UNITED STATES PATENT AND TRADEMARK OFFICE
`_______________________
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`_______________________
`APPLE INC.,
`Petitioner
`v.
`UNILOC LUXEMBOURG S.A.
`Patent Owner
`_______________________
`Case No. IPR2018-01027
`U.S. PATENT NO. 8,712,723
`
`DECLARATION OF WILLIAM C. EASTTOM II (CHUCK EASTTOM)
`
`Apple v. Uniloc, IPR2018-01027
`Uniloc's Exhibit No. 2001
`
`

`

`
`
`I.
`
`II.
`
`TABLE OF CONTENTS
`
`INTRODUCTION ........................................................................................................3
`
`BACKGROUND AND QUALIFICATIONS .....................................................................3
`
`III.
`
`CLAIM CONSTRUCTION ............................................................................................4
`
`IV.
`
`THE ‘723 PATENT .....................................................................................................5
`
`V.
`
`ONE OF ORDINARY SKILL IN THE ART ......................................................................6
`
`VI.
`
`GENERAL ISSUES ......................................................................................................6
`
`A. Dominant Axis ....................................................................................................6
`
`B. Cadence Window ...............................................................................................8
`
`VII.
`
`SPECIFIC CLAIM ELEMENTS ................................................................................... 10
`
`A. Claim 4.1 wherein the dynamic motion criteria includes at least a lower
`threshold, ........................................................................................................ 10
`
`B. Claim 4.2 wherein the lower threshold is adjusted based on at least one of a
`rolling average of accelerations and… / Claim 19.1 wherein the dynamic
`motion criteria includes at least a lower threshold, wherein the lower
`threshold is adjusted based on at least one of a rolling average of
`accelerations and the orientation of the inertial sensor. ............................... 12
`
`C. Claim 1............................................................................................................. 18
`
`D. Claim 2............................................................................................................. 20
`
`E. Claim 6............................................................................................................. 21
`
`F. Claim 10 .......................................................................................................... 21
`
`VIII.
`
`CONCLUSIONS ....................................................................................................... 22
`
`IX.
`
`APPENDIX A – EASTTOM CV .....................................................................................1
`
`A. Education ...........................................................................................................1
`1. University Degrees ...........................................................................1
`2.
`Industry Certifications ......................................................................1
`3. Licenses ............................................................................................3
`
`
`
`1
`
`

`

`
`
`B. Publications ........................................................................................................4
`1. Books 4
`2. Papers, presentations, & articles. ....................................................5
`3. Patents .............................................................................................7
`
`C. Standards and Certification Creation.................................................................8
`
`D. Professional Awards and Memberships ............................................................9
`
`E. Speaking Engagements ......................................................................................9
`
`F. Litigation Support Experience ......................................................................... 13
`
`G. Testifying Experience ...................................................................................... 18
`
`H. Professional Experience .................................................................................. 20
`
`X.
`
`CONTINUING PROFESSIONAL EDUCATION ........................................................... 23
`
`A. References to my work ................................................................................... 25
`1. Media References ......................................................................... 25
`2. References to publications ........................................................... 25
`3. Universities using my books ......................................................... 31
`
`B. Training ........................................................................................................... 33
`
`C. Technical Skills ................................................................................................ 34
`
`
`
`
`
`
`
`
`2
`
`

`

`
`
`I.
`
`INTRODUCTION
`
`1.
`
`I have been retained by Uniloc to provide my expert opinions regarding
`
`validity of U.S. Patent No. 8,712,723 (“723 Patent”). Specifically, I have been asked to
`
`provide expert opinions regarding Claims 1-3, 5-7, and 10-18.
`
`2.
`
`I am being compensated for my time at my standard consulting rate of
`
`$300 per hour. I am also being reimbursed for expenses that I incur during the course of
`
`this work. My compensation is not contingent upon the results of my study or the
`
`substance of my opinions.
`
`II.
`
`BACKGROUND AND QUALIFICATIONS
`
`3.
`
`I have 25+ years of experience in the computer science industry including
`
`extensive experience with computer security, computer programming, and computer
`
`networking. I have authored 26 computer science books, including textbooks used at
`
`universities around the world. I hold 42 different computer industry certifications,
`
`including many in networking subjects. I am experienced with multiple programming
`
`languages. I also have extensive experience in computer networking. I have extensive
`
`experience with mobile devices, including all aspects of mobile devices (hardware and
`
`software). I am a Distinguished Speaker for the Association of Computing Machinery
`
`(ACM), and a reviewer for the IEEE Security and Privacy journal, as well as a reviewer for
`
`the International Journal of Cyber Warfare and Terrorism (IJCWT). My CV is attached as
`
`appendix A.
`
`
`
`3
`
`

`

`
`
`III.
`
`CLAIM CONSTRUCTION
`
`4.
`
`Fort the purposes of an IPR, claim terms are given their broadest
`
`reasonable meaning.
`
`5.
`
`The petitioner has adopted the definitions of dominant axis as “the axis
`
`most influenced by gravity.”
`
`6.
`
`The petitioner has adopted the definition of cadence window as “a window
`
`of time since a last step was counted that is looked at to detect a new step.”
`
`7.
`
`The petitioner has adopted the definition of a dominant axis logic to
`
`determine an orientation of a device with respect to gravity, to assign a dominant axis,
`
`and to update the dominant axis when the orientation of the device changes as
`
`“hardware, software, or both to determine an orientation of a device, to assign a
`
`dominant axis, and to update the dominant axis as the orientation of the device changes.”
`
`The petitioner seems to ignore the fact that software, by itself, cannot determine a
`
`dominant axis. Hardware with software/firmware, can.
`
`8.
`
`The petitioner has adopted the definition of a counting logic to count
`
`periodic human motions by monitoring accelerations relative to the dominant axis by
`
`counting the periodic human motions when accelerations showing a motion cycle that
`
`meets motion criteria is detected within a cadence window as “hardware, software, or
`
`both to count periodic human motions by monitoring accelerations relative to the
`
`dominant axis by counting the periodic human motions when accelerations showing a
`
`motion cycle that meets motion criteria is detected within a cadence window.” The
`
`
`
`4
`
`

`

`
`
`petitioner seems to ignore the fact that software, by itself, cannot determine motion.
`
`Hardware with software/firmware, can.
`
`9.
`
`The petitioner has adopted the definition of a cadence logic to update the
`
`cadence window as actual cadence changes as “hardware, software, or both to update
`
`the cadence window as actual cadence changes.”
`
`10. While the petitioner has made some claims in claim construction that
`
`ignore the actual functionality of the hardware and software involved, for the purposes
`
`of this proceeding I will use the petitioners adopted definitions in performing my analysis
`
`and forming my opinions.
`
`IV.
`
`THE ‘723 PATENT
`
`11.
`
`The '723 patented invention is, at its core a specialized motion sensor for
`
`human activity. "A method for monitoring human activity using an inertial sensor includes
`
`continuously determining an orientation of the inertial sensor, assigning a dominant axis,
`
`updating the dominant axis as the orientation of the inertial sensor changes, and counting
`
`periodic human motions by monitoring accelerations relative to the dominant axis."
`
`12.
`
`According to the invention of the ’723 Patent, a device to monitor human
`
`activity using an inertial sensor assigns a dominant axis after determining the orientation
`
`of an inertial sensor. he orientation of the inertial sensor is continuously determined, and
`
`the dominant axis is updated as the orientation of the inertial sensor changes.
`
`13. While motion sensors did exist at the time of the invention, the specific
`
`methodologies used by the '723 patent are novel and inventive.
`
`
`
`5
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`

`

`
`
`V.
`
`ONE OF ORDINARY SKILL IN THE ART
`
`14.
`
`Patent claims must be viewed from the perspective of one of ordinary skill
`
`in the art. A Person of Ordinary Skill in the Art (POSA) in November 1999 would have been
`
`one with a bachelor’s degree in engineering, computer science, or related technical area
`
`with 2 years of experience related to accelerometers or similar devices. Additional
`
`experience can compensate for a lack of a degree.
`
`15.
`
`I am aware that the petitioner has a somewhat different view of a POSA.
`
`While I disagree with a few of the nuances of petitioner’s definition of a POSA, our
`
`definitions are substantially similar. Even if one adopts the petitioners view of a POSA, it
`
`would not alter my opinions.
`
`VI.
`
`GENERAL ISSUES
`
`A.
`
`Dominant Axis
`
`16.
`
`In general, the petitioner conflates the dominant axis in the ‘723 patent with
`
`the Z axis in Fabio and Pasolini. This is incorrect for several reasons.
`
`17.
`
`In Pasolini, the only mention of orientation is
`
`“For example, the main vertical axis can be identified at each acquisition
`of a new acceleration sample, block 30 of FIG. 4, so as to take into
`account variations in the orientation of the pedometer device 1, and
`consequently of the accelerometer 2 arranged inside it.”
`
`This depends entirely on the vertical axis but tries to account for “variations
`
`18.
`
`in the orientation of the pedometer device” It should be noted that Fabio, does not even
`
`mention orientation. It is clear that Pasolini is only concerned about a single axis and
`
`assumes that axis will be the main axis. This is made clear many places in Pasolini, a
`
`sample of such data is provided here:
`
`
`
`6
`
`

`

`
`
`“In use, the accelerometer 2 detects the component along the detection axis z of the
`vertical acceleration generated during the step, and produces a corresponding
`acceleration signal A.”
`
`“The accelerometer 2 could be equipped with a number of axes of
`measurement, for example three mutually orthogonal axes of measurement,
`and be built, for example, as described in “3-axis Digital Output Accelerometer
`For Future Automotive Applications”, B. Vigna et al., AMAA 2004. In this case,
`according to one embodiment of the present invention, the algorithm
`implemented by the processing unit 3 envisages identifying the main vertical axis
`to be used for step detection as the axis of detection that has the highest mean
`acceleration value Accm (on account of gravity). For example, the main vertical
`axis can be identified at each acquisition of a new acceleration sample, block 30
`of FIG. 4, so as to take into account variations in the orientation of the
`pedometer device 1, and consequently of the accelerometer 2 arranged inside
`it.”
`
`
`19.
`
`It is clear that Pasolini did not account for changing axis, and in fact it seems
`
`likely that was not even contemplated. That is in stark contrast to the ‘723 patent wherein
`
`any direction might become dominant and detecting the currently dominant axis is crucial
`
`(note the emphasis are added).
`
`“Embodiments of the present invention are designed to monitor human activity
`using an inertial sensor. In one embodiment, a dominant axis is assigned after
`determining an orientation of an inertial sensor”
`
`“In one embodiment, the dominant axis setting logic 140 determines an
`orientation of the electronic device 100 and/or the inertial sensor(s) within the
`electronic device 100. The orientation may be determined based upon the
`rolling averages of accelerations created by the rolling average logic 135.”
`
`
`
`20.
`
`This is not a trivial difference. A POSA would immediately understand the
`
`significant advantages that the ‘723 patent has over Fabio or Pasolini. And in fact, the ‘723
`
`patent explicitly discussed the advantages this technology presents over the prior art. In the
`
`background of the invention section, the ‘723 inventor point out the deficiencies of the
`
`prior art stating:
`
`
`
`7
`
`

`

`
`
`“Steps may be accurately counted regardless of the placement and/or orientation of
`the device on a user. Steps may be accurately counted whether the electronic device
`100 maintains a fixed orientation or changes orientation during operation. The
`electronic device 100 may be carried in a backpack, pocket, purse, hand, or
`elsewhere, and accurate steps may still be counted.”
`
`
`21.
`
`The petitioner completely ignores the fact that with the ‘723 patent, any
`
`axis can be the dominant axis, and that this provides a significant advantage over the prior
`
`art. The issue of the dominant axis is significant to the very claims the petitioner is
`
`challenging. Dominant axis is addressed three times in claim 1 alone, then again in claim
`
`2. Claim 3 depends on claim 1. Then in claim 10 dominant axis is again discussed, in this
`
`instance four times. Then again in claim 11. Claims 12 and 13 depend on claim 10.
`
`22.
`
`Claim 14 returns to explicitly discussing the dominant axis three times, and
`
`again in claim 15. Claims 16 and 17 depend on claim 14, and claim 18 depends on claim
`
`17.
`
`23.
`
`Once one understands that the dominant axis in the ‘723 patent is
`
`substantially different than the simple vertical axis in Fabio and Pasolini, and further
`
`conveys a significant advantage, then the challenged claims can be immediately seen as
`
`not being obvious nor anticipated by Fabio or Pasolini alone or in combination.
`
`B.
`
`Cadence Window
`
`24.
`
`Claim 1 recites “updating the cadence window as actual cadence changes.”
`
`Claim 5 recites “updating the cadence window as a cadence of the motion cycle changes.”
`
`Claim 10 recites “a cadence logic to update the cadence window as actual cadence
`
`changes.” Claim 12 states “the cadence logic to update a dynamic cadence window.”
`
`Claim 14 states “updating the cadence window as actual cadence changes.”
`
`
`
`8
`
`

`

`
`
`25.
`
`The petitioner claims “Fabio discloses this limitation. Ex.1003, p.78. First,
`
`the limitation is substantially similar to the limitation in [1.4] and is taught by Fabio as
`
`discussed above. Second, Fabio discloses that the updating of its validation window is
`
`performed by its control unit (i.e., a cadence logic): “the control unit 5 executes a first
`
`validation test” that is performed as described in [1.4]. Ex.1003, p.XX; see also Ex.1006,
`
`4:22-40.” However, what Fabio actually states is shown here (note that portion
`
`underlined in red is the portion the petitioner cited):
`
`
`
`26.
`
`The petitioner has extracted a small phrase, not even a complete sentence.
`
`And in doing so has removed the context. What is being describes is a test of the
`
`regularity of the individual step. This is the first validation test. Even if one supposes that
`
`“regularity of the individual step” to be synonymous with “cadence”, this excerpt is not
`
`describing updating the “regularity of the individual step”. This in no way describes
`
`
`
`9
`
`

`

`
`
`updating anything even analogous to the cadence window. It must also be noted that
`
`Fabio only discusses updating with respect to updating the number of steps, not anything
`
`even analogous to the cadence window.
`
`VII.
`
`SPECIFIC CLAIM ELEMENTS
`
`27.
`
`Several claims discussed in the petitioner’s brief and Dr. Pasadino’s
`
`declaration stand out as requiring specific commentary. Those claims are discussed in this
`
`section
`
`Claim 4.1 wherein the dynamic motion criteria includes at least a lower
`A.
`threshold,
`
`
`
`28.
`
`The petitioner states “The combination of Fabio and Pasolini renders this
`
`limitation obvious. First, as described in [1.3.1], Fabio teaches using “motion criteria” to
`
`recognize a step in that the acceleration signal AZ shows a positive peak, higher than a
`
`positive acceleration threshold AZP, followed by a negative peak, smaller than a negative
`
`acceleration threshold AZN.”
`
`29.
`
`I first note that Fabio does not even contain the term “motion criteria”
`
`despite the petitioner putting that in quotes. Neither does Pasolini. The full quote from
`
`Fabio is:
`
`
`
`10
`
`

`

`
`
`30.
`
`The petitioner further mischaracterizes Fabio in the following diagram
`
`
`
`
`
`31. What Fabio actually describes is a combination of a positive peak (so
`
`named because it is above a positive threshold) combined with a negative peak (so named
`
`because it is smaller than a negative threshold) and further the negative peak must follow
`
`the positive peak.
`
`32.
`
`The ‘723 patent teaches “At block 820, processing logic determines
`
`whether acceleration along the dominant axis is greater than a lower threshold. If the
`
`measurement is not greater than the lower threshold, no step may be recognized or
`
`
`
`11
`
`

`

`
`
`counted for that measurement (block 840). If the measurement is greater than the lower
`
`threshold, the processing logic continues to block 825.”
`
`33.
`
`First the ‘723 patent requires the acceleration be along the dominant axis,
`
`a concept that is not present in Fabio nor Pasolini. Secondly the ‘723 patent requires that
`
`“acceleration along the dominant axis is greater than a lower threshold”, Fabio instead
`
`describes “a negative peak, smaller than a negative acceleration threshold.” Fabio
`
`teaches a positive peak followed by a negative peak within a specific time frame. And
`
`that negative peak is compared to a ‘predetermined amplitude”. Essentially Fabio and
`
`Pasolini teach a more inefficient and cumbersome method of detecting a step. The ‘723
`
`patent has a streamlined, elegant method that uses fewer steps and depends on the
`
`dominant axis.
`
`34.
`
`Fabio and Pasolini, alone or in combination, teach away from the ‘723
`
`patents step detection method.
`
`Claim 4.2 wherein the lower threshold is adjusted based on at least one
`B.
`of a rolling average of accelerations and… / Claim 19.1 wherein the dynamic
`motion criteria includes at least a lower threshold, wherein the lower
`threshold is adjusted based on at least one of a rolling average of accelerations
`and the orientation of the inertial sensor.
`
`35.
`
`The petitioner states “The combination of Fabio, Pasolini, and Richardson
`
`renders this
`
`limitation obvious because Pasolini teaches updating the negative
`
`acceleration threshold based on averaging the acceleration samples for each step, and
`
`Richardson teaches computing a moving average of acceleration samples for a current set
`
`of acceleration data that is maintained in a buffer. Ex.1003, p.63. More specifically,
`
`Pasolini teaches that the negative acceleration threshold (referred to as S-, as described
`
`
`
`12
`
`

`

`
`
`above in [4.1]) is “not fixed and equal to a given pre-set value, but [is] calculated in a self-
`
`adaptive way … based on the values assumed by the detected acceleration.””
`
`36.
`
`It is notable that the petitioner does not even claim the concept of
`
`averaging is present in Fabio or Pasolini. In either Fabio or Pasolini, the only mention of
`
`average is in reference to average speed, with not even a suggestion that this leads to
`
`adjusting the lower threshold. The entire quote from Pasolini, from which the petitioner
`
`extracted a portion of a phrase, clarifies this:
`
`
`
`37.
`
`In Pasolini there the positive and negative reference thresholds are
`
`
`
`modified at each acquisition of a new sample of the acceleration signal. This is done
`
`specifically to account for different terrain, increase in speed or gait. This is precisely the
`
`
`
`13
`
`

`

`
`
`opposite of averaging values. In fact, averaging values would negate Pasolini’s stated
`
`purpose, since differences in terrain, speed, or gait would be lost due to averaging.
`
`38.
`
`The petitioner states “A POSITA would have recognized that the
`
`acceleration datum CalAcc is based on an “average of accelerations” because CalAcc is
`
`calculated from the new acceleration sample and the values of the previously acquired
`
`accelerations. Thus, when each new acceleration sample is acquired, the CalAcc value is
`
`updated to account for the previous acceleration samples.” This is incorrect. As previously
`
`explained, Pasolini teaches away from averaging.
`
`39.
`
`As already discussed Pasolini and Fabio teach away from a rolling average.
`
`A rolling average would actually prevent Pasolini and Fabio from functioning as intended.
`
`A rolling average would negate the ability of Pasolini to account for different terrain,
`
`increase in speed or gait.
`
`40.
`
`In paragraph 67 of his declaration, Dr. Paradiso stated in his declaration ‘In
`
`Pasolini, a lower threshold S- is determined based on an acceleration datum CalAcc that
`
`is calculated using the current acceleration data and the “mean value Accm of the
`
`acceleration sample” that was “calculated at the previous acquisition.’ In fact, the CalcAcc
`
`is precisely the opposite. The following excerpts from Pasolini demonstrate this:
`
`a. “Next, block 11, the processing unit 3 determines a first acceleration
`
`datum CalAcc, and consequently modifies the values of the reference
`
`thresholds.” EX1005, 4:20-22.
`
`This quote from Pasolini shows the CalAcc to be a ‘first acceleration datum’ not a
`
`mean or average.
`
`
`
`14
`
`

`

`
`
`b. “"The algorithm then proceeds, block 12, with the search for the positive
`
`phase of the step, by comparing the value of the acceleration datum
`
`CalAcc with the positive reference threshold S+ to detect a positive
`
`acceleration peak of the acceleration signal A" EX1005, 4:24-28.
`
`Again, CalAcc is a single first acceleration datum, not a mean or average. If it were
`
`an average that would significantly alter the process of comparing with the positive
`
`reference threshold. In fact, if CalAcc were a mean or average, then it would be more
`
`difficult to detect acceleration. The comparison of an average to an acceleration peak
`
`would, by definition be substantially the same at every test.
`
`c. “"Until a positive phase of the step is found, block 13, the algorithm
`
`proceeds with acquisition of a new acceleration datum CalAcc in block
`
`11..." EX1005, 4:29-31.
`
`Again, the actual language of Pasolini makes it abundantly clear that CalAcc is not
`
`an average or a mean, but rather a single datum.
`
`41.
`
`A POSITA would readily see that not only is the CalAcc in Pasolini not an
`
`average, but if it where converted to an average, Pasolini’s ability to detect acceleration
`
`would be significantly compromised.
`
`42.
`
`Pasolini actually provides the formula for calculating Accm:
`
`43.
`
`It is obvious from this formula that Accm is not a mean or average. Accm
`
`is an acceleration value. Pasolini does discuss taking the average of the Accm, but Accm
`
`
`
`
`
`15
`
`

`

`
`
`itself is not a mean or average. Furthermore, Pasolini is quite clear on how the CalAcc
`
`value is determined. In every step is it acquired directly from sensor data, it is not the
`
`product of any formula, including averaging/means. As examples of this fact are the
`
`following excerpts from Pasolini:
`
`a. “Until a positive phase of the step is found, block 13, the algorithm
`
`proceeds with acquisition of a new acceleration datum CalAcc in block 11
`
`(and corresponding modification of the reference thresholds), and with
`
`the comparison of said new acceleration datum with the positive reference
`
`threshold S+.”
`
`b. “In detail, the time interval Mask is incremented, block 17, a new
`
`acceleration datum CalAcc is acquired (and the values of the reference
`
`thresholds are modified accordingly), block 18 (which is equivalent to block
`
`11), and the algorithm returns to block 14”
`
`44.
`
`Figure 4 of Pasolini makes this even more clear:
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
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`16
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`

`

`37
`
`
`
`
`
`DETERMIHA'I'JUN DF ACCELERATION DATUM
`Cam: AND THRESHOLD ADAPTATIGN
`
`11.13
`
`ACQUISITION or
`ACCELERATION SAHPLE Am
`
`at)
`
`ELIMINATION 0F I:-.I::. DUMPONENT
`AN D DETERMINATION OF 02%
`
`31
`
`32
`
`Gallium:- Env'
`
`ND-YES
`Em" = m'Em“
`{[13:13
`
`En'I.I"=I:I:2
`
`35
`
`42
`
`5* = B' Emr‘
`H3 «= II
`
`35
`
`40
`
`5-: er Env-
`
`41
`
`3- = s,
`
`STEIF
`
`Fig.4
`
`17
`17
`
`
`
`

`

`
`
`45.
`
`CalAcc is acquired and compared to Env. Env may be changed, but CalAcc
`
`is not altered in any way. It is not summed, averaged, or calculated. But rather simply
`
`acquired from sensors.
`
`46.
`
`In fact, Pasolini explicitly states that CalAcc is not a mean “In detail, in an
`
`initial block 30, the processing unit 3 acquires from the accelerometer 2 a new
`
`acceleration sample Acc of the acceleration A. Then, block 31, the d.c. component of said
`
`acceleration value (due substantially to the acceleration of gravity) is eliminated so as to
`
`determine the acceleration datum CalAcc, with zero mean value, which will be used
`
`subsequently in the algorithm.” The old value is eliminated so a new value can be
`
`calculated with a zero-mean value. This is the complete opposite of using a mean or
`
`averaging for CalAcc.
`
`47.
`
`A POSITA would understand that eliminating the “d.c. component” so that
`
`CalAcc can be determined with “zero mean value” is referring to the mean amplitude of
`
`the waveform, and not an averaging of any kind. A POSITA would have known that when
`
`describing a periodic function in the time domain, the DC bias, DC component, DC offset,
`
`or DC coefficient is the mean amplitude of the waveform. If the mean amplitude is zero,
`
`there is no DC bias, and a POSITA would understand that having a DC bias, or DC
`
`component is generally undesirable.
`
`C.
`
`Claim 1
`
`48.
`
`Claim 1 includes the following segment “assigning a dominant axis with
`
`respect to gravity based on an orientation of the inertial sensor:” The petitioner has
`
`claimed that monitoring human activity is the equivalent of “selecting the acceleration
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`signal corresponding to the detection axis nearest to the vertical” of Fabio. However,
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`what the petitioner ignores is that the ‘723 patent has much broader applications than
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`Fabio or Pasolini. Both prior art patents cited by the petitioner explicitly state they are
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`pedometers and are for measuring walking or running. The ‘723 patent states ‘may be
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`used to count steps or other periodic human motions.’ (emphasis added) The nature of
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`the ‘723 patent is such that it is not limited to steps, but rather any human motion. If one
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`assumes only a limited range of activity, walking or running, then one might assume that
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`vertical means gravity. However, the ‘723 patent does is not limited to only walking or
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`running. As will be seen in the next section of claim 1, the ‘721 patent very clearly
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`anticipated an unlimited range of orientation, that would not be present in a simple
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`pedometer. It is also noteworthy that both the two prior art patents the petitioner cites
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`are by the same inventor, Fabio Pasolini. Therefore, it is abundantly clear what the
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`inventor meant. And in both patents, he was merely concerned with a pedometer.
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`49.
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`Claim 1 also has a segment “detecting a change in the orientation of the
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`inertial sensor and updating the dominant axis based on the change”. It should be first
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`noted that this language plainly anticipates scenarios in which the vertical axis will not be
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`the dominant axis, contrary to Fabio or Pasolini.
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`50.
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`The petitioner has claimed that the ‘723 patent claim language is
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`equivalent to “the main vertical axis can be identified at each acquisition of a new
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`acceleration sample” in the Pasolini patent. However, the complete passage from Pasolini
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`is “For example, the main vertical axis can be identified at each acquisition of a new
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`acceleration sample, block 30 of FIG. 4, so as to take into account variations in the
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`orientation of the pedometer device 1, and consequently of the accelerometer 2 arranged
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`inside it.” It is clear that Pasolini was only attempting to accommodate minor variations
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`in the main vertical axis, not a change in the dominant axis. This is a significant difference
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`and illustrates one of the many advantages of the ‘723 patent over Pasolini.
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`D.
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`Claim 2
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`51.
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`Claim 2 states “The method of claim 1, further comprising: using
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`acceleration measurements along only the dominant axis to count steps.” The petitioner
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`has stated that:
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`“The combination of Fabio and Pasolini renders this limitation obvious.
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`Ex.1003, p.51. First, as discussed above in [1.3.0], Fabio teaches using an
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`acceleration signal produced on the “axis nearest to the vertical” or the
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`“detection axis Z” to count steps. Ex.1003, p.51. Fabio also teaches that
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`the “acceleration signal AZ[] is correlated to the accelerations undergone
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`by the inertial sensor 3 itself along the detection axis Z.”
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`52. What the petitioner is ignoring is that both cited prior art patents
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`specifically state the Z axis. This is not surprising since both are explicitly limited to walking
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`or running. One would therefore assume that the person was upright, and the Z axis
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`would always be the dominant axis. However, the ‘723 patent is a significant
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`improvement over that. The ‘723 patent does not assume that any particular axis is
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`dominant, but rather measures to determine the dominant axis. This allows the ‘723
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`patented invention to be applied to a wide range of human activities. This is one of the
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`novel advantages of the ‘723 patent.
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`E.
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`Claim 6
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`53.
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`Claim 6 states “The method of claim 5, further comprising: switching the
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`device from the active mode to the non-active mode when a number of expected periodic
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`human motions are not identified in the appropriate cadence windows.”
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`54.
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`The petitioner claims that this is anticipated by Fabio citing “[W]hen an
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`interruption in the locomotion is detected, the second counting procedure is terminated,
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`and execution of the first counting procedure resumes (block 110).”
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`55.
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`However, Fabio does not teach the device going to a non-active mode.
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`Rather Fabio teaches the second counting procedure ceasing and the first counting
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`procedure resuming. The first counting procedure is not a non-active mode, but rather a
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`temporary pause. This is demonstrated by the following quote from Fabio “When a
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`sequence of steps corresponding to a regular gait of the user is recognized, the first
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`counting procedure is interrupted. Alternatively, the first counting procedure terminates
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`when a time interval TC that has elapsed from the last step recognized is longer than a
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`first time threshold TS1, for example 10 s. On exit from the first calculation procedure, the
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`control unit 5 sets a state flag FST to a first value C, if a sequence of steps that satisfies the
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`conditions of regularity has been recognized, and to a second value PD, if the first time
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`threshold TS1 has been exceeded.”
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`F.
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`Claim 10
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`56.
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`Claim 10 contains the following “a dominant axis logic to determine an
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`orientation of a device with respect to gravity”. The petitioner has claimed that this is
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`obvious in light of Pasolini and Fabio. However, both Pasolini and Fabio both assume the
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`Z-axis is always the dominant axis with respect to gravity. There is no indication that the
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`inventors ever contemplated the possibility of the dominant axis being an