Waymo LLC

Patent Trials and Appeals Board

Nov 4, 2020

15847064 - (D) (P.T.A.B. Nov. 4, 2020)

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.
15/847,064 12/19/2017 Jiajun Zhu XSDV 3.0-460
CON CON RE I
6919
146033 7590 11/04/2020
WAYMO LLC
BOTOS CHURCHILL IP LAW
430 Mountain Ave
Suite 401
New Providence, NJ 07974
EXAMINER
LILLIS, EILEEN DUNN
ART UNIT PAPER NUMBER
3993
NOTIFICATION DATE DELIVERY MODE
11/04/2020 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):
pto@bciplaw.com
waymo@bciplaw.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte JIAJUN ZHU, DAVID I. FERGUSON, and DMITRI A. DOLGOV
____________

Appeal 2020-005723
Application 15/847,064
Patent US 9,216,737 B1
Technology Center 3900
____________

Before MICHELLE R. OSINSKI, JILL D. HILL, and
CYNTHIA L. MURPHY, Administrative Patent Judges.

OSINSKI, Administrative Patent Judge.
DECISION ON APPEAL

STATEMENT OF THE CASE
The above-identified application seeks reissue of US 9,216,737 B1.
Appellant1 appeals under 35 U.S.C. § 134(a) from the Examiner’s decision
rejecting claims 1–49 in this reissue application. We have jurisdiction over
the appeal under 35 U.S.C. § 6(b). A telephonic oral hearing was held on
September 30, 2020.2

1 We use the term “Appellant” to refer to “applicant” as defined in 37 C.F.R.
§ 1.42. Appellant identifies the real party in interest as Waymo LLC.
Appeal Br. 2.
2 The record includes a transcript of the oral hearing.
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We AFFIRM.
THE CLAIMED SUBJECT MATTER
Claims 1, 8, 13, 21, 28, 33, and 41 are independent. Claims 1 and 8
are reproduced below.
1. A method comprising:
controlling, by one or more computing devices, an
autonomous vehicle in accordance with a first control strategy;
receiving, by the one or more computing devices, sensor
data indicating a detection of a first object;
classifying, by the one or more computing devices, the
first object based on the sensor data;
accessing, by the one or more computing devices,
behavior data based on a classification of the first object, wherein
the behavior data identifies potential actions of the first object
that are to result in a change in control strategy, and wherein at
least one of the potential actions identified in the behavior data
is the action of changing from traveling on a first road element
to travelling on a second road element;
determining, by the one or more computing devices, that
the first object has performed an action identified in the behavior
data; and
based on the determination, altering the control strategy of
the autonomous vehicle by the one or more computing devices.
Appeal Br. 56 (Claims App.).

8. A method comprising:
controlling, by one or more computing devices, an
autonomous vehicle;
receiving, by the one or more computing devices, sensor
data indicating a position of a first object external to the
autonomous vehicle;
classifying, by the one or more computing devices, the
first object based on the sensor data;
accessing, by the one or more computing devices, map
data having a plurality of road elements;
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comparing the sensor data with the map data;
identifying, by the one or more computing devices, that
the first object is travelling on a first road element from the
plurality of road elements;
determining, by the one or more computing devices, that
based on the comparison of the sensor data with the map data,
the first object has travelled from the first road element to a
second road element; and
altering, by the one or more computing devices, at least
one of a position, heading, speed, and acceleration of the
autonomous vehicle based on the determination that the first
object has travelled from the first road element to the second road
element.
Appeal Br. 57 (Claims App.).
EVIDENCE
The Examiner relied on the following evidence in rejecting the claims
on appeal:
Sakai US 8,676,487 B2 Mar. 18, 2014
Kitahama US 8,983,679 B2 Mar. 17, 2015

THE REJECTIONS3
I. Claims 1–49 stand rejected under 35 U.S.C. § 112, first paragraph, as
failing to comply with the written description requirement. Final
Act. 2–4.
II. Claims 13–20 and 33–49 stand rejected under 35 U.S.C. § 102(b) as
anticipated by Kitahama. Id. at 5–7.

3 Rejections of various claims on the grounds of nonstatutory double
patenting (Final Act. 12–26) have been withdrawn (Ans. 3) and are not
before us on appeal.
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III. Claims 1–49 stand rejected under 35 U.S.C. § 103(a) as unpatentable
over Kitahama and Sakai. Id. at 7–12.
OPINION
Rejection I
The Examiner finds that because there is no algorithm or flow of logic
described in the Specification to guide one of ordinary skill in the art as to
how to program a computer to perform at least the steps of “classifying . . .
[by one or more computing devices] the first object” and “accessing . . . [by
one or more computing devices] behavior data” as recited in claim 1, for
example, the Specification does not include written description support for
this subject matter. Final Act. 3–4.
More particularly, with respect to “classifying . . . [by one or more
computing devices] the first object,” as claimed, the Examiner explains that,
although the “[S]pecification indicates that ‘the computer 110 may classify
the object based on the data received by the vehicle 101’s sensors,’”
“[m]issing here is an explanation as to how the computer performs that
function or achieves that desired result of classifying the object.” Final Act.
30. The Examiner takes the position that the disclosure “that ‘the sensor
data could be used to classify objects as being a pedestrian, bicycle, or
vehicle’ . . . is . . . insufficient to describe how the computer performs the
function/result of ‘classifying’ in the context of claim 6 (for example).” Id.
The Examiner further explains:
Object classification (in context) would appear to require more
than simply determining the speed, heading and acceleration that
is associated with an object, because raw sensor data does not
inherently distinguish between detected objects. In addition, the
“sensors” identified in [the Specification] at columns 5–7, lines
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18–9, are described as providing distance and speed data, but
such data would appear to require computer processing which is
not described.
Id.
More particularly, with respect to “accessing . . . [by one or more
computing devices] behavior data,” as claimed, the Examiner explains:
The . . . [S]pecification indicates that “database 138 may include
a set of actions or behaviors of interest”. Response at 15, citing
to column 7, lines 41–50. Missing here is an explanation as to
how the computer performs the function or achieves the desired
result of accessing behavior data. In the [E]xaminer’s opinion,
the suggestion in [the Specification] that “instructions 132 may
allow for computer system 110 to identify when a detected
vehicle has performed one or more of the behaviors of interest”
([i]d[.]), is insufficient to describe how the computer performs
the claimed function/result of “accessing ... behavior data”.
Moreover, given a database (137) that includes the detected
position and movement associated with a vehicle (as opposed to
the claimed “object”) (col. 7, ll. 33–40), and a database (136) that
includes a road graph or “map”, simply “combining both sets of
data" ([i]d[.]) would not appear to inherently result in the access
of “behavior data” associated with that detected vehicle. In this
regard, the detected vehicle data is local (i.e. relative to vehicle
101: cols. 7–8, ll. 51–2), but the map data would appear to be
global. [The Specification] does not describe how the
local/global data would be reconciled with “a set of actions or
behaviors of interest” contained in database 138.
Final Act. 31.
Whether a specification complies with the written description
requirement of 35 U.S.C. § 112, first paragraph, is a question of fact and is
assessed on a case-by-case basis. See, e.g., Purdue Pharma L.P. v.
Faulding, Inc., 230 F.3d 1320, 1323 (Fed. Cir. 2000) (citing Vas-Cath Inc. v.
Mahurkar, 935 F.2d 1555, 1561 (Fed. Cir. 1991)). The disclosure, as
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originally filed, need not literally describe the claimed subject matter (i.e.,
using the same terms or in haec verba) in order to satisfy the written
description requirement. The specification, however, must convey with
reasonable clarity to those skilled in the art that, as of the filing date,
Appellant was in possession of the claimed invention. See id. When
examining computer-implemented functional claims, such as claim 1, the
Examiner should determine whether Appellant’s Specification discloses the
computer and the algorithm (e.g., the necessary steps and/or flowcharts) that
perform the claimed function in sufficient detail such that one of ordinary
skill in the art can reasonably conclude that the inventor possessed the
claimed subject matter at the time of filing. It is not enough that one skilled
in the art could write a program to achieve the claimed function because a
specification must explain how the inventor intends to achieve the claimed
function to satisfy the written description requirement. See, e.g., Vasudevan
Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683 (Fed. Cir.
2015). “The written description requirement is not met if the specification
merely describes a ‘desired result.’” Id.
The Office provides guidance (“2019 § 112 Guidance”) for addressing
whether a claim meets the requirements of 35 U.S.C. § 112 “where
functional language is used to claim [a] computer-implemented invention.”
(Computer-Implemented Functional Claim Limitations for Compliance With
35 U.S.C. 112, 84 Fed. Reg. 57 (Jan. 7, 2019)). The 2019 § 112 Guidance
incorporates Federal Circuit precedent pertaining specifically to computer-
implemented inventions, such as Vasudevan, and particularly addresses
written-description issues “related to the examination of computer-
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implemented inventions that recite only the idea of a solution or outcome to
a problem but fail to recite details of how the solution or outcome is
accomplished.” 2019 § 112 Guidance, 84 Fed. Reg. at 61.
The 2019 § 112 Guidance instructs examiners that, “[w]hen
examining computer implemented, software-related claims,” they “should
determine whether the specification discloses the computer and the
algorithm(s) that achieve the claimed function in sufficient detail that one of
ordinary skill in the art can reasonably conclude that the inventor possessed
the claimed subject matter at the time of filing.” 2019 § 112 Guidance, 84
Fed. Reg. at 61. An algorithm can be expressed in “any understandable
terms,” such as “in prose” or “as a flow chart.” Id. at 62. It is not sufficient,
however, “that one skilled in the art could theoretically write a program to
achieve the claimed function, rather the specification itself must explain how
the claimed function is achieved to demonstrate that the applicant had
possession of it.” Id.; see also Vasudevan, 782 F.3d at 682–83.
The 2019 § 112 Guidance tells examiners that “[t]he level of detail
required to satisfy the written description requirement varies depending on
the nature and scope of the claims and on the complexity and predictability
of the relevant technology,” and “[i]nformation that is well known in the art
need not be described in detail in the specification.” 2019 § 112 Guidance,
84 Fed. Reg. at 61. Here, for example, a recital of actual “computer code”
and/or details about “electrical signals” (see Appeal Br. 13) would not be
necessary to satisfy the written description requirement. On the other hand,
“sufficient information must be provided to show that the inventor had
possession of the invention as claimed.” 2019 § 112 Guidance, 84 Fed. Reg.
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at 61. Here, this “sufficient information” would be details about how
disclosed computer 110 is programmed to achieve the desired functions of
classifying objects and accessing behavior data.
The 2019 § 112 Guidance instructs examiners “to compare the scope
of the claim with the scope of the description to determine whether applicant
has demonstrated possession of the claimed invention.” 2019 § 112
Guidance, 84 Fed. Reg. at 61. This is not to say that written-description
support for a claim extends only to details expressly set forth in the
specification. See id. However, details set forth in the specification must
convey possession of the full scope of the claimed invention. Here, the
details set forth in Appellant’s Specification must convey possession of
programming the computer 110 to achieve the claimed functions of
classifying objects and accessing behavior data.
Classifying
Claims 1–20 require a computer (e.g., “one or more computing
devices” and/or a “one or more processors”) to classify “[a] first object
based on the sensor data.” Appeal Br. (Claims App.) Claims 6–7 (which
depend from claim 1) and claims 26–27 (which depend from claim 21)
require the first object to be “classified as a vehicle.” (Id.)
When a human is driving a vehicle, he/she sees “objects external to
the vehicle such as other vehicles, obstacles in the roadway, traffic signals,
signs, trees, etc.” Spec. 5:18–20. The human driver’s thought process with
respect to a detected object depends, of course, upon what the detected
object is. If a human driver sees “a sign indicating that the speed limit is 35
mph,” and the vehicle’s current speed is 50 mph, and the driver knows that
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he/she needs to “slow down” the vehicle. Spec. 7:13–15. If a human driver
sees “an object is obstructing the intended path of the vehicle,” the driver
knows that he/she needs to “maneuver the vehicle around the obstruction.”
Id. 7:16–19. Thus, a human driver’s thought process while driving a vehicle
invariably includes classifying a detected object.
The classifying function recited in claims 1–20, “without the
computer, amount[s] to normal human perception/judgement” that would be
employed when a human driver navigates a vehicle. Ans.14 n.2. The
Specification may convey possession of the Appellant’s idea to computerize
a human driver’s classification of detected objects via the computer 110, but
the Specification does not convey possession of the necessary steps of a
program to perform such a classification of detected objects.
The functional diagram depicted in Figure 1 shows, schematically, a
database 137 containing “[o]bject [d]ata” and “classification data,” but there
is no discussion in the Specification as to what is meant, even generally, by
“object data” and “classification data.” The flow chart depicted in Figure 7
does not mention “object data” or “classification data,” but instead only
includes a block 720 labeled as “Identify classification and state of each
detected object.”
The Specification does say that “the computer 110 may classify [a
detected] object based on the data received by vehicle 101’s sensors” and,
that, “[f]or example, the sensor data could be used to classify objects as
being a pedestrian, a bicycle or vehicle.” Spec. 9:4–7. As noted by the
Examiner, there is no indication in the rejection that the Specification lacked
adequate details regarding the raw sensor data received by the controller
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110. See Ans. 19–20. Rather, the Examiner’s written description concerns
reside solely in what happens after the computer 110 receives sensor data.
The raw sensor data received by the computer 110 would “require additional
computer processing to distinguish between detected objects,” and “the
algorithm for performing that additional processing has not been adequately
described or otherwise identified” in the Specification. Id. at 23.
Accessing Behavior Data
Claims 1–7 and 13–20 require the computer 110 to access “behavior
data” that is “based on a classification of the first object” and defines the
behavior data in terms of “a first object” Appeal Br. (Claims App.)
Claims 21–27 and 33–40 do not require the computer 110 to classify a first
object based on sensor data, but they also define the behavior data in terms
of “a first object.” Id. Specifically, these claims require the behavior data to
identify “potential actions of the first object that are to result in a change of
control strategy,” and require that “at least one of the potential actions of the
first object is the first object changing from traveling on a first road element
[(e.g., a first lane)] to travelling on a second road element [(e.g., a second
lane)].” Id.
When a human driver is driving a vehicle on a multi-lane roadway,
he/she is aware of the lane arrangement of the roadway, his/her vehicle’s
position in this lane arrangement, and other nearby vehicles’ positions in this
lane arrangement. The human driver also knows, due to his/her driving
experience, that a nearby vehicle could behave in certain ways, including
changing lanes on the roadway. The human driver further knows that if a
nearby vehicle changes lanes, it might be necessary to alter the position,
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heading, speed, and acceleration of his/her vehicle. Thus, the human driver
is on the lookout for this excepted behavior by a nearby vehicle so that,
when it occurs, he/she can respond appropriately.
The “behavior data” recited in claims 1–7, 13–27, and 33–40,
“without the computer, amount[s] to normal human perception/judgement”
that would be employed when a human driver is driving a non-autonomous
vehicle. Ans. 14 n.2. The Specification may convey possession of the
Appellant’s idea of the computer 110 storing a human driver’s knowledge
about potential behaviors of nearby objects for later access, but the
Specification does not convey possession of the necessary steps of a
program to store and access such potential behaviors of nearby objects.
The functional diagram depicted in Figure 1 shows, schematically, a
database 138 containing “[b]ehaviors of interest.” The flow chart depicted
in Figure 7 does not mention a database of behaviors of interest, but instead
only includes a block 740 labeled as “Has a detected vehicle performed a
behavior of interest?”. The Specification describes the database 138 as
including “a set of actions or behaviors of interest such as [a detected]
vehicle changing lanes or routes,” so that the computer 110 can “identify
when a detected vehicle has performed one or more behaviors of interest.”
Spec. 7:41–43. Inasmuch as “behaviors of interest” are described elsewhere
in the Specification, they are only described in the context of a detected
vehicle traveling along a multi-lane highway (see id. 7:20–c8:2); following a
path 630 (see id. 8:10–16), or coming to stop for a period of time (see id.
8:51–54).
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Claims 1–5, 13–20–25, and 33–40 do not require the first object to be
classified as a vehicle, and, therefore, have a claim scope encompassing a
detected object that is not a vehicle. The Specification discloses that sensor
data could be used to classify non-vehicle objects such as a “pedestrian” or a
“bicycle.” Spec. 9:5–7. The Specification does not describe “behavior data”
for a pedestrian and/or “behavior data” for a bicycle, which is assessable by
the computer 110. Even when the detected object is classified as a vehicle
(claims 6, 7, 26, 27), the Specification does not describe an algorithm or
flow of logic causing the computer 110 to access behavior data for a vehicle,
as opposed to behavior data for a pedestrian or bicycle.
Moreover, claims 1–5, 13–20–25, and 33–40 have a claim scope
encompassing an almost infinite number of detected objects and behavior
data associated therewith. The details set forth in the Specification about
behavior data (i.e., behavior data is described only in the context of a nearby
vehicle) does not convey possession of programming a computer
commensurate with the scope of these claims.4
Accessing Map Data
Claims 8–12, 28–32, and 41–49 require the computer 110 to access
“map data having a plurality of road elements.” Appeal Br. (Claims App.).
Claims 8–12 require the computer 110 to classify a first object “based on the
sensor data,” but they do not recite any relationship between the classified
first object and the map data that is accessed by the computer 110. Id.

4 We note that neither the claims nor the Specification describe what
happens if the first object cannot be classified by the computer 110 and/or if
there is no behavior data for the first object.
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Claims 28–32 and 41–49 only require the sensor data to indicate “a position
of a first object,” and they likewise do not recite any relationship between
the first object’s position and the map data that is accessed by the
computer 110.
The functional diagram depicted in Figure 1 shows, schematically, a
database 136 containing, among other things, a “Detailed Map,” and the
Specification says that “a road graph of the environment” is “stored in
database 136.” Spec. 7:47–48. The flow chart depicted in Figure 7 includes
a block 730 labeled “Access road graph data corresponding to objects’
environment,” and the Specification says that, when nearby vehicles 510 and
520 are detected, “road graph data” may be accessed that represents “the
current environment of vehicles 510 and 520.” Spec. 7:55–58. The
Specification shows, in Figure 6, “a road graph 600” that can be accessed by
the computer 110. Spec. 7:56–59.
The Specification, however, does not describe what “map data” is
actually stored in the database 136. According to the Examiner, “the road
graph data would appear to be global” (Ans. 26), which would align with the
computer 110 being in communication with “a geographic position
component” that determines the vehicle 101’s “latitude” and “longitude”
positions (Spec 4:4–8). However, this same paragraph also says that “[t]he
location of the vehicle,” determined by the geographic position component,
can also include, as well, “relative location information, such as location
relative to other cars immediately around it which can often be determined
with less noise than absolute geographical location.” Spec. 4:17–20.
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In any event, we agree with the Examiner that the Specification does
not adequately describe the computer 110 “reconciling” the map data with
the sensed data. Ans. 26. More particularly, Appellant’s Specification
describes no algorithm or flow of logic explaining how the computer 110 is
programmed to access (e.g., from the database 136 or elsewhere) not only
map data, but map data that corresponds with the location of the vehicle 101
and/or objects detected thereby.5
The Examiner’s findings and determinations reflect that the § 112
rejection of claims 1–49 was formulated by following the 2019 § 112
guidance. See Final Act. 2–4, Ans. 13–20. We agree with the Examiner
that, per this Guidance, and thus per the controlling case law that the
Guidance assimilates, claims 1–49 do not satisfy the written-description
requirement in that Appellant’s Specification describes “an outline of
desired results that the computer is expected to achieve,” but “missing” from
the Specification is “the algorithm or logic flow” which would convey
possession of the computer being programmed to do so. Ans. 13–14. In
sum, we agree with the Examiner’s determination that there is no algorithm
or flow of logic described in Appellant’s Specification sufficient to guide
one of ordinary skill in the art to program a computer to perform at least the
steps of (i) classifying, by one or more computing devices, an object based
on sensor data, (ii) accessing, by one or more computing devices, behavior
data, and (iii) accessing, by one or more computing devices, map data. Each

5 As indicated above, claims 8–12, 28–32, and 41–49 do not expressly
require a relationship between the detected first object and the map data, but
without such a relationship, the later comparison of sensor data and map data
would seem to be nonsensible.
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of independent claims 1, 8, 13, 21, 28, 33, and 41 recite at least one of these
steps. We sustain the rejection of claims 1–49 under 35 U.S.C. § 112, first
paragraph, as failing to comply with the written description requirement.
Rejection II
Claim 13
The Examiner finds that Kitahama discloses all of the limitations of
independent claim 13, including, among other things, a processor configured
to perform all of the recited functions. Final Act. 5–6 (citing Kitahama
5:46–56, 7:17–27, 47–59, 8:29–46; see also id. at 33 (citing Kitahama 5:62–
6:12, 8:29–46) (“[T]he processor in Kitahama is capable of performing the
function(s) recited in claim 13 because that processor actually does utilize
the classification of an object, such as ‘attributes of the recognized object’
(Kitahama), to ‘access behavior data’ including ‘potential actions’, such as
‘states of the recognized objects’ (Kitahama). Similarly, the processor in
Kitahama is capable of performing the ‘determine . . . an action’ and ‘alter
the control strategy’ functions recited in claim 13, because the processor
actually does recognize or ‘determine’ an object state or ‘action’, and can
correct or ‘alter’ the movement trajectory or ‘control strategy’ using
movement strategies.”).
More particularly, as to the processor being configured to “classify the
first object based on the sensor data,” as claimed, the Examiner takes the
position that Kitahama “recognizes[s] the physical attributes of the detected
objects.” Ans. 33.
More particularly, as to the processor being configured to “access
behavior data based on a classification of the first object,” as claimed, the
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Examiner points to Kitahama “recogniz[ing] the movement states of the
detected objects.” Ans. 33. The Examiner takes the position that claim 13
“do[es] not recite a database,” but “even if claim 13 was interpreted as
requiring the structure of a database, the actual contents of that database
would appear immaterial to the processor being capable of functioning to
broadly ‘access’ the database.” Id. at 31–32.
More particularly, as to the recitation that “the behavior data identifies
potential actions of the first object,” as claimed, the Examiner takes the
position that “the ‘potential actions’ and ‘behavior data’ claim recitations are
satisfied by Kitahama.” Ans. 34. The Examiner states in particular:
In Kitahama, the ECU 20 actually does access behavior data
associated with a detected object. October 2019 Office [A]ction
at 32–33. Kitahama indicates that the environment recognition
unit 21 “recognizes the states of the recognized objects”. Such
recognition is not only based upon the recognized attributes (i.e.
“classification”) of the detected object, but also includes the
recognition of different states of movement, such as: “a moving
state (particularly, a traveling state), a crossing state, a moving
speed, a moving direction, or unknown”. Kitahama at column 7,
lines 23–27. These states of movement can also be characterized
as “potential actions” and/or “behavior data”, and further must
be determined by comparison with known behavior data in order
for a state of movement to potentially be “unknown”. Moreover,
the ECU 20 in Kitahama is structurally capable of accessing
database hardware that contains information relating to potential
actions of a detected object that are to result in a change in control
strategy. The ECU 20 actually does access at least two databases
(col. 6, ll. 6–12), so the structural capability is clearly
demonstrated in Kitahama.
Ans. 34–35.
More particularly, as to the processor being configured to “determine
that the first object has performed an action identified in the behavior data,”
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as claimed, the Examiner points to Kitahama “determin[ing] whether the
attribute and state associated with a detected object may result in the object
being an obstacle to the host/automatic vehicle.” Ans. 33. The Examiner
takes the position that “th[e] broad functional statement [of this claim
limitation] simply refers to ‘an action identified in the behavior data’, which
may or may not be one of the ‘potential actions’ that is recited together with
the access function.” Ans. 32.
More particularly, as to the processor being configured to “alter the
control strategy of the autonomous vehicle based on the determination,” as
claimed, the Examiner points to Kitahama “correct[ing] the movement
trajectory using a movement strategy.” Ans. 33.
Appellant argues that when “Kitahama ‘automatically generates
movement trajectory based on movement strategies according to the
traveling scene’ (Kitahama col. 5[,] ll. 64–65), that applies to a current
situation in the environment and has nothing to do with identifying ‘potential
actions’ from ‘behavior data’ in the manner claimed in Appellant’s
claim 13” and “[t]he rejection fails to assert or to explain where Kitahama
discloses that ‘behavior data identifies potential actions.’” Appeal Br. 27–
28; see also id. at 29 (boldface omitted) (“The ‘surrounding environment
recognition unit’ ‘recognizes attributes of the recognized objects’ (id.
col.[ ]7[,] ll. 14–16), which as best understood is currently observed
information [regarding] the ‘traveling state’ of the object. This is irrelevant
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to behavior data that ‘identifies potential actions of the first object that are to
result in a change in control strategy’ as claimed.”).6
The Examiner’s position is that Kitahama’s surrounding environment
recognition unit 21 recognizes the states of recognized objects (Kitahama
7:23–27), and these recognized states (e.g., stop state, moving state, crossing
state) of recognized objects may be characterized as potential (or possible)
actions of recognized objects. Ans. 34. Moreover, the Examiner’s position
is that in order for Kitahama’s surrounding environment recognition unit 21
to be able to recognize a state of a recognized object (e.g., stop state, moving

6 Appellant also argues that “[i]t is improper (and erroneous) for the
Examiner to assert that a limitation is missing in Kitahama for one claim but
is present in that reference for another claim.” Appeal Br. 27. Although the
Examiner has stated that “Kitahama does not explicitly describe performing
the action of accessing behavior data based upon the object classification” in
connection with an alternative obviousness rejection (Final Act. 7–8), the
Examiner has explicitly stated in connection with the anticipation rejection
of claim 13 before us that “Kitahama discloses an electronic control unit . . .
which functions to . . . recognize the movement states of the detected objects
(or ‘access behavior data based on a classification’).” Ans. 33; see also id.
at 34 (“In Kitahama, the ECU 20 actually does access behavior data
associated with a detected object. . . . Kitahama indicates that the
environment recognition unit 21 ‘recognizes the states of the recognized
objects’. Such recognition is not only based upon the recognized attributes
(i.e. ‘classification’) of the detected object, but also includes the recognition
of different states of movement, such as: ‘a moving state (particularly, a
traveling state), a crossing state, a moving speed, a moving direction, or
unknown’. Kitahama at column 7, lines 23–27. These states of movement
can also be characterized as ‘potential actions’ and/or ‘behavior data’, and
further must be determined by comparison with known behavior data in
order for a state of movement to potentially be ‘unknown’.”). We do not
find the alternative rejections articulated by the Examiner to constitute a
basis for a finding of reversible error.
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state, crossing state) rather than having to label the state of the recognized
object as “unknown,” there must be a comparison with known data. Id. In
other words, the Examiner takes the position that Kitahama’s programming
with respect to recognizing and labeling of a current state of a recognized
object necessarily involves accessing known behavior data for a recognized
object (i.e., potential or possible actions of the recognized object such as
stopping, moving, crossing etc.) for comparison in order to be able to
accurately label the current state of the recognized object. Appellant’s
argument does not respond with sufficient particularity to the findings by the
Examiner to persuade us of error by the Examiner.
Appellant also argues that “[a]nother deficiency of Kitahama is that it
does not teach ‘determine that the first object has performed an action
identified in the behavior data.’” Appeal Br. 29. More particularly,
Appellant argues that Kitahama’s “movement strategy candidates are based
on observed actions by the detected objects, which is not a determination
‘that the first object has performed an action identified in the behavior data”
as claimed.” Id. (boldface omitted). The Examiner’s position is that
Kitahama discloses that in the case where an object is an obstacle to the
traveling of the host vehicle, the surrounding environment recognition unit
determines the traveling scene, which involves recognizing a state of a
recognized object, and when the state of a recognized object is determined,
this constitutes a determination that a recognized object has performed a
potential (or possible) action (i.e., at least one of the recognized states of a
stop state, moving state, or crossing state). Ans. 33. Appellant has not
persuaded us of error in the Examiner’s findings.
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Appellant also argues that “because there is no teaching in Kitahama
regarding behavior data, Kitahama necessarily does not disclose alteration of
‘the control strategy of the autonomous vehicle based on the determination’
in the manner claimed.” Appeal Br. 29. The Examiner’s position is that
Kitahama “can correct or ‘alter’ the movement trajectory or ‘control
strategy’ using movement strategies.” Final Act. 33 (citing Kitahama 5:62–
6:12, 8:29–46). Kitahama describes movement strategies being set
according to a determined traveling scene. Kitahama 8:29–46. According to
the Examiner, a change in the current traveling scene will result in a change
in movement strategy so as to constitute an alteration of control strategy
based on a determination of a state of a recognized object (i.e., at least one
of the recognized states of a stop state, moving state, or crossing state),
which is itself a determination that the recognized object has performed a
potential (or possible) action. Appellant has not persuaded us of error in the
Examiner’s findings.
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s determination that Kitahama discloses all of the limitations
of independent claim 13. Accordingly, we sustain the rejection of claim 13
under 35 U.S.C. § 102(b) as anticipated by Kitahama.
Claims 14–20
In rejecting dependent claims 14–20, the Examiner stated in the Final
Action that “claims 14–19, reciting a further limitation(s) related to a prior
functional requirement would not appear to define a new structural
requirement(s) of the claimed system that would distinguish the processor(s)
disclosed in Kitahama” and with respect to claim 20 that “in Kitahama the
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processor(s) further functions to control an autonomous vehicle in the
recited manner, as indicated at column 12, lines 9–24.” Final Act. 6.
Appellant argues that “[t]he Examiner has failed to meet his burden of
setting forth a prima facie case for anticipation with regard to the rejections
of claims 14–19.” Appeal Br. 30. The Examiner responds in the Answer by
providing additional explanation and including citation to specific passages
within Kitahama with respect to claims 14–19. Ans. 36–37 (citing Kitahama
5:65–6:4, 7:14–59, 8:31–38, Fig. 2). Appellant does not respond with
sufficient particularity in the Reply Brief to the specific findings made in the
Answer. See Reply Br. 11 (solely arguing the purported deficiencies with
respect to independent claim 13).
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s determination that Kitahama discloses all of the limitations
of claims 14–20. Accordingly, we sustain the rejection of claims 14–20
under 35 U.S.C. § 102(b) as anticipated by Kitahama.
Claims 33–40
The Examiner finds that claim 33 “recites the subject matter of
claim 13,” but lacks a recitation relating to “classification of a sensed
object.” Final Act. 6. The Examiner also finds that “claims 34–40 recite the
same subject matter as that of claims 14–20.” Id. Accordingly, the
Examiner relies on the previous analysis provided in connection with
independent claim 13 and its dependent claims. Id. Appellant essentially
relies on the same arguments and reasoning presented in connection with the
rejection of independent claim 13 (Appeal Br. 31–35), and for similar
reasons discussed above in connection with claim 13, we do not find such
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arguments and reasoning persuasive of error. Accordingly, we sustain the
rejection of claims 33–40 under 35 U.S.C. § 102(b) as anticipated by
Kitahama.
Claim 41
The Examiner finds that Kitahama discloses all of the limitations of
independent claim 41, including among other things, that the processor is
“configured to . . . access map data having a plurality of road elements; [and]
determine, based on a difference between the sensor data and the map data,
that a first object has travelled from the first road element to a second road
element,” as claimed. Final Act. 6. More particularly, the Examiner finds
that “in Kitahama the processor(s) further functions to access map data, as
indicated at column 10, lines 12–31 (‘navigation device’).” Id. The
Examiner further explains the position taken in the rejection:
In Kitahama, the processor can utilize information that is
obtained from a navigation device, which would inherently
include “map data”, in generating a display image of the vehicle
traveling scene (“[t]he basic image . . . may be an image that is
obtained by adding the recognized object to an image (CG
image) that is generated by a navigation device”). Moreover, the
processor in Kitahama is capable of performing the road element
function recited in claim 41, because that processor actually can
account for detected object movement (col. 7, ll. 14–27: “moving
state . . . crossing state . . . moving speed . . . moving direction”).
Id. at 34. The Examiner further explains the position taken in the
rejection in the Answer:
Considering the map data together with the “determine”
function recited in claim 41, Kitahama clearly demonstrates the
structural capability of the ECU 20 (processor) to access map
data and determine whether a detected object has travelled on
two road elements. Kitahama at column 10, lines 12-31. Brief
at 36. As explained in these passages, the ECU 20 can utilize
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information that is obtained from a navigation device (which
would inherently include “map data”) that generates a basic
display image of the autonomous vehicle traveling scene. As
further explained in Kitahama, a display image representing a
potential corrective movement strategy (as generated by the
display control unit 27) is added to the basic image generated by
the navigation device, in order to display the detected object
information (including movement) relative to the traveling scene
(and the autonomous vehicle). Thus, the broad functional
recitations of claim 41: “access map data having a plurality of
road elements”; and “determine, based on a difference between
the sensor data and the map data, that a first object has travelled
from the first road element to the second road element”, does not
structurally distinguish the capability of the ECU 20 disclosed in
Kitahama.
Ans. 38–39.
Thus, the Examiner explains in the Final Action and Answer that the
addition of a “recognized object to an image (CG image) that is generated by
a navigation device” as set forth in Kitahama (Kitahama 10:28–29) would
necessarily require accessing map data having a plurality of road elements
by virtue of the reference to a navigation device and would necessarily
require determining that a recognized object has traveled from the first road
element to a second road element by virtue of the recognized object being
displayed on the navigation device image accurately.
As to altering at least one of the position, heading, speed, and
acceleration of the autonomous vehicle based on the determination that the
first object has travelled from the first road element to the second road
element, the Examiner takes the position that “Kitahama clearly
demonstrates the structural capability of the ECU 20 (processor) to
implement a corrective movement strategy.” Ans. 39 (citing Kitahama
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5:65–6:4). For example, as Kitahama describes recognition of “a slow
vehicle ahead,” “a pedestrian on the street,” or “a bicycle on the street”
(Kitahama 7:55–60), the particular lane in which the slow vehicle,
pedestrian or bicycle is located in would have bearing on a desired
movement strategy (id. at 8:35–38) (“movement strategies for changing
lanes” “or movement strategies for going around a pedestrian or a bicycle”)).
Appellant argues that Kitahama’s “risk map” shown in Figure 6 is
“irrelevant to the claimed features” and the Examiner has not explained
adequately how Kitahama teaches the claimed limitations. Appeal Br. 36–
37; Reply Br. 12–13. Appellant does not respond with sufficient
particularity to the explanation provided by the Examiner in the Final Action
and Answer, and thus we are not persuaded of error in the rejection
articulated by the Examiner.
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s finding that Kitahama discloses all of the limitations of
independent claim 41. Accordingly, we sustain the rejection of claim 41
under 35 U.S.C. § 102(b) as anticipated by Kitahama.
Claims 42–45, 48, and 49
The Examiner finds that Kitahama discloses all of the limitations of
dependent claims 42–45, 48, and 49. Final Act. 6–7 (citing Kitahama 7:60–
8:16, 10:12–31, 12:9–24). The Examiner provides additional clarification in
the Answer as to the basis for the rejection of these claims. Ans. 40–42
(5:65–6:4, 7:14–27, 7:60–8:16, 12:9–24). Appellant argues the Examiner
improperly relies on pro forma rejections of other claims, rather than
addressing the limitations of claims 42–45, 48, and 49. Appeal Br. 37. We
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do not find this argument persuasive in light of the clarifying statements
made by the Examiner in the Answer. In addition, Appellant does not
respond with sufficient particularity in the Reply Brief to the specific
findings made in the Answer. See Reply Br. 13 (solely arguing the
purported deficiencies with respect to independent claim 41).
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s finding that Kitahama discloses all of the limitations of
dependent claims 42–45, 48, and 49. Accordingly, we sustain the rejection
of claims 42–45, 48, and 49 under 35 U.S.C. § 102(b) as anticipated by
Kitahama.
Claims 46 and 47
With respect to claims 46 and 47, the Examiner finds that “Kitahama
discloses a partially autonomous mode, as indicated at column 8, lines 47–
60.” Final Act. 6. Appellant argues that “[w]hile the passenger of Kitahama
can use a ‘movement strategy candidate[]’ to ‘change the movement
strategies’, that is not a partially autonomous mode that enables a driver to
directly ‘control one or more of steering, acceleration, braking and
emergency braking’ in the manner claimed.” Appeal Br. 38 (boldface
omitted). Appellant continues that “[i]n contrast, as explained by Kitahama,
the system ‘receives a passenger’s change operation of the movement
strategies, and if the movement strategies have been changed, it regenerates
the movement trajectory based on the movement strategies.’ (Kitahama
col.[ ]6[,] ll. 1–4).” Id.
With respect to claim 46, the Examiner responds that “Kitahama
clearly discloses the structural capability of the ECU 20 (processor) to fully
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control the vehicle or partially control the vehicle.” Ans. 41 (citing
Kitahama 8:47–60). Kitahama discloses a “movement strategy setting unit
23 [that] sets movement strategy candidates for the passenger to change the
movement strategies.” Kitahama 8:47–50. In the absence of a specific
explanation from Appellant as to why Kitahama’s disclosure of passenger
involvement and/or control in the selection of a movement strategy does not
constitute a partially autonomous mode, we are not persuaded of error in the
Examiner’s findings regarding dependent claim 46.
With respect to claim 47, the Examiner responds that:
[i]n Kitahama, the structural capability is clearly disclosed
because the partial autonomous mode includes passenger control
of at least steering and braking with the selection of a corrective
movement strategy (col. 8, ll. 47-60). The claim does not recite
“directly control”, and even if it did, the passenger can still effect
the change of movement strategy which results in control of
steering and/or braking. Brief at 38.
Ans. 41. Again, in the absence of a specific explanation from Appellant as
to why Kitahama’s disclosure of passenger involvement and/or control in the
selection of a movement strategy does not result, through the selection of a
particular movement strategy, in at least indirect control by the vehicle
operator of one or more of steering, acceleration, braking and emergency
braking, we are not persuaded of error in the Examiner’s findings regarding
dependent claim 47. That is, Appellant does not explain adequately why the
Examiner’s position articulated in the Answer is in error.
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s finding that Kitahama discloses all of the limitations of
dependent claims 46 and 47. Accordingly, we sustain the rejection of claims
46 and 47 under 35 U.S.C. § 102(b) as anticipated by Kitahama.
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Rejection III
Claims 1–7
In connection with an obviousness rejection of all of the pending
claims, the Examiner finds that Kitahama discloses most of the limitations of
independent claim 1, but takes the position that “Kitahama does not
explicitly describe performing the action of accessing behavior data based
upon the object classification.” Final Act. 7–8. The Examiner turns to
Sakai, finding that Sakai teaches “a motion prediction apparatus that predicts
motion of a mobile body (object) which is detected near a host (autonomous)
vehicle” and that “[t]he motion prediction apparatus determines a degree of
normality associated with the movement of the object, and then selects a
motion prediction model based upon the determined degree of normality.”
Id. at 8 (citing Sakai 1:65–2:22). The Examiner concludes that it would
have been obvious “to associate an object motion prediction apparatus, as
taught in Sakai, with the movement trajectory generator disclosed in
Kitahama” in order to “provide an improvement in travel safety and
efficiency.” Id.; Sakai 2:37–43. The Examiner takes the position that:
the improved autonomous vehicle control would include
performing the method step of accessing behavior data (i.e.
“motion prediction models” in Sakai). In Sakai, the motion
prediction apparatus determines a classification parameter
commensurate with the degree of normality for the detected
object movement, and uses that classification parameter to select
the motion prediction model (s) (col. 3, ll. 1–19). Considering
Kitahama, the classification of the detected objects (col. 7, ll. 14–
27) would have been recognized as being consistent with the
teachings in Sakai, because the classification also includes object
movement (“moving state ... crossing state ... moving speed ...
moving direction”).
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Final Act. 8–9.
As to the claimed step of “accessing, by the one or more computing
devices, behavior data based on a classification of the first object, wherein
the behavior data identifies potential actions of the first object that are to
result in a change in control strategy” (Appeal Br. 56 (Claims App.)), the
Examiner finds that “[t]he identified teaching in Sakai that is related to
prediction models corresponds directly to the claim 1 recitation: ‘potential
actions of the first object that are to result in a change in control strategy’.”
Final Act. 34. The Examiner further finds:
Sakai implements the object motion prediction apparatus with
traveling information acquisition means 10, which utilizes
sources of prior knowledge such as road maps and traffic rules in
combination with sensor data, to establish the environment
surrounding the autonomous vehicle. Sakai at column 9, lines 5–
51. As explained in Sakai, the prior knowledge is stored in a
database that can be accessed by the motion prediction apparatus
according to the present location of the autonomous vehicle.
Id[.] at lines 24–51. The database includes information such as
road arrangements that are relative to the autonomous vehicle,
and the sensor data is used to calculate information about a
detected object that is relative to the autonomous vehicle (such
as position and speed information). As further explained in
Sakai, the prediction of a potential action(s) of a detected object
involves estimating a degree of normality associated with the
behavior, or state of movement, of the object. Sakai at column
9, lines 52–66. Thus, Sakai reasonably teaches the claim 1
terminology: “accessing ... behavior data based on a
classification of the first object”, because the information that is
used by the object motion prediction apparatus is stated to
include “behavior” data associated with the object.
Ans. 45–46.
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As to the claimed step of “determining, by the one or more computing
devices, that the first object has performed an action identified in the
behavior data” (Appeal Br. 56 (Claims App.)), the Examiner finds that
“Kitahama . . . teaches the claimed step involving ‘determining’ . . . in
response to a classified object performing actions including moving and/or
changing direction” and “[t]he determination in Kitahama that a classified
object has performed an action, is entirely consistent with the terminology of
claim 1.” Ans. 44 (citing Appeal Br. 39) (underlining omitted). The
Examiner explains “that the ‘action identified in the behavior data’, as
recited in the ‘determining’ step of claim 1, is not even required by the claim
language to be the ‘at least one action’ that was defined in the ‘accessing’
step.” Final Act. 35.
Appellant argues that “predicting motion of another object based on
‘past time motions’ of that object is not what is claimed in claim 1.” Appeal
Br. 39. Thus, “Appellant submits that Sakai does not teach or suggest
‘accessing, by the one or more computing devices, behavior data based on a
classification of the first object, wherein the behavior data identifies
potential actions of the first object that are to result in a change in control
strategy, and wherein at least one of the potential actions identified in the
behavior data is the action of changing from traveling on a first road element
to travelling on a second road element.’” Id. (boldface omitted).
Appellant does not explain adequately how Kitahama’s surrounding
environment recognition unit recognizing a state of a recognized object (e.g.,
stop state, moving state, crossing state), and having been modified in
accordance with the teachings of Sakai so as to select for the recognized
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object a movement prediction model for predicting the movement region of
the recognized object, fails to teach accessing behavior data based on a
classification of the recognized object. We, thus, are not persuaded that the
Examiner erred in finding that modified Kitahama’s movement prediction
model identifies potential actions of the recognized object (e.g., a range of
movement for the recognized object) and is accessed when one of a plurality
of movement prediction models is selected for a recognized object.
Although a “degree-of-normality acquisition device may acquire the
degree of normality based on a history of movement of the mobile body”
(Sakai 3:33–35), and this degree of normality may be used to select a
movement prediction model from the plurality of movement prediction
models (id. at 2:4–11), this does not prevent Sakai from accessing a plurality
of movement prediction models that identify potential actions of the object
(i.e., a range of movement of a recognized object). Appellant’s argument
does not respond with sufficient particularity to the findings by the Examiner
to persuade us of error by the Examiner.
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s determination that Kitahama and Sakai renders obvious the
subject matter of independent claim 1. Accordingly, we sustain the rejection
of claim 1, and claims 2–7 which depend therefrom and for which Appellant
relies on the same arguments and reasoning (Appeal Br. 38–39), under
35 U.S.C. § 103(a) as unpatentable over Kitahama and Sakai.
Claims 13–27 and 33–40
In rejecting claims 13–27 and 33–40, the Examiner relies directly or
indirectly on the previous analysis provided in connection with independent
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claim 1 and its dependent claims. Final Act. 10–11. Appellant essentially
relies on the same arguments and reasoning presented in connection with the
rejection of independent claim 1. Appeal Br. 41–47, 49–52. For similar
reasons discussed above in connection with Rejection II and the rejection of
claim 1 under Rejection III, we do not find such arguments and reasoning
persuasive of error. Accordingly, we sustain the rejection of claims 13–27
and 33–40 under 35 U.S.C. § 103(a) as unpatentable over Kitahama and
Sakai.
Claims 8–12
With respect to independent claim 8, the Examiner states:
in Kitahama the recognition unit accounts for traffic rules in
recognizing the surrounding environment, as noted above (col. 7,
ll. 34–46). Kitahama also suggests that “other means may be
used” to recognize the surrounding environment (col. 14, ll. 7–
9). Sakai implements the detected object motion prediction
apparatus with traveling information acquisition means 10,
which utilizes map data and traffic rules as sources of prior
knowledge, in combination with sensor data, to establish the
surrounding environment (col. 9, ll. 5–51). Thus, the reasonable
combination of the Kitahama and Sakai disclosures would
obviously include the use of map data in defining the surrounding
environment, where that environment includes a travelling object
(i.e. vehicle).
Final Act. 10. The Examiner adds that:
at least Sakai teaches that the detected mobile body (object)
could be “weaving” (e.g. col. 3, ll. 36–49), and this action would
obviously involve “changing from traveling on a first road
element to traveling on a second road element” . . . Sakai
expressly discloses traveling information acquisition means 10
which uses prior knowledge that include “road maps” stored in a
database (col. 9, ll. 5–34). Further in Sakai, an object detection
means 11 uses the information in the traveling information
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acquisition means 10 to determine the past/current position,
speed and travel direction of a mobile body relative to the host
or “autonomous vehicle” (col. 9, ll. 35–51). Once the motion or
movement (or “behavior”) associated with a mobile body (object
vehicle) is established, the motion prediction apparatus
determines a degree of normality thereof, and uses the degree of
normality to select a prediction model(s) of future motion or
movement for that detected mobile body (cols. 1–2, ll. 65–22).
Thus, in addition to the detected “weaving” on road elements
discussed above (cl. 1), if the object vehicle effected a (complete)
travel lane change, the selection of the motion prediction model
would have logically been made by “comparing the sensor data
with the map data”, as recited in claim 8. This is evident because
the road maps stored in the prior knowledge database would
obviously have been accessed in order for the motion prediction
apparatus to determine the movement associated with the
detected object vehicle (col. 9, ll. 52–66: traffic rules/lane
compliance).
Id. at 34–36. The Examiner also explains in the Answer that Sakai’s
teaching of “estimating a degree of normality associated with the behavior,
or state of movement, of the detected object . . . would obviously involve a
‘comparison’ of the sensor data and the map data, in order to predict whether
a detected object will become an obstacle relative to the autonomous
vehicle.” Ans. 46.
Appellant argues that Kitahama’s “risk map” shown in Figure 6 is
“irrelevant to the claimed features” and the Examiner has not explained
adequately how Kitahama teaches the claimed limitations. Appeal Br. 40;
Reply Br. 15. Appellant also argues that “[a]s best understood, there is no
enabling teaching in Sakai of ‘comparing the sensor data with the map data’
as claimed.” Appeal Br. 41; Reply Br. 15. Accordingly, Appellant argues,
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“the applied combination” fails to teach the claimed “determining” and
“altering” steps. Id.
We understand the Examiner to be explaining that Sakai’s disclosure
of the storage of “road maps” and “various traffic rules . . . (e.g., . . .
dividing lines (yellow lines, white lines)” along with a determination of “the
degree of weaving of [a] nearby object” (Sakai 9:26–29, 61–62) would
necessarily implicate a comparison of sensor data with map data in order to
determine the degree of weaving (or conversely, traffic rule/lane
compliance) in Sakai, and that a determination of weaving necessarily
implicates a determination that an object has traveled from a first road
element to a second road element. We also understand the Examiner to be
explaining that the degree of weaving by a detected mobile body would be
utilized in Kitahama in connection with ensuring a desirable movement
strategy. This explanation from the Examiner remain unaddressed by
Appellant, and Appellant, therefore, fails to respond with sufficient
particularity to the explanation made by the Examiner to persuade us of error
in the rejection articulated by the Examiner. Appeal Br. 40–41; Reply
Br. 15–16.
For the foregoing reasons, Appellant does not apprise us of error in
the Examiner’s determination that Kitahama and Sakai render obvious the
subject matter of claim 8. Accordingly, we sustain the rejection of claim 8,
and claims 9–12 which depend therefrom and for which Appellant relies on
the same arguments (Appeal Br. 41; Reply Br. 16) under 35 U.S.C. § 103(a)
as unpatentable over Kitahama and Sakai.
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Claims 28–32 and 41–49
In rejecting claims 28–32 and 41–49, the Examiner relies directly or
indirectly on the previous analysis provided in connection with independent
claim 8 and its dependent claims. Final Act. 10–12. Appellant essentially
relies on the same arguments and reasoning presented in connection with the
rejection of independent claim 8 (Appeal Br. 47–49, 52–55), and for similar
reasons discussed above in connection with Rejection II and/or the rejection
of claim 8 under Rejection III, we do not find such arguments and reasoning
persuasive of error. Accordingly, we sustain the rejection of claims 28–32
and 41–49 under 35 U.S.C. § 103(a) as unpatentable over Kitahama and
Sakai.

CONCLUSION
In summary:
Claims
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–49 112, first
paragraph
Written Description 1–49
13–20,
33–49
102(b) Kitahama 13–20, 33–
49

1–49 103(a) Kitahama, Sakai 1–49
Overall
Outcome
1–49

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