Acquisio Inc.

Patent Trials and Appeals Board

Apr 6, 2020

14101674 - (D) (P.T.A.B. Apr. 6, 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.
14/101,674 12/10/2013 Bryan Michael Minor 050906-1490 1487
24504 7590 04/06/2020
THOMAS | HORSTEMEYER, LLP
3200 WINDY HILL ROAD, SE
SUITE 1600E
ATLANTA, GA 30339
EXAMINER
RETTA, YEHDEGA
ART UNIT PAPER NUMBER
3688
NOTIFICATION DATE DELIVERY MODE
04/06/2020 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):
docketing@thomashorstemeyer.com
ozzie.liggins@tkhr.com
uspatents@tkhr.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte BRYAN MICHAEL MINOR and TAMAS FRAJKA
Appeal 2019-003315
Application 14/101,674
Technology Center 3600
Before ALLEN R. MACDONALD, JEREMY J. CURCURI, and
IFTIKHAR AHMED, Administrative Patent Judges.
CURCURI, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject claims 1–21. We have jurisdiction under
35 U.S.C. § 6(b).
We AFFIRM.

1 We use the word Appellant to refer to “applicant” as defined in 37 C.F.R.
§ 1.42(a). Appellant identifies the real party in interest as Web.com Group,
Inc. Appeal Br. 2.
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CLAIMED SUBJECT MATTER
The claims are directed to “managing advertising campaigns across
those online advertising channels.” Spec. ¶ 1. Claim 1, reproduced below, is
illustrative of the claimed subject matter:
1. A method for directing online advertising across
multiple online advertising channels, the method comprising:
calculating, by a master computing device, formulated
data from channel data;
separating the formulated data and channel data into
three separate data sets, wherein each data set of the three
separate data sets comprises a same type of data and a differing
amount of data from one another;
building, by the master computing device, a plurality of
predictive models using a first data set of the three separate data
sets;
testing, by the master computing device, individual
predictive models of the plurality of predictive models using a
second data set of the three separate data sets, a first set of
results being generated using the second data set;
selecting, by the master computing device, a first subset
of predictive models based at least in part on the first set of
results;
testing, by the master computing device, each predictive
model in the first subset of predictive models using a third data
set of the three separate data sets, a second set of results being
generated using the third data set;
comparing, by the master computing device, the second
set of results with the first set of results associated with the first
subset of predictive models to identify overfit;
determining, by the master computing device, a third
subset of predictive models based at least in part on a removal
of predictive models having overfit from consideration;
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predicting, by the master computing device, using the
third subset of predictive models, results of advertisements
purchased in the multiple online advertising channels;
allocating, by the master computing device, an
advertising budget for a given time period for each of the
multiple online advertising channels based on the predicted
results;
communicating the budget allocations across a network
from the master computing device to one or more slave
computing devices responsible for purchasing online
advertisements within the multiple online advertising channels;
bidding, by the one or more slave computing devices, for
online advertisements within the multiple online advertising
channels based on the communicated budget allocations;
transmitting, via the one or more slave computing
devices, performance results over the network to the master
computing device, the performance results being associated
with the bidding for online advertisements;
adjusting, by the master computing device, the budget
allocation for one or more of the multiple online advertising
channels based on the performance results; and
communicating the adjusted budget allocation across the
network from the master computing device to the slave
computing devices responsible for purchasing advertisements
within the one or more of the multiple online advertising
channels.
REFERENCES
The prior art relied upon by the Examiner is:
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Name Reference Date
Pednault et al. US 2003/0176931 A1 Sept. 18, 2003
Kamath et al. US 2011/0071900 A1 Mar. 24, 2011
Wu et al. US 8,140,381 B1 Mar. 20, 2012
Ogawa US 2012/0158485 A1 June 21, 2012
Hegeman US 2013/0124308 A1 May 16, 2013

REJECTIONS
Claims 1–21 are rejected under 35 U.S.C. § 101 as directed to a
judicial exception without significantly more. Final Act. 2–4.
Claims 1, 5, 6, 8–12, 14, and 16–19 are rejected under 35 U.S.C.
§ 103 as obvious over Ogawa and Pednault. Final Act. 4–6.
Claims 2 and 3 are rejected under 35 U.S.C. § 103 as obvious over
Ogawa, Pednault, and Kamath. Final Act. 6–7.
Claims 4, 13, and 20 are rejected under 35 U.S.C. § 103 as obvious
over Ogawa, Pednault, and Wu. Final Act. 7.
Claims 7, 15, and 21 are rejected under 35 U.S.C. § 103 as obvious
over Ogawa, Pednault, and Hegeman. Final Act. 7–8.
OPINION
The Judicial Exception Rejection of Claims 1–21
The Examiner determines the following:
the claims are directed to determining an advertising
budget using a prediction model for biding across multiple
channels[.]
With respect to claims 1–21, the independent claims
include[] the steps of calculating data, separating data, building
predictive models, testing predictive models, comparing results,
predictive models, predicting results, transmitting results,
bidding and adjusting and communicating budget allocations.
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With respect to the independent claims, the claims further
offer descriptive limitations of elements found in the
independent claims which are addressed above and do not serve
to confer the subject matter eligibility to the claimed invention.
These claim elements are considered to be abstract ideas
because they are similar to the following which were found to
be abstract by a controlling court:
“collecting information, analyzing it and displaying
certain results of the collect[ion] and analysis” (Electric Power
Group) as seen in the steps “preparing data, calculating data,
separating data ... communicating results,”.
“an algorithm for determining the optimal number of
visits” (Maucorps) as seen in the steps “building predictive
models ... , choosing models ... , predicting results ... adjusting”.
“using advertising as an exchange or currency”
(Ultramercial) as seen in the steps of [“]bidding ... , purchasing
advertisement ....”.
Final Act. 2–3; see also Final Act. 3–4 (determining the claims do not recite
significantly more than an abstract idea), Ans. 3–4.
An invention is patent-eligible if it claims a “new and useful process,
machine, manufacture, or composition of matter.” 35 U.S.C. § 101.
However, the U.S. Supreme Court has long interpreted 35 U.S.C. § 101 to
include implicit exceptions: “[l]aws of nature, natural phenomena, and
abstract ideas” are not patentable. E.g., Alice Corp. v. CLS Bank Int’l, 573
U.S. 208, 216 (2014).
In determining whether a claim falls within an excluded category, we
are guided by the Court’s two-part framework, described in Mayo and Alice.
Id. at 217–18 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc.,
566 U.S. 66, 75–77 (2012)). In accordance with that framework, we first
determine what concept the claim is “directed to.” See Alice, 573 U.S. at
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219 (“On their face, the claims before us are drawn to the concept of
intermediated settlement, i.e., the use of a third party to mitigate settlement
risk.”); see also Bilski v. Kappos, 561 U.S. 593, 611 (2010) (“Claims 1 and 4
in petitioners’ application explain the basic concept of hedging, or protecting
against risk.”).
Concepts determined to be abstract ideas, and thus patent ineligible,
include certain methods of organizing human activity, such as fundamental
economic practices (Alice, 573 U.S. at 219–20; Bilski, 561 U.S. at 611);
mathematical formulas (Parker v. Flook, 437 U.S. 584, 594–95 (1978)); and
mental processes (Gottschalk v. Benson, 409 U.S. 63, 67 (1972)). Concepts
determined to be patent eligible include physical and chemical processes,
such as “molding rubber products” (Diamond v. Diehr, 450 U.S. 175, 191
(1981)); “tanning, dyeing, making waterproof cloth, vulcanizing India
rubber, smelting ores” (id. at 182 n.7 (quoting Corning v. Burden, 56 U.S.
252, 267–68 (1853))); and manufacturing flour (Benson, 409 U.S. at 69
(citing Cochrane v. Deener, 94 U.S. 780, 785 (1876))).
In Diehr, the claim at issue recited a mathematical formula, but the
Court held that “a claim drawn to subject matter otherwise statutory does not
become nonstatutory simply because it uses a mathematical formula.”
Diehr, 450 U.S. at 187; see also id. at 191 (“We view respondents’ claims as
nothing more than a process for molding rubber products and not as an
attempt to patent a mathematical formula.”). Having said that, the Court
also indicated that a claim “seeking patent protection for that formula in the
abstract . . . is not accorded the protection of our patent laws, and this
principle cannot be circumvented by attempting to limit the use of the
formula to a particular technological environment.” Id. (citation omitted)
(citing Benson and Flook); see, e.g., id. at 187 (“It is now commonplace that
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an application of a law of nature or mathematical formula to a known
structure or process may well be deserving of patent protection.”).
If the claim is “directed to” an abstract idea, we turn to the second
step of the Alice and Mayo framework, where “we must examine the
elements of the claim to determine whether it contains an ‘inventive
concept’ sufficient to ‘transform’ the claimed abstract idea into a patent-
eligible application.” Alice, 573 U.S. at 221 (quotation marks omitted).
“A claim that recites an abstract idea must include ‘additional features’ to
ensure ‘that the [claim] is more than a drafting effort designed to
monopolize the [abstract idea].’” Id. (alterations in original) (quoting Mayo,
566 U.S. at 77). “[M]erely requir[ing] generic computer implementation[]
fail[s] to transform that abstract idea into a patent-eligible invention.” Id.
In January 2019, the U.S. Patent and Trademark Office (USPTO)
published revised guidance on the application of § 101. 2019 Revised Patent
Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019)
(“2019 Revised Guidance”).2 “All USPTO personnel are, as a matter of
internal agency management, expected to follow the guidance.” Id. at 51;
see also October 2019 Update at 1.
Under the 2019 Revised Guidance and the October 2019 Update, we
first look to whether the claim recites:

2 In response to received public comments, the Office issued further
guidance on October 17, 2019, clarifying the 2019 Revised Guidance.
USPTO, October 2019 Update: Subject Matter Eligibility (the “October
2019 Update”) (available at
https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_update.p
df).
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(1) any judicial exceptions, including certain groupings of abstract
ideas (i.e., mathematical concepts, certain methods of organizing
human activity such as a fundamental economic practice, or mental
processes) (“Step 2A, Prong One”); and
(2) additional elements that integrate the judicial exception into a
practical application (see MPEP § 2106.05(a)–(c), (e)–(h) (9th ed.
Rev. 08.2017, Jan. 2018)) (“Step 2A, Prong Two”).3
2019 Revised Guidance, 84 Fed. Reg. at 52–55.
Only if a claim (1) recites a judicial exception and (2) does not
integrate that exception into a practical application, do we then look, under
Step 2B, to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not
“well-understood, routine, conventional” in the field (see MPEP
§ 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities
previously known to the industry, specified at a high level of
generality, to the judicial exception.
2019 Revised Guidance, 84 Fed. Reg. at 52–56.

3 This evaluation is performed by (a) identifying whether there are any
additional elements recited in the claim beyond the judicial exception, and
(b) evaluating those additional elements individually and in combination to
determine whether the claim as a whole integrates the exception into a
practical application. See 2019 Revised Guidance - Section III(A)(2), 84
Fed. Reg. 54–55.
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Are the claims patent-eligible?
Step 1
Claim 1 recites a method, which falls with the “process” category of
35 U.S.C. § 101. Claim 12 recites a system, which falls with the “machine”
category of 35 U.S.C. § 101. Claim 19 recites a non-transitory computer
readable storage medium, which falls with the “manufacture” category of 35
U.S.C. § 101. Thus, we must determine whether the claims recite a judicial
exception and fail to integrate the exception into a practical application. See
84 Fed. Reg. at 54–55. If both elements are satisfied, the claim is directed to
a judicial exception under the first step of the Alice/Mayo test. See id.
Step 2A, Prong One
Independent claim 1 recites the following steps:
[i] calculating, by a master computing device, formulated
data from channel data;
[ii] separating the formulated data and channel data into
three separate data sets, wherein each data set of the three
separate data sets comprises a same type of data and a differing
amount of data from one another;
[iii] building, by the master computing device, a plurality
of predictive models using a first data set of the three separate
data sets;
[iv] testing, by the master computing device, individual
predictive models of the plurality of predictive models using a
second data set of the three separate data sets, a first set of
results being generated using the second data set;
[v] selecting, by the master computing device, a first
subset of predictive models based at least in part on the first set
of results;
[vi] testing, by the master computing device, each
predictive model in the first subset of predictive models using a
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third data set of the three separate data sets, a second set of
results being generated using the third data set;
[vii] comparing, by the master computing device, the
second set of results with the first set of results associated with
the first subset of predictive models to identify overfit;
[viii] determining, by the master computing device, a
third subset of predictive models based at least in part on a
removal of predictive models having overfit from
consideration;
[ix] predicting, by the master computing device, using
the third subset of predictive models, results of advertisements
purchased in the multiple online advertising channels;
[x] allocating, by the master computing device, an
advertising budget for a given time period for each of the
multiple online advertising channels based on the predicted
results;
[xi] communicating the budget allocations across a
network from the master computing device to one or more slave
computing devices responsible for purchasing online
advertisements within the multiple online advertising channels;
[xii] bidding, by the one or more slave computing
devices, for online advertisements within the multiple online
advertising channels based on the communicated budget
allocations;
[xiii] transmitting, via the one or more slave computing
devices, performance results over the network to the master
computing device, the performance results being associated
with the bidding for online advertisements;
[xiv] adjusting, by the master computing device, the
budget allocation for one or more of the multiple online
advertising channels based on the performance results; and
[xv] communicating the adjusted budget allocation across
the network from the master computing device to the slave
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computing devices responsible for purchasing advertisements
within the one or more of the multiple online advertising
channels.
These steps [i]–[xv], overall, describe the process of allocating advertising
budgets based on predictive models, and subsequently adjusting the
advertising budgets based on performance results. Thus, steps [i]–[xv]
describe “advertising, marketing or sales activities or behaviors” and are
thus “commercial or legal interactions.” Memorandum, 84 Fed. Reg. at 52.
Thus, these steps recite the abstract concept of “[c]ertain methods of
organizing human activity.” Id.
In addition, the process of allocating advertising budgets based on
predictive models described by steps [i]–[x] can be done by human thought
and a pen and paper, and is one or more of “observation, evaluation,
judgment, opinion.” Id. Thus, these steps also recite the abstract concept of
“[m]ental processes.” Id.
In addition, the process of subsequently adjusting the advertising
budgets based on performance results described by steps [xi]–[xv] can be
done by human thought and a pen and paper, and is one or more of
“observation, evaluation, judgment, opinion.” Id. Thus, these steps also
recite the abstract concept of “[m]ental processes.” Id
The Federal Circuit has held that if a method can be performed by
human thought alone, or by a human using pen and paper, it is merely an
abstract idea and is not patent-eligible under § 101. CyberSource Corp. v.
Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (“[A] method
that can be performed by human thought alone is merely an abstract idea and
is not patent-eligible under § 101.”). The abstract idea, even when automated
to reduce the burden on the user of what once could have been done with
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pen and paper, remains an abstract idea. CyberSource, 654 F.3d at 1375
(“That purely mental processes can be unpatentable, even when performed
by a computer, was precisely the holding of the Supreme Court in
Gottschalk v. Benson.”).
Thus, we determine claim 1 recites a judicial exception.
We also determine claims 12 and 19 recited a judicial exception for
the same reasons discussed above with respect to claim 1.
Step 2A, Prong Two
Because claims 1, 12, and 19 recite a judicial exception, we next
determine if the claims recite additional elements that integrate the judicial
exception into a practical application.
In addition to the limitations of claim 1 discussed above that recite
abstract concepts, claim 1 further recites the following: “multiple online
advertising channels,” “a master computing device,” “a network,” and “one
or more slave computing devices.” Claim 12 further recites the “master
computing device” comprising “a data parser,” “a model builder,” “a model
tester,” and “a prediction and allocation module.”
The Specification does not provide additional details that would
distinguish the additional limitations from a generic implementation. See
Spec. ¶¶ 11 (“Each of master system 105 and slave modules 110, 120, 130
and 140 is a server or other computing system, comprising one or more
individual computing devices as known in the art, running program code that
performs the functions described herein.”), 12 (“network 150 which, in one
example, is the [I]nternet but can be any known form of network
communications”), 14 (“online advertising channels referred to herein can
be any one of now known or in the future developed channel”), 18 (“Data
parser 220 is programming code or computing logic that calculates
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formulated data from channel data, optionally cleans up data, and separates
data into sets.”), 20 (“Model builder 225 is programming code or computing
logic that builds the predictive models 205.”), 21 (“Model tester 230 is
programming code or computing logic that chooses which of the predictive
models 205 to use.”), 22 (“Prediction and allocation module 235 is
programming code or computing logic that determines slave module budget
allocations and allocation adjustments.”). There is no indication that the
computers used in the invention are anything other than general purpose
computers. See Spec. ¶ 11.
We do not find the recited computer-related limitations are sufficient
to integrate the judicial exception into a practical application. Specifically,
there is no improvement to the functioning of the computer, but, instead, the
computer merely implements the abstract idea. In this case, we do not see
any particular machine or manufacture that is integral to the claim; nor do
we see any transformation. That is, we do not see any of the additionally
recited elements applying or using the judicial exception in any meaningful
way beyond generally linking the judicial exception to the recited elements.
Accordingly, we determine that claims 1, 12, and 19 are directed to a
judicial exception because they do not recite additional elements that
integrate the recited judicial exception into a practical application.
Is there something else in the claims that ensures that they are
directed to significantly more than a patent ineligible concept?
Step 2B
Because claims 1, 12, and 19 are directed to a judicial exception, we
next determine, according to Alice, whether the claims recite an element, or
combination of elements that is enough to ensure that the claims are directed
to significantly more than a judicial exception.
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In addition to the limitations of claim 1 discussed above that recite
abstract concepts, claim 1 further recites the following: “multiple online
advertising channels,” “a master computing device,” “a network,” and “one
or more slave computing devices.” Claim 12 further recites the “master
computing device” comprising “a data parser,” “a model builder,” “a model
tester,” and “a prediction and allocation module.”
2019 Revised Guidance, Section III (B) (footnote 36) states:
In accordance with existing guidance, an examiner’s
conclusion that an additional element (or combination of
elements) is well understood, routine, conventional activity must
be supported with a factual determination. For more information
concerning evaluation of well-understood, routine, conventional
activity, see MPEP 2106.05(d), as modified by the USPTO
Berkheimer Memorandum.
The Berkheimer Memorandum, Section III (A)(1) states:
A specification demonstrates the well-understood, routine,
conventional nature of additional elements when it describes the
additional elements as well-understood or routine or
conventional (or an equivalent term), as a commercially
available product, or in a manner that indicates that the additional
elements are sufficiently well-known that the specification does
not need to describe the particulars of such additional elements
to satisfy 35 U.S.C. § 112(a). A finding that an element is well-
understood, routine, or conventional cannot be based only on the
fact that the specification is silent with respect to describing such
element.
The Specification discloses “Each of master system 105 and slave
modules 110, 120, 130 and 140 is a server or other computing system,
comprising one or more individual computing devices as known in the art,
running program code that performs the functions described herein.” Spec.
¶ 11 (emphasis added). The Specification further discloses “network 150
which, in one example, is the [I]nternet but can be any known form of
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network communications.” Spec. ¶ 12 (emphasis added). The Specification
further discloses “online advertising channels referred to herein can be any
one of now known or in the future developed channel.” Spec. ¶ 14 (emphasis
added). Thus, the Specification describes the “multiple online advertising
channels,” “master computing device,” “network,” and “one or more slave
computing devices” as conventional. See Spec. ¶¶ 11, 12, 14; see also Final
Act. 3–4, Ans. 4 (“The claims reciting a generic computer (server), and
generic components (models) merely describing the functions of the abstract
idea itself, without particularity is simply not enough for an inventive
concept under step two.”).
The Specification discloses “Data parser 220 is programming code or
computing logic that calculates formulated data from channel data,
optionally cleans up data, and separates data into sets.” Spec. ¶ 18 (emphasis
added). The Specification further discloses “Model builder 225 is
programming code or computing logic that builds the predictive models
205.” Spec. ¶ 20 (emphasis added). The Specification further discloses
“Model tester 230 is programming code or computing logic that chooses
which of the predictive models 205 to use.” Spec. ¶ 21 (emphasis added).
The Specification further discloses “Prediction and allocation module 235 is
programming code or computing logic that determines slave module budget
allocations and allocation adjustments.” Spec. ¶ 22 (emphasis added). Thus,
the Specification describes the “data parser,” “model builder,” “model
tester,” and “prediction and allocation module” in a manner that indicates
that these elements are sufficiently well-known that the Specification does
not need to describe the particulars of such additional elements to satisfy 35
U.S.C. § 112(a). See Spec. ¶¶ 18, 20, 21, 22; see also Final Act. 3–4, Ans. 4
(“The claims reciting a generic computer (server), and generic components
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(models) merely describing the functions of the abstract idea itself, without
particularity is simply not enough for an inventive concept under step two.”).
Further, the Specification does not provide additional details about the
computers that would distinguish the recited components from generic
implementation individually and generic implementation in the combination.
See Spec. ¶¶ 11, 12, 14, 18, 20, 21, 22.
In view of Appellant’s Specification, the claimed computer
components are reasonably determined to be generic, purely conventional
computer elements. Thus, the claims do no more than require generic
computer elements to perform generic computer functions, rather than
improve computer capabilities.
Accordingly, we determine that claims 1, 12, and 19 are not directed
to significantly more than a patent ineligible concept.
Appellant’s principal arguments
Appellant presents the following principal arguments:
i. The claims are not directed to an abstract idea because the
claims improve existing technologies. See Appeal Br. 10–11 (citing Finjan,
Inc., v. Blue Coat Systems, Inc., No. 2016-2520 (Fed. Cir. Jan. 2018))
(“Existing technologies do not involve this specific technological process for
efficiently managing online advertising campaigns using an automated
multi-layer architecture.”).
ii. The claims are not directed to an abstract idea because the
claims are not analogous to Electric Power Group, LLC. See Appeal Br. 11–
12 (citing Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed.
Cir. 2016)) (“[T]he present claims are directed towards new techniques for
analyzing data using a multi-layer architecture to deal with the complexity
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and range of online advertising possibilities, the rapidity of purchasing
online advertising, and the speed of obtaining online advertising results.”).
iii. The claims are not directed to an abstract idea because the
claims are not analogous to In re Maucorps. See Appeal Br. 12–13. (“[T]he
present claims are directed towards a specific technical approach for dealing
with the complexity and range of online advertising possibilities, the rapidity
of purchasing online advertising, and the speed of obtaining online
advertising results.”).
iv. The claims are not directed to an abstract idea. See Appeal Br.
14–17 (“[T]he present claims are related to improved systems for using a
multi-layer architecture to deal with the complexity and range of online
advertising possibilities, the rapidity of purchasing online advertising, and
the speed of obtaining online advertising results.”); see also Reply Br. 3–8
(discussing Step 2A, Prong 1). 8–10 (discussing Step 2A, Prong 2).
v. The claims recite significantly more than any abstract idea. See
Appeal Br. 18–21. For example, the Examiner “does not provide support as
outlined in the Berkheimer memorandum.” Appeal Br. 19. For example, “the
present claims are directed to at least one inventive concept as an ordered
combination of elements.” Appeal Br. 19 (citing BASCOM Global Internet
Services, Inc. v. AT&T Mobility LLC, No. 2015-1763 (Fed. Cir. June 27,
2016)). For example, “the claims here relate to a specific computer based
approach to deal with the complexity and range of online advertising
possibilities, the rapidity of purchasing online advertising, and the speed of
obtaining online advertising results.” Appeal Br. 21 (citing Amdocs, Ltd. v.
Openet Telecom, Inc., 841 F.3d 1288 (Fed. Cir. 2016)).
Regarding Appellant’s arguments (i)–(iv), these arguments do not
show any error because, as we explain above, the elements of claims 1, 12,
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and 19 are the abstract concepts of “[c]ertain methods of organizing human
activity” and “[m]ental processes.” 84 Fed. Reg. at 52. We also explain that
the claims do not recite additional elements that integrate the recited judicial
exception into a practical application, and are not directed to significantly
more than a patent ineligible concept. In short, the claims recite a computer
system for performing the abstract idea. Thus, on the record before us, the
claim limitations do not improve the functionality of the various hardware
components, nor do they achieve an improved technological result in
conventional industry practice. McRO, Inc. v. Bandai Namco Games Am.,
Inc., 837 F.3d 1299, 1316 (Fed. Cir. 2016). More particularly, we do not
agree that the claimed invention improves the technology as a whole.
Regarding Appellant’s arguments (v), these arguments do not show
any error because, as we explain above, the claims do not recite additional
elements that integrate the recited judicial exception into a practical
application, and are not directed to significantly more than a patent ineligible
concept. Our analysis above considers the claims in light of current USPTO
guidance, including the Berkheimer Memorandum. Further, as we explain
above, the Specification does not provide additional details about the
computer that would distinguish the recited components from conventional
components, and from generic implementation individually and generic
implementation in the combination. See Spec. ¶¶ 11, 12, 14, 18, 20, 21, 22.
We, therefore, sustain the Examiner’s rejection of claims 1, 12, and
19. We also sustain the Examiner’s rejection of claims 2–11, 12–18, 20, and
21, which are not separately argued with particularity.

Appeal 2019-003315
Application 14/101,674
19
The Obviousness Rejection of Claims 1, 5, 6, 8–12, 14, and 16–19 over
Ogawa and Pednault
The Examiner finds Ogawa and Pednault, collectively, teach all
limitations of claim 1. Final Act. 5–6. In particular, the Examiner finds
Ogawa and Pednault, collectively, teach
separating the formulated data and channel data into three
separate data sets, wherein each data set of the three separate data
sets comprises a same type of data and a differing amount of data
from one another
as recited in claim 1. See Final Act. 5–6.
The Examiner finds Ogawa teaches “separating the formulated data
and channel data into three separate data sets” as recited in claim 1. Final
Act. 5 (citing Ogawa ¶ 24). Ogawa discloses “a first set of information is
obtained, including statistical information relating to advertisement
campaign performance relating to multiple advertising campaigns and
associated with each of multiple online advertising channels and each of
multiple offline advertising channels.” Ogawa ¶ 24. The Examiner finds
“Ogawa does not [disclose] training predictive models using different sets of
data and comparing the outcome of each model.” Final Act. 5.
The Examiner finds “Pednault teaches training predictive model[s]
and use of at least three separate sets of data to train the models ... and
comparing each output for selecting a trained model for predicting an
outcome.” Final Act. 5 (citing Pednault ¶¶ 47–53, 83, 130–136); see also
Ans. 5–6 (finding Pednault discloses the use of holdout sets and folds).
The Examiner reasons “[i]t would have been obvious to one of
ordinary skill in the art before the effective filing date of the claimed
invention to implement Pe[]dnault’s training models in Ogawa’s prediction
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model in order to estimate the effectiveness of each model for making a
selection of a predictive model.” Final Act. 5–6.
Appellant presents the following principal arguments:
i. Ogawa does not teach
separating the formulated data and channel data into three
separate data sets, wherein each data set of the three separate data
sets comprises a same type of data and a differing amount of data
from one another
as recited in claim 1. See Appeal Br. 22–23; see also Reply Br. 10–11.
ii.
However, nothing in Pednault appears to show or suggest
at least “separating the formulated data and channel data into
three separate data sets, wherein each data set of the three
separate data sets comprises a same type of data and a differing
amount of data from one another,” as recited in claim 1. Rather,
Pednault merely discusses segmenting data and generating
models for each data segment and that the techniques for
generating the models are scalable with respect to the volume of
data they can handle. Indeed, while Pednault discusses
segmenting data, nothing in Pednault appears to show or suggest
“separating the . . . data into three separate data sets,” such that
each data set comprises “a same type of data and a differing
amount of data from one another,” as recited in claim 1.
Accordingly, Appellant respectfully submits that Pednault fails
to overcome the deficiencies of Ogawa.
Appeal Br. 24; see also Appeal Br. 23–24 (citing Pednault ¶¶ 2, 83, 130,
131); see also Reply Br. 12–13.
iii. Ogawa and Pednault do not teach the “building,” “testing,”
“selecting,” “testing,” “comparing,” and “determining” steps of claim 1. See
Appeal Br. 24–27; see also Reply Br. 13–14.
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Appellant’s arguments persuade us that the Examiner erred in
rejecting claim 1.
Regarding Appellant’s argument (i), this argument does not persuade
us of any error because the Examiner does not find Ogawa teaches the
argued limitation; rather, the Examiner finds Ogawa and Pednault,
collectively, teach the argued limitation. See Final Act. 5–6.
Regarding Appellant’s arguments (ii) and (iii), when we consider
these two arguments together, they persuade us the Examiner erred in
rejecting claim 1. We recognize that Ogawa discloses data “relating to
advertising campaign performance.” Ogawa ¶ 24. We also recognize that
Pednault discloses training predictive models using different sets of data. See
Final Act. 5; see also Pednault Abstract (“[D]ata records are partitioned into
a plurality of segments and separate predictive models are constructed for
each segment.”). To the extent that we interpret the disclosures in Pednault
that describe segmenting as teaching three separate data sets, claim 1 recites
using the three separate data sets in a specific way, including “building...
predictive models using a first data set,” “testing... predictive models... using
a second data set,” “testing... predictive models using a third data set.” We
do not see these specific steps in Pednault. To the extent Pednault discloses
training models and using holdout sets and folds (see Final Act. 5, Ans. 5–
6), such disclosures are not sufficient to teach the argued limitations of claim
1 as recited in combination.
We, therefore, do not sustain the Examiner’s rejection of claim 1.
We also do not sustain the Examiner’s rejection of claims 5, 6, and 8–
11, which depend from claim 1.
We also do not sustain the Examiner rejection of independent claim
12 for the same reasons discussed above with respect to claim 1. We also do
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not sustain the Examiner’s rejection of claims 14 and 16–18 with depend
from claim 12.
We also do not sustain the Examiner rejection of independent claim
19 for the same reasons discussed above with respect to claim 1.

The Obviousness Rejection of Claims 2 and 3 over Ogawa, Pednault, and
Kamath
Claims 2 and 3 depend from claim 1. The Examiner does not find
Kamath cures the deficiency of Ogawa and Pednault. See Final Act. 6–7.
We, therefore, do not sustain the Examiner’s rejection of claims 2 and
3.

The Obviousness Rejection of Claims 4, 13, and 20 over Ogawa, Pednault,
and Wu
Claims 4, 13, and 20 depend from claims 1, 12, and 19, respectively.
The Examiner does not find Wu cures the deficiency of Ogawa and
Pednault. See Final Act. 7.
We, therefore, do not sustain the Examiner’s rejection of claims 4, 13,
and 20.

The Obviousness Rejection of Claims 7, 15, and 21 over Ogawa, Pednault,
and Hegeman
Claims 7, 15, and 21 depend from claims 1, 12, and 19, respectively.
The Examiner does not find Hegeman cures the deficiency of Ogawa and
Pednault. See Final Act. 7–8.
We, therefore, do not sustain the Examiner’s rejection of claims 7, 15,
and 21.
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CONCLUSION
The Examiner’s overall decision to reject claims 1–21 is affirmed.
DECISION SUMMARY
In summary:
Claims
Rejected
35 U.S.C.
§
References/Basis Affirmed Reversed
1–21 101 Judicial
Exception
1–21
1, 5, 6, 8–
12, 14, 16–
19
103 Ogawa, Pednault 1, 5, 6, 8–
12, 14, 16–
19
2, 3 103 Ogawa, Pednault,
Kamath
2, 3
4, 13, 20 103 Ogawa, Pednault,
Wu
4, 13, 20
7, 15, 21 103 Ogawa, Pednault,
Hegeman
7, 15, 21
Overall
Outcome
1–21

TIME PERIOD FOR RESPONSE
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