IN THE UNITED STATES PATENT AND TRADEMARK OFFICE
`
`Gopalakrishnan
`In re Patent of:
`U.S. Patent No.:
`9,572,499
`February 21, 2017
`Issue Date:
`Appl. Serial No.: 14/730,122
`Filing Date:
`June 3, 2015
`Title:
`METHODS AND SYSTEMS FOR ARRHYTHMIA TRACKING
`AND SCORING
`
` Attorney Docket No.: 50095-0032IP1
`
`DECLARATION OF DR. BERNARD R. CHAITMAN
`
`1
`
`APPLE 1003
`
`
`
`Gopalakrishnan
`In re Patent of:
`U.S. Patent No.:
`9,572,499
`February 21, 2017
`Issue Date:
`Appl. Serial No.: 14/730,122
`Filing Date:
`June 3, 2015
`Title:
`METHODS AND SYSTEMS FOR ARRHYTHMIA TRACKING
`AND SCORING
`
` Attorney Docket No.: 50095-0032IP1
`
`DECLARATION OF DR. BERNARD R. CHAITMAN
`
`1
`
`APPLE 1003
`
`
`
`Table of Contents
`
`
`
`
`
`ASSIGNMENT .............................................................................................. 5
`
` QUALIFICATIONS ...................................................................................... 5
`
` SUMMARY OF CONCLUSIONS FORMED ............................................ 7
`
` BACKGROUND KNOWLEDGE ONE OF SKILL IN THE ART
`
`WOULD HAVE HAD PRIOR TO THE PRIORITY DATE OF THE
`
`’499 PATENT ................................................................................................. 8
`
`
`
`LEGAL PRINCIPLES .................................................................................. 9
`
`A. CLAIM CONSTRUCTION ................................................................................... 9
`
`B. PRIORITY ...................................................................................................... 10
`
`C. ANTICIPATION .............................................................................................. 10
`
`D. OBVIOUSNESS .............................................................................................. 10
`
` MATERIALS CONSIDERED ................................................................... 12
`
` TECHNOLOGY OVERVIEW .................................................................. 20
`
`A. ARRHYTHMIA ............................................................................................... 20
`
`B. ELECTROCARDIOGRAPHY (ECG) ................................................................. 21
`
`C. PHOTOPLETHYSMOGRAPHY (PPG) ............................................................... 22
`
`D. HEART RATE (HR) ....................................................................................... 24
`
`2
`
`
`
`E. HEART RATE VARIABILITY (HRV) .............................................................. 24
`
`F. MACHINE LEARNING ALGORITHMS .............................................................. 26
`
` OVERVIEW OF THE ’499 PATENT ....................................................... 27
`
` OVERVIEW OF THE PROSECUTION HISTORY............................... 29
`
`
`
`SUMMARY OF THE PRIOR ART .......................................................... 30
`
`A. SHMUELI ...................................................................................................... 30
`
`B. OSORIO ........................................................................................................ 36
`
`C. HU 1997 OVERVIEW ..................................................................................... 41
`
` ANALYSIS OF SHMUELI IN VIEW OF OSORIO ............................... 43
`
`A. THE COMBINATION OF SHMUELI AND OSORIO ............................................. 43
`
`B. CLAIM 1 ....................................................................................................... 57
`
`C. CLAIM 2 ....................................................................................................... 98
`
`D. CLAIM 3 .....................................................................................................100
`
`E. CLAIM 4 .....................................................................................................102
`
`F. CLAIM 5 .....................................................................................................104
`
`G. CLAIM 6 .....................................................................................................107
`
`H. CLAIM 10 ...................................................................................................114
`
`I.
`
`J.
`
`CLAIM 11 ...................................................................................................122
`
`CLAIM 12 ...................................................................................................138
`
`3
`
`
`
`K. CLAIM 13 ...................................................................................................138
`
`L. CLAIM 14 ...................................................................................................139
`
`M. CLAIM 15 ...................................................................................................139
`
`N. CLAIM 16 ...................................................................................................139
`
`O. CLAIM 20 ...................................................................................................139
`
` ANALYSIS OF SHMUELI IN VIEW OF OSORIO AND HU 1997 ...140
`
`A. THE COMBINATION OF SHMUELI, OSORIO AND HU 1997 ............................140
`
`B. CLAIM 7 .....................................................................................................145
`
`C. CLAIM 8 .....................................................................................................146
`
`D. CLAIM 9 .....................................................................................................148
`
`E. CLAIM 17 ...................................................................................................151
`
`F. CLAIM 18 ...................................................................................................151
`
`G. CLAIM 19 ...................................................................................................151
`
` CONCLUSION ..........................................................................................152
`
`
`
`
`
`
`
`4
`
`
`
`I, Dr. Bernard R. Chaitman, of St. Louis, Missouri, declare that:
`
`
`1.
`
`ASSIGNMENT
`I have been retained on behalf of Apple Inc. (“Apple” or “Petitioner”)
`
`to offer technical opinions related to U.S. Patent No. 9,572,499 (“The ’499 patent”)
`
`(APPLE-1001). I understand that Apple is requesting that the Patent Trial and
`
`Appeal Board (“PTAB” or “Board”) to institute an inter partes review (“IPR”)
`
`proceeding of the ’499 patent.
`
`2.
`
`I have been asked to provide my independent analysis of the ’499
`
`patent in light of the prior art publications cited in this declaration.
`
`3.
`
`I am not and never have been, an employee of Apple. I received no
`
`compensation for this declaration beyond my normal hourly compensation based
`
`on my time actually spent analyzing the ’499 patent, the prior art publications cited
`
`below, and issues related thereto, and I will not receive any added compensation
`
`based on the outcome of any IPR or other proceeding involving the ’499 patent
`
` QUALIFICATIONS
`I am over the age of 18 and am competent to write this declaration. I
`
`4.
`
`have personal knowledge, or have developed knowledge of these technologies
`
`based upon education, training, or experience, of the matters set forth herein.
`
`5.
`
`I am an Emeritus Professor of Medicine, and Director of
`
`Cardiovascular Research at St Louis University School of Medicine. I am also a
`
`5
`
`
`
`Board-Certified Cardiologist and have practiced Internal Medicine and
`
`Cardiovascular Disease for four decades. I am currently licensed in the State of
`
`Missouri and Florida. I also serve as the Chair for Clinical Event Committees and
`
`Data and Safety Monitoring Boards for numerous clinical trials sponsored by
`
`National Heart Lung and Blood Institute (NHLBI) and industry. I am currently a
`
`member of the Editorial Board of nine journals that include Circulation, Journal of
`
`the American College of Cardiology, and the European Heart Journal. I also
`
`founded and am the Medical Director of St Louis University Core ECG Laboratory
`
`that provides ECG analysis for numerous NHLBI and industry sponsored clinical
`
`trials that test various treatment strategies.
`
`6.
`
`I received a Bachelor of Science degree in 1965 and a medical degree
`
`1969, both from McGill University in Montreal, Canada. I completed my Internal
`
`Medicine training at McGill University and Royal Victoria Hospital in 1972. I then
`
`completed post-graduate training in Cardiovascular Diseases at the University of
`
`Oregon (from 1972-1974) and University of Montreal (from 1974-1975).
`
`7.
`
`I have a long and distinguished career in academic medicine and have
`
`published more than 400 peer-reviewed papers and more than 600 abstracts, book
`
`chapters, and short communications. My areas of expertise in Cardiovascular
`
`Medicine include rest and exercise ECG analysis, diagnostic noninvasive testing,
`
`large scale multinational clinical trials testing different treatment strategies, and
`
`6
`
`
`
`drug development. I have received funding from the National Heart Lung and
`
`Blood Institute (NHLBI) for more than 3 decades and also funding by the
`
`Department of Defense. My experience is recognized internationally and I have
`
`lectured abroad and published frequently with cardiologists in Europe.
`
`8.
`
`I have served as a consultant to the Food and Drug Administration on
`
`ECG related issues, and the use of the rest and exercise ECG as a diagnostic
`
`instrument. I also served as a committee member for the American Heart
`
`Association, American College of Cardiology, and the European Society of
`
`Cardiology in matters related to ECG analysis and the use of ECG analysis as a
`
`diagnostic and prognostic tool. I served on grant review committees for the
`
`NHLBI, the Veterans Administration Review Board, and the American Heart
`
`Association.
`
` SUMMARY OF CONCLUSIONS FORMED
`This Declaration explains the conclusions that I have formed based on
`
`9.
`
`my analysis. To summarize those conclusions:
`
`
`
`Ground 1: Based upon my knowledge and experience and my review of the
`
`prior art publications in this declaration, I believe that claims 1-6, 10-16 and
`
`20 of the ’499 patent are rendered obvious by Shmueli in view of Osorio.
`
`
`
`Ground 2: Based upon my knowledge and experience and my review of the
`
`prior art publications in this declaration, I believe that claims 7-9 and 17-19
`
`7
`
`
`
`of the ’499 patent are rendered obvious by Shmueli in view of Osorio and
`
`Hu 1997.
`
` BACKGROUND KNOWLEDGE ONE OF SKILL IN THE ART
`WOULD HAVE HAD PRIOR TO THE PRIORITY DATE OF THE ’499
`PATENT
`10.
`
`I have been informed that a person of ordinary skill in the art is a
`
`hypothetical person who is presumed to have the skill and experience of an
`
`ordinary worker in the field at the time of the alleged invention. Based on my
`
`knowledge and experience in the field and my review of the ’499 patent and file
`
`history, I believe that a person of ordinary skill in the art in this matter would have
`
`had at least a combination of Bachelor’s Degree (or a similar Master’s Degree, or
`
`higher degree) in an academic area emphasizing health science, or a related field,
`
`and two or more years of work experience with cardiac monitoring technologies
`
`(e.g., as a cardiologist). Additional education or industry experience may
`
`compensate for a deficit in one of the other aspects of the requirements stated
`
`above.
`
`11. My analysis and conclusions set forth in this declaration are based on
`
`the perspective of a person of ordinary skill in the art having this level of
`
`knowledge and skill as of the date of the alleged invention of the ’499 patent
`
`(“POSITA”). Based on instruction from Counsel, I have applied December 12,
`
`2014 (“Critical Date”), as the date of the alleged invention of the ’499 patent.
`
`8
`
`
`
`However, my analysis of the prior art and the conclusions I have formed as set
`
`forth herein would also apply even if the date of the alleged invention as claimed
`
`was December 12, 2013 (“earliest possible effective filing date”).
`
`12. Based on my experiences, I have a good understanding of the
`
`capabilities of a POSITA. Indeed, I have taught, mentored, advised, and
`
`collaborated closely with many such individuals over the course of my career.
`
` LEGAL PRINCIPLES
`13.
`I am not a lawyer and I will not provide any legal opinions in this IPR.
`
`Although I am not a lawyer, I have been advised that certain legal standards are to
`
`be applied by technical experts in forming opinions regarding the meaning and
`
`validity of patent claims.
`
`A. Claim construction
`I understand that claim terms are generally given their plain and
`
`14.
`
`ordinary meaning in light of the patent’s specification and file history as
`
`understood by a person of ordinary skill in the art at the time of the purported
`
`invention. In that regard, I understand that the best indicator of claim meaning is its
`
`usage in the context of the patent specification as understood by a POSITA. I
`
`further understand that the words of the claims should be given their plain meaning
`
`unless that meaning is inconsistent with the patent specification or the patent’s
`
`history of examination before the Patent Office. I also understand that the words
`
`9
`
`
`
`of the claims should be interpreted as they would have been interpreted by a
`
`POSITA at the time of the invention was made (not today).
`
`B.
`Priority
`I understand that a continuation application is a later-filed application
`
`15.
`
`that has the same disclosure (specification and figures) as an earlier filed
`
`application to which the later-filed application claims priority. A continuation is
`
`generally entitled to the same priority date as the later-filed application to which it
`
`claims priority.
`
`C. Anticipation
`I understand that a patent claim is invalid as anticipated if each and
`
`16.
`
`every element as set forth in the claim is found, either expressly or inherently
`
`described, in a single prior art reference. I also understand that, to anticipate, the
`
`reference must teach all of the limitations arranged or combined in the same way
`
`as recited in the claim. I do not rely on anticipation in this declaration.
`
`17. With respect to inherency, I understand that the fact that a certain
`
`result or characteristic may occur or be present in the prior art is not sufficient to
`
`establish the inherency of that result or characteristic. Instead, the inherent
`
`characteristic must necessarily flow from the teaching of the prior art.
`
`D. Obviousness
`I understand that a patent claim is invalid if the claimed invention
`
`18.
`
`would have been obvious to a person of ordinary skill in the field at the time of the
`
`10
`
`
`
`purported invention, which is often considered the time the application was filed.
`
`Thus, even if all of the claim limitations are not found in a single prior art
`
`reference that anticipates the claim, the claim can still be invalid.
`
`19. To obtain a patent, a claimed invention must have, as of the priority
`
`date, been nonobvious in view of the prior art in the field. I understand that an
`
`invention is obvious when the differences between the subject matter sought to be
`
`patented and the prior art are such that the subject matter as a whole would have
`
`been obvious at the time the invention was made to a person having ordinary skill
`
`in the art.
`
`20.
`
`I understand that, to prove that prior art or a combination of prior art
`
`renders a patent obvious it is necessary to: (1) identify the particular references
`
`that, singly or in combination, make the patent obvious; (2) specifically identify
`
`which elements of the patent claim appear in each of the asserted references; and
`
`(3) explain a motivation, teaching, need, market pressure or other legitimate reason
`
`that would have inspired a person of ordinary skill in the art to combine prior art
`
`references to solve a problem.
`
`21.
`
`I also understand that certain objective indicia can be important
`
`evidence regarding whether a patent is obvious or nonobvious. Such indicia
`
`include:
`
`
`
`Commercial success of products covered by the patent claims;
`
`11
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`A long-felt need for the invention;
`
`Failed attempts by others to make the invention;
`
`Copying of the invention by others in the field;
`
`Unexpected results achieved by the invention as compared to the closest
`
`prior art;
`
`Praise of the invention by the infringer or others in the field;
`
`The taking of licenses under the patent by others;
`
`Expressions of surprise by experts and those skilled in the art at the making
`
`of the invention; and
`
`The patentee proceeded contrary to the accepted wisdom of the prior art.
`
`22. To the extent these factors have been brought to my attention, if at all,
`
`I have taken them into consideration in rendering my opinions and conclusions.
`
` MATERIALS CONSIDERED
`23. My analysis and conclusions set forth in this declaration are based on
`
`my educational background and experiences in the field (see Section IV). Based on
`
`my above-described experience, I believe that I am considered to be an expert in
`
`the field. Also, based on my experiences, I understand and know of the capabilities
`
`of persons of ordinary skill in the field during the early 1990s–2010s, and I taught,
`
`participated in organizations, and worked closely with many such persons in the
`
`field during that time frame.
`
`12
`
`
`
`24. As part of my independent analysis for this declaration, I have
`
`considered the following: the background knowledge/technologies that were
`
`commonly known to persons of ordinary skill in this art during the time before the
`
`earliest claimed priority date for the ’499 patent; my own knowledge and
`
`experiences gained from my work experience in the field of the ’499 patent and
`
`related disciplines; and my experience in working with others involved in this field
`
`and related disciplines.
`
`25.
`
`In addition, I have analyzed the following publications and materials:
`
` U.S. Patent No. 9,572,499 to Gopalakrishnan (“the ’499 patent”) (APPLE-
`
`1001)
`
` the Prosecution History of the ’499 patent (“the Prosecution History”)
`
`(APPLE-1002)
`
` PCT Patent Publication WO2012/140559 (“Shmueli”) (APPLE-1004)
`
` U.S. Patent Publication 2014/0275840 (“Osorio”) (APPLE-1005)
`
` Li Q, Clifford GD, “Signal quality and data fusion for false alarm reduction
`
`in the intensive care unit,” J Electrocardiol. 2012 Nov-Dec; 45(6):596-603
`
`(“Li 2012”) (APPLE-1006)
`
` U.S. Patent Publication 2008/0004904 (“Tran”) (APPLE-1007)
`
` U.S. Patent Publication 2014/0107493 (“Yuen”) (APPLE-1008)
`
` U.S. Patent Publication 2015/0119725 (“Martin”) (APPLE-1009)
`
`13
`
`
`
` U.S. Provisional Application No. 61/794,540 (“Osorio Provisional”)
`
`(APPLE-1010)
`
` Lee J, Reyes BA, McManus DD, Mathias O, Chon KH. Atrial fibrillation
`
`detection using a smart phone. Annu Int Conf IEEE Eng Med Biol Soc.
`
`2012; 2012:1177-800 (“Lee 2013”) (APPLE-1011)
`
` Tsipouras MG, Fotiadis DI. Automatic arrhythmia detection based on time
`
`and time-frequency analysis of heart rate variability. Comput Methods
`
`Programs Biomed. 2004 May; 74(2):95-108 (“Tsipouras 2004”) (APPLE-
`
`1012)
`
` Lu S, Zhao H, Ju K, Shin K, Lee M, Shelley K, Chon KH. Can
`
`photoplethysmography variability serve as an alternative approach to obtain
`
`heart rate variability information? J Clin Monit Comput. 2008 Feb; 22(1):23-
`
`9 (“Lu 2008”) (APPLE-1013)
`
` Selvaraj N, Jaryal A, Santhosh J, Deepak KK, Anand S. Assessment of heart
`
`rate variability derived from finger-tip photoplethysmography as compared
`
`to electrocardiography. J Med Eng Technol. 2008 Nov-Dec; 32(6):479-84
`
`(“Selvaraj 2008”) (APPLE-1014)
`
` Lu G, Yang F, Taylor JA, Stein JF. A comparison of photoplethysmography
`
`and ECG recording to analyse heart rate variability in healthy subjects. J
`
`Med Eng Technol. 2009; 33(8):634-41 (“Lu 2009”) (APPLE-1015)
`
`14
`
`
`
` Suzuki T, Kameyama K, Tamura T. Development of the irregular pulse
`
`detection method in daily life using wearable photoplethysmographic sensor.
`
`Annu Int Conf IEEE Eng Med Biol Soc. 2009; 2009:6080-3 (“Suzuki
`
`2009”) (APPLE-1016)
`
` Reed MJ, Robertson CE, Addison PS. Heart rate variability measurements
`
`and the prediction of ventricular arrhythmias. QJM. 2005 Feb; 98(2):87-95
`
`(“Reed 2005”) (APPLE-1017)
`
` Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate
`
`of heart rate variability? A review on studies comparing
`
`photoplethysmographic technology with an electrocardiogram. Int J Cardiol.
`
`2013 Jun 5; 166(1):15-29 (“Schafer 2013”) (APPLE-1018)
`
` K. Douglas Wilkinson, “The Clinical Use of the Sphygmomanometer,” The
`
`British Medical Journal, 1189-90 (Dec. 27, 1924) (“Wilkinson”) (APPLE-
`
`1019)
`
` U.S. Pat. No. 6,095,984 (“Amano”) (APPLE-1020)
`
` B.K. Bootsma et. al, “Analysis of R-R intervals with atrial fibrillation at rest
`
`and during exercise.” Circulation 1970; 41:783-794 (APPLE-1021)
`
` Frits L. Meijler and Fred H. M. Wittkampf, “Role of the Atrioventricular
`
`Node in Atrial Fibrillation” Atrial Fibrillation: Mechanisms and
`
`Management, 2nd ed. 1997 (“Meijler”) (APPLE-1022)
`
`15
`
`
`
` Heart Diseases _ Definition of Heart Diseases by Merriam-Webster
`
`(APPLE-1023)
`
` Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS. Heart
`
`rate variability: a review. Med Biol Eng Comput. 2006 Dec; 44(12):1031-51
`
`(“Acharya 2006”) (APPLE-1024)
`
` Saime Akdemir Akar, Sadık Kara, Fatma Latifoğlu, Vedat Bilgiç. Spectral
`
`analysis of photoplethysmographic signals: The importance of
`
`preprocessing. Biomedical Signal Processing and Control, 2013; 8(1):16-22
`
`(Akar 2013) (APPLE-1025)
`
` U.S. Provisional Application No. 61/915,113 (APPLE-1026)
`
` U.S. Provisional Application No. 61/953,616 (APPLE-1027)
`
` U.S. Provisional Application No. 61/969,019 (APPLE-1028)
`
` U.S. Provisional Application No. 61/970,551 (APPLE-1029)
`
` U.S. Provisional Application No. 62/014516 (APPLE-1030)
`
` U.S. Patent Publication No. 2012/0203491 (“Sun”) (APPLE-1031)
`
` U.S. Patent No. 9,808,206 (“Zhao”) (APPLE-1032)
`
` Kleiger RE, Stein PK, Bigger JT Jr. Heart rate variability: measurement and
`
`clinical utility. Ann Noninvasive Electrocardiol. 2005 Jan; 10(1):88-101
`
`(“Kleiger 2005”) (APPLE-1033)
`
` Chen Z, Brown EN, Barbieri R. Characterizing nonlinear heartbeat
`
`16
`
`
`
`dynamics within a point process framework. IEEE Trans Biomed Eng. 2010
`
`Jun; 57(6):1335-47 (“Chen 2010”) (APPLE-1034)
`
` Karvonen, J., Vuorimaa, T. Heart Rate and Exercise Intensity During Sports
`
`Activities. Sports Medicine 5, 303–311 (1988) (“Karvonen 1988”) (APPLE-
`
`1035)
`
` Yu C, Liu Z, McKenna T, Reisner AT, Reifman J. A method for automatic
`
`identification of reliable heart rates calculated from ECG and PPG
`
`waveforms. J Am Med Inform Assoc. 2006 May-Jun; 13(3):309-20 (“Yu
`
`2006”) (APPLE-1036)
`
` AliveCor v Apple ITC Complaint Exhibit 11 (499 Infringement Chart)
`
`(APPLE-1037)
`
` Tavassoli, & Ebadzadeh, Mohammad & Malek,. (2012). Classification of
`
`cardiac arrhythmia with respect to ECG and HRV signal by genetic
`
`programming. Canadian Journal on Artificial Intelligence, Machine
`
`Learning and Pattern Recognition. 3. 1-13 (“Tavassoli 2012”) (APPLE-
`
`1038)
`
` Asl BM, Setarehdan SK, Mohebbi M. Support vector machine-based
`
`arrhythmia classification using reduced features of heart rate variability
`
`signal. Artif Intell Med. 2008 Sep;44(1):51-64 (“Asl 2008”) (APPLE-1039)
`
` Yaghouby F., Ayatollahi A. (2009) An Arrhythmia Classification Method
`
`17
`
`
`
`Based on Selected Features of Heart Rate Variability Signal and Support
`
`Vector Machine-Based Classifier. In: Dössel O., Schlegel W.C. (eds) World
`
`Congress on Medical Physics and Biomedical Engineering, September 7 -
`
`12, 2009, Munich, Germany. IFMBE Proceedings, vol 25/4. Springer,
`
`Berlin, Heidelberg (“Yaghouby 2009”) (APPLE-1040)
`
` Dallali, Adel & Kachouri, Abdennaceur & Samet, Mounir. (2011).
`
`Integration of HRV, WT and neural networks for ECG arrhythmias
`
`classification. ARPN Journal of Engineering and Applied Sciences. VOL. 6.
`
`74-82 (“Dallali 2011”) (APPLE-1041)
`
` Sajda P. Machine learning for detection and diagnosis of disease. Annu Rev
`
`Biomed Eng. 2006;8:537-65 (“Sajda 2006”) (APPLE-1042)
`
` Aarron Smith. Smartphone Ownership – 2013 Update. Pew Research
`
`Center. June 5, 2013 (“Smith 2013”) (APPLE-1043)
`
` C. Narayanaswami and M. T. Raghunath, “Application design for a smart
`
`watch with a high resolution display,” Digest of Papers. Fourth International
`
`Symposium on Wearable Computers, 2000, pp. 7-14 (“Narayanaswami
`
`2000”) (APPLE-1044)
`
` Thong, YK & Woolfson, M & Crowe, JA & Hayes-Gill, Barrie & Challis,
`
`Richard. (2002). Dependence of inertial measurements of distance on
`
`accelerometer noise,” Meas. Measurement Science and Technology. 13.
`
`18
`
`
`
`1163 (“Thong 2002”) (APPLE-1045)
`
` AliveCor’s ITC Complaint filed on April 20, 2021 in “Certain Wearable
`
`Electronic Devices With ECG Capability and Components Thereof” ITC-
`
`337-3545-20210420 (“ITC Complaint”) (APPLE-1046)
`
` Marcovitch, Harvey. Black’s Medical Dictionary. London: A. & C. Black,
`
`2005 (APPLE-1047)
`
` U.S. Pat. No. 7,894,888 (“Chan”) (APPLE-1048)
`
` Hu YH, Palreddy S, Tompkins WJ. A patient-adaptable ECG beat classifier
`
`using a mixture of experts approach. IEEE Transactions on Bio-medical
`
`Engineering. 1997 Sep;44(9):891-900 (“Hu 1997”) (APPLE-1049)
`
` Strath SJ, Swartz AM, Bassett DR Jr, et al. Evaluation of heart rate as a
`
`method for assessing moderate intensity physical activity. Medicine and
`
`Science in Sports and Exercise. 2000 Sep;32(9 Suppl):S465-70 (“Strath
`
`2000”) (APPLE-1050)
`
` U.S. Provisional Application No. 61/895,995 (“Martin Provisional”)
`
`(APPLE-1054)
`
` AliveCor’s District Court Complaint filed on May 25, 2021 in AliveCor, Inc.
`
`v. Apple Inc., 3:21-cv-03958 (N.D.Cal. May 25, 2021) (“Antitrust
`
`Complaint”) (APPLE-1055)
`
`19
`
`
`
` TECHNOLOGY OVERVIEW
`A. Arrhythmia
`26. Cardiac arrhythmias refer to a group of disorders of the heart rate or
`
`heart rhythm. Atrial fibrillation is “the most common cardiac arrhythmia.”
`
`APPLE-1001, 1:35-36. Arrhythmic activity can include the heart beating too fast
`
`(tachycardia), too slow (bradycardia), or irregularly (variations in heart rate). Id.
`
`While tachycardia and bradycardia may be diagnosed based on heart rate,
`
`variations in heart rate (e.g., atrial fibrillation) are diagnosed based on heart rate
`
`variability (“HRV”) analysis. As a hypothetical example, when a patient goes into
`
`atrial fibrillation, a common rhythm disturbance, an HRV analysis would detect the
`
`irregularity, even though heart rate of the patient may stay with the normal range of
`
`between 60-100 bpm. For example, Tsipouras 2004 discloses detecting arrhythmia
`
`by training a machine learning algorithm (e.g., neural networks) with HRV data.
`
`APPLE-1012, Abstract. Tsipouras 2004 states that “Our study is based on the
`
`analysis of the RR-interval duration so the proposed method is capable of detecting
`
`arrhythmia types that produce irregularities on the RR intervals, the HRV or the
`
`heart rhythm.” APPLE-1012, p. 106. Compared to looking at the raw heart rate
`
`signal (e.g., ECG), HRV analysis is more robust because it involves extracting the
`
`RR intervals and is less affected by noise. APPLE-1039, p. 52.
`
`27. Since 1903, different detection techniques have been employed to
`
`20
`
`
`
`detect irregular pulse rhythms or irregular heartbeats. See, e.g., APPLE-1019
`
`(describing use of a sphygmomanometer as early as 1924 to make “obvious the
`
`variation in the sound heard over the artery” to identify pulse irregularity);
`
`APPLE-1020, 9:12-28 (describing use of “plethysmogram” in 1997 to detect
`
`arrhythmia). By 1977, both detecting possible atrial fibrillation using irregular
`
`pulse rhythms or heartbeats and techniques to quantitatively characterize
`
`irregularities were well-known. By 2009, examples of known arrhythmia detection
`
`techniques included: neural networks, wavelet transforms, support vector
`
`machines, fuzzy logic and rule-based algorithms. APPLE-1040, p. 1928. A
`
`POSITA would have understood that many of these techniques (e.g., support
`
`vector machines, neural networks) are machine learning algorithms.
`
`B.
`Electrocardiography (ECG)
`28. Eletrocardiography (ECG) measures the electrical activity of the
`
`heart, which can be indicative of various heart diseases. APPLE -1004, 1:14-17.
`
`ECG recording uses Ag/AgCl electrodes attached to specific anatomical positions.
`
`APPLE-1015, p. 635. Clinical ECG recording commonly uses 12 leads for
`
`determination of the complex temporal dynamics of each cardiac cycle. Id.
`
`29. An ECG represents electrical activity of the heart based on
`
`depolarization and repolarization of the atria and ventricles, which typically show
`
`up as five distinct waves on the ECG readout—P-wave, Q-wave, R-wave, S-wave,
`
`21
`
`
`
`and T-wave. A QRS complex is a combination of the Q, R, and S waves occurring
`
`in succession and represents the electrical impulse of a heartbeat as it spreads
`
`through the ventricles during ventricular depolarization. An R-R interval represents
`
`a time elapsed between successive R-waves of a QRS complex of the ECG that
`
`occur between successive heart beats. If R-R interval durations over a time period
`
`are close to one another in value, then ventricular rhythm is understood to be
`
`“regular.” APPLE-1022, 110-112. In contrast, if there are significant variations in
`
`the R-R interval durations over a time period, then the ventricular rhythm is
`
`understood to be “irregular.” Id.
`
`30.
`
`In conventional clinical practice, ECG and telemetry are used at a
`
`hospital to diagnose cardiac arrhythmias. APPLE-1016, p. 6080. As an irregular
`
`heartbeat caused by arrhythmia does not necessarily occur during examination at
`
`the hospital, a Holter ECG has been used for measuring one or more leads of an
`
`ECG in daily life. Id. A Holter ECG device is not ideal because it still requires
`
`attaching some electrodes to the patients’ chest. Id. In addition, a Holter device
`
`typically only monitors the patient for a certain period (e.g., 24 hours, 48 hours or
`
`72 hours) and thus it may not detect a cardiac arrhythmia if it does not occur
`
`during the monitoring period. APPLE-1004, 1:26-2:3.
`
`C.
`Photoplethysmography (PPG)
`31. Photoplethysmography (PPG) is a simple noninvasive optical
`
`22
`
`
`
`technique for monitoring beat-to-beat relative blood volume changes in the
`
`microvascular bed of peripheral tissues. APPLE-1014, p. 479. PPG is sometimes
`
`also referred to as blood oxygen saturation, pulse oximeter, oximetry, and SpO2.
`
`APPLE-1004, 7:25-27. Its basic principle requires a light source to illuminate
`
`subcutaneous tissue and a photo detector with spectral characteristics matching
`
`those of the light source. APPLE-2018, p. 16.
`
`32. As the pulse period derived from PPG data is directly related to
`
`cardiac activity, the physiological information derived from RR intervals of ECG
`
`can also be derived from the pulse period of a PPG reading. APPLE-1014, p. 480.
`
`This is because under normal conditions, the electrical impulse of the heart (ECG)
`
`stimulates a cardiac contraction resulting in a spread of the pulsatile wave of blood
`
`to the periphery (PPG). APPLE-1014, p. 480. Thus, a PPG signal includes
`
`information about both heart rate and heart rate variability. APPLE-1025, p. 16.
`
`Many studies verify the high correlation between RR intervals (RRI) obtained from
`
`ECG and PP intervals (PPI) obtained from PPG. APPLE-1025, p. 16; APPLE-
`
`1018, Fig. 1.
`
`23
`
`
`
`
`
`APPLE-1018, Fig. 1.
`
`33. Compared to ECG, PPG is attractive because it only requires
`
`attaching a single sensor to the hand of the user. APPLE-1018, p.16.
`
`D. Heart Rate (HR)
`34. Heart rate (HR) is the reciprocal of the RR interval and measures the
`
`number of heartbeats per unit of time. APPLE-1034, p. 5. It was long recognized
`
`that an individual’s heart rate varies with his/her activity level (exercise intensity).
`
`APPLE-1035, p. 303. As discussed above, an individual’s heart rate and the
`
`corresponding RR interval can be determined using either ECG or PPG data.
`
`APPLE-1036, Abstract and Fig. 1.
`
`E. Heart Rate Variability (HRV)
`35. Heart rate variability (HRV) is defined as the variation of RR
`
`24
`
`
`
`intervals with respect to time and reflects beat-to-beat heart rate (HR) variability.
`
`APPLE-1025, p. 16; APPLE-1012, p. 95. HRV analysis is an important tool in
`
`cardiology used to diagnose various types of arrhythmias. APPLE-1012, Abstract
`
`and pp. 95-96 (“Therefore, HRV analysis became a critical tool in cardiology for
`
`the diagnosis of heart diseases.”). HRV analysis has become popular because heart
`
`rate (HR) is a nonstationary signal and its variation may contain indicators of heart
`
`diseases. APPLE-1024, Abstract.
`
`36. By the Critical Date, it was known that HRV can be accurately
`
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
