throbber
Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 1 of 33
`6:20-cv-1112
`
`Exhibit “1”
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 2 of 33
`I 1111111111111111 1111111111 lllll lllll 111111111111111 111111111111111 IIII IIII
`USO 10595731B2
`
`c12) United States Patent
`Gopalakrishnan et al.
`
`(IO) Patent No.: US 10,595,731 B2
`(45) Date of Patent:
`Mar.24,2020
`
`(54) METHODS AND SYSTEMS FOR
`ARRHYTHMIA TRACKING AND SCORING
`
`(71) Applicant: AliveCor, Inc.
`
`(72)
`
`Inventors: Ravi Gopalakrishnan, San Francisco,
`CA (US); Lev Korzinov, San
`Francisco, CA (US); Fei Wang, San
`Francisco, CA (US); Euan Thomson,
`Los Gatos, CA (US); Nupur
`Srivastava, San Francisco, CA (US);
`Omar Dawood, San Francisco, CA
`(US); Iman Abuzeid, San Francisco,
`CA (US); David E. Albert, Oklahoma
`City, OK (US)
`
`(73) Assignee: AliveCor, Inc., Mountain View, CA
`(US)
`
`(52) U.S. Cl.
`CPC ........ A61B 5102055 (2013.01); A61B 510022
`(2013.01); A61B 510245 (2013.01); A61B
`5102405 (2013.01); A61B 5102416 (2013.01);
`A61B 51046 (2013.01); A61B 51681 (2013.01);
`A61B 516898 (2013.01);
`(Continued)
`(58) Field of Classification Search
`USPC .................................................. 600/508-509
`See application file for complete search history.
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`7,846,106 B2
`9,839,363 B2
`
`12/2010 Andrews et al.
`12/2017 Albert
`(Continued)
`
`( *) Notice:
`
`Subject to any disclaimer, the term ofthis
`patent is extended or adjusted under 35
`U.S.C. 154(b) by O days.
`
`Primary Examiner - Nicole F Lavert
`(74) Attorney, Agent, or Firm - Womble Bond Dickinson
`(US) LLP; Daniel E. Ovanezian
`
`(21) Appl. No.: 16/588,201
`
`(22)
`
`Filed:
`
`Sep. 30, 2019
`
`(65)
`
`(63)
`
`(51)
`
`Prior Publication Data
`
`Jan. 23, 2020
`US 2020/0022594 Al
`Related U.S. Application Data
`
`Continuation of application No. 16/153,446, filed on
`Oct. 5, 2018, now Pat. No. 10,426,359, which is a
`(Continued)
`
`Int. Cl.
`A61B 51024
`A61B 510205
`
`(2006.01)
`(2006.01)
`(Continued)
`
`ABSTRACT
`(57)
`A dashboard centered around arrhythmia or atrial fibrillation
`tracking is provided. The dashboard includes a heart or
`cardiac health score that can be calculated in response to
`data from the user such as their ECG and other personal
`information and cardiac health influencing factors. The
`dashboard also provides to the user recommendations or
`goals, such as daily goals, for the user to meet and thereby
`improve their heart or cardiac health score. These goals and
`recommendations may be set by the user or a medical
`professional and routinely updated as his or her heart or
`cardiac health score improves or otherwise changes. The
`dashboard is generally displayed from an application pro(cid:173)
`vided on a smartphone or tablet computer of the user.
`
`30 Claims, 16 Drawing Sheets
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 3 of 33
`
`US 10,595,731 B2
`Page 2
`
`Related U.S. Application Data
`
`continuation of application No. 15/393,077, filed on
`Dec. 28, 2016, now Pat. No. 10,159,415, which is a
`continuation of application No. 14/730,122, filed on
`Jun. 3, 2015, now Pat. No. 9,572,499, which is a
`continuation of application No. 14/569,513, filed on
`Dec. 12, 2014, now Pat. No. 9,420,956.
`
`(52)
`
`(2006.01)
`(2018.01)
`
`A61B 5111
`Gl6H 50/30
`U.S. Cl.
`CPC .......... A61B 517264 (2013.01); A61B 517275
`(2013.01); A61B 51746 (2013.01); G16H
`20140 (2018.01); G16H 40167 (2018.01); A61B
`5/021 (2013.01); A61B 5/02438 (2013.01);
`A61B 5/0452 (2013.01); A61B 511118
`(2013.01); Gl6H 10/60 (2018.01); Gl6H
`15/00 (2018.01); Gl6H 40/63 (2018.01);
`Gl6H 50/30 (2018.01)
`
`(60)
`
`(51)
`
`Provisional application No. 62/014,516, filed on Jun.
`19, 2014, provisional application No. 61/970,551,
`filed on Mar. 26, 2014, provisional application No.
`61/969,019, filed on Mar. 21, 2014, provisional
`application No. 61/953,616, filed on Mar. 14, 2014,
`provisional application No. 61/915,115, filed on Dec.
`12, 2013.
`
`Int. Cl.
`A61B 510245
`A61B 51046
`A61B 5100
`G16H 20140
`G16H 40167
`Gl6H 40/63
`Gl6H 15/00
`Gl6H 10/60
`A61B 5/021
`A61B 5/0452
`
`(2006.01)
`(2006.01)
`(2006.01)
`(2018.01)
`(2018.01)
`(2018.01)
`(2018.01)
`(2018.01)
`(2006.01)
`(2006.01)
`
`(56)
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`2007 /0213624 Al *
`
`9/2007 Reisfeld .
`
`2012/0109675 Al
`2012/0197148 Al*
`
`5/2012 Ziegler et al.
`8/2012
`Levitan .
`
`2012/0289790 Al
`2014/0125619 Al*
`
`11/2012
`5/2014
`
`2014/0163393 Al
`2014/0276154 Al
`2015/0057512 Al
`2015/0122018 Al
`2015/0305684 Al
`
`6/2014
`9/2014
`2/2015
`5/2015
`10/2015
`
`* cited by examiner
`
`Jain et al.
`Panther ............... G06F 3/04883
`345/173
`
`Mccombie et al.
`Katra et al.
`Kapoor
`Yuen
`Gross
`
`A61B 5/0402
`600/504
`
`A61B 5/02405
`600/515
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 4 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 1 of 16
`
`US 10,595,731 B2
`
`100,
`
`105
`
`115
`
`109
`
`109a
`
`111
`
`Fl0.1
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 5 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 2 of 16
`
`US 10,595,731 B2
`
`200
`
`!- 202
`~~ ............... I,..,.,.,,..,.,,
`
`206
`I
`TradH:ionat HRV Me;1$urnm€nts,
`1, tlme dcimain measures,
`2. freque1v:y dornainineasurns,
`3, ni:in,Hnear HRV
`
`204
`I
`Non Trnd}tional flR-lnti:,:rval tvteasuremer.tt stKh .as
`1, RR(n,i) for B1gemni, Tf:igemni,
`2. pi:,,riodk autore,gress:h1e roo,J'!ng average {MRMA}
`
`I
`
`208 - -! Feature Sefoction
`
`210-
`
`Real Time
`AFIB
`Predictk>n
`
`OffHne training
`~---~· Based on labeled _
`Data using
`,,.. · · ·
`Random Forests
`
`212
`
`1'1(,: 2
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 6 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 3 of 16
`
`US 10,595,731 B2
`
`300
`
`l- 302
`........__------!---........... --""'
`!
`!
`RR Interval Extraction
`~l,,,,,,,,,,,,,,,~~3o=s ~,_~ _ _
`
`$ ....
`
`I
`
`310
`I
`
`304 -
`
`306
`
`r···
`
`T~ad~ti~r5:,-,f }t~v: ~1:S:asurs:~l"i~t'ft5;,
`1. ·um~ d<.1w.:Sjr~ .m~as:SJr~~:•
`:t -fr~tl::.H,~~:cv ~k":!P.~.a.ki Hma~-;.t:,~~;.
`J .. n<>n-liri;z;-sr H!lV
`
`Ncr: ·ff:,kdltlcmii rm-ir:te:va!
`tvl~~~.;Jrem:e.r:>:ts~ :~>;,t:~ .as- R:~{n;,;$}:;
`f>~f kt~fo·~; auto,~J ~~:s $.h:'½-·,s-~::;fS:::~
`,Wel'<iJi:'2 {i'Ai'iMA.}
`
`Feat,i,<35 fmi'i', Rat">' slgr,;i/
`1. Waveletf>:;.atwe,$
`2~ $h9j.~ .. ha~~d- ~~~t>;:pJ:'.$ him>
`Hi!&eit tl',::;r-,;;form
`
`•. _.
`312 -[~·--·-f_ea_t_u_.r_e_s_e_le_c_t_io_n_. _____________ .:~-----~1
`4
`Real Time
`AFlB
`Predictkm
`
`314 ····-
`
`316
`
`O'mine Training
`Based on Labeled
`~"""""""""""""'i Data using Random
`Forests
`
`PJG. 3
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 7 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 4 of 16
`
`US 10,595,731 B2
`
`400
`
`4(12
`
`404
`
`406
`
`408
`
`416
`
`r~:-~;;~;;;;'""'""'""'"w,,;4
`
`✓/
`
`f
`420
`
`4.rn
`
`..... ,." ·.]
`1 r~~~-::=tr1ft t~~~~mt R1? :t<:G
`h~1~f:pr~t;~t:o~~~ t~.:: :tv.idk.:l!
`pfi.":P'ii>~J.::,c;;.,::I
`A»' ••••••••••••••-'""""••••"""W"•'""""°'"'"'"'°••m
`
`/
`
`422
`
`FlG,4
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 8 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 5 of 16
`
`US 10,595,731 B2
`
`500
`
`508
`
`He:art He:a!th
`5ctire
`
`504 l
`
`1
`
`Step3:
`
`502
`I
`
`Step 1:
`
`506-
`
`tount•Prer:nature.
`Beats/ cafout.:ite HRT
`
`Heart: Health
`........ ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.,;-..,·1 Scare based
`on Arrhythmia
`
`510
`
`FIG. 5
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 9 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 6 of 16
`
`US 10,595,731 B2
`
`600
`
`pu_:;. 6
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 10 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 7 of 16
`
`US 10,595,731 B2
`
`~,,gr;,Vt-fo!~
`®tmaiJahOOrma!:
`Arff:-i~h~n~i

`t4r:§it•J.>:1fo;s{: t?t;s~g:e~ {Tlif)}
`
`FIG. 7
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 11 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 8 of 16
`
`US 10,595,731 B2
`
`Consumer Application transition to Medical
`Application
`
`--weight
`•SM!
`•Cho!estf.':Wl
`»Sl:t--ep
`•Caffunle
`--Acttvffy
`•Diet
`,.Stfl:.!SS
`--Almho! etc
`
`He..irt ~~le f-ai:tws
`That .a Patient Can
`fafluence
`
`-'
`
`ECG Ov:errfld, anafy$iJ,
`Arryrim:1ia, A·Ffb
`~tect:!an,1-HW,
`Brady/la®y; Premature
`~ats
`
`- Riu~ct:on
`
`Managed and
`Tracked by
`Da$hboard
`
`f,'J(;. 8
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 12 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 9 of 16
`
`US 10,595,731 B2
`
`902
`
`I
`
`;,.!,,
`
`------------------------------------------------------------------,
`Provide User Access to
`Cardiac Disease
`
`:
`
`900
`
`904
`I
`----------------------------------------------------
`Provide User Biometric Sensor(s} For
`Coupling to User Computing Device
`and Cardiac Disease Management
`System
`
`····i
`
`l
`j
`l
`l
`l
`
`_/-"
`
`J
`
`/"/❖
`_1.(.<~
`
`· .... ~.~.~.~.~~~.~~: .. ~.~.~~~~........ I
`\. ·:,
`\
`\
`\,
`... :-,.~
`
`906-·-r Download Cardiac Disease Management
`~ System.onto.Urer.Colputing.device-
`

`
`1
`1 Receive User Personal Information Input to i ..
`908 - -! Cardiac Disease Management System
`(:_----
`
`9 l0--1 t::~,:~~:~i~fc~fo:i~:~;;~;;;;~ ;
`, -J -1
`1 , .......................................... r ......................................... .
`
`912 --l Generate Heart Health Score
`
`::
`....,tr
`
`1 Generate Recommendation(s) and/or
`914-- j Goal(s) for User
`~ ....................................................................................... ~~-............................................................................................................ ·
`
`I
`
`i
`1:~\i~~~; n;~;;;;;;;;~;;;ti;~i;i~~;;;;;~-- ~ 1
`l
`918 -t.W~=-~:~-=UWJ ............................................. ••••
`
`916
`
`FIG. 9
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 13 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 10 of 16
`
`US 10,595,731 B2
`
`1014
`I
`
`-
`
`0
`
`/
`
`0
`
`((l!lti!'l<ioo~ Hit
`mi:i!l!lorl~
`
`N----
`
`!
`1002
`
`i>r<stll$~Qf fel;lliv'!l:S
`aia:l l!~aw.lf,~s 1-l!t
`tnformat!on
`
`tl>~tllm'lin~
`lm1(!.i!a1lly, tri,1Ajl!1~
`alert
`
`'
`
`'
`-
`
`S<!tljl,~~$~00
`_,, tCG wl!ts «int:.usitO!l'
`mi:i!liti:ir
`
`I
`1004
`
`I
`1006
`
`I
`1008
`
`FlG.W
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 14 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 11 of 16
`
`US 10,595,731 B2
`
`AFlB PATIENT DASHBOARD
`
`1100~
`
`HEARTSCOHE
`
`OCSTSCORE
`
`7DAY
`
`HEART RATE
`
`AFIBDASH ·*
`GOALS *
`@
`*
`
`6
`
`0:13secs
`AVG DURA TlON
`0:1 Osecs 1 :OOmln
`7/7
`LONGEST MEtfADHERE
`
`AF!B EP!S.ODES
`
`SHORTEST
`
`SCROLL CONTINUES
`~---------------------------------
`
`!
`
`:}.
`:i
`*
`i
`:f.
`:i
`:i
`
`FIG.11
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 15 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 12 of 16
`
`US 10,595,731 B2
`
`,--1100
`
`1100b
`
`,11ooc:
`
`BLOOD PRESSURE
`
`,JAN .24
`
`(
`
`180
`
`0 i
`
`)
`
`INFLUENCERS
`
`CAFFEINE
`
`STRESS
`
`◄ e ►
`
`ALCOHOL
`
`NUTRITION
`
`c··f~)
`
`BLOOD PRESSURE
`
`I
`I
`L --------------------------------
`
`Fl(,. llA
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 16 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 13 of 16
`
`US 10,595,731 B2
`
`·1200~
`
`t200a
`
`PATIENT GOALS
`
`70AYGOALS@
`
`DAY:1
`
`2
`
`3 4
`
`5 6
`
`7
`
`TODAY'S GOALS:
`
`~
`RECORDED MY ECG
`0
`GET AT LEAST 7 HRS SLEEP
`NIGHTLY (recommended)
`[tJ
`
`VvALKED 20 M!NUTES
`
`$MAE GOALS
`
`NE\NGOALS
`
`FIG.12
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 17 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 14 of 16
`
`US 10,595,731 B2
`
`@ NEWGOALS
`
`1200~
`
`1200b ··· □
`□
`□
`
`TAKE DAILY MEDICATIONS
`
`REDUCE CAFFEINE IN TAKE
`(reenmrnended)
`
`REDUCE ALCOHOL INTAKE
`
`□
`□
`□
`
`MEDITATE FOR 5 MIN DAILY
`
`TAKE BLOOD PRESSURE
`READING DAILY
`
`GET AT LEAST 7 HRS SLEEP
`N!GHTL Y (recommended)
`
`FJ(i. l2A
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 18 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 15 of 16
`
`US 10,595,731 B2
`
`AUVECOR
`
`HAVE YOU MET
`YOUR GOA.LS FOR.
`TODAY?
`
`[~~!~~] I UPDATE I
`
`FlO. 13
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 19 of 33
`
`U.S. Patent
`
`Mar.24,2020
`
`Sheet 16 of 16
`
`US 10,595,731 B2
`
`y--1400
`
`1412
`
`1402
`
`1404
`
`L?ff"" 1·4
`l.' . \.i . ...
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 20 of 33
`
`US 10,595,731 B2
`
`1
`METHODS AND SYSTEMS FOR
`ARRHYTHMIA TRACKING AND SCORING
`
`CROSS-REFERENCE TO RELATED
`APPLICATIONS
`
`5
`
`2
`for subjects to administer without the aid of a medical
`professional. For example, the use of Holter monitors
`requires a patient to wear a bulky device on their chest and
`precisely place a plurality of electrode leads on precise
`locations on their chest. These requirements can impede the
`activities of the subject, including their natural movement,
`bathing, and showering. Once an ECG is generated, the ECG
`is sent to the patient's physician who may analyze the ECG
`and provide a diagnosis and other recommendations. Cur-
`10 rently, this process often must be performed through hospital
`administrators and health management organizations and
`many patients do not receive feedback in an expedient
`manner.
`
`This application is a continuation of U.S. application Ser.
`No. 16/153,446, filed Oct. 5, 2018, now U.S. Pat. No.
`10,426,359, issued Oct. 1, 2019, which is a continuation of
`U.S. application Ser. No. 15/393,077, filed Dec. 28, 2016,
`now U.S. Pat. No. 10,159,415, issued Dec. 25, 2018, which
`is a continuation of U.S. application Ser. No. 14/730,122,
`filed Jun. 3, 2015, now U.S. Pat. No. 9,572,499, issued Feb.
`21, 2017, which is a continuation of U.S. application Ser.
`No. 14/569,513 filed Dec. 12, 2014, now U.S. Pat. No. 15
`9,420,956, issued Aug. 23, 2016, which claims the benefit of
`U.S. Provisional Application No. 61/915,113, filed Dec. 12,
`2013, which application is incorporated herein by reference,
`U.S. Provisional Application No. 61/953,616 filed Mar. 14,
`2014, U.S. Provisional Application No. 61/969,019, filed 20
`Mar. 21, 2014, U.S. Provisional Application No. 61/970,551
`filed Mar. 26, 2014 which application is incorporated herein
`by reference, and U.S. Provisional Application No. 62/014,
`516, filed Jun. 19, 2014, which application is incorporated
`herein by reference.
`
`SUMMARY
`
`Disclosed herein are devices, systems, and methods for
`managing health and disease such as cardiac diseases,
`including arrhythmia and atrial fibrillation. In particular, a
`cardiac disease and/or rhythm management system, accord(cid:173)
`ing to aspects of the present disclosure, allows a user to
`conveniently document their electrocardiograms (ECG) and
`other biometric data and receive recommendation(s) and/or
`goal(s) generated by the system or by a physician in
`25 response to the documented data. The cardiac disease and/or
`rhythm management system can be loaded onto a local
`computing device of the user, where biometric data can be
`conveniently entered onto the system while the user may
`continue to use the local computing device for other pur-
`30 poses. A local computing device may comprise, for example,
`a computing device worn on the body (e.g. a head-worn
`computing device such as a Google Glass, a wrist-worn
`computing device such as a Samsung Galaxy Gear Smart
`Watch, etc.), a tablet computer (e.g. an Apple iPad, an Apple
`35 iPod, a Google Nexus tablet, a Samsung Galaxy Tab, a
`Microsoft Surface, etc.), a smartphone (e.g. an Apple
`iPhone, a Google Nexus phone, a Samsung Galaxy phone,
`etc.)
`A portable computing device or an accessory thereof may
`40 be configured to continuously measure one or more physi(cid:173)
`ological 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 communi(cid:173)
`cation with the portable computing device. The portable
`45 computing device may have loaded onto (e.g. onto a non(cid:173)
`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 receiv(cid:173)
`ing the continuously measured physiological signal(s), ana-
`50 lyzing 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 physi(cid:173)
`ological signal(s). The heart rate may be measured by one or
`more electrodes provided on the computing device or acces-
`55 sory, 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
`60 variability (R-R variability or HRV), and heart rate turbu(cid:173)
`lence (HRT) may be determined. These parameters and
`further parameters may be analyzed to detect and/or predict
`one or more of atrial fibrillation, tachycardia, bradycardia,
`bigeminy, trigeminy, or other cardiac conditions. A quanti-
`65 tative heart health score may also be generated from the
`determined parameters. One or more of the heart health
`score, detected heart conditions, or recommended user
`
`BACKGROUND
`
`The present disclosure relates to medical devices, sys(cid:173)
`tems, and methods. In particular, the present disclosure
`relates to methods and systems for managing health and
`disease such as cardiac diseases including arrhythmia and
`atrial fibrillation.
`Cardiovascular diseases are the leading cause of death in
`the world. In 2008, 30% of all global death can be attributed
`to cardiovascular diseases. It is also estimated that by 2030,
`over 23 million people will die from cardiovascular diseases
`annually. Cardiovascular diseases are prevalent in the popu(cid:173)
`lations of high-income and low-income countries alike.
`Arrhythmia is a cardiac condition in which the electrical
`activity of the heart is irregular or is faster (tachycardia) or
`slower (bradycardia) than normal. Although many arrhyth(cid:173)
`mias are not life-threatening, some can cause cardiac arrest
`and even sudden cardiac death. Atrial fibrillation is the most
`common cardiac arrhythmia. In atrial fibrillation, electrical
`conduction through the ventricles of heart is irregular and
`disorganized. While atrial fibrillation may cause no symp(cid:173)
`toms, it is often associated with palpitations, shortness of
`breath, fainting, chest, pain or congestive heart failure. Atrial
`fibrillation is also associated with atrial clot formation,
`which is associated with clot migration and stroke.
`Atrial fibrillation is typically diagnosed by taking an
`electrocardiogram (ECG) of a subject, which shows a char(cid:173)
`acteristic atrial fibrillation waveform
`To treat atrial fibrillation, a patient may take medications
`to slow heart rate or modify the rhythm of the heart. Patients
`may also take anticoagulants to prevent atrial clot formation
`and stroke. Patients may even undergo surgical intervention
`including cardiac ablation to treat atrial fibrillation.
`Often, a patient with arrhythmia or atrial fibrillation is
`monitored for extended periods of time to manage the
`disease. For example, a patient may be provided with a
`Holter monitor or other ambulatory electrocardiography
`device to continuously monitor a patient's heart rate and
`rhythm for at least 24 hours.
`Current ambulatory electrocardiography devices such as
`Holter monitors, however, are typically bulky and difficult
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 21 of 33
`
`US 10,595,731 B2
`
`3
`action items based on the heart health score may be dis(cid:173)
`played to the user through a display of the portable com(cid:173)
`puting device.
`The biometric data may be uploaded onto a remote server
`where one or more cardiac technicians or cardiac specialists 5
`may analyze the biometric data and provide ECG interpre(cid:173)
`tations, diagnoses, recommendations such as lifestyle rec(cid:173)
`ommendations, and/or goals such as lifestyle goals for
`subject. These interpretations, diagnoses, recommendations,
`and/or goals may be provided to the subject through the 10
`cardiac disease and/or rhythm management system on their
`local computing device. The cardiac disease and/or rhythm
`management system may also include tools for the subject to
`track their biometric data and the associated interpretations,
`diagnoses, recommendations, and/or goals from the cardiac 15
`technicians or specialists.
`An aspect of the present disclosure includes a dashboard
`centered around arrhythmia or atrial fibrillation tracking.
`The dashboard includes a heart score that can be calculated
`in response to data from the user such as their ECG and other
`personal information such as age, gender, height, weight,
`body fat, disease risks, etc. The main driver of this heart
`score will often be the incidence of the user's atrial fibril(cid:173)
`lation. Other drivers and influencing factors include the
`aforementioned personal information. The heart score will
`be frequently related to output from a machine learning
`algorithm that combines and weights many if not all of
`influencing factors.
`The dashboard will often display and track many if not all
`of the influencing factors. Some of these influencing factors 30
`may be entered directly by the user or may be input by the
`use of other mobile health monitoring or sensor devices. The
`user may also use the dashboard as an atrial fibrillation or
`arrhythmia management tool to set goals to improve their
`heart score.
`The dashboard may also be accessed by the user's phy(cid:173)
`sician ( e.g. the physician prescribing the system to the user,
`another regular physician, or other physician) to allow the
`physician to view the ECG and biometric data of the user,
`view the influencing factors of the user, and/or provide 40
`additional ECG interpretations, diagnoses, recommenda(cid:173)
`tions, and/or goals.
`Another aspect of the present disclosure provides a
`method for managing cardiac health. Biometric data of a
`user may be received. A cardiac health score may be 45
`generated in response to the received biometric data. One or
`more recommendations or goals for improving the generated
`cardiac health score may be displayed to the user. The
`biometric data may comprise one or more of an electrocar(cid:173)
`diogram (ECG), dietary information, stress level, activity 50
`level, gender, height, weight, age, body fat percentage, blood
`pressure, results from imaging scans, blood chemistry val(cid:173)
`ues, or genotype data. The recommendations or goals may
`be updated in response to the user meeting the displayed
`recommendations or goals. The user may be alerted if one or 55
`more recommendations or goals have not been completed by
`the user, for example if the user has not completed one or
`more recommendations or goals for the day.
`The analysis applied may be through one or more of the
`generation of a heart health score or the application of one 60
`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
`of the heart rate monitoring enabled portable computing
`devices or accessories provided to a plurality of users. The 65
`training population of users may have been previously
`identified as either having atrial fibrillation or not having
`
`4
`atrial fibrillation prior to the generation of data for continu(cid:173)
`ously 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
`atrial fibrillation or other conditions therefrom. After the
`machine learning algorithm has been trained, the machine
`learning algorithm may recognize atrial fibrillation from the
`continuously measured heart rate data of a new user who has
`not yet been identified as having atrial fibrillation 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-transi(cid:173)
`tory 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.)
`A set of instructions for managing cardiac health may be
`downloaded from the Internet. These set of instructions may
`be configured to automatically generate the cardiac health
`20 score. The cardiac health score may be generated using a
`machine learning algorithm. The machine learning algo(cid:173)
`rithm may generate the cardiac health score of the user
`and/or the recommendations and/or goals in response to
`biometric data from a plurality of users. The set of instruc-
`25 tions may be configured to allow a medical professional to
`access the received biometric data. The cardiac health score
`and/or the recommendations and/or goals may be generated
`by the medical professional.
`The set of instructions may be stored on a non-transitory
`computer readable storage medium of one or more of a
`body-worn computer, a tablet computer, a smartphone, or
`other computing device. These set of instructions may be
`capable of being executed by the computing device. When
`executed, the set of instructions may cause the computing
`35 device to perform any of the methods described herein,
`including the method for managing cardiac health described
`above.
`Another aspect of the present disclosure provides a sys(cid:173)
`tem for managing cardiac health. The system may comprise
`a sensor for recording biometric data of a user and a local
`computing device receiving the biometric data from the
`sensor. The local computing device may be configured to
`display a cardiac health score and one or more recommen(cid:173)
`dations or goals for the user to improve the cardiac health
`score in response to the received biometric data.
`The system may further comprise a remote server receiv(cid:173)
`ing the biometric data from the local computing device. One
`or more of the local computing device or the remote server
`may comprise a machine learning algorithm which generates
`one or more of the cardiac health score or the one or more
`recommendations or goals for the user. The remote server
`may be configured for access by a medical professional.
`Alternatively, or in combination, one or more of the cardiac
`health score or one or more recommendations or goals may
`be generated by the medical professional and provided to the
`local computing device through the remote server.
`The sensor may comprise one or more of a hand-held
`electrocardiogram (ECG) sensor, a wrist-worn activity sen(cid:173)
`sor, a blood pressure monitor, a personal weighing scale, a
`body fat percentage sensor, a personal thermometer, a pulse
`oximeter sensor, or any mobile health monitor or sensor.
`Often, the sensor is configured to be in wireless communi(cid:173)
`cation with the local computing device. The local computing
`device comprises one or more of a personal computer, a
`laptop computer, a palmtop computer, a tablet computer, a
`smartphone, a body-worn computer, or the like. The bio-
`metric data may comprise one or more of an electrocardio-
`
`

`

`Case 6:20-cv-01112-ADA Document 1-1 Filed 12/07/20 Page 22 of 33
`
`US 10,595,731 B2
`
`5
`
`6
`FIG. 9 shows an exemplary method for cardiac disease
`and rhythm management;
`FIG. 10 shows an exemplary method for monitoring a
`subject to determine when to record an electrocardiogram
`(ECG);
`FIG. 11 shows an exemplary screenshot of a first aspect
`of a dashboard application;
`FIG. llA shows an exemplary screenshot of a second
`aspect of a dashboard application;
`FIG. 12 shows an exemplary screenshot of a first aspect
`of a goals and recommendations page of the cardiac disease
`and rhythm management system interface or mobile app;
`FIG. 12A shows an exemplary screenshot of a second
`15 aspect of a goals and recommendations page of the cardiac
`disease and rhythm management system interface or mobile
`app;
`FIG. 13 shows an exemplary screenshot of a user's local
`computing device notifying the user with a pop-up notice to
`meet their daily recommendations and goals; and
`FIG. 14 shows an embodiment comprising a smart watch
`which includes at least one heart rate monitor and at least
`one activity monitor.
`
`5
`gram (ECG), dietary information, stress level, activity level,
`gender, height, weight, age, body fat percentage, or blood
`pressure.
`Other physiological signals or parameters such as physi(cid:173)
`cal activity, heart sounds, blood pressure, blood oxygen(cid:173)
`ation, blood glucose, temperature, activity, breath composi(cid:173)
`tion, weight, hydration levels, an electroencephalograph
`(EEG), an electromyography (EMG), a mechanomyogram
`(MMG), an electrooculogram (EOG), etc. may also be
`monitored. The user may also input user-related health data 10
`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 atrial fibrillation or other
`conditions. The machine learning algorithm may be config(cid:173)
`ured to identify atrial fibrillation 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 and/or 20
`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 25
`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.
`
`INCORPORATION BY REFERENCE
`
`All publications, patents, and patent applications men(cid:173)
`tioned in this specification are herein incorporated by ref(cid:173)
`erence to the same extent as if each individual publication,
`patent, or patent application was specifically and individu(cid:173)
`ally indicated to be incorporated by reference.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`The novel features of the subject matter disclosed herein
`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:
`FIG. 1 shows a system for cardiac disease and rhythm
`management;
`FIG. 2 shows a flow chart of a method 200 for predicting
`and/or detecting atrial fibrillation from R-R interval mea(cid:173)
`surements;
`FIG. 3 shows a flow chart of a method for predicting
`and/or detecting atrial fibrillation from R-R interval mea(cid:173)
`surements and for predicting and/or detecting atrial fibril(cid:173)
`lation from raw heart rate signals;
`FIG. 4 shows an embodiment of the system and method
`of the ECG monitoring described herein;
`FIG. 5 shows a flow chart of an exemplary method to
`generate a heart health score in accordance with many
`embodiments;
`FIG. 6 shows an exemplary method of generating a heart
`score;
`FIG. 7 shows a schematic diagram of the executed appli(cid:173)
`cation described herein;
`FIG. 8 shows exemplary screenshots of the executed
`application;
`
`DETAILED DESCRIPTION
`
`Devices, systems, and methods for managing health and
`disease such as cardiac diseases, including arrhythmia and
`atrial fibrillation, are disclosed. In particular, a cardiac
`30 disease and/or rhythm management system, according to
`aspects of the present disclosure, allows a user to conve(cid:173)
`niently document their electrocardiograms (ECG) and other
`biometric data and receive recommendation(s) and/or
`goal(s) generated by the system or by a physician in
`35 response to the documented data.
`The term "atrial fibrillation," denoting a type of cardiac
`arrhythmia, may also be abbreviated in either the figures or
`description herein as "AFIB."
`FIG. 1 shows a system 100 for cardiac disease and rhythm
`40 management. The system 100 may be prescribed for u

This document is available on Docket Alarm but you must sign up to view it.


Or .

Accessing this document will incur an additional charge of $.

After purchase, you can access this document again without charge.

Accept $ Charge
throbber

Still Working On It

This document is taking longer than usual to download. This can happen if we need to contact the court directly to obtain the document and their servers are running slowly.

Give it another minute or two to complete, and then try the refresh button.

throbber

A few More Minutes ... Still Working

It can take up to 5 minutes for us to download a document if the court servers are running slowly.

Thank you for your continued patience.

This document could not be displayed.

We could not find this document within its docket. Please go back to the docket page and check the link. If that does not work, go back to the docket and refresh it to pull the newest information.

Your account does not support viewing this document.

You need a Paid Account to view this document. Click here to change your account type.

Your account does not support viewing this document.

Set your membership status to view this document.

With a Docket Alarm membership, you'll get a whole lot more, including:

  • Up-to-date information for this case.
  • Email alerts whenever there is an update.
  • Full text search for other cases.
  • Get email alerts whenever a new case matches your search.

Become a Member

One Moment Please

The filing “” is large (MB) and is being downloaded.

Please refresh this page in a few minutes to see if the filing has been downloaded. The filing will also be emailed to you when the download completes.

Your document is on its way!

If you do not receive the document in five minutes, contact support at support@docketalarm.com.

Sealed Document

We are unable to display this document, it may be under a court ordered seal.

If you have proper credentials to access the file, you may proceed directly to the court's system using your government issued username and password.


Access Government Site

We are redirecting you
to a mobile optimized page.





Document Unreadable or Corrupt

Refresh this Document
Go to the Docket

We are unable to display this document.

Refresh this Document
Go to the Docket