Apple Inc.

Patent Trials and Appeals Board

May 11, 2021

2021001964 (P.T.A.B. May. 11, 2021)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
16/371,838 04/01/2019 Brent M. Ledvina 090911-
P34127USC1-1127106
1035
65656 7590 05/11/2021
Apple / Kilpatrick Townsend & Stockton LLP
Mailstop: IP Docketing - 22
1100 Peachtree Street
Suite 2800
Atlanta, GA 30309
EXAMINER
SCHWARTZ, JOSHUA L
ART UNIT PAPER NUMBER
2642
NOTIFICATION DATE DELIVERY MODE
05/11/2021 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
KTSDocketing2@kilpatrick.foundationip.com
ipefiling@kilpatricktownsend.com
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte BRENT M. LEDVINA, ROBERT W. BRUMLEY,
ROBERT WILLIAM MAYOR, WILLIAM J. BENCZE,
ALEJANDRO J. MARQUEZ, SHANG-TE YANG,
XU CHEN, INDRANIL S. SEN and MOHIT NARANG
___________

Appeal 2021–001964
Application 16/371,838
Technology Center 2600

____________

Before CARL W. WHITEHEAD JR., JEFFREY S. SMITH and
MICHAEL J. ENGLE, Administrative Patent Judges.

WHITEHEAD JR., Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE1

1 Rather than reiterate Appellant’s arguments and the Examiner’s
determinations, we refer to the Appeal Brief (filed July 31, 2020), the Reply
Brief (filed January 22, 2021), the Final Action (mailed March 20, 2020) and
the Answer (mailed December 8, 2020), for the respective details.

Appeal 2021-001964
Application 16/371,838

2

Appellant2 is appealing the final rejection of claims 2–21 under
35 U.S.C. § 134(a). See Appeal Brief 9. Claims 2 and 14 are independent.
Claim 1 is cancelled. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
Introduction
According to Appellant, the claimed subject matter relates to a
method
for allowing a mobile device (e.g., a key fob or a consumer
electronic device, such as a mobile phone, watch, or other
wearable device) to interact with a vehicle such that a location of
the mobile device can be determined by the vehicle, thereby
enabling certain functionality of the vehicle.
See Specification ¶ 5.
Representative Claim3
2. A method for determining a current location of a mobile device
relative to a vehicle, the method comprising:
receiving a set of signal values measured using one or more device
antennas of the mobile device, the set of signal values providing one or more
signal properties of signals from one or more vehicle antennas having
various locations in the vehicle, wherein the one or more signal properties of
a signal change with respect to a distance between a device antenna of the

2 Appellant to refer to “applicant” as defined in 37 C.F.R. § 1.42(a).
Appellant identifies Apple Inc. as the real party in interest. Appeal Brief 3.

3 “Applicant respectfully submits that the combination of the references used
in the 35 U.S.C. § 103(a) fails to disclose or suggest each and every feature
of claim 2.” Appeal Brief 10; see also Appeal Brief 7, 14, 17. Accordingly,
we select independent claim 2 as the representative claim. See 37 C.F.R.
§ 41.37(c)(1)(iv).
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mobile device that received the signal and a vehicle antenna that emitted the
signal;
storing a machine learning model, wherein:
an input to the machine learning model comprises the one or
more signal properties of the signals from the one or more vehicle
antennas;
an output of the machine learning model comprises a
classification of the current location of the mobile device as being
within a region of a set of regions in a vicinity of the vehicle; and
the machine learning model is trained using various sets of
signal values measured at various locations across the set of regions;
and
providing the set of signal values to the machine learning model to
obtain a current classification of a particular region of the set of regions, the
particular region corresponding to the current location of the mobile device.

References
Name4 Reference Date
Tapia US 2016/0021503 A1 January 21, 2016
Reese US 2016/0328661 A1 November 10, 2016
Verkin US 2017/0327083 A1 November 16, 2017
Sieber US 2018/0083349 A1 March 22, 2018
Ledvina US 10,285,013 B2 May 7, 2019

4 All reference citations are to the first named inventor only.

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Rejections on Appeal5
Claims 2–12, 14–19 and 21 stand rejected under 35 U.S.C. § 103 as
being unpatentable over Verkin and Tapia. Final Action 3–9; see Answer 3
(Statement of rejected claims amended to include dependent claim 21).
Claims 13 and 20 stand rejected under 35 U.S.C. § 103 as being
unpatentable over Verkin, Tapia and Sieber. Final Action 9–11.
Claims 2–12, 14–19 and 21 stand rejected under 35 U.S.C. § 103 as
being unpatentable over Verkin and Reese. Final Action 11–17; see Answer
3 (Statement of rejected claims amended to include dependent claim 21).
Claims 13 and 20 stand rejected under 35 U.S.C. § 103 as being
unpatentable over Verkin, Reese and Sieber. Final Action 17–19.

5 The Examiner rejects claims 2–20 on the ground of nonstatutory
obviousness-type double patenting. See Final Action 19–26. The Examiner
further indicates on a PTO-90C form mailed December 8, 2020 that
Appellant’s Terminal Disclaimer filed June 5, 2020 was not approved.
Appellant does not proffer arguments addressing the merits of the
nonstatutory obviousness-type double patenting. See Appeal Brief 10; see
also Answer 3. Accordingly, we summarily sustain the Examiner’s
nonstatutory obviousness-type double patenting rejection of claims 2–20.
See 37 C.F.R. § 41.37(c)(1)(iv) (“The arguments shall explain why the
examiner erred as to each ground of rejection contested by appellant. Except
as provided for in §§ 41.41, 41.47 and 41.52, any arguments or authorities
not included in the appeal brief will be refused consideration by the Board
for purposes of the present appeal. ”).
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ANALYSIS
We have reviewed the Examiner’s rejections in light of Appellant’s
arguments that the Examiner has erred. We adopt as our own (1) the
findings and reasons set forth by the Examiner in the action from which this
appeal is taken and (2) the reasons set forth by the Examiner in the
Examiner’s Answer in response to Appellant’s Appeal Brief, except where
noted.

Obviousness over Verkin and Tapia
Appellant argues, “Verkin pertains to assessing the reliability of a
determination of the relative position between a device and a vehicle using
radio signals from vehicle antennas” and therefore “Verkin does not disclose
or suggest using a set of signal values to obtain a classification of a
particular region of a set of regions in a vicinity of a vehicle in which a
mobile device is in; nor does Verkin disclose or suggest using a machine
learning model to do so, as claim 2 pertains.” Appeal Brief 10. Appellant
states that support for the “classification of a particular region” limitation is
located in paragraph 99 of the Specification and Figure 11. See Appeal
Brief 8. Paragraph 99 is reproduced below (emphasis added):
[0099] Distance information 1115 (such as time stamps, signal
strengths, or an actual distance) can be measured on the mobile
device using signals received from one or more vehicle antennas.
These measurements along with any required ancillary data (such
as gyrometer, accelerometer, and coarse location data) are used
with the machine learning model 1130 to make a region decision
1145. Additional input features to model 1130 could be a channel
impulse response and ratio of the received power for the first path
to the second path.

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Paragraph 99 of the Specification as well as Appellant’s Figure 11 are
silent in regard to classifying a particular region as Appellant argues. We
find, in light of Appellant’s Specification, that Verkin discloses
classification of the current location of both the mobile device and particular
regions. Verkin discloses, “If the device, i.e., the electronic key which may
be configured as an RFID transponder, is in a range (of several meters), it
receives the LF query signal, may decode it, and may retransmit it in the
UHF frequency band having new encoding” and the “signal transmitted by
the electronic key may be decoded again in the vehicle.” Verkin ¶ 13.
Verkin does not disclose a machine learning model as recited in claim
2, however, the Examiner relies upon Tapia for disclosing a machine
learning model wherein “an input to the machine learning model comprises
the one or more signal properties of the signals from the one or more vehicle
antennas” and “an output of the machine learning model comprises a
classification of the current location or the mobile device as being within a
region of a set of regions in a vicinity of the vehicle.” Final Action 4
(emphasis added) (citing Tapia ¶¶ 8, 10, 12, 21).
Appellant contends, “[I]n Tapia, the data inputs to the geolocation
model may include signal characteristics of the telecommunication signals
that are from the user device and received by either a network cell or a Wi-
Fi access point.” Appeal Brief 12; see Tapia ¶ 23. Appellant argues,
“Tapia, however, does not disclose or suggest that the geographical areas
pertain to regions in a vicinity of a vehicle.” Appeal Brief 12. We agree
with Appellant that Tapia does not disclose regions pertaining to a vehicle,
however, the Examiner relies upon Verkin to disclose the vehicle whereas
Tapia discloses, “machine learning may use the trained geolocation model to
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estimate the geolocation of a user device” while “the classification algorithm
may classify the data inputs using the trained geolocation model to
determine a geographical area that has the highest probability of
corresponding to the geolocation of the user device.” Tapia ¶ 12 (emphasis
added); see Final Action 4. Tapia further discloses:
the geolocation engine 102 may use the trained geolocation
model and a classification algorithm to classify the data inputs
that are relevant for a user device. Such data inputs may include
time of the day, one or more signal characteristics (e.g., signal
strength, signal quality, etc.) of the telecommunication signal
from the user device received by a network cell, interference to
the network cell from nearby network cells, location of the
network cell, and/or so forth. Accordingly, the classification
algorithm may calculate a probability that the user device is
located in each of multiple geographical areas.

Tapia ¶ 21 (emphasis added). Accordingly, we do not find Appellant’s
arguments persuasive of Examiner error because it is the combination of the
references that discloses the claimed invention. See Final Action 4. Here,
Verkin discloses “a method for assessing a reliability of a determination of
the relative position between a device for accessing a vehicle” and Tapia
discloses a machine learning model as recited in claim 2. Verkin, Abstract;
Tapia ¶ 21. “As our precedents make clear, however, the analysis need not
seek out precise teachings directed to the specific subject matter of the
challenged claim, for a court can take account of the inferences and creative
steps that a person of ordinary skill in the art would employ.” KSR Int’l v.
Teleflex Inc., 550 U.S. 398, 418 (2007). We sustain the Examiner’s
obviousness rejection of independent claim 2 as well as the obviousness
rejections of dependent claims 3–21, not argued separately. See Appeal
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Brief 7, 14, 17.

Obviousness over Verkin and Reese
Appellant argues that Reese does not address Verkin’s deficiencies in
regard to claim 2. See Appeal Brief 14. The Examiner finds that Reese
discloses a machine learning model wherein “an input to the machine
learning model comprises the one or more signal properties of the signals
from the one or more vehicle antennas” and “an output of the machine
learning model comprises a classification of the current location of the
mobile device as being within a region of a set of regions in a vicinity of the
vehicle.” Final Action 12 (emphasis added). The Examiner further finds that
Reese provides, “the set of signal values to the machine learning model to
obtain a current classification of a particular region of the set of regions, the
particular region corresponding to the location of the mobile device (Reese
at ¶[¶]30, 33, 59, and 67.).”
Appellant argues:
In Reese, the input to the machine learning model is the current
geolocation and recent past geolocations of the mobile device.
This is in contrast to claim 2, which recites that “an input to the
machine learning model comprises the one or more signal
properties of the signals from the one or more vehicle antennas.”

In Reese, the output of the machine learning model are predicted
future geolocations of the mobile device. This is in contrast to
amended claim 2, which recites “an output of the machine
learning model comprises a classification of the current location
of the mobile device as being within a region of a set of regions
in a vicinity of the vehicle.”
Appeal Brief 15; see Reese ¶¶ 5, 6, Abstract.
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Paragraph 99 of the Specification, as well as, Appellant’s Figure 11
are silent in regard to the classification of the current location of the mobile
device as Appellant argues. See Appeal Brief 8. Also, Reese discloses a
description of related art in paragraphs 5 and 6. Reese discloses, “The
present invention relates generally to predictive information retrieval system
and, more specifically, to scalable complex event processing with
probabilistic machine learning models to predict subsequent geolocations of
individuals.” Reese ¶ 3.
Nonetheless, Reese discloses:
the server 18 may route incoming geolocation events (e.g.,
indications from one of the mobile computing devices 16 that the
mobile computing device is currently at a geolocation, such as a
given place), to both the event stream ingest module 32 and the
geolocation event log 20, the latter of which may update a record
and store the event for subsequent training and updates to
models.
Reese ¶ 33 (emphasis added); see Final Action 12.
Accordingly, we do not find Appellant’s arguments persuasive of
Examiner error because it is the combination of the references that discloses
the claimed invention. See Final Action 12. Here, Verkin discloses “a
method for assessing a reliability of a determination of the relative position
between a device for accessing a vehicle” and Reese discloses a machine
learning model as recited in claim 2. Verkin, Abstract; see Reese ¶ 33; Final
Action 12, 13; KSR Int’l, 550 U.S. at 418. We sustain the Examiner
obviousness rejection of independent claim 2, as well as, the obviousness
rejections of dependent claims 3–21, not argued separately. See Appeal
Brief 7, 14, 17.
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CONCLUSION

Claims
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
2–20 nonstatutory
obviousness-type
double patenting
2–20
2–12, 14–
19, 21
103 Verkin, Tapia 2–12, 14–
19, 21

13, 20 103 Verkin, Tapia,
Sieber
13, 20
2–12, 14–
19, 21
103 Verkin, Reese 2–12, 14–
19, 21

13, 20 103 Verkin, Reese,
Sieber
13, 20
Overall
Outcome
2–21

No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1). See 37 C.F.R.
§ 1.136(a)(1)(v).

AFFIRMED