Cintra Holding US Corp.

Patent Trials and Appeals Board

Nov 15, 2021

2021005115 (P.T.A.B. Nov. 15, 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.
16/828,697 03/24/2020 Ricardo Sanchez Gomez CIN875/4-001US 4419
22892 7590 11/15/2021
VINSON & ELKINS L.L.P.
1001 Fannin Street
Suite 2500
HOUSTON, TX 77002-6760
EXAMINER
NELSON, FREDA ANN
ART UNIT PAPER NUMBER
3628
NOTIFICATION DATE DELIVERY MODE
11/15/2021 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):
ggoel@velaw.com
iptldocket@velaw.com
sbrown@velaw.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte RICARDO SANCHEZ GOMEZ, JOHN FRANCIS BRADY,
and VAMSI KRISHNA NADIMPALLI
____________

Appeal 2021-005115
Application 16/828,697
Technology Center 3600
____________

Before MICHAEL C. ASTORINO, NINA L. MEDLOCK, and
BRADLEY B. BAYAT, Administrative Patent Judges.

MEDLOCK, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Appellant1 appeals under 35 U.S.C. § 134(a) from the Examiner’s
final rejection of claims 1–21. We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

1 We use the term “Appellant” to refer to “applicant” as defined in 37 C.F.R.
§ 1.42. Our decision references Appellant’s Appeal Brief (“Appeal Br.,”
filed April 30, 2021) and Reply Brief (“Reply Br.,” filed August 25, 2021),
the Examiner’s Answer (“Ans.,” mailed June 25, 2021), Advisory Action
(“Advisory Act.,” mailed December 21, 2020), and Final Office Action
(“Final Act.,” mailed October 1, 2020). Appellant identifies
Cintra Holding US Corp. as the real party in interest (Appeal Br. 3).
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CLAIMED INVENTION
The Specification states, “[t]he present invention relates to systems,
methods, and computer readable storage media containing instructions for
detecting an anomalous traffic condition” and “[s]ome embodiments further
relate to calculating a decision in response to the detection of an anomalous
traffic condition, such as selecting a route, setting a speed limit, or setting a
toll rate” (Spec. ¶ 1).
Claims 1, 8, and 15 are the independent claims on appeal. Claim 1,
reproduced below with bracketed notations added, is illustrative of the
claimed subject matter:
1. A computer-implemented method for managing
traffic on a roadway, comprising:
[(a)] receiving, for a time interval, traffic data
comprising a plurality of data attributes, composed of:
roadway traffic data received from sensors mounted
on or in proximity to the roadway, and
calculated traffic data;
[(b)] determining a traffic management decision using on
a predetermined traffic management model,
[(c)] determining whether a vector composed of the
traffic data and traffic management decision is within a cluster of
anomalous vectors and is therefore an anomalous vector, and
[(d)] adjusting the traffic management decision based on
a traffic management adjustment rule associated with the
anomalous vector, wherein the traffic management adjustment
rule is determined by:
[(d1)] receiving, for a plurality of historical time
intervals, historical traffic data comprising the plurality of
data attributes, and a historical traffic management
decision corresponding to each time interval,
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[(d2)] collating the historical traffic data and the
historical traffic management decision into a plurality of
historical vectors, wherein each vector in the plurality of
historical vectors comprises the historical traffic data and
historical traffic management decision corresponding to a
single time interval;
[(d3)] identifying a plurality of anomalous vectors
from within the plurality of historical vectors, where each
vector in the plurality of anomalous vectors is anomalous
relative to the plurality of historical vectors;
[(d4)] clustering the set of anomalous vectors into a
plurality of anomalous vector clusters according to a
distance metric; and
[(d5)] analyzing the vectors in a vector cluster to
determine the traffic management adjustment rule
associated with the vector cluster.
Appeal Br. 17–18 (Claims App.).
REJECTION
Claims 1–21 are rejected under 35 U.S.C. § 101 as directed to a
judicial exception without significantly more.
ANALYSIS
Under 35 U.S.C. § 101, “[w]hoever invents or discovers any new and
useful process, machine, manufacture, or composition of matter, or any new
and useful improvement thereof, may obtain a patent therefor.” 35 U.S.C.
§ 101. The Supreme Court, however, has long interpreted § 101 to include
an implicit exception: “[l]aws of nature, natural phenomena, and abstract
ideas” are not patentable. Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 216
(2014).
The Supreme Court, in Alice, reiterated the two-step framework
previously set forth in Mayo Collaborative Services v. Prometheus
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Laboratories, Inc., 566 U.S. 66 (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 Corp., 573 U.S. at 217.
The first step in that analysis is to “determine whether the claims at issue are
directed to one of those patent-ineligible concepts.” Id. If the claims are not
directed to a patent-ineligible concept, e.g., an abstract idea, the inquiry
ends. Otherwise, the inquiry proceeds to the second step where the elements
of the claims are considered “individually and ‘as an ordered combination’”
to determine whether there are additional elements that “‘transform the
nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo,
566 U.S. at 79, 78). This 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 (alteration in original).
The U.S. Patent and Trademark Office (the “USPTO”) published
revised guidance on January 7, 2019, for use by USPTO personnel in
evaluating subject matter eligibility under 35 U.S.C. § 101. 2019 REVISED
PATENT SUBJECT MATTER ELIGIBILITY GUIDANCE, 84 Fed. Reg. 50 (Jan. 7,
2019) (the “2019 Revised Guidance”).2 That guidance revised the USPTO’s
examination procedure with respect to the first step of the Mayo/Alice
framework by (1) “[p]roviding groupings of subject matter that [are]
considered an abstract idea”; and (2) clarifying that a claim is not “directed

2 The MANUAL OF PATENT EXAMINING PROCEDURE (“MPEP”) incorporates
the revised guidance and subsequent updates at Section 2106 (9th ed.
Rev. 10.2019, rev. June 2020).
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to” a judicial exception if the judicial exception is integrated into a practical
application of that exception. Id. at 50.
The first step, as set forth in the 2019 Revised Guidance (i.e.,
Step 2A), is, thus, a two-prong test. In Step 2A, Prong One, we look to
whether the claim recites a judicial exception, e.g., one of the following
three groupings of abstract ideas: (1) mathematical concepts; (2) certain
methods of organizing human activity, e.g., fundamental economic
principles or practices, commercial or legal interactions; and (3) mental
processes. 2019 Revised Guidance, 84 Fed. Reg. at 54. If so, we next
determine, in Step 2A, Prong Two, whether the claim as a whole integrates
the recited judicial exception into a practical application, i.e., whether the
additional elements recited in the claim beyond the judicial exception, apply,
rely on, or use 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. Id. at 54–55. Only if
the claim (1) recites a judicial exception and (2) does not integrate that
exception into a practical application do we conclude that the claim is
“directed to” the judicial exception, e.g., an abstract idea. Id.
If the claim is determined to be directed to a judicial exception under
revised Step 2A, we next evaluate the additional elements, individually and
in combination, in Step 2B, to determine whether they provide an inventive
concept, i.e., whether the additional elements or combination of elements
amounts to significantly more than the judicial exception itself; only then, is
the claim patent eligible. 2019 Revised Guidance, 84 Fed. Reg. at 56.
Here, in rejecting the pending claims under 35 U.S.C. § 101, the
Examiner determined that independent claims 1, 8, and 15 recite “a certain
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method of organizing human activity or a mathematical concept” and,
therefore, an abstract idea (Final Act. 10–12). The Examiner also
determined that the additional elements recited in independent claims 1, 8,
and 15 do not integrate the recited abstract idea into a practical application
(id. at 12–14); that the additional elements are not sufficient to amount to
significantly more than the abstract idea itself (id. at 14–15); and that the
dependent claims are patent ineligible because they merely narrow the
previously recited abstract idea (id. at 15–16).
Independent Claims 1, 8, and 15
Step One of the Mayo/Alice Framework (2019 Revised Guidance,
Step 2A)
Appellant argues the independent claims as a group (Appeal
Br. 9–15). We select independent claim 1 as representative. Claims 8 and
15 stand or fall with claim 1. See 37 C.F.R. § 41.37(c)(1)(iv).
We are not persuaded, as an initial matter, by Appellant’s argument
that the Examiner has failed to establish a prima facie case of patent
ineligibility (Appeal Br. 10–11). Appellant charges that the Examiner “has
failed to identify any alleged patent ineligible concept recited in the pending
claims under Step 2A, Prong 1” and has instead “merely copied the shared
text of independent [c]laims 1, 8, and 15, and then alleged that the claim
recites ‘a certain method of organizing human activity or a mathematical
concept’” (id. at 10 (citing Final Act. ¶¶ 27, 28)). Appellant, thus, maintains
that it has been “left merely to guess at the alleged patent ineligible concept
or concepts in the claim,” and “[a]s a result, [the] Examiner has failed to
present a prima facie case that the claims are patent ineligible” (id.
at 10–11).
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Even if, as Appellant contends, the Examiner failed to provide a
comprehensive analysis in paragraphs 27 and 28 of the Final Office Action,
the Examiner clearly set forth the bases for the rejection in the “Response to
Arguments,” at pages 2–9 of the Final Office Action. There, the Examiner
noted that the claims recite “receiving, for a time interval, traffic data[,] . . .
determining a traffic management decision[,] . . . and adjusting the traffic
management decision”; and citing paragraphs 46 and 63 of Appellant’s
Specification,3 the Examiner explained that these limitations, when
considered in light of the Specification and under a broadest reasonable
interpretation, recite “a certain method of organizing human activity, e.g.,

3 The Examiner noted that paragraph 46 explains that the traffic data
management system performs calculations and makes decisions based on
input data (received in real time) for a time interval on a road segment, and
describes that these calculations can include “determining a preferred route
from a plurality of routes (i.e., managing personal behavior or relationships
or interactions between people, such as following rules or instructions)”;
“determining a speed limit for a road segment (i.e., managing personal
behavior or relationships or interactions between people, such as following
rules or instructions)”; or “determining a toll rate for a road segment (i.e.,
managing personal behavior or relationships or interactions between people,
such as following rules or instructions; a fundamental economic princip[le];
or commercial interaction, such as sales activities or behaviors)” (Final
Act. 4) (emphasis omitted). The Examiner further noted that paragraph 63
explains that the “predetermined decision making algorithm” can produce a
decision, such as “a decision to set or adjust a toll price (i.e., managing
personal behavior or relationships or interactions between people, such as
following rules or instructions; a fundamental economic princip[le]; or
commercial interaction, such as sales activities or behaviors) (in the case of a
MTLS)” or “to set or adjust other traffic-related features (i.e., managing
personal behavior or relationships or interactions between people, such as
following rules or instructions) such as speed limits, traffic flow control
devices, automatic gates, lighting or signage, and other similar features” (id.)
(emphasis omitted).
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managing personal behavior or relationships or interactions between people,
such as following rules or instructions” (Final Act. 3–4).
The Federal Circuit has observed repeatedly that “the prima facie case
is merely a procedural device that enables an appropriate shift of the burden
of production.” Hyatt v. Dudas, 492 F.3d 1365, 1369 (Fed. Cir. 2007)
(citing In re Oetiker, 977 F.2d 1443, 1445 (Fed. Cir. 1992)). The court has,
thus, held that the USPTO “satisfies its initial burden of production by
‘adequately explain[ing] the shortcomings it perceives so that the applicant
is properly notified and able to respond.”’ In re Jung, 637 F.3d 1356, 1362
(Fed. Cir. 2011) (quoting Hyatt, 492 F.3d at 1369–70). Thus, what is
required of the Office is that it sets forth the statutory basis of the rejection
in a sufficiently articulate and informative manner as to meet the notice
requirement of 35 U.S.C. § 132. Id.; see also Chester v. Miller, 906 F.2d
1574, 1578 (Fed. Cir. 1990) (“Section 132 is violated when a rejection is so
uninformative that it prevents the applicant from recognizing and seeking to
counter the grounds for rejection.”). Here, the Examiner did precisely what
§ 132 requires. And, in doing so, the Examiner established a prima facie
case of patent ineligibility.
We also are not persuaded by Appellant’s further argument that the
Examiner erred in determining that claim 1 is directed to an abstract idea
(Appeal Br. 11–14). The Federal Circuit has explained that “the ‘directed
to’ inquiry applies a stage-one filter to claims, considered in light of the
specification, based on whether ‘their character as a whole is directed to
excluded subject matter.’” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327,
1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network,
Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the
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claims is on a specific improvement in relevant technology or on a process
that itself qualifies as an “abstract idea” for which computers are invoked
merely as a tool. See id. at 1335–36. Here, it is clear from the Specification
(including the claim language) that claim 1 focuses on an abstract idea, and
not on an improvement to technology and/or a technical field.
The Specification is titled “SYSTEMS AND METHODS FOR
DETECTING AND RESPONDING TO ANOMALOUS TRAFFIC
CONDITIONS,” and discloses in the Background section that public
roadways are frequently monitored by one or more detection devices, e.g.,
vehicle counters, speed monitors, license plate readers, and other sensing
technologies (Spec. ¶ 2). Roadway data also can be collected from
individual drivers, either through their cars or their mobile devices, which
report GPS location, speed, and other metrics, and allow drivers to manually
report roadway events (e.g., stopped vehicle, police presence) (id.). This
data then “can be used to provide services to drivers [e.g., implementing
traffic-aware navigation technologies and providing real-time alerts to
drivers of unexpected roadway conditions],” as well as to operators of the
public roadways (e.g., dynamically setting speed limits or toll rates,
estimating roadway wear, developing plans for future highway construction
projects) (id. ¶¶ 2, 3).
The Specification discloses that, for many of these services, the
roadway data must be analyzed and a decision made by a computing system
programmed with complex decision-making logic — a process that “can
occasionally produce an incorrect result, resulting in non-optimal decision-
making (e.g., picking the wrong route, setting a non-optimal toll, or setting a
traffic-inducing speed limit)” (Spec. ¶ 4). The Specification, thus, describes,
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as a non-limiting example, managed toll lanes, where a single road segment
has public, toll-free unmanaged lanes, and one or more managed toll lanes
(id. ¶ 5). “On such roads, the toll charged for use of the managed lanes is
often dynamic, and can be adjusted in response to traffic conditions,” e.g.,
current roadway information or a prediction of future roadway conditions
(id.). The Specification describes that “[t]hese adjustments can be
performed automatically . . . through various algorithms set by human
programmers to accomplish such goals as achieving a predetermined speed
on the managed lanes, maximizing throughput throughout the entire
roadway, and maximizing toll revenue”; however, according to the
Specification, “current systems are limited in that they often produce
incorrect or non-optimal results in response to anomalous or unexpected
roadway conditions” (id. ¶ 6). Therefore, the Specification explains, “[w]hat
is needed . . . is a method for determining whether a traffic-based decision is
being based on an anomalous set of circumstances, or producing an
anomalous result in need of correction” and “for addressing such anomalous
conditions by, for example, providing alternative decision-making strategies
in those circumstances” (id. ¶ 7).
The claimed invention is ostensibly intended to meet this need by
providing a system and method for detecting an anomalous combination of
traffic data inputs and traffic management outputs, relative to historical data.
Claim 1, thus, recites a method for managing data on a roadway comprising:
(1) receiving, for a time interval, current traffic data comprising a plurality
of data attributes, i.e., “receiving, for a time interval, traffic data comprising
a plurality of data attributes, composed of: roadway traffic data received
from sensors mounted on or in proximity to the roadway, and calculated
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traffic data” (step (a)); (2) “determining a traffic management decision [e.g.,
a toll rate] using . . . a predetermined traffic management model [e.g., a toll
setting model]” (step (b)); (3) “determining whether a vector composed of
the traffic data and traffic management decision [e.g., the toll rate] is within
a cluster of anomalous vectors and is therefore an anomalous vector”
(step (c)); and (4) adjusting the traffic management decision, e.g., the toll
rate, based on a traffic management adjustment rule, e.g., a toll adjustment
rate, associated with the anomalous vector, i.e.,
adjusting the traffic management decision based on a
traffic management adjustment rule associated with the
anomalous vector, wherein the traffic management adjustment
rule is determined by:
receiving, for a plurality of historical time intervals,
historical traffic data comprising the plurality of data
attributes, and a historical traffic management decision
corresponding to each time interval,
collating the historical traffic data and the historical
traffic management decision into a plurality of historical
vectors, wherein each vector in the plurality of historical
vectors comprises the historical traffic data and historical
traffic management decision corresponding to a single
time interval;
identifying a plurality of anomalous vectors from
within the plurality of historical vectors, where each vector
in the plurality of anomalous vectors is anomalous relative
to the plurality of historical vectors;
clustering the set of anomalous vectors into a
plurality of anomalous vector clusters according to a
distance metric; and
analyzing the vectors in a vector cluster to
determine the traffic management adjustment rule
associated with the vector cluster[,]
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(step (d)). We agree with the Examiner that these limitations, when given
their broadest reasonable interpretation, recite a method of organizing
human activity and, therefore, an abstract idea (Final Act. 10–12).
Appellant argues that claim 1 is “not directed to a ‘method of
organizing human activity’ or a ‘commercial or legal interaction,’ . . . but
instead claim[s] a method for managing traffic to ‘maximiz[e] throughput
throughout the entire tollway’” (Appeal Br. 11) (last alteration in original).
Appellant notes that claim 1 expressly recites that “the invention is ‘for
managing traffic on a roadway,’” and asserts that although the Specification
describes that tolling is one possible traffic management decision, the
purpose of the toll is not merely marketing or sales, but to manage traffic
flow (id. (citing Spec. ¶¶ 45, 47, 49, and 50, and observing that the
Specification describes example embodiments as “managed toll lane
system[s]” and “traffic management system[s]” and identifies, as “technical”
effects, “reducing travel time for vehicles” and “improved roadway
safety”)).
Appellant argues that claim 1 is “not analogous to any of the
USPTO’s illustrative examples [of certain methods of organizing human
activity], such as using advertising as currency, offer-based price
optimization, or structuring a sales force or marketing company” (Appeal
Br. 11–12 (citing MPEP § 2106.04)). But, by Appellant’s own admission,
“claim 1 involves a method for directing the travel of a large plurality of real
vehicles on public roadways, such as in a particular region at a particular
time” and “recites ‘determining a traffic management decision’ and
‘adjusting the traffic management decision’ that under a broadest reasonable
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interpretation . . . includes a management action for the travel of real
vehicles on public roadways” (Reply Br. 4).
Appellant maintains that claim 1 does not recite an abstract idea. Yet,
we fail to see why, and Appellant does not explain why, the subject matter
recited in claim 1, as thus characterized, and under a broadest reasonable
interpretation, does not constitute managing personal behavior and
relationships or interactions between people, which is one of “[c]ertain
methods of organizing human activity” that are an abstract idea. See 2019
Revised Guidance, 84 Fed. Reg. at 52.
We also find no parallel here between claim 1 and the hypothetical
claim in Example 39 of the USPTO’s 2019 “Subject Matter Eligibility
Examples: Abstract Ideas” (the “Eligibility Examples”).4 The USPTO
describes in the “Background” section of Example 39 that prior methods for
performing facial detection, i.e., identifying human faces in digital images,
use neural networks to classify images as either containing a human face or
not, based on the model being previously trained on a set of facial and non-
facial images. Eligibility Examples 8. “However, these prior methods
suffer from the inability to robustly detect human faces in images where
there are shifts, distortions, and variations in scale and rotation of the face
pattern in the image.” Id. To address this problem, the hypothetical
applicant in Example 39 developed a method of training a neural network
for facial detection using a combination of features to more robustly detect
human faces. The first of these features involves the creation of an
expanded training set of facial images developed by applying mathematical

4 Available at https://www.uspto.gov/sites/default/files/documents/
101_examples_37to42_20190107.pdf.
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transformation functions on an acquired set of facial images; the neural
networks are then trained with this expanded training set using stochastic
learning with backpropagation, which is a type of machine learning
algorithm. Id. The second feature of the hypothetical applicant’s invention
involves limiting the number of false positives (caused by the introduction of
the expanded training set when classifying non-facial images) by retraining
the system with an updated training set containing the false positives
produced after face detection has been performed on a set of non-facial
images. Id.
Applying the 2019 Revised Guidance, the Office determines that the
hypothetical claim5 does not recite any of the judicial exceptions enumerated

5 The claim in Example 39 recites:
A computer-implemented method of training a neural network
for facial detection comprising:
collecting a set of digital facial images from a database;
applying one or more transformations to each digital facial
image including mirroring, rotating, smoothing, or contrast
reduction to create a modified set of digital facial images;
creating a first training set comprising the collected set of
digital facial images, the modified set of digital facial images,
and a set of digital non-facial images;
training the neural network in a first stage using the first
training set;
creating a second training set for a second stage of training
comprising the first training set and digital non-facial images that
are incorrectly detected as facial images after the first stage of
training; and
training the neural network in a second stage using the
second training set.
Eligibility Examples 8–9.
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in the 2019 Revised Guidance. Eligibility Examples 9. And, the Office
concludes that the claim is patent eligible. Id.
Citing paragraph 6 of the Specification, Appellant notes here that
claim 1 presupposes an existing traffic management system that may, in
certain circumstances “produce incorrect or non-optimal results in response
to anomalous or unexpected roadway conditions” (Appeal Br. 9). And
Appellant asserts that claim 1 recites a method for identifying and
responding to such anomalous conditions using a set of machine learning
(“ML”) models trained on historical data (id.; see also id. at 9–10 (“The
training of such models is described in the limitations following the phrase
‘wherein the traffic management adjustment rule is determined by[,]’ [i.e.,
steps (d1) through (d5)])).
Appellant maintains that claim 1 recites “the training and use of a ML
model for detecting anomalies in the traffic management context, in the
same way that Example 39 recited using artificial neural networks in the
facial recognition context,” i.e., that claim 1 recites a specific method for
training such a model:
1. Collecting a set of training data – “receiving, for a
plurality of historical time intervals, historical traffic data
comprising the plurality of data attributes, and a historical
traffic management decision corresponding to each interval.”
2. Training an anomaly detection model – “identifying a
plurality of anomalous vectors from within the plurality of
historical vectors, where each vector in the plurality of
anomalous vectors is anomalous relative to the plurality of
historical vectors[.”]
3. Training a clustering model – “clustering the set of
anomalous vectors into a plurality of anomalous vector clusters
according to a distance metric[.”]
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(Appeal Br. 12). Yet, as the Examiner observes, claim 1 merely recites
“determining a traffic management decision using . . . a predetermined
traffic management model” and “adjusting the traffic management decision
based on a traffic management adjustment rule”; the claim is silent on the
training of a neural network or algorithm (Final Act. 6) and “silent regarding
‘identifying and responding to such anomalous conditions using a set of
machine learning (‘ML’) models trained on historical data’” (Ans. 4–5).
Appellant asserts in the Reply Brief that “although claim 1 does not
use the explicit terms ‘training’ and ‘ML model[,]’ this does not preclude the
fact that the actual limitations recited in the claim do describe training an
ML model or an equivalent thereof” (Reply Br. 6). Appellant maintains that
“[l]earning and training of the traffic management model occurs in the step
of ‘adjusting the traffic management decision . . . associated with the
anomalous vector’” and that “[t]he basis for the training is the anomalous
vector, which is associated with a traffic management adjustment rule” (id.
at 7). Yet, we fail to see how, and Appellant does not adequately explain
how, claim 1, thus, recites the training of a machine model or an equivalent
thereof.
We also are not persuaded by Appellant’s argument that claim 1 is not
directed to an abstract idea because any alleged abstract idea is integrated
into a practical application (Appeal Br. 13–14).
The 2019 Revised Guidance references MPEP, Ninth Edition
(rev. Jan 2018) (available at https://www.uspto.gov/web/offices/
pac/mpep/old/mpep_E9R08-2017.htm) § 2106.05(a)–(c) and (e) in
non-exhaustively listing considerations indicative that an additional element
or combination of elements may have integrated a recited judicial exception
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into a practical application. 2019 Revised Guidance, 84 Fed. Reg. at 55.
In particular, the Guidance describes that an additional element may have
integrated the judicial exception into a practical application if, inter alia,
“[the] additional element reflects an improvement in the functioning of a
computer, or an improvement to other technology or technical field.” Id.
At the same time, the Guidance makes clear that merely including
instructions to implement an abstract idea on a computer, or merely using a
computer as a tool to perform an abstract idea; adding insignificant extra-
solution activity to the judicial exception; or only generally linking the use
of the judicial exception to a particular technological environment or field
are not sufficient to integrate the judicial exception into a practical
application. Id.
Appellant asserts here that “any alleged judicial exception in the
claims [has] been integrated into a practical application of managing traffic”
and that “the additional limitations make clear that . . . [c]laim 1 reflects an
improvement in the field of traffic management” (Appeal Br. 13). Pointing
to paragraph 6, Appellant notes that the Specification describes that current
systems are limited in that they often produce incorrect or non-optimal
results in response to anomalous or unexpected roadway conditions (id.).
And Appellant argues that the pending claims “solve this technical problem
in the field of traffic management” by “‘adjusting the traffic management
decision rate based on a traffic management adjustment rule associated with
the anomalous vector,’ and basing such an adjustment on a specific
anomaly-correcting algorithm’” (id. at 13–14).
Appellant maintains, “[a]s a result, . . . whatever abstract ideas may be
recited in the claims . . . are integrated into a practical application” (Appeal
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Br. 13–14). But, that argument is not persuasive at least because the
relevant question under Step 2A, Prong 2 is not whether the claimed
invention itself (i.e., responding to detected anomalous roadway conditions
by adjusting a traffic management decision) is a practical application;
instead, the question is whether the claim includes additional elements
beyond the judicial exception that integrate the judicial exception into a
practical application. The only additional elements recited in claim 1
beyond the abstract idea are “sensors mounted on or in proximity to the
roadway” and, although not explicitly recited, a processor, i.e., generic
computer components (see, e.g., Spec. ¶¶ 26–28). “And after Alice, there
can remain no doubt: recitation of generic computer limitations does not
make an otherwise ineligible claim patent-eligible.” DDR Holdings, LLC v.
Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014).
We also are not persuaded by Appellant’s argument that any parallel
exists here between hypothetical claim 1 in Example 40 of the Eligibility
Examples and claim 1 here at issue (Appeal Br. 13–14).
The USPTO describes in the “Background” section of Example 40
that network visibility tools enable close monitoring of computer network
traffic, applications, performance, and resources, and that the data acquired
through these network visibility tools are useful in optimizing network
performance, resolving network issues, and improving network security.
Eligibility Examples 10. One industry standard network visibility protocol
is NetFlow. Id. In a typical setup, a NetFlow exporter generates and exports
network traffic statistics (in the form of NetFlow records) to at least one
NetFlow collector that analyzes the statistics. Id. However, because
NetFlow records are large, the continual generation and export of NetFlow
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records substantially increases the traffic volume on the network, which
hinders network performance. Id.
The hypothetical applicant’s invention addresses this issue by varying
the amount of network data collected based on monitored events in the
network. The system, thus, collects NetFlow protocol data and exports a
NetFlow record only when an abnormal network condition is detected.
Eligibility Examples 10.
Applying the 2019 Revised Guidance, the Office determines that
hypothetical claim 1 recites a mental process and, therefore, an abstract idea,
i.e., that, other than the nominal recitation of a generic network appliance,
nothing precludes the step of “comparing at least one of the collected traffic
data to a predefined threshold” from practically being performed in the
mind.6 Eligibility Examples 11. Nonetheless, the Office determines that
claim 1 is patent eligible because it integrates the recited abstract idea into a
practical application, i.e.,

6 Claim 1 of Example 40 recites:
A method for adaptive monitoring of traffic data through
a network appliance connected between computing devices in a
network, the method comprising:
collecting, by the network appliance, traffic data relating
to the network traffic passing through the network appliance, the
traffic data comprising at least one of network delay, packet loss,
or jitter;
comparing, by the network appliance, at least one of the
collected traffic data to a predefined threshold; and
collecting additional traffic data relating to the network
traffic when the collected traffic data is greater than the
predefined threshold, the additional traffic data comprising
Netflow protocol data.
Eligibility Examples 10.
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the method limits collection of additional Netflow protocol data
to when the initially collected data reflects an abnormal
condition, which avoids excess traffic volume on the network
and hindrance of network performance. The collected data can
then be used to analyze the cause of the abnormal condition. This
provides a specific improvement over prior systems, resulting in
improved network monitoring.
Id. (emphasis added).
Appellant argues here that pending claim 1 goes further than the
hypothetical claim in Example 40 in that it “actively manage[s] (not merely
monitor[s]) vehicular traffic by identifying and correcting anomalous
behavior” (Appeal Br. 14). Yet, as Appellant ostensibly acknowledges (id.),
the Office concluded that hypothetical claim 1 in Example 40 is patent
eligible because the combination of additional elements integrates the
judicial exception (i.e., the mental process of comparing collected traffic
data to a predetermined threshold) into a practical application — improved
network monitoring. Although identifying and addressing anomalous
roadway conditions may well provide certain benefits, e.g., “reducing travel
time for vehicles, improving revenue generation from toll roads, and
improved roadway safety from actively managed roadways” (id. at 14–15),
we are not persuaded that this amounts to a technological improvement
comparable to the one in Example 40, as opposed to an improvement to the
abstract idea of managing traffic on a roadway, which is not enough for
patent eligibility. See SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1170
(Fed. Cir. 2018) (“[P]atent law does not protect such claims[, i.e., claims to
an asserted advance in the realm of abstract ideas], without more, no matter
how groundbreaking the advance.”).
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We conclude, for the reasons outlined above, that claim 1 recites a
method of organizing human activity, i.e., an abstract idea, and that the
additional elements recited in the claim beyond the abstract idea are no more
than generic computer components used as tools to perform the recited
abstract idea. As such, they do not integrate the abstract idea into a practical
application. See Alice Corp., 573 U.S. at 223–24 (“[W]holly generic
computer implementation is not generally the sort of ‘additional featur[e]’
that provides any ‘practical assurance that the process is more than a drafting
effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo,
566 U.S. at 77)). Accordingly, we agree with the Examiner that claim 1 is
directed to an abstract idea.
Step Two of the Mayo/Alice Framework (2019 Revised Guidance,
Step 2B)
Having determined under step one of the Mayo/Alice framework that
claim 1 is directed to an abstract idea, we next consider under Step 2B of the
2019 Revised Guidance, the second step of the Mayo/Alice framework,
whether claim 1 includes additional elements or a combination of elements
that provides an “inventive concept,” i.e., whether an additional element or
combination of elements adds specific limitations beyond the judicial
exception that are not “well-understood, routine, conventional activity” in
the field (which is indicative that an inventive concept is present) or simply
appends well-understood, routine, conventional activities previously known
to the industry to the judicial exception. 2019 Revised Guidance,
84 Fed. Reg. at 56.
Appellant first argues that claim 1 “recite[s] significantly more than
merely a judicial exception” because the claim recites an improvement to a
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technological field, i.e., the field of traffic management (Appeal Br. 14–15).
But, that argument is not persuasive of Examiner error for the reasons
outlined above.
Appellant also attempts to draw an analogy here between claim 1 and
claim 4 in Example 46 of the USPTO’s October 2019 Update: Subject
Matter Eligibility Life Sciences & Data Processing Examples (“October
2019 Examples”) (Appeal Br. 15).7 That argument fares no better.
Appellant notes that claim 4 recites “a processor” for “obtaining
animal-specific information” and “a herd monitor” for collecting “animal-
specific information”; yet, “[t]he USPTO guidance recognize[s] that this
claim [does] not recite an abstract idea at all” (Appeal Br. 15). And
Appellant argues that claim 1 is similar to claim 4 (and, ostensibly therefore,

7 Claim 4, which appears at page 33 of Appendix 1 to the October 2019
Update (available at https://www.uspto.gov/sites/default/files/documents
/peg_oct_2019_update.pdf), reads:
4. A system for monitoring health and activity in a herd of
dairy livestock animals comprising:
a memory;
a processor coupled to the memory programmed with
executable instructions, the instructions including a livestock
interface for obtaining animal-specific information for a plurality
of animals in the herd, wherein the animal-specific information
comprises animal identification data and at least one of body
position data, body temperature data, feeding behavior data, and
movement pattern data; and
a herd monitor including (a) a radio frequency reader for
collecting the animal-specific information from a plurality of
animal sensors attached to the animals in the herd when the
animal sensors are within proximity to the radio frequency
reader, each animal sensor having a radio frequency transponder,
and (b) a transmitter for transmitting the collected animal-
specific information to the livestock interface.
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also patent eligible) in that “[c]laim 1 . . . recites ‘sensors mounted on or in
proximity to the roadway’ that provide ‘roadway traffic data,’ which is
analogous to the ‘herd monitor’ that collects ‘animal-specific information’”
(id.).
The difficulty with Appellant’s argument is that the Office did not
determine that claim 4 does not recite an abstract idea based on the mere
presence of physical components, i.e., a processor and a herd monitor, for
collecting information. Instead, the Office determined that the claimed
system is intended for use in monitoring the health and activity of dairy
livestock animals, and that claim 4 does not recite “a mathematical concept,
mental process, or a method of organizing human activity such as a
fundamental economic concept or managing interactions between people.”
October 2019 Examples 40–41. The Office also notes that although “the
claim involves the observation of natural phenomena or laws of nature (the
behavior of the livestock animals), such limited involvement does not rise to
the level of this claim actually reciting a natural phenomenon or law of
nature” (id. at 41). Appellant has not demonstrated that a comparable
situation is present here.
We are not persuaded, on the present record, that the Examiner erred
in rejecting independent claim 1 under 35 U.S.C. § 101. Therefore, we
sustain the Examiner’s rejection of claim 1, and independent claims 8 and
15, which fall with claim 1.
Dependent Claims 2–7, 9–14, and 16–21
Appellant argues that claims 2–7, 9–14, and 16–21 are allowable
based on their dependence from independent claims 1, 8, and 15,
respectively (Appeal Br. 15). We are not persuaded for the reasons outlined
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above that the Examiner erred in rejecting independent claims 1, 8, and 15
under 35 U.S.C. § 101. Therefore, we sustain the Examiner’s rejection of
claims 2–7, 9–14, and 16–21 under § 101 for substantially the same reasons.
CONCLUSION
In summary:
Claim(s)
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–21 101 Eligibility 1–21
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED