Pawel Gocek et al.

Patent Trials and Appeals Board

Nov 13, 2019

14303802 - (D) (P.T.A.B. Nov. 13, 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.
14/303,802 06/13/2014 Pawel Gocek DE920140039US1 6137
103765 7590 11/13/2019
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
11/13/2019 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 STOPA
Appeal 2018-007779
Application 14/303,802
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
STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision rejecting claims 13, 16, 17, 21, 28, 35–39, 42, and 43.

1 We use the word Appellant to refer to “applicant” as defined in 37 C.F.R.
§ 1.42(a). Appellant identifies the real party in interest as International
Business Machines Corp. See Appeal Br. 2.
Appeal 2018-007779
Application 14/303,802

2
Claims 1–12, 14, 15, 18–20, 22–27, 29–34, 40, and 41 have been canceled.2
See Final Act. 1–3.3 We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.
CLAIMED SUBJECT MATTER
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. Claims 13 and 17 are
independent. Claim 13, reproduced below, is illustrative of the claimed
subject matter:
13. A system comprising:
an artificial neural network having an at least one input
neuron and an at least one output neuron; and;
one or more computer processor circuits that are
configured to host a bundling application that is configured to:
identify a software component having a first value
for a first identification attribute and a second value for a
second identification attribute;
generate an input vector derived from the first value
and the second value;

2 Claims 33, 34, 40, and 41 were canceled in an Amendment dated Nov. 14,
2017. See Adv. Act. 2 (mailed Feb. 2, 2018). The Examiner includes
canceled claims 33, 34, 40, and 41 in the listing of the pending claims
because the Amendment was filed after the Final Office Action. See Final
Act. 1–3.
3 We refer to Appellant’s Specification (“Spec.”), filed June 13, 2014;
Appeal Brief (“Appeal Br.”), filed Jan. 11, 2018; and Reply Brief (“Reply
Br.”), filed July 25, 2018. We also refer to the Examiner’s Final Office
Action (“Final Act.”), mailed Aug. 22, 2017; and Answer (“Ans.”) mailed
June 5, 2018.
Appeal 2018-007779
Application 14/303,802

3
load the input vector into the at least one input
neuron of the artificial neural network;
obtain a yielded output vector from the 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
determine, 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 both prior to
and subsequent to the obtaining the yielded output vector.
REFERENCES
The prior art relied upon by the Examiner as evidence is:
Name Reference Date
Kephart et al. (“Kephart”) US 5,675,711 Oct. 7, 1997
Moore US 7,412,430 B1 Aug. 12, 2008
Dudek et al. (“Dudek”) US 2013/0055202 A1 Feb. 28, 2013
Simard et al. (“Simard”) US 2015/0019204 A1 Jan. 15, 2015 (filed
Nov. 8, 2013)
Appeal 2018-007779
Application 14/303,802

4
REJECTIONS4, 5
1. The Examiner rejects claims 13, 16, 17, 21, 28, 35–39, 42, and
43 under 35 U.S.C. § 101 as being directed to patent-ineligible subject
matter. See Final Act. 7–8.
2. The Examiner rejects claims 35 and 42 under 35 U.S.C.
§ 112(a), as failing to comply with the written description requirement. See
Final Act. 9–11.6
3. The Examiner rejects claims 13, 16, 17, 21, 28, and 37–39
under 35 U.S.C. § 103 as being unpatentable over Kephart and Dudek. See
Final Act. 13–24.
4. The Examiner rejects claims 35 and 42 under 35 U.S.C. § 103
as being unpatentable over Kephart, Dudek, and Moore.7 See Final Act. 25–
29.
5. The Examiner rejects claims 36 and 43 under 35 U.S.C. § 103
as being unpatentable over Kephart, Dudek, and Simard. See Final Act. 29–
30.

4 See Footnote 2 (supra). Claims 33, 34, 40, and 41 were canceled after
mailing of the Final Office Action. The Examiner includes canceled claims
33, 34, 40, and 41 in the claim rejections. We correct this harmless error in
the listing of the rejected claims for clarity.
5 The Examiner withdrew the double patenting rejection of claims 13, 16,
17, 21, 28, and 33–43, as well as the written description rejection of claims
36 and 43. See Ans. 3. We do not address Appellant’s arguments with
respect to the withdrawn rejections. See Appeal Br. 7, 16.
6 See Footnote 5 (supra). The Examiner includes claims 36 and 43 in the
rejection. See Final Act. 9–11. We correct the listing of the rejected claims
for clarity.
7 The Examiner omits the Dudek reference from the statement of the
rejection. We correct this harmless error for clarity.
Appeal 2018-007779
Application 14/303,802

5
RELATED APPEAL
Appellant indicates that an Appeal was filed for a related patent
application, U.S. Patent Application No. 14/468,623 (“’623 Appl.”). See
Appeal Br. 3. The Notice of Appeal for the ’623 Appl. was filed on
November 14, 2017 and the ’623 Appl. is also the subject of an Appeal to
the Board. That appeal has been assigned Appeal No. 2018-006351. The
Board rendered a decision in Appeal No. 2018-006351 (“Decision”), which
was mailed on Aug. 15, 2019. In that Decision, the Board affirmed (with a
Dissenting Opinion) the Examiner’s 35 U.S.C. § 101 rejection of claims 1, 2,
7, 10, 12, 13, 20, and 21. The Board also affirmed the Examiner’s
obviousness rejections of claims 1, 2, 7, 10, 12, 13, 20, and 21, and reversed
the Examiner’s written description rejection of claim 20. The scope of the
claims in the instant application are substantially similar to the at-issue
claims in the ’623 Application.
OPINION
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.
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 Servs. v. Prometheus Labs., Inc.,
Appeal 2018-007779
Application 14/303,802

6
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. The
framework requires us first to consider “whether the claims at issue are
directed to one of those patent-ineligible concepts.” Alice, 573 U.S. at 217.
If so, we then examine “the elements of [the] claim both individually and ‘as
an ordered combination’ to determine whether the additional elements
‘transform the nature of the claim’ into a patent-eligible application.” Alice,
573 U.S. at 217 (quoting Mayo, 566 U.S. at 78, 79). That is, we examine the
claim for an “inventive concept,” “an element or combination of elements
that is ‘sufficient to ensure that the patent in practice amounts to
significantly more than a patent upon the [ineligible concept] itself.’” Alice,
573 U.S. at 217–18 (alteration in original) (quoting Mayo, 566 U.S. at 72–
73).
The Patent Office recently published revised guidance concerning this
framework and 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
Appeal 2018-007779
Application 14/303,802

7
(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”).8
See 2019 Revised Guidance, 84 Fed. Reg. at 51–52, 55.
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. See 2019 Revised Guidance, 84 Fed. Reg.
at 54.
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.9

8 All references to the MPEP are to the Ninth Edition, Revision 08-2017
(rev. Jan. 2018).
9 Items (3) and (4) are collectively referred to as “Step 2B” hereinafter and
in the 2019 Revised Guidance.
Appeal 2018-007779
Application 14/303,802

8
See 2019 Revised Guidance, 84 Fed. Reg. at 56. With these principles in
mind, we turn to the merits of the § 101 rejection. The Examiner rejects
Appellant’s claims 13, 16, 17, 21, 28, 35–39, 42, and 43 as being directed to
patent-ineligible subject matter. See Final Act. 7–8; Ans. 4–12. Appellant
does not separately argue the claims with specificity and, instead, argues
claims 13, 16, 17, 21, 28, 35–39, 42, and 43 together for this rejection. See
Appeal Br. 7–13. Accordingly, we address the Examiner’s rejection of
independent claim 13 and the claims not separately argued by Appellant as a
group based on claim 13, as permitted by 37 C.F.R. § 41.37(c)(1)(iv).
Statutory Subject Matter
We find that claim 13 recites a “system” (infra). Appellant’s
“system” uses an artificial neural network and one or more computer
processors to implement a number of functions. See claim 13 (Appeal
Br. 22 (Claim App.)). Accordingly, we analyze Appellant’s system as a
process, which is a statutory category of invention (subject matter)
(USPTO’s Step 1).
Abstract Idea
The Examiner rejects Appellant’s claim 13 as being directed to patent-
ineligible subject matter. See Final Act. 7–8; Ans. 4–10. Specifically, the
Examiner concludes claim 13 “is directed to a judicial exception (i.e., . . . an
abstract idea) without . . . ‘significantly more.’” Final Act. 7; see Ans. 8–9.
More specifically, the Examiner rejects Appellant’s claim 1 as reciting
“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
Appeal 2018-007779
Application 14/303,802

9
1344 (Fed. Cir. 2014)) that are “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)). Final Act. 7;
see Ans. 5–10.
Appellant contends the Examiner erred in rejecting the claims as
being directed to patent-ineligible subject matter. See Appeal Br. 7–13;
Reply Br. 2–12. Specifically, Appellant contends, with respect to the first
step of the Alice analysis, that “the Examiner has failed to establish even a
prima facie case of unpatentability” because “the Examiner has not provided
any detailed, reasoned basis for explaining how these identified abstract
ideas are applicable to the limitations of claim[] 13.” Appeal Br. 8–9.
Appellant also contends claim 13 (and the other pending claims) is “not
directed to an abstract idea,” but instead is “directed to technical solutions to
technical problems related to software bundling and artificial neural
networks” (Appeal Br. 10), and because a neural network is not a mental
process. See Appeal Br. 10–12; Reply Br. 3–4. Appellant further contends
that the Examiner, in the Answer, presented new arguments that
mischaracterize claim 13 (and the other pending claims), and cited
inapposite precedent in support of the Examiner’s ineligibility
determination. See Reply Br. 2–12.
For the reasons discussed below, we conclude Appellant’s claim 13
(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.
Appeal 2018-007779
Application 14/303,802

10
We begin our analysis by broadly but reasonably construing
Appellant’s claim 13 (see Appeal Br. 22 (Claims App.)). Claim 13 recites
“[a] system” including “an artificial neural network” and “one or more
computer processor circuits” where the processor circuits are “configured to
host a bundling application” that in turn “is configured to” perform
particular functions. In other words, Claim 13 recites a system including an
artificial neural network (“ANN”) and a processor executing an application
to perform a software bundling process (see Spec. ¶¶ 1, 3, 16, 48–49) (see
also, discussion of statutory subject matter (supra)). 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 24 October 2019). This is consistent with
Applicant’s description of the ANN:
[A]n artificial neural network may refer to a statistical model
incorporating numerical parameters that are adjusted through a
learning algorithm, such that the model is capable of
approximating functions of its input values. In some
embodiments, an artificial neural network may be a
computational tool that is capable of deriving functions based on
patterns found in learned examples derived from training data
loaded into the neural network.
Spec. ¶ 25.
The bundling application (of claim 13) is configured to (i.e., the
software bundling process includes a (step, sub-process, or) function to)
“identify a software component” where the software component has “a first
value for a first identification attribute and a second value for a second
Appeal 2018-007779
Application 14/303,802

11
identification attribute.” That is, identifying software (a software component
of a software bundle) that has two attributes (a first identification attribute
and a second identification attribute)—i.e., utilizing attributes (metadata) to
identify software. See Spec. ¶¶ 28, 48, 49; Fig. 6. Hereinafter we refer to
this step as “Step A.”
Claim 13 also recites a software bundling process function to
“generate an input vector derived from the first value and the second value.”
In other words, 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 13 further recites a software bundling process function to “load
the input vector into the at least one input neuron of the artificial neural
network.” That is, the process includes entering (loading) the input vector
into a node (neuron) of the ANN. See Spec. ¶¶ 32, 36, 44, 48, 49; Fig. 5.
Hereinafter we refer to this step as “Step C.”
Claim 13 additionally recites a software bundling process function to
“obtain a yielded output vector from the 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, reading
(obtaining) an output vector (a data representation that is an alphanumeric
string) from a node (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 13 continues, by reciting a software bundling process function
to “determine, based on the yielded output vector, that the software
Appeal 2018-007779
Application 14/303,802

12
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 13 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 13 recites a system including an ANN and a
processor executing an application to perform a process for managing
bundled software (licenses) to identify software components, generate an
input vector, input the vector into a neural network, produce an output vector
and associate the software component with a software bundle. Hereinafter,
we refer to this process as the “bundled software management process.”
This is consistent with how Appellant describes the claimed invention—
claim 13 is “directed to . . . software bundling and artificial neural
networks.” Appeal Br. 10; see also Abstract; Spec. ¶ 1.
Appellant’s contentions (supra) focus on the Examiner’s purported
failure to present a prima facie case (see, e.g., Appeal Br. 8–9). Here, in
rejecting the claims (in particular claim 13) under 35 U.S.C. § 101, the
Examiner analyzed the claims using the Mayo/Alice two-step framework,
Appeal 2018-007779
Application 14/303,802

13
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 on
August 22, 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 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. 10). See
Appeal Br. 10–12; Reply Br. 3–4. Claim 13, however, recites no substantive
limitations 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 13 recites (the function) “obtain” an “output
vector” that “corresponds to a software bundle” and “determine, 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
Appeal 2018-007779
Application 14/303,802

14
software management process utilizing an artificial neural network, claim 13
(and the other pending claims) does not explicitly recite the ANN
performing any processing, analysis, or calculations. The entity performing
the functions (“identify” (in Step A), “generate” (in Step B), “load” (in Step
C), “obtain” (in Step D), and “determine” (in Step E)) is the bundling
application, which is executed by the processor (configured to perform the
functions). Claim 13, instead, simply recites that a “yielded output vector”
is obtained from a neuron (node) of the ANN. Although obtaining an output
(from the ANN) implies utilizing 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 practically 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
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. 11 (“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 13 does not specifically recite the ANN
performing any function. Claim 13 simply recites “obtain[ing] 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.
Appeal 2018-007779
Application 14/303,802

15
Claims reciting performing information analysis, and the collection
and exchange (outputting) of information related to such analysis, have been
determined by our reviewing court to be an abstract concept that is not
patent eligible. See SAP Am., 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. at 1168 (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., 830 F.3d at 1354 (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
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.”). Indeed, even if the
analysis requires one to access and gather data from a database or utilize a
Appeal 2018-007779
Application 14/303,802

16
pen and paper in the analysis (such as to graphically represent a data set),
such analysis may still be an abstract mental process. See CyberSource, 654
F.3d at 1372 (“[E]ven if some physical steps are required to obtain
information from the database . . . such data-gathering steps cannot alone
confer patentability.” A claim focused on verifying credit card transaction
information is directed to “unpatentable mental processes” because the
claim’s steps “can be performed in the human mind, or by a human using a
pen and paper.”).
In summary, we conclude Appellant’s claim 13 recites a judicial
exception (USPTO’s Step 2A, Prong 1; see 2019 Revised Guidance).
Specifically, claim 13 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 association between the software component(s) and bundled
software as discussed supra. The bundled software management process
consists of mental processes that can be practically 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, even if the claim recites that a generic
Appeal 2018-007779
Application 14/303,802

17
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.”
(emphasis omitted)). Because each of the limitations discussed above
encompasses an act that people can perform in their minds or using pen and
paper, claim 1 recites mental processes. Appellant’s arguments have not
persuaded us otherwise.
Practical Application
We next consider whether claim 13 integrates the abstract idea into a
practical application (USPTO’s Step 2A, Prong 2). See Revised Guidance,
84 Fed. Reg. at 51. In doing so, we consider whether there are any
additional elements beyond the abstract idea that, individually or in
combination, “integrate the [abstract idea] into a practical application, using
one or more of the considerations laid out by the Supreme Court and the
Federal Circuit.” Revised Guidance, 84 Fed. Reg. at 54–55.
Appellant’s claim 13 recites additional elements beyond the abstract
bundled software management process (the judicial exception) (supra). The
additional elements in claim 13 include the recited “artificial neural
network” (“ANN”) and “one or more computer processor circuits” (the
processor) that executes the “bundling application” “configured to” perform
the bundled software management process. Appeal Br. 22 (Claims App).
The written description, or lack thereof, indicates that each of these elements
encompass commonplace generic components. Appellant’s Specification
does not even mention computer processor circuits or a processor, and does
Appeal 2018-007779
Application 14/303,802

18
not describe a “computer” or the “artificial neural network” with any
specificity. For example, Appellant’s Specification describes “computers”
as “any relevant computer system or combination of computer systems
including, for example, servers, desktops, laptops, mobile phones, smart
phones, tablets, personal or enterprise digital assistants, and the like.” Spec.
¶ 21. Also, Appellant’s Specification describes the “artificial neural
network” as a “statistical model incorporating numerical parameters” or a
“computational tool that is capable of deriving functions based on patterns”
where a user “may obtain software bundling information about an unbundled
software component (e.g., a newly discovered software component for
which a bundle association is unknown . . .) by using an artificial neural
network.” Spec. ¶ 25. Further, the Specification explains that “[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.” Spec. ¶ 26. In summary, Appellant’s written description does
not portray computers or the ANN operating in a new way. Instead the
written description depicts these components as generic components
operating in their accustomed manner.
Other than disclosing that these additional elements performing their
accustomed functions utilizing standard techniques, the written description
describes these components in functional, result-oriented terms with no
technical details. See, e.g., Spec. ¶¶ 21, 25–26, 48–49 (describing the ANN
with respect to the bundled software management process). These
descriptions show that additional elements are generic. See Hybritech Inc. v.
Monoclonal Antibodies, Inc., 802 F.2d 1367, 1384 (Fed. Cir. 1986) (“[A]
patent need not teach, and preferably omits, what is well known in the art.”);
Appeal 2018-007779
Application 14/303,802

19
Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed.
Cir. 2017) (“The claimed mobile interface is so lacking in implementation
details that it amounts to merely a generic component (software, hardware,
or firmware) that permits the performance of the abstract idea, i.e., to
retrieve the user-specific resources.”).
Appellant contends claim 13 (as well as independent claim 17)
provides “technical solutions to technical problems related to software
bundling and artificial neural networks” (Appeal Br. 10) and constitutes an
“improve[ment to] computer functionality” (i.e., computer-related
technology) (Appeal Br. 11) and 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. 11)—similar to DDR Holdings (DDR
Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014)). See
Appeal Br. 10–12; Reply Br. 2–12. Appellant additionally contends
“Appellant’s claims are actually quite similar to those found eligible in
McRO and Enfish” (Reply Br. 5 (citing McRO, Inc. v. Bandai Namco Games
Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016) and Enfish, LLC v. Microsoft
Corp., 822 F.3d 1327 (Fed. Cir. 2016)) in that the claim provides “a specific
set of rules to improve a computer-implemented process” (similar to McRO)
(Reply Br. 6). See Reply Br. 4–9. In other words, Appellant contends 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.
Appeal 2018-007779
Application 14/303,802

20
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, however, disagree with Appellant’s
contentions. Appellant’s additional elements, in particular 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 claim recites generic computer elements (the
processor and the ANN) that are utilized as tools to carry out the functions
recited in the bundled software management process—identifying software
components, generating an input vector and inputting the vector into a neural
network, producing an output vector, and associating the software
component with a software bundle, as well as carrying 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 computers as tools to perform common data processing functions
(and to perform an implicit calculation) that can be mental processes (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 claim 13 (and the other pending 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
Appeal 2018-007779
Application 14/303,802

21
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 Global Internet
Servs., Inc. v. AT&T Mobility LLC , 827 F.3d 1341, 1349–52 (Fed. Cir.
2016)), 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 13 is
not a technological improvement or an improvement in a technology.
Appellant’s claim 13 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 claim(s) 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. 10–12; Reply Br. 2–12.
As discussed supra, nothing in claim 1, precludes a human from
performing the bundled software management process. Performing such
information processing functionality is the reason computers 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.
11–12; Spec. ¶ 2 (“may help to ensure license compliance and efficient
resource allocation”))—does not change the abstract-idea analysis. See
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363,
Appeal 2018-007779
Application 14/303,802

22
1370 (Fed. Cir. 2015) (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 eligible.”); see also FairWarning IP, LLC v. Iatric
Sys., Inc., 839 F.3d 1089, 1095 (Fed. Cir. 2016).
Even if Appellant’s claimed system including the ANN also includes
an improved algorithm and processing of information by the ANN using
such an improved algorithm to produce an output vector—features that are
not recited in claim 13 (under our broad but reasonable interpretation)—the
claim still does not specify any improvement in how the processor
(computer) and the ANN perform the underlying mathematical analysis
necessary to perform the algorithm. In other words, only the abstract ideas
in claim 13 are potentially new (although we make no determination as to
novelty or obviousness), not the way the computer and 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. 9–12), such activity may also be interpreted as
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
Appeal 2018-007779
Application 14/303,802

23
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.
Inventive Concept
Having concluded Appellant’s claims are directed to an abstract idea
under the 2019 Revised Guidance (Step 2A analysis), we consider whether
claim 13 has an inventive concept, that is, whether the claim has additional
elements that “‘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). As
discussed above, this requires us to evaluate whether the additional claim
elements add “a specific limitation or combination of limitations that are not
well-understood, routine, conventional activity in the field” or “simply
append[] well-understood, routine, conventional activities previously known
to the industry, specified at a high level of generality.” Revised Guidance,
84 Fed. Reg. at 56.
The Examiner determined that Appellant’s claim 13 (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.” Final Act. 7; see Final Act. 7–8; Ans. 5–12.
Appellant, on the other hand, reiterates the limitations of claims 13
(and 17) and contends claim 13 “similar to the claims in [BASCOM], include
nonconventional arrangements of pieces that amount to significantly more
than an abstract idea” and this “unconventional arrangement . . . amounts to
an inventive concept.” Appeal. Br. 13; see Appeal Br. 12; Reply Br. 4–12.
Appeal 2018-007779
Application 14/303,802

24
Appellant also contends the Examiner did not properly indicate the
additional claim elements and did not provide sufficient explanation or
support that the claim limitations recite well-understood, routine, or
conventional activities. See Appeal Br. 9.10
Appellant fails to persuade us of error in the Examiner’s rejection
with respect to the second Alice step (USPTO’s Step 2B). We agree with the
Examiner that Appellant’s claim 13 (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 13 (and the other pending claims)
merely recites additional non-abstract elements (above)—specifically the
“ANN” and “processor circuits” (the processor) that execute the “bundling
application” to perform the bundled software management process. Appeal
Br. 22 (Claims App)—generic computer elements, in particular the
processor, that carry out common data processing functions recited in the
bundled software management process (the abstract idea). Specifically,
Appellant’s Specification describes a collection of conventional (generic)
computers performing traditional computer functions—these computer

10 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
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 explicit arguments with respect to
Berkheimer and, accordingly, such arguments are waived.
Appeal 2018-007779
Application 14/303,802

25
components include the above-discussed processor and ANN interacting
with the bundling application and a bundling database (not recited in the
claim) through a network (also not recited in the claim). See, e.g., Spec.
¶¶ 21, 22, 25, 26; Fig. 1. In particular, Appellant’s Specification describes
the 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” (Spec. ¶ 26)), and Spec. ¶ 47 (“[m]any other types of artificial
neural networks are contemplated with many different variations”). Also, to
the extent that the written description describes the functions performed by
these elements, in particular the ANN, Appellant’s Specification describes
the functions at a high level of generality and largely does not describe the
particulars of how the claimed invention implements these functions. See,
e.g., Spec. ¶¶ 48–49 (describing the functions in the validity dependent data
handling process). 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); see also Berkheimer
Memorandum at 3 (explaining that a specification that describes additional
elements “in a manner that indicates that the additional elements are
sufficiently well-known that the specification does not need to describe the
particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” can
show that the elements are well-understood, routine, and conventional).
To the extent Appellant contends the Examiner failed to properly
present and support the Examiner’s rejection with respect to the inventive
concept analysis (supra), the Examiner cited to Appellant’s Specification as
Appeal 2018-007779
Application 14/303,802

26
well as case precedent to support the Examiner’s determinations that the
claims recite well-understood, routine, and conventional components and
activities. See Ans. 4 (citing Electric Power Group), 7–8 (citing Spec. ¶ 47);
Ans. 9–12 (citing case precedent and further explaining that the claims do
not demonstrate an inventive concept).
For at least the reasons above, we are not persuaded of Examiner error
in the rejection of claim 13 under 35 U.S.C. § 101. Thus, we sustain the
Examiner’s rejection under § 101 of independent claim 13, independent
claim 17, and dependent claims 16, 21, 28, 35–39, 42, and 43, which depend
from claims 13 and 17, respectively, and which were not separately argued
with specificity.
Written Description Rejection—35 U.S.C. § 112(a)
The Examiner rejects claims 35 and 42 as failing to comply with the
written description requirement. See Final Act. 8–11; Ans. 12–13.
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 35 (Appeal
Br. 26 (Claims App.)))—are supported by the Specification. See Appeal Br.
13–16 (citing Spec. ¶¶ 28, 32, 34; Figs. 3, 6); Reply Br. 12–13.
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 & Co.,
598 F.3d 1336, 1351 (Fed. Cir. 2010).
Appeal 2018-007779
Application 14/303,802

27
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
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 claims 35 and 42
as lacking sufficient written description support.
Obviousness Rejections of Claims 13, 16, 17, 21, 28, 36–39, and 43
Appellant argues independent claim 13, independent claim 17, and
dependent claims 16, 21, 28, 36–39, and 43, together as a group with respect
to the 35 U.S.C. § 103 rejection. See Appeal Br. 17–19. We select
independent claim 13 as representative of Appellant’s arguments with
respect to claims 13, 16, 17, 21, 28, 36–39, and 43. 37 C.F.R.
§ 41.37(c)(1)(iv).
The Examiner rejects claims 13, 16, 17, 21, 28, and 37–39 over
Kephart and Dudek. See Final Act. 13–24. The Examiner also rejects
claims 36 and 43 over Kephart, Dudek, and Simard. See Final Act. 29–30.
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. 17–19; Reply Br. 14–16. Specifically,
Appellant contends, inter alia, the Examiner’s rationale (the stated
Appeal 2018-007779
Application 14/303,802

28
motivations) is “merely conclusory arguments” (Appeal Br. 17) and the
Examiner has not provided reasoning why one would combine Kephart’s
virus classifier with Dudek’s method of discovering products that are
licensed together (see Appeal Br. 18). See Appeal Br. 17–19; Reply
Br. 14–16.
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
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.’” Final Act. 18 (quoting Dudek ¶ 2)
(emphasis omitted). 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. 13 (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
Final Act. 18; Ans. 13. 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 or the
Appeal 2018-007779
Application 14/303,802

29
claimed invention. In contrast to Appellant’s arguments (see Appeal Br. 17;
Reply Br. 14–16), 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. 2012). Here, Appellant argues the references individually and does not
address the specific arguments set 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. 15; see Reply
Br. 14–16. 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
Appeal 2018-007779
Application 14/303,802

30
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
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).
Appeal 2018-007779
Application 14/303,802

31
Accordingly, Appellant’s contentions do not persuade us of error in the
Examiner’s obviousness rejection of representative independent claim 13.
Therefore, we affirm the Examiner’s rejection of representative claim 13,
independent claim 17, and dependent claims 16, 21, 28, 36–39, and 43, not
separately argued with particularity (supra).
Obviousness Rejection of Claims 35 and 42
The Examiner rejects claims 35 and 42 over Kephart, Dudek, and
Moore. See Final Act. 25, 27–29. 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.
19–20; Reply Br. 16–18.
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.’” Final Act. 28–29 (quoting
Moore’s Abstract) (emphasis omitted). 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. 14. “The [motivation for combining Kephart and Dudek
with Moore] is . . . suggested by Moore[—]to improve the accuracy of
representation.” Ans. 14 (citing Moore, Abstract).
For the same reasons as claim 13 (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 Final Act. 27–29;
Appeal 2018-007779
Application 14/303,802

32
Ans. 14. 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
persuade us of error in the Examiner’s obviousness rejection of claims 35
and 42. Therefore, we affirm the Examiner’s rejection of claims 35 and 42.
CONCLUSION
For the reasons discussed above, we determine that claims 13, 16, 17,
21, 28, 35–39, 42, and 43 are directed to an abstract idea and do not
demonstrate an inventive concept. We also determine that claims 13, 16, 17,
21, 28, 35–39, 42, and 43 are obvious in view of the cited prior art. We
further find claims 35 and 42 have adequate written description support.
Appellant has not shown that the Examiner erred in rejecting claims
13, 16, 17, 21, 28, 35–39, 42, and 43 under 35 U.S.C. § 101. Appellant has
also not shown that the Examiner erred in rejecting claims 13, 16, 17, 21, 28,
35–39, 42, and 43 under 35 U.S.C. § 103. Appellant has shown that the
Examiner erred in rejecting claims 35 and 42 under 35 U.S.C. § 112(a). We
therefore sustain the Examiner’s rejection of claims 13, 16, 17, 21, 28, 35–
39, 42, and 43.
Appeal 2018-007779
Application 14/303,802

33
DECISION SUMMARY
In summary:
Claims
Rejected
35 U.S.C.
§
Reference(s)/Basis  Affirmed Reversed
13, 16, 17,
21, 28, 35–
39, 42, 43
101 Patent Eligible
Subject Matter
13, 16, 17,
21, 28, 35–
39, 42, 43

35, 42 112(a) Written Description 35, 42
13, 16, 17,
21, 28, 35–
39, 42, 43
103 Kephart, Dudek 13, 16, 17,
21, 28, 35–
39, 42, 43

Overall
Outcome

13, 16, 17,
21, 28, 35–
39, 42, 43

TIME PERIOD FOR RESPONSE
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(1)(iv). See 37
C.F.R. § 41.50(f).

AFFIRMED

Appeal 2018-007779
Application 14/303,802

34

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 2018-007779
Application 14/303,802
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 13, 16, 17, 21, 28, 35–39, 42, and 43 under 35 U.S.C. § 103 and
reversing the Examiner’s decision to reject claims 35 and 42 under 35
U.S.C. § 112(a). But I disagree with the Majority’s decision to affirm the
rejection of claims 13, 16, 17, 21, 28, 35–39, 42, and 43 under 35
U.S.C. § 101 as directed to patent-ineligible subject matter.
In particular, the claims are directed to a computer system comprising
an Artificial Neural Network (ANN). I disagree with the Majority that the
claims recite a patent-ineligible abstract idea under § 101.
Appeal 2018-007779
Application 14/303,802

35
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. 10 (citing Hargrave’s Communications Dictionary,
Wiley, 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.
Appeal 2018-007779
Application 14/303,802

36
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. 16. I
respectfully disagree.
The Claims
Independent claim 13 recites,
A system comprising:
an artificial neural network having an at least one input
neuron and an at least one output neuron; and
one or more computer processor circuits that are
configured to host a bundling application that is configured to:
identify a software component having a first value
for a first identification attribute and a second value for a
second identification attribute;
generate an input vector derived from the first
value and the second value;
load the input vector into the at least one input
neuron of the artificial neural network;
Appeal 2018-007779
Application 14/303,802

37
obtain a yielded output vector from the 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
determine, 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 both prior to and subsequent to the
obtaining the yielded output vector.
Indeed, the input to the artificial neural network (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. 10.
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 instance, “Claims do not
recite a mental process when they do not contain limitations that can
practically be performed in the human mind, for instance when the human
mind is not equipped to perform the claim limitations.” October 2019
Update: Subject Matter Eligibility 1 (Oct. 17, 2019)
https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_update.p
Appeal 2018-007779
Application 14/303,802

38
df (emphasis added); see also USPTO, Subject Matter Eligibility Examples:
Abstract Ideas 8–9 (Jan. 7, 2019),
https://www.uspto.gov/sites/default/files/documents/101_examples_37to42_2
0190107.pdf (applying the Guidance to a method for training a neural
network for facial detection in Example 39 and concluding that “the steps
are not practically performed in the human mind”) (emphasis added).
Here, I disagree with the Majority that the claims recite a process that
can be practically performed in the human mind. For instance, claim 13
recites the ANN itself and the claim requires circuits configured to interact
with the ANN.11 So if the recited ANN is “a computer system or set of
algorithms within a computer system” as the Majority determines (Maj. Op.
10), then the recited ANN must require a computer running those
algorithms. In fact, claim 13 expressly requires “computer processor
circuits” and the ANN to “load the input vector into the at least one input
neuron of the artificial neural network” and “obtain a yielded output vector
from the at least one output neuron of the artificial neural network”
(emphasis added). Because the functions require an interaction between the
ANN and the circuits, the recited functions cannot practically be performed
in the human mind but for some generically recited computer components.
Thus, the claims do not set forth or describe an abstract mental process. See,
e.g., Guidance, 84 Fed. Reg. at 52 n. 15 (collecting cases).
For this reason, I respectfully dissent.

11 Independent claim 17 recites similar functions.