Petitioner’s Oral Hearing Demonstratives
`
`Apple Inc. (Petitioner)
`v.
`AliveCor, Inc. (Patent Owner)
`
`9,572,499 | IPR2021-00970 | APL2021046011
`10,595,731 | IPR2021-00971 | APL2021046012
`10,638,941 | IPR2021-00972 | APL2021046013
`
`Before Hon. Robert A. Pollock, Eric C. Jeschke, and David Cotta
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`1
`
`APPLE 1086
`Apple v. AliveCor
`IPR2021-00972
`
`1
`
`
`
`Apple Inc. (Petitioner)
`v.
`AliveCor, Inc. (Patent Owner)
`
`9,572,499 | IPR2021-00970 | APL2021046011
`10,595,731 | IPR2021-00971 | APL2021046012
`10,638,941 | IPR2021-00972 | APL2021046013
`
`Before Hon. Robert A. Pollock, Eric C. Jeschke, and David Cotta
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`1
`
`APPLE 1086
`Apple v. AliveCor
`IPR2021-00972
`
`1
`
`
`
`Instituted Grounds
`
`’499 IPR
`IPR2021-00970
`
`’731 IPR*
`IPR2021-00971
`
`Ground
`Obviousness over Shmueli, Osorio
`Obviousness over Shmueli, Osorio, Hu1997
`
`Claim(s) Challenged
`1-6, 10-16, 20
`
`7-9, 17-19
`
`Ground
`Obviousness over Shmueli
`Obviousness over Shmueli, Osorio
`Obviousness over Shmueli, Osorio, Li2012
`Obviousness over Shmueli, Osorio, Kleiger2005
`Obviousness over Shmueli, Osorio, Chan
`
`Claim(s) Challenged
`1, 7, 12, 13, 16, 17, 23-26, 30
`
`1, 2, 4, 7, 12-14, 16-18, 20, 23-26, 30
`3, 5, 6, 19, 21, 22
`8, 9, 10, 11, 27-29
`15
`
`’941 IPR
`IPR2021-00972
`
`Ground
`Obviousness over Shmueli, Osorio
`Obviousness over Shmueli, Osorio, Lee2013
`Obviousness over Shmueli, Osorio, Chan
`
`Claim(s) Challenged
`1, 5, 7–9, 11, 12, 16, 18, 19, 20, 22, 23
`
`2-4, 6, 13, 14, 15, 17
`10, 21
`
`* The record from IPR2021-00971 is primarily referenced throughout to address topics covered in all three IPRs
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`2
`
`2
`
`
`
`Applied Prior Art
`
`The Shmueli-Osorio Combination
`has been applied in all IPRs
`
`Shmueli
`’731
`
`Hu1997
`’499
`
`Shmueli +
`Osorio
`’499
`’731
`’941
`
`Lee2013
`’941
`
`Osorio
`
`Kleiger2005
`’731
`
`Li2012
`’731
`
`Chan
`’731
`’941
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`3
`
`3
`
`
`
`Table Of Contents
`
`The Challenged Patents
`
`Key Asserted Prior Art (Shmueli, Osorio)
`
`The Shmueli-Osorio Combination
`
`Topics for Discussion
`
`1 – Arrhythmia Detection (All IPRs)
`
`2 – “Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`3 – Atrial Fibrillation (’941 IPR)
`
`4 – Machine Learning (’499, ’731 IPRs)
`
`5 – POSITA
`
`Other Reference Slides
`
`5
`
`12
`
`21
`
`25
`
`25
`
`32
`
`42
`
`51
`
`69
`
`77
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`4
`
`4
`
`
`
`The Challenged Patents
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`5
`
`5
`
`5
`
`
`
`The Challenged Patents
`
`’941 Patent
`
`’499 and ’731 Patents
`
`’941 Patent, Fig. 7 (annotated)
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`6
`
`’499 Patent, Fig. 10 (annotated)
`
`6
`
`
`
`The ’941 Patent
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`7
`
`’941 Petition, 7 (citing APPLE-1001, Fig. 1) (annotated)
`
`7
`
`
`
`The ’941 Patent
`
`“The ’941 patent relates to cardiac monitoring to sense the presence of an intermittent
`arrhythmia in an individual.”
`
`’941 Petition, 5; APPLE-1001, Abstract, 1:17-22
`
`APPLE-1001, Claim 1; see ’941 Petition, 31-53
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`8
`
`8
`
`
`
`The ’731 and ’499 Patents
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`9
`
`’499 Patent, Fig. 10
`
`9
`
`
`
`The ’731 Patent
`
`’731 Patent, Claim 1; see ’731 Petition, 13-28
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`10
`
`10
`
`
`
`The ’499 Patent
`
`’499 Patent, Claim 1; see ’499 Petition, 28-46
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`11
`
`11
`
`
`
`Key Asserted Prior Art
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`12
`
`12
`
`12
`
`
`
`Shmueli (PCT Patent Publication WO 2012/140559)
`
`Shmueli’s Monitoring Device
`
`APPLE-1004, Figs. 1A, 1B (cited in ’731 Petition, 9)
`
`APPLE-1004, Figs. 1A, 1B (cited in ’731 Petition, 9)
`
`“…the terms ‘oxygen saturation in the blood’, ‘blood oxygen saturation”,
`‘pulse oximeter’, oximetry, SpO2, and photoplethysmography have the
`same meaning and may be used interchangeably….”
`APPLE-1004, 7:25-27 (cited in ’731 Petition, 8)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`13
`
`13
`
`
`
`Shmueli (PCT Patent Publication WO 2012/140559)
`
`“Measuring ECG typically requires connecting the patient to an ECG
`measuring device via a plurality of wires connected to the patient in
`predefined places of the body.”
`
`APPLE-1004, 2:21-3:3 (cited in ’731 Petition, 7-8)
`
`“The prior art does not consider a requirement to enable a patient to perform
`ECG measurement as soon as an irregular heart activity develops and without
`requiring the ECG to be constantly wired to the patient.”
`APPLE-1004, 9:21-23(cited in ’731 Petition, 12)
`
`“The present invention…provid[es] a combined oximetry and
`electrocardiogram measuring system and a method in which the oximetry
`measurement is performed continuously and/or repeatedly, and the ECG
`measurement is triggered upon detection of an intermittent irregular heart-
`related event.”
`
`APPLE-1004, 9:23-28 (cited in ’731 Petition, 10)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`14
`
`14
`
`
`
`Shmueli (PCT Patent Publication WO 2012/140559)
`
`Shmueli’s Monitoring Technique
`
`’731 Petition, 22 (citing APPLE-1004, Fig. 7)
`
`“…Petitioner has shown sufficiently that one of ordinary skill in the art would have understood that
`Shmueli to disclose detecting arrhythmia based on PPG data and confirming the diagnosis with
`an ECG measurement.”
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`’731 Institution Decision, 45
`
`15
`
`15
`
`
`
`Osorio (U.S. Pat. App. No. 2014/0275840)
`
`APPLE-1005, Fig. 4A (annotated) (cited in ’731 Petition, 58)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`16
`
`16
`
`
`
`Osorio (U.S. Pat. App. No. 2014/0275840)
`
`“…the present disclosure relates to a
`method of detecting a pathological
`body state of a patient, comprising
`receiving a body signal of the patient;
`determining a first body data variability
`(BDV) from said body signal;
`determining an activity level of said
`patient; determining a non-pathological
`range for said first BDV, based at least
`in part on said activity level; comparing
`said first BDV to said non-pathological
`range for said first BDV; and detecting a
`pathological body state when said BDV
`is outside said non-pathological range.”
`APPLE-1005, [0003] (cited in ’731 Petition, 39)
`
`“In one embodiment, the severity 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.).”
`APPLE-1005, [0071] (cited in ’731 Petition, 71)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`17
`
`17
`
`
`
`Osorio (U.S. Pat. App. No. 2014/0275840)
`
`Efimov Testimony
`Q. Okay. Do you have an understanding
`of how a person of skill in the art in
`2013 would understand the term
`"pathological state"?
`
`…A
`
`. Pathological state could be also
`understood as an irregular heart
`condition.
`
`Chaitman Testimony
`Q. What does it mean to you as a
`clinician when somebody says it's
`pathological?
`
`A. “Pathological” means it falls
`outside the norm.
`
`Ex. 2017, 94:19-22
`
`APPLE-1069, 50:17-22 (cited ’731 Reply, 1-2)
`
`Efimov Testimony
`Q. Would a pathological state in 2013
`to a person of ordinary skill in the art
`include an arrhythmia?
`
`A. Yes. Arrhythmia would be
`included, yes. And also heart failure,
`diabetes, you know, metabolic condition,
`and many, many others.
`APPLE-1069, 50:17-22 (cited ’731 Reply, 1-2)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`18
`
`18
`
`
`
`Osorio (U.S. Pat. App. No. 2014/0275840)
`
`“…a certain pathological state (e.g., epileptic seizure)”
`APPLE-1005, [0037] (cited in ’731 Reply, 11)
`
`“In one embodiment, the pathological state is an epileptic event, e.g., an
`epileptic seizure”
`
`APPLE-1005, [0046] (cited in ’731 Reply, 11)
`
`“This disclosure relates to medical device systems and methods capable of
`detecting a pathological body state of a patient, which may include epileptic
`seizures, and responding to the same.”
`
`APPLE-1005, [0002] (cited in ’731 Reply, 11)
`
`…
`
`APPLE-1005, Claims 1, 7 (cited in ’731 Reply, 11)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`19
`
`19
`
`
`
`Osorio (U.S. Pat. App. No. 2014/0275840)
`
`“In one embodiment, the severity 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.).”
`
`APPLE-1005, [0071] (cited in ’731 Petition, 71)
`
`“In one embodiment, the pathological
`state is an epileptic event, e.g., an
`epileptic seizure. For example, if the
`body signal is heart rate, then an
`instantaneous heart rate above the non-
`pathological heart rate range
`determined by the BDV range
`determination module 260 may indicate
`a tachycardia episode frequently seen
`with epileptic seizures originating from
`or spreading to certain brain regions,
`and an instantaneous heart rate below
`the non-pathological heart rate range
`may indicate a bradycardia episode
`occasionally seen with epileptic
`seizures originating from certain brain
`regions.”
`
`APPLE-1005, [0046] (cited in ’731 Reply, 13)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`20
`
`20
`
`
`
`The Shmueli-Osorio Combination
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`21
`
`21
`
`21
`
`
`
`The Shmueli-Osorio Combination
`
`Osorio improves Shmueli’s detection of irregular heart conditions:
`
`Improve detection accuracy
`using activity level monitoring
`
`HRV to improve detection
`
`’731 Petition, 58 (citing APPLE-1005, Fig. 4A)
`
`’731 Petition, 40 (citing APPLE-1005, FIG. 8)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`22
`
`22
`
`
`
`The Shmueli-Osorio Combination
`
`Osorio improves Shmueli’s detection of irregular heart conditions:
`
`Chaitman Testimony
`“A POSITA also would have been motivated
`to incorporate Osorio’s HRV analysis
`because it is less affected by noise.”
`APPLE-1003 (’731 IPR), ¶159 (cited in 731 IPR Petition, 48);
`APPLE-1039, 52
`
`Chaitman Testimony
`“A POSITA would have been aware that the
`normal heart rate ranges from 60-100bpm,
`and that failure to account for physical
`activity or stress, that might elevate heart
`rates above 100 bpm during normal daily
`activity would result in triggered alerts from
`physiologic and not pathologic events that
`occur during normal daily activity. Indeed,
`Osorio explicitly describes the benefits
`(e.g., improved accuracy, reliability, and
`reduced false detection) of using activity
`level to detect an irregular heart
`condition. APPLE-1005, [0029], [0036].
`With these benefits in mind, a POSITA
`would have been motivated to incorporate
`Osorio’s activity sensor and activity level
`analysis techniques into Shmueli’s heart
`monitoring device.”
`APPLE-1003 (’731 IPR), ¶151 (cited in ’731 Petition, 43)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`23
`
`23
`
`
`
`The Shmueli-Osorio Combination
`
`The Shmueli-Osorio Device
`
`Apple’s ’731 Petition
`The Shmueli-Osorio Combination would
`have:
`
`(1)
`
`Increased accuracy
`
`(2) Provided continuously monitoring
`heart rhythm with an oximetry
`sensor
`
`(3)
`
`Improved user satisfaction
`’731 Petition, 54;
`APPLE-1005, [0029]; APPLE-1003, ¶167
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`24
`
`’731 Petition, 53 (modifying APPLE-1004, FIG. 6);
`APPLE-1003 (’731 IPR), ¶167
`
`24
`
`
`
`Five Topics For Discussion
`
`1. Arrhythmia Detection (All IPRs)
`
`2.
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`3. Atrial Fibrillation (’941 IPR)
`
`4. Machine Learning (’499, ’731 IPRs)
`
`5. POSITA (All IPRS)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`25
`
`25
`
`
`
`Arrhythmia Detection (All IPRs)
`
`Apple’s ’731 Petition
`“As discussed in Section III.A.1 and
`explained by Dr. Chaitman, a POSITA would
`have understood and found obvious that
`the term “irregular heart condition” refers to
`arrhythmia.”
`
`’731 Petition, 13; APPLE-1003 (’731 IPR), ¶¶72-73
`
`AliveCor’s ’731 Preliminary Response
`“…a POSITA would not automatically
`assume that this otherwise undefined term
`refers to arrhythmia, as opposed to another
`heart condition..”
`
`’731 Response, 44
`
`’731 Institution Decision
`“…one of ordinary skill in the art would have understood Shmueli’s use of “irregular
`heart condition” as referring to—or at a minimum, encompassing—arrhythmia,
`and, thus, disclosing the detection of arrhythmia.”
`
`’731 Institution Decision, 33-34
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`26
`
`26
`
`
`
`Arrhythmia Detection (All IPRs)
`
`AliveCor’s ’731 Response
`“…Shmueli’s disclosure of the
`heart condition genus is not a
`disclosure of the arrhythmia
`species.”
`
`’731 Response, 44
`
`’AliveCor’s ’731 Response
`Acknowledges that irregular
`heart conditions includes the
`following conditions:
`
`•Arrhythmia
`•Heart attack
`•Angina pectoris
`•Cardiomyopathy
`•Congenital heart disease
`•Coronary heart disease
`•Heart-valve defect
`
`’731 Response, 44-45 (citing APPLE 1023, APPLE 1047)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`27
`
`27
`
`
`
`Arrhythmia Detection (All IPRs)
`
`Chaitman Declaration
`“…a POSITA would have
`understood that [irregular heart
`condition] refers to arrhythmia,
`which is one of the most obvious (if
`not the most obvious) types of
`“irregular heart condition[s]” that
`can be determined
`APPLE-1003, ¶48 (citing APPLE-1016, p. 6081;
`APPLE-1020, Abstract, 44:29-32;
`APPLE-1011, Abstract;
`APPLE-1023, 2;
`APPLE-1047, 320-21;
`APPLE-1001, 1:40-42;
`APPLE-1004, 8:11-13, 15:3-5);
`(cited in ’731 Petition, 10-11)
`
`Apple’s ’731 Reply
`“If ‘irregular heart conditions’ is
`understood to include ‘numerous’
`conditions and Shmueli discloses
`detecting ‘various irregular heart
`conditions,’ then it would have
`been obvious that arrhythmia
`(which is the most common type
`of heart condition) is one
`example detected by Shmueli.”
`’731 Reply, 9 (citing APPLE-1004, 12:29-31)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`28
`
`28
`
`
`
`Arrhythmia Detection (All IPRs)
`
`Goldreich – U.S. Pat. No. 7,598,878
`“The signal that is collected from the
`SpO2 sensor may also optionally be
`used for producing other heart
`related information. For example,
`processing the signal that reflects
`the intensity of the reflected IR light
`may produce information such as
`heart rate, PWTT, irregularity of
`heart rate etc.”
`APPLE-1061, 16:54-58 (cited in ’731 Reply, 8)
`
`APPLE-1061, Claim 5 (cited in ’731 Reply, 8)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`29
`
`29
`
`
`
`Arrhythmia Detection (All IPRs)
`
`Shmueli
`“Another common solution is a Holter
`device, which is practically a small ECG
`device connected to the patient for
`typically 24 hours, recording the ECG
`signal. Hopefully, the heart-related event
`occurs during the recording time. The
`Holter does not limit the mobility range of
`the patient but has a time limit of its
`operation. For events that are not
`sufficiently frequent this solution does not
`work. Also important and highly
`disadvantageous is the Holter device uses
`electric contacts at the end of electric
`cables. Thus, the patient has to be
`constantly wired to the Holter device.”
`APPLE-1004, 2:21-3:3 (cited in ’731 Reply, 7-8)
`
`Efimov Testimony
`Another common solution is a Holter
`device, which is practically a small
`ECG device connected to the patient
`for typically 24 hours, recording the
`ECG signal. Hopefully, the heart-related
`event occurs during the recording time.
`The Holter does not limit the mobility
`range of the patient but has a time limit
`of its operation.
`APPLE-1004, 2:21-3:3 (cited in ’731 Reply, 8)
`
`Efimov Testimony
`Q. So is it fair that a Holter monitor
`was used -- one of the primary uses of
`a Holter monitor was to determine
`whether a patient may have cardiac
`arrhythmia?
`
`A.· Yes.
`
`APPLE-1069, 30:24-31:9 (cited in ’731 Reply, 8)
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`30
`
`30
`
`
`
`Arrhythmia Detection (All IPRs)
`
`Apple’s ’941 Reply
`“U.S. 2005/177051 to Almen
`discloses a wrist-watch with heart
`rate sensors, including an ECG and
`a pulse oximeter that is used to
`detect arrhythmia.”
`
`’941 Reply, 11; APPLE-1062, [0014], [0051], [0055], [0062]
`
`Efimov Testimony
`Q. Dr. Efimov, I believe you state in your
`declarations that arrhythmias can be
`intermittent; is that correct?
`
`A. Yes. Especially such as atrial
`fibrillation. Many arrhythmias could be
`intermittent.
`
`APPLE-1069, 23:25-24:4 (cited in ’941 Reply, 8)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`31
`
`31
`
`
`
`Five Topics For Discussion
`
`1. Arrhythmia Detection (All IPRs)
`
`2. “Confirmation” of Arrhythmia Detection (’731, ’941
`IPRs)
`
`3. Atrial Fibrillation (’941 IPR)
`
`4. Machine Learning (’499, ’731 IPRs)
`
`5. POSITA (All IPRS)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`32
`
`32
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`’731 Patent
`
`’941 Patent
`
`…
`
`…
`
`’731 Patent, Claim 1 (cited in ’941 Petition, 51)
`
`’941 Patent, Claim 1 (cited in ’941 Petition, 51)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`33
`
`33
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`Efimov Testimony
`“PPG monitoring is …historically has
`been found to be less reliable in
`detecting arrhythmias, especially atrial
`arrhythmias, such as atrial
`fibrillation….Compared to an ECG, heart
`rate estimation is more challenging when
`using a PPG signal.”
`Ex. 2016, ¶16 (cited in ’731 POR, 10-11; ; ’941 POR, 9); Ex. 2023, 2, 9
`
`Pereira
`“ECG remains the gold standard for the
`electrophysiological definition and
`recognition of arrhythmias, including AF
`diagnosis”
`
`Ex. 2023, 3 (cited in ’731 POR, 11; ’941 POR, 10)
`
`Chaitman Testimony
`“A POSITA would have recognized that Shmueli’s focus on
`enabling ECG measurements “as soon as” an irregular heart
`condition is detected enables ECG data to be used to
`confirm the detection of the irregular heart condition
`using PPG data, thereby improving detection accuracy
`compared to prior art heart monitoring devices.”
`Ex. 2016, ¶16 (cited in ’731 POR, 10-11)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`34
`
`34
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`APPLE-1004, FIG. 7 (cited in ’731 Petition, 27; ’941 Petition, 14)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`35
`
`35
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`Shmueli
`“The software program proceeds to element 38 to derive
`from the SpO2 measurement physiological parameters
`such as pulse rate, pulse amplitude, pulse shape, rate of
`blood flow, etc. Then, the software program scans the
`derived physiological parameters to detect various
`irregularities of the heart condition. The scanning for an
`irregular heart condition preferably uses heart-irregularity
`detection parameters (element 39) stored in the memory
`unit 28. When an irregular heart condition is detected
`(element 40) the software program continues to element
`41. However, the SpCh measurement (element 37)
`preferably continues and optionally also the derivation of
`physiological parameters as well as the detection of
`irregular heart conditions (element 38).”
`
`APPLE-1004, 13:14-22 (cited in ’731 Reply, 16)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`36
`
`36
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`Chaitman Testimony
`“…a POSITA would have understood that the software at element
`50, element 39 and element 38 causes the processing device to
`confirm the presence of the arrhythmia based on the ECG
`data, by searching for correlations between the PPG and
`ECG data, modifying detection parameters, and confirming the
`presence of arrhythmia.
`
`APPLE-1003, ¶112 (cited in ’731 Petition, 27);
`APPLE-1003, ¶154 (cited in ’941 Petition, 68)
`
`APPLE-1004, FIG. 7 (cited in ’731 Petition, 27; ’941 Petition, 14)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`37
`
`37
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`Chaitman Testimony
`“…the software proceeds to element 51 to determine a set of stop
`conditions (element 52), such as whether ‘the irregular heart condition has
`stopped.’ APPLE-1004, 13:22-29. Shmueli discloses that when the
`software program detects that “the irregular heart condition has
`stopped” (element 51), the software program notifies the user that the ECG
`measurement has stopped (element 53) and stops the ECG measurement
`(element 54).”
`
`APPLE-1003 (’731 IPR), ¶113 (cited in ’731 Petition); APPLE-1004, 13:22-29
`
`APPLE-1004, FIG. 7 (cited in ’731 Petition, 27; ’941 Petition, 14)
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`38
`
`38
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`AliveCor’s ’731 Response
`“...all detection of irregular
`heart conditions in Shmueli is
`by the SpO2 measurement.”
`
`’731 Response, 52
`
`Apple’s ’731 Reply
`“…Shmueli’s ECG data still
`has a role. As shown in FIG.
`7, Shmueli’s ECG analysis
`(element 50) leads to new
`detection parameters
`(element 39) used for more
`accurate detection of the
`irregular heart condition
`(element 38) with SpO2/PPG
`data.”
`
`’731 Response, 52
`
`’731 Reply, 15-16;
`’941 Reply, 20
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`39
`
`39
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`AliveCor’s ’731 Sur-Reply
`“Shmueli’s ‘correlation’ of ECG and
`SpO2 measurements is used to
`update detection parameters.”
`
`’731 Response, 52
`“And those detection parameters
`are merely used to detect the
`presence of an ECG signal.”
`’731 Sur-Reply, 18
`
`Apple’s ’731 Petition
`“…a POSITA would have
`understood that the software at
`element 50, element 39 and
`element 38 causes the processing
`device to confirm the presence
`of the arrhythmia based on the
`ECG data.”
`
`’731 Petition, 27; ’941 Petition, 51-53
`
`APPLE-1004, FIG. 7 (cited in ’731 Petition, 27; ’941 Petition, 14)
`
`APPLE-1004, FIG. 7 (cited in ’731 Petition, 27; ’941 Petition, 14)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`40
`
`40
`
`
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`Shmueli
`“When the software program detects
`that a condition for stopping the ECG
`measurement is met (element 51)
`using stop conditions (element 52)
`preferably stored in the memory unit 28
`the software program preferably notifies
`the user that the ECG measurement
`has stopped (element 53) and stops
`the ECG measurement (element 54).”
`APPLE-1004, 14:22-29 (cited in ’731 Reply, 15; ’941 Reply, 20)
`
`Shmueli
`Examples of conditions for stopping the
`ECG measurement:
`• The irregular heart condition has
`stopped.
`• The heart condition returned to
`normal.
`• A predefined period elapsed.
`• A predefined number ofheart beats
`were counted.
`APPLE-1004, 14:22-29 (cited in ’731 Reply, 15; ’941 Reply, 20)
`
`APPLE-1004, FIG. 7 (cited in ’731 Petition, 27; ’941 Petition, 14)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`41
`
`41
`
`
`
`Five Topics For Discussion
`
`1. Arrhythmia Detection (All IPRs)
`
`2.
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`3. Atrial Fibrillation (’941 IPR)
`
`4. Machine Learning (’499, ’731 IPRs)
`
`5. POSITA (All IPRS)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`42
`
`42
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`Claim 1
`
`…
`
`Chaitman Testimony
`“By 1977, both detecting possible atrial
`fibrillation using irregular pulse rhythms or
`heartbeats and techniques to quantitatively
`characterize irregularities were well-known.”
`APPLE-1003 (’941 IPR), 27 (cited in ’941 Petition, 2)
`
`Claim 2
`
`’941 Patent, Claims 1 and 2
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`43
`
`43
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`Apple’s ’941 Petition
`“…both Shmueli and Lee 2013
`disclose detecting arrhythmia
`using PPG data, further confirming
`their combinability.”
`
`’941 Reply, 26
`
`Chaitman Testimony
`“A POSITA would have therefore found it
`obvious to implement the Shmueli-
`Osorio- Lee-2013 device without
`invention such that it executes Lee-
`2013’s software application to detect AF
`using PPG data collected by oximetry
`sensor 13”
`APPLE-1003 (’941 IPR), ¶154 (cited in ’941 Petition, 68)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`44
`
`44
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`Lee-2013
`“Atrial fibrillation is the most common
`sustained arrhythmia. Over 3 million
`Americans are currently diagnosed, and the
`prevalence of AF is increasing with the aging
`of the U.S. population (Go et al., 2001).”
`
`APPLE-1011, 1 (cited in ’941 Petition, 66)
`
`Efimov Testimony
`Q. Would you agree that atrial fibrillation is
`the most common cardiac arrhythmia
`present?
`
`A. Statistically, yes. This is what is referred
`to.
`
`APPLE-1069, 23:5-9 (cited in ’941 Reply, 10)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`45
`
`45
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`’AliveCor’s ’731 Response
`“As recognized in the Suzuki paper cited
`by Petitioner, “[t]here are 8 kinds of
`arrhythmia” recognized by the widely
`used Minnesota Code Manual of
`Electrocardiographic Finding (June
`1982).”
`
`’731 Response, 5 (citing APPLE-1016, 1)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`46
`
`46
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`Motivation to Combine
`
`Chaitman Testimony
`“The combined Shmueli-Osorio-
`Lee-2013 device would have
`also provided an improvement
`over Lee-2013’s technique
`using a mobile device to detect
`AF since the combined device
`leverages the wrist-mounted
`form factor of the Shmueli’s
`heart monitoring device
`without requiring the user to
`carry a separate mobile device.”
`
`APPLE-1003 (’941 IPR), ¶152 (cited in ’941 Petition, 66)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`47
`
`47
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`Apple’s Key Position
`A POSITA would have found it obvious to incorporate Lee-2013’s teachings of detecting
`atrial fibrillation using PPG data into the Osorio-Shmueli combination
`
`Apple’s ’941 Petition
`“Shmueli and Osorio each describes techniques for generally detecting
`arrhythmias, but do not address detection of specific types of arrhythmias,
`such as AF.”
`
`’941 Petition, 66
`
`Lee2013
`“Atrial fibrillation is the most common
`sustained arrhythmia.”
`APPLE-1011, 26 (cited in ’941 Petition)
`
`Chaitman Testimony
`“Given the prominence of AF, a
`POSITA would have recognized that
`incorporating AF detection into the
`Shmueli-Osorio device provides a new
`capability for classifying an arrhythmia
`as AF using PPG data.”
`APPLE-1003, ¶152 (cited in ’941 Petition, 66)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`48
`
`48
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`AliveCor’s ’941 Response
`“…Lee 2013 teaches not to use a separate
`ECG.”
`
`’941 Response, 56
`
`Lee-2013
`“Given the ever-growing popularity of cell
`phones and smartphones, a smartphone-
`based AF detection application provides
`patients and their caregivers with access to an
`inexpensive and easy-to-use monitor for AF
`outside of the traditional health care
`establishment. Because the application does
`not involve a separate ECG sensor and
`instead employs built-in hardware, it is
`both novel and cost-effective. We believe
`this package will lead to better acceptance and
`more widespread use than existing out-of-
`hospital arrhythmia monitors.”
`APPLE-1011, 29 (cited in ’941 Response, 56-57)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`49
`
`49
`
`
`
`Atrial Fibrillation (’941 IPR)
`
`AliveCor’s ’941 Sur-Reply
`“Apple presents no argument that
`the references, alone or in
`combination, would confirm the
`presence of atrial fibrillation using
`an ECG sensor.”
`
`’941 Sur-Reply, 19-20
`
`Chaitman Testimony
`“Given the prominence of AF, a
`POSITA would have recognized that
`incorporating AF detection into the
`Shmueli-Osorio device provides a new
`capability for classifying an arrhythmia as
`AF using PPG data.”
`
`APPLE-1003 (’941 IPR), ¶152 (cited in ’941 Petition,66)
`
`Chaitman Testimony
`The combination “…improves the accuracy of
`AF detection provided by Lee-2013 alone
`since the Shmueli-Osorio-Lee-2013 device
`uses ECG data to confirm AF detection
`based on PPG data.”
`APPLE-1003 (’941 IPR), ¶153 (cited in ’941 Petition,67)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`50
`
`50
`
`
`
`Five Topics For Discussion
`
`1. Arrhythmia Detection (All IPRs)
`
`2.
`
`“Confirmation” of Arrhythmia Detection (’731, ’941 IPRs)
`
`3. Atrial Fibrillation (’941 IPR)
`
`4. Machine Learning (’499, ’731 IPRs)
`
`5. POSITA (All IPRs)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`51
`
`51
`
`
`
`Machine Learning (’499, ’731 IPRs)
`
`Apple’s Key Positions
`Machine learning was well-known:
`
`1) Using machine learning to detect arrhythmia was well-known
`
`2) The ’499 and ’731 patents only recite broad categories of well-known machine
`learning methods
`
`3) Machine learning was known to offer significant benefits
`
`’731 Reply, 18-20; ’499 Reply, 18-20
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`52
`
`52
`
`
`
`Machine Learning (’499, ’731 IPRs)
`
`1) Using machine learning to detect arrhythmia was well-known
`
`Asl 2008
`“This paper presents an effective cardiac
`arrhythmia classification algorithm using
`the heart rate variability (HRV) signal. The
`proposed algorithm is based on the
`generalized discriminant analysis (GDA)
`feature reduction scheme and the support
`vector machine (SVM) classifier.”
`
`APPLE-1039, Abstract (cited in ’731 Petition, 69; ’731 Reply, 20; and
`in APPLE-1003 (’499 IPR), ¶214)
`
`Dallali 2011
`“Integration of HRV, WT and neural
`networks for ECG arrhythmias
`classification.”
`APPLE-1041, p.74 (cited in ’731 Petition, 67; ’731 Reply, 20; and in
`APPLE-1003 (’731 IPR), ¶¶117, 259)
`
`Yaghouby 2009
`“Some examples of these automatic arrhythmia
`detection and classification techniques are neural
`networks [1,2], wavelet transforms [3], support
`vector machines [4], fuzzy logic [5] and the rule-
`based algorithms [6].”
`“In this paper we present an arrhythmia
`classification method using Heart Rate Variability
`(HRV) signal features and Support Vector
`Machine (SVM) Classifier.”
`APPLE-1040, 1928 (cited in ’731 Petition, 67; ’731 Reply, 20; and in
`APPLE-1003 (’731 IPR), ¶¶27, 117, 259)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`53
`
`53
`
`
`
`Machine Learning (’499, ’731 IPRs)
`
`1) Using machine learning to detect arrhythmia was well-known
`
`Tavassoli 2012
`Classification of cardiac arrhythmia with respect
`to ECG and HRV signal by genetic
`programming.”
`APPLE-1038, Abstract (cited in ’731 Petition, 69; ’731 Reply, 20; and in
`APPLE-1003 (’731 IPR), ¶263)
`
`Tsipouras 2004
`“We have developed an automatic arrhythmia
`detection system, which is based on heart rate
`features only. Initially, the RR interval duration
`signal is extracted from ECG recordings and
`segmented into small intervals. The analysis is
`based on both time and time—frequency (t—f)
`features. Time domain measurements are
`extracted and several combinations between the
`obtained features are used for the training of a
`set of neural networks.”
`
`APPLE-1012, Abstract (cited in ’731 Petition, 69; ’731 Reply, 20; and in
`APPLE-1003 (’731 IPR), ¶¶26, 35, 117, 263)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`54
`
`54
`
`
`
`Machine Learning (’499, ’731 IPRs)
`
`2) The ’499 and ’731 patents only recite broad categories of well-known
`machine learning methods
`
`’731 Patent
`“Any 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.”
`
`APPLE-1001 (’731 IPR), 9:67-10:9 (cited in ’731 Reply, 19)
`
`Apple’s ’731 Reply
`“The types of learning listed in the patent
`were all generic categories known in the
`art—the claimed ‘machine learning
`algorithm’ is nothing more than generic
`functional language that adds no inventive
`concept.”
`
`’731 Reply, 19
`
`Efimov Testimony
`Q: And those were all machine learning
`algorithms or structures that were already
`known by December of 2013; correct?
`
`…A
`
`: Well, based on this text, which I couldn’t
`recall, yes, they’d already been known.
`APPLE-1069, 170:9-14 (cited in ’731 Reply, 20)
`
`DEMONSTRATIVE EXHIBIT - NOT EVIDENCE
`
`55
`
`55
`
`
`
`Machine Learning (’499, ’731 IPRs)
`
`3) Machine learning was known to offer significant benefits
`
`Sajda 2006
`“Machine learning offers a principled approach for
`developing sophisticated, automatic, and
`objective algorithms for analysis of high-
`dimensional and multimodal biomedical data….
`recent developments in machine learning … have
`made significant impacts in the detection and
`diagnosis of disease in biomedicine.”
`APPLE-1042, Abstract (cited in ’731 Petition, 68; ’731 Reply, 20;
`and in APPLE-1003 (‘’731 IPR), ¶260))
`
`Yaghouby 2009
`“BSVM is a classification algorithm based on
`SVM which is able to solve the multi-class
`classification problems. here, five types of the
`most life threatening cardiac arrhythmias … can be
`discriminated by BSVM and selected features with
`the average accuracy of 99.78%.”
`
`Efimov Testimony
`“there are two uses of machine learning, I
`would say, in medicine in terms of benefits.
`One is to assist a physician or cardiologist
`or radiologist in looking through a lot of
`data… And another one is to basically work
`in real life, in the world, on the wearable
`device, for example, and basically alert the
`user or potential patient that, let’s say, an
`arrhythmia was detected”
`
`APPLE-1069, 185:17-24 (cited in ’731 Reply, 20)
`
`Dallali 2011
`“The classification accuracy of the ANNs
`introduced classifier up to 90.5% was achieved,
`and a 99.5% of sensitivity.”
`
`APPLE-1040, 1928 (cited in ’731 Petition, 67; ’731 Reply, 20;
`and in APPLE-1003 (’731 IPR), ¶¶117, 259)
`DE
Accessing this document will incur an additional charge of $.
After purchase, you can access this document again without charge.
Accept $ Charge
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.
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.
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
