Gil, Alvaro E. et al.

Patent Trials and Appeals Board

Aug 30, 2019

14221723 - (D) (P.T.A.B. Aug. 30, 2019)

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.
14/221,723 03/21/2014 Alvaro E. Gil 20130255-US-NP/3019-3 1813
144650 7590 08/30/2019
Budzyn IP Law, LLC
120 Eagle Rock Avenue, Suite 328
East Hanover, NJ 07936
EXAMINER
KING JR., JOSEPH W
ART UNIT PAPER NUMBER
3696
NOTIFICATION DATE DELIVERY MODE
08/30/2019 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):
Conduent.PatentDocketing@conduent.com
lbudzyn@budzyn-ip.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte ALVARO E. GIL, EDGAR A. BERNAL, and
NATHAN GNANASAMBANDAM
____________

Appeal 2018-007772
Application 14/221,723
Technology Center 3600
____________

Before IRVIN E. BRANCH, ADAM J. PYONIN, and
MICHAEL M. BARRY, Administrative Patent Judges.

BARRY, Administrative Patent Judge.

DECISION ON APPEAL
Appellants1 appeal under 35 U.S.C. § 134(a) from a final rejection of
claims 1, 4, 8–19, 22, and 26–33 which are all the pending claims. See App.
Br. 5 and Final Act. 1. We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

1 Appellants identify Conduent Business Services, LLC as the real party in
interest. App. Br. 2.
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Introduction
Appellants describes the invention as “related to the field of loan risk
assessment and the determination of risk associated with a plurality of loan
accounts.” Spec. ¶ 1. “The invention is specifically directed towards . . .
loan risk prediction via utilization of multiple algorithms to independently
select features from a plurality of loan account histories X, the plurality of
loan account histories containing variables x describing each loan account.”
Id.; see also id. ¶ 5 (identifying “a need exists for loan risk prediction which
facilitates assessment of future risk and other statistics regarding a plurality
of loan account histories”).
Claims 1, 19, and 33 are independent; claim 1 is representative:
1. A computer implemented method for loan risk
prediction comprising:
receiving by a computing device a plurality of loan
account histories X relating to millions of loan accounts
containing millions of variables x transmitted from a
computerized database;
utilizing by said computing device a plurality of
algorithms to independently select features from said plurality
of loan account histories X, the selected features being functions
of the received variables x;
grouping said selected features selected from said
plurality of loan account histories X into a first data structure xf,
applying by said computing device at least one voting
algorithm to said selected features selected from said plurality
of loan account histories X and grouping results into a second
data structure xr, wherein the second data structure xr is used by
the computing device to form a data structure Xr, said data
structure Xr used to generate a linear model, the linear model
indicating risk associated with each of said received plurality of
loan account histories X on a periodic basis for a time period
into the future;
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generating by the computing device a third data structure
xl of nonlinear interaction terms from the second data structure
xr;
after generating by the computing device the third data
structure xl, then generating by the computing device a fourth
data structure xNL wherein xNL equals selectively one of xr U xl
and x U xl;
after generating by the computing device the fourth data
structure xNL then executing a nonlinear model that selects
features from the fourth data structure xNL to form a fifth data
structure xNLR, wherein the fifth data structure xNLR is used to
form a data structure XNLR by selecting first elements of X
whose indices are in xNLR; and
generating, using data structure XNLR, a nonlinear model
y = f (XNLR), where f is a nonlinear function, the nonlinear model
y indicating risk associated with each of said received plurality
of loan account histories X; and
calculating a risk for each of the loan accounts using the
nonlinear model y on a periodic basis for a time period in the
future.
App. Br. 13–14 (Claims App’x) (formatting adjusted).
The Rejection
The Examiner rejected claims 1–20 under 35 U.S.C. § 101 as
ineligibly directed to a judicial exception. Final Act. 5–10.
§ 101 General Legal Framework and the USPTO Guidance
An invention is patent-eligible if it claims a “new and useful process,
machine, manufacture, or composition of matter.” 35 U.S.C. § 101. The
Supreme Court, however, has long interpreted 35 U.S.C. § 101 to include
implicit exceptions: “[l]aws of nature, natural phenomena, and abstract
ideas” are not patentable. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S.
208, 216 (2014) (internal quotation marks and citation omitted).
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In determining whether a claim falls within an excluded category, we
are guided by the Supreme Court’s two-step 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 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, 69
(1972)). Concepts determined to be patent eligible include physical and
chemical processes, such as “molding rubber products” in Diamond v.
Diehr, 450 U.S. 175, 191 (1981).
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 (internal citation
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,
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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 early 2019, the PTO published revised guidance on the application
of § 101. 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed.
Reg. 50–57 (Jan. 7, 2019) (“Guidance”). Under the Guidance, we first look,
in step one of the Alice/Mayo analysis, to whether the claim recites:
(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) (“prong one”); and
(2) additional elements that integrate the judicial exception
into a practical application (“prong two”) (see MPEP
§ 2106.05(a)–(c), (e)–(h)).2
See 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 to whether the claim adds “significantly more”
under step two of the Alice/Mayo analysis, i.e., whether the claim:
(3) adds a specific limitation beyond the judicial exception that
are not “well-understood, routine, conventional” in the field
(see MPEP § 2106.05(d)); or simply appends well-understood,
routine, conventional activities previously known to the
industry, specified at a high level of generality, to the judicial
exception.
See Guidance, 84 Fed. Reg. at 56.
Our Analysis
Appellants argue the independent claims together as a group and
present no separate arguments for the dependent claims. App. Br. 5–12. We

2 All references to the MPEP are to the 9th Ed, Rev. 08.2017 (Jan. 2018).
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select claim 1 as representative of all claims for this appeal. 37 C.F.R.
§ 41.37(c)(1)(iv).
Alice/Mayo Step One, Guidance Step 2A, Prong One
Under step 2A, prong one of the Guidance, we determine whether the
claims recite a patent-ineligible concept. We must analyze the claim’s
recitations in a way that is “[]tethered [to] the language of the claim[]” and,
accordingly, we assess what the claim 1 recites at the same level of
generality or abstraction expressed in the claim. Enfish, LLC v. Microsoft
Corp., 822 F.3d 1327, 1337 (Fed. Cir. 2016).
Claim 1 recites3 receiving “a plurality of loan account histories X
relating to [numerous]4 loan accounts containing [numerous] variables x,”
using “algorithms to independently select features from said plurality of loan

3 All quotes in this paragraph are from claim 1 and we omit all “computing
device” and “data structure” limitations at this stage of our analysis (we
consider them in our analysis under prong two of step 2A of the Guidance
and under step two of the Alice/Mayo framework). We note Appellants
describe “data structure” in the Specification as follows:
A “data structure,” as discussed within the context of this
patent application and related patent applications refers to a
computer-based storage unit allowing for the storage of single
or multiple types of data. The data structure may take the form
of any computer-based storage unit functioning at any level of
an OSI model, including computer files, .csv files, matrixes, a
linked-list, arrays, tree structures, objects, variables, text files,
SQL-databases or database entries, packets, frames, or any
presently existing or after-arising equivalent. The “data
structure” for the purposes defined herein can actually be one or
multiple computer-storage units transmitted sequentially or in
parallel.
Spec. ¶ 24.
4 We substitute “numerous” for “millions of.” See infra note 6.
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account histories X, the selected features being functions of the received
variables x,” “grouping said selected features selected from said plurality of
loan account histories X,” applying a “voting algorithm to said selected
features selected from said plurality of loan account histories X and grouping
results into [] xr,” using xr “to generate a linear model, the linear model
indicating risk associated with each of said received plurality of loan account
histories X on a periodic basis for a time period into the future,” generating
“nonlinear interaction terms from” xr, then “then generating . . . xNL wherein
xNL equals selectively one of xr U xl and x U xl,” “then executing a nonlinear
model that selects features from . . . xNL to form . . . xNLR, wherein . . . xNLR is
used to form . . . XNLR by selecting first elements of X whose indices are in
xNLR,” “generating, using [] XNLR, a nonlinear model y = f (XNLR), where f is a
nonlinear function, the nonlinear model y indicating risk associated with
each of said received plurality of loan account histories X,” “and calculating
a risk for each of the loan accounts using the nonlinear model y on a periodic
basis for a time period in the future.”
In view of the foregoing, we agree with the Examiner that claim 1
recites “calculating risk for various loan accounts, including the use of loan
history data for numerous loan accounts and a voting algorithm to generate
various data to perform the risk calculations.” Final Act. 4–5.5 Mitigating
risk for loans is a fundamental economic practice. See, e.g., Alice, 573 U.S.

5 In view of the Specification (see supra note 3), we treat “data structure” as
a computer-related term of art and consider it as a claim element in addition
to the recited abstract idea under the analytical frameworks of Alice/Mayo
and the Guidance. We note the Examiner’s determination of the abstract
idea recited by claim 1 included “to generate various data structures”
(emphasis added to highlight we omit “structures” from our determination).
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at 219–20 (concluding that using third parties to mediate settlement risk is a
“fundamental economic practice”). Thus, claim 1 recites an idea for a
fundamental economic practice in combination with a particular
mathematical algorithm. “Adding one abstract idea . . . to another abstract
idea . . . does not render the claim non-abstract.” RecogniCorp, LLC v.
Nintendo Co. LTD., 855 F.3d 1322, 1327 (Fed. Cir. 2017). In accordance
with the Guidance, because fundamental economic practices are one of
certain methods of organizing human activity that courts have determined to
be abstract, and mathematical algorithms are abstract ideas in the category of
mathematical concepts, we determine that claim 1, thus, recites an abstract
idea. See Guidance, 84 Fed. Reg. at 52.
Our analysis now proceeds to step 2A, prong two of the Guidance, to
determine whether or not claim 1 integrates the recited idea into a practical
application. See Guidance, 84 Fed. Reg. at 54.
Alice/Mayo Step One, Guidance Step 2A, Prong Two
To determine whether the judicial exception is integrated into a
practical application, we identify whether there are “any additional elements
recited in the claim beyond the judicial exception(s)” and evaluate those
elements to determine whether they integrate the judicial exception into a
practical application. Guidance, 84 Fed. Reg. at 54–55 (emphasis added);
see also MPEP § 2106.05(a)–(c), (e)–(h).
Here is claim 1, with the additional limitations beyond those that
recite the abstract idea shown in italics—
1. A computer implemented method for loan risk
prediction comprising:
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receiving by a computing device a plurality of loan
account histories X relating to millions of 6 loan accounts
containing millions of variables x transmitted from a
computerized database;
utilizing by said computing device a plurality of
algorithms to independently select features from said plurality
of loan account histories X, the selected features being
functions of the received variables x;
grouping said selected features selected from said
plurality of loan account histories X into a first data structure
xf,
applying by said computing device at least one voting
algorithm to said selected features selected from said plurality
of loan account histories X and grouping results into a second
data structure xr, wherein the second data structure xr is used
by the computing device to form a data structure Xr, said data
structure Xr used to generate a linear model, the linear model
indicating risk associated with each of said received plurality of
loan account histories X on a periodic basis for a time period
into the future;
generating by the computing device a third data structure
xl of nonlinear interaction terms from the second data structure
xr;
after generating by the computing device the third data
structure xl, then generating by the computing device a fourth
data structure xNL wherein xNL equals selectively one of xr U xl
and x U xl;
after generating by the computing device the fourth data
structure xNL then executing a nonlinear model that selects

6 Appellants specifically argue that the quantitative aspect of “allow[ing] for
processing of ‘millions of loan accounts containing millions of variables’”
demonstrates the invention constitutes an improvement in computing. Reply
Br. 3. Regarding claim scope related to “millions of,” as recited, we note
claim 1 recites receiving “a plurality” of loan account histories (i.e., two or
more) and that these histories relate to millions of loan accounts containing
millions of variables x.
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features from the fourth data structure xNL to form a fifth data
structure xNLR, wherein the fifth data structure xNLR is used to
form a data structure XNLR by selecting first elements of X
whose indices are in xNLR; and
generating, using data structure XNLR, a nonlinear model
y = f (XNLR), where f is a nonlinear function, the nonlinear
model y indicating risk associated with each of said received
plurality of loan account histories X; and
calculating a risk for each of the loan accounts using the
nonlinear model y on a periodic basis for a time period in the
future.
Beyond the claim limitations reciting the abstract ideas for calculating
loan risk predictions using a particular mathematical algorithm, claim 1
recites computer and data processing limitations: “computer implemented,”
“by a computing device,” “a computerized database,” and several “data
structure” limitations for computer storage of variable data. Claim 1 also
includes a numerical limitation that the loan histories are related to “millions
of” loan accounts that contain millions of variables. Reciting requirements
for a large number (i.e., millions of) variables does not constitute improving
any underlying technology (ordinarily skilled artisans have long understood
that computers routinely handle millions of variables).
Thus, claim 1’s computer-related and numerical limitations amount to
little more than instructions to carry out the abstract idea using a computer,
and do not serve to integrate the recited abstract idea of “calculating risk for
various loan accounts, including the use of loan history data for numerous
loan accounts and a voting algorithm to generate various data to perform the
risk calculations idea” into a practical application. In other words, these
technological recitations add specificity to the claim and limit its use to
computer technology, but none of these computer limitations serves to
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improve a technology or technical field. Instead, the technological
recitations constitute routine uses of technology to automate the underlying
method of calculating risk for numerous loan accounts. Thus, claim 1 does
not recite any additional elements that constitute an improvement to “the
functioning of the computer itself” or “‘any other technology or technical
field.’” See MPEP § 2106.05(a) (quoting Alice, 573 U.S. at 225). Neither
do these limitations qualify as applying the judicial exception with “a
particular machine,” because these components provide their conventional
functions and require no more than general purpose equipment. See MPEP
§ 2106.05(b); see also Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716–
17 (Fed. Cir. 2014); SiRF Tech., Inc. v. Int’l Trade Comm’n, 601 F.3d 1319,
1333 (Fed. Cir. 2010) (“In order for the addition of a machine to impose a
meaningful limit on the scope of a claim, it must play a significant part in
permitting the claimed method to be performed, rather than function solely
as an obvious mechanism for permitting a solution to be achieved more
quickly.”).
The Supreme Court has “rejected the argument that ‘implement[ing] a
principle in some specific fashion’ will ‘automatically fal[l] within the
patentable subject matter of § 101.’” Alice, 573 U.S. at 222 (quoting Flook,
437 U.S. at 593). Claim 1 further does not effect a particular transformation
of the recited articles, which are simply used for their ordinary purposes, nor
does it add any other meaningful (technological) limitations, i.e., limitations
beyond simply “linking the use” of the abstract idea to generic technology.
See MPEP § 2106.05 (c), (e)–(f); see also id. at (g)–(h) (use of well-known
limitations beyond the judicially excepted matter constitutes “insignificant
extra-solution activity” (g) and claim limitations “merely indicating a field
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of use or technological environment in which to apply a judicial exception
do not amount to significantly more” (h)).
Accordingly, because the recited judicial exception is not integrated
into a practical application, the Examiner did not err in determining claim 1
is directed to an abstract idea. Thus, we proceed to step two of the Alice/
Mayo analysis (step 2B of the Guidance).
Alice/Mayo Step Two; Guidance Step 2B
In step two of the Alice/Mayo analysis, we consider whether there are
additional limitations that, individually or as an ordered combination, ensure
the claims amount to “significantly more” than the abstract idea. Alice, 573
U.S. at 217–18 (citing Mayo, 566 U.S. at 72–73, 77–79). As stated in the
Guidance, many of the considerations to determine whether the claims
amount to “significantly more” under step two of the Alice framework are
already considered as part of determining whether the judicial exception has
been integrated into a practical application. Guidance, 84 Fed. Reg. at 56.
Thus, at this point of our analysis, we determine if claim 1 adds a specific
limitation, or combination of limitations, that is not well-understood, routine,
conventional activity in the field; or whether, in addition to the recited
judicial exception, they recite only well-understood, routine, conventional
activities at a high level of generality. Id.
Here, Appellants’ claims do not recite specific limitations (or a
combination of limitations) that are beyond what was well-understood,
routine, and conventional. The Examiner finds, and we agree, that beyond
the limitations for the judicial exception, the technological limitations
recited in the independent claims constitute the use of technology that was
well known to those of ordinary skill prior to the invention. Final Act. 9–11;
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see also Ans. 4–6. The disclosure in Appellants’ Specification of the
claimed technological features is at a generic level. See, e.g., Spec. ¶¶ 1, 6–
14, 23–24, 41–42. There is no discussion of any special functionality or
considerations for a technological aspect of any technological component
recited in the independent claims.
Appellants contend claim 1 “provides an improvement in computing.”
App. Br. 7; see also id. at 6–10 (arguing claim 1 is akin to the patent-eligible
claim-at-issue in Amdocs7 and not akin to the patent-ineligible claim-at-issue
in FairWarning8). This argument is unpersuasive.
Unlike the high-level or generic technological terms in Appellants’
claim 1, the claim at issue in Amdocs included a specifically defined term
claim term—“enhance”—which the Federal Circuit defined “as meaning ‘to
apply a number of field enhancements in a distributed fashion.’” 841 F.3d at
1300. The court further “took care to note,” in the context of the claim term
“enhance,” that “‘distributed’ means that the network usage records are
processed close to their sources before being transmitted to a centralized
manager,” and the claim term “enhance” specifically requires “‘in a
distributed fashion’ and the ‘close to the source’ of network information
requirements.” Id. In other words, the claim at issue in Amdocs included a
narrow, defined technological term. Appellants’ claim 1, on the other hand,
includes no narrow technological limitations. Appellants do not persuade us
the Examiner errs in determining claim 1 is more akin to the claim at issue in

7 Amdocs (Israel) Limited v. Openet Telecom, Inc., 841 F.3d 1288 (Fed. Cir.
2016).
8 FairWarning IP, LLC v. Iatric Systems, Inc., 839 F.3d 1089 (Fed. Cir.
2016).
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FairWarning and other cases determining the claim(s) at issue to be patent
ineligible. See Ans. 4–5; see also Final Act. 9–11.
Appellants further contend “[t]he subject invention relies on multiple
creations of data structures which result in transformation of data.” App. Br.
11 (citing Spec. ¶¶ 31, 33 for the proposition that “[i]n all, it is clear that
data is transformed with the subject invention”). For authority, Appellants
cite the Federal Circuit decision In re Bilski: “Sufficient transformation
occurs in data when ‘the nature of the data has been changed such that it has
a different function or is suitable for a different use.’” App. Br. 10 (citing
545 F.3d 943, 962–63 (Fed. Cir. 2008)) (noting “cited by Bilski v. Kappos,
561 U.S. 593 (2010)”). This is unpersuasive.9 Certainly, claims that create
particular data structures may be patent-eligible. See, e.g., Enfish, 822 F.3d
at 1339. Here, however, the recited data structures are generic computer
storage for holding data generated by the recited abstract algorithm. See
Spec. ¶ 24. In other words, claim 1’s multiple recitations of first, second,
third, etc. data structures amount to mere instructions to implement the
recited abstract algorithm using generic computer technology.
Conclusion
Thus, Appellants do not persuade us the Examiner erred, and we
sustain the rejection under 35 U.S.C. § 101 of claims 1, 4, 8–19, 22, and 26–
33, and. In doing so, as consistent with our discussion above, we adopt the
Examiner’s findings and reasons as set forth in the Final Office Action from
which this appeal is taken and as set forth in the Answer.

9 We note the quoted language that Appellants attribute to In re Bilski does
not appear at the cited location (or elsewhere) in that case.
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DECISION
We affirm the rejection of claims 1, 4, 8–19, 22, and 26–33 under 35
U.S.C. § 101.
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1)(iv).
AFFIRMED