IN THE UNITED STATES PATENT AND TRADEMARK OFFICE
`
`Albert et al.
`In re Patent of:
`U.S. Patent No.:
`10,638,941
`May 5, 2020
`Issue Date:
`Appl. Serial No.: 16/158,112
`Filing Date:
`October 11, 2018
`Title:
`DISCORDANCE MONITORING
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`Attorney Docket No.: 50095-0034IP1
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`DECLARATION OF DR. BERNARD R. CHAITMAN
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`1
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`APPLE 1003
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`APPLE-1003
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`Table of Contents
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`ASSIGNMENT .................................................................................................................. 4
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`QUALIFICATIONS ......................................................................................................... 4
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`SUMMARY OF CONCLUSIONS FORMED ............................................................... 6
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`BACKGROUND KNOWLEDGE ONE OF SKILL IN THE ART WOULD HAVE
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`HAD PRIOR TO THE PRIORITY DATE OF THE ’941 PATENT ........................... 7
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`LEGAL PRINCIPLES ..................................................................................................... 8
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`A.
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`B.
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`C.
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`CLAIM INTERPRETATION ................................................................................................... 8
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`PRIORITY .......................................................................................................................... 9
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`ANTICIPATION ................................................................................................................... 9
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`D. OBVIOUSNESS ................................................................................................................. 10
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` MATERIALS CONSIDERED ....................................................................................... 11
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` TECHNOLOGY OVERVIEW ...................................................................................... 18
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`A. ARRHYTHMIA ................................................................................................................. 18
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`B.
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`C.
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`ELECTROCARDIOGRAPHY (ECG) .................................................................................... 20
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`PHOTOPLETHYSMOGRAPHY (PPG) .................................................................................. 21
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`D. HEART RATE (HR) .......................................................................................................... 23
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`E.
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`HEART RATE VARIABILITY (HRV) ................................................................................. 23
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` OVERVIEW OF THE ’941 PATENT .......................................................................... 24
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` OVERVIEW OF THE PROSECUTION HISTORY .................................................. 27
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`APPLE-1003
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`INTERPRETATION OF THE ’941 PATENT CLAIMS............................................ 28
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`A.
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`“DISCORDANCE” (CLAIMS 1 AND 12) .............................................................................. 28
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`SUMMARY OF THE PRIOR ART .............................................................................. 30
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`A. OVERVIEW OF SHMUELI .................................................................................................. 30
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`B.
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`C.
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`OVERVIEW OF OSORIO .................................................................................................... 35
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`OVERVIEW OF LEE-2013 ................................................................................................. 39
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`D. OVERVIEW OF CHAN ....................................................................................................... 40
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` ANALYSIS OF SHMUELI AND OSORIO ................................................................. 42
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`A.
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`B.
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`C.
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`THE COMBINATION OF SHMUELI AND OSORIO ................................................................ 43
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`APPLICATION OF SHMUELI AND OSORIO TO CLAIM 1 ...................................................... 54
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`APPLICATION OF SHMUELI AND OSORIO TO CLAIMS 5 AND 7-11 .................................... 76
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`D. APPLICATION OF SHMUELI AND OSORIO TO CLAIM 12 .................................................... 82
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`E.
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`APPLICATION OF SHMUELI AND OSORIO TO CLAIMS 16 AND 18-23 ................................ 85
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` ANALYSIS OF SHMUELI, OSORIO, AND LEE-2013 ............................................. 87
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`A.
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`B.
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`C.
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`THE COMBINATION OF SHMUELI, OSORIO, AND LEE-2013 .............................................. 87
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`APPLICATION OF SHMUELI, OSORIO, AND LEE-2013 TO CLAIMS 2-4 AND 6 .................... 89
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`APPLICATION OF SHMUELI, OSORIO, AND LEE-2013 TO CLAIMS 13-15 AND 17 .............. 92
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` ANALYSIS OF SHMUELI, OSORIO, AND CHAN .................................................. 93
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`A.
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`B.
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`THE COMBINATION OF SHMUELI, OSORIO, AND CHAN .................................................... 93
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`APPLICATION OF SHMUELI, OSORIO, AND CHAN TO CLAIMS 10 AND 21 ......................... 95
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` CONCLUSION ............................................................................................................... 97
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`APPLE-1003
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`I, Dr. Bernard R. Chaitman, of St. Louis, Missouri, declare that:
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`1.
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`ASSIGNMENT
`I have been retained on behalf of Apple Inc. (“Apple” or “Petitioner”)
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`to offer technical opinions related to U.S. Patent No. 10,638,941 (“The ’941
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`patent”) (APPLE-1001). I understand that Apple is requesting that the Patent Trial
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`and Appeal Board (“PTAB” or “Board”) to institute an inter partes review (“IPR”)
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`proceeding of the ’941 patent.
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`2.
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`I have been asked to provide my independent analysis of the ’941
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`patent based on the prior art publications cited in this declaration.
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`3.
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`I am not and never have been, an employee of Apple. I received no
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`compensation for this declaration beyond my normal hourly compensation based
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`on my time actually spent analyzing the ’941 patent, the prior art publications cited
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`below, and issues related thereto, and I will not receive any added compensation
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`based on the outcome of any IPR or other proceeding involving the ’941 patent
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` QUALIFICATIONS
`I am over the age of 18 and am competent to write this declaration. I
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`4.
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`have personal knowledge, or have developed knowledge of these technologies
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`based upon education, training, or experience, of the matters set forth herein.
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`5.
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`I am an Emeritus Professor of Medicine, and Director of
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`Cardiovascular Research at St Louis University School of Medicine. I am also a
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`APPLE-1003
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`Board-Certified Cardiologist and have practiced Internal Medicine and
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`Cardiovascular Disease for four decades. I am currently licensed in the State of
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`Missouri and Florida. I also serve as the Chair for Clinical Event Committees and
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`Data and Safety Monitoring Boards for numerous clinical trials sponsored by
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`National Heart Lung and Blood Institute (NHLBI) and industry. I am currently a
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`member of the Editorial Board of nine journals that include Circulation, Journal of
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`the American College of Cardiology, and the European Heart Journal. I also
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`founded and am the Medical Director of St Louis University Core ECG Laboratory
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`that provides ECG analysis for numerous NHLBI and industry sponsored clinical
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`trials that test various treatment strategies.
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`6.
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`I received a Bachelor of Science degree in 1965 and a medical degree
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`1969, both from McGill University in Montreal, Canada. I completed my Internal
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`Medicine training at McGill University and Royal Victoria Hospital in 1972. I then
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`completed post-graduate training in Cardiovascular Diseases at the University of
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`Oregon (from 1972-1974) and University of Montreal (from 1974-1975).
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`7.
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`I have a long and distinguished career in academic medicine and have
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`published more than 400 peer-reviewed papers and more than 600 abstracts, book
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`chapters, and short communications. My areas of expertise in Cardiovascular
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`Medicine include rest and exercise ECG analysis, diagnostic noninvasive testing,
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`large scale multinational clinical trials testing different treatment strategies, and
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`drug development. I have received funding from the National Heart Lung and
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`Blood Institute (NHLBI) for more than 3 decades and also funding by the
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`Department of Defense. My experience is recognized internationally and I have
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`lectured abroad and published frequently with cardiologists in Europe.
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`8.
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`I have served as a consultant to the Food and Drug Administration on
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`ECG related issues, and the use of the rest and exercise ECG as a diagnostic
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`instrument. I also served as a committee member for the American Heart
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`Association, American College of Cardiology, and the European Society of
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`Cardiology in matters related to ECG analysis and the use of ECG analysis as a
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`diagnostic and prognostic tool. I served on grant review committees for the
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`NHLBI, the Veterans Administration Review Board, and the American Heart
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`Association.
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` SUMMARY OF CONCLUSIONS FORMED
`This Declaration explains the conclusions that I have formed based on
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`9.
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`my analysis. To summarize those conclusions:
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`
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`Ground 1: Based upon my knowledge and experience and my review of the
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`prior art publications in this declaration, I believe that claims 1, 5, 7-9, 11,
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`12, 16, 18-20, 22, and 23 of the ’941 patent are made obvious by Shmueli
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`and Osorio.
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`Ground 2: Based upon my knowledge and experience and my review of the
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`prior art publications in this declaration, I believe that claims 2-4, 6, 13-15,
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`and 17 of the ’941 patent are made obvious by the combination of Shmueli,
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`Osorio, and Lee-2013.
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`
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`Ground 3: Based upon my knowledge and experience and my review of the
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`prior art publications in this declaration, I believe that claims 10 and 21 of
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`the ’941 patent are made obvious by the combination of Shmueli, Osorio,
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`and Chan.
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` BACKGROUND KNOWLEDGE ONE OF SKILL IN THE ART
`WOULD HAVE HAD PRIOR TO THE PRIORITY DATE OF
`THE ’941 PATENT
`I have been informed that a person of ordinary skill in the art is a
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`10.
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`hypothetical person who is presumed to have the skill and experience of an
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`ordinary worker in the field at the time of the alleged invention. Based on my
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`knowledge and experience in the field and my review of the ’941 patent and file
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`history, I believe that a person of ordinary skill in the art in this matter would have
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`had at least a combination of Bachelor’s Degree (or a similar Master’s Degree, or
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`higher degree) in an academic area emphasizing health science, or a related field,
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`and two or more years of work experience with cardiac monitoring technologies
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`(e.g., as a cardiologist). Additional education or industry experience may
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`compensate for a deficit in one of the other aspects of the requirements stated
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`above.
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`11. My analysis and conclusions set forth in this declaration are based on
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`the perspective of a person of ordinary skill in the art having this level of
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`knowledge and skill as of the date of the alleged invention of the ’941 patent
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`(“POSITA”). Based on instruction from Counsel, I have applied May 13, 2015
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`(“Critical Date”), as the date of the alleged invention of the ’941 patent.
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`12. Based on my experiences, I have a good understanding of the
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`capabilities of a POSITA. Indeed, I have taught, mentored, advised, and
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`collaborated closely with many such individuals over the course of my career.
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` LEGAL PRINCIPLES
`13.
`I am not a lawyer and I will not provide any legal opinions in this IPR.
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`Although I am not a lawyer, I have been advised that certain legal standards are to
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`be applied by technical experts in forming opinions regarding the meaning and
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`validity of patent claims.
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`A. Claim Interpretation
`I understand that claim terms are generally given their plain and
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`14.
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`ordinary meaning based on the patent’s specification and file history as understood
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`by a person of ordinary skill in the art at the time of the purported invention. In that
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`regard, I understand that the best indicator of claim meaning is its usage in the
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`context of the patent specification as understood by a POSITA. I further
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`understand that the words of the claims should be given their plain meaning unless
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`APPLE-1003
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`that meaning is inconsistent with the patent specification or the patent’s history of
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`examination before the Patent Office. I also understand that the words of the
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`claims should be interpreted as they would have been interpreted by a POSITA at
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`the time of the invention was made (not today).
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`B.
`Priority
`I understand that a continuation application is a later-filed application
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`15.
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`that has the same disclosure (specification and figures) as an earlier filed
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`application to which the later-filed application claims priority. A continuation is
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`generally entitled to the same priority date as the later-filed application to which it
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`claims priority.
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`C. Anticipation
`I understand that a patent claim is invalid as anticipated if each and
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`16.
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`every element as set forth in the claim is found, either expressly or inherently
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`described, in a single prior art reference. I also understand that, to anticipate, the
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`reference must teach all of the limitations arranged or combined in the same way
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`as recited in the claim. I do not rely on anticipation in this declaration.
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`17. With respect to inherency, I understand that the fact that a certain
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`result or characteristic may occur or be present in the prior art is not sufficient to
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`establish the inherency of that result or characteristic. Instead, the inherent
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`characteristic must necessarily flow from the teaching of the prior art.
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`D. Obviousness
`I understand that a patent claim is invalid if the claimed invention
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`18.
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`would have been obvious to a person of ordinary skill in the field at the time of the
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`purported invention, which is often considered the time the application was filed.
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`Even if all of the claim limitations are not found in a single prior art reference that
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`anticipates the claim, the claim can still be invalid.
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`19. To obtain a patent, a claimed invention must have, as of the priority
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`date, been nonobvious in view of the prior art in the field. I understand that an
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`invention is obvious when the differences between the subject matter sought to be
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`patented and the prior art are such that the subject matter as a whole would have
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`been obvious at the time the invention was made to a person having ordinary skill
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`in the art.
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`20.
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`I understand that, to prove that prior art or a combination of prior art
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`makes a patent obvious it is necessary to: (1) identify the particular references that,
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`singly or in combination, make the patent obvious; (2) specifically identify which
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`elements of the patent claim appear in each of the asserted references; and (3)
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`explain a motivation, teaching, need, market pressure or other legitimate reason
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`that would have inspired a person of ordinary skill in the art to combine prior art
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`references to solve a problem.
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`21.
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`I also understand that certain objective indicia can be important
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`evidence regarding whether a patent is obvious or nonobvious. Such indicia
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`include:
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`Commercial success of products covered by the patent claims;
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`A long-felt need for the invention;
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`Failed attempts by others to make the invention;
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`Copying of the invention by others in the field;
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`Unexpected results achieved by the invention as compared to the closest
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`prior art;
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`Praise of the invention by the infringer or others in the field;
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`The taking of licenses under the patent by others;
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`Expressions of surprise by experts and those skilled in the art at the making
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`of the invention; and
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`The patentee proceeded contrary to the accepted wisdom of the prior art.
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`22. To the extent these factors have been brought to my attention, if at all,
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`I have taken them into consideration in rendering my opinions and conclusions.
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` MATERIALS CONSIDERED
`23. My analysis and conclusions set forth in this declaration are based on
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`my educational background and experiences in the field (see Section IV). Based on
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`my above-described experience, I believe that I am considered to be an expert in
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`the field. Also, based on my experiences, I understand and know of the capabilities
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`APPLE-1003
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`of persons of ordinary skill in the field during the early 1990s–2010s, and I taught,
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`participated in organizations, and worked closely with many such persons in the
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`field during that time frame.
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`24. As part of my independent analysis for this declaration, I have
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`considered the following: the background knowledge/technologies that were
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`commonly known to persons of ordinary skill in this art during the time before the
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`earliest claimed priority date for the ’941 patent; my own knowledge and
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`experiences gained from my work experience in the field of the ’941 patent and
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`related disciplines; and my experience in working with others involved in this field
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`and related disciplines.
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`25.
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`In addition, I have analyzed the following publications and materials:
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` U.S. Pat. No. 10,638,941 to Albert et. al (“the ’941 patent”) (APPLE-1001)
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` Excerpts from the Prosecution History of the ’941 patent (“the Prosecution
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`History”) (APPLE-1002)
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` PCT Patent Publication WO2012/140559 (“Shmueli”) (APPLE-1004)
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` U.S. Patent Publication 2014/0275840 (“Osorio”) (APPLE-1005)
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` Li Q, Clifford GD, “Signal quality and data fusion for false alarm reduction
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`in the intensive care unit,” J Electrocardiol. 2012 Nov-Dec; 45(6):596-603
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`(“Li-2012”) (APPLE-1006)
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` U.S. Patent Publication 2008/0004904 (“Tran”) (APPLE-1007)
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`APPLE-1003
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` U.S. Patent Publication 2014/0107493 (“Yuen”) (APPLE-1008)
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` U.S. Patent Publication 2015/0119725 (“Martin”) (APPLE-1009)
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` Lee J, Reyes BA, McManus DD, Mathias O, Chon KH. Atrial fibrillation
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`detection using a smart phone. Annu Int Conf IEEE Eng Med Biol Soc.
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`2012; 2012:1177-800 (“Lee-2013”) (APPLE-1011)
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` Tsipouras MG, Fotiadis DI. Automatic arrhythmia detection based on time
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`and time-frequency analysis of heart rate variability. Comput Methods
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`Programs Biomed. 2004 May; 74(2):95-108 (“Tsipouras-2004”) (APPLE-
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`1012)
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` Lu S, Zhao H, Ju K, Shin K, Lee M, Shelley K, Chon KH. Can
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`photoplethysmography variability serve as an alternative approach to obtain
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`heart rate variability information? J Clin Monit Comput. 2008 Feb; 22(1):23-
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`9 (“Lu-2008”) (APPLE-1013)
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` Selvaraj N, Jaryal A, Santhosh J, Deepak KK, Anand S. Assessment of heart
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`rate variability derived from finger-tip photoplethysmography as compared
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`to electrocardiography. J Med Eng Technol. 2008 Nov-Dec; 32(6):479-84
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`(“Selvaraj-2008”) (APPLE-1014)
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` Lu G, Yang F, Taylor JA, Stein JF. A comparison of photoplethysmography
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`and ECG recording to analyse heart rate variability in healthy subjects. J
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`Med Eng Technol. 2009; 33(8):634-41 (“Lu-2009”) (APPLE-1015)
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` Suzuki T, Kameyama K, Tamura T. Development of the irregular pulse
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`detection method in daily life using wearable photoplethysmographic sensor.
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`Annu Int Conf IEEE Eng Med Biol Soc. 2009; 2009:6080-3 (“Suzuki-
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`2009”) (APPLE-1016)
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` Reed MJ, Robertson CE, Addison PS. Heart rate variability measurements
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`and the prediction of ventricular arrhythmias. QJM. 2005 Feb; 98(2):87-95
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`(“Reed-2005”) (APPLE-1017)
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` Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate
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`of heart rate variability? A review on studies comparing
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`photoplethysmographic technology with an electrocardiogram. Int J Cardiol.
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`2013 Jun 5; 166(1):15-29 (“Schafer-2013”) (APPLE-1018)
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` K. Douglas Wilkinson, “The Clinical Use of the Sphygmomanometer,” The
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`British Medical Journal, 1189-90 (Dec. 27, 1924) (“Wilkinson”) (APPLE-
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`1019)
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` U.S. Pat. No. 6,095,984 (“Amano”) (APPLE-1020)
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` B.K. Bootsma et. al, “Analysis of R-R intervals with atrial fibrillation at rest
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`and during exercise.” Circulation 1970; 41:783-794 (“Bootsama-1970”)
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`(APPLE-1021)
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` Frits L. Meijler and Fred H. M. Wittkampf, “Role of the Atrioventricular
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`Node in Atrial Fibrillation” Atrial Fibrillation: Mechanisms and
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`Management, 2nd ed. 1997 (“Meijler-1997”) (APPLE-1022)
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` Heart Diseases, Definition of Heart Diseases by Merriam-Webster (APPLE-
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`1023)
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` Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim CM, Suri JS. Heart
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`rate variability: a review. Med Biol Eng Comput. 2006 Dec; 44(12):1031-51
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`(“Acharya-2006”) (APPLE-1024)
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` Saime Akdemir Akar, Sadık Kara, Fatma Latifoğlu, Vedat Bilgiç. Spectral
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`analysis of photoplethysmographic signals: The importance of
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`preprocessing. Biomedical Signal Processing and Control, 2013; 8(1):16-22
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`(“Akar-2013”) (APPLE-1025)
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` U.S. Patent Publication No. 2012/0203491 (“Sun”) (APPLE-1031)
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` U.S. Patent No. 9,808,206 (“Zhao”) (APPLE-1032)
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` Kleiger RE, Stein PK, Bigger JT Jr. Heart rate variability: measurement and
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`clinical utility. Ann Noninvasive Electrocardiol. 2005 Jan; 10(1):88-101
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`(“Kleiger-2005”) (APPLE-1033)
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` Chen Z, Brown EN, Barbieri R. Characterizing nonlinear heartbeat
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`dynamics within a point process framework. IEEE Trans Biomed Eng. 2010
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`Jun; 57(6):1335-47 (“Chen-2010”) (APPLE-1034)
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` Karvonen, J., Vuorimaa, T. Heart Rate and Exercise Intensity During Sports
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`Activities. Sports Medicine 5, 303–311 (1988) (“Karvonen-1988”) (APPLE-
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`1035)
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` Yu C, Liu Z, McKenna T, Reisner AT, Reifman J. A method for automatic
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`identification of reliable heart rates calculated from ECG and PPG
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`waveforms. J Am Med Inform Assoc. 2006 May-Jun; 13(3):309-20 (“Yu-
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`2006”) (APPLE-1036)
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` Tavassoli, & Ebadzadeh, Mohammad & Malek,. (2012). Classification of
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`cardiac arrhythmia with respect to ECG and HRV signal by genetic
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`programming. Canadian Journal on Artificial Intelligence, Machine
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`Learning and Pattern Recognition. 3. 1-13 (“Tavassoli-2012”) (APPLE-
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`1038)
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` Asl BM, Setarehdan SK, Mohebbi M. Support vector machine-based
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`arrhythmia classification using reduced features of heart rate variability
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`signal. Artif Intell Med. 2008 Sep;44(1):51-64 (“Asl-2008”) (APPLE-1039)
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` Yaghouby F., Ayatollahi A. (2009) An Arrhythmia Classification Method
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`Based on Selected Features of Heart Rate Variability Signal and Support
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`Vector Machine-Based Classifier. In: Dössel O., Schlegel W.C. (eds) World
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`Congress on Medical Physics and Biomedical Engineering, September 7 -
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`12, 2009, Munich, Germany. IFMBE Proceedings, vol 25/4. Springer,
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`Berlin, Heidelberg (“Yaghouby-2009”) (APPLE-1040)
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` Dallali, Adel & Kachouri, Abdennaceur & Samet, Mounir. (2011).
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`Integration of HRV, WT and neural networks for ECG arrhythmias
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`classification. ARPN Journal of Engineering and Applied Sciences. VOL. 6.
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`74-82 (“Dallali-2011”) (APPLE-1041)
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` Sajda P. Machine learning for detection and diagnosis of disease. Annu Rev
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`Biomed Eng. 2006;8:537-65 (“Sajda-2006”) (APPLE-1042)
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` Aarron Smith. Smartphone Ownership – 2013 Update. Pew Research
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`Center. June 5, 2013 (“Smith-2013”) (APPLE-1043)
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` C. Narayanaswami and M. T. Raghunath, “Application design for a smart
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`watch with a high resolution display,” Digest of Papers. Fourth International
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`Symposium on Wearable Computers, 2000, pp. 7-14 (“Narayanaswami-
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`2000”) (APPLE-1044)
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` Thong, YK & Woolfson, M & Crowe, JA & Hayes-Gill, Barrie & Challis,
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`Richard. (2002). Dependence of inertial measurements of distance on
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`accelerometer noise,” Meas. Measurement Science and Technology. 13.
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`1163 (“Thong-2002”) (APPLE-1045)
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` AliveCor’s ITC Complaint filed on April 20, 2021 in “Certain Wearable
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`Electronic Devices With ECG Capability and Components Thereof” ITC-
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`337-3545-20210420 (“ITC Complaint”) (APPLE-1046)
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` Marcovitch, Harvey. Black’s Medical Dictionary. London: A. & C. Black,
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`2005 (APPLE-1047)
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`APPLE-1003
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` U.S. Pat. No. 7,894,888 (“Chan”) (APPLE-1048)
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` Discordance, Definition of Discordance by Merriam-Webster Dictionary
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`(APPLE-1049)
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` Strath SJ, Swartz AM, Bassett DR Jr, et al. Evaluation of heart rate as a
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`method for assessing moderate intensity physical activity. Medicine and
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`Science in Sports and Exercise. 2000 Sep; 32 (9 Suppl):S465-70 (“Strath-
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`2000”) (APPLE-1050)
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` U.S. Provisional Application No. 61/895,995 (“Martin Provisional”)
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`(APPLE-1054)
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` AliveCor’s District Court Complaint filed on May 25, 2021 in AliveCor, Inc.
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`v. Apple Inc., 3:21-cv-03958 (N.D.Cal. May 25, 2021) (“Antitrust
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`Complaint”) (APPLE-1055)
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` TECHNOLOGY OVERVIEW
`A. Arrhythmia
`26. Cardiac arrhythmias refer to a group of disorders of the heart rate or
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`heart rhythm. Types of arrhythmias include “atrial fibrillation and supraventricular
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`tachycardia.” APPLE-1001, 1:17-22. Arrhythmic activity can include the heart
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`beating too fast (tachycardia), too slow (bradycardia), or irregularly (variations in
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`heart rate). While tachycardia and bradycardia may be diagnosed based on heart
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`rate, variations in heart rate (e.g., atrial fibrillation) are diagnosed based on heart
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`APPLE-1003
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`rate variability (HRV) analysis. As a hypothetical example, when the patient goes
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`into an atrial fibrillation, a common rhythm disturbance, an HRV analysis would
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`detect the irregularity, but the heart rate may of the patient may stay with the
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`normal range of below 100 bpm. For example, Tsipouras-2004 discloses detecting
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`arrhythmia by training a machine learning algorithm (e.g., neural networks) with
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`HRV data. APPLE-1012, Abstract. Tsipouras-2004 states that “Our study is based
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`on the analysis of the RR-interval duration so the proposed method is capable of
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`detecting arrhythmia types that produce irregularities on the RR intervals, the HRV
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`or the heart rhythm.” APPLE-1012, 106. Compared to looking at the raw heart rate
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`signal (e.g., ECG), HRV analysis is more robust because it involves extracting the
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`RR intervals and is less affected by noise. APPLE-1039, 52.
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`27. Since 1903, different detection techniques have been employed to
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`detect irregular pulse rhythms or irregular heartbeats. See, e.g., APPLE-1019
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`(describing use of a sphygmomanometer as early as 1924 to make “obvious the
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`variation in the sound heard over the artery” to identify pulse irregularity);
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`APPLE-1020, 9:12-28 (describing use of “plethysmogram” in 1997 to detect
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`arrhythmia). By 1977, both detecting possible atrial fibrillation using irregular
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`pulse rhythms or heartbeats and techniques to quantitatively characterize
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`irregularities were well-known. By 2009, examples of known arrhythmia detection
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`techniques included: neural networks, wavelet transforms, support vector
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`APPLE-1003
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`machines, fuzzy logic and rule-based algorithms. APPLE-1040, p. 1928. A
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`POSITA would have understood that many of these techniques (e.g., support
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`vector machines, neural networks) are machine learning algorithms.
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`B.
`Electrocardiography (ECG)
`28. Eletrocardiography (ECG) measures the electrical activity of the
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`heart, which can be indicative of various heart diseases. APPLE -1004, 1:14-17.
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`ECG recording uses Ag/AgCl electrodes attached to specific anatomical positions.
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`APPLE-1015, 635. Clinical ECG recording commonly uses 12 leads for
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`determination of the complex temporal dynamics of each cardiac cycle and to
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`detect abnormal patterns in the ECG waveform. Id.
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`29. An ECG represents electrical activity of the heart based on
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`depolarization and repolarization of the atria and ventricles, which typically show
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`up as five distinct waves on the ECG readout—P-wave, Q-wave, R-wave, S-wave,
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`and T-wave. A QRS complex is a combination of the Q, R, and S waves occurring
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`in succession and represents the electrical impulse of a heartbeat as it spreads
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`through the ventricles during ventricular depolarization. An R-R interval represents
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`a time elapsed between successive R-waves of a QRS complex of the ECG that
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`occur between successive heart beats. If R-R interval durations over a time period
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`are close to one another in value, then ventricular rhythm is understood to be
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`“regular.” APPLE-1022, 110-112. In contrast, if there are significant variations in
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`APPLE-1003
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`the R-R interval durations over a time period, then the ventricular rhythm is
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`understood to be “irregular.” Id.
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`30.
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`In conventional clinical practice, ECG and telemetry are used at a
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`hospital to diagnose cardiac arrhythmias. APPLE-1016, p. 6080. As an irregular
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`heartbeat caused by arrhythmia does not necessarily occur during examination at
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`the hospital, a Holter ECG has been used for measuring one or more leads of an
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`ECG in daily life. Id. A Holter ECG device is not ideal because it still requires
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`attaching some electrodes to the patients’ chest. Id. In addition, a Holter device
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`typically only monitors the patient for a certain period (e.g., 24 hours, 48 hours or
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`72 hours) and thus it may not detect a cardiac arrhythmia if it does not occur
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`during the monitoring period. APPLE-1004, 1:26-2:3.
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`C.
`Photoplethysmography (PPG)
`31. Photoplethysmography (PPG) is a simple noninvasive optical
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`technique for monitoring beat-to-beat relative blood volume changes in the
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`microvascular bed of peripheral tissues. APPLE-1014, 479. PPG is sometimes also
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`referred to as blood oxygen saturation, pulse oximeter, oximetry, and SpO2.
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`APPLE-1004, 7:25-27. Its basic principle requires a light source to illuminate
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`subcutaneous tissue and a photo detector with spectral characteristics matching
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`those of the light source. APPLE-1018, 16.
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`32. As the pulse period derived from PPG data is directly related to
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`APPLE-1003
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`cardiac activity, the information derived from RR intervals of ECG can also be
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`derived from the pulse period of a PPG reading. APPLE-1014, p. 480. This is
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`because under normal conditions, the electrical impulse of the heart (ECG)
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`stimulates a cardiac contraction resulting in a spread of the pulsatile wave of blood
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`to the periphery (PPG). APPLE-1014, p. 480. A PPG signal includes information
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`about both heart rate and heart rate variability. APPLE-1025, p. 16. Many studies
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`verify the high correlation between RR intervals (RRI) obtained from ECG and PP
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`intervals (PPI) obtained from PPG. APPLE-1025, p. 16; APPLE-1018, Fig. 1.
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`APPLE-1018, Fig. 1
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`Compared to ECG, PPG is attractive because it only requires attaching a single
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`sensor to the hand of the user. APPLE-1018, 16.
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`APPLE-1003
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`D. Heart Rate (HR)
`33. Heart rate (HR) is the reciprocal of the RR interval and measures the
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`number of heartbeats per unit of time. APPLE-1034, 5. It was long recognized that
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`an individual’s heart rate varies with his/her activity level (exercise intensity).
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`APPLE-1035, 303. As discussed above, an individual’s heart rate and the
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`corresponding RR interval can be determined using either ECG or PPG data.
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`APPLE-1036, Abstract, Fig. 1.
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`E. Heart Rate Variability (HRV)
`34. Heart rate variability (HRV) is defined as the variation of RR intervals
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`with respect to time and reflects beat-to-beat heart rate (HR) variability. APPLE-
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`1025, 16; APPLE-1012, 95. HRV analysis is an important tool in cardiology for
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`the diagnosis of various types of arrhythmia. APPLE-1012, Abstract, 95-96
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`(“Therefore, HRV analysis became a critical tool in cardiology for the diagnosis of
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`heart diseases.”). HRV analysis has become popular because heart rate (HR) is a
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`nonstationary signal and its variation may contain indicators of heart diseases.
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`APPLE-1024, Abstract.
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`35. By the Critical Date, it was known that HRV can be accurately
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`determined based on either ECG data or PPG data. See, e.g., APPLE-1013,
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`Abstract (“Our results demonstrate that the parameters of PPGV are highly
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`correlated with the parameters of HRV.”); APPLE-1014, Abstract (“HRV can also
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`APPLE-1003
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`be reliably estimated from the PPG based PP interval method.”); APPLE-1015,
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`Abstract (“Our results confirm that PPG provides accurate interpulse intervals
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`from which HRV measures can be accurately derived in healthy subjects under
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`ideal conditions, suggesting this technique may prove a practical alternative to
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`ECG for HRV analysis.”). Kleiger 2005 discloses that methods for quantifying
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`HRV are categorized as: time domain, spectral or frequency domain, geometric,
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`and nonlinear. APPLE-1033, 88. For example, SDNN, the standard deviation of all
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`normal RR intervals during a 24-hour period, is a commonly used time domain
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`measure of HRV. Id.
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`36.
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`If the RR intervals