Klarna AB

Patent Trials and Appeals Board

Feb 18, 2021

2020005406 (P.T.A.B. Feb. 18, 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.
14/918,169 10/20/2015 Mikael Hussain 0101560-001US0 4881
22504 7590 02/18/2021
DAVIS WRIGHT TREMAINE, LLP/SEATTLE
IP DOCKETING DEPT.
DAVIS WRIGHT TREMAINE LLP
920 FIFTH AVE., SUITE 3300
SEATTLE, WA 98104-1610
EXAMINER
KHATTAR, RAJESH
ART UNIT PAPER NUMBER
3693
NOTIFICATION DATE DELIVERY MODE
02/18/2021 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):
patentdocket@dwt.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte MIKAEL HUSSAIN
__________________

Appeal 2020-005406
Application 14/918,169
Technology Center 3600
____________________

Before JOSEPH A. FISCHETTI, BIBHU R. MOHANTY, and
JAMES P. CALVE, Administrative Patent Judges.

CALVE, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the decision
of the Examiner to reject claims 1–3 and 5–21, which are all of the pending
claims.2 We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42. Appellant
identifies Klarna AB as the real party in interest. Appeal Br. 3.
2 Claim 4 is cancelled. Appeal Br. 18 (Claims App.).
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CLAIMED SUBJECT MATTER
The claims relate to mitigating the risk of default by a consumer on a
transaction conducted over the Internet by generating a fidelity score based
on information collected about a consumer to reflect a projected likelihood
of a consumer defaulting on payment. See Spec. ¶¶ 1, 13, 16–20.
Claim 1, 5, and 13 are independent. Representative claim 1 recites:
1. A computer-implemented method, comprising:
under the control of one or more computer systems that
execute instructions,
providing executable instructions to a client
computing device associated with a user that, as a result of
being executed by the client computing device, causes the client
computing device to:
collect client data that includes:
personally identifiable information
about the user;
an identifier associated with the client
computing device; and
a measurement captured by the client
computing device associated the user interacting
with the client computing device, the measurement
including:
an action performed by the user
to an object displayed in a user interface of
the client computing device;
an identity of the object; and
a time value indicating a time at
which the action was performed to the
object; and
provide the client data to the one or more
computer systems;
obtaining stored transactional data associated with
one or more previous transactions involving the user;
obtaining verification data verifying that the
personally identifiable information is accurate;
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transforming the stored transactional data, the
verification data, and the client data that includes the
measurement into a set of variable values usable as input into a
machine learning algorithm that is trained to infer
characteristics about the user from the set of variable values;
obtaining, as a result of inputting the set of
variable values into the machine learning algorithm, a fidelity
score output by the machine learning algorithm; and
based at least in part on the fidelity score and
without obtaining additional information about the user from a
third party:
determining a payment or credit option to
display in the user interface for a current transaction; and
updating, contents of the user interface to
provide the payment or credit option.

REJECTION
Claims 1–3 and 5–21 are rejected under 35 U.S.C. § 101 as directed to
a judicial exception without significantly more.
ANALYSIS
Patent Eligibility of Claims 1–20
Appellant argues the claims as a group. See Appeal Br. 9–14. We
select claim 1 as representative. See 37 C.F.R. § 41.37(c)(1)(iv).
The Examiner determines that claim 1 recites certain methods of
organizing human activity of commercial or legal interactions by providing
payment/credit options for a current transaction and the determining step can
be carried out using pen and paper as a mental process. Final Act. 3; Ans.
3–4. The Examiner determines that the abstract idea is applied on a generic
computing device and machine learning algorithm recited at high levels of
generality and used as tools that do not integrate the abstract idea into a
practical application or provide significantly more. Final Act. 3–5; Ans. 5.
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Principles of Law
Section 101 of the Patent Act states:
Whoever invents or discovers any new and useful
process, machine, manufacture, or composition of matter, or
any new and useful improvement thereof, may obtain a patent
therefor, subject to the conditions and requirements of this title.
35 U.S.C. § 101. This provision contains an implicit exception: “Laws of
nature, natural phenomena, and abstract ideas are not patentable.” Alice
Corp. v. CLS Bank Int’l, 573 U.S. 208, 216 (2014).
To distinguish patents that claim laws of nature, natural phenomena,
and abstract ideas from those that claim patent-eligible applications, we first
determine whether the claims are directed to a patent-ineligible concept. Id.
at 217. If they are, we consider the elements of each claim, individually and
“as an ordered combination,” to determine if additional elements “‘transform
the nature of the claim’ into a patent-eligible application” as an “inventive
concept” sufficient to ensure the claims in practice amount to significantly
more than a patent on the ineligible concept itself. Id. at 217–18.
The USPTO has issued guidance about this framework. 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019)
(“Revised Guidance”). Under the Revised Guidance, to determine whether a
claim is “directed to” an abstract idea, we evaluate whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract ideas
listed in the Revised Guidance (i.e., mathematical concepts, certain methods
of organizing human activities such as a fundamental economic practice, or
mental processes); and (2) additional elements that integrate the judicial
exception into a practical application (see MPEP §§ 2106.05(a)–(c), (e)–(h)
(9th ed. Rev. 10.2019 June 2020) (“MPEP”)). Id. at 52–55.
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Only if a claim (1) recites a judicial exception and also (2) does not
integrate that exception into a practical application, do we then consider
whether the claim either (3) adds a specific limitation beyond a 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. Id. at 56.
Step 1: Are the Claims Within a Statutory Category?
Claim 1 recites a method, which is a statutory category of invention
under 35 U.S.C. § 101, namely, a process. Final Act. 2; see Appeal Br. 8.
Step 2A, Prong One: Do the Claims Recite a Judicial Exception?
We agree with the Examiner that claim 1 recites certain methods of
organizing human activity to facilitate commercial or legal interactions by
providing payment/credit options. Final Act. 3; Ans. 3–4. The Revised
Guidance enumerates this concept as certain methods of organizing human
activity––fundamental economic principles or practices (mitigating risk) and
commercial interactions (legal interactions and sales activities). See Revised
Guidance, 84 Fed. Reg. at 52. Some limitations recite mental processes. Id.
The method addresses the needs of merchants and payment services to
mitigate the risk of consumers defaulting on a transaction conducted over the
Internet from a consumer’s computing device. Spec. ¶ 1. A merchant may
contact a private credit bureau to obtain a consumer’s credit score during an
online transaction, but this process is costly in terms of time and money. Id.
So, the method collects data about a consumer to generate a fidelity score to
indicate a likelihood of default on a transaction and to select a payment or a
credit option to offer the consumer. See id. ¶¶ 11–17, 25–27, 48–50, 59–61.
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The first limitations recite data collection as (1) collecting a user’s
personally identifiable information (PII), an identifier of a client computing
device, and a measurement of the user interacting with the client computing
device, (2) obtaining transaction data of previous user transactions; and
(3) obtaining verification data of a user’s PII. Appeal Br. 16 (Claims App.).
See Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir.
2016) (“[M]erely selecting information, by content or source, for collection,
analysis, and display does nothing significant to differentiate a process from
ordinary mental processes, whose implicit exclusion from § 101 undergirds
the information-based category of abstract ideas.”); In re TLI Commc’ns
LLC Patent Litig., 823 F.3d 607, 613 (Fed. Cir. 2016) (“[T]he claims . . . are
simply directed to the abstract idea of classifying and storing digital images
in an organized manner. . . . [W]e have applied the ‘abstract idea’ exception
to encompass inventions pertaining to methods of organizing human
activity.”); Content Extraction and Transmission LLC v. Wells Fargo Bank,
N.A., 776 F.3d 1343, 1348 (Fed. Cir. 2014) (collecting data, recognizing
certain data in the collected data, and storing recognized data recite mental
steps humans perform such as banks reviewing checks, recognizing data in
checks (e.g., amount, account number, account holder), and storing data);
Berkheimer v. HP Inc., 881 F.3d 1360, 1367 (Fed. Cir. 2018) (“The parsing
and comparing of [certain] claims . . . are similar to the collecting and
recognizing of Content Extraction . . . and the classifying in an organized
manner of TLI . . . .”); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d
1366, 1372–73 (Fed. Cir. 2011) (obtaining information about credit card
transactions made from an Internet address and creating a map of credit card
numbers to compare to transactions can be performed in the human mind).
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The Specification describes an embodiment in which a merchant’s
checkout page 122 causes a user’s computing device to collect PII entered
by a user (e.g., name, address, telephone number, email address, date of
birth, etc.) and interactions of the user with web pages 120 of a merchant’s
website. Spec. ¶ 20, Fig. 1. User interactions include mouse clicks, mouse
and cursor movements on a page, and user inputs via input/output devices.
Id. ¶ 22. User interactions also may be measured by image sensors for gaze
detection, accelerometers, gyroscopes, microphones, global positioning
system receivers, and other sensors of behavioral data. Id. ¶¶ 22, 23. The
Specification’s high-level description of user measurement features using
generic input/output devices and generic sensors to perform basic data
capture confirms the abstract nature of these steps as recited in claim 1 at a
high level of generality without any technical details of the collection steps.
In Electric Power, the claims similarly collected measurements from
data streams in real time, time stamped and synchronized from a power grid,
including voltages, power, phase angles, oscillation modes, damping, and
other measured power system data. See Elec. Power, 830 F.3d at 1351–52.
Nonetheless, the court held that collecting such measured information, even
when limited to particular content, did not take the claims out of the abstract
realm of mental processes. Id. at 1353, 1355.
Similarly, claims to collecting a user’s physiological heart rate data
relating to arrhythmia events for analysis and display required only generic
data analysis and therefore were abstract like those in Electric Power. See
CardioNet, LLC v. InfoBionic, Inc., 816 F. App’x 471, 475 (Fed. Cir. 2020)
(citing Elec. Power and Content Extraction). Here, claim 1 recites similar
generic data collection steps of user PII and measured interaction data.
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The “stored transactional data” is described as previous transactions
by the user such as types and amounts of purchases and payment history.
Spec. ¶¶ 13, 29. Verification data can be obtained from a third party credit
bureau to verify a user’s PII such as name, address, and the like. Id. ¶ 39.
Again, the Specification’s broad description of these limitations confirms the
claim’s focus on these data collection steps as abstract mental processes.
The final limitations analyze the collected data to generate “variable
values” (scores) for the collected transactional data, verification data, and
client data (PII and measured interaction data), and output a “fidelity score”
by a machine learning algorithm without technical details of this process.
The fidelity score is used to determine and provide payment or credit options
customized to the user on a user interface. Appeal Br. 16–17 (Claims App.).
The method mitigates the risk in a sales transaction by calculating a
“fidelity score” for a user and recommending appropriate loan/credit options
based on the user’s fidelity score. The Specification indicates the method
replaces credit scores provided by a private credit bureau and mitigates the
risk of default by the consumer. Spec. ¶ 1. Mitigating risk is a fundamental
economic practice and an abstract idea when recited at such a high level of
generality. Alice, 573 U.S. at 219 (claims to using a third party to mitigate
settlement risk recite a fundamental economic practice long prevalent in our
system of commerce); Credit Acceptance Corp. v. Westlake Servs., 859 F.3d
1044, 1054 (Fed. Cir. 2017) (claims to processing an application to finance a
purchase of a product by calculating a user credit score recite a fundamental
economic practice long prevalent in our system of commerce). The claimed
credit scoring method also saves the cost of obtaining a credit assessment of
a user from a third party credit service bureau. See Spec. ¶¶ 35, 46.
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In Clarilogic, similar claims to a method of certifying financial data
recited an abstract idea. Clarilogic, Inc. v. FormFree Holdings Corp., 681
F. App’x 950, 954 (Fed. Cir. 2017). The method received a request for
certified financial data, collected financial data, transformed the data into a
desired format, and validated the data by applying an algorithm engine to
generate a credit report. Id. at 952–54. The steps of gathering financial
information of potential borrowers for analysis and displaying certain results
of the collection and analysis recited an abstract idea as in Electric Power.
Id. at 954. The algorithm engine was applied to the collected data in some
unclaimed manner that did not make the claim patent eligible. Id.
The court explained.
Peculiar to this case is that the algorithm engine mentioned in
the claim is not claimed, identified, or explained. To be sure,
claiming an algorithm does not alone render subject matter
patent eligible . . . . But a method for collection, analysis, and
generation of information reports, where the claims are not
limited to how the collected information is analyzed or
reformed, is the height of abstraction.
Id.; see Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314,
1324 (Fed. Cir. 2016) (claims to the collection of information to generate a
“credit grading” to facilitate anonymous loan shopping is an abstract idea).
In Mortgage Grader, a borrower’s credit grading was calculated so
lenders could provide loan pricing information based on the credit grading.
Mortg. Grader, 811 F.3d at 1324. The steps of collecting data, calculating a
credit grading, and providing loan pricing all could be performed by humans
without a computer. Id. (citing CyberSource, 654 F.3d at 1377 (computation
methods that can be performed in the human mind embody the basic tools of
scientific and technological work free to all men and reserved to none)).
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Here, claim 1 similarly uses a generic machine learning algorithm to
output a fidelity score using “variable values” in some unspecified manner.
The Specification indicates that a fidelity score may be generated by logistic
regression, random forest, supervised learning algorithm, risk algorithm,
regression model, or other classification algorithm. See Spec. ¶¶ 16, 47, 68.
This description confirms the broad scope of claim 1. See Ericsson Inc. v.
TCL Commc’n Tech. Holdings Ltd., 955 F.3d 1317, 1325 (Fed. Cir. 2020)
(“[T]he specification may be ‘helpful in illuminating what a claim is directed
to . . . [but] the specification must always yield to the claim language’ when
identifying the ‘true focus of a claim.’”) (quoting ChargePoint, Inc. v.
SemaConnect, Inc., 920 F.3d 759, 766 (Fed. Cir. 2019)); Synopsys, Inc. v.
Mentor Graphics Corp., 839 F.3d 1138, 1149 (Fed. Cir. 2016) (“The § 101
inquiry must focus on the language of the Asserted Claims themselves.”).
Using a fidelity score based on user information to offer customized
payment or credit options recites a fundamental practice. See Intellectual
Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1369–70 (Fed.
Cir. 2015) (citing Alice, 573 U.S. at 219); Bridge & Post, Inc. v. Verizon
Commc’ns, Inc., 778 F. App’x 882, 887 (Fed. Cir. 2019) (providing tailored
information to a user based on data provided by a user is a fundamental
practice); see also Bozeman Fin. LLC v. Fed. Reserve Bank of Atlanta, 955
F.3d 971, 978 (Fed. Cir. 2020) (“Verifying financial documents to reduce
transactional fraud is a fundamental business practice that, without more, is
not eligible for patent protection.”); buySAFE, Inc. v. Google, Inc., 765 F.3d
1350, 1354–55 (Fed. Cir. 2014) (underwriting a transaction by creating a
transaction performance guaranty for online transactions is a long-familiar
commercial transaction for contractual relationships and an abstract idea).
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Combining one abstract idea (certain methods of organizing human
for a fundamental economic practice) with another abstract idea (mental
processes) does not make an abstract idea non-abstract. See RecogniCorp,
LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir. 2017) (“Adding one
abstract idea (math) to another abstract idea (encoding and decoding) does
not render the claim non-abstract.”); Apple, Inc. v. Ameranth, Inc., 842 F.3d
1229, 1240 (Fed. Cir. 2016) (“An abstract idea can generally be described at
different levels of abstraction.”); Synopsys, Inc. v. Mentor Graphics Corp.,
839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is
still an abstract idea.”).
Outputting the results of the abstract data collection and analysis is
part of the same abstract idea. See Elec. Power, 830 F.3d at 1354 (“And we
have recognized that merely presenting the results of abstract processes of
collecting and analyzing information, without more (such as identifying a
particular tool for presentation), is abstract as an ancillary part of such
collection and analysis.”). Thus, presenting payment or credit options in a
generic user interface does not make claim 1 non-abstract. See Appeal Br. 9.
The Examiner’s citation of limitations reciting transactional data and
payment and credit options for a current transaction (Final Act. 3; Ans. 3–4)
together with the Specification’s description of these and other features also
supports the Examiner’s determination that the focus of the claim involves a
commercial or legal interaction (Final Act. 3). See Appeal Br. 10.
Appellant asserts the claims are rooted in computer technology similar
to claim 2 of Example 37 of the 2019 Subject Matter Eligibility Examples at
https://www.uspto.gov/sites/default/files/ documents/101_examples_37to42
20190107.pdf (January 7, 2019) (2019 PEG Examples). Appeal Br. 10.
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Example 37 of the 2019 PEG Examples illustrates why claim 1 here is
not patent eligible. Claim 2 of Example 37 is a method of rearranging icons
on a graphical user interface (GUI) by receiving a user selection to organize
each icon based on specific criteria of the amount of use of each icon and
automatically moving the most used icons to a position on the GUI closest to
the start icon based on the determined use. 2019 PEG Examples 3. Claim 1
here does not recite features of a GUI such as icons. Instead, it just displays
credit and payment options without any technical details of that process.
The steps of measuring an action performed by the user to an object
displayed in a user interface, an identity of the object, and a time value at
which the action is performed simply collect data of user interactions with a
user interface at a high level of generality that the Specification confirms to
involve measurements of generic activities using generic components with
time stamps of the activities. See Spec. ¶¶ 22, 23.
Measuring/collecting data from sensors in real time with time stamps,
without more, recites the abstract idea identified above. Elec. Power, 830
F.3d at 1351–53. Collecting a persistent device identifier and historic and
location-centric information to create a user profile to deliver customized
information to the user recites a fundamental practice as discussed above.
See Bridge and Post, 778 F. App’x at 886–87. Tracking a user’s location,
navigation data, and the time of day the user navigates to a website to serve
customized information is a fundamental practice. See Intellectual Ventures,
792 F.3d at 1369–70; see also In re Morsa, 809 F. App’x 913, 917 (Fed. Cir.
2020) (requesting user demographic and psychographic information to
match to a specific advertiser is a fundamental economic practice of
organizing human activity through customizing information).
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The Specification describes this process as follows:
Executable code may be embedded in one or more of the set of
web pages 120 to collect details (also referred to as a set of
measurements) about user interactions with the webpages. For
example, each of the set of web pages 120 may include
JavaScript or other client-side executable code that keeps a
record of certain actions performed by a user (also referred to
herein as a set of measurements). The code may be provided by
the merchant or may be embedded in the merchant web pages
in an HTML inline frame (iframe), with the iframe source being
provided by a payment service provider (which may be the
same provider as the provider of system of the present
disclosure). The record may be a set of measurements of
actions performed by the user, and each measurement of the set
of measurements may include information such as a user or
session identifier for the particular user/session, a detected
action (e.g., onClick, onDblClick, onMouseOver, onMouseOut,
onMouseDown, onMouseUp, onMouseMove, and onSubmit
event triggers, etc.) performed by the user using a user input
device (e.g., mouse, trackball, touch screen, keyboard, light
pen, game controller, fingerprint scanner, etc.), and a timestamp
for the detected action.
Spec. ¶ 22. As claimed, the measurement collects data based on user actions
with an “object” displayed in a user interface. See Accenture Global Servs.
GmbH v. Guidewire Software, Inc., 728 F.3d 1336, 1345 (Fed. Cir. 2013)
(the complexity of implementing software or detail in the specification does
not make a claim reciting only an abstract idea patent-eligible).
In Internet Patents, furnishing icons on a webpage and a browser with
Back and Forward navigation functions to display an online application that
users filled with data recited the abstract idea of retaining information in the
navigation of online forms with no details. Internet Patents Corp. v. Active
Network, Inc., 790 F.3d 1343, 1348 (Fed. Cir. 2015) (noting maintaining the
state is not described with any mechanism for accomplishing such a result).
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Similar claims to collecting, transmitting, analyzing, and storing data
to detect fraudulent and/or invalid clicks for pay-per-click advertising based
on the time between two requests by the same device recited an abstract
idea. See Zuili v. Google LLC, 722 F. App’x 1027, 1030 (Fed. Cir. 2018).
Appellant cites Example 39 of the 2019 PEG Examples as an example
of why claim 1 here is not an abstract idea. Appeal Br. 10–11. Claim 1 of
Example 39 recites a method of training a neural network for facial detection
by collecting digital facial images and transforming the images to create a
first training set used to train the neural network in a first stage followed by
a second stage that trains the network with a second training set of images
that were detected incorrectly as facial images during the first stage. 2019
PEG Examples 8–9. Here, claim 1 recites a machine learning algorithm that
is trained to infer characteristics about a user from variable values generated
from transactional data, verification data, and client data in an unspecified
way without any technical details. The algorithm outputs a fidelity score.
The Specification describes the algorithm as including a logistic regression,
random forest similar to supervised models described in various applications
incorporated in the Specification, a supervised learning algorithm, a neural
network, a vector machine, or another classification algorithm. Spec. ¶¶ 16,
35, 47, 68. The Specification’s description confirms the abstract nature of
this feature. A “machine learning algorithm” as such is not described in the
Specification. The description of other concepts used to calculate a fidelity
score confirms the abstract nature of the claimed machine learning algorithm
and fidelity score. Even if the Specification provided more technical details,
such details would not make claim 1 patent eligible where it recites only an
abstract idea as discussed above. See Accenture, 728 F.3d at 1345.
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The fidelity score can be a numeric value or range but its calculation
is not described in any technical detail thus confirming its broad scope in
claim 1 as a mental process. Spec. ¶¶ 56–63. A scoring engine is broadly
described as collecting and processing various client data at a high level of
generality. See id. ¶¶ 13, 20, 25–29. The broad description of the scoring
function confirms that transforming transactional, verification, and client
data into variable values encompasses mental processes as recited in claim 1.
See Mortg. Grader, 811 F.3d at 1324 (claimed steps of applying for a loan
and calculating a borrower’s credit grading to provide loan pricing can be
performed by humans); Spec. ¶ 29 (prior transactions with faithful payments
may weigh toward more favorable risk assessment; no prior transactions or
unreliable transactions may weigh towards a less favorable risk assessment).
Descriptions of analysis of other client data illustrate similar mental
processes. Using Internet Brower that was preinstalled on a computer may
suggest the user is less fickle than a user who uses Google Chrome browser.
Spec. ¶ 15. Hovering a cursor over a submit button for a threshold amount
of time may indicate a user applies careful consideration before completing a
transaction, suggesting that the user may be more reliable in making credit
commitments than a user who clicks quickly on the submit button. Id. Gaze
detection may indicate how long a user spent reading certain portions of the
terms and conditions. Id. ¶ 23. Detecting background noise may indicate a
user is transacting business in a noisy area (public bus or sporting event) and
may not be giving a transaction sufficient consideration. Id. Similar mental
processes are described for verification and behavioral data. Id. ¶¶ 39–41.
Accordingly, we determine claim 1 recites the abstract idea identified
above as do claims 2, 3, and 5–21, which fall with claim 1.
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Step 2A, Prong Two: Integration into a Practical Application
We next consider whether claim 1 recites additional elements that
integrate the abstract idea into a practical application. Revised Guidance,
84 Fed. Reg. at 54. We determine claim 1 lacks additional elements that
improve a computer or other technology or implement the abstract idea in
conjunction with a particular machine or manufacture that is integral to the
claim. Nor does it include an additional element that transforms or reduces a
particular article to a different state or thing or applies the abstract idea in a
meaningful way beyond linking it to a particular technological environment.
See Revised Guidance, 84 Fed. Reg. at 55; Final Act. 3–4; Ans. 4–5.
Appellant argues that claim 1 recites a practical application by making
a risk assessment of a user. Appeal Br. 11. Appellant argues that integration
results from the ordered combination of collecting user measurements by a
client computing device, transforming the measurements into variable values
input into a machine learning algorithm trained to infer user characteristics,
and outputting a fidelity score without additional information from a third
party to determine a payment/credit option. Appeal Br. 11.
These features recite the abstract idea identified under Prong One and
therefore cannot be additional elements that integrate that abstract idea into a
practical application. Revised Guidance, 84 Fed. Reg. at 55 n.24 (“USPTO
guidance uses the term ‘additional elements’ to refer to claim features,
limitations, and/or steps that are recited in the claim beyond the identified
judicial exception.”); Alice, 573 U.S. at 221 (a claim to an abstract idea must
include “additional features” to ensure it does not monopolize the abstract
idea) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S.
66, 77 (2012)).
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Outputting results of data collection and analysis is an ancillary part
of the abstract idea. Elec. Power, 830 F.3d at 1354. Claim 1 does not recite
features of a user interface as does Example 37 of the 2019 PEG Examples.
See Appeal Br. 12. Example 37’s claims organize icons automatically on a
GUI. 2019 PEG Examples 2–3. Such features are not claimed here.
Core Wireless illustrates why claim 1 is not integrated into a practical
application. Core Wireless Licensing S.A.R.L. v. LG Elecs., Inc., 880 F.3d
1356 (Fed. Cir. 2018). The claimed user interface displayed a summary
window of un-launched applications reached from a menu without launching
the applications. Id. at 1362–63. This display of application data improved
prior art interfaces that displayed an index and required users to navigate to
an application and open it to access data. Id. at 1363. Here, claim 1 recites
no similar features. It displays the results of abstract processes on a generic
interface that is not a particular machine that is integral to the claim.
The Specification describes algorithms for generating fidelity scores
without details of training them to infer user characteristics. Spec. ¶¶ 16, 35,
47, 68. As claimed, this feature is a mental process that humans perform to
score credit/loan applications. See Mortg. Grader, 811 F.3d at 1324.
The method “may save 1.3 seconds or more of transaction processing
time.” Spec. ¶ 46; Appeal Br. 11–12. Any such improvements in speed or
network latency by eliminating the need to obtain a credit score from a credit
bureau results from generic computer automation rather than a technological
improvement to computers or other technology. See Credit Acceptance, 859
F.3d at 1055; see Spec. ¶¶ 1, 35, 46, 59.
Accordingly, we determine that claim 1 lacks additional elements that
are sufficient to integrate the abstract idea into a practical application.
Appeal 2020-005406
Application 14/918,169

18
Step 2B: Do the Claims Include an Inventive Concept?
We next consider whether claim 1 recites any additional elements,
individually or as an ordered combination, that provide an inventive concept.
Alice, 573 U.S. at 217–18. This step is satisfied when claim limitations
involve more than well-understood, routine, and conventional activities
known in industry. Berkheimer, 881 F.3d at 1367; see Revised Guidance, 84
Fed. Reg. at 56 (Step 2B looks for a limitation beyond a judicial exception
that is not “well-understood, routine, conventional” activity in the field).
The “client computing device” is any type of computing device with
processors and memory and capable of receiving input from a user and
communicating with components of the risk engine 234 through a network.
Spec. ¶¶ 33, 73. The machine learning algorithm is a generic algorithm. Id.
¶¶ 16, 35, 47, 68. These elements do not provide an inventive concept. See
BSG Tech, LLC v BuySeasons, Inc, 899 F.3d 1281 1290–91 (Fed. Cir. 2018)
(“If a claim’s only ‘inventive concept’ is the application of an abstract idea
using conventional and well-understood techniques, the claim has not been
transformed into a patent-eligible application of an abstract idea.”); SAP
Am., Inc. v. Investpic, LLC, 898 F.3d 1161, 1169 (Fed. Cir. 2019) (“[T]his
court has ruled many times that ‘such invocations of computers . . . that are
not even arguably inventive are insufficient to pass the test of an inventive
concept in the application of an abstract idea.’”) (citation omitted); Bancorp
Services, LLC v. Sun Life Assurance Company of Canada (U.S.), 687 F.3d
1266, 1280 (Fed. Cir. 2012) (“The district court correctly held that without
the computer limitations nothing remains in the claims but the abstract idea
of managing a stable value protected life insurance policy by performing
calculations and manipulating the results.”); Final Act. 4; Ans. 5–6.
Appeal 2020-005406
Application 14/918,169

19
As an ordered combination, the limitations recite no more than they
do individually. See Elec. Power, 830 F.3d at 1355–56 (claims requiring
nothing besides off-the-shelf, conventional computer, network, and display
technology for gathering, sending, and presenting desired information do not
state an inventive concept for application of the information-based abstract
idea); Ans. 6 (machine learning algorithm and computer send and receive
data as is known in the art). The generic description of these elements in the
Specification indicates they are well-known enough in the art that further
details are not required to describe them and their generic functions.
Accordingly, we determine claim 1 lacks an inventive concept to
transform the abstract idea into patent eligible subject matter. We sustain
the rejection of claim 1 and claims 2, 3, and 5–21, which fall therewith.
CONCLUSION
In summary:
Claims
Rejected
35 U.S.C.
§
Reference(s)/
Basis
Affirmed Reversed
1–3, 5–21 101 Eligibility 1–3, 5–21
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED