The Boeing Company

Patent Trials and Appeals Board

Apr 28, 2021

2020005835 (P.T.A.B. Apr. 28, 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.
15/690,909 08/30/2017 Nils KNEUPER 17-1110-US-NP 1040
116502 7590 04/28/2021
Perman & Green, LLP
99 Hawley Lane
Stratford, CT 06614
EXAMINER
LINHARDT, LAURA E
ART UNIT PAPER NUMBER
3663
MAIL DATE DELIVERY MODE
04/28/2021 PAPER
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.
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte NILS KNEUPER and
RALF RENÉ SHU-ZHONG CABOS
____________

Appeal 2020-005835
Application 15/690,909
Technology Center 3600
____________

Before MICHAEL J. FITZPATRICK, WILLIAM A. CAPP, and
LEE L. STEPINA, Administrative Patent Judges.

CAPP, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Appellant1 seeks our review under 35 U.S.C. § 134(a) of the final
rejection of claims 1 and 3–21 under 35 U.S.C. § 103 over Bailey
(US 2011/0054718 A1, pub. Mar. 3, 2011), and of claim 4 over Bailey and a
Wikipedia article on Machine Learning. We have jurisdiction under 35
U.S.C. § 6(b).

1 We use the word “Appellant” to refer to “applicant” as defined in 37
C.F.R. § 1.42(a). Appellant identifies The Boeing Company as the
Applicant and real party in interest. Appeal Br. 1.
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We AFFIRM.

THE INVENTION
Appellant’s invention relates to computer software used to generate
aircraft flight plans. Spec. ¶ 1. Claim 1, reproduced below, is illustrative of
the subject matter on appeal.
1. An aircraft flight planning apparatus comprising:
a database including
a plurality of forecasting models configured to
generate predictions of a predetermined characteristic on
which at least a portion of an aircraft flight plan is based,
where the predetermined characteristic includes at least a
portion of a weather forecast, and
at least one data matrix of test predictions for the
predetermined characteristic generated by each of the
plurality of forecasting models, each of the at least one
data matrix of test prediction includes a plurality of test
prediction data points; and
an aircraft flight planning controller coupled to the
database, the aircraft flight planning controller being configured
to receive analysis forecast data having at least one
analysis data point,
select a forecasting model, from the plurality of
forecasting models, based on a comparison between the
at least one analysis data point and the plurality of test
prediction data points of a respective forecasting model,
and
provide a prediction of the predetermined
characteristic generated with the forecasting model,
selected from the plurality of forecasting models, that
corresponds to a test prediction data point that is
representative of the at least one analysis data point.
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OPINION
Eligible Subject Matter
Before delving into the merits of the art rejection, we would be remiss
if we failed to mention that Appellant’s claims appear to recite little more
than using computer software for data collection, analysis, and display.
Such is generally considered an abstract idea in the form of a mental process
under our Guidelines for analysis under 35 U.S.C. § 101. See 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019).
It is now well settled that mere recitation of a generic computer cannot
transform a patent-ineligible abstract idea into a patent-eligible invention.
Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 223 (2014). Merely stating an
abstract idea while adding the words “apply it” is not enough to confer
eligibility. Id.
Although the preamble of claim 1 recites an apparatus, the only
structural element in the body of the claim is a “controller,” which is
claimed in the most generic way possible. Claims App. We can also infer
structure in the form of computer memory by recitation in claim 1 of a
“database.” Id. The remainder of the claim language is highly conceptual in
nature and is directed to functions that are performed by software. Id.
However, in view of the strength of the Examiner’s art rejection, we
decline to exercise our discretion to enter a new ground of rejection under
Section 101. The Board enters a new ground of rejection at its discretion,
and no inference should be drawn from a failure to exercise that discretion.
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See 37 C.F.R. § 41.50(b); see also MPEP § 1213.02. In the event that
Appellant continues prosecution after resolution of this appeal, the Examiner
may want to evaluate the eligibility of this application under Section 101.
Unpatentability of Claims 1, 3, and 5–21
over Bailey
Claim 1
The Examiner finds that Bailey discloses the invention substantially
as claimed except for comparing an analysis data point to a plurality of test
prediction data points of a forecasting model. Final Act. 3–4. The Examiner
concludes that it would have been obvious to a person of ordinary skill in the
art at the time of the invention to compare an analysis data point to test data
points. Id. at 5. According to the Examiner, a person of ordinary skill in the
art would have done this to predict how an aircraft’s trajectory would be
affected by weather. Id.
Appellant first argues that Bailey fails to disclose “forecasting
models” as claimed. Appeal Br. 6. Appellant’s Specification defines a
weather forecasting model as computer software that generates a weather
forecast based on raw weather data and re-analysis data. Spec. ¶ 2. In the
Answer, the Examiner finds, correctly, that Bailey’s “predicted weather” is
equivalent to Appellant’s claimed “weather forecasts,” as both terms relate
to estimates of possible future weather conditions. Ans. 4.
Bailey is directed to a system that dynamically selects weather
information for use in planning and updating flight trajectories. Bailey ¶ 3.
Bailey identifies a flight trajectory for an aircraft and then identifies weather
information for the flight trajectory. Id. ¶ 10. Such weather information
may be formatted as “weather band” information. Id. In Bailey, “weather
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band” information is weather data that is specific to a geospatial point. Id.
A geospatial point can be a particular latitude and longitudinal, at a
particular altitude, and at a particular time. “Weather band” information
may include temperature, pressure, humidity, turbulence, wind speed, and
wind direction. Id. ¶ 7. Bailey’s weather band information may be based on
current or predicted weather. Id. ¶¶ 8, 63. Bailey explicitly contemplates
that an aircraft’s flight trajectory may be affected when actual weather
differs from the predicted weather that is used to calculate a flight trajectory.
Id. ¶ 8. Bailey continually evaluates incoming and updated weather
information to dynamically select weather for a flight plan. Id. ¶¶ 61–62.
Bailey generates aircraft predictions 418, which includes information
associated with a number of points in time based on predicted weather. Id.
¶ 68. Bailey’s weather band processor 402 gathers weather information
from ground and aircraft sources and passes such information through data
filtering 422 and then on to selection module 426. Id. ¶ 69. Selection
module 426 processes filtered information 424 and applies selection
criteria 427 to an aircraft trajectory. Id. ¶ 71. Selection module 426 predicts
how the aircraft trajectory may change from its flight plan based on weather
information 414 received in filtered information 424. Id.
A person of ordinary skill in the art, having read Bailey and
Appellant’s disclosure, would immediately recognize that both inventions
merely attempt to use modern computer and telecommunications technology
to automate a flight planning process that has been performed manually for
decades prior to the date of invention.2 An aircraft operator will select a
route of flight from point A to point B and then calculate how long it will

2 See EB6 aviation slide rule article at https://en.wikipedia.org/wiki/E6B.
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take to fly such route at airspeeds and altitudes that will optimize fuel
consumption and other economic considerations relevant to a flight, such as
cost per passenger mile per unit of time.
A person of ordinary skill in the art also understands that weather
considerations will affect the flight. If there is a head wind or a tail wind,
such will affect fuel consumption and its attendant costs. Such will also
affect the aircraft’s arrival time, which may affect ground facilities at the
destination, such as terminal gate availability. A person of ordinary skill in
the art also understands that there is an interaction between an aircraft’s
performance, its flight trajectory, and weather. For example, when faced
with a strong head wind at cruise altitude, an aircraft might descend to a
lower altitude to encounter less head wind, but may then experience an
increased rate of fuel consumption at the lower altitude.
A person of ordinary skill in the art also understands that a flight plan
initially begins purely as a forecast or prediction, but that, after takeoff, the
flight plan is updated during the flight to account for, among other things,
differences between what was estimated or predicted in the original flight
plan and what the aircraft actually experiences during the course of a flight.
Bailey, among other things, relies on forecast weather information and such
information would have been produced using a “forecasting model” within
the meaning of claim 1.
The invention of Bailey is almost identical to what is described and
claimed in Appellant’s disclosure. Owing to the highly conceptual nature of
Appellant’s claim language, coupled with the variability of expression made
possible by the flexibility of the English language, we view Bailey and
Appellant’s invention as little more than two different ways of describing the
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same thing. We view Appellant’s arguments for patentability, for the most
part, as an effort to mischaracterize and distort the meaning of words in
order to seemingly create differences between the prior art and the claimed
invention where no differences, in fact, exist.
With the foregoing in mind, we find Appellant’s “forecasting model”
argument unpersuasive. A weather forecast that forecasts a particular wind
direction and velocity, at a particular altitude, and a particular time amounts
to a “prediction of [a] predetermined characteristic” within the meaning of
claim 1. See Spec. ¶ 33 (explaining that a “predetermined characteristic”
may be wind speed or any other suitable meteorological variable). The
Examiner does not err in finding that the forecasting model limitation of
claim 1 is met by Bailey.
Appellant next argues that there is nothing in Bailey that discloses or
suggests any part of the system of Bailey that generates weather forecasts or
“predicted weather.” Appeal Br. 6. We disagree. Figure 3 of Bailey is an
illustration of a weather selection environment. Bailey ¶¶ 17, 55, Fig. 3. A
person of ordinary skill in the art would understand that weather forecasts
are generated from weather information 318 in weather selection system 306
of Bailey. Id. Claim 1 merely recites an apparatus that has: (1) a database;
and (2) a flight planning controller. Claims App. As correctly found by the
Examiner, there is no recital in claim 1 that limits where the various physical
components of the apparatus are located. Ans. 5. The Examiner does not err
in finding that Bailey’s system includes the generation of weather forecasts.
Appellant next argues that Bailey fails to disclose a data matrix of test
predictions generated by the forecasting models as claimed. Appeal Br. 7.
We understand a “test prediction” to be little more than an item of data from
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a weather forecast, which is substantially identical to Bailey’s “weather
band.” Bailey ¶ 7. Appellant construes the claim language “at least one data
matrix of test predictions” as a plurality of forecast data points. Id.
In response, the Examiner finds that Bailey teaches a number of
databases. Ans. 6 (citing Bailey ¶ 55). The Examiner interprets “matrix” as
a mathematical term for two or more dimensional information. Id. The
Examiner finds that current and predicted weather band information for
various waypoints constitutes a multi-dimensional data set that reads on the
definition of a matrix. Id. Therefore, according to the Examiner, “the
weather band’s predicted weather for waypoints along a flight path includes
at least one data matrix of test predictions.” Id.
Bailey’s processor 314 accesses a “number of databases 316.” Bailey
¶ 55. The processor receives “weather information” using a number of
databases which information is obtained from a number of operation
centers 302 and a number of aircraft 304. Id. Weather information 317 is
also received by weather selection system 306 from a number of other
ground systems 301, such as weather reporting stations and weather
monitoring stations. Id. ¶¶ 51, 55, Fig. 3. Bailey features weather selection
system 306. Id. ¶ 53. Weather selection system 306 interacts with a number
of operations centers and a number of aircraft 304. Id. Bailey’s weather
band selection system continually evaluates information received in order to
dynamically select weather for a flight plan. Id. ¶¶ 60–61. Given the
foregoing context, a person of ordinary skill in the art would understand that
Bailey receives computer generated weather forecast information from a
plurality of sources. Thus, a person of ordinary skill in the art would
understand that, in selecting information for a weather “band,” Bailey’s
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system selects from among data that is received from a plurality of sources.
Data from this plurality of sources is stored in databases 316. Id. ¶ 55. A
person of ordinary skill in the art would understand that weather forecast
data stored in databases 316 constitutes a data matrix within the meaning of
claim 1. The Examiner does not err in finding that Bailey discloses a data
matrix of test predictions as claimed.
Next, Appellant argues that Bailey fails to disclose a comparison
between an analysis data point and a test prediction data point. Appeal
Br. 8. According to Appellant:
[T]here is nothing in Bailey that in any way, shape, or form that
[sic] discloses or suggests “analysis data points” and “test
prediction data points” and to assert otherwise is to blatantly
create disclosure in Bailey where no such disclosure exists.
Id. at 9.
In response, the Examiner states that Bailey’s processor, which makes
weather band selections, reads on Appellant’s controller. Ans. 7. According
to the Examiner, Bailey’s weather band selection process accesses the most
recently acquired weather obtained by the aircraft to determine the weather
information that is pertinent to the aircraft. Id. The Examiner finds such
recently acquired weather information to satisfy the “analysis data point”
limitation of claim 1. Id. (citing Bailey ¶ 80). Current weather data
obtained by the aircraft is used to rank the weather bands and the selected
weather band then anticipates the weather for the upcoming flight path. Id.
(citing Bailey ¶¶ 73, 80). The Examiner finds that the selected weather band
is selected based on: (1) the most recently acquired weather measured by
aircraft — analysis data points; and (2) the predicted weather for the
waypoints — test prediction data points. Id.
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In reply, Appellant argues that Bailey merely filters data without
performing a comparison of one item of data with another. Reply Br. 4.
Appellant goes so far as to argue that Bailey does not use the word
“compare” or any of its derivatives. Id.
Appellant’s argument based on the literal word “compare” and its
derivatives is unpersuasive. It is well settled that a prior art reference need
not satisfy an ipsissimis verbis test. In re Gleave, 560 F.3d 1331, 1334 (Fed.
Cir. 2009). Bailey uses the word “select” or a derivative thereof more
than 100 times. See generally Bailey. Reading how the word “select” is
used in context throughout Bailey, it is understood that a selection is made
based on comparing an item of data to a criterion or another item of data.
According to Appellant’s Specification, “analysis forecast data” is
obtained from data sources such as airline operation control centers, aircraft
onboard sensors, or any other suitable weather data center. Spec. ¶ 35. In
claim 1, analysis forecast data is compared with test prediction data points
obtained from forecasting models. Claims App. Appellant’s argument that
Bailey merely filters data cannot be sustained from the record before us.
Reply Br. 4. In Bailey, a weather band for waypoint 512 might include a
head wind component of X knots. Bailey ¶ 79. When Bailey’s aircraft 506
reaches waypoint 512, two things happen: (1) current weather conditions
are obtained for waypoint 5123; and (2) the remainder of the flight plan that
is down range of waypoint 512 is updated based on forecast head wind data
along the route. Id. ¶ 80. Forecast weather band data for the remainder of
the flight plan that is down range of waypoint 512 is obtained from sources

3 This information could be obtained from aircraft’s onboard navigation/
sensor system which can measure, among other things, current head wind
component, or it could be obtained from sources external to the aircraft.
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such as other aircraft along the same route or weather forecasting stations.
Id.
When aircraft 506 follows trajectory 502 along flight path 500
at a later time than aircraft 504, aircraft 506 may receive the
benefit of the weather information detected by aircraft 504 as
well as the current weather detected by aircraft 506. The current
weather detected by aircraft 506 may also be used to update the
dated weather in the onboard computer of aircraft 506. The
dated weather may be . . . weather information uploaded
preflight into the onboard computer of aircraft 506 . . . The
information obtained by aircraft 504 along trajectory 502 may
be used to anticipate the weather aircraft 506 will encounter on
points 508, 510, 512, and 514 of trajectory 502 for flight
path 500.
Id. Bailey’s system evaluates weather information from a plurality of
sources, processes such information through section module 426, and
updates the flight plan with weather band data that is selected by the
selection module. Id. ¶¶ 69–71. “Selection module 426 processes filtered
information 424 and applies selection criteria 427 to an aircraft trajectory
received.” Id. ¶ 71.
Thus, Bailey sends filtered data to selection module 426 where it is
“processed” using selection criteria and then applied to an aircraft trajectory.
Bailey ¶ 71. Appellant’s argument that Bailey’s selection process is not
equivalent to Appellant’s comparison process as found by the Examiner is
not persuasive. Appeal Br. 10. Appellant fails to explain how Bailey
receives weather forecast information from a plurality of sources and then
processes such information using selection criteria without engaging in a
comparison process. Bailey ¶ 71, Fig. 4. The Examiner does not err in
finding that Bailey compares analysis data points to test prediction data
points. Final Act. 4.
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The Examiner’s findings of fact are supported by a preponderance of
the evidence, and the Examiner’s legal conclusion of unpatentability is well-
founded. In view thereof, we sustain the Examiner’s unpatentability
rejection of claim 1.
Claim 8
Claim 8 is an independent claim that is substantially similar to
claim 1, except that it recites a method instead of an apparatus. Claims App.
Appellant argues that Bailey fails to disclose selecting a forecasting
model based on comparing an analysis data point and a data matrix of test
predictions. Appeal Br. 11–12. As discussed above, we fully considered
this argument with respect to the rejection of claim 1 and found it to be
unpersuasive. It is equally unpersuasive here. The same is true with respect
to the remainder of the arguments Appellant advances against the rejection
of claim 8. Appeal Br. 12–16.
We sustain the Examiner’s unpatentability rejection of claim 8.
Claim 15
Claim 15 is an independent claim that is substantially similar to
claim 1, except that it recites a method instead of an apparatus. Claims App.
In traversing the rejection, Appellant presents only arguments that we
previously considered with respect to claim 1. Appeal Br. 16–19. Such
arguments are no more persuasive with respect to claim 15 than they were
with respect to the rejection of claim 1.
We sustain the Examiner’s rejection of claim 15.
Claims 5 and 14
Claim 5 depends from claim 1 and adds the limitation: “wherein each
of the plurality of forecasting models is one or more of trained and tested
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using training data common to all of the plurality of forecasting models.”
Claims App. Claim 14 depends directly from claim 13 and indirectly from
claim 8 and adds the limitation: “wherein the test prediction data point from
the data matrix of test predictions that is representative of the at least one
analysis data point is determined by creating a distance matrix to identify
which of the plurality of test prediction data points has a smallest distance to
the at least one analysis data point.” Id.
On page 19 of the Appeal Brief, in a paragraph numbered as “1D,”
Appellant states: “Claims 3-7, 9-14, and 16-21 depend from claims 1, 8,
and 15 and are patentable at least by reason of their respective
dependencies.” Appeal Br. 19. Later, on page 21, in a paragraph numbered
only as “1D-2,” Appellant argues with respect to claim 5 that Bailey merely
filters data and that data filtering differs from training and testing of a
forecasting model. Appeal Br. 21. Similarly, in paragraph 1D-3 on page 21,
Appellant argues with respect to claim 14 that Bailey fails to disclose
trajectory predictions that read on the smallest distance. Id.
The Examiner’s Answer does not address Appellant’s arguments
concerning claims 5 and 14 on page 21 of the Appeal Brief. Ans. 11. Based
on the following remarks in the Answer, it appears to us that the Examiner
understood Appellant’s remarks on page 19 of the Appeal Brief as
Appellant’s entire position on claims 5 and 14. Id.
Appellant argues that claims 3-7, 9-14, and 16-21 depend
from claims 1, 8, and 15 and are patentable at least by reason of
their respective dependences (see argument 1D).
The examiner disagrees, and because there was no issue
raised with the dependent claims, these claims 1, 8, and 15 rise
and fall with the independent claims.
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Id. Thus, it appears that the Examiner mistakenly overlooked Appellant’s
arguments under paragraphs 1D-2 and 1D-3 on page 21 of the Appeal Brief.
The instant situation presents us with a procedural dilemma. Our
rules require that any claims that an Appellant intends to argue separately or
as a subgroup “shall” be argued under a separate subheading. See 37 C.F.R.
§ 41.37(c)(1)(iv). The word “shall” denotes a mandatory requirement.
Moreover, such rules further require that the separate subheading identify
the claim(s) “by number.” Id. Appellant’s numbering of paragraphs in the
Appeal Brief, clearly fails to comply with the subheading requirements of
Rule 41.37. On the one hand, we normally would prefer to dispose of
arguments on their substantive merits. However, on the other hand, it
appears that Appellant’s failure to abide by our rules and provide an Appeal
Brief with appropriate subheadings may have misled the Examiner which, in
turn, may have deprived us of the opportunity to hear and consider the
Examiner’s response to Appellant’s arguments. Procedural rules, such as
Rule 41.37, serve an important purpose and are not to be ignored or
neglected at the whim or carelessness of the Appellant. In this particular
situation, had Appellant filed an Appeal Brief with the proper subheadings,
we likely would have the benefit of the competing positions of Appellant
and the Examiner on this matter. We determine that, if any hardship should
befall either Appellant or the Examiner due to Appellant’s failure to abide
by our rules in this particular instance, it should befall Appellant.
Under the circumstances, due to Appellant’s failure to provide proper
subheadings under our rules, we will treat claims 5 and 14 as having been
grouped with claims 1 and 8 respectively and, thus, they fall therewith. See
Subheading 1, Appeal Br. 5.
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Claims 3, 6, 7, 9–13, and 16–21
These claims depend from one of claims 1, 8, and 15 and are not
separately argued. They fall with their respective independent claims.
See 37 C.F.R. § 41.37(c)(1)(iv) (failure to separately argue claims
constitutes a waiver of arguments for separate patentability).
Unpatentability of Claim 4
Claim 4 depends from claim 1 and adds the limitation: “wherein each
of the plurality of forecasting models are machine learning models.” Claims
App. We deem it expedient to consider the Examiner’s alternative grounds
of rejection of claim 4 together.
The Examiner finds that, in Bailey, a data set and weather
band/forecast is selected and Bailey’s system and then predicts how the
aircraft trajectory may change from its flight plan based on weather
information. Final Act. 5 (citing Bailey ¶ 71). The Examiner finds that such
integration of weather data to predict how the trajectory of the aircraft may
change based on a weather model amounts to a teaching that is equivalent to
“machine learning.” Id. at 6, 15.
The data filtering process [of Bailey] is capable of being a
complex algorithm that is a combination of data mining and
computational statistics to filter the data sets and identify the
accuracy and relevance of the information in the database for
that particular aircraft request which is the description of
machine learning. The prior art uses all relevant information to
predict how the trajectory may change, which is a description of
a machine learning data-driven predication.
Id. at 15 (citing Machine Learning, p. 1).
The Examiner concludes that it would have been obvious to a person
of ordinary skill in the art at the time of the invention to have applied
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machine learning techniques to the data filtering and selection features of
Bailey. Final Act. 14–15. According to the Examiner, a person of ordinary
skill in the art would have done this to better predict how the aircraft’s
trajectory may change due to weather along the route. Id. at 15.
Appellant argues that Bailey cannot be modified as proposed by the
Examiner because, to do so, would change the manner in which Bailey
operates. Appeal Br. 22 (“the databases and the weather band selection as
they are explicitly described in Bailey would be wholly eliminated”).
In response, the Examiner states that a data driven decisions process,
such as Bailey, compares analysis data points measured by the aircraft to
various weather band test prediction data points. Ans. 12. The Examiner
finds that such comparison results in rank ordering for selection of an
appropriate weather band. Id. The Examiner finds that such is a machine
learning process without labeling the system as such in so many words. Id.
In reply, Appellant argues that Bailey’s disclosure of a data decision
approach “says nothing” about machine learning models. Reply Br. 5.
Appellant defines “machine learning” as “the scientific study of
algorithms and statistical models that computer systems use to perform a
specific task without using explicit instructions, relying on patterns and
inference instead.” Appeal Br. 20. According to Appellant, “[m]achine
learning models (or algorithms) build a mathematical model based on
sample data, known as “training data”, in order to make predictions or
decisions without being explicitly programmed to perform the task.” Id.
at 20–21. Appellant’s Specification concedes that weather forecasting
computers that use weather forecasting models are a form of machine
learning. Spec. ¶ 2. The Specification teaches that decision trees, random
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forecast algorithms, polynomial fit, and k-nearest neighbors are “suitable
machine learning models.” Id. ¶ 30.
To the extent that Bailey does not explicitly teach the use of a
“machine learning” model in its data filtering and selection modules, the
question is raised as to whether it would have been obvious to incorporate
machine learning techniques into Bailey’s system. Bailey already employs
telecommunications and computer processing technology to gather, filter,
analyze, select, and output flight planning data. Bailey, Figs. 1–5. Bailey
applies data processing techniques to aircraft performance data, in
combination with actual and predicted weather data, to predict an aircraft’s
trajectory. At the end of the day, “machine learning” is little more than just
another, known, data processing technique.
The prospect that Bailey may not explicitly disclose the use of
machine learning techniques is not fatal to the Examiner’s case. It is well
settled that a claimed invention may be obvious even when the prior art does
not teach each claim limitation, so long as the record contains some reason
that would cause one of skill in the art to modify the prior art to obtain the
claimed invention. Beckson Marine, Inc. v. NFM, Inc., 292 F.3d 718, 727
(Fed. Cir. 2002). In an obviousness analysis, “we do not ignore the
modifications that one skilled in the art would make to a device borrowed
from the prior art.” In re ICON Health & Fitness, Inc., 496 F.3d 1374, 1382
(Fed. Cir. 2007). This follows from the prospect that “[a] person of ordinary
skill is also a person of ordinary creativity, not an automaton.” KSR Int’l
Co. v. Teleflex Inc., 550 U.S. 398, 421 (2007).
It is well settled that, if a technique has been used to improve one
device, and a person of ordinary skill in the art would recognize that it would
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improve similar devices in the same way, using the technique is obvious
unless its actual application is beyond his or her skill. KSR, 550 U.S. at 417.
In the instant case, Appellant presents neither evidence nor persuasive
technical reasoning that tends to show that modifying Bailey’s data
processing features to incorporate machine learning techniques requires
more than ordinary skill or somehow produces unexpected results. The crux
of Appellant’s traverse of the Examiner’s rejection is contained in the
following statement:
Bailey cannot be modified to replace the databases and
selection of the weather bands as explicitly described in Bailey
with machine learning models as disclosed in Machine
Learning because doing so would clearly change the manner in
which Bailey operates (i.e., the databases and the weather band
selection as they are explicitly described in Bailey would be
wholly eliminated).
Appeal Br. 22. This statement is vague and conclusory at best. It lacks any
evidentiary support or persuasive technical reasoning as to how Bailey’s
operation would be altered by machine learning or how or why Bailey’s
databases would be “wholly eliminated.” It amounts to unsubstantiated
attorney argument that is entitled to little weight. See In re Geisler, 116
F.3d 1465, 1470 (Fed. Cir. 1997) (explaining that attorney arguments and
conclusory statements that are unsupported by factual evidence are entitled
to little probative value).
Here, the Examiner has supplied us with sufficient findings of fact and
reasons to modify the prior art to make out a prima facie case of
obviousness. See In re Berg, 320 F.3d 1310, 1315 (Fed. Cir. 2003)
(discussing an Examiner’s competence to make findings that can establish a
prima facie case of obviousness). When a prima facie case of obviousness is
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made, the burden shifts to Appellant to come forward with evidence and/or
argument supporting patentability. In re Giannelli, 739 F.3d 1375, 1379
(Fed. Cir. 2014). Appellant’s unsupported and conclusory arguments are not
sufficient to overcome the Examiner’s prima facie case. See In re De
Blauwe, 736 F.2d 699, 705 (Fed. Cir. 1984) (explaining that arguments
relying on factually unsupported assumptions to rebut a prima facie case of
obviousness “can hardly be considered factual evidence”).
We sustain each of the Examiner’s alternative grounds of rejection of
claim 4.
CONCLUSION
Claims
Rejected
35
U.S.C. §
Reference(s)/Basis Affirmed Reversed
1, 3-21 103 Bailey 1, 3-21
4 103 Bailey, Machine Learning 4
Overall
Outcome
1, 3-21

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)(iv).
AFFIRMED