Filed on behalf of: Fitbit, Inc.
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`Paper No. ____
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`UNITED STATES PATENT AND TRADEMARK OFFICE
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`FITBIT, INC.
`Petitioner
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`v.
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`BODYMEDIA, INC.
`Patent Owner
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`U.S. Patent No. 8,961,413
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`DECLARATION OF DR. MARK A. MUSEN
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`FITBIT EXHIBIT 1002
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`TABLE OF CONTENTS
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`IX. 
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`Page
`INTRODUCTION ............................................................................................................. 1 
`I. 
`QUALIFICATIONS .......................................................................................................... 1 
`II. 
`SUMMARY OF OPINIONS ............................................................................................. 5 
`III. 
`LEVEL OF ORDINARY SKILL IN THE ART ............................................................... 6 
`IV. 
`BACKGROUND OF THE RELEVANT FIELD .............................................................. 7 
`V. 
`BACKGROUND OF THE ’413 PATENT ...................................................................... 10 
`VI. 
`VII.  CLAIM CONSTRUCTION ............................................................................................. 13 
`A. 
`“third party data source” and “third party input data” (claim 8) .......................... 14 
`VIII.  PRIOR ART ..................................................................................................................... 14 
`Billon .................................................................................................................... 14 
`A. 
`Wyatt .................................................................................................................... 17 
`B. 
`Pardey .................................................................................................................. 18 
`C. 
`Tuorto ................................................................................................................... 20 
`D. 
`Amano .................................................................................................................. 21 
`E. 
`CERTAIN REFERENCES TEACH OR SUGGEST ALL THE CLAIMED
`FEATURES OF CLAIMS 1–12 OF THE ’413 PATENT............................................... 23 
`Ground 1: Billon Teaches All the Features of Claims 1–4 and 6–11 .................. 24 
`A. 
`1. 
`Claim 1 ..................................................................................................... 24 
`2. 
`Claim 2 ..................................................................................................... 30 
`3. 
`Claim 3 ..................................................................................................... 30 
`4. 
`Claim 4 ..................................................................................................... 31 
`5. 
`Claim 6 ..................................................................................................... 33 
`6. 
`Claim 7 ..................................................................................................... 34 
`7. 
`Claim 8 ..................................................................................................... 35 
`8. 
`Claim 9 ..................................................................................................... 36 
`9. 
`Claim 10 ................................................................................................... 37 
`10. 
`Claim 11 ................................................................................................... 39 
`Ground 2: Billon and Wyatt Teach or Suggest All the Features of Claim 5 ........ 40 
`1. 
`Claim 5 ..................................................................................................... 40 
`Ground 3: Billon and Pardey Teach or Suggest All the Features of Claim
`12.......................................................................................................................... 44 
`1. 
`Claim 12 ................................................................................................... 44 
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`B. 
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`C. 
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`X. 
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`D. 
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`Ground 4: Billon and Tuorto Teach or Suggest All the Features of Claims
`4 and 10 ................................................................................................................ 49 
`1. 
`Claim 4 ..................................................................................................... 49 
`2. 
`Claim 10 ................................................................................................... 51 
`Ground 5: Billon and Amano Teach or Suggest All the Features of Claim 8 ...... 52 
`1. 
`Claim 8 ..................................................................................................... 52 
`CONCLUSION ................................................................................................................ 56 
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`E. 
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`I, Mark A. Musen, declare as follows:
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`I.
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`INTRODUCTION
`1.
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`I have been retained by Fitbit Inc. (“Petitioner”) as an independent
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`expert consultant in this proceeding before the United States Patent and Trademark
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`Office regarding U.S. Patent No. 8,961,413 (“the ’413 patent”), which I understand
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`is labeled as Ex. 1001 in this proceeding. I have been asked to consider, among
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`other things, whether certain references teach or suggest the features recited in
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`claims 1–12 of the ’413 patent. My opinions are set forth below.
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`2.
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`I am being compensated at my normal consulting rate for the time I
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`spend on this matter. No part of my compensation is dependent on the outcome of
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`this proceeding or any other proceeding involving the ’413 patent. I have no other
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`interest in this proceeding.
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`II. QUALIFICATIONS
`3.
`I am a Professor of Medicine (Biomedical Informatics) at Stanford
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`University, where I have served on the faculty since 1988.
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`4. My undergraduate degree (1977) and medical degree (1980) are from
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`Brown University. During my time at Brown, I was a research assistant in the
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`Laboratory for Advanced Methods in Biological Data Acquisition, where I
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`programmed computers to control laboratory instruments and to acquire both
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`analog and digital signals from
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`those
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`instruments
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`to perform biological
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`experiments. I subsequently pursued clinical training in Internal Medicine at
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`Stanford University Hospital, and obtained my license to practice medicine (1981)
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`and became certified by the American Board of Internal Medicine (1983).
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`5.
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`In 1983, I entered the graduate program at Stanford University in
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`Medical Information Sciences (now called “Biomedical Informatics”), where I
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`received my Ph.D. in 1988. In graduate school, I took courses in databases,
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`programming languages, artificial intelligence, algorithms and data structures,
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`clinical decision-support systems, decision analysis, and multivariate statistics.
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`My dissertation research concerned new methods for the engineering of clinical
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`decision-support systems, and led to a line of investigation that I have continued
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`into the present time.
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`6.
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`At Stanford, I am the Director of the Stanford Center for Biomedical
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`Informatics Research. The faculty members in the Center teach students and
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`conduct research related to all aspects of the use of information technology in
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`medicine and healthcare—including new methods for analysis of data from
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`electronic health records, new architectures for clinical decision support, new
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`algorithms for interpreting biomedical images, and the use of genomic data to
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`inform clinical diagnosis. Faculty members in the Center also include Stanford
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`physicians responsible for operational aspects of all healthcare information
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`technology—both server-based and mobile—at Stanford Health Care and Stanford
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`Children’s Health.
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`7.
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`In my own research program, I am principal investigator of the Center
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`for Expanded Data Annotation and Retrieval, one of the eleven centers of
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`excellence that the National Institutes of Health (NIH) have supported as part of
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`the Big Data to Knowledge (BD2K) Program since 2014. I chair the BD2K
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`Centers Steering Committee and the BD2K Metadata Working Group. I am also
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`principal investigator of the National Center for Biomedical Ontology, one of the
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`seven National Centers for Biomedical Computing created by the NIH in 2005.
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`8.
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`At Stanford, I teach students in the Biomedical Informatics graduate
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`program. I offer a large classroom-based course entitled, “Modeling Biomedical
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`Systems,” where I teach methods of conceptual modeling, object-oriented design,
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`and the engineering of computing systems that assist users with medical decision
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`making.
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`9.
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`I have just completed a four-year term as a member of the National
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`Advisory Council on Biomedical Imaging and Bio-engineering. In this capacity, I
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`participated in numerous policy discussions regarding programs at the National
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`Institute of Biomedical Imaging and Bio-engineering (NIBIB) of the NIH, where a
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`major thrust is the use of mobile technology to aid healthcare in the developing
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`world. Along with other members of the Council, I provided a second level of peer
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`review for grant applications to the Institute that had already been refereed by
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`national experts in bio-engineering.
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`10.
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`I have received many honors and awards for my research. I have been
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`elected a Fellow of the American College of Medical Informatics (1989); I
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`received the Donald A. B. Lindberg Award for Innovation in Informatics from the
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`American Medical Informatics Association (2006); members of my research team
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`and I received the “Ten Years” Award from the Semantic Web Science
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`Association in 2014. Within the academic medicine community, I have been
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`named a Fellow of the American College of Physicians (1990) and elected to
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`membership in both the American Society for Clinical Investigation (1997) and the
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`Association of American Physicians (2010). I have served as scientific program
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`chair for several international conferences, including the American Medical
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`Informatics Association Annual Symposium (2003), the International Semantic
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`Web Conference (2005), and the International Conference on Knowledge Capture
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`(2011). I am the founding co-editor-in-chief of the journal Applied Ontology.
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`11.
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`I serve as a consultant to the American Medical Association, to the
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`World Health Organization, to other academic organizations, and to industry. My
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`curriculum vitae documents more than 400 scientific publications in journals,
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`books, and peer-reviewed conferences, as well as invited presentations on my work
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`in biomedical information technology at numerous international meetings.
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`III. SUMMARY OF OPINIONS
`12. All of the opinions contained in this Declaration are based on the
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`documents I reviewed and my knowledge and professional judgment. In forming
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`the opinions expressed in this Declaration, I reviewed the documents mentioned in
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`this Declaration, including the ’413 patent (Ex. 1001), the prosecution history file
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`of the ’413 patent (Ex. 1003), U.S. Patent Nos. 7,689,437 (“the ’437 patent”) and
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`6,605,038 (“the ’038 patent”) (Exs. 1005 and 1006), the prosecution history file of
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`U.S. Application No. 10/638,588 (Ex. 1008), European patent EP 0 681 447 to
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`Billon et al. (“Billon”) and a certified translation in English thereof (Ex. 1009),
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`U.S. Patent No. 5,907,282 to Tuorto et al. (“Tuorto”) (Ex. 1010), U.S. Patent No.
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`6,078,549 to Wyatt et al. (“Wyatt”) (Ex. 1011), U.S. Patent No. 5,999,846 to
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`Pardey et al. (“Pardey”) (Ex. 1012), U.S. Patent No. 6,030,342 to Amano et al.
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`(“Amano”) (Ex. 1004), the prosecution history file of pending inter partes
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`reexamination control nos. 95/002,371, 95/002,376, and 95/002,354 (Exs. 1013–
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`1015) involving the ’437, ’707, and ’038 patents, respectively, a portion of a
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`treatise by Gilad J. Kuperman et al. entitled “HELP: A Dynamic Hospital
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`Information System” (Ex. 1016), a journal article by Norman J. Holter entitled
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`“New Method for Heart Studies: Continuous Electrocardiography of Active
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`Subjects Over Long Periods is Now Practical” (Ex. 1017), and a portion of a
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`treatise edited by Jan van Bemmel and myself, “Handbook of Medical
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`Informatics” (Ex. 1018). My opinions are additionally guided by my appreciation
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`of how a person of ordinary skill in the art would have understood the claims of the
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`’413 patent at the time of the alleged invention, which I have been asked to assume
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`is June 16, 2000.
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`13. Based on my experience and expertise, it is my opinion that certain
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`references teach or suggest all the features recited in claims 1–12 of the ’413
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`patent.
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`IV. LEVEL OF ORDINARY SKILL IN THE ART
`14. At the time of the alleged invention, in June 2000, a person of
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`ordinary skill in the art would have had at least two years of relevant college-level
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`coursework in an engineering field with one to two years of post-education
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`relevant work experience.
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`15.
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`In determining the level of ordinary skill, I have been asked to
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`consider, for example, the types of problems encountered in the art, prior solutions
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`to those problems, the rapidity with which innovations are made, the sophistication
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`of the technology, and the educational level of active workers in the field. Active
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`workers in the field would have had at least several years of college-level
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`coursework in a relevant engineering field, as noted above. Depending on the level
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`of education, it would have taken between 1–2 years for a person to become
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`familiar with the problems encountered in the art and to become familiar with the
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`prior and current solutions to those problems.
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`16.
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`In my capacity as a professor at Stanford University, a large
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`proportion of the students whom I train and supervise would also be considered
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`persons of ordinary skill in the art under the above level of skill during the relevant
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`timeframe.
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`17. A person of ordinary skill in the art in June 2000 would be familiar
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`with the general use of sensors to measure physiological data in a medical context,
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`database and computer science concepts relating to the storage and retrieval of
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`those data for processing and analysis, and have at least some experience relating
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`to the use of physiological sensors in wearable medical devices.
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`V. BACKGROUND OF THE RELEVANT FIELD
`18. The disclosures in the ’413 patent reflect trends in health information
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`technology that have been progressing since the advent of clinical computing in the
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`1960s. For decades, there has been increasing interest in making the collection of
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`physiological data less intrusive, in facilitating the analysis of those data in a
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`location remote from the source of data collection, in relying on patients
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`themselves to assist in data entry, and in providing analyses that will benefit
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`patients directly. The current explosion of activity in “mHealth” in general and in
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`wearable physiological sensors in particular can be traced to ideas that predate the
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`’413 patent—and even the Internet—by dozens of years.
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`19. Some of the very first experiments in clinical computing in the United
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`States were performed in the early 1960s by Homer Warner and his colleagues at
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`LDS Hospital in Salt Lake City, Utah. In 1964, Warner led what is thought to be
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`the first demonstration of remote signal acquisition in the cardiac catheterization
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`laboratory. See Gilad J. Kuperman, Reed M. Gardner, and T. Allan Pryor, HELP:
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`A Dynamic Hospital Information System at 5 (1991) (Ex. 1016). Warner’s system
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`acquired physiological signals from sensors that physicians inserted via catheters
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`or that they placed on the patient’s skin, converted those analog signals to digital
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`form, stored the data in a database, and interpreted the signals automatically to
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`offer patient-specific, situation-specific analyses.
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`20. As technology advanced, the natural objective was to take advantage
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`of those advances to facilitate acquisition of signals in less intrusive ways. The
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`increasing use of wearable sensors and of wireless communication was a well-
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`known way to achieve this goal. In 1961, Holter demonstrated that the acquisition
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`and analysis of ECG signals could be achieved “by the use of long-period,
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`continuous recording of heart potentials with a portable, self-contained
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`instrument.” Norman J. Holter, New Method for Heart Studies: Continuous
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`Electrocardiography of Active Subjects Over Long Periods is Now Practical, 134
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`Science 1214, 1220 (1961) (Ex. 1017). For more than 50 years, people have worn
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`sensors such as Holter monitors to enable continuous acquisition and interpretation
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`of ECG signals, EEG signals, and other data. Such devices routinely have input
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`methods that allow their users to create time-stamped annotations to the signals,
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`denoting life events (such as going to bed or taking medication) or the onset of
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`subjective symptoms (such as palpitations). Over time, Holter monitors and other
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`wearable “telematic” devices have become smaller, more portable, more
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`sophisticated in their capabilities, and more integrated into the networked clinical
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`computing infrastructure of health-care organizations and of patient homes.
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`21.
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`In 1997, when Professor Jan van Bemmel and I published our
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`introductory Handbook of Medical Informatics, the notion of physiological sensors
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`that would communicate signals
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`to remote computers wirelessly was a
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`commonplace idea. See Jan van Bemmel and Mark A. Musen, Handbook of
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`Medical Informatics (1997) (Ex. 1018). In Chapter 2 of the Handbook, entitled
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`“Information and Communication,” we describe a routine scenario where “[i]n
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`some instances it might be of interest to fix a wireless transmitter on the body (e.g.,
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`in the case of Holter monitoring . . .) to implant a transducer or even a transmitter
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`in the body (e.g., to measure intracranial pressure).” Id. at 22. At the time when the
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`technology presented in the ’413 patent was first described, there was already an
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`established industry engaged in the development of wearable devices that had
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`physiological sensors. It was considered natural by those in the art for such devices
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`to communicate wirelessly with remote computers that could analyze, display, and
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`archive the sensor data. It also was taken for granted by those in the art that such
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`wearable devices could assist individuals or clinicians with the evaluation and
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`management of health-related situations.
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`VI. BACKGROUND OF THE ’413 PATENT
`22. The ’413 patent describes a system that monitors an individual’s
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`physiological information using sensors, and stores and reports that information in
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`various ways, as shown in Figure 1 below. See, e.g., Ex. 1001 at 4:18–7:31 and
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`7:32–10:49.
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`23. The system includes sensor device 10, which the ’413 patent explains
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`is preferably worn on the user’s body. Id. at 4:31–36. This device includes one or
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`more sensors in proximity to the individual that generate signals representative of
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`physiological characteristics of the individual, such as pulse rate, skin temperature,
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`core body temperature, and activity level. Id. at 4:40–55 and Table 1. These
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`physiological characteristics of the individual are referred to as “physiological
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`parameters” by the ’413 patent. Id. at 4:46–55. Data indicative of these
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`physiological parameters either can be taken from the signals themselves, or can be
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`based on the signals and “calculated by [a] microprocessor.” Id. at 4:55–60.
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`24. According to the ’413 patent, the sensors and the methods of
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`generating signals representative of these physiological parameters using the
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`sensors were “well known.” Id. at 4:60–65. Table 1 of the ’413 patent (excerpted
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`below) provides examples of these physiological parameters and the corresponding
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`sensors used to generate data representative of those parameters.
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`25. The ’413 patent explains that a microprocessor in the device 10 can
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`“summarize and analyze the data.” Id. at 5:46–47. By doing so, the sensor device
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`10 can “derive information relating to an individual’s physiological state” based on
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`the data collected from the sensors. Id. at 6:40–43. The ’413 patent explains that
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`this derived information can include, for example, “sleep onset/wake” information,
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`which can be derived from sensor data that may include “[b]eat-to-beat variability,
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`heart rate, pulse rate, respiration rate, skin temperature, core temperature, heat
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`flow, galvanic skin response, EMG, EEG, EOG, blood pressure, [and/or] oxygen
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`consumption.” Id. at Table 2. As with the generation of signals representative of
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`physiological parameters using various sensors, the ’413 patent explains that such
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`methods of programming a microprocessor to derive information relating to an
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`individual’s physiological status from the sensor signals were “known.” Id. at
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`6:43–46. Table 2 of the ’413 patent (excerpted below) provides examples of
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`information relating to an individual’s physiological state that can be derived—
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`which is referred to interchangeably as “derived information” or “derived
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`parameters” by the ’413 patent—and the types of physiological parameters that
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`“can be used” to derive such information.
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`26. The ’413 patent provides no specific algorithms or methodologies for
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`deriving any of the exemplary physiological status data. Rather, the ’413 patent
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`explains that microprocessor 10 “is programmed to derive such information using
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`known methods based on the data indicative of one or more physiological
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`parameters.” Id. at 6:44–46.
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`27. The ’413 patent explains that status information derived by the
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`microprocessor can be sent to memory 22 in the sensor device 10 for storage. Id. at
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`7:58–61 and Fig. 2. These stored data, or data obtained in real-time, can be
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`uploaded to a remote “central monitoring unit 30” and stored in a database for later
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`processing and presentation to a user via a communications network such as the
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`Internet. Id. at 8:19–23 and 8:30–32.
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`VII. CLAIM CONSTRUCTION
`28.
`I understand that in this proceeding, a claim receives its broadest
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`reasonable construction in light of the specification of the patent in which it
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`appears. I also understand that in these proceedings, any term that is not construed
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`should be given its plain and ordinary meaning under the broadest reasonable
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`construction. I have followed these principles in my analysis below.
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`A.
`“third party data source” and “third party input data” (claim 8)
`29. Claim 8 recites a “third party data source in electronic communication
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`with said transceiver unit which third-party data source delivers third party input
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`data in an electronic input signal.” Ex. 1001 at 27:6–11.
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`30.
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`I understand that Petitioner has offered that the broadest reasonable
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`construction of “third party data source” should encompass a “separate device from
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`the processing unit within the housing” and the broadest reasonable construction of
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`“third party input data” should encompass “data obtained from the separate
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`device.” See Ex. 1003 at 436–37; Ex. 1001 at 9:18–49 and 9:67–10:22. I have used
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`this construction unless otherwise noted, and agree that this construction is
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`consistent with the applicant’s characterization of the term during prosecution of
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`the ’413 patent. Id.
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`VIII. PRIOR ART
`A. Billon
`31. Billon teaches a device that can “specifically recognize and quantify
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`sleep phases” of an individual by “surveying several physiological data elements
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`simultaneously.” Ex. 1009 at ¶¶ 0023–0024.1 Billon teaches that this device
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`includes a “first sensor adapted for placement in contact with a subject’s skin,
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`which reacts to changes in said subject’s instantaneous blood pressure, delivering a
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`signal representative of said instantaneous blood pressure,” a “second sensor
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`isolated from said subject’s skin and reacting to said subject’s movements,
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`delivering a signal representative of said movements,” and computational
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`components for “process[ing] and analyz[ing]” signals from the sensors. Id. at
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`¶ 0025. The sensors and computational components are mounted “in an
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`autonomous portable housing” worn by an individual, such as in a “wristwatch.”
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`Id. at ¶¶ 0051, 0055, and Fig. 4.
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`32. Billon teaches that the first sensor is a piezoelectric sensor that detects
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`changes in pressure caused by the passage of blood through veins in the
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`1 References herein are to the certified translation of Billon included in Ex. 1009.
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`individual’s wrist—which Billon also describes as arterial
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`tension—and
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`automatically generates a signal representative of the individual’s blood pressure.
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`Id. at ¶¶ 0091 and 0094. This signal is further processed to automatically generate
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`signals representative of the individual’s heart rate, respiratory amplitude, and
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`respiratory frequency. Id. at ¶ 0083.
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`33. Billon teaches that the second sensor is a piezoelectric sensor that
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`detects movements of the individual’s wrist and automatically generates a signal
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`representative of these movements—which Billon also describes as actimetric
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`measurements. Id. at ¶¶ 0057, 0091 and 0100.
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`34. Billon teaches that the computational components in the wristwatch
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`are contained within an application-specific integrated circuit mounted within the
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`wristwatch’s housing. Id. at ¶ 0091. As discussed above, the computational
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`components process signals from the sensors to derive five physiological factors:
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`the individual’s arterial tension, heart rate, actimetry, respiratory amplitude, and
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`respiratory frequency. Id. at ¶ 0083. Billon explains that these factors are then
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`analyzed using a probabilistic model in order to determine the individual’s time of
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`sleep onset and waking, which includes an estimate of the individual’s transition to
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`and from the “slow light sleep, slow deep sleep, paradoxical sleep, and waking”
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`sleep phases. Id. at ¶ 0059.
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`35. Billon teaches that a connection component permits the wristwatch to
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`communicate with other devices, and in particular, to transmit the derived sleep-
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`phase data to other devices for use in a variety of biomedical applications. Id. at
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`¶¶ 0052 and 0054.
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`B. Wyatt
`36. Wyatt teaches “a system for measuring sleep time and wake time
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`intervals that can be automatically actuated by the user without assistance by
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`another.” Ex. 1011 at 3:62–65. This allows the system to “provide useful sleep
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`pattern information without the expense, distraction and inconvenience of clinical
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`probes and professional monitoring.” Id. at 3:47–49; Ex. 1009 at ¶¶ 0016–0017.
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`The system taught by Wyatt includes a “housing 10 sized and constructed to be
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`held by a user’s hand 12, and particularly pinched between a finger tip 14 and
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`thumb tip 16 of the hand.” Id. at 6:32–34 and Fig. 1.
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`37. Wyatt teaches that a timer electrically connected to a switch
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`positioned in the housing records “sleep time or time to sleep onset” based on
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`whether or not the switch is actuated. See id. at 6:38–56. Wyatt teaches that the
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`individual time stamps the time to bed by “pinching the switches between [his or
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`her] finger and thumb,” which “initiates the recording of awake time by the awake
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`timer.” Id. at 10:17–21. Upon the “onset of sleep, the thumb and fingers of the
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`[individual]’s hand relax and separate, and thus release the pressure that keeps the
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`switches closed,” which activates the “recording of sleep time” by the system. Id.
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`at 10:30–35. Wyatt teaches that the individual time stamps a wake time by
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`actuating a switch to indicate he or she is awake and “to stop recording of sleep
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`time.” Id. at 10:35–39; 11:3–6, and 11:11–13.
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`C.
`Pardey
`38. Pardey
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`teaches a device “primarily
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`intended for
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`. . . general
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`practitioners for use as a screening tool for subjects who claim to be insomniacs
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`and for members of the public who wish to monitor the quality of their own sleep.”
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`Ex. 1012 at 2:30–34. The device “generates and displays a summary index which
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`provides a simple objective indicator of the degree of insomnia suffered by [a]
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`subject.” Id. at 2:5–7. The device is “worn by [the] subject during the night, during
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`which time it continually acquires and analyses the electrical signal . . . from the
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`subject’s scalp.” Id. at 2:21–24, 7:8–9, and 7:49–50 (“During data acquisition, the
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`device continually acquires EEG [electroencephalogram] signals from the subject’s
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`head.”). These signals “relate[] to the sleep stage type being experienced by the
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`subject.” Id. at 1:59–61. Pardey teaches that based on these recorded signals, the
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`device generates a hypnogram, which reflects the subject’s identified sleep phases
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`during the night. See id. at 1:48–49. The device analyzes this hypnogram to
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`“provide[] one or more simple indices of sleep quality which indicate how well the
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`subject slept.” Id. at 2:24–26.
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`39. Pardey teaches that the summary index of sleep quality “reduces the
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`time taken by [a] physician to make a decision on whether additional treatment is
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`required,” and “[p]referably . . . comprises a Yes/No value indicating whether or
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`not the subject suffers from some form of insomnia.” Id. at 2:34–40. Pardey
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`teaches that this summary index is determined using a “low-cost device,” with
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`“[n]o clinical expertise in sleep scoring . . . required to interpret the results” and
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`“[n]o training is required to fit [the sensors].” Id. at 12:60–64. Pardey further
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`teaches that this measure of sleep quality provides a simple “indicat[or] that the
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`subject should refer to either a general practitioner or a sleep laboratory for further
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`investigation.” Id. at 2:26–29.
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`D.
`Tuorto
`40. Tuorto teaches a sleep detection and alarm system intended for use
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`during driving or other activities that require an individual to stay awake. Ex. 1010
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`at 1:6–15. Tuorto describes this system as one that senses physiological changes in
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`an individual associated with the onset of sleep, and sounds an alarm upon
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`detection of these physiological changes. Id.
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`41. Tuorto teaches that the sleep detection system “preferably takes the
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`form of a wrist band” with a strap and a housing. Id. at 3:61–63. Tuorto teaches
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`that the housing includes a piezoelectric crystal 42 for “sensing pressure
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`variations” between the individual’s wrist and the housing, a microphone for
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`“sensing sounds generated by the pulsing heart of the user to determine the heart
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`rate,” a “skin conductivity sensor 52 for measuring the amount of sweat generated”
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`by the individual, microprocessor 36, an analog/digital converter, and a beeper
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`alarm. Id. at 3:66–4:10 and Figs. 1 and 3.
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`42.
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`Tuorto teaches that “[i]t is generally known that the amount of sweat
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`produced by a person is directly related to his or her metabolic rate, and therefore
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`to the level of alertness,” and that “during the period immediately preceding sleep,
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`a person experiences a reduction in perspiration, heart pulse—rate and blood
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`pressure.” Id. at 4: 13-21. Using the inputs from the sensors, the microprocessor of
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`the Tuorro system determines the “state of alertness” of the individual, and if
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`appropriate, sends a signal to activate the beeper alarm. Id. at 4:26-39.
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`E.
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`Amano
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`43.
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`Amcmo teaches a system that includes a Wristwatch sensor device with
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`physiological sensors. Ex. 1004 at Figs. 3A and 3B; 12:41-42. This wristwatch
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`device includes a sensor that detects “body motion” and generates an electronic
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`signal representative of “Whether [an individual] is in a state of rest or activity
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`(eXercise),” id. at 6:57—7:7, a sensor that “measure[s] the skin temperature around
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`the radial artery” and generates an electronic signal
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`representative of the
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`individual’s “deep body temperature,” id. at 11:6–13 and 13:8–18, and a sensor
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`that “measur[es] the pulse pressure around the [individual’s] radial artery” and
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`generates an electronic signal representative of the individual’s pulse rate, id