`571-272-7822
`
`Paper 42
`Entered: December6, 2022
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`APPLE,INC.,
`Petitioner,
`
`V.
`
`ALIVECOR,INC.,
`Patent Owner.
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Before ROBERT A. POLLOCK, ERIC C. JESCHKE,and
`DAVID COTTA, Administrative Patent Judges.
`
`POLLOCK, Administrative Patent Judge.
`
`JUDGMENT
`Final Written Decision
`Determining All Challenged Claims Unpatentable
`35 U.S.C. § 318(a)
`
`Denying In-Part and Dismissing In-Part as Moot
`Patent Owner’s Motion to Exclude Evidence
`37 CFR. $ 42.64
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`A. Background
`
`I.
`
`INTRODUCTION
`
`Apple, Inc. (“Petitioner’’) filed a Petition for an inter partes review of
`
`claims 1—30 of U.S. Patent No. 10,595,731 B2 (Ex. 1001, “the *731 patent’’).
`
`Paper 2 (“Pet.”). AliveCor, Inc. (“Patent Owner”) timely filed a Preliminary
`
`Response. Paper 6 (“Prelim. Resp.”). Petitioner further filed an authorized
`
`Reply to the Preliminary Response (Paper 7); Patent Ownerfiled a
`
`responsive Sur-reply (Paper 8). Taking into account the arguments and
`
`evidence presented, we determined the information presented in the Petition
`
`established that there was a reasonable likelihood that Petitioner would
`
`prevail in demonstrating unpatentability of at least one challenged claim of
`
`the *731 patent, and weinstituted this inter partes review asto all challenged
`
`claims. Paper 10 (“DI’).
`
`After institution, Patent Ownerfiled a Patent Owner Response (Paper
`
`26, “PO Resp.’’); Petitioner filed a Reply to the Patent Owner Response
`
`(Paper 29, “Reply”); Patent Ownerfiled a (corrected) Sur-reply (Paper 36,
`
`“PO Sur-reply”’).
`
`Patent Owneralso filed a motion to exclude (Paper 34, “Mot.”);
`
`Petitioner opposed the motion (Paper 36, “Opp. Mot.”); and Patent Owner
`
`filed a reply in support of its motion (Paper 38, “Reply Mot.”).
`
`An oral hearing was held on September 14, 2022, and a transcript of
`
`the hearing is included in the record. Paper 41 (“Tr.”).
`
`Wehavejurisdiction under 35 U.S.C. § 6. This decision is a Final
`
`Written Decision under 35 U.S.C. § 318(a) as to the patentability of claims
`
`1-30 of the ’731 patent. For the reasons discussed below, we hold that
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Petitioner has demonstrated by a preponderance of the evidence that claims
`
`1—30 are unpatentable.
`
`B. Real Parties-in-Interest
`
`Petitioner identifies itself, Apple Inc., as the real party-in-interest. Pet.
`
`88. Patent Owner, identifies itself, AliveCor, Inc., as the real party-in-
`
`interest. Paper 6, 2.
`
`C. Related Matters
`
`According to Patent Owner:
`
`U.S. Patent No. 10,595,731 has been asserted by Patent
`Owner against Petitioner in A/iveCor, Inc. v. Apple, Inc., Case
`No. 6:20-cv-01112-ADA,filed in the United States District
`Court for the Western District of Texas, and in Investigation
`No. 337-TA-1266 before the International Trade Commission,
`In the Matter of Certain Wearable Electronic Devices with
`ECG Functionality and Components Thereof. Apple also filed
`IPR petitions against the other patents asserted in those actions:
`IPR2021-00970 (USP 9,572,499) and IPR2021-00972 (USP
`10,638,941).
`
`Paper6, 2; see Pet. 88. We further note that the ’731 patent at issue here is
`
`related by a chain of continuation applications to Application No.
`
`14/730,122, which issued as U.S. Patent No. 9,572,499 (“the °499 patent’’),
`
`challenged in IPR2021-00970. See Ex. 1001, code (63). As such, the ’731
`
`and °499 patents share substantially the same specification.
`
`D. Priority Date of the ’731 Patent
`
`The *731 patent claimspriority to, inter alia, a series of provisional
`
`applications filed between December 12, 2013, and June 19, 2014. Ex. 1001,
`
`code (60); see Prelim. Resp. 4; Pet. 2 & nn. 1-3. Petitioner contends that the
`
`claims of the ’731 patent are not entitled the benefit of the earliest of those
`
`applications such that the critical date is March 14, 2014, the filing date of
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`provisional application No. 61/953,616. Pet. 2-3. Because Patent Owner
`
`does not contest this assertion, or the prior art status of any asserted
`
`reference, we need not determine whether the challenged claimsare entitled
`
`to the benefit of the earliest filed provisional application. See generally
`
`Prelim. Resp. 4; PO Resp. 17, 19.
`
`E. Asserted Grounds of Unpatentability
`
`Petitioner asserts the following grounds of unpatentability (Pet. 1):
`
`
`pf BARIT8108 Shmuel
`7
`1, 2, 4, 7, 12-14, 16-18,
`§ 103
`Shmueli, Osorio?
`20, 23-26, 30
`3,5, 6,19, 21, 22
`
`
`
`3
`
`
`
`
`
`
`
`§ 103
`
`8-11, 27-29
`
`§ 103
`
`Shmueli, Osorio,
`Li 2012+
`Shmueli, Osorio,
`or
`4
`Kleiger
`Shmueli, Osorio,
`§ 103
`15
`5
`Chan®
`
`' The Leahy-Smith America Invents Act (“AIA”) included revisions to
`35 U.S.C. § 103 that becameeffective on March 16, 2013. Because we
`determine the priority date of the challenged claimsis no earlier than the
`°731 patent’s filing date of March 14, 2014 (see infra), we apply the AIA
`versions of the statutory bases for unpatentability.
`*WO2012/140559, publ. Oct. 18, 2012. Ex. 1004.
`7 U.S. 2014/0275840, publ. Sept. 18, 2014. Ex. 1005.
`* Li Q, Clifford GD, “Signal quality and datafusion forfalse alarm
`reduction in the intensive care unit,” 45(6) J Electrocardiol. 596-603 (2012).
`(“Lior “Li-2012”) Ex. 1006.
`> Kleiger RE, Stein PK, “Bigger JT Jr. Heart rate variability: measurement
`and clinicalutility.” 10(1) Ann Noninvasive Electrocardiol. 88—101 (2005).
`(“Kleiger”) Ex. 1033.
`° US. Pat. No. 7,894,888, issued Feb. 22, 2011. Ex. 1048.
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`In support of its patentability challenge, Petitioner relies on, inter alia,
`
`the Declaration of Dr. Bernard R. Chaitman, M.D. Ex. 1003. Patent Owner
`
`similarly relies on the Declarations of Dr. Igor Efimov, Ph.D. Ex. 2001;
`
`Ex. 2016.
`
`F. The ’731 Patent and Relevant Background
`
`The ’731 patent relates to medical devices, systems, and methods for
`
`detecting cardiac conditions, including cardiac arrhythmias. Ex. 1001, 1:29-
`
`33, 2:17—25. In general:
`
`In response to the continuous measurementand 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
`further parameters may be analyzed to detect and/or predict one
`or more ofatrial fibrillation, tachycardia, bradycardia,
`bigeminy, trigeminy, or other cardiac conditions.
`
`Id. at 2:57—-64; see id. at 18:52—63 (Table2, listing atrial fibrillation, sinus
`
`and supraventricular tachycardias, bradycardia, bigeminy, and trigemini
`
`amongthe types of arrhythmias).
`
`According to Dr. Chaitman, “HRV analysis is an importanttool in
`
`cardiology to help diagnose various types of arrhythmia.” Ex. 1003 4 35.
`
`“HRV is defined as the variation of RR intervals with respect to time and
`
`reflects beat-to-beat heart rate (HR) variability,” and “can be accurately
`
`determined based on either ECG [electrocardiogram] data or PPG
`
`[photoplethysmography] data.” /d. J] 35-36. “An R-R interval represents a
`
`time elapsed between successive R-waves of a QRS complex’ of the ECG
`
`7 «TE]lectrical activity of the heart based on depolarization and repolarization
`of the atria and ventricles .
`.
`. typically show[s] up as five distinct waves on
`[an] ECG readout — P-wave, Q-wave, R-wave, S-wave, and T-wave.” Ex.
`1003 4 29. “A QRS complex is a combination of the Q, R, and S waves
`
`5
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`that occur between successive heart beats.” /d. ¥ 29. “If the RR intervals
`
`over a time period are close to each other in value, then ventricular rhythm is
`
`understood to be ‘regular.’ In contrast, if there are significant variations in
`
`the RR intervals over a time period, then the ventricular rhythm is
`
`understood to be “irregular.’” /d. § 37 (citations omitted).
`
`The Specification explains that during cardiac arrhythmia, “the
`
`electrical activity of the heart is irregular or is faster (tachycardia) or slower
`
`(bradycardia) than normal,” and in some forms, “can cause cardiac arrest
`
`and even sudden cardiac death.” Ex. 1001, 1:40-44. The ’731 patent
`
`identifies atrial fibrillation as the most common form of cardiac
`
`arrhythmia—which occurs whenelectrical conduction throughthe atria of
`
`the heart is irregular, fast, and disorganized, leadingto irregular activation of
`
`ventricles. /d. at 1:44—49. Althoughatrial fibrillation may cause no
`
`symptoms,it is associated with palpitations, shortness of breath, fainting,
`
`chest pain, congestive heart failure, as well as atrial clot formation, which
`
`can lead to clot migration and stroke. /d. at 1:44—51. “Atrial fibrillation is
`
`typically diagnosed by taking an electrocardiogram (ECG) of a subject,
`
`which showsa characteristic atrial fibrillation waveform.” /d. at 1:52—54.
`
`The Specification discloses body-worn devices for detecting the
`
`occurrence of arrhythmias using a combination of ECG and PPGelectrodes.
`
`See, e.g., claim 1. PPG, or photoplethysmography,uses an optical sensor to
`
`detect the fluctuation of blood flow, and can provide a measure ofheart rate.
`
`Id. at 25:21—24. According to the Specification, fluctuations in heart rate not
`
`explained by changing activity levels may be interpreted as an advisory
`
`occurring in succession and represents the electrical impulse of a heartbeat
`as it spreads through the ventricles during ventricular depolarization.” /d.
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`condition for recording an ECG,or electrocardiogram, which1s a typical
`
`method for diagnosing episodes of arrhythmia. /d. at 1:52—54, 1:60-65,
`
`25:1-35.
`
`The collected data may also be analyzed using machinelearning
`
`algorithms to, for example, determine appropriate trigger thresholds, detect
`
`and predict health conditions, or provide a heart health score. See, e.g., id. at
`
`3:43-4:16, 8:38-41, 9:8-11, 12:44—64. “The machine learning based
`
`algorithm(s) may allow software application(s) to identify patterns and/or
`
`features of the R-R interval data and/or the raw heart rate signals or data to
`
`predict and/or detectatrial fibrillation or other arrhythmias.” /d. at 9:8—11. In
`
`particular,
`
`[a]ny number of machine learning algorithms or methods may
`be trained to identify atrial fibrillation or other conditions such
`as arrhythmias. These may include the use of decision tree
`learning such as with a random forest, association rule learning,
`artificial neural network, inductive logic programming, support
`vector machines, clustering, Bayesian networks, reinforcement
`learning, representation learning similarity and metric learning,
`sparse dictionary learning,or the like.
`
`Id. at 9:66-10:9.
`
`Figure 14, reproduced below, shows one embodimentof a body-worn
`
`device. /d. at 6:21—23.
`
`
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Figure 14, shows “smart watch 1400 which includesat least one heart
`
`rate monitor 1402 andat least one activity monitor 1404,” such as an
`
`accelerometer. /d. at 24:66—25:1, 25:13-30. Analysis of signals from these
`
`monitors can be used to “determine if heart rate and activity measurements
`
`represent an advisory condition for recording an ECG,”andtrigger signals
`
`for recording an ECG if an advisory condition is detected. /d. at 25:1—12.
`
`Figure 10, illustrated below, shows another embodiment involving a
`
`body-worn device. /d. at 6:3-—5.
`
`
`
`~~ Ho Record <
`
` <
`
`
`
`SARKSERLLERINNSIDIEESE|,geea
`
`ArineniRRR!
`
`. Te 3
`- SRR x.
`y
`
`SeREARS
`becnennnanqngrgrnnnoninnnenrwed:
`
`
`
`
`Figure 10 illustrates “a method for monitoring a subject to determine when
`
`to record an electrocardiogram (ECG).” /d. at 23:20—22. Accordingto the
`
`Specification:
`
`In FIG. 10, a subject is wearing a continuousheart rate monitor
`(configured as a watch 1010, including electrodes 1016), shown
`in step 1002. The heart rate monitor transmits (wirelessly 1012)
`heart rate information that is received by the smartphone 1018,
`as shown in step 1004. The smartphone includesa processor
`that may analyze the heart rate information 1004, and when an
`irregularity is determined, may indicate 1006 to the subject that
`an ECG should be recorded.
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Id. at 23:22—30. In some embodiments, the ECG deviceis “present in
`
`a smart watch band or a smart phone.”/d. at 25:36—37. “The ECG,
`
`heart rate, and rhythm information can be displayed on the computer
`
`or smartphone, stored locally for later retrieval, and/or transmitted in
`
`real-time to a web server.” /d. at 25:48—S0.
`
`G. Challenged Claims
`
`Petitioner challenges claims 1—30, of which claims 1, 17, and 25 are
`
`independent. Of these, claim 1 recites:
`
`1. A smart watch to detect the presence of an arrhythmia of a
`user, comprising:
`
`a processing device;
`
`a photoplethysmography (“PPG”) sensor operatively coupled to
`the processing device;
`
`an ECG sensor, comprising two or more ECG electrodes, the
`ECGsensoroperatively coupled to the processing device;
`
`a display operatively coupled to the processing device; and
`
`a memory,operatively coupled to the processing device, the
`memory having instructions stored thereon that, when executed
`by the processing device, cause the processing deviceto:
`
`receive PPG data from the PPG sensor;
`
`detect, based on the PPG data, the presence of an
`arrhythmia;
`
`receive ECG data from the ECG sensor; and
`
`confirm the presence of the arrhythmia based on the ECG
`data.
`
`Independentclaims 17 and 25 recite similar limitations but are respectively
`
`drawn to “[a] method to detect the presence of an arrhythmia of a user on a
`
`smart watch,” and “non-transitory computer-readable storage medium
`
`including instructions.”
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Among the dependent claims, claims 2, 14, and 18 relate to the use of
`
`motion sensor(inertial) data; claims 4 and 20 relate to “determin[ing]
`
`heartrate variability (“HRV’) data from the PPG data, and detect[ing], based
`
`on the HRV data, the presence of the arrhythmia’; claims 3, 5, 6, 19, 21, and
`
`22 recite “a machinelearning algorithm trained to detect arrhythmias”; and
`
`claim 15 recites a device “configured to display an ECG rhythm strip for the
`
`ECGdata.”
`
`H. Overview of the Asserted References
`
`1) Shmueli (Exhibit 1004)
`
`Shmueli, titled “Pulse Oximetry Measurement Triggering ECG
`
`Measurement,” addresses “solutions .
`
`.
`
`. for monitoring infrequent events of
`
`irregular ECG.” Ex. 1004, 2.8 According to Shmueli, “[t]he present
`
`invention preferably performs measurements of intermittent irregular heart-
`
`related events without requiring the fixed wiring of the ECG deviceto the
`
`patient.” /d. at 8.
`
`Shmueli discloses body-worn cardiac monitoring devices “equipped
`
`with two types of sensing devices: an oximetry (SpO2) measuring unit and
`
`an ECG measuring unit.” /d.? Shmueli’s Figures 1A, 1B, and 4, reproduced
`
`below, exemplify one embodiment(annotations by Petitioner in red):
`
`§ Throughoutthis opinion, wecite to the native pagination. For clarity with
`respect to citations to Shmueli, we understand the native pagination to be the
`numbersat the top of the page.
`” As used by Shmueli “the terms ‘oxygen saturation in the blood’, ‘blood
`oxygen saturation’, ‘pulse oximeter’, oximetry, SpO, and
`photoplethysmography have the same meaning and may be used
`interchangeably, except for those places where a difference between such
`termsis described.” /d. at 7; see Tr. 6:22-7:12, 73:18-21, 95:7-11.
`
`10
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`
`
`Pet. 12. Figures 1A, 1B, and 3 show three viewsof a wrist-mountheart
`
`monitoring device having three ECG electrodes 14 and a PPG sensor13.
`
`Ex. 1004, 6, 9-10. Figure 1A shows two of the ECGelectrodes, 14/16, on
`
`the face of the device. /d. at 9. Figure 1B showsa third ECG electrode,
`
`14/15, along with PPG sensor 13, of the back of the device. /d. Figure 3
`
`shows the device as worn on a patient’s wrist, with PPG sensor 13 and ECG
`
`electrode 14/15 in contact with the patient’s left wrist and ECG electrodes
`
`14/16 in contact with two fingers of the patient’s nght hand. /d. Petitioner
`
`annotates each of Figures 1A, 1B, and 3 with arrowsidentifying the ECG
`
`electrodes. Petitioner has also annotated Figure 1B with an arrow identifying
`
`PPGsensor 13. In connection with these devices, Shmueli discloses
`
`a methodfor triggering measurementof electrocardiogram
`(ECG)signal of a subject, the method including the stepsof:
`continuously measuring SpO2at least one of a wrist and a
`finger of the subject, detecting an irregular heart condition from
`the SpO2 measurement, notifying the subject to perform an
`ECG measurement, andinitiating ECG measurementat least
`partially at the wrist.
`
`Id. at 2; see Abstract.
`
`Shmueli explains that “[dJeriving heart beat rate from oximetry, as
`
`well as other artifacts of the heart activity and blood flow, is .. . known in
`
`11
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`the art,” as are various body-worn oximetry devices. /d. at 8. Shmueli further
`
`explains that the use of oximetry in combination with ECG measurementsis
`
`also knownin the art. /d. Shmueli states, for example, that “US patent No.
`
`7,598,878 (Goldreich) describes a wrist mounted device equipped with an
`
`ECG measuring device and a SpO, measuring device.” /d. However,
`
`Shmueli, notes “Goldreich does not teach interrelated measurements of ECG
`
`and SpO,”and, thus, does not “enable a patient to perform ECG
`
`measurementas soon as an irregular heart activity develops and without
`
`requiring the ECGto be constantly wired to the patient.” /d. According to
`
`Shmueli:
`
`The present invention resolves this problem by providing a
`combined oximetry and electrocardiogram measuring system
`and a method in which the oximetry measurement is performed
`continuously and/or repeatedly, and the ECG measurementis
`triggered upon detection of an intermittent irregular heart-
`related events without requiring the fixed wiring of the ECG
`device to the patient.
`
`Id. Consistent with this disclosure, Shmueli claims:
`
`1. A methodfor triggering measurementof electrocardiogram
`(ECG)signal of a subject, the method comprising the stepsof:
`
`continuously measuring SpO2at least one of a wrist and a
`finger of said subject;
`
`detecting an irregular heart condition from said SpO2
`measurement;
`
`notifying said subject to perform an ECG measurement;
`and
`
`initiating ECG measurementat least partially at said wrist.
`
`Id. at 16.
`
`12
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Shmueli Figure 7 is reproduced below:
`
` Csrart )
`SEAR
`
`TUNOTEUSER
`
`a MEASURE SOG
`
`| ae
`43. pgnenaenna ;
`
`“LE RECORD SPO? AND
`39
`~ 37
`50 ‘ECG MEASUREMENTS
`é
`zy a8 wf
`
` SEARCH
`DETECT IRREGULAR /DETECTION es
`|
`HEART CONDITION
`} PARAMSTERS jf 52
`MOR RELEATION
`‘,
`i
`
`ONDYwitonsy
`‘STOP ECC?
`
`STOP
`
`NO
`
`
`
`
`
` le a43
`Roepf verection /
`wee
`
`Joeeeeseeeeeeeeeeseneeeetenepeneeeteeneseeeeeteeeteseeed
`PARAMETE5f
`CNOFIFY [3
`
`
`reiitatMUNICATE GATA
`
`
`“Fig. 7 1s a simplified flow chart of a software program preferably executed
`
`by the processor of the wrist-mounted heart monitoring device.” /d. at 7; see
`
`also id. at 12—13 (further describing the steps of the software program
`
`illustrated in Figure 7).
`
`2) Osorio (Exhibit 1005)
`
`Osorio,titled “Pathological State Detection Using Dynamically
`99 66
`
`Determined Body Data Variability Range Values,”
`
`“relates to medical
`
`device systems and methods capable of detecting a pathological bodystate
`
`of a patient, which mayincludeepileptic seizures, and respondingto the
`
`same.” Ex. 1005 4 2. Although broadly referencing “a pathological body
`
`state,” Osorio repeatedly exemplifies such conditions in terms of detecting
`
`epileptic events. See, e.g., id. 4 37 (referencing values that may “be
`
`indicative of a certain pathological state (e.g., epileptic seizure)’), §] 46 (“In
`
`13
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`one embodiment, the pathologicalstate is an epileptic event, e.g., an
`
`epileptic seizure.”), § 56 (“HRV range maybe taken as an indication of an
`
`occurrence of a pathological state, e.g., an epileptic seizure”), J] 66 (“The
`
`dynamic relationship between non-pathological HRVs andactivity levels
`
`may be exploited to detect pathological states such as epileptic seizures”).
`
`Consistent with the broad disclosure and narrow exemplification in
`
`the body of its specification, Osorio’s claim | 1s directed to “[a] method for
`
`detecting a pathological body state of a patient,” whereas claim 7 limits the
`
`pathological state to an epileptic event. /d. at claim 1, claim 7; also compare
`
`id. at claim 14, with claim 17 (similarly limiting a pathological state to an
`
`epileptic event).
`
`According to Osorio, the disclosed methods, systems, and related
`
`devices, detect a pathological state of a patient by determining when a body
`
`data variability value, or “BDV,”is outside of a “value range,” and where
`
`the threshold levels of that range vary in responseto the patient’s physical
`
`activity (measured by, e.g., an accelerometer) or mental/emotionalstate. See,
`
`e.g., id. at code (57), JF 3-8, 28, 33, 35. In this respect, Osorio states that
`
`“false negative and false positive detections of pathological events may be
`
`reduced by dynamically determining pathological or non-pathological ranges
`
`for particular body indices based on activity type and level or other variables
`
`(e.g., environmental conditions).” /d. § 36.
`
`14
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Osorio’s Figure | is reproduced below.
`
`
`
`went
`guppy
`
`bo
`of
`
`|
`ii
`| conmunication |
`|
`gb
`UNE
`
` power
`
`Figure 1 showsa schematic representation of medical device system
`
`100, including kinetic sensor(s) 212 and body signal sensor(s) 282 connected
`
`to medical device 200 by leads 211 and 281, respectively. /d. § 33.
`
`“TA|ctivity sensor(s) 212 may each be configured to collect at least one
`
`signal from a patient relating to an activity level of the patient,” and include,
`
`for example, an accelerometer, an inclinometer, a gyroscope, or an
`
`ergometer. /d. Figure | also showsa current body data variability (BDV)
`
`module 265, which may “may comprise an QO) saturation variability (O2SV)
`
`module 330 configured to determine O2SV from O> saturation data,” and
`
`15
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`“an HRV module 310 configured to determine HRV from heart rate data.”
`
`Id. Jf 10, 13, 53, Fig. 2C. Osorio discloses that “medical device system 100
`
`maybe fully or partially implanted, or alternatively may be fully external.”
`
`Id. § 33.
`
`Figure 8, reproduced below, shows one embodiment of Osorio’s
`
`monitoring method.
`
`
`
`Figure 8 showsthat an activity level is determined at 810, and a non-
`
`pathological BDV rangeis determined at 820 based onthe activity level. /d.
`
`477. A current BDV is determined at 840 and compared to the non-
`
`pathological BDV range at 850. /d. 478. If the current BDV 1s outside the
`
`non-pathological range, then a pathological state is determined at 860 and a
`
`further action, such as warning,treating, or logging the occurrence and/or
`
`severity of the pathological state, is taken at 870. /d.
`
`16
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`According to Osorio, body indices that may be the subject of BDV
`
`monitoring include:
`
`heart rhythm variability, a heart rate variability (HRV), a
`respiratory rate variability (RRV), a blood pressure variability
`(BPV), a respiratory rhythm variability, respiratory sinus
`arrhythmia, end tidal CO2 concentration variability, power
`variability at a certain neurological index frequency band(e.g.,
`beta), an EKG morphology variability, a heart rate pattern
`variability, an electrodermal variability (e.g., a skin resistivity
`variability or a skin conductivity variability), a pupillary
`diameter variability, a blood oxygen saturation variability, a
`kinetic activity variability, a cognitive activity variability,
`arterial pH variability, venous pH variability, arterial-venous
`pHdifference variability, a lactic acid concentration variability,
`a cortisol level variability, or a catecholamine level variability.
`
`Id. § 43; see also id. § 42 (similar) §§] 45-46 (monitoring heart rate for
`
`episodes of tachycardia and bradycardia). “In one embodiment, the severity
`
`[of a pathological state] may be measured by a magnitude and/or duration of
`
`a pathological state such as a seizure, a type of autonomic change associated
`
`with the pathological state (e.g., changes in heart rate, breathing rate, brain
`
`electrical activity, the emergence of one or more cardiac arrhythmias,etc.).”
`
`Id. 471.
`
`With respect to HRV,in particular, Osorio teaches: “By monitoring
`
`the patient’s activity level, HR, and HRV,it is possible to determine when
`
`the patient’s HRVfalls outside the non-pathological ranges as the patient’s
`
`activity levels change over time.” /d. 9 66. Osorio’s Figure 4A, reproduced
`
`below, showsheart rate variability as a function of activity level. See id.
`
`q 58.
`
`17
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`
`
`eeeeeeee
`FIG. 4A
`ACTRATY
`
`Figure 4A plots a patient’s heart rate (HR) on the Y-axis and a
`
`patient’s activity level on the X-axis. /d. Markers Al though A4 represent
`
`increasing activity from a sleep state (A1) through vigorousactivity (A4). /d.
`
`Boundary lines 410 and 420, respectively, represent the upper and lower
`
`limits of non-pathological heart rate, and include representative ranges R1
`
`through R4. /d. at Fig. 4A. According to Osorio,
`
`the upper and lower boundsof the non-ictal!'°! HR region
`increase as activity level increases (e.g., from a sleep state to a
`resting, awake state) and reach their highest values for
`strenuous exertion. In addition, the width of the non-
`pathological HR ranges narrowsasactivity levels and heart
`rates increase, which is consistent with the known reduction in
`HRVathigh levels of exertion. When the patientis in a non-
`pathological state (e.g., when an epileptic patient is not having a
`seizure), for a particular activity level the patient’s HRV should
`
`10 <Tctal” refers to the active, middle stage of a seizure and corresponds with
`intense electrical brain activity. See https://epilepsyfoundation.org.au/
`understanding-epilepsy/seizures/seizure-phases/.
`
`18
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`fall within a non-pathological HRV range associated with that
`activity level.
`
`Id. ¥ 58.
`
`Osorio further presents Figure 11 as “depict[ing] pathological and
`
`non-pathological BDV (e.g., HRV) value ranges.” Jd. F923, 91. In this
`
`illustration, Osorio showsthat HRV values falling below 0.5 bpm and above
`
`4 bpm are always pathological whenactivity level is low (e.g., resting or
`
`walking), whereas intermediate HRV values (0.5—4 bpm) may be
`
`pathological when consideredin light of the patient’s activity level. /d.
`
`Osorio further notes that the boundaries between normal and pathological
`
`may be adjusted based on an individual’s physiology. “For example, in an
`
`epilepsy patient also suffering from tachycardia, and having baseresting
`
`heart rate of 100-110 bpm,a decline in heart rate to 70 bpm may be
`
`indicative of a seizure slowing downthe heart rate, even though a heart rate
`
`of 70 bpm is generally ‘normal’ across a typical population.” /d. ¥ 45.
`
`3)
`
` Kleiger (Exhibit 1033)
`
`Kleiger is a review article regarding the measurement and clinical
`
`utility of heart rate variability (HRV). Ex. 1033, Title. Kleiger discloses
`
`various methods for quantifying HRV including time domain, spectral or
`
`frequency domain, geometric, and nonlinear methods. /d. at 88. According
`
`to Kleiger:
`
`The greatest variation of heart rate occurs with circadian
`changes, particularly the difference between night and day heart
`rate, mediated by complex and poorly understood
`neurohormonal rhythms. Exercise and emotion also have
`profoundeffects on heart rate. Fluctuations in heart rate reflect
`autonomic modulation and have prognostic significance in
`pathological states.
`
`7d. (internal citation numbers omitted).
`
`19
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Long-term, usually 24-hour recordings, can be used to assess
`autonomic nervous responses during normaldaily activities in
`health, disease, and in response to therapeutic interventions,
`e.g., exercise or drugs. RR interval variability is useful for
`assessing risk of cardiovascular death or arrhythmic events,
`especially when combined with othertests, e.g., left ventricular
`ejection fraction or ventricular arrhythmias.
`
`Td. at Abstract.
`
`A) Li 2012 (Exhibit 1006)
`
`Li 2012 investigates algorithms for reducing cardiac monitor false
`
`alarms (“FA”) in an intensive care setting. Ex. 1006, 1. Li 2012 explains that
`
`a lack of integration between different sensors results in frequent false
`
`alarms in intensive care units. /d. at Abstract. To reduce these false alarms,
`
`Li 2012
`
`present[s] a novel framework for FA reduction using a machine
`learning approach to combineup to 114 signal quality and
`physiological features extracted from the electrocardiogram,
`photoplethysmograph, and optionally the arterial blood pressure
`waveform. A machinelearning algorithm wastrained and
`evaluated on a database of 4107 expert-labeled life-threatening
`arrhythmias, from 182 separate ICU visits.
`
`Id. According to Li 2012, the resulting algorithm reduced false alarms with
`
`without substantial suppression of true alarms. /d. at Abstract, 7, Table 6.
`
`For example, “[f]or the ventricular tachycardia alarms, the best FA [false
`
`alarm] suppression performance was 30.5% with a TA [true alarm]
`
`suppression rate below 1%.” /d. at Abstract.
`
`5) Chan (Exhibit 1048)
`
`Chan discloses:
`
`A wristwatch worn by a user for measuring a three-lead ECG
`[that] includes three electrodes placed separately on the front,
`either side, and back or strap thereof. The wristwatch further
`
`20
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`includes an electrode panel having the electrode on the front or
`either side of the watch, sensing elements, pressure, infrared or
`impedancedetectors, and circuits. The electrode panelis
`capable of sensing the contact or press of fingers to trigger the
`ECG measuring. While the electrode in the back-side of the
`watch contacts the hand wearing the watch, the electrode and
`electrode panel on the front or either side of the watch are
`pressed by fingers from the other hand, and the electrode in the
`strap contacts the abdomenorleft leg simultaneously. Thus, a
`three-lead ECG can be measured. ECG data can be transmitted
`to a personal or hospital computer by wireless networksor flash
`memory.
`
`Ex. 1048, Abstract.
`
`Chan’s Figures 1A and 1B, reproduced below, show an embodiment
`
`of the disclosed three-lead ECG wristwatch.
`
`FIG.AA
`
`FIG.IB
`
`Figures 1A and 1B, respectively, show the front and rear of a three-lead
`
`ECG wristwatch. /d. at 2:21—22. Figure 1A shows ECGelectrode 4, sensing
`
`element 6 (which can detect “pressure, impedanceor infrared for
`
`recognizing the contact or press madebyfingersto initiate an ECG
`
`21
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`measurement’), and display 7, which may be an LCD. /d. at 2:44—56.
`
`Display 7 can display text (e.g., time, heart rate, and, condition (normal vs
`
`arrhythmia) as well as “graph/animation, for an event reminding 13 and
`
`ECG waveforms14.” /d. at 2:56—59; see also id. at 4:56—59 (stating, with
`
`reference to Figure 7, that “display 57 can show users time,heart rate,
`
`waveforms and any other information 61, such as activity level and
`
`temperature, if needed”’).
`
`Chan Figure 2 is reproduced below.
`
`
`
`Figure 2 shows an embodimentof the three-lead ECG watch havinga third
`
`lead 5 on the strap 11. /d. at 2:24—25, 3:1-4.
`
`22
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`Chan Figure 3B is reproduced below.
`
`
`
`Figure 3B “demonstrate[s] how to place the wristwatch to make electrodes
`
`be contacted by both hands.” /d. at 2:26—28, 3:5-22.
`
`I.
`
`ANALYSIS
`
`A. Legal Standards
`
`“In an IPR, the petitioner has the burden from the onset to show with
`
`particularity why the patent it challenges is unpatentable.” Harmonic Inc.v.
`
`Avid Technology, Inc., 815 F.3d 1356, 1363 (citing 35 U.S.C. § 312(a)(3)
`
`(requiring inter partes review petitions to identify “with particularity .
`
`.
`
`. the
`
`evidence that supports the grounds for the challenge to each claim”)). This
`
`burden of persuasion never shifts to Patent Owner. See Dynamic Drinkware,
`
`LLC vy. Nat’l Graphics, Inc., 800 F.3d 1375, 1378 (Fed. Cir. 2015)
`
`(discussing the burden of proof in inter partes review).
`
`In KSR International Co. v. Teleflex Inc., 550 U.S. 398 (2007), the
`
`Supreme Court reaffirmed the framework for determining obviousnessset
`
`23
`
`
`
`IPR2021-00971
`Patent 10,595,731 B2
`
`forth in Graham v. John Deere Co., 383 U.S. 1 (1966). The KSR Court
`
`summarized the four factual inquiries set forth in Graham (383 U.S. at 17-
`
`18) that are applied in determining whether a claim is unpatentable as
`
`obvious under 35 U.S.C. § 103 as follows: (1) determining the scope and
`
`content of the prior art; (2) ascertaining the differences between the priorart
`
`and the claimsat issue; (3) resolving the level of ordinary skill in t