 1A showsa flow chart of a method for predicting AFIB from R-R interval
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`measurements;
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`[0009] FIG. 1B showsa flow chart of a method used for predicting AFIB from rawheart rate data;
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`[0010] FIG. 2A showsa flow chart of a method to generate a heart health score from heart rate data;
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`[0011] FIG. 2B showsa flow chart of a method to generate a heart health score from heart rate and
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`other physiological data;
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`[0012] FIG. 3A showsa schematic diagram of an exemplary mobile application for tracking hear
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`health being unlocked to provide further features; and
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`[0013] FIG. 3B showsscreenshots of the mobile application of FIG. 3A being unlocked to provide
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`further features.
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`[0014] Atrial Fibrillation Detection/Prediction
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`DETAILED DESCRIPTION
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`[0015] Aspects of the present disclosure provide systems and methods for detecting and/or
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`predicting atrial fibrillation (AFIB) or other arrhythmias of a user by applying one or more machine
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`learning-based algorithms. A portable computing device (or an accessory usable with the portable
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`computing device) may provide R-R intervals and/or raw heart rate signals as input to an application
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`loaded and executed on the portable computing device. The raw heart r