eBay Inc.

Patent Trials and Appeals Board

Oct 12, 2021

2021001542 (P.T.A.B. Oct. 12, 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.
14/902,391 12/31/2015 Yaron Zakai-or 2043.K79US1 4205
49845 7590 10/12/2021
SCHWEGMAN LUNDBERG & WOESSNER/EBAY
P.O. BOX 2938
MINNEAPOLIS, MN 55402
EXAMINER
KYEREME-TUAH, AKOSUA P
ART UNIT PAPER NUMBER
3623
NOTIFICATION DATE DELIVERY MODE
10/12/2021 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
SLW@blackhillsip.com
USPTO@SLWIP.COM
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte YARON ZAKAI-OR and KIRA RADINSKY
____________

Appeal 2021–001542
Application 14/902,391
Technology Center 3600
____________

Before HUBERT C. LORIN, ANTON W. FETTING, and
MICHAEL C. ASTORINO, Administrative Patent Judges.

FETTING, Administrative Patent Judge.

DECISION ON APPEAL

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STATEMENT OF THE CASE1
Yaron Zakai-or and Kira Radinsky (Appellant2) seeks review under
35 U.S.C. § 134 of a final rejection of claims 33–53, the only claims pending
in the application on appeal. Claims 1–32 have been cancelled. We have
jurisdiction over the appeal pursuant to 35 U.S.C. § 6(b).
The Appellant invented a form of sales prediction. Specification 1:5.
An understanding of the invention can be derived from a reading of
exemplary claim 43, which is reproduced below (bracketed matter and some
paragraphing added).
43. A method executed by a processor, the method comprising:
[1] retrieving
from a data source
one or more groups of parameters;
[2] for each group of parameters in the one or more groups of
parameters,
generating one or more groups of new parameters,
the generation of the one or more groups of new
parameters comprising:
[2.1] automatically analyzing each parameter in the
group;

1 Our decision will make reference to the Appellant’s Appeal Brief (“Appeal
Br.,” filed July 30, 2020) and Reply Brief (“Reply Br.,” filed December 28,
2020), and the Examiner’s Answer (“Ans.,” mailed October 27, 2020), and
Final Action (“Final Act.,” mailed February 14, 2020).
2 We use the word “Appellant” to refer to “applicant” as defined in
37 C.F.R. § 1.42. Appellant identifies the real party in interest as eBay, Inc.
(Appeal Br. 2).

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[2.2] identifying a set of new parameter types and
a set of new parameter values based on an analysis
of the parameters;
[2.3] based on the generated one or more groups of
new parameters,
using a trained neural network to generate a
prediction model associated with each group
of new parameters;
and
[2.4] applying each prediction model
to generate a probability of a successful
future interaction with the one or more
potential customers.3
The Examiner relies upon the following prior art:
Name Reference Date
Mathur US 5,740,324 Apr. 14, 1998
Dockery US 2006/0129447 A1 June 15, 2006
Kasiolas US 2007/0088703 A1 Apr. 19, 2007
Kagarlis US 2011/0178905 A1 July 21, 2011

Claims 33–53 stand rejected under 35 U.S.C. § 101 as directed to a
judicial exception without significantly more.
Claims 33–39, 42–48, and 51–53 stand rejected under 35 U.S.C. § 103
as unpatentable over Dockery and Mathur.

3 There appears to be no antecedent basis for “the one or more potential
customers.” This does not appear in corresponding claims 33 and 52, and so
this appears to be a clerical error. Accordingly, we treat this limitation as
though the “the” were “omitted as in claims 33 and 52.
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Claims 40 and 49 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Dockery, Mathur, and Kasiolas.
Claims 41 and 50 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Dockery, Mathur, and Kagarlis.

ISSUES
The issues of eligible subject matter turn primarily on whether the
claims recite more than abstract conceptual advice of results desired.
The issues of obviousness turn primarily on whether the art describes
generating new parameters.

FACTS PERTINENT TO THE ISSUES
The following enumerated Findings of Fact (FF) are believed to be
supported by a preponderance of the evidence.
Facts Related to the Prior Art
Dockery
01. Dockery is directed to sales management, and in particular
analyzing sales data for optimizing sales and marketing activities.
Dockery para. 2.
02. Dockery describes a business planning solution designed to
improve the effectiveness of the sales force by directing their
efforts and resources toward market segments that are most likely
to respond to the sales effort. A preliminary process for directing
the efforts of the sales force to responsive market segment
involves suitable identification or definitions of the market
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segments. Suitable definitions of the market segments may be
analytically derived from market response data. Dockery para. 22.
03. Dockery describes suitable market segments (i.e., categories of
the target physician population). Several categories of physicians
may be defined according to suitably selected physician
characteristics. The definitions of the physician categories may be
based on physician characteristics that are obtained or developed
from sales data and other data. The data may include both current
sales and market data and/or historical sales and market data. The
data may be relevant to a predetermined period of time, for
example, one year or six months. Exemplary physician
characteristics on which the definitions of the several physician
categories are based may include characteristics such as the
physician’s rate of prescribing a particular pharmaceutical
product, and the volume or dosage of the particular product
prescribed by the physician. Other physician characteristics that
may be used to define the physician categories may refer to the
increase or decrease in either the physician’s rate of prescribing a
product, or in the average dose prescribed by the physician.
Additional physician characteristics, which are relevant for
defining the physician categories, may include the rate at which a
physician prescribes alternate or competing pharmaceutical
products instead of the marketed pharmaceutical product. The
definition of the physician categories also may be based on
demographic characteristics such as the physician’s medical
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specialty, age, gender, and the geographic location or zip code of
the physician's medical practice. Dockery para. 24.
04. Dockery describes selection of the physician categories and
scheduling of the sales calls based on suitable “market response”
modeling of sales experience data, which may include both
ongoing and historical experience data. The sales experience data
for this purpose may have been generated, for example, in the
course of previous sales contacts regarding the particular
pharmaceutical product. Preferably, the sales experience data
includes data from sales contacts with at least some of the
physicians in each of the categories of physicians. Relevant sales
experience data may be collected and assembled for use on a
continuous or periodic basis. Relevant sales experience data may
include, for example, the frequency or number of all types of sales
calls, and the frequency or number of sales calls specifically
regarding the particular pharmaceutical product made on each
physician. The relevant sales experience data also may identify
physicians who have previously purchased or prescribed the
pharmaceutical product, and the frequency at which these
physicians have purchased or prescribed the pharmaceutical
product. Additionally, the relevant sales experience data may
include volume or use data (e.g., the amount of the pharmaceutical
product purchased or prescribed by each of the physicians).
Dockery para. 27.
05. Dockery describes an optimization module configured to process
the input data using suitable optimization algorithms and models
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to predict sales force effort (i.e., reach and frequency) that may be
required for reaching the quantitative sales and financial goals of
the client. The algorithms and models may be designed to
optimize sales force effort at a product brand or product portfolio
level. Further, the algorithms and models may be designed to take
in to account variables such as aggregated market segments,
statistical CSO scenarios, and sales force structure. The
algorithms and models may take into account marketing and
promotional channel variables (e.g., sample, continuation medical
education/dinner channels, and hospital/specialty sales force
structure). The module may include suitable algorithms and
models for computing return on investment (ROI) data for the sale
effort under different sales activity and market scenarios.
Dockery para. 38.
Mathur
06. Mathur is directed to a neural network tool for process system
identification and a method for making the tool. Mathur 1:11–13.
07. Mathur describes a general purpose approach for process system
identification. System identification is viewed as a function
approximation problem, where the inputs to the function are the
input and output of the process, and the outputs of the function are
estimates of model parameters. This approach, which requires no
mathematical analysis, utilizes the learning capabilities of neural
networks, and can be used for a wide variety of applications.
Mathur 1:15–23.
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08. Mathur describes a vector of samples (scaled appropriately)
being used as input to a trained neural network, when a sufficient
number of samples have been received. The output of the network
is subjected to some post processing (scaling and/or translation) in
a post processor to obtain a delay estimate. Mathur 17:21–26.
ANALYSIS
Claim 43 recites little more than the conceptual idea of analyzing
existing data to generate further data by analyzing attributes and values from
the original data and developing a prediction model that uses the generated
data to come up with a probability. Claim 43 also recites using a trained
neural network, but only in the sense of invoking a generic and conventional
computer component for its known use to be used according to its known
features and manner.
Claims 33–53 rejected under 35 U.S.C. § 101 as directed to a judicial
exception without significantly more
STEP 14
Claim 43, as a method claim, nominally recites one of the enumerated
categories of eligible subject matter in 35 U.S.C. § 101. The issue before us
is whether it is directed to a judicial exception without significantly more.
STEP 2
The Supreme Court
set forth a framework for distinguishing patents that claim laws
of nature, natural phenomena, and abstract ideas from those that

4 For continuity of analysis, we adopt the steps nomenclature from 2019
Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7,
2019) (“Revised Guidance”).
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claim patent-eligible applications of those concepts. First, . . .
determine whether the claims at issue are directed to one of those
patent-ineligible concepts. If so, we then ask, “[w]hat else is
there in the claims before us? To answer that question, . . .
consider the elements of each claim both individually and “as an
ordered combination” to determine whether the additional
elements “transform the nature of the claim” into a patent-
eligible application. [The Court] described step two of this
analysis as a search for an “‘inventive concept’”—i.e., an
element or combination of elements that is “sufficient to ensure
that the patent in practice amounts to significantly more than a
patent upon the [ineligible concept] itself.”
Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 217–18 (2014) (citations
omitted) (citing Mayo Collaborative Servs. v. Prometheus Labs, Inc.,
566 U.S. 66 (2012)). To perform this test, we must first determine what the
claims are directed to. This begins by determining whether the claims recite
one of the judicial exceptions (a law of nature, a natural phenomenon, or an
abstract idea). Then, if the claims recite a judicial exception, determining
whether the claims at issue are directed to the recited judicial exception, or
whether the recited judicial exception is integrated into a practical
application of that exception, i.e., that the claims “apply, rely on, or use the
judicial exception in a manner that imposes a meaningful limit on the
judicial exception, such that the claim is more than a drafting effort designed
to monopolize the judicial exception.” Revised Guidance, 84 Fed. Reg. at
54. If the claims are directed to a judicial exception, then finally
determining whether the claims provide an inventive concept because the
additional elements recited in the claims provide significantly more than the
recited judicial exception.
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STEP 2A Prong 1
At a high level, and for our preliminary analysis, we note that method
claim 43 recites retrieving and generating parameter data, analyzing and
identifying parameter data, generating model data, and generating
probability data. Retrieving data is receiving data. Identifying data is
analyzing data. Thus, claim 43 recites receiving, generating, and analyzing
data. None of the limitations recites technological implementation details
for any of these steps, but instead recite only results desired by any and all
possible means.
From this we see that claim 43 does not recite the judicial exceptions
of either natural phenomena or laws of nature.
Under Supreme Court precedent, claims directed purely to an abstract
idea are patent in-eligible. As set forth in the Revised Guidance, which
extracts and synthesizes key concepts identified by the courts, abstract ideas
include (1) mathematical concepts5, (2) certain methods of organizing
human activity6, and (3) mental processes7. Among those certain methods of
organizing human activity listed in the Revised Guidance are commercial or

5 See, e.g., Gottschalk v. Benson, 409 U.S. 63, 71–72 (1972); Bilski v.
Kappos, 561 U.S. 593, 611 (2010); Mackay Radio & Telegraph Co. v. Radio
Corp. of Am., 306 U.S. 86, 94 (1939); SAP America, Inc. v. InvestPic, LLC,
898 F.3d 1161, 1163 (Fed. Cir. 2018).
6 See, e.g., Bilski, 561 U.S. at 628; Alice, 573 U.S. at 219-20; Ultramercial,
Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed Cir. 2014); Smart Sys.
Innovations, LLC v. Chicago Transit Auth., 873 F.3d 1364, 1383 (Fed. Cir.
2017); In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1160–61 (Fed.
Cir. 2018).
7 See, e.g., Benson, 409 U.S. at 67; CyberSource Corp. v. Retail Decisions,
Inc., 654 F.3d 1366, 1371–72 (Fed. Cir. 2011); Intellectual Ventures I LLC
v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016).
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legal interactions. Like those concepts, claim 43 recites the concept of
managing marketing and sales interactions. Specifically, claim 43 recites
operations that would ordinarily take place in advising one to predict
customer interactions from model probability forecasts based on parameters.
The advice to predict customer interactions from model probability forecasts
based on parameters involves generating a probability of a successful future
interaction with potential customers, which is an economic act. For
example, claim 43 recites “generate a probability of a successful future
interaction with the one or more potential customers,” which is an activity
that would take place whenever one is deciding commercial activity.
The Examiner determines the claims to be directed to the recited
limitations. Final Act. 5–6. The preamble to claim 43 does not recite what it
is to achieve, but the steps in claim 43 result in managing marketing and
sales interactions by predicting customer interactions from model probability
forecasts based on parameters absent any technological mechanism other
than a conventional computer for doing so.
As to the specific limitations, limitation 1 recites receiving data.
Limitation 2 recites generic and conventional generating and analyzing of
parameter and model data, which advise one to apply generic functions to
get to these results. The limitations thus recite advice for predicting
customer interactions from model probability forecasts based on parameters.
To advocate predicting customer interactions from model probability
forecasts based on parameters is conceptual advice for results desired and
not technological operations.
The Specification at column 1, line 5 describes the invention as
relating to sales prediction. Thus, all this intrinsic evidence shows that claim
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43 recites managing marketing and sales interactions. This is consistent
with the Examiner’s determination.
This in turn is an example of commercial or legal interactions as a
certain method of organizing human activity because managing marketing
and sales interactions is a form of organizing commercial interactions. The
concept of managing marketing and sales interactions by predicting
customer interactions from model probability forecasts based on parameters
is one idea for deciding how to adapt such managing. The steps recited in
claim 43 are part of how this might conceptually be premised.
Our reviewing court has found claims to be directed to abstract ideas
when they recited similar subject matter. Simio, LLC v. Flexsim Software
Prod., Inc., 983 F.3d 1353, 1362 (Fed. Cir. 2020) (abstract idea of using
graphics instead of programming to create object-oriented simulations).
Alternately, this is an example of concepts performed in the human
mind as mental processes because the steps of receiving, generating, and
analyzing data mimic human thought processes of observation, evaluation,
judgment, and opinion, perhaps with paper and pencil, where the data
interpretation is perceptible only in the human mind. See In re TLI
Commc’ns LLC Patent Litig., 823 F.3d 607, 611 (Fed. Cir. 2016);
FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093–94 (Fed. Cir.
2016). Claim 43, unlike the claims found non-abstract in prior cases, uses
generic computer technology to perform data reception, generation, and
analysis and does not recite an improvement to a particular computer
technology. See, e.g., McRO, Inc. v. Bandai Namco Games Am. Inc., 837
F.3d 1299, 1314–15 (Fed. Cir. 2016) (finding claims not abstract because
they “focused on a specific asserted improvement in computer animation”).
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As such, claim 43 recites receiving, generating, and analyzing data, and not
a technological implementation or application of that idea.
From this we conclude that at least to this degree, claim 43 recites
managing marketing and sales interactions by predicting customer
interactions from model probability forecasts based on parameters, which is
a commercial and legal interaction, one of certain methods of organizing
human activity identified in the Revised Guidance, and, thus, an abstract
idea.
STEP 2A Prong 2
The next issue is whether claim 43 not only recites, but is more
precisely directed to this concept itself or whether it is instead directed to
some technological implementation or application of, or improvement to,
this concept i.e., integrated into a practical application.8
At the same time, we tread carefully in construing this
exclusionary principle lest it swallow all of patent law. At some
level, “all inventions . . . embody, use, reflect, rest upon, or apply
laws of nature, natural phenomena, or abstract ideas.” Thus, an
invention is not rendered ineligible for patent simply because it
involves an abstract concept. “[A]pplication[s]” of such
concepts “‘to a new and useful end,’” we have said, remain
eligible for patent protection. Accordingly, in applying the § 101
exception, we must distinguish between patents that claim the
“‘buildin[g] block[s]’” of human ingenuity and those that
integrate the building blocks into something more.
Alice, 573 U.S. at 217 (citations omitted).
Taking the claim elements separately, the operation performed by the
computer at each step of the process is expressed purely in terms of results,

8 See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S.
175 (1981).
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devoid of implementation details. Step 1 is a pure data gathering step.
Limitations describing the nature of the data do not alter this. Step 2 recites
generic computer processing expressed in terms of results desired by any
and all possible means and so present no more than conceptual advice. The
recitation of using a trained neural network is no more than the conceptual
idea of using a conventional computer module for its intended use according
to its conventional operation.
we perceive no “inventive concept” that transforms the abstract
idea of collecting, displaying, and manipulating XML data into
a patent-eligible application of that abstract idea. Rather, the
claims recite both a generic computer element—a processor—
and a series of generic computer “components” that merely
restate their individual functions—i.e., organizing, mapping,
identifying, defining, detecting, and modifying. That is to say,
they merely describe the functions of the abstract idea itself,
without particularity. This is simply not enough under step two.
Intellectual Ventures I LLC v. Capital One Financial Corporation, 850 F.3d
1332, 1341 (Fed. Cir. 2017).
All purported inventive aspects reside in how the data is interpreted
and the results desired, and not in how the process physically enforces such
a data interpretation or in how the processing technologically achieves those
results.
Viewed as a whole, Appellant’s claim 43 simply recites the concept of
managing marketing and sales interactions by predicting customer
interactions from model probability forecasts based on parameters as
performed by a generic computer. This is no more than conceptual advice
on the parameters for this concept and the generic computer processes
necessary to process those parameters, and do not recite any particular
implementation.
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Claim 43 does not, for example, purport to improve the functioning of
the computer itself. Nor does it effect an improvement in any other
technology or technical field. The 23+ pages of specification do not bulge
with disclosure, but only spell out different generic equipment9 and
parameters that might be applied using this concept and the particular steps
such conventional processing would entail based on the concept of
managing marketing and sales interactions by predicting customer
interactions from model probability forecasts based on parameters under
different scenarios. They do not describe any particular improvement in the
manner a computer functions. Instead, claim 43 at issue amounts to nothing
significantly more than an instruction to apply managing marketing and
sales interactions by predicting customer interactions from model probability
forecasts based on parameters using some unspecified, generic computer.
Under our precedents, that is not enough to transform an abstract idea into a
patent-eligible invention. See Alice, 573 U.S. at 225–26.
None of the limitations reflects an improvement in the functioning of
a computer, or an improvement to other technology or technical field,
applies or uses a judicial exception to effect a particular treatment or
prophylaxis for a disease or medical condition, implements a judicial
exception with, or uses a judicial exception in conjunction with, a particular

9 The Specification describes any kind of electronic device with data
processing capabilities, including, by way of non-limiting example, personal
computer, a server, a computing system, a communication device, a
processor (e.g., digital signal processor (DSP), a microcontroller, a field
programmable gate array (FPGA), an application specific integrated circuit
(ASIC), etc.), any other electronic computing device, and or any
combination thereof. Spec. 9:2–8.
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machine or manufacture that is integral to the claim, effects a transformation
or reduction of a particular article to a different state or thing, or applies or
uses the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological
environment, such that the claim as a whole is more than a drafting effort
designed to monopolize the exception.
We conclude that claim 43 is directed to achieving the result of
managing marketing and sales interactions by advising one to predict
customer interactions from model probability forecasts based on parameters,
as distinguished from a technological improvement for achieving or
applying that result. This amounts to commercial or legal interactions,
which fall within certain methods of organizing human activity that
constitute abstract ideas. The claim does not integrate the judicial exception
into a practical application.
STEP 2B
The next issue is whether claim 43 provides an inventive concept
because the additional elements recited in the claim provide significantly
more than the recited judicial exception.
The introduction of a computer into the claims does not generally alter
the analysis at Mayo step two.
the mere recitation of a generic computer cannot transform a
patent-ineligible abstract idea into a patent-eligible invention.
Stating an abstract idea “while adding the words ‘apply it’” is not
enough for patent eligibility. Nor is limiting the use of an
abstract idea “‘to a particular technological environment.’”
Stating an abstract idea while adding the words “apply it with a
computer” simply combines those two steps, with the same
deficient result. Thus, if a patent’s recitation of a computer
amounts to a mere instruction to “implement[t]” an abstract idea
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“on . . . a computer,” that addition cannot impart patent
eligibility. This conclusion accords with the preemption concern
that undergirds our § 101 jurisprudence. Given the ubiquity of
computers, wholly generic computer implementation is not
generally the sort of “additional feature[e]” that provides any
“practical assurance that the process is more than a drafting effort
designed to monopolize the [abstract idea] itself.”
Alice, 573 U.S. at 223–24 (citations omitted).
“[T]he relevant question is whether the claims here do more than
simply instruct the practitioner to implement the abstract idea [] on a generic
computer.” Alice, 573 U.S. at 225. They do not.
Taking the claim elements separately, the function performed by the
computer at each step of the process is purely conventional. Using a
computer for receiving, generating, and analyzing data amounts to electronic
data query and retrieval—one of the most basic functions of a computer.
The limitation of using a trained neural network is not a technological
implementation step, but a recitation of a conventional computer module,
viz. a neural network, which is recited as being used according to its
conventional purpose in a conventional manner. All of these computer
functions are generic, routine, conventional computer activities that are
performed only for their conventional uses. See Elec. Power Grp. LLC v.
Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). See also In re Katz
Interactive Call Processing Patent Litig., 639 F.3d 1303, 1316 (Fed. Cir.
2011) (“Absent a possible narrower construction of the terms ‘processing,’
‘receiving,’ and ‘storing,’ . . . those functions can be achieved by any
general purpose computer without special programming”). None of these
activities is used in some unconventional manner nor does any produce some
unexpected result. Appellant does not contend it invented any of these
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activities. In short, each step does no more than require a generic computer
to perform generic computer functions. As to the data operated upon, “even
if a process of collecting and analyzing information is ‘limited to particular
content’ or a particular ‘source,’ that limitation does not make the collection
and analysis other than abstract.” SAP America, Inc. v. InvestPic LLC, 898
F.3d 1161, 1168 (Fed. Cir. 2018).
Considered as an ordered combination, the computer components of
Appellant’s claim 43 add nothing that is not already present when the steps
are considered separately. The sequence of data reception-generation-
analysis is equally generic and conventional. See Ultramercial, Inc. v. Hulu,
LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting,
offering for exchange, display, allowing access, and receiving payment
recited an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc.,
876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis,
modification, generation, display, and transmission), Two-Way Media Ltd. v.
Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir.
2017) (sequence of processing, routing, controlling, and monitoring). The
ordering of the steps is therefore ordinary and conventional.
We conclude that claim 43 does not provide an inventive concept
because the additional elements recited in the claim do not provide
significantly more than the recited judicial exception.
REMAINING CLAIMS
Claim 43 is representative. The remaining method claims merely
describe process parameters. We conclude that the method claims at issue
are directed to a patent-ineligible concept itself, and not to the practical
application of that concept.
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As to the structural claims, they
are no different from the method claims in substance. The
method claims recite the abstract idea implemented on a generic
computer; the system claims recite a handful of generic computer
components configured to implement the same idea. This Court
has long “warn[ed] . . . against” interpreting § 101 “in ways that
make patent eligibility ‘depend simply on the draftsman’s art.’”
Alice, 573 U.S. at 226. As a corollary, the claims are not directed to any
particular machine.
LEGAL CONCLUSION
From these determinations we further determine that the claims do not
recite an improvement to the functioning of the computer itself or to any
other technology or technical field, a particular machine, a particular
transformation, or other meaningful limitations. From this we conclude the
claims are directed to the judicial exception of the abstract idea of certain
methods of organizing human activity as exemplified by the commercial and
legal interaction of managing marketing and sales interactions by advising
one to predict customer interactions from model probability forecasts based
on parameters, without significantly more.
APPELLANT’S ARGUMENTS
As to Appellant’s Appeal Brief arguments, we adopt the Examiner’s
determinations and analysis from Final Action 5–7 and Answer 3–4 and
reach similar legal conclusions. We now turn to the Reply Brief.
We are not persuaded by Appellant's argument that
the pending independent claims recite at least five processes
that require retrieval of at least one group of parameters and
then generating a group of new parameters. According to the
claims, the generation of the new group of parameters includes
automatically analyzing each parameter in the group and then
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identifying a set of new parameter types and a set of new
parameter values. Moreover, the claims recite generating a
prediction model associated with each group of the new
parameters. Appellant submits that a person would not be
capable of performing all of these steps in their mind, nor with
pen and paper.
Reply Br. 2. Appellant’s characterization that he “submits” this argument is
appropriate as it is a conclusory argument. Appellant offers no evidence that
with a sufficiently minimal data set all such steps could not readily be
performed with paper and pencil. Appellant appears to be arguing that a
computer is needed because of data volume and complexity. But this is
insufficient to confer eligibility.
we do not rely on the pen and paper test to reach our holding of
patent eligibility in this case. At the same time, we note that, in
viewing the facts in FairWarning’s favor, the inability for the
human mind to perform each claim step does not alone confer
patentability. As we have explained, “the fact that the required
calculations could be performed more efficiently via a computer
does not materially alter the patent eligibility of the claimed
subject matter.”
FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1098 (Fed. Cir.
2016) (citations omitted).
Appellant next argues that the present claims are patent-eligible
because they are similar to the USPTO’s Example 39 in the “Subject Matter
Eligibility Examples: Abstract Ideas,” published January 7, 2019 (“2019
Eligibility Examples”). Reply Br. 3. More particularly, Appellant argues
The claims, which were found eligible, in Example 39 included
features related to neural networks, which are similar to features
recited in the pending claims.
In Example 39, the claim was found eligible because the claims
did "not recite any mathematical relationships, formulas, or
calculations. While some of the limitations may be based on
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mathematical concepts, the mathematical concepts are not
recited in the claims. Further, the claim does not recite a mental
process because the steps are not practically performed in the
human mind. Finally, the claim does not recite any method of
organizing human activity such as a fundamental economic
concept or managing interactions between people." Similarly,
the present claims do not recite "mathematical relationships,
formulas, or calculations." In addition, the claims cannot be
practically performed in the mind, as discussed above. Moreover,
the claims do not recite a method "of organizing human activity
such as a fundamental economic concept or managing
interactions between people."
Id. (footnotes omitted).
The difficulty with Appellant’s argument is that Example 39 was
deemed patent-eligible because it provided training of a neural network for
facial detection. See 2019 Eligibility Examples, 8–9. The guidelines said
expanded training set is developed by applying mathematical
transformation functions on an acquired set of facial images.
These transformations can include affine transformations, for
example, rotating, shifting, or mirroring or filtering
transformations, for example, smoothing or contrast reduction.
The neural networks are then trained with this expanded training
set using stochastic learning with backpropagation which is a
type of machine learning algorithm that uses the gradient of a
mathematical loss function to adjust the weights of the network.
Unfortunately, the introduction of an expanded training set
increases false positives when classifying non-facial images.
Accordingly, the second feature of applicant’s invention is the
minimization of these false positives by performing an iterative
training algorithm, in which the system is retrained with an
updated training set containing the false positives produced after
face detection has been performed on a set of non-facial images.
This combination of features provides a robust face detection
model that can detect faces in distorted images while limiting the
number of false positives.
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Id. Thus, Example 39 addressed technological difficulties related to
analyzing graphic images to identify and analyze facial images within them.
Appellant has neither identified nor demonstrated that the present claims
provide such image analysis. Instead, the Specification says the claims are
directed to automatically generating a statistical model capable of providing
probabilities of successful future interactions with one or more potential
customers of a company (Specification 3), which would include analyzing
conventional transaction textual data. Such analysis of textual data is the
bread and butter of mental processes.

Claims 33–39, 42–48, and 51–53 rejected under 35 U.S.C. § 103(a) as
unpatentable over Dockery and Mathur
As to Appellant’s Appeal Brief arguments, we adopt the Examiner’s
determinations and analysis from Final Action 8–22 and Answer 4–6 and
reach similar legal conclusions. We now turn to the Reply Brief.
We are not persuaded by Appellant's argument that Dockery fails to
describe “generating a group of new parameters from a set of parameters and
generating a prediction model based on the group of new parameters.”
Reply Br. 4. This argument is carried through Reply Br. 4–7 and is the only
argument presented. As the Examiner determined, Dockery describes
suitable market segments (i.e., categories of the target physician population).
Several categories of physicians may be defined according to suitably
selected physician characteristics. The definitions of the physician
categories may be based on physician characteristics that are obtained or
developed from sales data and other data. Final Act. 9. See also FF 03.
Dockery describes an optimization module configured to process the input
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data using suitable optimization algorithms and models to predict sales force
effort (i.e., reach and frequency) that may be required for reaching the
quantitative sales and financial goals of the client. Ans. 5. See also FF 05.
The problem for Appellant is that the claims do not precisely define or
narrow the scope of what new parameters encompass. The categories
Dockery generates are within the scope of such new data or parameters.

Claims 40 and 49 rejected under 35 U.S.C. § 103(a) as unpatentable over
Dockery, Mathur, and Kasiolas
Appellant relies on the above arguments which are equally
unpersuasive here.

Claims 41 and 50 rejected under 35 U.S.C. § 103(a) as unpatentable over
Dockery, Mathur, and Kagarlis
Appellant relies on the above arguments which are equally
unpersuasive here.
CONCLUSIONS OF LAW
The rejection of claims 33–53 under 35 U.S.C. § 101 as directed to a
judicial exception without significantly more is proper.
The rejection of claims 33–39, 42–48, and 51–53 under 35 U.S.C. §
103(a) as unpatentable over Dockery and Mathur is proper.
The rejection of claims 40 and 49 under 35 U.S.C. § 103(a) as
unpatentable over Dockery, Mathur, and Kasiolas is proper.
The rejection of claims 41 and 50 under 35 U.S.C. § 103(a) as
unpatentable over Dockery, Mathur, and Kagarlis is proper.
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CONCLUSION
The rejection of claims 33–53 is affirmed.
In summary:
Claims
Rejected
35 U.S.C. § Basis  Affirmed Reversed
33–53 101 Eligibility 33–53
33–39, 42–48,
51–53
103 Dockery
Mathur
33–39, 42–48,
51–53

40, 49 103 Dockery,
Mathur,
Kasiolas
40 49
41, 50 103 Dockery,
Mathur,
Kagarlis
41 50
Overall Outcome 33–53

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)(2011).

AFFIRMED