Trials@uspto.gov
`571-272-7822
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`Paper No. 7
`Filed: March 11, 2019
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`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`SAMSUNG ELECTRONICS AMERICA, INC.,
`Petitioner,
`
`v.
`
`UNILOC 2017 LLC
`Patent Owner.
`____________
`
`Case IPR2018-01756
`Patent 7,653,508 B1
`____________
`
`
`Before SALLY C. MEDLEY, JOHN F. HORVATH, and
`SEAN P. O’HANLON, Administrative Patent Judges.
`
`HORVATH, Administrative Patent Judge.
`
`
`
`DECISION
`Denying Institution of Inter Partes Review
`35 U.S.C. § 314(a)
`
`
`
`
`
`
`571-272-7822
`
`
`
`
`
`Paper No. 7
`Filed: March 11, 2019
`
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`SAMSUNG ELECTRONICS AMERICA, INC.,
`Petitioner,
`
`v.
`
`UNILOC 2017 LLC
`Patent Owner.
`____________
`
`Case IPR2018-01756
`Patent 7,653,508 B1
`____________
`
`
`Before SALLY C. MEDLEY, JOHN F. HORVATH, and
`SEAN P. O’HANLON, Administrative Patent Judges.
`
`HORVATH, Administrative Patent Judge.
`
`
`
`DECISION
`Denying Institution of Inter Partes Review
`35 U.S.C. § 314(a)
`
`
`
`
`
`
`
`IPR2018-01756
`Patent 7,653,508 B1
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`I. INTRODUCTION
`
`A. Background
`Samsung Electronics America, Inc. (“Petitioner”)1 filed a Petition
`requesting inter partes review of claims 18, 11–16, 19, and 20 (“the
`challenged claims”) of U.S. Patent No. 7,653,508 B1 (Ex. 1001, “the ’508
`patent”). Paper 1 (“Pet.”). Uniloc 2017 LLC (“Patent Owner”)2, filed a
`Preliminary Response. Paper 6 (“Prelim. Resp.”). We have jurisdiction
`under 35 U.S.C. § 314. For the reasons discussed below, we exercise our
`discretion under 35 U.S.C. § 314(a) to deny institution of inter partes
`review.
`B. Related Matters
`Petitioner and Patent Owner identify the following as matters that
`could affect, or be affected by, a decision in this proceeding: Uniloc USA,
`Inc. v. Samsung Elec. Am., Inc., 2-17-cv-00650 (EDTX); Uniloc USA, Inc. v.
`Huawei Devices USA, Inc., 2-17-cv-00737 (EDTX); Uniloc USA, Inc. v.
`HTC Am., Inc., 2-17-cv-01629 (WDWA); Uniloc USA, Inc. v. Apple Inc.,
`4-8-cv-00364 (NDCA); Uniloc USA, Inc. v. LG Elec. USA, Inc., 4-18-cv-
`02918 (NDCA); Apple Inc. v. Uniloc Luxembourg S.A., Case IPR2018-
`00387 (PTAB); Apple Inc. v. Uniloc Luxembourg S.A., Case IPR2018-00389
`(PTAB); Apple Inc. v. Uniloc Luxembourg S.A., Case IPR2018-00424
`(PTAB); Apple Inc. v. Uniloc Luxembourg S.A., Case IPR2018-01026
`(PTAB); Apple Inc. v. Uniloc Luxembourg S.A., Case IPR2018-01027
`
`
`1 Petitioner identifies Samsung Electronics Co., Ltd. as a real party-in-
`interest. Pet. 1.
`2 Patent Owner identifies Uniloc USA, Inc. and Uniloc Licensing USA LLC
`as real parties-in-interest. Paper 3, 1.
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`(PTAB); Apple Inc. v. Uniloc Luxembourg S.A., Case IPR2018-01028
`(PTAB); LG Elec., Inc. v. Uniloc 2017 LLC, Case IPR2018-01458 (PTAB);
`LG Elec., Inc. v. Uniloc 2017 LLC, Case IPR2018-01577 (PTAB); HTC
`Corp. v. Uniloc 2017 LLC, Case IPR2018-01589 (PTAB); HTC Corp. v.
`Uniloc 2017 LLC, Case IPR2018-01631 (PTAB); Samsung Elec. Am., Inc. v.
`Uniloc 2017 LLC, Case IPR2018-01653 (PTAB); Samsung Elec. Am., Inc. v.
`Uniloc 2017 LLC, Case IPR2018-01757 (PTAB). Pet. 1–3; Paper 3, 1–2.
`C. Evidence Relied Upon3
`
`References
`Tamura
`
`Fabio
`
`Pasolini
`
`US 7,698,097 B2
`
`US 7,463,997 B2
`
`Richardson
`
`US 5,976,083
`
`
`
`US 2006/0010699 A1
`
`Jan. 19, 2006
`
`Effective Date4
`
`Exhibit
`
`Oct. 2, 2006
`
`Oct. 2, 2006
`
`Nov. 2, 1999
`
`1005
`
`1006
`
`1008
`
`1009
`
`D. Asserted Grounds of Unpatentability
`Petitioner asserts the following grounds of unpatentability:
`Reference(s)
`Basis
`Claim(s) Challenged
`Tamura and Pasolini
`§ 103(a)
`1, 2, 11, and 12
`Tamura, Pasolini, and Fabio
`§ 103(a)
`3–5, 13, and 14
`Tamura, Pasolini, Fabio, and
`§ 103(a)
`5
`Richardson
`Fabio
`§ 102(e)
`6–8, 15, 16, 19, and 20
`
`3 Petitioner relies upon the Declaration of Dr. Irfan Essa (Ex. 1002). Patent
`Owner relies upon the Declaration of William C. Easttom II (Ex. 2001).
`4 Petitioner relies on the filing dates of Fabio and Pasolini as the effective
`date for determining their availability as prior art. Pet. 5.
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`II. ANALYSIS
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`A. The ’508 Patent
`The ’508 patent relates to “a method of . . . counting periodic human
`motions such as steps.” Ex. 1001, 1:5–7. A “portable electronic device
`[100] that includes one or more inertial sensors. . . . measure[s] accelerations
`along a single axis or multiple axes.” Id. at 2:21–25. The accelerations may
`be linear or rotational. Id. at 2:25–26.
`Figure 1 of the ’508 patent is reproduced below.
`
`Figure 1 of the ’508 patent is a block diagram illustrating electronic device
`100. Id. at 1:43–44. Device 100 includes acceleration measuring logic 105,
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`dominant axis logic 127, and step counting logic 130. Id. at 2:18–21.
`Acceleration measuring logic 105 includes one or more inertial sensors that
`measure linear or rotational acceleration data along one or more axes at a
`fixed or variable sampling rate determined by timer 170. Id. at 2:21–26,
`2:35–40. Device 100 counts “steps or other periodic human motions.” Id. at
`2:26–27. The ’508 patent defines a “step,” as “any user activity having a
`periodic set of repeated movements.” Id. at 3:33–36. Device 100 counts
`steps “regardless of the placement and/or orientation of the device on a
`user,” and regardless of whether the device “maintains a fixed orientation or
`changes orientation during operation.” Id. at 2:28–32.
`
`Dominant axis logic 127 includes cadence logic 132, rolling average
`logic 135, and dominant axis setting logic 140. Id. at 2:64–67. Cadence
`logic 132 analyzes measured acceleration data to detect “a period and/or
`cadence of [a] motion cycle” based on user activity such as running or
`walking. Id. at 3:12–16. Cadence logic 132 also determines “a cadence
`window 150 to be used by the step counting logic 130.” Id. at 3:9–12.
`Cadence window 150 is “a window of time since a last step was counted that
`is looked at to detect a new step.” Id. at 3:65–67. Cadence window 150 is
`initially set to a default value, and is updated after each step once a
`minimum number of steps have been detected to reflect the cadence or
`period of the detected steps. Id. at 4:21–27, 4:62–5:3. The cadence or
`stepping period can be determined as a “rolling average of the stepping
`periods over previous steps.” Id. at 3:60–61. The minimum and maximum
`of cadence window 150 can be “determined by measuring lengths of time
`since the most recent step was counted.” Id. at 4:10–13.
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`Cadence logic 132 also determines “one or more sample periods to be
`used by the rolling average logic 135.” Id. at 3:9–11. A sample period can
`be “the length of, or longer than, [a] stepping period,” including a “multiple
`of the stepping period.” Id. at 5:30–37. Rolling average logic 135 “creates
`one or more rolling averages of accelerations as measured by the inertial
`sensor(s) over the sample period(s) set by the cadence logic 132.” Id. at
`5:38–40. The rolling averages may be “used for determining an orientation
`of the electronic device, [and] for determining thresholds” against which
`acceleration measurements are compared. Id. at 5:40–44.
`Dominant axis setting logic 140 determines an orientation of device
`100 or the inertial sensor(s) within device 100 “based upon the rolling
`averages of accelerations created by the rolling average logic 135.” Id. at
`6:7–11. “The axis with the largest absolute rolling average” over a given
`sampling period, i.e., the “axis most influenced by gravity,” is determined to
`be the dominant axis. Id. at 6:12–17, 6:22–25. This allows “a new
`dominant axis [to] be assigned when the orientation of the electronic device
`100 and/or the inertial sensor(s) attached to or embedded in the electronic
`device 100 changes,” because the rolling average changes over time as the
`orientation of device 100 changes over time. Id. at 6:15–21. Dominant axis
`setting logic 140 can also set the dominant axis to be a virtual “axis that is
`defined as approximately aligned to gravity,” and that is determined “by
`doing trigonometric calculations on the actual axes based on the
`gravitational influence” on those axes. Id. at 6:24–33.
`Step counting logic 130 includes measurement selection logic 145,
`measurement comparator 155, and threshold comparator 160. Id. at 6:37–
`41. Measurement selection logic 145 “monitor[s] accelerations relative to
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`the dominant axis, and select[s] only those measurements with specific
`relations to the dominant axis,” such as those that are approximately parallel
`or perpendicular to the dominant axis. Id. at 6:43–50. “Selected
`measurements [are] forwarded to the measurement comparator 155 and the
`threshold comparator 160 to determine whether a step has occurred.” Id. at
`6:56–58.
`A method for determining whether a step has occurred is disclosed in
`Figure 8 of the ’508 patent, which is reproduced below.
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`Figure 8 is a flow diagram of a method for recognizing that a step has
`occurred. Id. at 1:64–67, 12:25–27. Acceleration data is measured (805) at
`a sampling rate, filtered (810) to remove low and high frequency
`components, and used to assign a dominant axis (812). Id. at 12:31–44, Fig.
`8. The sampled acceleration data must pass several criteria to be counted as
`a step, including falling within a cadence window (815). Id. at 12:45–50,
`Fig. 8. The sampled acceleration along the dominant axis must be greater
`than a lower threshold (820), which can be the rolling average acceleration
`along the dominant axis. Id. at 7:9–12, 12:51–57, 12:64–66, 13:4–6, Fig. 8.
`The sampled acceleration along the dominant axis must be greater than
`previously measured accelerations (825). Id. at 7:9–12, 13:33–40, 13:53–56,
`Fig. 8. Finally, the sampled acceleration must be lower than an upper
`threshold (830), which “prevent[s] sudden accelerations such as taps from
`being counted as steps.” Id. at 13:59–14:3, Fig. 8.
`Device 100 is battery operated and has multiple operating modes to
`preserve battery life, including sleep mode 305, entry mode 315, stepping
`mode 325, and exit mode 335. Id. at 8:16–18. The power level of device
`100 is linked to these modes. Id. at 8:18–19. The different modes and the
`relationships between them are shown in Figure 3 of the ’508 patent, which
`is reproduced below.
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`Figure 3 of the ’508 patent is a state diagram showing the different modes of
`electronic device 100. Id. at 1:45–50. When no acceleration data is
`measured, device 100 is in sleep mode 305. Id. at 8:20–22. When
`acceleration data is detected, device 100 enters entry mode 315 to detect
`steps in the acceleration data. Id. at 8:22–25. If a predetermined number
`(N) of steps are detected in a sampling period, device 100 enters stepping
`mode 325; otherwise it reverts to sleep mode 305. Id. at 8:25–28. In
`stepping mode 325, steps are detected and counted as described above until
`no steps are detected within the cadence window, at which point device 100
`enters exit mode 335. Id. at 8:30–37. In exit mode 335, device 100
`determines whether a predetermined number (X) of steps are detected at a
`particular cadence. Id. at 8:38–40. If so, device 100 reverts to stepping
`mode 325; if not, device 100 reverts to entry mode 315. Id. at 8:41–44.
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`The operation of device 100 in entry mode 315 is shown in Figure 5,
`which is reproduced below.
`
`Figure 5 of the ’508 patent is a flow chart of device 100 operating in entry
`mode 315. Id. at 1:54–56. After setting a sampling rate (504), a first step is
`detected in the acceleration data (510), a default cadence window is set
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`(514), and a temporary or buffered step count is set to one (520). Id. at
`9:55–10:2, 10:25. The default cadence window may be set “such that steps
`will be counted for most or all possible stepping cadences, whether a user is
`walking slowly or sprinting.” Id. at 10:3–6. Next, the acceleration data is
`searched for additional steps using the criteria discussed above (524) with
`respect to Figure 8, including whether the acceleration data falls within the
`cadence window. Id. at 10:25–30.
`
`When additional steps are detected in the acceleration data (524), they
`are added to the buffered step count (560). Id. at 10:46–47. If the number of
`steps in the buffered step count reaches predetermined number M (564), a
`new cadence window is set based on the cadence of the M steps (574), and
`the acceleration data is searched for additional steps (524). Id. at 10:47–58.
`If the number of steps in the buffered step count reaches a predetermined
`number N, the N buffered steps are added to an actual step count, and device
`100 enters stepping mode 325 (584). Id. at 10:57–11:3. In stepping mode
`325, the cadence window is dynamically updated based on the rolling
`average of previously measured stepping periods. Id. at 11:13–17.
`As discussed above, measured acceleration data is counted as a step
`only when it falls within cadence window 150. Id. at 10:25–30, 12:45–46,
`Fig. 8. Cadence window 150 has minimum and maximum times measured
`from the time of the most recently detected step. Id. at 4:10–13. If
`measured acceleration data falls outside cadence window 150 because it is
`too early and time remains within cadence window 150 (530), the
`acceleration data is searched for additional steps (524). Id. at 10:32–36. If
`measured acceleration data falls outside cadence window 150 because it is
`too late, no time remains within cadence window 150 (530), the buffered
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`step count is reset (534), and the acceleration data is searched for another
`first step (540/510). Id. at 10:36–43.
`B. Illustrative Claims
`Of the challenged claims, claims 1, 6, 11, and 15 are independent.
`Other challenged claims depend from these claims. Claim 1 is reproduced
`below.
`
`1. A method of monitoring human activity using an
`inertial sensor, comprising;
`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.
`
`
`Ex. 1001, 15:10–18. Claim 11 recites an inertial sensor based device having
`a dominant axis logic and a counting logic to perform the method recited in
`claim 1. Id. at 16:6–12. Claim 6 recites a method of monitoring human
`activity that alternates between active and non-active modes, and is
`reproduced below.
`6. A method of monitoring human activity using an
`inertial sensor, comprising:
`running a device that includes the inertial sensor in
`a non-active mode, in which periodic human
`motions are buffered;
`switching the device from the non-active mode to
`an active mode, after identifying a number of
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`periodic human motions within appropriate cadence
`windows; and
`during the active mode, counting each of the
`periodic human motions to enable the monitoring of
`human activity.
`Id. at 15:37–47. Claim 15 recites a device having an inertial sensor and
`counting logic, mode logic, and a buffer to perform the method recited in
`claim 6. Id. at 16:30–38.
`C. Overview of the Prior Art
`1. Tamura
`Tamura discloses “a mobile terminal apparatus 1 . . . with the function
`of a pedometer.” Ex. 1005 ¶ 24. The mobile terminal can be a mobile
`phone or a personal digital assistant. Id. ¶ 18. Figure 1 of Tamura,
`reproduced below, schematically illustrates mobile terminal 1.
`
`
`Figure 1 of Tamura is a schematic illustration of a mobile terminal having
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`the functionality of a pedometer. Id. ¶¶ 13, 24. The mobile terminal
`includes processing unit 10, detecting unit 20, GPS unit 34, and display unit
`36. Id. ¶ 19. Detecting unit 20 includes magnetic sensor 22 and tilt angle
`sensor 24, and detects “position, azimuth, bearing, altitude, and so forth.”
`Id.
`
`Tilt angle sensor 24 “is an acceleration sensor for detecting
`acceleration components in three axis directions,” including an “X axis and
`[a] Y axis [that] are placed orthogonally to each other in a horizontal plane,
`and [a] Z axis [that] is placed in the direction of gravity.” Id. ¶ 21. When a
`user of mobile terminal 1 is walking, “tilt angle sensor 24 detects not only
`the acceleration components corresponding to the tilt angle of the mobile
`terminal apparatus 1 but also low-frequency acceleration components in
`response to the movement of the user.” Id. ¶ 24. Processing unit 10
`“performs a frequency analysis on the detection results and counts the
`number of [a] user’s steps based . . . on the acceleration components of a
`frequency within a predetermined range.” Id. ¶ 25. To count steps,
`processing unit 10 uses “detection results along an axis within the tilt angle
`sensor 24 which most approximates the axis of gravity” because walking
`“add[s] an acceleration component mainly in the direction of gravity.” Id.
`The detection results can be “corrected based on the detection results along
`the other two axes.” Id. The “axis [that] most approximates the axis of
`gravity” is “calculated based on changes in the resistance values of the
`respective [tilt angle sensor] axes and the calculated values of the [tilt angle
`sensor’s] pitch angle and roll angle.” Id. The pitch and roll angles are “the
`angles relative to the direction of gravity,” and are calculated based on
`“gravitational acceleration changes” detected by tilt angle sensor 24 when
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`“the posture of the mobile terminal apparatus 1 inclines.” Id. ¶¶ 20–21.
`That is, processing unit 10 “detects acceleration components of a frequency
`in a predetermined range, based on a detection result along an axis among
`the axes of the tilt angle sensor which most approximates a gravity axis, and
`counts the number of user’s steps.” Id. ¶ 6.
`2. Pasolini
`Pasolini discloses a pedometer that uses a step detection “algorithm
`for self-adaptive computation of acceleration thresholds.” Ex. 1008, 1:10–
`12. The pedometer may be housed in a portable device, such as a mobile
`phone, and includes an accelerometer having multiple detection axes. Id. at
`2:60–63, 8:11–13, 8:31–34. The step detection algorithm “identif[ies] 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).” Id.
`at 8:18–20. The algorithm identifies the main vertical axis “at each
`acquisition of a new acceleration sample . . . to take into account variations
`in the orientation of the pedometer device 1 and consequently of the
`accelerometer 2 arranged inside it.” Id. at 8:20–24.
`Pasolini’s step detection algorithm “compares the value of [measured]
`acceleration signal A . . . with a positive reference threshold S+ and with a
`negative reference threshold S-, for identifying, respectively, the positive
`phase (positive acceleration peak) and the negative phase (negative
`acceleration peak) of the step.” Id. at 3:35–41. The negative acceleration
`phase must occur “within a certain time interval Mask,” of the positive
`acceleration phase for acceleration signal A to be counted as a step. Id. at
`4:47–50, 4:66–5:3. “[T]he values of the positive and negative reference
`thresholds S+, S- are not fixed . . . but are calculated in a self-adaptive way.”
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`Id. at 3:42–45. In particular, thresholds S+ and S- “are modified at each
`acquisition of a new sample of the acceleration signal A, as a function of the
`value of a positive and negative amplitude envelope of the acceleration
`signal.” Id. at 3:48–54. This allows the pedometer to “adapt[] to variations
`in the detection conditions . . . due, for example, to different types of terrain
`or to an increase in the speed of the gait.” Id. at 3:54–59.
`3. Fabio
`Fabio discloses a method for “controlling a pedometer based on the
`use of inertial sensors.” Ex. 1006, 1:10–11. The pedometer can be
`“integrated within a portable electronic device, such as a cell phone.” Id. at
`2:34–36. The method involves:
`generating a signal correlated to movements of a
`user of the pedometer; detecting steps of the user
`based on the signal; checking whether sequences of
`the detected steps satisfy pre-determined conditions
`of regularity; updating a total number of valid steps
`if the conditions of regularity are satisfied; and
`preventing updating of the total number of valid
`steps if the conditions of regularity are not satisfied.
`Id. at 1:62–2:3. Fabio detects user steps from sampled acceleration data AZ
`according to a method illustrated in Figures 5 and 6, which are reproduced
`below.
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`Figure 5 of Fabio is a graph illustrating quantities used to detect user steps
`from acceleration data. Id. at 2:22–23. A step is detected when a positive
`peak of acceleration signal AZ is greater than threshold AZP, and a negative
`peak is less than threshold AZN and falls within fixed time window TW
`measured from the positive peak. Id. at 4:15–21.
`
` A detected step is validated as a step when it falls within a variable
`time window TV, which is illustrated in Figure 6, reproduced below.
`
`
`Figure 6 of Fabio is a graph illustrating quantities used to validate user steps
`detected from acceleration data. Id. at 2:24–25. Figure 6 illustrates a
`sequence of user steps detected at times TR(1), TR(2) . . . TR(K-2), TR(K-1),
`and TR(K) according to the method disclosed in Figure 5. The time between
`steps detected at times TR(K-1) and TR(K-2) is ∆TK-1; the time between steps
`detected at times TR(K) and TR(K-1) is ∆TK. Id. at 4:28–35. For the step
`detected at time TR(K) to be validated, it must fall within variable time
`window TV, i.e., TR(K) must be greater than TR(K-1) + ½ (∆TK-1) and less
`than TR(K-1) + 2 (∆TK-1). Id. at 4:35–52. Although Figure 6 depicts TV
`having variable width ∆TK-1 (e.g., because it depends on the variable time
`between previous steps) that is asymmetric about time TR(K-1) + ∆TK-1,
`Fabio discloses TV can be “symmetrical and a have a different amplitude.”
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`Id. at 4:52–53. Fabio further discloses time window TV ensures “the
`duration ∆TK of a current step K is substantially homogeneous with respect
`to the duration ∆TK-1 of an immediately preceding step.” Id. at 4:28–31.
`When counting steps, Fabio discloses that isolated “or very brief
`sequences of steps are far from significant and should preferably be ignored
`because they are, in effect, irrelevant.” Id. at 1:47–50. To ignore such steps,
`Fabio discloses a two-stage counting procedure, illustrated in Figure 3,
`reproduced below.
`
`
`Figure 3 of Fabio is a flow chart depicting a two-stage counting procedure.
`Id. at 2:17–19. Initially, valid step counter NVT, valid control step counter
`NVC, and invalid step counter NINV are set to zero (100). Id. at 3:13–18.
`First counting procedure 110 counts steps by sampling acceleration signal
`AZ at a predetermined frequency. Id. at 3:19–21. First counting procedure
`110 terminates when “a regular gait of the user is recognized,” or when “a
`time interval . . . that has elapsed from the last step recognized is longer than
`a first time threshold.” Id. at 3:27–36. If the first time threshold expires the
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`pedometer is placed in a wait or power down state, otherwise second
`counting procedure 130 continues to count steps. Id. at 3:37–41, 3:50–53.
`
`Fabio illustrates first counting procedure 110 in Figure 4, which is
`reproduced below.
`
`
`Figure 4 of Fabio is a flowchart illustrating first counting procedure 110. Id.
`at 2:20–21, 3:58–59. Acceleration data AZ is sampled (200), and if the time
`Tc between the sample time and the time of the last detected step is greater
`than a first threshold TS1 (205), state flag FST is set (210), and survey
`procedure 140 is called. Id. at 3:60–4:2; see also Fig. 3. Otherwise, if Tc is
`less than a second, shorter, threshold TS2 (215), sample AZ is checked to see
`if it is recognized (225) and validated (230) as a step according to the
`procedures disclosed in Figures 5 and 6. Id. at 4:2–55. That is, sample AZ
`must have positive and negative peaks, and fall within time window TV
`from a preceding step.
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`If sample AZ is recognized (225) and validated (230) as a step, the
`number of valid control steps NVC is incremented (255) and compared to a
`threshold NT2 (260). Id. at 5:10–22. If NVC is less than NT2, another sample
`of acceleration data is obtained (200) and the process continues. Id. at 5:22–
`27. However, if NVC is equal to NT2 (260), the number of valid control steps
`NT2 is added to the number of valid steps NVT, state flag FST is set to the
`count value C (i.e., NVC), and second counting procedure 130 is called (265).
`Id. at 5:30–39; see also Fig. 3. If the acceleration sample is recognized
`(225) but not validated (230) as a step because it falls outside of time
`window TV, an invalid step count NINV is incremented (235), a new
`acceleration data sample is obtained (step 200), and the process is repeated.
`Fabio’s second counting procedure 130 is illustrated in Figure 7,
`which is reproduced below.
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`Figure 7 of Fabio is a flowchart illustrating second counting procedure 130.
`Id. at 6:12–13. Acceleration data AZ is sampled (300), and if the time Tc
`between the sample time and the time of the last detected step is greater than
`threshold TS2 (305), second counting procedure 130 is terminated. Id. at
`6:14–20; see also Fig. 3. Otherwise, sample AZ is checked to see if it is both
`recognized (315) and validated (320) as a step according to the procedures
`disclosed in Figures 5 and 6 (320). Id. at 6:21–39. If sample AZ is
`recognized and validated as a step, the number of valid steps NVT is
`incremented (325), another sample of acceleration data is obtained (300),
`and the process continues searching for valid steps. Id. at 6:40–53. Second
`counting procedure 130 terminates when (a) the time Tc between an
`acceleration data sample and the time of the last detected step is greater than
`threshold TS2 (305), or (b) a sample is not validated as a step (320) and the
`incremented number of invalid steps NINV (340) reaches threshold NT4 (345).
`Id. at 6:14–20, 6:54–7:6. When second counting procedure 130 terminates,
`first counting procedure 110 is executed. Id., Fig. 3.
`
`In addition to disclosing the two-stage counting procedure described
`above, Fabio discloses that when the pedometer’s inertial sensor contains
`two or more detection axes, step recognition is “performed by selecting the
`acceleration signal corresponding to the detection axis nearest to the
`vertical.” Id. at 8:21–25. The nearest-to-vertical axis “is selected on the
`basis of the value of the DC component of the respective acceleration signal,
`which is correlated to the contribution of the acceleration of gravity.” Id. at
`8:27–30. This allows steps to be counted “independently of how [the
`pedometer] is oriented.” Id. at 8:32–33.
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`Fabio uses survey procedure 140 to identify the nearest-to-vertical
`detection axis. See id. at 7:21–59, Fig. 3. Survey procedure 140 executes
`whenever first counting procedure 110 terminates for failure to detect a step
`within a time TS1 from a previously detected step. Id. at 3:29–32, 3:50–53,
`Fig. 3. Survey procedure 140 initially determines and stores mean value
`AZM of the DC component of acceleration signal AZ in non-volatile memory.
`Id. at 7:27–31. Mean value AZM represents the inertial sensor’s acceleration
`due to gravity along its detection axis, and indicates the pedometer’s
`orientation. Id. at 7:30–37. The pedometer then enters a sleep state, and
`periodically wakes up (e.g., every 10 seconds) to determine an updated mean
`value AZM' of the DC component of acceleration signal AZ. Id. at 7:46–48. If
`AZM' is substantially the same as AZM, the pedometer goes back to sleep. Id.
`at 7:54–59. Otherwise, first counting procedure 110 is executed, i.e., first
`counting procedure is executed whenever the pedometer’s orientation
`changes. Id. at 7:51–54.
`4. Richardson
`Richardson discloses a “fitness monitoring device [that] includes a
`pedometer for determining and outputting data representing the locomotion
`of [an] individual.” Ex. 1009, 1:18–20. The device samples data from
`accelerometer subsystem 025, and alternately stores the sampled data in one
`of two buffers A/B. Id. at 28:29–36. While new data is stored in one of the
`buffers, an acceleration baseline is determined from data previously stored in
`the other buffer by “comput[ing] at each sample time a moving average of
`acceleration.” Id. at 28:34–39. A graph of the measured and moving
`average acceleration from Richardson’s pedometer is shown in Figure 13a,
`reproduced below.
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`Figure 13a is plot of Richardson’s measured acceleration versus time (solid
`lines) overlayed with a plot of moving average acceleration versus time
`(dashed lines). To detect a step, Richardson requires a peak in the measured
`acceleration to be “above a minimum height, never less than 0.1 G above
`baseline, which is usually around 1 G.” Id. at 28:56–58. Richardson further
`discloses the minimum height threshold is dynamic because it “is adjusted
`upwards when peak accelerations 168 have been higher, and is adjusted
`downwards when peak accelerations 168 have been lower.” Id. at 28:58–60.
`D. Multiple Petitions
`Numerous claims of the ’508 patent have been challenged in four
`previously filed inter partes review proceedings. We instituted trial in
`IPR2018-00387 (“the Apple IPR”) on the patentability of (a) claims 1, 2,
`11, and 12 over Pasolini, (b) claims 6–8, 15, 16, and 19 over Fabio, and
`(c) claims 3, 4, 13, and 14 over Pasolini and Fabio. See Apple Inc. v. Uniloc
`Luxembourg S.A., Case IPR2018-00387, slip op. 5–6, 27 (Paper 8) (July 23,
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`2018, PTAB). We denied institution of trial in IPR2018-01026 (“the Apple
`II IPR”) on the patentability of claim 5 over Pasolini, Fabio, and Richardson.
`See Apple Inc. v. Uniloc 2017 LLC, Case IPR2018-01026, slip op. 7, 23
`(Paper 9) (Oct. 18, 2018, PTAB). We instituted trial in IPR2018-01577, a
`copycat of the Apple IPR, after which we joined LG Electronics, Inc. and its
`real parties-in-interest to the Apple IPR and terminated the proceeding. See
`LG Electronics, Inc. v. Uniloc 2017 LLC, Case IPR2018-01577, slip op. 6–7
`(Paper 8) (Jan. 15, 2019, PTAB). Finally, we instituted trial in IPR2018-
`01589 (“the HTC IPR”), a near copycat of the Apple IPR, after which we
`joined HTC Corp. and its real parties-in-interest to the Apple IPR but
`maintained the proceeding to consider HTC’s challenge to claim 20—a
`challenge not presented in the Apple IPR. See HTC Corp.. v. Uniloc 2017
`LLC, Case IPR2018-01589, slip op. 6–7 (Paper 9) (Feb. 27, 2019, PTAB).
`Patent Owner argues we should deny the instant Petition because “it
`relies on the same art and substantially the same arguments . . . already
`before the Board [in these] four other pending IPR proceedings.” Prelim.
`Resp. 36. Patent Owner further argues Fabio, Pasolini, and Richardson are
`“the same references as in the prior petitions,” and that although Tamura is a
`new reference it is used “in substantially the same manner as the Pasolini
`reference was used in the previous petitions.” Id. at 38. Patent Owner also
`argues Petitioner “had the benefit of Patent Owner’s preliminary responses
`and patent owner response to earlier petitions (e.g., in each of IPR2018-
`00387, IPR2018-01026, IPR2018-01589, and IPR2018-01577) and further
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`had the benefit of the Board’s decision on whether to institute review in
`IPR2018-00387 and IPR2018-01026.”5 Id. at 37.
`Petitioner argues we should not deny the Petition because Petitioner
`has not previously challenged the claims of the ’508 patent and is not a party
`to any previously filed inter partes review challenging the claims of the ’508
`patent. Pet. 66. Petitioner further argues we should not deny the Petition
`because
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