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. 8,073,707
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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 ’707 PATENT ...................................................................... 10 
`VI. 
`VII.  CLAIM CONSTRUCTION ............................................................................................. 14 
`A. 
`“life activities data” (claims 5, 6, and 10) ............................................................ 14 
`VIII.  PRIOR ART ..................................................................................................................... 15 
`Amano ’342 .......................................................................................................... 15 
`A. 
`Amano ’837 .......................................................................................................... 18 
`B. 
`Goodman .............................................................................................................. 20 
`C. 
`CERTAIN REFERENCES TEACH OR SUGGEST ALL THE CLAIMED
`FEATURES OF CLAIMS 1–24 OF THE ’707 PATENT............................................... 22 
`Ground 1: Amano ’342 Teaches All the Features of Claims 1, 19, 23, and
`A. 
`24.......................................................................................................................... 22 
`1. 
`Claim 1 ..................................................................................................... 22 
`2. 
`Claim 19 ................................................................................................... 33 
`3. 
`Claim 23 ................................................................................................... 34 
`4. 
`Claim 24 ................................................................................................... 36 
`Ground 2: Amano ’837 and Goodman Teach or Suggest All the Features
`of Claims 1–24 ..................................................................................................... 38 
`1. 
`Claim 1 ..................................................................................................... 38 
`2. 
`Claim 2 ..................................................................................................... 54 
`3. 
`Claim 3 ..................................................................................................... 55 
`4. 
`Claim 4 ..................................................................................................... 56 
`5. 
`Claim 5 ..................................................................................................... 58 
`6. 
`Claim 6 ..................................................................................................... 60 
`7. 
`Claim 7 ..................................................................................................... 61 
`8. 
`Claim 8 ..................................................................................................... 62 
`9. 
`Claim 9 ..................................................................................................... 66 
`10. 
`Claim 10 ................................................................................................... 67 
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`B. 
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`X. 
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`11. 
`Claim 11 ................................................................................................... 68 
`Claim 12 ................................................................................................... 69 
`12. 
`Claim 13 ................................................................................................... 69 
`13. 
`Claim 14 ................................................................................................... 71 
`14. 
`Claim 15 ................................................................................................... 74 
`15. 
`Claim 16 ................................................................................................... 74 
`16. 
`Claim 17 ................................................................................................... 76 
`17. 
`Claim 18 ................................................................................................... 78 
`18. 
`Claim 19 ................................................................................................... 79 
`19. 
`Claim 20 ................................................................................................... 80 
`20. 
`Claim 21 ................................................................................................... 81 
`21. 
`Claim 22 ................................................................................................... 83 
`22. 
`Claim 23 ................................................................................................... 84 
`23. 
`Claim 24 ................................................................................................... 85 
`24. 
`CONCLUSION ................................................................................................................ 88 
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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. 8,073,707 (“the ’707 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 1–24 of the ’707 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 ’707 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 ’707 patent (Ex. 1001), the prosecution history file
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`of the ’707 patent (Ex. 1003), U.S. Patent No. 7,689,437 (“the ’437 patent”) (Ex.
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`1005), U.S. Patent No. 6,030,342 to Amano et al. (“Amano ’342”) (Ex. 1004), U.S.
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`Patent No. 6,616,613 to Goodman (“Goodman”) (Ex. 1007), and the prosecution
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`history file of pending inter partes reexamination control nos. 95/002,371, and
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`95/002,376 (Exs. 1013–1014) involving the ’437 and ’707 patents, respectively, a
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`portion of a treatise by Gilad J. Kuperman et al. entitled “HELP: A Dynamic
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`Hospital Information System” (Ex. 1016), a journal article by Norman J. Holter
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`entitled “New Method for Heart Studies: Continuous Electrocardiography of
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`Active Subjects Over Long Periods is Now Practical” (Ex. 1017), and a portion of
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`a 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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`’707 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 1–24 of the ’707
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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 ’707 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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`’707 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 ’707 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 ’707 PATENT
`22. The ’707 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 ’707 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 data indicative of
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`physiological characteristics of the individual, such as pulse rate, skin temperature,
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`core body temperature, and body movement. Id. at 4:39–55 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 ’707 patent. Id. at 4:47–55. 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 ’707 patent, the sensors and the methods of
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`generating data indicative of these physiological parameters using the sensors were
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`“well known.” Id. at 4:60–65. Table 1 of the ’707 patent (excerpted below)
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`provides examples of these physiological parameters and the corresponding
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`sensors used to generate data indicative of those parameters.
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`25. The ’707 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 ’707 patent explains that
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`this derived information can include, for example, “calories burned” information,
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`which can be derived from sensor data that may include “[h]eart rate, pulse rate,
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`respiration rate, heat flow, activity, [and] oxygen consumption.” Id. at Table 2. As
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`with the generation of data indicative of physiological parameters, the ’707 patent
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`explains that such methods of programming a microprocessor to derive
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`information relating to an individual’s physiological status from the sensor data
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`were “known.” Id. at 6:43–46. Table 2 of the ’707 patent (excerpted below)
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`provides examples of information relating to an individual’s physiological state
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`that can be derived—which is referred to interchangeably as “derived information”
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`or “derived parameters” by the ’707 patent—and the types of data indicative of
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`physiological parameters that “can be used” to derive these physiological status
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`parameters.
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`26. The ’707 patent provides no specific algorithms or methodologies for
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`deriving any of the exemplary physiological status data. Rather, the ’707 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 ’707 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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`28. For example, the ’707 patent explains that using an individual’s data,
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`the central monitoring unit may generate Web pages customized for that
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`individual. Id. at 14:33–47 and Figs. 5–11. One such a page is depicted below in
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`Fig. 7, which displays an individual’s daily calorie intake, expenditure, and
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`targeted expenditure (identified as section 215 but not labeled in the figure), as
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`well as time spent “exercising aerobically or engaging in a vigorous lifestyle
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`activity as input by the user and/or sensed by sensor device 10.” Id. at 16:46–17:10
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`and Figs 6–7.
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`VII. CLAIM CONSTRUCTION
`29.
`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.
`“life activities data” (claims 5, 6, and 10)
`30. The term “life activities data” appears in claims 5, 6, and 10 of the
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`’707 patent. Claim 5 recites “an input device for providing life activities data of the
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`individual to the system.” Ex. 1001 at 21:48–50. Claim 6 recites that the output
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`device presents indicators of a derived parameter in relation to indicators of “life
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`activities data” of the individual or another derived parameter. Id. at 21:51–60.
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`Claim 10 recites that “data generated by the system and [the] life activities data is
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`aggregated into a database accessible to a user.” Id. at 22:1–3.
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`31.
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`I understand that Petitioner has offered that, in view of the
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`specification, the broadest reasonable construction of “life activities data” includes
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`at least “data relating to the eating, sleep, exercise (e.g., type or duration of
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`exercise), mind centering or relaxation, and/or daily living habits, patterns and/or
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`activities of the individual (e.g., medication or supplements taken),” particularly
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`under its broadest reasonable construction.
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`VIII. PRIOR ART
`A. Amano ’342
`32. Amano ’342 teaches a system for detecting, monitoring, and reporting
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`a status of an individual. In particular, the system taught by Amano ’342 includes a
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`wristwatch device that functions as, inter alia, an electronic “calorie expenditure
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`measuring device.” Ex. 1004 at 1:7–15 and 12:41–42. The wristwatch has a sensor
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`that detects “body motion” and generates data indicative of “whether [an
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`individual] is in a state of rest or activity (exercise).” Id. at 6:57–7:7. The
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`wristwatch also has a sensor that “measure[s] the skin temperature around the
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`radial artery” and generates data indicative of the individual’s “deep body
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`temperature.” Id. at 11:6–13 and 13:8–18. The wristwatch also has a sensor that
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`“measur[es] the pulse pressure around the [individual’s] radial artery” and
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`generates data indicative of the individual’s pulse rate. Id. at 12:66–13:7.
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`33. Amano ’342 teaches that a processing unit within the wristwatch
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`applies regression formulas to the sensor data to derive certain physiological
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`parameters such as the individual’s calorie expenditure. See, e.g., id. at 18:6–50.
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`Amano ’342 teaches that the wristwatch can also transmit the individual’s derived
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`calorie expenditure data and/or physiological data to an external central monitoring
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`unit for storage and further analysis. Id. at 21:52–23:12. Amano ’342 teaches that
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`the face of the wristwatch includes an LCD output device, which can present
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`indicators of derived physiological status data, such as the individual’s calorie
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`expenditure over time. See, e.g., id. at 19:27–33 and Fig. 19. Amano ’342 teaches
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`that the wristwatch can also present to a user indicators of an individual’s
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`physiological data used to determine the calorie expenditure, such as the
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`individual’s pulse rate and deep body temperature over time. See, e.g., id. at 23:66–
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`24:14, 27:56–67, 26:45–27:12, and Figs. 19, 37, and 40.
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`In addition, Amano ’342 teaches that the wristwatch’s processing unit
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`34.
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`derives an individual’s respiration rate in their basal or deep sleep state. This and
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`other “standard” parameters from the individual’s basal metabolic state are
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`determined based on input from the physiological sensors. First, the processing
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`unit of the wristwatch taught by Amano ’342 determines when the individual is in a
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`state of deep sleep by analyzing input from the body motion sensor and the body
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`temperature sensor. Id. at 17:1–24, 17:43–54. Second, Amano ’342 teaches that
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`once a state of deep sleep is determined, the processing unit reads the input from
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`the pulse sensor for a period of time. Id. at 17:24–29, 17:48–54. Third, Amano ’342
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`teaches that the processing unit analyzes the pulse sensor data through
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`interpolation and fast Fourier transform techniques to derive the individual’s
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`standard respiration rate. Id. at 25:24–28, 25:52–26:1, and 26:25–33.
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`B.
`Amano ’837
`35. Like the ’707 patent, Amano ’837 teaches a system for detecting,
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`monitoring, and reporting a status of an individual. In particular, Amano ’837
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`teaches a “health management device” that includes a wristwatch “exercise support
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`device which provides appropriate suggestions and guidance to the user.” Ex. 1006
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`at 1:4–14. The system taught by Amano ’837 includes a first sensor in proximity to
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`the individual’s finger that detects changes in “light…being reflected via blood
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`vessels under the skin” and generates data in the form of a pulse wave that is
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`indicative of the amount of blood in capillaries at the point of measurement at a
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`given time. Id. at 16:19–27. The system taught by Amano ’837 also includes a
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`second sensor in proximity to the individual that detects movement and generates
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`data indicative of the individual’s body movement. Id. at 16:28–31 and 19:36–44.
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`36. A processing unit in the system taught by Amano ’837 is programmed
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`to generate a variety of derived parameters from the sensor data relating to the
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`individual’s physiological status and exercise. These derived parameters include
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`the individual’s exercise pulse rate, id. at 20:36–55, resting pulse rate, id. at 19:23–
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`56, and exercise amount/calories burned,
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`id. at 20:36–21:20. In some
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`embodiments, the system taught by Amano ’837 augments the processing
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`capabilities of the wristwatch with a central monitoring unit in the form of a
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`connected personal computer. Id. at 30:64–39:16.
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`37. The wristwatch of the system taught by Amano ’837 includes an LCD
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`output device. See, e.g., id. at 18:3–5. The LCD can present to a user indicators of
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`derived parameters in visual relation to indicators of data from the physiological
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`sensors. See, e.g., id. at 17:39–18:9. For example, before and after the individual
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`exercises, Amano ’837 teaches that the LCD display can present to a user the
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`derived parameter of the individual’s resting pulse rate in the lower right section of
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`the display (108-3) in simultaneous visual relation to waveform data from the pulse
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`wave sensor, which is displayed in the lower left section of the display (108-D).
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`See id. at 17:56–59, 22:43–45, 19:27–33, and Fig. 4.
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`C. Goodman
`38. Goodman teaches “a physiological signal monitoring system” that
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`relies on measurements from plethysmography (PPG) and thermistor sensors to
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`derive a variety of cardiovascular parameters by analyzing pulse wave contour data
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`from the PPG sensor. Ex. 1007 at 3:65–4:20 and 8:56–63. The system taught by
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`Goodman includes an embodiment in which a PPG sensor and a thermistor sensor
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`are disposed in the housing of a computer peripheral, such as a mouse. See, e.g., id.
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`at 8:64–66, 9:57–10:7, 12:49–63, and Fig. 2. Goodman teaches that these sensors
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`could also be incorporated into a wristwatch like the one part of the Amano ’342
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`system. Id. at 8:66–9:5 (“[I]t would be also possible to implement the invention by
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`incorporating PPG sensor 12 within the casing of . . . some other type of stand
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`alone data processing and transmitting device (e.g. a watch).”), and 13:43–50. A
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`processing unit is programmed to generate derived physiological parameters
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`“related to the state of the [individual’s] cardiovascular system as well as
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`respiratory function” using the sensor data, display the data on an output device,
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`and then transmit that data to a central monitoring unit over the Internet. Id. at
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`16:39–41 and 18:14–18.
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`39. Once the sensors have collected physiological data from an individual,
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`Goodman teaches that “it is a simple matter” to convey these data to a Web server
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`over a communications network such as the Internet. Id. at 31:51–53 and Figs. 1
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`and 16. According to Goodman, transmission of the individual’s data to a remote
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`Web server allows for “more sophisticated [data] analysis” capabilities than is
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`possible at the device worn by the individual. Id. at 34:33–35. Goodman explains
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`that once an individual’s physiological data are transmitted to the Web server, the
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`data are “accessible to the user and health professionals authorized by the user
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`anywhere[,] any time,” which Goodman suggests would facilitate “improved
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`communication of information and biofeedback functionality.” Id. at 3:59–61,
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`34:35–38 and Fig. 17.
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`IX. CERTAIN REFERENCES TEACH OR SUGGEST ALL THE
`CLAIMED FEATURES OF CLAIMS 1–24 OF THE ’707 PATENT
`40.
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`In my opinion, Amano ’342 teaches all the features recited in claims