`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`
`———————
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`
`———————
`
`
`
`APPLE INC.,
`Petitioner,
`
`v.
`
`UNILOC Luxembourg S.A.,
`Patent Owner
`
`———————
`
`Declaration of Joseph A. Paradiso, PhD
`under 37 C.F.R. § 1.68
`
`
`
`
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`Apple v. Uniloc USA
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`Page 1 of 63
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`Apple Ex. 1003
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`Paradiso Decl.
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`Inter Partes Review of U.S. 7,881,902
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`TABLE OF CONTENTS
`
`I.
`
`INTRODUCTION ........................................................................................... 1
`
`II. QUALIFICATIONS AND PROFESSIONAL EXPERIENCE ...................... 2
`
`III. LEVEL OF ORDINARY SKILL IN THE ART ............................................. 7
`
`IV. RELEVANT LEGAL STANDARDS ............................................................. 9
`
`A. Anticipation ............................................................................................ 10
`
`B. Obviousness ........................................................................................... 10
`
`V. OVERVIEW OF THE ’902 PATENT .......................................................... 11
`
`A. Summary of the ’902 Patent .................................................................. 11
`
`B. Prosecution History of the ’902 Patent .................................................. 14
`
`VI. BROADEST REASONABLE INTERPRETATION ................................... 14
`
`A. “cadence window” ................................................................................. 15
`
`VII.
`
`IDENTIFICATION OF HOW THE CLAIMS ARE UNPATENTABLE .... 15
`
`A. Claim 5 is obvious over Fabio in view of Pasolini. ............................... 15
`
`1. State of the Art at the Time of the ’902 Patent .............................. 15
`
`2. Summary of Fabio .......................................................................... 17
`
`3. Summary of Pasolini ...................................................................... 20
`
`4. Reasons to Combine Fabio and Pasolini ........................................ 23
`
`5. Detailed Analysis ........................................................................... 26
`
`B. Claim 8 is obvious over Fabio in view of Pasolini, further in view of
`Tsuji. ...................................................................................................... 44
`
`1. Summary of Tsuji ........................................................................... 44
`
`2. Reasons to Combine Fabio, Pasolini, and Tsuji ............................ 47
`
`
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`3. Detailed Analysis ........................................................................... 51
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`VIII. CONCLUSION .............................................................................................. 60
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`Inter Partes Review of U.S. 7,881,902
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`I.
`
`INTRODUCTION
`
`1.
`
`I am making this declaration at the request of Apple Inc. in the matter
`
`of the inter partes review of U.S. Patent No. 7,881,902 (“the ’902 Patent”) to
`
`Kahn, et al.
`
`2.
`
`I am being compensated for my work in this matter at the rate of
`
`$500/hour. I am also being reimbursed for reasonable and customary expenses
`
`associated with my work and testimony in this investigation. My compensation is
`
`not contingent on the outcome of this matter or the specifics of my testimony.
`
`3.
`
`I have been asked to provide my opinions regarding whether claim 8
`
`of the ’902 Patent is unpatentable, either because it is anticipated or would have
`
`been obvious to a person having ordinary skill in the art (“POSITA”) at the time of
`
`the alleged invention, in light of the prior art. It is my opinion that all of the
`
`limitations of claim 8 would have been obvious to a POSITA.
`
`4.
`
`In the preparation of this declaration, I have studied:
`
`a)
`
`b)
`
`c)
`
`The ’902 Patent, Ex. 1001;
`
`The prosecution history of the ’902 Patent, Ex. 1002;
`
`U.S. Patent No. 7,463,997 to Fabio Pasolini et al. (“Pasolini”),
`
`Ex. 1005;
`
`d)
`
`U.S. Patent No. 7,698,097 to Fabio Pasolini et al. (“Fabio”), Ex.
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`1006;
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`
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`1
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`e)
`
`f)
`
`U.S. Patent No. 7,297,088 to Tsuji (“Tsuji”), Ex. 1010; and
`
`Excerpts from Robert L. Harris, INFORMATION GRAPHICS: A
`
`COMPREHENSIVE ILLUSTRATED REFERENCE (1996) (“Harris”),
`
`Ex.1011.
`
`5.
`
`In forming the opinions expressed below, I have considered:
`
`a)
`
`The documents listed above, and
`
`b) My own knowledge and experience based upon my work in the
`
`field of MEMS (micro-electro-mechanical systems) devices
`
`and body motion sensing systems, as described below.
`
`II. QUALIFICATIONS AND PROFESSIONAL EXPERIENCE
`
`6. My complete qualifications and professional experience are described
`
`in my Curriculum Vitae, a copy of which can be found in Ex. 1004. The following
`
`is a brief summary of my relevant qualifications and professional experience.
`
`7.
`
`As shown in my curriculum vitae, I have devoted my career to various
`
`fields of physical, electrical, and computer science with more than two decades
`
`focused on embedding sensing, including wearable and wireless sensors. I have
`
`twenty years of experience in wearable devices and computing, during which I
`
`invented and fielded many types of wearable activity tracking devices that utilized
`
`a variety of power management and wakeup protocols.
`
`
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`2
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`8.
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`I am the Alexander W. Dreyfoos (1954) Professor in Media Arts and
`
`Sciences at the Massachusetts Institute of Technology (MIT), where I direct the
`
`Responsive Environments Group, which explores how sensor networks augment
`
`and mediate human experience, interaction and perception. I also have served as a
`
`co-director of the Things That Think Consortium, a group of MIT Media Lab
`
`researchers and industrial partners focused on the future of embedded computation
`
`and sensing, and I am now serving as our Associate Department Head.
`
`9.
`
`I received my B.S. in electrical engineering and physics summa cum
`
`laude from Tufts University in 1977 and my Ph.D. in physics from MIT in 1981.
`
`From 1981 to 1984, I did post-doctoral research at the Swiss Federal Institute of
`
`Technology (ETH) in Zurich, working on sensor technology for high-energy
`
`particle physics. From 1984-1994, I was a physicist at the Draper Laboratory in
`
`Cambridge, Massachusetts, where, as a member of the NASA Systems and
`
`Advanced Sensors and Signal Processing Directorates, my research included
`
`spacecraft control systems and sensor technology for both sonar systems and high-
`
`energy physics. I also worked at Draper Lab as an undergraduate (1974-1978) on
`
`software for advanced strategic inertial measurement units and guidance systems.
`
`From 1992-1994, I directed the development of precision alignment sensors for the
`
`GEM muon detector at the Superconducting Supercollider, and worked on design
`
`of particle detectors at the CERN Large Hadron Collider (LHC).
`
`
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`3
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`10.
`
`I joined the MIT Media Lab in 1994. The MIT Media Lab was
`
`founded in 1985 to actively promote a unique, anti-disciplinary culture that focuses
`
`on research projects joining different technological and academic fields. As
`
`described further below, researchers at the MIT Media Lab have pioneered areas
`
`such as wearable computing, tangible interfaces, and affective computing.
`
`Examples of products or platforms spun off from the Media Lab research include
`
`electronic ink readers such as the Amazon Kindle and Barnes & Noble Nook, the
`
`popular video game Guitar Hero, the MPEG-4 structured audio format, the first
`
`bionic lower-leg system for amputees, wireless mesh networks developed by
`
`Nortel, and the Mercury RFID Reader, commercialized by spin-off ThingMagic.
`
`Today, the Lab is supported by more than 70 sponsors/members, comprising some
`
`of the world’s leading corporations and representing the fields of electronics,
`
`entertainment, fashion, health care, greeting cards, and telecommunications, among
`
`others. Faculty members, research staff, and students at the Media Lab work in
`
`more than 25 research groups on more than 350 projects that range from digital
`
`approaches for treating neurological disorders, to a stackable, electric car for
`
`sustainable cities, to advancing imaging technologies that can see around corners.
`
`11. Upon joining the Media Lab, I focused on developing new sensing
`
`modalities for human-computer interaction, then by 1997 evolved my research into
`
`wearable wireless sensing and distributed sensor networks. This work anticipated
`
`
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`4
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`and influenced transformative products and industries that have blossomed in
`
`recent years. For example, the sensor-laden wireless shoe I developed for
`
`interactive dance in 1997 is recognized as a watershed in the field of wearable
`
`wireless sensing and was an inspiration for the Nike+, one of the very first activity
`
`trackers and the first commercial product to integrate dynamic music with
`
`monitored exercise. My team went on to pioneer clinical gait analysis with
`
`wearable wireless sensors in collaboration with the Massachusetts General
`
`Hospital (MGH) in 2002, and then broke new ground in sports medicine with
`
`another MGH collaboration that developed an ultra-wide-range wireless inertial
`
`measurement unit system for evaluating professional baseball pitchers in 2007. My
`
`team and I have also been leaders on wearable sensing for Human-Computer
`
`Interfaces, over the past decade fielding, for example, wristbands to measure finger
`
`position, wristbands to enable pointing interaction and control of heating and
`
`lighting, and even a wireless touchpad mounted on a fingernail.
`
`12. Leading to over 300 publications, 17 issued patents, and a string of
`
`awards in the Pervasive Computing, Human Computer Interaction, and sensor
`
`network communities, my research has become the basis for widely established
`
`curricula. Many of these publications are directed to wearables. I have also advised
`
`over 55 graduate (M.S. and Ph.D) theses for students who have done their work in
`
`my research group, and served as a reader for roughly 100 MS and PhD students in
`
`Ex. 1003
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`5
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`other groups and at other universities. Some of my own students have gone on to
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`prominence in their own careers that have involved wearables—for example, Dr.
`
`Nan-Wei Gong (Ph.D 2013) was the R&D lead of Project Jacquard (integrating
`
`electronics and textiles) at Google ATAP before becoming founder and CEO of
`
`her own companies with a wearable focus ‘Circular2’ and ‘Figure8,’ and Dr. Stacy
`
`Morris Bamberg (Ph.D 2004) became a tenured professor at the University of Utah
`
`doing wearable gait analysis, then started a company in this space (Veristride). I
`
`have given over 280 invited talks, panel appearances, and seminars worldwide,
`
`recently keynoting on topics relating to ubiquitous sensing and the Internet of
`
`Things (IoT) for prestigious venues ranging from the Sensors Expo (the main
`
`industrial sensors conference) to the World Economic Forum. I am frequently
`
`asked to address industrial groups on wearables and IoT, and often engage with the
`
`Media Lab’s extensive list of industrial partners in strategizing these areas.
`
`13.
`
`I belong to and participate in various professional organizations. I am
`
`a senior member of the IEEE (Institute of Electrical and Electronics Engineers),
`
`and also belong to the ACM (Association for Computer Machinery). I also belong
`
`to the APS American Physical Society (the major professional society in physics),
`
`and am a senior member in the AIAA (the American Institute of Aeronautics and
`
`Astronautics). Within the IEEE, I belong to the Signal Processing Society, the
`
`Controls Society, and the Computer Society. As detailed in my CV, I have served
`
`
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`on many Technical Program Committees (TPCs, which solicit, review, and select
`
`papers for academic conferences) and journal editorial boards, plus have organized
`
`academic conferences in areas such as wireless sensor networks, wearable
`
`computing and wearable sensing, human-computer interfaces, ubiquitous
`
`computing, etc.
`
`14. One of the themes of my research has been on low-power embedded
`
`systems and energy harvesting. I have written several well-regarded papers on
`
`these topics that well predate the ’902 Patent—for example, the review article that
`
`I wrote for IEEE Pervasive Computing in 2005, ‘Energy Scavenging for Mobile
`
`and Wireless Electronics’ has become their most popular article and is widely
`
`cited. My work on smart wakeup systems (e.g., as described in my papers such as
`
`‘A Framework for the Automated Generation of Power-Efficient Classifiers for
`
`Embedded Sensor Nodes’ and ‘CargoNet: A Low-Cost MicroPower Sensor Node
`
`Exploiting Quasi-Passive Wakeup for Adaptive Asynchronous Monitoring of
`
`Exceptional Events,’ both presented at SenSys 2007), are of relevance here.
`
`III. LEVEL OF ORDINARY SKILL IN THE ART
`
`15.
`
`I understand there are multiple factors relevant to determining the
`
`level of ordinary skill in the pertinent art, including (1) the levels of education and
`
`experience of persons working in the field at the time of the invention; (2) the
`
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`sophistication of the technology; (3) the types of problems encountered in the field;
`
`and (4) the prior art solutions to those problems.
`
`16.
`
`I am familiar with accelerometers (including those found in portable
`
`devices such as mobile phones). I am also aware of the state of the art at the time
`
`the application resulting in the ’902 Patent was filed. I have been informed by
`
`Apple’s counsel that the earliest alleged priority date for the ’902 Patent is
`
`December 22, 2006. Based on the technologies disclosed in the ’902 Patent, a
`
`POSITA would be someone knowledgeable concerning accelerometers and the
`
`analysis of the data generated thereby. That person would have (i) a Bachelor’s
`
`degree in Electrical Engineering, Computer Engineering, Computer Science, or
`
`equivalent training, as well as (ii) approximately two years of experience
`
`working in hardware and/or software design and development related to MEMS
`
`devices and body motion sensing systems. Lack of work experience can be
`
`remedied by additional education, and vice versa. Such academic and industry
`
`experience would be necessary to appreciate what was obvious and/or
`
`anticipated in the industry and what a POSITA would have thought and
`
`understood at the time. Based on this criteria, as of the relevant time frame for
`
`the ’902 Patent, I possessed at least such experience and knowledge of a
`
`POSITA, as well as trained many of them by then, hence am qualified to opine
`
`on the ’902 Patent.
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`
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`8
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`17. For purposes of this Declaration, in general, and unless otherwise
`
`noted, my statements and opinions, such as those regarding my experience and the
`
`understanding of a POSITA generally (and specifically related to the references I
`
`consulted herein), reflect the knowledge that existed in the field as of December
`
`22, 2006. Unless otherwise stated, when I provide my understanding and analysis
`
`below, it is consistent with the level of a POSITA prior to the priority date of the
`
`’902 Patent.
`
`IV. RELEVANT LEGAL STANDARDS
`
`18.
`
`I understand that prior art to the ’902 Patent includes patents and
`
`printed publications in the relevant art that predate the priority date of the alleged
`
`invention recited in the ’902 Patent. For purposes of this Declaration, I have been
`
`asked to apply December 22, 2006, the earliest alleged priority date, as the priority
`
`date.
`
`19.
`
`I am not an attorney. In preparing and expressing my opinions and
`
`considering the subject matter of the ’902 Patent, I am relying on certain basic
`
`legal principles that counsel have explained to me. These principles are discussed
`
`below.
`
`20.
`
`I understand that a claim is unpatentable if it is anticipated under 35
`
`U.S.C. § 102 or obvious under 35 U.S.C. § 103.
`
`
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`A. Anticipation
`
`21.
`
`I have been informed by counsel that a patent claim is unpatentable as
`
`anticipated if each element of that claim is present either explicitly or inherently in
`
`a single prior art reference. I have also been informed that, to be an inherent
`
`disclosure, the prior art reference must necessarily disclose the limitation, and the
`
`fact that the reference might possibly practice or contain a claimed limitation is
`
`insufficient to establish that the reference inherently teaches the limitation.
`
`B. Obviousness
`
`22.
`
`I have been informed that a claimed invention is unpatentable under
`
`35 U.S.C. § 103 if the differences between the invention and the prior art are such
`
`that the subject matter as a whole would have been obvious at the time the
`
`invention was made to a person having ordinary skill in the art to which the subject
`
`matter pertains. I have also been informed by counsel that the obviousness analysis
`
`takes into account factual inquiries including the level of ordinary skill in the art,
`
`the scope and content of the prior art, and the differences between the prior art and
`
`the claimed subject matter.
`
`23.
`
`I have been informed by counsel that the Supreme Court has
`
`recognized several rationales for combining references or modifying a reference to
`
`show obviousness of claimed subject matter. Some of these rationales include the
`
`following: (a) combining prior art elements according to known methods to yield
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`predictable results; (b) simple substitution of one known element for another to
`
`obtain predictable results; (c) use of a known technique to improve a similar device
`
`(method, or product) in the same way; (d) applying a known technique to a known
`
`device (method, or product) ready for improvement to yield predictable results; (e)
`
`choosing from a finite number of identified, predictable solutions, with a
`
`reasonable expectation of success; and (f) some teaching, suggestion, or motivation
`
`in the prior art that would have led one of ordinary skill to modify the prior art
`
`reference or to combine prior art reference teachings to arrive at the claimed
`
`invention.
`
`V. OVERVIEW OF THE ’902 PATENT
`
`A.
`
`Summary of the ’902 Patent
`
`24. The ’902 patent is directed to an electronic device that “may be used
`
`to count steps or other periodic human motions.” Ex. 1001, 2:29-30. To detect
`
`periodic human motions, the electronic device “includes one or more inertial
`
`sensors,” such as an accelerometer. Ex. 1001, 2:25-26, 1:18. The inertial sensor
`
`measures acceleration data to detect a motion cycle. Ex. 1001, 2:38-43, 3:47-48.
`
`The ’902 patent explains that the “period and/or cadence of the motion cycle may
`
`be based upon user activity,” such as rollerblading, biking, running, walking, or
`
`any other activity having a periodic set of repeated movements. Ex. 1001, 3:16-17,
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`3:36-38.
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`25. To reduce power consumption, the electronic device in the ’902 patent
`
`operates in different modes. Ex. 1001, 8:20-23. As recited in claims 1-4, one of
`
`these modes is a “sleep mode.” The “sleep mode” in the ’902 patent is described as
`
`a power level that “reduces power consumption and prolongs battery life.” Ex.
`
`1001, 8:66-67. The electronic device enters the sleep mode when “no relevant
`
`acceleration is detected.” Ex. 1001, 10:41-41. While in the sleep mode, “a
`
`sampling function is periodically executed,” where the function “samples
`
`acceleration data at a set sampling rate for a set time period.” Ex. 1001, 9:5-9. The
`
`device terminates the sleep mode “[w]hen acceleration is detected.” Ex. 1001,
`
`9:39-41.
`
`26. Claims 5-6 and 9-10 differ from claims 1-4 in that they are not related
`
`to the sleep mode, but instead are directed to determining a step cadence window
`
`“used to count steps.” Ex. 1001, 4:21-22. According to the ’902 patent, a cadence
`
`window “is a window of time since a last step was counted that is looked at to
`
`detect a new step.” Ex. 1001, 3:66-4:1. The ’902 patent describes how “[i]f fewer
`
`than the required number of steps” are detected, “the cadence window may have a
`
`default minimum and maximum value.” Ex. 1001, 4:63-66. However, “[o]nce
`
`enough steps have been detected to determine a dynamic stepping cadence or
`
`period,” the dynamic cadence window “continuously updates as a user’s cadence
`
`changes.” Ex. 1001, 5:1-2, 4:24-26.
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`27. Claim 10 of the ’902 Patent is further directed to assigning a dominant
`
`axis based on the orientation” of the mobile device with respect to gravity. See,
`
`e.g., Ex. 1001, claim 10. In the ’902 Patent, the dominant axis is “the axis most
`
`influenced by gravity,” which “may change over time (e.g. as the electronic device
`
`is rotated).” Ex. 1001, 6:16-21. Figure 9 of the ’902 Patent, reproduced below in
`
`part, provides a method for assigning a dominant axis based on taking
`
`measurements of acceleration data:
`
`
`
`Ex. 1001, Fig. 9.
`
`28. The “cadence window” and the “dominant axis” concepts claimed in
`
`the ’902 Patent were not novel. As shown in this Declaration, (1) U.S. Patent No.
`
`7,698,097 to Fabio Pasolini et al. (“Fabio”) describes a validation interval (cadence
`
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`window) that is a window of time since a last step was counted that is looked at to
`
`detect a new step and (2) both Fabio and U.S. Patent No. 7,463,997 to Fabio
`
`Pasolini et al. (“Pasolini”) describe detecting steps using a dominant axis of a tri-
`
`axial accelerometer, or, in other words, using the axis most influenced by gravity.
`
`B.
`
`Prosecution History of the ’902 Patent
`
`29. The ’902 patent issued on February 1, 2011 from the U.S. Patent
`
`Application No. 12/694,135 filed January 26, 2010. The ’902 patent is a
`
`continuation of the U.S. Patent No. 7,653,508, filed on December 22, 2006.
`
`30. The first action by the Office during prosecution was a Notice of
`
`Allowance that issued on September 24, 2010. Ex. 1002, p. 34.
`
`31.
`
`In the Notice of Allowance the Examiner stated that the cited art (not
`
`included in this petition) failed to teach or suggest the limitations of original claim
`
`12 (issued claim 1) and original claim 25 (issued claim 5). Ex. 1002, p.5.
`
`VI. BROADEST REASONABLE INTERPRETATION
`
`32.
`
`It is my understanding that in order to properly evaluate the ’902
`
`Patent, the terms of the claims must first be interpreted. It is my understanding that
`
`for the purposes of this inter partes review, the claims are to be given their
`
`broadest reasonable interpretation in light of the specification. It is my further
`
`understanding that claim terms are given their ordinary and accustomed meaning
`
`as would be understood by one of ordinary skill in the art, unless the inventor has
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`set forth a special meaning for a term. In order to construe the following claim
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`terms, I have reviewed the entirety of the ’902 Patent, as well as its prosecution
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`history.
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`A.
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` “cadence window”
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`33. This term appears in at least claim 5. The ’902 specification
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`specifically defines this term as “a window of time since a last step was counted
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`that is looked at to detect a new step.” Ex. 1001, 3:66-4:1.
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`34. Thus, for the purposes of this proceeding, the term “cadence window”
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`as used in the claims includes “a window of time since a last step was counted that
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`is looked at to detect a new step.”
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`VII. IDENTIFICATION OF HOW THE CLAIMS ARE UNPATENTABLE
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`A. Claim 5 is obvious over Fabio in view of Pasolini.
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`35.
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`It is my opinion that claim 5 is obvious over U.S. Patent No.
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`7,698,097 to Fabio Pasolini et al. (“Fabio”) in view of U.S. Patent No. 7,463,997 to
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`Fabio Pasolini et al. (“Pasolini”).
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`1.
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`State of the Art at the Time of the ’902 Patent
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`36. By the time the’902 Patent was filed on December 22, 2006, others
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`were actively working on pedometer devices that monitored user’s steps. One such
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`developer was Fabio Pasolini, who designed motion detection systems using
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`MEMS that could be implemented in phones or other portable electronic devices.
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`15
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`See Ex. 1006, 2:33-36; Ex. 1005, 8:31-34. The pedometer devices that Mr. Pasolini
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`designed use an inertial sensor, such as an accelerometer, to count steps of the user
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`while the user was carrying the device. Ex. 1006, 1:10-11, 2:49-64; Ex. 1005, 3:30-
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`35.
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`37. To detect and identify the user’s steps, Mr. Pasolini’s devices analyze
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`positive and negative acceleration peaks provided by the accelerometer. Ex. 1006,
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`4:12-21; Ex. 1005, 3:35-41. In this way, Mr. Pasolini’s devices provide features
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`that help avoid “false positives” with respect to the step recognition. Ex. 1006,
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`7:16-19; Ex. 1005, 1:61-2:3. These step-recognition features are described in two
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`of Mr. Pasolini’s issued patents – U.S. Patent No. 7,698,097 (“Fabio”) and U.S.
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`Patent No. 7,463,997 (“Pasolini”) – that were both filed on October 2, 2006 and
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`share the same inventive entity (Fabio Pasolini and Ivo Binda).
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`38. Both of Mr. Pasolini’s patents describe a number of features in
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`common with the pedometer devices. These features include, for example, an
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`accelerometer with multiple axes of detection, so that step recognition is
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`advantageously performed using the accelerations measured by the axis that is
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`most aligned with gravity. Ex. 1006, 8:20-32; Ex. 1005, 8:15-24.
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`39. The references differ in that the Pasolini reference provides additional
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`detail regarding step detection using linear and multi-axes accelerometers,
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`including describing that the pedometer updates the vertical axis with each
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`16
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`acquisition of an acceleration sample to take into account variations of the
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`orientation of the pedometer device during use. Ex. 1005, 8:20-24. The Fabio
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`reference, on the other hand, describes applying a regularity condition to the
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`detected step data so that a step is counted when it occurs within a “validation
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`interval,” which is identified as a window of time since a previous step was
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`counted. Ex. 1006, 4:35-39, 7:16-19, FIG. 6.
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`40. As described in more detail below, the disclosures provided in the
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`Fabio and Pasolini references render obvious each and every element of challenged
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`claims.
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`2.
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`Summary of Fabio
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`41. Fabio is directed to “controlling a pedometer based on the use of
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`inertial sensors.” Ex. 1006, 1:10-11. An example of Fabio’s pedometer device 1 as
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`“integrated within a portable electronic device, such as a cell phone 2” (Ex. 1006,
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`2:33-36) is reproduced below:
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`
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`17
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`Ex. 1006, Fig. 1.
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`
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`42. Fabio describes that its pedometer 1 includes an “inertial sensor 3
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`[that] supplies at output an acceleration signal AZ, which is correlated to the
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`accelerations undergone by the inertial sensor 3 itself along the detection axis Z.”
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`Ex. 1006, 2:56-59. Fabio’s pedometer performs step recognition by sampling the
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`acceleration signal AZ to identify characteristics including “a positive peak, higher
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`than a positive acceleration threshold AZP, followed by a negative peak, smaller
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`than a negative acceleration threshold AZN.” Ex. 1006, 4:12-21.
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`43. Fabio notes that “there are many random events that can interfere with
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`correct recognition of the step. Impact or other external vibrations and given
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`movements of the user can, in fact, give rise to so-called ‘false positives’.” Ex.
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`1006, 1:38-41. For this reason, Fabio describes a pedometer having various
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`features to overcome these step detection challenges. Some of these features
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`include “checking whether sequences of the detected steps satisfy pre-determined
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`conditions of regularity; updating a total number of valid steps if the conditions of
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`regularity are satisfied; and preventing updating of the total number of valid steps
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`if the conditions of regularity are not satisfied.” Ex. 1006, 1:62-2:3. More
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`precisely, Fabio explains that “the last step is validated if the instance of
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`recognition of the current step TR(K) falls within a validation interval TV[.]”
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`Ex. 1006, 4:35-39. Fabio shows the instant of recognition of a current step
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`occurring within the validation interval after an instant of recognition of a previous
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`step in Figure 6 (below).
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`Instant of recognition of current step
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`Instant of
`recognition of
`previous step
`
`
`
`
`Validation Interval
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`
`
`Ex. 1006, FIG. 6 (annotated).
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`
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`19
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`44. Using this step validation technique, Fabio’s device is able to more
`
`accurately count steps and adapt to changes in the user’s pace. Specifically, Fabio
`
`teaches that “[p]ossible isolated irregularities are ignored and do not interrupt or
`
`suspend updating of the count, which is, instead, interrupted when prolonged
`
`pauses occur or in the presence of significant discontinuities in locomotion.” Ex.
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`1006, 7:16-19.
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`45. Fabio additionally describes sampling the acceleration signal from a
`
`detection axis most influenced by gravity. Specifically, Fabio teaches using an
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`inertial sensor “with two or three axes of detection” so that “step recognition can
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`advantageously be performed by selecting the acceleration signal
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`corresponding to the detection axis nearest to the vertical.” Ex. 1006, 8:20-25.
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`According to Fabio, “[t]he detection axis nearest to the vertical is the axis along
`
`which the contribution of gravity is greater.” Ex. 1006, 8:30-32.
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`3.
`
`Summary of Pasolini
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`46. As discussed above, Pasolini shares the same inventive entity as Fabio
`
`(Fabio Pasolini and Ivo Binda) and was filed on the same day. See Ex. 1005; Ex.
`
`1006. Pasolini is similarly directed to “a pedometer device and to a step detection
`
`method using an algorithm for self-adaptive computation of acceleration
`
`thresholds.” Ex. 1005, 1:10-12. Like Fabio, Pasolini describes that its “pedometer
`
`device 1 … may advantageously be housed inside a portable device, in particular a
`
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`mobile phone.” Ex. 1005, 8:31-34. An example of Pasolini’s pedometer is shown
`
`in Figure 8, reproduced below:
`
`
`
`Ex. 1005, FIG. 8.
`
`47. Like Fabio, Pasolini also describes that its pedometer includes an
`
`inertial sensor that is an “accelerometer 2 … having a vertical detection axis z.”
`
`Ex. 1005, 2:60-64. Pasolini’s pedometer similarly “acquires at pre-set intervals
`
`samples of the acceleration signal A generated by the accelerometer 2, and
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`executes appropriate processing operations for counting the number of steps.” Ex.
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`1005, 3:30-35. Like Fabio, these processing operations include “identifying,
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`respectively, the positive p