Pawel Gocek et al.

Patent Trials and Appeals Board

Aug 15, 2019

14468623 - (D) (P.T.A.B. Aug. 15, 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.
14/468,623 08/26/2014 Pawel Gocek DE920140039US2 1069
103765 7590 08/15/2019
IBM Corp-Rochester Drafting Center
1701 North Street Building 256-3
Department SHCB
Endicott, NY 13760
EXAMINER
LEE, TSU-CHANG
ART UNIT PAPER NUMBER
2121
NOTIFICATION DATE DELIVERY MODE
08/15/2019 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):
rocdrctr@us.ibm.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte PAWEL GOCEK, PIOTR KANIA, MICHAL PALUCH,
and TOMASZ STOPA1
____________

Appeal 2018-006351
Application 14/468,623
Technology Center 2100
____________

Before JAMES R. HUGHES, LARRY J. HUME, and
JASON M. REPKO, Administrative Patent Judges.

Opinion for the Board filed by Administrative Patent Judge,
JAMES R. HUGHES.

Opinion dissenting-in-part filed by Administrative Patent Judge,
JASON M. REPKO

HUGHES, Administrative Patent Judge.

DECISION ON APPEAL

1 International Business Machines Corp. (“Appellant”) is the applicant as
provided in 37 C.F.R. § 1.46 and is identified as the real party in interest.
Appeal Br. 2.
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STATEMENT OF THE CASE
Appellant seeks our review under 35 U.S.C. § 134(a) of the
Examiner’s decision rejecting claims 1, 2, 7, 10, 12, 13, 20, and 21. Claims
3–6, 8, 9, 11, and 14–19 have been canceled.2 See Non-Final Act. 1–2;
Appeal Br. 9.3 We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
Appellant’s Invention
The invention relates generally “to software bundling, and more
specifically, to managing software bundling using an artificial neural
network.” Spec. ¶ 1; see Spec. ¶¶ 3, 48–49; Abstract.
Representative Claim
Independent claim 1, reproduced below, further illustrates the
invention:
1. A method comprising:
identifying a software component having a first value for
a first identification attribute and a second value for a second
identification attribute;
generating an input vector derived from the first value and
the second value;

2 Claims 18 and 19 were canceled in an Amendment dated Nov. 14, 2017.
See Adv. Act. 2 (mailed Mar. 7, 2018). The Examiner includes canceled
claims 18 and 19 in the listing of the pending claims because the
Amendment was filed after the Non-Final Office Action. See Non-Final
Act. 1–2.
3 We refer to Appellant’s Specification (“Spec.”), filed Aug. 26, 2014
(claiming benefit of US 14/3030,802 filed June 13, 2014); Appeal Brief
(“Appeal Br.”), filed Jan. 11, 2018; and Reply Brief (“Reply Br.”), filed
June 1, 2018. We also refer to the Examiner’s Non-Final Office Action
(“Non-Final Act.”), mailed Aug. 23, 2017; Advisory Action (“Adv. Act.”)
mailed Mar. 7, 2018; and Answer (“Ans.”) mailed Apr. 5, 2018.
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loading the input vector into an at least one input neuron
of an artificial neural network; and
obtaining a yielded output vector from an at least one
output neuron of the artificial neural network, wherein the
yielded output vector corresponds to a software bundle of a
plurality of software bundles; and
determining, based on the yielded output vector, that the
software component is associated with the software bundle,
wherein the association between the software component and the
software bundle exists but is unknown prior to the obtaining the
yielded output vector from the at least one output neuron of the
artificial neural network, and wherein the association comprises
a relationship between the software component and the software
bundle such that the software component is licensed with other
software components as part of the software bundle.
Appeal Br. 20 (Claims App.).
Rejections on Appeal 4, 5
1. The Examiner rejects claims 1, 2, 7, 10, 12, 13, 20, and 21
under 35 U.S.C. § 101 as being directed to patent-ineligible subject matter.
See Non-Final Act. 5, 7–8.

4 The application on appeal has an effective filing date of June 13, 2014.
Therefore, the Leahy-Smith America Invents Act (AIA) amendments to the
U.S. Code (§§ 112, 103) are applicable. See MPEP § 2159.02 (9th ed. 2018)
(the amended sections “apply to any patent application that contains . . . a
claimed invention that has an effective filing date that is on or after
March 16, 2013.”) because this application has an effective filing date later
than the AIA’s effective date for applications, this decision refers to the AIA
versions of §§ 112 and 103.
5 The Examiner has withdrawn the Provisional Obviousness-Type Non-
Statutory Double Patenting Rejection (see Non-Final Act. 5–6). See Ans. 3.
The Examiner has also withdrawn the Written Description Rejection of
claim 21 (see Non-Final Act. 10–12). See Ans. 3. Claims 18 and 19 have
been canceled, but the Examiner includes canceled claims 18 and 19 in the
statement of rejection for the §§ 101, 112(a), and 112(b) rejections. We
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2. The Examiner rejects claim 20 under 35 U.S.C. § 112(a), as
failing to comply with the written description requirement. See Non-Final
Act. 10, 12.
3. The Examiner rejects claims 1, 2, 7, 12, and 13 under 35 U.S.C.
§ 103 as being unpatentable over Kephart et al. (US 5,675,711, issued Oct.
7, 1997) (“Kephart”) and Dudek et al. (US 2013/0055202 A1, published
Feb. 28, 2013) (“Dudek”). See Non-Final Act. 13–23.
4. The Examiner rejects claim 10 under 35 U.S.C. § 103 as being
unpatentable over Kephart, Dudek, and Dwivedi et al. (US 9,135,610 B2,
issued Sept. 15, 2015 (filed Mar. 29, 2011)) (“Dwivedi”). See Non-Final
Act. 24.
5. The Examiner rejects claim 20 under 35 U.S.C. § 103 as being
unpatentable over Kephart, Dudek, and Moore (US 7,412,430 B1, issued
Aug. 12, 2008). See Non-Final Act. 25, 28–29.
6. The Examiner rejects claim 21 under 35 U.S.C. § 103 as being
unpatentable over Kephart, Dudek, and Simard et al. (US 2015/0019204 A1;
published Jan. 15, 2015 (filed Nov. 8, 2013)) (“Simard”). See Non-Final
Act. 29.
RELATED APPEAL
Appellant indicates that an Appeal was filed for a related patent
application, U.S. Patent Application No. 14/303,802 (“’802 Appl.”). See
Appeal Br. 3. The Notice of Appeal for the ’802 Appl. was filed on

amend the statements of the rejections for clarity. We do not address
Appellant’s arguments concerning the withdrawn rejections or the rejections
directed to the canceled claims.
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November 14, 2017 and the ’802 Appl. is also the subject of an Appeal to
the Board. That appeal has been assigned Appeal No. 2018-007779 for
which the Board has not issued a decision.
ISSUES
Based upon our review of the record, Appellant’s contentions, and the
Examiner’s findings and conclusion, the issues before us follow:
1. Did the Examiner err in concluding Appellant’s claims were
directed to patent-ineligible subject matter, without significantly more, under
35 U.S.C. § 101?
2. Did the Examiner err in finding Appellant’s claims failed to
comply with the written description requirement under 35 U.S.C. § 112(a)?
3. Did the Examiner provide a proper rationale for combining
Kephart and Dudek to show that the combination of references would have
collectively taught or suggested “determining . . . that the software
component is associated with the software bundle . . . such that the software
component is licensed with other software components as part of the
software bundle” as recited in Appellant’s claim 1?
ANALYSIS
Subject Matter Eligibility—35 U.S.C. § 101
Under 35 U.S.C. § 101, a patent may be obtained for “any new and
useful process, machine, manufacture, or composition of matter, or any new
and useful improvement thereof.” The Supreme Court has “long held that
this provision contains an important implicit exception: Laws of nature,
natural phenomena, and abstract ideas are not patentable.” Alice Corp. v.
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CLS Bank Int’l, 573 U.S. 208, 216 (2014) (quoting Ass’n for Molecular
Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 589 (2013)).
The Supreme Court, in Alice, reiterated the two-step framework
previously set forth in Mayo Collaborative Services v. Prometheus Labs.,
Inc., 566 U.S. 66, 77–80 (2012), “for distinguishing patents that claim laws
of nature, natural phenomena, and abstract ideas from those that claim
patent-eligible applications of those concepts.” Alice, 573 U.S. at 217.
Assuming that a claim nominally falls within one of the statutory categories
of machine, manufacture, process, or composition of matter, the first step in
the analysis is to “determine whether the claims at issue are directed to one
of those patent-ineligible concepts” (id.), e.g., to an abstract idea. 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, but are not limited to, certain methods of organizing human activity,
such as fundamental economic practices (Alice, 573 U.S. at 219–20; Bilski,
561 U.S. at 611); mathematical formulas (Parker v. Flook, 437 U.S. 584,
594–95 (1978)); and mental processes (Gottschalk v. Benson, 409 U.S. 63,
67 (1972)). Concepts determined to be patent eligible include physical and
chemical processes, such as “molding rubber products” (Diamond v. Diehr,
450 U.S. 175, 191 (1981)); “tanning, dyeing, making water-proof cloth,
vulcanizing India rubber, smelting ores” (id. at 182 n.7 (quoting Corning v.
Burden, 56 U.S. (15 How.) 252, 267–68 (1854))); and manufacturing flour
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(Benson, 409 U.S. at 69 (citing Cochrane v. Deener, 94 U.S. 780, 785
(1876))).
In Diehr, the claim at issue recited a mathematical formula, but the
Supreme Court held that “[a] claim drawn to subject matter otherwise
statutory does not become nonstatutory simply because it uses a
mathematical formula.” Diehr, 450 U.S. at 176; see also id. at 191 (“We
view respondents’ claims as nothing more than a process for molding rubber
products and not as an attempt to patent a mathematical formula.”). The
Supreme Court continued by qualifying its findings, indicating that a claim
“seeking patent protection for that formula in the abstract . . . is not accorded
the protection of our patent laws, . . . and this principle cannot be
circumvented by attempting to limit the use of the formula to a particular
technological environment.” Id. (citing Benson and Flook); see, e.g., id. at
187 (“It is now commonplace that an application of a law of nature or
mathematical formula to a known structure or process may well be deserving
of patent protection.”).
If the claims are not directed to an abstract idea, the inquiry ends.
Otherwise, the inquiry proceeds to the second step of the Alice and Mayo
framework where the elements of the claims are considered “individually
and ‘as an ordered combination’ to determine whether the additional
elements ‘transform the nature of the claim’ into a patent-eligible
application.” Alice, 573 U.S. at 217 (quoting Mayo, 566 U.S. at 78–79).
This second step is described as “a search for an ‘inventive concept’—i.e.,
an element or combination of elements that is ‘significantly more than . . .
the [ineligible concept] itself.’” Id. at 217–218 (alteration in original)
(quoting Mayo, 566 U.S. at 72–73). “A claim that recites an abstract idea
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must include ‘additional features’ to ensure ‘that the [claim] is more than a
drafting effort designed to monopolize the [abstract idea].’” Alice, 573 U.S.
at 221 (quoting Mayo, 566 U.S. at 77). “[M]erely requir[ing] generic
computer implementation[] fail[s] to transform that abstract idea into a
patent-eligible invention.” Id.
The Court acknowledged in Mayo that “all inventions at some level
embody, use, reflect, rest upon, or apply laws of nature, natural phenomena,
or abstract ideas.” Mayo, 566 U.S. at 71. We, therefore, look to whether the
claims focus on a specific means or method that improves the relevant
technology or are instead directed to a result or effect that itself is the
abstract idea and merely invoke generic processes and machinery. See
Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336 (Fed. Cir. 2016).
The PTO recently published revised guidance on the application of
§ 101. USPTO’s 2019 Revised Patent Subject Matter Eligibility Guidance,
84 Fed. Reg. 50 (Jan. 7, 2019) (hereinafter “2019 Revised Guidance”).
Under that guidance, we first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract
ideas (i.e., mathematical concepts, mental processes, or certain
methods of organizing human activity such as a fundamental
economic practice or managing personal behavior or relationships or
interactions between people) (hereinafter “Step 2A, prong 1”); and
(2) additional elements that integrate the judicial exception into a
practical application (see MPEP § 2106.05(a)–(c), (e)–(h))
(hereinafter “Step 2A, prong 2”).6
See 2019 Revised Guidance, 84 Fed. Reg. at 51–52, 55.

6 All references to the MPEP are to the Ninth Edition, Revision 08–2017
(rev. Jan. 2018).
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A claim that integrates a judicial exception into a practical application
applies, relies on, or uses 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. See 2019
Revised Guidance, 84 Fed. Reg. at 54. When the judicial exception is so
integrated, then the claim is not directed to a judicial exception and is patent
eligible under 35 U.S.C. § 101. Id.
Only if a claim: (1) recites a judicial exception and (2) does not
integrate that exception into a practical application, do we then evaluate
whether the claim provides an inventive concept. See 2019 Revised
Guidance 84 Fed. Reg. at 56; Alice, 573 U.S. at 217–18.
For example, we look to whether the claim:
(3) adds a specific limitation beyond the judicial exception that is not
“well-understood, routine, conventional” in the field (see MPEP
§ 2106.05(d)); or
(4) simply appends well-understood, routine, conventional activities
previously known to the industry, specified at a high level of
generality, to the judicial exception.7
See 2019 Revised Guidance, 84 Fed. Reg. at 56.
Eligibility Analysis—Revised Guidance Steps 1 and 2A, Prong 1
Turning to the first step of the eligibility analysis, “the first step in the
Alice inquiry . . . asks whether the focus of the claims is on the specific
asserted improvement in computer capabilities . . . or, instead, on a process
that qualifies as an ‘abstract idea’ for which computers are invoked merely
as a tool.” Enfish, 822 F.3d at 1335–36. “The abstract idea exception

7 Items (3) and (4) are collectively referred to as “Step 2B” hereinafter and
in the 2019 Revised Guidance.
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prevents patenting a result where ‘it matters not by what process or
machinery the result is accomplished.’” McRO, Inc. v. Bandai Namco
Games Am. Inc., 837 F.3d 1299, 1312 (Fed. Cir. 2016) (quoting O’Reilly v.
Morse, 56 U.S. (15 How.) 62, 113 (1853)).
The Examiner rejects Appellant’s claims 1, 2, 7, 10, 12, 13, 20, and 21
as being directed to patent ineligible subject matter. See Non-Final Act. 5,
7–8; Ans. 3–9. Appellant does not separately argue the dependent claims
with specificity (Appellant does present a generalized argument that the
Examiner has not properly rejected the dependent claims (see Appeal Br. 8–
9 and infra)). See Appeal Br. 6–12. Accordingly, we address the
Examiner’s rejection of independent claim 1 and the claims not separately
argued by Appellant as a group based on claim 1.
The Examiner rejects Appellant’s claim 1 as being directed to patent
ineligible subject matter and concludes claim 1 “is directed to a judicial
exception (i.e., . . . an abstract idea) without . . . ‘significantly more.’” Non-
Final Act. 7. Specifically, the Examiner rejects Appellant’s claim 1 as being
directed to patent ineligible subject matter because claim 1 recites
“organizing software entities through mathematical functional correlations
of extracted attribute information” similar to Electric Power Group (Elec.
Power Grp., LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016)) and
Digitech (Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d
1344 (Fed. Cir. 2014)), and “mental processes which could be accomplished
by a human with pencil and paper which are identified as abstract ideas by
the courts” similar to CyberSource (CyberSource Corp. v. Retail Decisions,
Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011)). Non-Final Act. 7; see Ans. 3–6.
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Appellant contends the Examiner erred in rejecting the claims as
being directed to patent-ineligible subject matter. See Appeal Br. 6–12;
Reply Br. 2–10. Specifically, Appellant contends, with respect to the first
step of the Alice analysis, “the Examiner has failed to meet the initial burden
of presenting a prima facie case of unpatentability” and “the claims are not
directed to an abstract idea.” Appeal Br. 6; see Appeal Br. 7–11; Reply
Br. 2–10. Appellant also contends “[w]ith respect to the dependent claims,”
the Examiner’s “failure to establish a prima facie case [is] particularly
egregious” because “the Examiner’s eligibility analysis for all seven of the
dependent claims amounts to a statement that the claims depend from the
independent claim.” And, “[t]his lack of explanation supporting these
rejections is plainly improper.” Appeal Br. 8.
For the reasons discussed below, we conclude Appellant’s claim 1
(and the other pending claims) recites abstract ideas, these abstract ideas are
not integrated into a practical application, nor do they include an inventive
concept. In view of the 2019 Revised Guidance, we clarify and expand the
Examiner’s reasoning as follows.
We begin our analysis by broadly but reasonably construing
Appellant’s claim 1 (see Appeal Br. 20 (Claims App.)). Claim 1 recites “[a]
method” comprising “identifying a software component” where the software
component has “a first value for a first identification attribute and a second
value for a second identification attribute.” That is, a process (for managing
software bundling using an artificial neural network—see Spec. ¶ 1),
including the step of identifying software (a software component of a
software bundle) that has two attributes (a first identification attribute and a
second identification attribute). In other words utilizing attributes
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(metadata) to identify software. See Spec. ¶¶ 28, 48, 49; Fig. 6.8
Hereinafter we refer to this step as “Step A.”
Claim 1 also recites “generating an input vector derived from the first
value and the second value.” In other words, the process includes the step of
generating a data structure or data representation that is an alphanumeric
string (an input vector) from the identification attribute values (first and
second values). See Spec. ¶¶ 32, 48, 49; Fig. 5. Hereinafter we refer to this
step as “Step B.”
Claim 1 further recites “loading the input vector into an at least one
input neuron of an artificial neural network.” That is, the process includes
entering (loading) the input vector into a node (neuron) of the artificial
neural network (“ANN”). See Spec. ¶¶ 32, 36, 44, 48, 49; Fig. 5.
Hereinafter we refer to this step as “Step C.” We note that a neural network
is a computer system or set of algorithms within a computer system designed
to function similar to a brain. See “neural network (NN).” Hargrave’s
Communications Dictionary, Wiley, 1st ed. (2001) available at
https://search.credoreference.com/content/entry/hargravecomms/neural_net
work_nn/0 (last accessed 31 July 2019).
Claim 1 additionally recites “obtaining a yielded output vector from
an at least one output neuron of the artificial neural network, wherein the
yielded output vector corresponds to a software bundle of a plurality of
software bundles.” In other words, the process includes the step of acquiring
(e.g., reading) (obtaining an output of) an output vector from a node

8 Throughout this Decision, we give the claim limitations the broadest
reasonable interpretation consistent with the Specification. See In re Morris,
127 F.3d 1048, 1054 (Fed. Cir. 1997).
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(neuron) of the ANN where the output vector corresponds to a software
bundle. See Spec. ¶¶ 44, 46, 48, 49; Figs. 5, 6. Hereinafter we refer to this
step as “Step D.”
Claim 1 continues, reciting “determining, based on the yielded output
vector, that the software component is associated with the software bundle,”
where “the association” (between the software component and the software
bundle) “exists but is unknown prior to the obtaining the yielded output
vector . . . and . . . comprises a relationship between the software component
and the software bundle such that the software component is licensed with
other software components as part of the software bundle.” That is, making
a determination (determining), based on the output vector, that a software
component is associated with a software bundle. Claim 1 also characterizes
the purpose of determining the association between the software component
and the software bundle—to identify a relationship between the software
component (identified in Step A) and another licensed software component
that is part of a software bundle. Hereinafter we refer to this step as
“Step E.”
In summary, claim 1 recites a process for managing bundled software
(licenses) using an artificial neural network by identifying software
components, generating an input vector, inputting the vector into a neural
network, producing an output vector and associating the software component
with a software bundle. Hereinafter, we refer to this process as the “bundled
software management process.”
We find that claim 1 recites a “method” (supra)—the bundled
software management process. A process is a statutory category of
invention (subject matter) (USPTO’s Step 1). Utilizing our interpretation of
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claim 1 (supra), we analyze whether the claim is directed to an abstract idea
(USPTO’s Step 2A).
Here, Appellant’s claims generally, and independent claim 1 in
particular (as summarized, supra), recite a process for managing software
bundling using an artificial neural network. See Abstract; Spec. ¶ 1. This is
consistent with how Appellant describes the claimed invention—“claim 1
. . . is directed to . . . software bundling and artificial neural networks.”
Appeal Br. 9.
Appellant’s contentions (supra) focus on the Examiner’s purported
failure to present a prima facie case (see, e.g., Appeal Br. 7–8). Here, in
rejecting the claims (in particular claim 1) under 35 U.S.C. § 101, the
Examiner analyzed the claims using the Mayo/Alice two-step framework,
consistent with the guidance set forth in the USPTO’s “2014 Interim
Guidance on Patent Subject Matter Eligibility,” 79 Fed. Reg. 74618
(Dec. 16, 2014), in effect at the time the rejection was made, i.e., on
August 23, 2017. The Examiner notified Appellant of the reasons for the
rejection “together with such information and references as may be useful in
judging of the propriety of continuing the prosecution of . . . [the]
application.” 35 U.S.C. § 132. See Non-Final Act. 7–8. Contrary to
Appellant’s assertions, in doing so, the Examiner set forth a prima facie case
of unpatentability such that the burden of production shifted to Appellant to
demonstrate that the claims are patent-eligible.
Appellant also contends (supra) the at-issue claims are not abstract
because the claims require “the use of artificial neural networks performing
technical operations related to software bundling” (Appeal Br. 9). See
Appeal Br. 9–11; 2–7. Claim 1, however, recites no substantive limitations
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on how the bundled software management process generates or derives
vectors (the input vector and output vector), or how the association between
the software component and the software bundle is determined (based on the
output vector). The limitations are entirely functional in nature, or
characterize various data (structures and values) utilized in Steps A–E—for
example, claim 1 recites “obtaining” an “output vector” that “corresponds to
a software bundle” and “determining, based on the . . . output vector, that the
software component is associated with the software bundle” where “the
software component is licensed with other software components as part of
the software bundle.” Although Appellant contends the claims describe
purported technological improvements or advances provided by the recited
bundled software management process utilizing an artificial neural network,
claim 1 (and the other pending claims) does not explicitly recite the ANN
performing any processing, analysis, or calculations. The entity performing
the steps (“identifying” (in Step A), “generating” (in Step B), “loading” (in
Step C), “obtaining” (in Step D), and “determining” (in Step E)) is not
recited. Claim 1, instead, simply recites that a “yielded output vector” is
obtained from a neuron (node) of the ANN. This implies utilizing a
computer (in this case the ANN) to perform data analysis or calculations and
output a data structure (an output vector). How the output vector is
“yielded,” that is, how the output vector is calculated utilizing the ANN, is
not specified.
A person can perform the function of limitations A, B, C, D, and E
(delineated above) mentally, or by using pen and paper. See, e.g.,
Appellant’s Fig. 7. Nowhere does Appellant point to specific claim
limitations that distinguish over a human process. To the extent Appellant
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argues utilizing an ANN to perform a function—determine associations with
licensed software bundles—represents specific technological improvements,
we disagree. See, e.g., Appeal Br. 10 (“the claimed invention actually
improves computer functionality through inventive techniques that utilize
artificial neural networks in a novel and non-obvious manner for improved
software bundling.”). Claim 1 does not specifically recite the ANN
performing any function. Claim 1 simply recites “obtaining a yielded output
vector from an at least one output neuron of the artificial neural network,”
which implies the ANN outputting a calculation—the “yielded output
vector.” The ANN is not recited in the association determination.
Performing information analysis, and the collection and exchange
(outputting) of information related to such analysis, may be an abstract
concept that is not patent eligible. See SAP America, Inc. v. InvestPic, LLC,
898 F.3d 1161, 1165, 1167–68 (Fed. Cir. 2018) (Claims reciting “[a] method
for providing statistical analysis” (id. at 1165), were determined to be
“directed to an abstract idea” (id. at 1168). “As many cases make clear,
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” (id. (citation and quotation marks
omitted)). See also Intellectual Ventures I LLC v. Capital One Fin. Corp.,
850 F.3d 1332, 1340 (Fed. Cir. 2017) (identifying the abstract idea of
collecting, displaying, and manipulating data); Elec. Power Grp., LLC v.
Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (characterizing collecting
information, analyzing information by steps people go through in their
minds, or by mathematical algorithms, and presenting the results of
collecting and analyzing information, without more, as matters within the
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realm of abstract ideas); Content Extraction & Transmission LLC v. Wells
Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1345, 1347 (Fed. Cir. 2014)
(finding the “claims generally recite . . . extracting data . . . [and]
recognizing specific information from the extracted data” and that the
“claims are drawn to the basic concept of data recognition”); Bancorp
Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278
(Fed. Cir. 2012) (“[T]he 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.”).
In summary, we conclude Appellant’s claim 1 recites a judicial
exception (USPTO’s Step 2A, Prong 1; see 2019 Revised Guidance).
Specifically, claim 1 recites a process for managing software bundling using
an artificial neural network— the bundled software management process—
for associating software (software components) with other software
(bundled, licensed software) by identifying software components, generating
an input vector, inputting the vector into a neural network, using an artificial
neural network to produce (yield) an output vector and determining an
associating between the software component(s) and bundled software as
discussed supra. The bundled software management process consists of
mental processes performed in the human mind (or utilizing pen and paper)
including observation, evaluation, or judgment. See 2019 Revised
Guidance, 84 Fed. Reg. at 52, 53 (listing “[m]ental processes—concepts
performed in the human mind (including an observation, evaluation,
judgment, opinion)” as one of the “enumerated groupings of abstract ideas”
(footnote omitted)). The revised guidance explains that “mental processes”
include acts that people can perform in their minds or using pen and paper,
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even if the claim recites that a generic computer component performs the
acts. See 2019 Revised Guidance, 84 Fed. Reg. at 52 n.14 (“If a claim,
under its broadest reasonable interpretation, covers performance in the mind
but for the recitation of generic computer components, then it is still in the
mental processes category unless the claim cannot practically be performed
in the mind.”)
Eligibility Analysis—Revised Guidance Step 2A, Prong 2
Appellant’s claim 1 also recites an additional element beyond the
abstract bundled software management process (the judicial exception)
(supra)—the artificial neural network (“ANN”). See claim 1 (Appeal Br. 20
(Claims App)). We evaluate this additional element to determine whether
the additional element integrates the bundled software management process
(the judicial exception) into a practical application of the exception
(USPTO’s Step 2A, Prong 2; see 2019 Revised Guidance).
Appellant contends the “claimed invention is not a generic use of a
generic computer as a tool. Rather the claimed invention actually improves
computer functionality through inventive techniques that utilize artificial
neural networks in a novel and non-obvious manner for improved software
bundling” (Appeal Br. 10)—that claim 1 (and the other pending claims)
recites improvements to computer functionality—similar to McRO, Enfish,
BASCOM (BASCOM Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827
F.3d 1341 (Fed. Cir. 2016)), and DDR Holdings (DDR Holdings, LLC v.
Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014)). See Appeal Br. 9–11;
Reply Br. 2–7. In other words, the claims recite a technological
improvement that amounts to more than simply utilizing a computer as a
tool to accomplish the bundled software management process.
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Appellant’s contentions correspond to the reasoning in MPEP
§§ 2106.05(a)–(c), where additional elements integrate the judicial exception
into a practical application. We disagree. Appellant’s additional element
(the ANN) do not apply or use the bundled software management process
(the judicial exception) in a manner that imposes a meaningful limit on the
judicial exception, such that it is more than a drafting effort designed to
monopolize the exception. See Alice, 573 U.S. at 221–24 (citing Mayo, 566
U.S. at 78–85). Rather, Appellant’s claims recite a computer (or network of
computers) (the ANN) that is utilized as a tool to carry out the implicit
calculation of the output vector (the “yielded output vector”) which is used
to determine an association with bundled software (the abstract idea).
Utilizing a computer as a tool to perform an implicit calculation that is part
of a mental process (an abstract idea) does not impose a meaningful limit on
the abstract idea. See MPEP § 2106.05(f); see also Alice, 573 U.S. at 223
(finding “if [the] recitation of a computer amounts to a mere instruction to
implement an abstract idea on a computer that addition cannot impart patent
eligibility” (quotations and internal citations omitted)).
Appellant’s claims can be distinguished from patent-eligible claims
such as those in McRO, Enfish, BASCOM, and DDR Holdings that are
directed to “a specific means or method that improves the relevant
technology” (McRO, 837 F.3d at 1314), or “a specific improvement to the
way computers operate” (Enfish, 822 F.3d at 1336), solving a technology-
based problem (BASCOM, 827 F.3d at 1349–52), or a method “rooted in
computer technology in order to overcome a problem specifically arising in
the realm of computer [technology]” (DDR Holdings, 773 F.3d at 1257).
Contrary to Appellant’s arguments, claim 1 is not a technological
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improvement or an improvement in a technology. Appellant’s claim 1 does
not “improve the functioning of the computer itself” or “any other
technology or technical field.” Alice, 573 U.S. at 225. Nor does it provide a
technological solution to a technological problem. See DDR Holdings, 773
F.3d at 1257; MPEP § 2106.05(a). Appellant fails to sufficiently and
persuasively explain how the instant claims are directed to an improvement
in the way computers operate, nor has Appellant identified any technical
advance or improvement or specialized computer components. See Appeal
Br. 17–19.
As discussed supra, nothing in claim 1, aside from “obtaining” a
“yielded output vector”—an output vector impliedly calculated using the
ANN—precludes a human from performing the bundled software
management process. Performing such a calculation is the reason computers
(and, in particular, artificial neural networks) exist. The mere automation of
a process that can be performed by a human is not sufficient to show an
improvement in computer functionality, and the fact that a computer or an
ANN may increase efficiency—be more efficient and accurate by reducing
the difficulty of tracking licenses (see Appeal Br. 10–11; Spec. ¶ 2 (“may
help to ensure license compliance and efficient resource allocation”))—does
not change the abstract-idea analysis. See Intellectual Ventures, 792 F.3d
at 1370 (holding that “merely adding computer functionality to increase the
speed or efficiency of the process does not confer patent eligibility on an
otherwise abstract idea”); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d
1359, 1363 (Fed. Cir. 2015) (“[R]elying on a computer to perform routine
tasks more quickly or more accurately is insufficient to render a claim patent
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eligible.”); see also FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089,
1095 (Fed. Cir. 2016).
Moreover, even if Appellant’s claimed process includes an improved
algorithm for determining an output vector, claim 1 does not specify any
improvement in how a computer (the ANN) performs the underlying
mathematical analysis necessary to perform the algorithm. In other words,
only the abstract ideas in claim 1 are potentially new (although we make no
determination as to novelty or obviousness), not the way a computer (the
ANN) operates.
To the extent Appellant contends inputting and outputting vectors (to
and from nodes of the ANN) demonstrates significant non-abstract subject
matter (see, e.g., Reply Br. 7–9), such activity is merely extra-solution
activity. See MPEP § 2106.05(g).
In summary, “the focus of the claims is not on such an improvement
in computers as tools, but on certain independently abstract ideas that use
computers as tools.” Elec. Power Grp., 830 F.3d at 1354; see also
MPEP § 2106.05(f) (emphasis omitted) (instructing Examiners to consider
“[w]hether the claim invokes computers or other machinery merely as a tool
to perform an existing process” in determining whether the claim recites
mere instructions to apply the exception), cited in 2019 Revised Guidance,
84 Fed. Reg. at 55, n.30. Thus, we conclude the claims are directed to an
abstract idea that is not integrated into a practical application.
Step 2B Analysis—“Significantly More”
Having concluded Appellant’s claims are directed to an abstract idea
under the 2019 Revised Guidance Step 2A analysis, we next address
whether the claims add significantly more to the alleged abstract idea. As
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directed by our reviewing court, we search for an “‘inventive concept’
sufficient to ‘transform the nature of the claim into a patent-eligible
application.’” McRO, 837 F.3d at 1312 (quoting Alice, 573 U.S. at 217).
The implementation of the abstract idea involved must be “more than
performance of ‘well-understood, routine, [and] conventional activities
previously known to the industry.’” Content Extraction, 776 F.3d at 1347–
48 (alteration in original) (quoting Alice, 573 U.S. at 225). The “inventive
concept” “must be significantly more than the abstract idea itself, and cannot
simply be an instruction to implement or apply the abstract idea on a
computer.” BASCOM, 827 F.3d at 1349 (citation omitted).
The Examiner rejects Appellant’s claims 1, 2, 7, 10, 12, 13, 20, and 21
as being directed to patent ineligible subject matter (supra), and concludes
that claim 1 (and the other pending claims) “does not include additional
elements that are sufficient to amount to significantly more than the judicial
exception because the additional claim elements are simply general purpose
computer systems which are recited at a high level of generality which
provide general purpose functionality.” Non-Final Act. 7. The Examiner
further explains that “[e]ven if the use of neural network is not part of the
abstract idea, . . . the use of neural network still fails to be significantly more
under step 2B of [Alice]. The usage of neural network . . . is well known and
conventional.” “The applicant’s own specification . . . provides evidence
that this is well known.” Ans. 5 (citing Spec. ¶ 47); see Ans. 5–7.
Appellant, on the other hand, reiterates the limitations of claim 1 (see
Appeal Br. 12) and contends claim 1 “includes significantly more than the
abstract idea” (Appeal. Br. 11) similar to the claims in BASCOM in that
“claim 1 . . . includes a non-conventional arrangement of pieces that
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amounts to significantly more than an abstract idea” (Appeal. Br. 12). See
Appeal Br. 11–12.
Appellant fails to persuade us of error in the Examiner’s rejection
with respect to the second Alice step. We agree with the Examiner that
Appellant’s claim 1 (and the other pending claims) does not evince an
“inventive concept” that is significantly more than the abstract idea itself. In
particular, Appellant fails to explain how the additional elements (above)
add specific limitations beyond the judicial exception that are not well-
understood, routine, and conventional in the field.
As previously discussed, claim 1 (and the other pending claims)
merely recites an additional non-abstract element (above)—specifically the
ANN—that implicitly performs the calculation of the output vector and does
not otherwise perform the steps of the bundled software management
process (the abstract idea).
Even so, Appellant’s Specification describes a computer system (not
recited in the claims) as a collection of conventional (generic) computers
components performing traditional computer functions. See, e.g., Spec.
¶¶ 21 and 22. Appellant’s Specification also describes the artificial neural
network (“ANN”) as being well-known or conventional—see, e.g., Spec.
¶¶ 25, 26 (“[i]t is contemplated that a wide variety of different types of
artificial neural networks could be suitable for use in some embodiments of
the present invention”), and Spec. ¶ 47 (“[m]any other types of artificial
neural networks are contemplated with many different variations”).9

9 Appellant’s Reply Brief was filed after the publication of the Berkheimer
decision (Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018)) and the
USPTO’s Berkheimer Memorandum (Changes in Examination Procedure
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Accordingly, Appellant’s Specification itself describes the additional
element, the ANN, as being well understood, routine, and conventional.
Such conventional computer processes operating on conventional computer
hardware “do not alone transform an otherwise abstract idea into patent-
eligible subject matter.” FairWarning, 839 F.3d at 1096 (citing DDR
Holdings, 773 F.3d at 1256).
For at least the reasons above, we are not persuaded of Examiner error
in the rejection of claim 1 under 35 U.S.C. § 101. Thus, we sustain the
Examiner’s rejection under § 101 of independent claim 1, and dependent
claims 2, 7, 10, 12, 13, 20, and 21, which depend from claim 1 and which
were not separately argued with specificity.
Dependent Claims 2, 7, 10, 12, 13, 20, and 21
Appellant argues the Examiner has not presented a prima facie case
with respect to the dependent claims. See supra; Appeal Br. 8–9. We
previously explained that the Examiner provided a prima facie case with
respect to claim 1 and the other pending claims. See supra. Additionally,
the Examiner provides additional explanation with respect to the dependent
claims in the Examiner’s Answer. See Ans. 7–9. Appellant does not explain
why the dependent claims are not directed to an abstract idea (see Appeal
Br. 8–9) and does not address the Examiner’s supplemental explanations

Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility
Decision (Berkheimer v. HP, Inc.) (April 19, 2018) available at
https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-
20180419.PDF. Appellant makes no arguments with respect to Berkheimer
and, accordingly, such arguments are waived. Additionally, the Examiner
cited to Appellant’s Specification as evidence of conventionality.
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(see Reply Br. 10). Thus, we are not persuaded of Examiner error in the
rejection of claims 2, 7, 10, 12, 13, 20, and 21 under 35 U.S.C. § 101.
The Written Description Rejection of Claim 20
The Examiner rejects claim 20 as failing to comply with the written
description requirement. See Non-Final Act. 10, 12; Ans. 9–10. Appellant
contends that the disputed features—“the input vector comprises a first
dimension corresponding to the first identification attribute and a second
dimension corresponding to the second identification attribute, wherein the
first dimension has at least three possible values, and wherein the second
dimension has at least two possible values” (claim 20 (Appeal Br. 23
(Claims App.)))—are supported by the Specification. See Appeal Br. 12–15
(citing Spec. ¶¶ 28, 32, 34; Figs. 3, 6); Reply Br. 10–12.
The test for sufficiency under the written description requirement “is
whether the disclosure of the application relied upon reasonably conveys to
those skilled in the art that the inventor had possession of the claimed
subject matter as of the filing date.” Ariad Pharms, Inc. v. Eli Lilly and Co.,
598 F.3d 1336, 1351 (Fed. Cir. 2010).
Appellant’s cited paragraphs describe multiple possible identification
attribute values (at least five different values), and multiple dimensions. See
Spec. ¶¶ 28, 32, 34; Appeal Br. 12–15. We find the above-described subject
matter from Appellant’s Specification provides sufficient written description
support for the claimed features the Examiner found lacking in such support.
In particular, the above description shows Appellant had possession of a first
dimension corresponding to a first identification attribute, a second
dimension corresponding to a second identification attribute, the first
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dimension (identification attribute) having three possible values, and the
second dimension (identification attribute) having two possible values.
We, therefore, find the Examiner erred in rejecting independent
claim 1 as lacking sufficient written description support.
Obviousness Rejection of Claims 1, 2, 7, 10, 12, 13, and 21
Appellant argues independent claim 1, and dependent claims 2, 7, 10,
12, 13, and 21, together as a group with respect to the 35 U.S.C. § 103
rejection. See Appeal Br. 17. We select independent claim 1 as
representative of Appellant’s arguments with respect to claims 1, 2, 7, 10,
12, 13, and 21. 37 C.F.R. § 41.37(c)(1)(iv).
The Examiner rejects claims 1, 2, 7, 12, and 13 over Kephart and
Dudek. See Non-Final Act. 13–23. The Examiner rejects claim over
Kephart, Dudek, and Dwivedi. See Non-Final Act. 24. And, the Examiner
rejects claim 21 over Kephart, Dudek, and Simard. See Non-Final Act. 29.
Appellant contends the Examiner does not provide a sufficient
rationale for combining Kephart and Dudek and, therefore, the Examiner’s
rejection is improper. See Appeal Br. 15–17; Reply Br. 12–14. Specifically,
Appellant contends, inter alia, the Examiner’s rationale (the stated
motivations) is “merely conclusory arguments” (Appeal Br. 16) and the
Examiner has not provided reasoning why one combine Kephart’s virus
classifier with Dudek’s method of discovering products that are licensed
together (see Appeal Br. 17). See Appeal Br. 15–17; Reply Br. 12–14.
The Examiner explains that “[i]t would thus have been obvious . . . to
advantageously add onto the teachings of [Kephart],” which “identif[ies]
unknown software component[s] using [an] artificial neural network, with
[the teachings] of Dudek” which describes “software components licensed
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with other software components in a bundle” “to select the predicative
identification attributes to be used by the classifier for software bundling.”
The suggestion or motivation “for doing so would have been to facilitate ‘a
collection of software components that is licensed or sold together, . . . to
serve a particular business need.’” Non-Final Act. 17 (quoting Dudek ¶ 2).
As further explained by the Examiner, “Kephart teaches identifying similar
software components with similar attributes characteristics via neural
network classifiers.” “Dudek teaches using certain attributes as being
indicative of two software components belonging to a common software
product.” The motivation [for combining Kephart and Dudek] is . . .
suggested by Dudek[—]to have the software be licensed together.” Ans. 10
(citing Dudek ¶¶ 14, 21).
We agree with the Examiner that the Examiner has provided a proper
rationale for combining the undisputed features of Kephart and Dudek. See
Non-Final Act. 17; Ans. 10. Appellant does not explain why the references
are incompatible (not analogous or not in the same field of endeavor), nor
does Appellant argue that the references teach away from one another. In
contrast to Appellant’s arguments (see Appeal Br. 17; Reply Br. 13–14) the
Examiner need not explain how to bodily combine the features of the prior
art references—“[t]he test for obviousness is not whether the features of a
secondary reference may be bodily incorporated into the structure of the
primary reference . . . . Rather, the test is what the combined teachings of
the references would have suggested to those of ordinary skill in the art.” In
re Keller, 642 F.2d 413, 425 (CCPA 1981) (emphasis added). See In re
Mouttet, 686 F.3d 1322, 1332–33 (Fed. Cir. 2013). Here, Appellant argues
the references individually and does not address the specific arguments set
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out by the Examiner. The references cited by the Examiner must be read,
not in isolation, but for what each fairly teaches in combination with the
prior art as a whole. See In re Merck & Co., 800 F.2d 1091, 1097 (Fed. Cir.
1986) (one cannot show non-obviousness by attacking references
individually where the rejections are based on combinations of references).
Also, contrary to Appellant’s arguments, this is not an instance where
the rejection attempts to combine references where one of the references
already solves the problem purportedly addressed by the reference—that the
“software components of Dudek are already licensed together; that is the
point of the software bundles described therein” (Reply Br. 13; see Reply
Br. 12–13. Instead, as pointed out by the Examiner (supra), Dudek teaches
attributes indicating that software components belong to a common software
product. See Dudek ¶¶ 14, 21.
The Supreme Court has held that in analyzing an obviousness
rationale, the Examiner “need not seek out precise teachings directed to the
specific subject matter of the challenged claim . . . [and may] take account of
the inferences and creative steps that a person of ordinary skill in the art
would employ.” KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007).
Also, the Examiner may consider “the background knowledge possessed by
a person having ordinary skill in the art.” KSR, 550 U.S. at 418 (quoting In
re Kahn, 441 F.3d 977, 988 (Fed. Cir. 2006)). Further, an artisan is
presumed to possess both skill and common sense. See KSR, 550 U.S. at
421 (“A person of ordinary skill is also a person of ordinary creativity, not
an automaton.”). For the all the reasons set forth above, we find the
Examiner provided a legally cognizable rationale for the combination of
Kephart and Dudek, in that the Examiner “articulated reasoning with some
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rational underpinning to support the legal conclusion of obviousness.” KSR,
550 U.S. at 418 (quoting In re Kahn, 441 F.3d at 988).
We further find that it would have been within the skill of an
ordinarily skilled artisan to combine Kephart’s neural network with Dudek’s
teaching of associating software components together with a common
software product (i.e., determining an association). See KSR, 550 U.S. at
417 (“[I]f a technique has been used to improve one device, and a person of
ordinary skill in the art would recognize that it would improve similar
devices in the same way, using the technique is obvious unless its actual
application is beyond his or her skill.”). We are not persuaded that
combining the respective familiar elements of the cited references in the
manner proffered by the Examiner would have been “uniquely challenging
or difficult for one of ordinary skill in the art” at the time of Appellant’s
invention. Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1162
(Fed. Cir. 2007) (citing KSR, 550 U.S. at 418).
Accordingly, Appellant’s contentions do not persuade us of error in
the Examiner’s obviousness rejection of representative independent claim 1.
Therefore, we affirm the Examiner’s rejection of representative claim 1, and
dependent claims 2, 7, 10, 12, 13, and 21, not separately argued with
particularity (supra).
Obviousness Rejection of Claim 20
The Examiner rejects claim 20 over Kephart, Dudek, and Moore. See
Non-Final Act. 25, 28–29. Appellant contends that the Examiner does not
provide a sufficient rationale for combining Kephart, Dudek, and Moore
and, therefore, the Examiner’s rejection is improper. See Appeal Br. 18–19;
Reply Br. 14–16.
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The Examiner explains that “[i]t would thus have been obvious . . . to
advantageously add onto the teachings of [Kephart],” which “identif[ies]
unknown software component[s] using [an] artificial neural network, with
[the teachings] of Moore” which describes “multiple linguistic value[s] for
[a] software attribute.” The suggestion or motivation “for doing so would
have been to ‘help improve the accuracy.’” Non-Final Act. 28–29 (quoting
Moore’s Abstract). As further explained by the Examiner, “Kephart teaches
identification attributes of vector dimensions for software identification, and
Moore teaches multiple values for software attributes.” Ans. 10–11. “The
[motivation] [for combining Kephart and Dudek with Moore] is . . .
suggested by Moore[—]to improve the accuracy of representation.” Ans. 11
(citing Moore, Abstract).
For the same reasons as claim 1 (supra), we agree with the Examiner
that the Examiner has provided a proper rationale for combining the
undisputed features of Kephart, Dudek, and Moore. See Non-Final Act. 28–
29; Ans. 10–11. In particular, Appellant does not explain why the references
are not analogous art or are not in the same field of endeavor, nor does
Appellant argue that the references teach away from one another. Further,
the Examiner need not explain how to bodily combine the features of the
prior art references.
We further find that it would have been within the skill of an
ordinarily skilled artisan to combine Kephart’s neural network with Dudek’s
teaching of associating software components together with a common
software product and also Moore’s teaching of multiple dimension values.
See KSR, 550 U.S. at 417–18. Accordingly, Appellant’s contentions do not
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persuade us of error in the Examiner’s obviousness rejection of claim 20.
Therefore, we affirm the Examiner’s rejection of claim 20.
CONCLUSION
Appellant has not shown that the Examiner erred in rejecting claims 1,
2, 7, 10, 12, 13, 20, and 21 under 35 U.S.C. § 101.
Appellant has shown that the Examiner erred in rejecting claim 20 as
failing to comply with the written description requirement under 35 U.S.C.
§ 112(a).
Appellant has not shown that the Examiner erred in rejecting claims 1,
2, 7, 10, 12, 13, 20, and 21 under 35 U.S.C. § 103.
DECISION
We affirm the Examiner’s rejection of claims 1, 2, 7, 10, 12, 13, 20,
and 21.
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1)(iv). See
37 C.F.R. § 41.50(f).
AFFIRMED

UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte PAWEL GOCEK, PIOTR KANIA, MICHAL PALUCH,
and TOMASZ STOPA
____________________

Appeal 2018-006351
Application 14/468,623
Technology Center 2100
____________________

Before JAMES R. HUGHES, LARRY J. HUME, and
JASON M. REPKO, Administrative Patent Judges.

REPKO, Administrative Patent Judge, Dissenting-in-Part

DISSENTING-IN-PART
I join the Majority in affirming the Examiner’s decision to reject
claims 1, 2, 7, 10, 12, 13, 20, and 21 under 35 U.S.C. § 103 and reversing
the Examiner’s decision to reject claim 20 under 35 U.S.C. § 112(a). But I
disagree with the Majority’s decision to affirm the rejection of claims 1, 2, 7,
10, 12, 13, 20, and 21 under 35 U.S.C. § 101 as directed to patent-ineligible
subject matter.
In particular, the claims are directed to a method of using an Artificial
Neural Network (ANN). I disagree with the Majority that the claims recite a
patent-ineligible abstract idea under § 101.
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Background
The Specification explains that tracking which software licenses
govern which components is a challenge. Spec. ¶ 17. For example, an
entity may be entitled to use a specific database software component for free
if it is bundled with one offering. Id. Yet if it is bundled with another
offering, the entity may have to pay for it. Id. There may be hundreds or
thousands of possible components or bundles. Id. ¶ 21. In Appellants’
invention, a software-asset administrator obtains software bundling
information about an unbundled software component by using an ANN.
Id. ¶ 25.
The Majority notes that “a neural network is a computer system or set
of algorithms within a computer system designed to function similar to a
brain.” See Maj. Op. 12 (citing Hargrave’s Communications Dictionary (1st
ed. 2001)). According to the Specification, an ANN uses a statistical model
incorporating numerical parameters that are adjusted through a learning
algorithm so the model can approximate functions of its input values. Spec.
¶ 25. The ANN can derive functions based on patterns found in the learned
examples derived from training data. Id.
After the ANN has been trained, the invention uses it to determine
bundling information for newly discovered software components. Id. ¶ 48.
Specifically, the system scans the network for new components. Id. The
system then generates an execution input vector with dimensions
corresponding to identification attributes. Id. Next, the system enters the
execution input vector into the ANN. Id. The ANN’s output vector can then
be converted into the name or identifier of the appropriate bundle. Id.
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Step 2A, Prong One of the Guidance
The USPTO published revised guidance on patent subject matter
eligibility. 2019 Revised Patent Subject Matter Eligibility Guidance, 84
Fed. Reg. 50 (USPTO Jan. 7, 2019) (“Guidance”). Step 1 of the USPTO’s
eligibility analysis asks whether the claimed subject matter falls within the
four statutory categories of invention. Id. at 53–54. Under Step 2A, Prong
One of the Guidance, we determine whether the claim recites a judicial
exception, including particular groupings of abstract ideas. Id. at 52–53.
The Guidance synthesizes the key concepts identified by the courts as
abstract ideas into three primary subject-matter groupings: mathematical
concepts, certain methods of organizing human activity (e.g., a fundamental
economic practice), and mental processes. Id. at 52.
Here, the Majority determines that the recited process can be
“performed in the human mind (or utilizing pen and paper).” Maj. Op. 17.
I respectfully disagree.
The Claims
Claim 1 recites several steps for generating the input vector and
loading it into the ANN’s input neuron and then obtaining the output vector
from the ANN’s output neuron. The following steps expressly require an
ANN:
loading the input vector into an at least one input neuron
of an artificial neural network;
obtaining a yielded output vector from an at least one
output neuron of the artificial neural network, wherein the
yielded output vector corresponds to a software bundle of a
plurality of software bundles; and
determining, based on the yielded output vector, that the
software component is associated with the software bundle,
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wherein the association between the software component and
the software bundle exists but is unknown prior to the obtaining
the yielded output vector from the at least one output neuron of
the artificial neural network,
App. Br. 20 (emphasis added).
Indeed, the input to the ANN need not be complex. For
instance, the recited vectors may include alphanumeric or binary
strings that represent specific values for variables. Spec. ¶ 32. Each
specific value may be represented in a single dimension—i.e., a
specific portion in the string corresponding to a specific variable. Id.
And, as the majority notes, a neural network is “designed to function
similar to a brain.” Maj. Op. 12.
Even so, processes that cause a computer to function like a
brain, such as those in the artificial-intelligence field, can be different
from the mental processes that are discussed in the Guidance. For
example, the Majority has not shown that the ANN itself relies on any
mental process to achieve the claimed function. On the contrary, if
the recited ANN is “a computer system or set of algorithms within a
computer system” (id.), then loading the ANN and obtaining the
output from it—as recited in claim 1—require a computer running
those algorithms. In this way, the claimed steps are different from
those that can practically be performed in the human mind but for
some generically recited computer components. See, e.g., Guidance,
84 Fed. Reg. at 52 n. 15 (collecting cases). Thus, the claims do not
recite an abstract mental process.
For this reason, I respectfully dissent.