Trials@uspto.gov
`571-272-7822
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`Paper No. 18
`Entered: November 5, 2019
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`APPLE, INC.,
`Petitioner,
`
`v.
`
`UNILOC 2017 LLC,
`Patent Owner.
`____________
`
`IPR2018-01028
`Patent 7,881,902 B1
`____________
`
`
`Before SALLY C. MEDLEY, JOHN F. HORVATH, and
`SEAN P. O’HANLON, Administrative Patent Judges.
`
`HORVATH, Administrative Patent Judge.
`
`
`
`JUDGMENT
`Final Written Decision
`Determining All Challenged Claims Unpatentable
`35 U.S.C. § 318(a)
`
`
`
`
`
`
`571-272-7822
`
`
`
`
`
`Paper No. 18
`Entered: November 5, 2019
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`____________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`____________
`
`APPLE, INC.,
`Petitioner,
`
`v.
`
`UNILOC 2017 LLC,
`Patent Owner.
`____________
`
`IPR2018-01028
`Patent 7,881,902 B1
`____________
`
`
`Before SALLY C. MEDLEY, JOHN F. HORVATH, and
`SEAN P. O’HANLON, Administrative Patent Judges.
`
`HORVATH, Administrative Patent Judge.
`
`
`
`JUDGMENT
`Final Written Decision
`Determining All Challenged Claims Unpatentable
`35 U.S.C. § 318(a)
`
`
`
`
`
`
`
`IPR2018-01028
`Patent 7,881,902 B1
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`
`I. INTRODUCTION
`
`A. Background
`Apple Inc. (“Petitioner”) filed a Petition requesting inter partes review
`of claim 8 (“the challenged claim”) of U.S. Patent No. 7,881,902 B1
`(Ex. 1001, “the ’902 patent”). Paper 2 (“Pet.”). Uniloc 2017 LLC (“Patent
`Owner”)1 filed a Preliminary Response. Paper 7 (“Prelim. Resp.”). Upon
`consideration of the Petition and Preliminary Response, we instituted inter
`partes review of claim 8 on the ground raised in the Petition. Paper 8 (“Dec.
`Inst.”)
`Patent Owner filed a Response to the Petition (Paper 10, “PO Resp.”),
`Petitioner filed a Reply (Paper 11, “Pet. Reply”), and Patent Owner filed a
`Sur-Reply (Paper 12, “PO Sur-Reply”). We held a consolidated oral hearing
`for this case and related cases involving the same parties and related patents
`on April 2, 2019, and the hearing transcript is included in the record. See
`Paper 17 (“Tr.”).
`We have jurisdiction under 35 U.S.C. § 6(b). This is a Final Written
`Decision under 35 U.S.C. § 318(a) and 37 C.F.R. § 42.73. For the reasons
`set forth below, we find Petitioner has shown by a preponderance of
`evidence that claim 8 of the ’902 patent is unpatentable.
`B. Related Matters
`Petitioner and Patent Owner identify numerous district court matters
`that could affect, or be affected by, a decision in this proceeding. See Pet. 1–
`2; Paper 3, (2); Paper 6, (2). In addition, our Institution Decision identifies
`
`
`1 Uniloc 2017 LLC identifies itself, Uniloc USA, Inc., and Uniloc Licensing
`USA LLC as real parties-in-interest. Paper 6 (1).
`
`2
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`Patent 7,881,902 B1
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`numerous inter partes reviews challenging claims of the ’902 patent and
`related U.S. Patent Nos. 7,653,508 B1 and 8,712,723 B1, that could affect,
`or be affected by, this proceeding. See Dec. Inst. 2–3.
`C. Evidence Relied Upon2
`
`Reference
`Pasolini
`
`Fabio
`
`Tsuji
`
`US 7,463,997 B2
`
`US 7,698,097 B2
`
`US 7,297,088 B2
`
`Effective Date3
`
`Exhibit
`
`Oct. 2, 2006
`
`Oct. 2, 2006
`
`Apr. 19, 2005
`
`1005
`
`1006
`
`1010
`
`
`
`8
`
`D. Instituted Ground of Unpatentability
`References
`Claim Challenged
`35 U.S.C. §
`Fabio, Pasolini, and Tsuji
`103(a)
`II. ANALYSIS
`
`A. The ’902 Patent
`The ’902 patent relates to “a method of . . . counting periodic human
`motions such as steps.” Ex. 1001, 1:9–11. The method involves the use of a
`“portable electronic device that includes one or more inertial sensors” that
`“measure accelerations along a single axis or multiple axes.” Id. at 2:24–28.
`
`
`2 Petitioner also relies upon the Declaration of Joseph A. Paradiso, Ph.D.
`(Ex. 1003).
`3 Petitioner relies on the filing dates of Pasolini, Fabio, and Tsuji as the
`effective date for determining their availability as prior art under 35 U.S.C.
`§ 102(e). Pet. 8–9.
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`Figure 1 of the ’902 patent is reproduced below.
`
`
`Figure 1 of the ’902 patent is a block diagram illustrating electronic device
`100. Id. at 1:47–48. Device 100 includes acceleration measuring logic 105
`(e.g., inertial sensors), dominant axis logic 127, and step counting logic 130.
`Id. at 2:19–24, 2:38–43, Fig. 1. Device 100 “may be used to count steps or
`other periodic human motions,” where a “step” is “any user activity having a
`periodic set of repeated movements.” Id. at 2:29–30, 3:34–38. According to
`the ’902 patent, device 100 accurately counts steps “regardless of the
`placement and/or orientation of the device on a user,” and regardless of
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`whether the device “maintains a fixed orientation or changes orientation
`during operation.” Id. at 2:31–35.
`
`Dominant axis logic 127 includes cadence logic 132 and rolling
`average logic 135. Id. at 2:66–3:2, Fig. 1. Inertial sensors 105 measure
`acceleration data, and cadence logic 132 analyzes this data to detect “a
`period and/or cadence of a motion cycle,” which may be based on user
`activity such as running or walking. Id. at 2:38–40, 3:14–18, 3:46–51.
`Cadence logic 132 determines “a cadence window 150 to be used by the step
`counting logic 130.” Id. at 3:11–14. 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 3:65–4:1. Cadence window 150 is initially set to a default value, and can
`be dynamically updated to reflect the cadence or period of detected steps
`once a minimum number of steps have been detected. Id. at 3:57–61, 4:22–
`28, 4:61–5:6. The cadence or stepping period can be determined as a
`“rolling average of the stepping periods over previous steps.” Id. at 3:61–62.
`Cadence logic 132 also determines “one or more sample periods to be
`used by the rolling average logic 135.” Id. at 3:11–14, 5:31–34. The sample
`periods can be set to “the length of, or longer than, the stepping period,”
`including a “multiple of the stepping period.” Id. at 5:34–37. Rolling
`average logic 135 “creates one or more rolling averages of accelerations . . .
`measured by the inertial sensor(s) over the sample period(s) set by the
`cadence logic 132.” Id. at 5:39–41. These rolling averages are used to
`determine an orientation of the electronic device and a threshold against
`which acceleration measurements are compared. Id. at 5:41–45.
`Dominant axis logic 127 includes dominant axis setting logic 140,
`which determines an orientation of device 100 or of the inertial sensor(s)
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`within device 100. Id. at 6:8–10. This may be done “based upon the rolling
`averages of accelerations created by the rolling average logic 135.” Id. at
`6:10–12. In particular, “[t]he axis with the largest absolute rolling average”
`over a given sampling period is identified as the “axis most influenced by
`gravity” and is designated the dominant axis. Id. at 6:14–18, 6:23–25. The
`’902 patent explains that because device orientation may change over time,
`the rolling average acceleration may change and “a new dominant axis may
`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.” Id. at 6:16–22. 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 found from “trigonometric calculations on the actual
`axes based on the gravitation influence” on those axes. Id. at 6:25–34.
`Step counting logic 130 includes measurement selection logic 145,
`measurement comparator 155, and threshold comparator 160. Id. at 6:38–
`41. Measurement selection logic 140 “monitor[s] accelerations relative to
`the dominant axis, and select[s] only those measurements with specific
`relations to the dominant axis.” Id. at 6:44–47. “Selected measurements
`[are] forwarded to the measurement comparator 155 and the threshold
`comparator 160 to determine whether a step has occurred.” Id. at 6:57–59.
`A method for determining whether a step has occurred is disclosed in
`Figure 8 of the ’902 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 2:1–4, 12:25–27. Acceleration measurement data is
`received and filtered to remove low and high frequency components. Id. at
`12:31–38, Fig. 8 (steps 805 and 810). A dominant axis is assigned as
`described above. Id. at 40–44, Fig. 8 (step 812). If the acceleration
`measurement falls outside the cadence window, it is discarded. Id. at 12:45–
`48, Fig. 8 (step 815). If the acceleration measurement falls within the
`cadence window, three additional tests are performed to determine whether a
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`step is counted. First, the acceleration along the dominant axis must be
`greater than a lower threshold, such as the rolling average acceleration along
`the dominant axis. Id. at 7:9–12, 12:51–55, 12:64–65, Fig. 8 (step 820).
`Second, the acceleration along the dominant axis must be greater than the
`previous measured accelerations along the dominant axis. Id. at 7:9–12,
`13:34–38, 13:53–56, Fig. 8 (step 825). Third, the acceleration along the
`dominant axis must be lower than an upper threshold, which “prevent[s]
`sudden accelerations such as taps from being counted as steps.” Id. at
`13:66–14:3, 7:9–12, 13:59–62, Fig. 8 (step 830).
`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 ’902 patent, which
`is reproduced below.
`
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`Figure 3 of the ’902 patent is a state diagram showing the different modes of
`electronic device 100. Id. at 1:52–54. 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.
`The method by which device 100 transitions from entry mode 315 to
`stepping mode 325 is shown in Figure 5, which is reproduced below.
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`Figure 5 of the ’902 patent is a flow chart of entry mode 315. Id. at 1:58–60.
`After setting a sampling rate (504), a first step is detected in the acceleration
`data (510), a default cadence window is set (514), and a temporary or
`buffered step count is set to one (520). Id. at 9:55–10:8, 10:25. Next
`additional steps are searched for in the acceleration data (524) using the
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`criteria discussed above, including whether the acceleration data falls within
`the default cadence window. Id. at 10:25–30, 12:45–46, Fig. 8.
`
`When additional steps are detected (524), they are added to the
`buffered step count (560). Id. at 10:46–47. If the buffered step count is less
`than a predetermined number M (564), additional steps are looked for in the
`acceleration data (524). Id. at 10:47–52. If the buffered step count is equal
`to M (564) and the cadence window is the default cadence window (570), a
`new cadence window is determined based on the cadence of the M steps
`(574). Id. at 10:53–57. The new cadence window is used to look for
`additional steps in the acceleration data (524) until a predetermined number
`of N steps is counted in the buffered step count (580). Id. at 10:57–67.
`When the buffered step count reaches N steps, the N steps are added to an
`actual step count (584), and device 100 enters stepping mode 325. Id. at
`10:67–11:3. 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 only counted as a
`step when it falls within the cadence window. Id. at 10:25–30, 12:45–46,
`Fig. 8. If the measured acceleration data falls outside the cadence window
`(530), the buffered step count is reset to zero (534). Id. at 10:36–37. If
`acceleration data is subsequently detected (540), the process begins again to
`look for a first step (510). Id. at 10:42–44. Otherwise, the device enters
`sleep mode (544). Id. at 10:41–42.
`B. The Challenged Claim
`Claim 8, the only challenged claim, depends from independent claim
`5. Claims 5 and 8 are reproduced below.
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`5. A method for a mobile device comprising:
`receiving acceleration data that meets stepping
`criteria from an accelerometer included in the
`mobile device;
`incrementing a step count in a step count buffer;
`when at least one of a) the step count is below a step
`count threshold, or b) a current user cadence fails to
`match a step cadence of a user profile, using a
`default step cadence window to identify a time
`frame within which to monitor for a next step; and
`when the step count is at or above the step count
`threshold, determining a dynamic step cadence
`window and using the dynamic step cadence
`window to identify the time frame within which to
`monitor for the next step.
`Ex. 1001, 15:46–16:6.
`8. The method of claim 5, wherein determining the
`dynamic step cadence window comprises:
`computing a rolling average of stepping periods of
`previously counted steps; and
`setting the dynamic step cadence window based on
`the rolling average of stepping periods.
`Id. at 16:22–27.
`C. Claim Construction
`This Petition was filed on May 4, 2018. Pet. 45. For petitions filed
`before November 13, 2018, claim terms of an unexpired patent are given
`their broadest reasonable interpretation in light of the specification of the
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`patent in which they appear. 37 C.F.R. § 42.100(b) (2017).4 Under the
`broadest reasonable interpretation standard, claim terms are generally given
`their ordinary and customary meaning, as would be understood by one of
`ordinary skill in the art, in the context of the entire disclosure. In re
`Translogic Tech., Inc., 504 F.3d 1249, 1257 (Fed. Cir. 2007). Only claim
`terms which are in controversy need to be construed and only to the extent
`necessary to resolve the controversy. See Vivid Techs., Inc. v. Am. Sci. &
`Eng’g, Inc., 200 F.3d 795, 803 (Fed. Cir. 1999).
`Petitioner proposes a construction for the term “cadence window,”
`which Petitioner argues is defined in the Specification to mean “a window of
`time since a last step was counted that is looked at to detect a new step.”
`Pet. 7 (citing Ex. 1001, 3:66–4:1). In our Institution Decision, we
`determined that this claim term does not require express construction. See
`Dec. Inst. 16. Neither party disputes that determination. See PO. Resp. 5
`(“Patent Owner agrees no specific construction is necessary here.”); Pet.
`Reply 1–20. Accordingly, we maintain our decision declining to expressly
`construe the term “cadence window.”
`D. Overview of the Prior Art
`1. 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:
`
`
`4 See Changes to the Claim Construction Standard for Interpreting Claims in
`Trial Proceedings Before the Patent and Trial Appeal Board, 83 Fed. Reg.
`51,340 (Oct. 11, 2018).
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`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 the sampled acceleration data
`AZ of its inertial sensor according to a method illustrated in Figures 5 and 6,
`which are reproduced below.
`
`
`
`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 recognized 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 a fixed time window TW
`measured from the positive peak. Id. at 4:15–21.
`
`A recognized step is validated when it falls within a validation
`interval TV, which is illustrated in Figure 6, reproduced below.
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`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 recognized 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 recognized at times TR(K-1) and TR(K-2) is ∆TK-1; the time
`between steps recognized at times TR(K) and TR(K-1) is ∆TK. Id. at 4:28–
`35. For the step recognized at time TR(K) to be validated as a step, it must
`fall within validation interval 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 TV is described as having an amplitude or “width” of 3/2 ∆TK-1
`that depends on the variable time between previous steps K-1 and K-2, Fabio
`discloses TV can “have a different amplitude.” Id. at 4:52–53. Fabio further
`discloses that TV is used to ensure “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,
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`Fabio discloses a two-stage counting procedure, illustrated in Figure 3,
`which is 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.
`Next, first counting procedure 110 counts steps by sampling acceleration
`data AZ at a predetermined frequency. Id. at 3:19–21. First counting
`procedure 110 terminates and sets a state flag to C when “a regular gait of
`the user is recognized,” or terminates and sets the state flag to PD 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 state flag is set to C, second
`counting procedure 130 executes; otherwise, survey procedure 140 executes
`and the pedometer is placed in a wait or power down state. Id. at 3:37–41,
`3:50–53.
`
`Fabio illustrates first counting procedure 110 in Figure 4, which is
`reproduced below.
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`Figure 4 of Fabio is a flowchart illustrating first counting procedure 110. Id.
`at 2:20–21, 3:58–59. Sampled acceleration data AZ is read (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 to PD (210) and
`survey procedure 140 is called. Id. at 3:60–4:2; see also id. at Fig. 3.
`Otherwise, the sample is checked to see if it is both recognized as a step
`(225) according to the procedure disclosed in Figure 5, and validated as a
`step (230) according to the procedure disclosed in Figure 6. Id. at 4:2–55.
`As discussed above, to be validated, a step must fall within validation
`interval TV.
`If the sample 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
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`acceleration data sample is read (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., NVT), and second counting procedure 130 is called
`(265). Id. at 5:30–39; see also id. at Fig. 3. If the acceleration sample is
`recognized (225) but not validated (230) as a step because it falls outside of
`validation interval TV, an invalid step count NINV is incremented (235), a
`new acceleration data sample is read (step 200), and the process is repeated.
`Id. at Fig. 4.
`Fabio’s second counting procedure 130 is illustrated in Figure 7,
`which is reproduced below.
`
`
`Figure 7 of Fabio is a flowchart illustrating second counting procedure 130.
`Id. at 6:12–13. Sampled acceleration data AZ is read (300), and if the time
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`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 id. at Fig. 3. Otherwise, the sample is checked to see if
`it is both recognized as a step (315) according to the procedure disclosed in
`Figure 5, and validated as a step (320) according to the procedure disclosed
`in Figure 6. Id. at 6:21–39. If the sample is recognized and validated as a
`step, the number of valid steps NVT is incremented (325), another
`acceleration data sample is read (300), and the process continues searching
`for valid steps. Id. at 6:40–53. Second counting procedure 130 continues
`until either (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. at Fig. 3.
`2. Pasolini
`Pasolini discloses a pedometer having a “step detection method using
`an algorithm for self-adaptive computation of acceleration thresholds.”
`Ex. 1005, 1:10–12. The pedometer can be housed inside 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 pedometer’s 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),” and does so “at each acquisition of a
`new acceleration sample . . . to take into account variations in the orientation
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`of the pedometer device 1 and consequently of the accelerometer 2 arranged
`inside it.” Id. at 8:16–24.
`Pasolini’s step detection algorithm “compares the value of the
`[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. Once a positive acceleration
`phase is identified, a negative acceleration phase is searched for “within a
`certain time interval Mask.” Id. at 4:47–50. If a negative step phase is
`found within the Mask interval, a step is counted, and the length (LPS) of
`the step is estimated as LPS = LP x f(S+
`max), where LP is a standard length
`max) is “a
`corresponding to 0.4 to 0.5 times the height of the user, and f(S+
`max reached by the positive reference
`function of the maximum value S+
`threshold S+ during the positive phase of the step.” Id. at 4:66–5:24.
`max) can be tabulated and stored in memory on the basis of
`Function f(S+
`experimental tests. Id. at 5:24–5:29.
`3. Tsuji
`Tsuji discloses “an electronic pedometer . . . mounted on a human
`body in order to electronically count the number of steps by a person.”
`Ex. 1010, 1:6–8. Figure 1 of Tsuji is reproduced below.
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`Figure 1 of Tsuji is a block diagram of Tsuji’s electronic pedometer. Id. at
`4:30–31. The pedometer includes walk sensor or accelerometer 100 for
`detecting and outputting an acceleration signal, and counter 102 for counting
`the number of user steps based on the acceleration signal. Id. at 4:32–39.
`Counter 102 includes filter 105 for filtering and outputting the signal
`received from accelerometer 100, walk cycle comparator 106 for comparing
`a cycle time of the signal received from filter 105 to a range of reference
`walk cycles (i.e., Ta +/- 10%), and walk cycle calculator 108 for calculating
`a reference walk cycle (i.e., Ta) as a moving average of a predetermined
`number of cycle times of accelerometer signals that have been determined to
`be walk signals by walk cycle comparator 106. Id. at 4:50–67, 6:51–7:47.
`E. Level of Ordinary Skill in the Art
`Petitioner contends a person of ordinary skill in the art would have
`had a Bachelor of Science degree or equivalent training in electrical
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`engineering, computer engineering, or computer science, and approximately
`two years of hardware or software design and development experience
`related to micro-electro-mechanical (MEM) devices and body motion
`sensing systems. Pet. 6 (citing Ex. 1003, 8). Patent Owner does not dispute
`Petitioner’s contention, and does not offer an alternative set of qualifications
`for a person of ordinary skill in the art. PO Resp. 2–3.
`We find Petitioner’s description to be consistent with the problems
`and solutions disclosed in the patent and prior art of record, and adopt it as
`our own for purposes of this Decision. See, e.g., In re GPAC Inc., 57 F.3d
`1573, 1579 (Fed. Cir. 1995).
`F. Patentability of Claim 5 over Fabio and Pasolini
`Claim 8 depends from independent claim 5. In challenging the
`unpatentability of claim 8, Petitioner repeats its analysis from IPR2018-
`00424 that claim 5 is unpatentable under 35 U.S.C. § 103(a) as obvious over
`Fabio and Pasolini. Pet. 9–32. We previously found claim 5 unpatentable
`over the combination of Fabio and Pasolini based on that analysis. See
`Apple Inc. v. Uniloc 2017 LLC, IPR2018-00424, Paper 21 at 43–52 (PTAB
`July 16, 2019) (Final Written Decision). Given that claim 8 depends from
`claim 5, we repeat Petitioner’s analysis of claim 5 here, as well as the
`reasons we find Petitioner’s analysis persuasive notwithstanding Patent
`Owner’s arguments to the contrary.
`Claim 5 is an independent claim reciting a method to be performed by
`a mobile device, and requires receiving acceleration data that meets stepping
`criteria from an accelerometer included in the mobile device. Ex. 1001,
`15:46–48. Petitioner demonstrates how Fabio teaches this limitation by
`disclosing a mobile pedometer that receives acceleration data AZ and
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`determines whether that data satisfies stepping criteria AZP and AZN. Pet.
`19–22 (citing Ex. 1006, 1:58–59, 2:23–26, 2:34–36, 2:49–64, Figs. 1, 5). In
`particular, Fabio’s pedometer determines whether the acceleration data has a
`positive peak greater than AZP followed by a negative peak less than AZN
`within a time window TW. See Ex. 1006, 4:12–21, Fig. 5. Patent Owner
`does not dispute these contentions. See PO Resp. 6–16; PO Sur-Reply 1–15.
`Claim 5 further requires incrementing a step count in a step count
`buffer. Ex. 1001, 15:49. Petitioner demonstrates how Fabio teaches this
`limitation by temporarily counting a number of valid control steps in first
`counting procedure 110 until “the number of valid control steps NVC reaches
`the second threshold number NT2.” Pet. 22–23 (citing Ex. 1006, 4:24–26,
`5:10–11, 5:40–45, Fig. 4). Relying on the testimony of Dr. Paradiso,
`Petitioner demonstrates that NVC is a step count buffer because it temporarily
`stores step counts until a regularity condition is reached, at which time NVC
`is added to total step count NVT. Id. at 23 (citing Ex. 1003, 32–33). Patent
`Owner does not dispute these contentions. See PO Resp. 6–16; PO Sur-
`Reply 1–15.
`
`Claim 5 further requires using a default step cadence window to
`identify a time frame within which to monitor for a next step when at least
`one of (a) the step count is below a step count threshold, or (b) a current user
`cadence fails to match a step cadence of a user profile. Ex. 1001, 15:50–
`16:2. Petitioner demonstrates how the combination of Fabio and Pasolini
`teaches or suggests using a default cadence window to monitor for a next
`step when a step count is below a step count threshold (i.e., under condition
`(a) above), which is sufficient to demonstrate the combination meets this
`limitation. Pet. 24–29 (citing Ex. 1006, 5:22–32, 5:42–45, Figs. 4, 6;
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`Ex. 1003, 33–36). In particular, notwithstanding Patent Owner’s arguments
`to the contrary, discussed infra, Petitioner demonstrates how Fabio’s first
`counting procedure 110 counts steps using a default cadence window when
`step count NVC is below threshold NT2. Id.
`Petitioner identifies Fabio’s default cadence window as either first
`timing threshold TS1 or validation interval TV modified to have a fixed
`width. Id. at 27–29. Petitioner argues that it would have been obvious to
`modify validation interval TV to have a fixed width because Fabio expressly
`teaches (a) that TV can have a different amplitude (i.e., width) than the 3/2
`ΔTK-1 width shown in Figure 6 (which depends on the variable time interval
`between previous steps K-1 and K-2), and (b) that TV is used to verify the
`compatibility between the timing of a current step and the frequency of
`previously counted steps. Id. at 28 (citing Ex. 1006, 4:52–55; Ex. 1003, 38–
`39). Dr. Paradiso testifies that these teachings would have suggested to a
`person skilled in the art the benefit of “modify[ing] the cadence window
`[TV] to a default value in order to increase compatibility with a user’s
`previous step as the user is beginning a new activity such as running or
`walking.” Ex. 1003, 38–39. Additionally, Petitioner argues that it would
`have been obvious to modify TV to have a fixed width because Pasolini
`teaches “establishing a default step length value (LP) that is used to
`determine the distance that a user travels.” Pet. 28 (citing Ex. 1003, 39;
`Ex. 1005, 5:21–22). Dr. Paradiso testifies that Pasolini’s teaching would
`have further motivated a person skilled in the art to modify Fabio’s
`validation interval TV to have a fixed width because “establish[ing] a default
`cadence window based on [a] user’s physical attributes would increase the
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`likelihood that the [user’s] first few steps are recognized as being
`compatible.” Ex. 1003, 39.
`We agree with Petitioner’s reasoning, and find it to have rational
`underpinnings. Fabio teaches using validation interval TV to determine the
`timing compatibility of steps, and the timing compatibility of a user’s first
`few steps can only be determined by comparing them to a default time
`interval because there is no other time interval against which to compare
`them. See Ex. 1006, 4:28–31 (“[V]alidation occurs when the duration ΔTK
`of a current step K is substantially homogeneous with respect to the duration
`ΔTK-1 of an immediately preceding step K-1 . . . .”).
`Patent Owner argues that Fabio’s validation interval TV, even when
`modified to have a default width, is not a default cadence window because it
`fails to monitor for a next step. See PO Resp. 7, 10–12; PO Sur-Reply 8–10.
`Specifically, Patent Owner argues:
`Fabio’s TV is retrospective at least in that it is used to validate
`only the immediately preceding step (shown in Fig. 6 as K-1) . . .
`:
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