Philip Morris Products S.A.

Patent Trials and Appeals Board

Apr 30, 2021

2020005214 (P.T.A.B. Apr. 30, 2021)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
14/409,664 12/19/2014 Florian Martin 4058.0060002 7918
155543 7590 04/30/2021
STERNE, KESSLER, GOLDSTEIN & FOX P.L.L.C.
1100 NEW YORK AVENUE, N.W.
WASHINGTON, DC 20005
EXAMINER
HARWARD, SOREN T
ART UNIT PAPER NUMBER
1631
NOTIFICATION DATE DELIVERY MODE
04/30/2021 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
e-office@sternekessler.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte FLORIAN MARTIN, ALAIN SEWER, JULIA HOENG, and
MANUEL CLAUDE PEITSCH
Appeal 2020-005214
Application 14/409,664
Technology Center 1600
Before DEBORAH KATZ, JOHN G. NEW, and ROBERT A. POLLOCK,
Administrative Patent Judges.
KATZ, Administrative Patent Judge.
DECISION ON APPEAL
Appellant1 seeks our review,2 under 35 U.S.C. § 134(a), of the
Examiner’s decision to reject claims 1–5, 8–15, 18, and 19. We have
jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

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 Phillip Morris
Products S.A. (Appeal Br. 4.)
2 We consider the Final Office Action issued March 12, 2019 (“Final Act.”),
the Appeal Brief filed March 23, 2020 (“Appeal Br.”), the Examiner’s
Answer issued on May 1, 2020 (“Ans.”), and the Reply Brief filed July 1,
2020 (“Reply Br.”).
Appeal 2020-005214
Application 14/409,664

2
Appellant’s Specification is directed to a system and method for
identifying biological entities and their properties that are representative of a
phenotype of interest. (See Spec. 2:10–12.) The method analyzes the
measured activities of biological entities using a network model of a
biological system. (See id. at 2:12–14.) The Specification describes the
network model as “a mathematical construct that is representative of a
dynamic biological system and that is built by assembling quantitative
information about various basic properties of the biological system.” (Id. at
15:6–8.)
Appellant’s claim 1 recites:
A computerized method for identifying biological entities
that are representative of a phenotype of interest, comprising
the steps of:

(a) providing, at a network modeling engine
instantiated on a processing device, a computational causal
network model that represents a biological system that
contributes to the phenotype and includes:
a plurality of nodes that represent biological
entities in the biological system; and
a plurality of edges connecting pairs of nodes
among the plurality of nodes and representing
relationships between the biological entities represented
by the nodes,
wherein one or more edges is associated with a
direction value that represents a causal activation or
causal suppression relationship between the biological
entities represented by the nodes, and wherein each node
is connected by an edge to at least one other node;

(b) receiving, at a network scoring engine instantiated
on the processing device, the network scoring engine in
communication with the network modeling engine, (i) a first
Appeal 2020-005214
Application 14/409,664

3
set of data corresponding to activities of a first subset of
biological entities obtained under a first set of conditions; and
(ii) a second set of data corresponding to activities of the first
subset of biological entities obtained under a second set of
conditions different from the first set of conditions, wherein the
first and second sets of conditions relate to the phenotype;

(c) calculating, with the network scoring engine, a set
of activity measures for a first subset of nodes corresponding to
the first subset of biological entities, the activity measure
representing a difference between the first set of data and the
second set of data;

(d) generating, with the network scoring engine, based
on the computational causal network model and the set of
activity measures, a set of activity values for a second subset of
nodes representing candidates of biological entities that
contribute to the phenotype but whose activities are not
measured, by identifying, for each particular node in the second
subset of nodes, an activity value that minimizes a difference
statement corresponding to an expression or executable
statement that represents a difference between the activity value
of the particular node and the activity value or activity measure
of nodes to which the particular node is connected by an om3
[sic] within the computational causal network model, wherein
the difference statement depends on the direction values of the
edges in the computational network connecting the nodes and
weight values associated with the edges connecting the nodes;
and

(e) generating, with the network scoring engine using
a machine learning technique, a classifier for the phenotypes
based on the set of activity measures, the set of activity values,
or both.

3 The Examiner identifies that the term “om” should read “edge” as
presented in previous claim sets. (Final Act. 2.)
Appeal 2020-005214
Application 14/409,664

4
(Appeal Br. 32–33.) Appellant’s independent claim 14 recites a “computer
program product comprising non-transitory computer-readable instructions”
for performing the method of claim 1. (See id. at 35–36.) Appellant’s
independent claim 15 recites a system for performing the method of claim 1.
(See id. at 36–37.).
The Examiner rejects claims 1–5, 8–15, 18, and 19 under 35 U.S.C.
§ 101 as being directed to patent-ineligible subject matter. (Final Act. 3–5.)
The Examiner also rejects claims 1–3, 5, 13–15, 18, and 19 under 35 U.S.C.
§ 103(a) over Ladd4 and Chuang.5 (Final Act. 7–11.) The Examiner rejects
claims 4 and 5 under 35 U.S.C. § 103(a) over Ladd, Chuang, and Abeel.6
(Final Act. 11–12.) The Examiner rejects claims 8–12 under 35
U.S.C. § 103(a) over Ladd, Chuang, and Toyoshiba.7 (Final Act. 12–14.)

35 U.S.C. § 101
The Examiner rejects claims 1–5, 8–15, 18, and 19 under § 101 as
being directed to patent-ineligible subject matter. (Final Act. 3–5; Ans. 4–
8.) Appellant argues claims 1–5, 8–15, 18, and 19 together. (See Appeal Br.
7–25.) We therefore analyze claim 1 as exemplary below.

4 Ladd et al., US Patent Application Publication 2009/0099784 A1,
published April 16, 2009.
5 Chuang et al., Network-based classification of breast cancer metastasis, 3
Molecular Systems Biology 140 (2007).
6 Abeel et al., Robust biomarker identification for cancer diagnosis with
ensemble feature selection methods, 26 Bioinformatics 392–98 (2010).
7 Toyoshiba et al., Gene Interaction Network Suggests Dioxin Induces a
Significant Linkage between Aryl Hydrocarbon Receptor and Retinoic Acid
Receptor Beta, 112 Environ. Health Perspect. 1217–24 (2004).
Appeal 2020-005214
Application 14/409,664

5
Standard for Subject Matter Eligibility
For issues involving subject matter eligibility under § 101, we apply
the two-step test set forth in Alice Corp. v. CLS Bank Int’l, 573 U.S. 208
(2014). In the first step, we “determine whether the claims at issue are
directed to a patent-ineligible concept.” (Id. at 218.) If the initial threshold
is met, we move to the second step, in which we “consider the elements of
each claim both individually and ‘as an ordered combination’ to determine
whether the additional elements ‘transform the nature of the claim’ into a
patent-eligible application.” (Id. at 217 (quoting Mayo Collaborative Servs.
v. Prometheus Labs., Inc., 566 U.S. 66, 78–79 (2012))). The second step is
“a search for an ‘inventive concept’—i.e., an element or combination of
elements that is ‘sufficient to ensure that the patent in practice amounts to
significantly more than a patent upon the [ineligible concept] itself.’” (Id. at
217–18 (quoting Mayo, 566 U.S. at 72–73) (alteration in original).)
The USPTO has published guidance on the application of § 101.
2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50
(Jan. 7, 2019) (“Guidance”). Under the Guidance and the October 2019
Update, we first look to whether the claim recites:
(1) any judicial exceptions, including certain groupings of abstract
ideas (i.e., mathematical concepts, certain methods of organizing
human activity such as a fundamental economic practice, or mental
processes) (“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) (9th ed.
Rev. 08.2017, Jan. 2018)) (“Step 2A, Prong 2”).8

8 This evaluation is performed by (a) identifying whether there are any
additional elements recited in the claim beyond the judicial exception, and
(b) evaluating those additional elements individually and in combination to
Appeal 2020-005214
Application 14/409,664

6
Guidance, 84 Fed. Reg. at 52–55.
Only if a claim (1) recites a judicial exception and (2) does not
integrate that exception into a practical application, do we then look, under
Step 2B, 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.

Guidance, 84 Fed. Reg. at 52–56.
Step 2A, Prong 1
Under Step 2A, Prong 1, of the Guidance, we determine whether the
claims recite any judicial exceptions, including certain groupings of abstract
ideas (i.e., mathematical concepts, certain methods of organizing human
activity, or mental processes). See 84 Fed. Reg. at 52, 54. The Examiner
finds that the claimed “computational causal network model” is a
mathematical construct that is representative of a dynamic biological system,
where the biological system is modeled as a mathematical graph. (Ans. 4,
citing Spec. 15:6–7, 28–29.) The Examiner finds that the claims recite
additional mathematical concepts, including “calculating,” “generating . . . a
score,” and other mathematical expressions. (Id. at 4–5.) Accordingly, the
Examiner finds that the claims recite numerous mathematical concepts that
are patent ineligible abstract ideas. (See id. at 5.)

determine whether the claim as a whole integrates the exception into a
practical application. See 2019 Revised Guidance - Section III(A)(2), 84
Fed. Reg. 54–55.
Appeal 2020-005214
Application 14/409,664

7
Appellant argues that the independent claims do not: (1) recite
mathematical formulas, (2) attempt to textually claim an equation, (3) recite
mathematical operators, and (4) expressly recite relationships between
variables and numbers. (Appeal Br. 12.) Appellant argues that although the
independent claims recite terms that may rely on mathematical concepts,
“they do not explicitly recite any mathematical concepts.” (Id. at 13.) As to
the specific claim elements, Appellant argues that “the claimed
computational causal network merely refers to inferred data based on
received input,” as thus is not a component of an algorithm. (Id. at 15.) To
further support their argument, Appellant refers to Patent Eligibility
Guidance Examples 39 and 41, issued with the 2019 Guidance. (See id. at
15–18.) Appellant argues that the claims are similar to Example 39, which
does not explicitly recite mathematical concepts, and are dissimilar to
Example 41, which explicitly recites mathematical formulas. (See id.)
We agree with the Examiner that the claims recite an abstract idea,
namely a mathematical concept. In step (d), independent claim 1 recites:
an activity value that minimizes a difference statement
corresponding to an expression or executable statement that
represents a difference between the activity value of the
particular node and the activity value or activity measure of
nodes to which the particular node is connected by an [edge]
within the computational causal network model, wherein the
difference statement depends on the direction values of the
edges in the computational network connecting the nodes and
weight values associated with the edges connecting the nodes
(Appeal Br. 32–33, 36 (claim 14(d)), 37 (claim 15 (b)(iii)).) The
Specification describes a specific expression of a difference statement in the
form of Equation 6. (See Spec. 26:12–22). The claims appear to textually
Appeal 2020-005214
Application 14/409,664

8
recite Equation 6, i.e., (the difference (-) between activity values (f(x), f(y))
of nodes (x and y) connected by an edge (x→y), depending on direction
values (sign(x→y)) and weight values (w(x→y)). (See id.) Additionally,
the claims recite other mathematical concepts including calculating a set of
activity measures representing a difference between data sets. For at least
these reasons we find that the claims are distinguishable from those of PEG
Example 39. Instead, the claims are similar to PEG Example 41 in that they
expressly recite a mathematical expression, although using text rather than
mathematical operators. Accordingly, we agree with the Examiner that the
claims recite an abstract idea in the category of mathematical concepts under
Step 2A, Prong 1 of the Guidance.
Step 2A, Prong 2
Under Step 2A, Prong 2, of the Guidance, we determine whether the
claims as a whole integrate the judicial exception into a practical application.
See 84 Fed. Reg. at 54–55. The Examiner finds that additional elements of
the claims recite mere instructions to apply the abstract idea, i.e.,
mathematical concept, using a generic computer, and therefore the claim
does not integrate the abstract idea into a practical application. (See Final
Act. 4.) Moreover, the Examiner finds that “[n]one of the dependent claims
recite any additional non-abstract elements; they are all directed to further
aspects of the information being analyzed, the manner in which that analysis
is performed, or the mathematical operations performed on the information.”
(Id.)
Appellant argues that the claims recite a specific combination of
elements for identifying biological entities that are representative of a
phenotype of interest. (See Appeal Br. 20.) Appellant argues that the claims
Appeal 2020-005214
Application 14/409,664

9
are similar to claims found patent eligible in Appeal No. 2017-003914 and
Appeal No. 2019-003792. (See id. at 20–21.) Finally, Appellant argues that
the claims are similar to those of PEG Example 37. (Id. at 21–22.)
We have considered, but are not persuaded, by Appellant’s arguments.
We do not agree that Appellant’s claims recite additional elements that
integrate the abstract idea, i.e., mathematical concept, into a practical
application. As acknowledged by Appellant’s Specification, a network
model of a biological system is a mathematical construct. (See Spec. 15:6–
8.) Accordingly, Appellant’s claims are directed to mathematical concepts
that improve the mathematical construct used to identify biological entities.
In other words, the claimed additional elements generally link the judicial
exception, i.e., mathematical concept, to a particular technological
environment, i.e., mathematical constructs of biological systems. But
“merely limiting the field of use of the abstract idea to a particular existing
technological environment does not render the claims any less abstract.”
(Affinity Labs of Texas, LLC v. DIRECTV, LLC, 838 F.3d 1253, 1259 (Fed.
Cir. 2016); see also Digitech Image Techs., LLC v. Elecs. for Imaging, Inc.,
758 F.3d 1344, 1351 (Fed. Cir. 2014) (“If a claim is directed essentially to a
method of calculating, using a mathematical formula, even if the solution is
for a specific purpose, the claimed method is nonstatutory.” (quoting Parker
v. Flook, 437 U.S. 584, 595 (1978)).
Because the alleged improvement is to a mathematical construct, we
find these claims to be readily distinguishable from those in Appeal Nos.
2017-003914 and 2019-003792. Moreover, as discussed by the Examiner,
the additional elements of the claims, i.e., a computerized process performed
on a processing device, all relate to applying the claimed mathematical
Appeal 2020-005214
Application 14/409,664

10
concepts using a generic computer and do not improve the computer itself.
(See Final Act. 4.) For this reason, we find the claims distinguishable from
the claim of PEG Example 37, which relates to a technological improvement
rather than merely applying the abstract idea on a computer.
Step 2B
Under Step 2B of the Guidance, we continue to evaluate patent
eligibility by considering whether the claim provides an inventive concept
that amounts to significantly more than the exception itself. (See Guidance,
84 Fed. Reg. at 56.) The Examiner finds that: (1) the only non-abstract
elements of the claims amount to mere instructions to apply the abstract idea
using a generic computer, and (2) that the claims do not provide an inventive
concept because the additional elements of the claims are elements of the
abstract idea itself. (See Ans. 6–8.) Because the abstract idea itself cannot
contribute to an inventive concept, the Examiner does not expressly apply
the “well-understood, routine and conventional” analysis to the additional
elements of the claims. (See id.; citing Genetic Techs. v. Merial LLC, 818
F.3d 1369, 1376 (Fed. Cir. 2016) (“The inventive concept necessary at step
two of the Mayo/Alice analysis cannot be furnished by the unpatentable law
of nature (or natural phenomenon or abstract idea) itself.”)
Appellant contends that, in addition to any alleged mathematical
concepts, the claims recite “details regarding specific components and
limitations of the network model,” “a receiving step,” and “actually
generating a classifier for the phenotypes using a machine learning
technique defined by specific inputs.” (Reply Br. 7–8.) Appellant contends
that the Examiner has not provided evidence that these additional claim
elements are routine, conventional, or well-known. (See id. at 8.)
Appeal 2020-005214
Application 14/409,664

11
We are not persuaded by Appellant’s argument. Rather we agree with
the Examiner that the elements cited by Appellant are either insignificant
extra-solution activity or part of the abstract idea itself. For example, the
receiving step relates to receiving first and second sets of data to be
analyzed. Although the data may be specific to biological entities, our
reviewing court has held that mere data-gathering steps cannot make an
otherwise nonstatutory claim statutory. See CyberSource Corp. v. Retail
Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011). Moreover, receiving
data for use in a network modeling engine is well-understood, routine, and
conventional as established by Ladd, discussed below.
Appellant’s other “additional elements,” are part of the mathematical
concept itself. For example, the details of the network model, including the
nodes, edges, and their relationships, are simply the data points making up
the mathematical construct. Similar network models of biological systems,
including biological entities represented as nodes connected by edges are
taught by Ladd, Chuang, and Toyoshiba, as discussed below. Likewise,
generating a classifier using a machine-learning technique is another
mathematical concept recited by the claims. The Specification
acknowledges that such machine-learning techniques are conventional. (See
Spec. 32:24–33:2.) In sum, Appellant does not identify any additional
features that constitute an inventive concept.
Accordingly, we are not persuaded that the Examiner erred in
rejecting Appellant’s pending claims as being directed to ineligible subject
matter under 35 U.S.C. § 101.

Appeal 2020-005214
Application 14/409,664

12
35 U.S.C. § 103(a)
The Examiner rejects Appellant’s claims as being obvious under 35
U.S.C. § 103(a). Specifically, the Examiner rejects claims 1–3, 5, 13–15,
18, and 19 over Ladd and Chuang (see Final Act. 7–11); claims 4 and 5 over
Ladd, Chuang, and Abeel (see Final Act. 11–12); and claims 8–12 over
Ladd, Chuang, and Toyoshiba (see Final Act. 12–14). Appellant does not
raise any separate arguments against any of the claims or groups of claims in
the separate rejections. (See Appeal Br. 26–31.) Thus, we focus on claim 1.
See 37 C.F.R. § 41.37(c)(1)(iv).
Ladd teaches a method for modelling the actions and interactions of
biomolecules within a biological system to understand the systematic nature
of biological events. (See Ladd ¶ 59.) Ladd specifically teaches a causal
system model (“CSM”) which connects “many biological elements or
‘nodes’ into a highly intricate network of relationships and/or connections to
form a systematically descriptive, inclusive, and scalable representation of a
biological system.” (Id. ¶ 60.) Ladd further teaches “edges,” as the nodes in
the model are linked by physical, chemical, or biological relationships, along
with a direction of causality between nodes. (See id. ¶ 74.) Ladd teaches
that operational data is mapped onto a working knowledge base
(“assembly”), and that “algorithms simulate the effect through the assembly
of hypothesized increases or decreases in the quantity or activity of nodes
within the assembly. This results in generation of a large number of
branching paths which involve nodes representative of data points in the
operational data set.” (Id. ¶ 76.) Ladd teaches evaluating the models for
richness (“number of nodes in the model which map onto the data is greater
Appeal 2020-005214
Application 14/409,664

13
than the number that would map by chance”), and concordance (“what
fraction of nodes correspond to the operational data”). (Id. ¶¶ 77, 122.)
The Examiner finds that Ladd teaches steps (a) through (d) of
Appellant’s claim 1, including: (a) providing a causal system model
comprising nodes that represent biological entities and directional
relationships between nodes (edges); (b) receiving quantitative data
corresponding to the entities for at least two different biological states; (c)
comparing node similarities and dissimilarities; and (d) applying algorithms
to the data to analyze concordance, which correspond to each step of the
recited method, except step [e]. (See Final Act. 8–10.)
The Examiner finds that Chuang teaches a network-based method for
analyzing differential gene expression in breast cancer. (See Final Act. 10.)
The Examiner finds Chuang applies a greedy search algorithm, which is a
machine-learning technique, to classify the data. (See id.) The Examiner
finds that a person of ordinary skill in the art would have been motivated to
combine Ladd’s causal system model with Chuang’s network-based system
for identifying biomarkers “because Chuang teaches that network based
biomarker identification has various advantages over methods than identify
biomarkers independently.” (Id. at 11.) The Examiner further finds that
because both Ladd and Chuang teach network-based methods for analyzing
biological pathways, a person of ordinary skill would have had a reasonable
expectation of success in “using a CSM to predict expressions of unobserved
genes from gene expression measurements, and then using the observed and
predicted measurements to create a classifier that distinguishes between”
phenotypes, e.g., metastatic and non-metastatic cancer. (Id.)
Appeal 2020-005214
Application 14/409,664

14
Appellant argues that Ladd does not teach the limitations of step (d),
particularly an “expression or executable statement,” and “a difference
statement would not represent a difference between an activity value
generated for a particular node and an activity value generated for a node
connected to the particular node’s edge.” (Appeal. Br. 29–30.) We address
each argument in turn.
First, Appellant argues that Ladd does not teach an “expression or
executable statement” because “Ladd does not disclose or suggest that the
‘case frames’ include ‘functional descriptions of mathematical
relationships.’” (Appeal Br. 29.)
We are not persuaded by Appellant’s argument. Ladd generally
teaches that causal modeling of relationships in biological systems may be
combined with artificial intelligence “to generate millions of potential
hypotheses, which are then evaluated through a number of algorithms to
produce a set of statistically significant hypotheses.” (Ladd ¶ 70.) More
specifically, Ladd teaches a class of algorithms that may be used to adjust
the causal model. (See Ladd ¶ 112.) For example, Ladd teaches applying
“combinatorial space search algorithms, such as a genetic algorithm,
simulated annealing, evolutionary algorithms, and the like, to the multiple
branching paths using as a fitness function the number of correctly simulated
data points in the candidate path combinations.” (Id. ¶ 118.) Moreover,
Ladd teaches applying richness and concordance algorithms to differentiate
between models. (See id. ¶¶ 121–123; Figs. 5–14.) Each of the specific
algorithms taught by Ladd include underlying functional descriptions of
mathematical relationships, and thus teach expressions or executable
Appeal 2020-005214
Application 14/409,664

15
statements within those algorithms. Accordingly, we are not persuaded that
the Examiner erred.
Second, Appellant argues that Ladd does not teach minimizing a
difference statement representing a difference between the activity value of a
particular node, and the activity value of nodes to which the particular node
is connected by an edge within the computational casual network model.
(See Reply Br. 8.) According to Appellant, Ladd’s fitness function is not
being used to minimize the difference between the results of the CSM and
the operational data. (Id. at 10.)
The Examiner responds that Ladd teaches preferring models with
“concordance,” which is synonymous with “minimal difference.” (Ans. 10.)
The Examiner finds that Ladd’s fitness function would have been used to
minimize the difference between the simulation results of the CSM and
operational data. (Id.) Accordingly, the Examiner finds that simulating a
highly concordant CSM would calculate activity values that minimize the
difference statement between the activity value of the particular node, and
the activity value of nodes connected by an edge to the particular node. (Id.)
We agree with the Examiner that the claims would have been obvious
over the cited prior art. Ladd teaches a causal network including a plurality
of nodes connected by directional edges, including nodes with and without
operational data. (See Ladd Figs. 5–14.) Ladd optimizes based on
concordance between simulation results and operational data, and teaches
various algorithms used to minimize the difference between activity levels in
the models. (See Ladd ¶ 122.) Part of Ladd’s concordance analysis
compares the predicted activity increase or decrease between nodes, and thus
minimizes a difference between the activity values of nodes connected by
Appeal 2020-005214
Application 14/409,664

16
edges. (See id.) Although, Ladd does not disclose an identical difference
statement to the claimed difference statement, the obviousness “analysis
need not seek out precise teachings directed to the specific subject matter of
the challenged claim, for a court can 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). The Examiner has
articulated reasoning for extrapolating a conclusion of prima facie
obviousness from the prior art, by applying such inferences and creative
steps. See Perfect Web Techs., Inc. v. InfoUSA, Inc., 587 F.3d 1324, 1330
(Fed. Cir. 2009). Appellant has not persuasively rebutted the Examiner’s
articulated reasoning.
Appellant does not persuade us that the Examiner erred in rejecting
claim 1 as being obvious. Appellant fails to present separate arguments for
the rejection of any other of the pending claims. Accordingly, we sustain
each of the rejections under 35 U.S.C. § 103(a).
DECISION SUMMARY
Upon consideration of the record and for the reasons given, we affirm
the Examiner’s rejection.
In summary:
Claim(s)
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–5, 8–
15, 18, 19
101 Eligibility 1–5, 8–15,
18, 19

1–3, 5,
13–15,
18, 19
103(a) Ladd, Chuang 1–3, 5, 13–
15, 18, 19

4, 5 103(a) Ladd, Chuang,
Abeel
4, 5
Appeal 2020-005214
Application 14/409,664

17
8–12 103(a) Ladd, Chuang,
Toyoshiba
8–12
Overall
Outcome
1–5, 8–15,
18, 19

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