Facebook, Inc.

Patent Trials and Appeals Board

Jun 10, 2020

14498894 - (D) (P.T.A.B. Jun. 10, 2020)

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.
14/498,894 09/26/2014 Yi Huang 2006.027US1 7268
160546 7590 06/10/2020
Mannava & Kang, P. C.
3201 Jermantown Road
Suite 525
Fairfax, VA 22030
EXAMINER
LONG, MEREDITH A
ART UNIT PAPER NUMBER
3688
NOTIFICATION DATE DELIVERY MODE
06/10/2020 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):
docketing@mannavakang.com
fb-pdoc@fb.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte YI HUANG,
PENG FAN, and MAYANK KUMAR
__________________

Appeal 2020-000252
Application 14/498,894
Technology Center 3600
____________________

Before JAMES P. CALVE, WILLIAM A. CAPP, and
JEREMY M. PLENZLER, Administrative Patent Judges.

CALVE, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the decision
of the Examiner to reject claims 1–4, 6–10, 12–17, 19, and 20. Final Act. 2.
Claims 5, 11, and 18 are cancelled. Appeal Br. 17, 19, 21 (Claims App.).
We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42. Appellant
identifies Facebook, Inc. as the real party in interest. Appeal Br. 2.
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CLAIMED SUBJECT MATTER
Claims 1, 9, and 14 are independent. Claim 1 is reproduced below.
1. A method comprising:
receiving, from a third party system, a plurality of
business rules specifying criteria for whether a
user of an online system is to be included in each
of a plurality of audience groups, each business
rule including one or more conditions for
associating a user with an audience group and each
audience group including one or more users;
generating a decision tree including the plurality of
business rules, the decision tree comprising a root
node and one or more paths, each path connecting
the root node with one or more nodes each
identifying at least one audience group, at least one
path comprising an intermediate node identifying a
first audience group and a leaf node identifying a
second audience group;
receiving contextual information from a client device
associated with a user of the online system, the
contextual information describing an interaction
between the user and content of the third party
system;
determining one or more audience groups associated with
the user based on a portion of the received
contextual information by traversing the decision
tree, wherein traversing the decision tree
comprises:
traversing one or more paths of the decision tree from the
root node using the contextual information,
storing identifiers of nodes traversed, and
associating the user with the audience groups
corresponding to the traversed nodes;
selecting an advertisement for presentation to the user
based on the one or more audience groups
associated with the user; and
providing the selected advertisement to the client device
of the user.
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REJECTION
Claims 1–4, 6–10, 12–17, 19, and 20 are rejected as directed to a
judicial exception to 35 U.S.C. § 101.
ANALYSIS
Patent Eligibility of Claims 1–4, 6–10, 12–17, 19, and 20
Appellant argues the claims as a group. Appeal Br. 5–15. We select
claim 1 as representative of the group. See 37 C.F.R. § 41.37(c)(1)(iv).
Examiner’s Determination
The Examiner determines claim 1 recites the abstract idea of “utilizing
business rules and user interactions to associate the user with an audience
group and provide an advertisement to the user based on their associated
audience group” using decision trees that can be done in the human mind.
Final Act. 3; Ans. 4. The Examiner determines that claim 1 lacks additional
elements that amount to significantly more than this abstract idea as it does
not claim a technical improvement to computers but instead applies a known
method of reducing burden in a computer environment. Final Act. 2–3.
Appellant’s Contentions
Appellant argues that the claims recite a specific improvement to how
a computer processes data and performs certain operations as follows:
Specifically, the claims are directed to a solution for reducing
an amount of processing burden needed by a computer when
performing certain operations such as associating a user with an
audience group, through the creation and traversal of a specific
type of decision tree that reduces path redundancy and allows
for multiple audience groups to be determined through a single
path traversal.

Appeal Br. 6.
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Appellant also argues the following benefits of the decision tree:
[T]he decision tree is constructed in a specific way so as to
“reduce a size of the decision tree and improve efficiency of
traversals of the decision tree” by sorting the individual
conditions present in a set of business rules and constructing the
tree “such that conditions occurring in a greater number of
business rules appear higher in the decision tree than conditions
occurring in fewer business rules.” ¶ [0049]. This results in a
tree that contains “at least one path comprising an intermediate
node identifying a first audience group and a leaf node
identifying a second audience group,” allowing for multiple
audience groups to be evaluated through traversal of single path
of the decision tree. See, e.g., Claim 1. Thus, the claim is
directed to reducing processing burden when associating users
with audience groups by generating a specific type of decision
tree that combines a set of business rules in a manner that
exhibits reduced size and improved traversal efficiency in
comparison to conventional decision trees, and traversing the
generated tree to identify audience groups.

Id. at 7.
Appellant contends that the present claims are analogous to a “self-
referential table” in Enfish, which held that the claims were not directed to
an abstract idea because they recited a specific type of data structure that
improved the way a computer stores and retrieves data in memory and
implemented a solution to a problem in the software arts. Id. at 7–8 (quoting
Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016)).
Appellant asserts that the claims are not directed to just any type of decision
tree, but to a specific type of decision tree that can classify a user into
multiple audience groups through a single path traversal, which process is
distinct from conventional decision trees, like the claims in McRO Inc. v.
Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). Id. at 9.
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Applicable Legal Principles
Section 101 of the Patent Act states:
Whoever 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,
subject to the conditions and requirements of this title.
35 U.S.C. § 101. This provision contains an implicit exception: “Laws of
nature, natural phenomena, and abstract ideas are not patentable.” Alice
Corp. Pty. v. CLS Bank Int’l, 573 U.S. 208, 216 (2014).
To distinguish patents that claim laws of nature, natural phenomena,
and abstract ideas from those that claim patent-eligible applications, we first
determine whether the claims are directed to a patent-ineligible concept. Id.
at 217. If they are, we consider the elements of each claim, individually and
“as an ordered combination,” to determine if additional elements “‘transform
the nature of the claim’ into a patent-eligible application” as an “inventive
concept” sufficient to ensure the claims in practice amount to significantly
more than a patent on the ineligible concept itself. See id. at 217–18.
The USPTO has issued guidance about this framework. 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019)
(“Revised Guidance”). Under the Revised Guidance, to determine whether a
claim is “directed to” an abstract idea, we evaluate whether the claim recites
(1) any judicial exceptions, including certain groupings of abstract ideas
listed in the Revised Guidance (i.e., mathematical concepts, certain methods
of organizing human activities such as a fundamental economic practice, or
mental processes); 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) (“MPEP”)). Id. at 52–55.
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Only if a claim (1) recites a judicial exception and also (2) does not
integrate that exception into a practical application, do we then consider
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. Id. at 56.
Step 1: Is Claim 1 Within a Statutory Category?
Claim 1 recites “[a] method” which is within a statutory category of
35 U.S.C. § 101, namely, a process. Therefore, we next consider whether it
recites a judicial exception.
Step 2A, Prong 1: Does Claim 1 Recite a Judicial Exception?
We agree with the Examiner that claim 1 recites an abstract idea. The
Revised Guidance now enumerates this abstract idea as certain methods of
organizing the human activity of commercial interactions of advertising
activities and mental processes. See Revised Guidance, 84 Fed. Reg. at 52.
The Specification describes this process as organizing online users
into one or more audience groups based on their interaction activities with
third party websites. Spec. ¶ 1. Users are placed into audience groups by
these activities so third party advertisers can target advertisements at them.
To allow a third party system to target advertisements
more effectively to users of an online system, the online system
generates one or more audience groups each including one or
more users of the online system. For example, an audience
group includes online system users having one or more
common characteristics.

Id. ¶ 4. Business rules identify user actions of audience groups. Id. ¶ 5.
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Claim 1 recites this concept in the final three limitations as follows:
associating the user with the audience groups corresponding to
the traversed nodes; selecting an advertisement for presentation
to the user based on the one or more audience groups associated
with the user; and providing the selected advertisement to the
client device of the user.

Appeal Br. 16–17 (Claims App.) (reformatted from original).
Other aspects of this abstract idea include obtaining business rules that
specify conditions that are used to associate users with audience groups by
receiving, from a third party system, a plurality of business
rules specifying criteria for whether a user of an online system
is to be included in each of a plurality of audience groups, each
business rule including one or more conditions for associating a
user with an audience group and each audience group including
one or more users.

Id. at 16. This step involves extra-solution activity of receiving rules used to
organize and categorize online activities of users into audience groups that
receive targeted marketing in later steps in claim 1 discussed above. It thus
recites another aspect of the abstract idea by receiving business rules used to
organize human activity for advertising. “Each business rule includes one or
more conditions for associating a user with an audience group.” Spec. ¶ 5.
Rules identify user interactions with website pages to associate a user with
an audience group. Id. Rules specify other criteria used to associate users
with audience groups. Id. ¶ 38. Rules may associate a user with an audience
group based on a user’s location or the location of content with which a user
interacts (e.g., a website for a hotel in Paris). Id. Rules may specify a time
or type of a user’s actions, e.g., interacting with a third party’s content by
viewing a website page, clicking, or interacting with an application. Id.
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The next step recites another aspect of this abstract idea as
generating a decision tree including the plurality of business
rules, the decision tree comprising a root node and one or more
paths, each path connecting the root node with one or more
nodes each identifying at least one audience group, at least one
path comprising an intermediate node identifying a first
audience group and a leaf node identifying a second audience
group.

Appeal Br. 16 (Claims App.).
This step generates a decision tree by combining plural business rules
into a decision tree with each path connecting the initial root node to one or
more nodes that identify an audience group. This step is specified at a very
high level of generality without any specific details of the process by which
the business rules are combined to generate the decision tree. Generating “at
least one path comprising an intermediate node identifying a first audience
group and a leaf node identifying a second audience group” captures the
concept of combining audience groups that share a common business rule or
condition along the same path so user activity that satisfies the business rule
for one audience group also satisfies the same business rule associated with
another audience group. This concept thus forms a path based on a business
rule that is common to more than one audience group. See Spec. ¶ 49.
Appellant argues that claim 1 improves computer operation and data
processing of large numbers of individual business rules (Appeal Br. 6), but
claim 1 only processes “a plurality of business rules” to generate a decision
tree comprising “one or more paths” with “one or more nodes” that identify
“at least one audience group.” At least one path is shared by two audience
groups, one at an intermediate node and one at a leaf node. Any redundancy
reduction is limited to one path and two business rules. See Spec. ¶¶ 49, 57.
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Appellant’s arguments that the claims reduce data processing burdens
by reducing redundancy and placing more common conditions higher in the
decision tree (Appeal Br. 6–7) are not commensurate with claim 1, which
recites neither concept. The claimed decision tree does not sort hundreds or
thousands of business rules or eliminate all redundancy. The Specification
discloses that the system may define business rules to associate users with a
large number groups. Spec. ¶¶ 48, 49, 57. We do not limit claim 1 to such
an embodiment when the language is so much broader. See Personalized
Media Commc’ns, LLC v. Apple Inc., 952 F.3d 1336, 1343 (Fed. Cir. 2020).
Nor does claim 1 recite the concept that “conditions occurring in a
greater number of business rules appear higher in the decision tree than
conditions occurring in fewer business rules” as asserted. See Appeal Br. 7.
This feature is not claimed. The step of generating a decision tree does not
require a particular placement of conditions in the tree.
Thus, claim 1 does not recite features that allegedly improve computer
performance and processing of a large number of business rules as Appellant
argues, such as sorting all business rules to reduce all redundancy. Reducing
redundancy in business rules of audience groups also depends on how many
audience groups share the same business rule(s) that can be combined on the
same path. None of these steps are recited either. See Spec. ¶ 49.
Claim 1 does not recite, nor does the Specification describe, advances
in databases or software used to sort conditions in business rules to reduce
redundancy and/or organize common conditions higher along paths in the
decision tree. Id. ¶¶ 48, 49, 57. The Specification describes the process at a
high level of generality as mental processes or steps that can be performed
by a person using pen and paper. Claim 1 recites even higher level steps.
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Figures 3C and 3D, reproduced below, illustrate how a fragment of a
business rule is restructured as path 306 and combined into a decision tree.

Appellant’s Figure 3C above illustrates fragment 305 of business rule
300 restructured as path 306. Fulfillment of conditions a and c associates a
user with audience group A1 307A. Spec. ¶ 46.

Figure 3D above illustrates path 306 to node 307A with paths to nodes
307B–307D. User activity that satisfies condition a or b and condition c or d
is associated with audience group A1 if the decision tree is traversed to any
of terminals 307A–307D. Id. ¶ 47.
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Exemplary conditions are that a user views a third party web page
with information about flights to Paris (a) or London (b), and the URL of the
web page contains the term “purchase” (c) or “book” (d). See id. ¶ 44.
Decision tree 310 can be generated with other business rules for other
audience groups as illustrated in Figure 3E, which is reproduced below.

Appellant’s Figure 3E above illustrates decision tree 320 for business
rules to associate a user with audience groups A1, A2, A3, and A4. Id. ¶ 48.
The same conditions associate a user with audience group A1. Satisfying
conditions b, c, and d or conditions c and e associates a user with audience
group A2. User activity that satisfies condition c also associates a user with
audience group A3. Satisfaction of conditions a and d associates a user with
audience group A4. See id.
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Decision tree 320 may associate a user with one or more of hundreds
or thousands of such audience groups. Id. However, claim 1 only requires
one or more nodes identifying at least one audience group and two audience
groups sharing a path. Therefore, we are not persuaded that claim 1 recites a
technical improvement to computers or to data processing or data structures
that provides any purported technical benefits beyond the abstract idea.
Such steps are similar to claims held to recite mental processes in
CyberSource Corporation v. Retail Decisions, Inc., 654 F.3d 1366 (Fed. Cir.
2011). There, the claims recited a method of detecting credit card fraud in
online transactions. The first step of obtaining information about other
transactions that used an Internet address identified with the credit card
transaction “can be performed by a human who simply reads records of
Internet credit card transactions from a preexisting database.” CyberSource,
654 F.3d at 1372. The second step of constructing a map of credit card
numbers can be performed by a person writing down a list of credit card
transactions made from an IP address. Id. (“There is no language in claim 3
or in the ’154 patent’s specification that requires the constructed ‘map’ to
consist of anything more than a list of a few credit card transactions.”). The
third step of using the map of credit card numbers to determine if the credit
card transaction is valid can be performed entirely in the human mind, e.g.,
by a person simply observing that numerous transactions using different
credit cards with different user names and billing addresses all originated
from the same IP address. Id. at 1373 (“Indeed, CyberSource’s CEO
admitted that, before CyberSource created a computer implemented fraud
detection system, ‘[w]e could see just by looking that more than half of our
orders were fraudulent.’”).
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Here, a person can receive and record business rules as a decision tree
that specifies the conditions for associating a user with an audience group as
claimed and as illustrated in Appellant’s Figures 3C and 3D where business
rules and conditions are mapped out in a decision tree format with an “OR”
operator that joins “AND” operators or conditions. See Spec. ¶¶ 45–48.
A person can generate a decision tree that combines plural business
rules as a mental process or with pen and paper as illustrated in Appellant’s
Figure 3E above. Common conditions of different rules may be combined
as desired. Patent attorneys essentially perform this task when they draft
claims in dependent claim format that groups together common elements of
a claim or claims to which dependent limitations refer to include when using
multiple dependent claim formats. See MPEP §§ 608.01(m), 608.01(n).
Similarly, claims to calculating a steradian region of space could be
implemented using a pen and paper. In re Coffelt, 680 F. App’x 1010, 1011
(Fed. Cir. 2017). Here the relationships of business rules, conditions, and
audience groups are depicted spatially as nodes and paths of a decision tree,
which can be generated by a person using pen and paper without even the
need to perform mathematical calculations that were required in Coffelt. Id.
Essentially, the step of generating a decision tree including a plurality
of business rules tabulates the business rules and their conditions to combine
paths of different business rules along their common conditions as illustrated
in Appellant’s Figure 3E. In a similar manner, claims to tabulating voter
ballots generated by a voting station(s) recite “human cognitive actions” that
are nothing more than abstract ideas that can be performed entirely in the
human mind. Voter Verified, Inc. v. Elec. Sys. & Software LLC, 887 F.3d
1376, 1385–86 (Fed. Cir. 2018) (citing CyberSource, 654 F.3d at 1373).
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Collecting plural business rules and their conditions for each audience
group and generating a decision tree with one or more paths and nodes with
at least one path comprising an intermediate and leaf node for two audience
groups organizes user activities into sets/subsets as paths and nodes. Claims
to similar data organization recited a method of organizing human activity in
In re Jobin, Appeal 2020-1067, 2020 WL 2298381 (Fed. Cir. May 8, 2020).
Despite its expansive language and its recitation of servers and
databases, claim 221 of Jobin’s application is, at bottom,
directed to the collection, organization, grouping, and storage of
data using techniques such as conducting a survey or
crowdsourcing. As the Board correctly concluded, this claim is
directed to a method of organizing human activity—a hallmark
of claims directed to abstract ideas.

Jobin, 2020 WL 2298381, at *4 (quoting Elec. Power Grp., LLC v. Alstom
S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (“[W]e have treated analyzing
information by steps people go through in their minds . . . without more, as
essentially mental processes within the abstract-idea category.”)); see also
Versata Dev. Grp., Inc. v. SAP Am., Inc., 793 F.3d 1306, 1333 (Fed. Cir.
2015) (holding organizational and product or group hierarchies used to set
price is an abstract idea with no concrete or tangible form or application).
The decision in Enfish LLC v. Microsoft Corporation, 822 F.3d 1327,
1336–37 (Fed. Cir. 2016) illustrates the distinction. The claim recited a
“means for configuring” computer memory using a four-step algorithm that
created a logical table in memory with rows corresponding to records and
columns corresponding to fields or attributes, assigned each row and column
an object identification number, and stored information about each column
in one or more rows to render the table self-referential so new columns could
be added simply by creating a new column definition record in a row.
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Here, claim 1 generates a decision tree with paths and nodes. These
elements do not represent advances in technology for decision trees. Paths
represent business rules connecting nodes that may identify audience groups.
At least one path leads to two audience groups via an intermediate node and
a leaf node that identify different groups. Claim 1 does not require a node to
identify more than one audience group. A user’s association with audience
groups depends on the audience group nodes traversed. See Spec. ¶ 47.
Claim 1 does not recite a particular sorting procedure for generating
the decision tree from a plurality of business rules to provide a common path
or to eliminate redundancy. Nor does the Specification describe how the
computer processing time used to process plural business rules to generate a
single decision tree with at least one common path for two audience groups
compares to the computer processing time required to process each business
rule separately to determine a user’s association with each audience group.
Claim 1 does not sort or process hundreds or thousands of business rules,
nodes, paths, or audience groups to generate a decision tree. Only a plurality
of business rules and associated nodes and paths are processed in some way
that is not recited in claim 1 to generate the decision tree.
Distilled to its essence, claim 1 recites the even broader concept that
multiple individual decision trees can be organized as a single decision tree
by combining some common conditions to reduce some redundancy. The
Specification indicates that this feature is just one embodiment. Spec. ¶ 57.
The reduction in the size of a combined decision tree compared to individual
decision trees depends on how many conditions are shared by the individual
decision trees and whether those common conditions are combined into a
single path. Claim 1 only requires this for “at least one path.”
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Next, audience groups are determined by traversing the decision tree:
receiving contextual information from a client device associated
with a user . . . describing an interaction between the user and
content of the third party system; determining one or more
audience groups associated with the user based on a portion of
the received contextual information by traversing the decision
tree, wherein traversing the decision tree comprises: traversing
one or more paths of the decision tree from the root node using
the contextual information, storing identifiers of nodes
traversed, and associating the user with the audience groups
corresponding to the traversed nodes.

Appeal Br. 16 (Claims App.) (reformatted from original). User contextual
information received from a client device is used to traverse the decision tree
to associate the user with audience groups. Spec. ¶ 45. The method does
not determine user contextual information unlike Example 37, which tracks
user selections of icons of a graphical user interface. See Reply Br. 9.
The purported innovation to the decision tree simply combines paths
of different audience groups where they share a common business rule or
condition. The decision tree includes a root node and paths that connect the
root node with nodes that identify an audience group(s). User contextual
information is compared to the decision tree and its paths and nodes starting
at the root node and traversing the paths and nodes in the usual manner.
Claim 1 encompasses an even broader concept of organizing human
activity, namely, that different business rules can be combined if they are
converted to the same format of a decision tree with paths for conditions of
the business rules and nodes that define audience groups. The Specification
describes how “[o]nce structured as a decision tree in which branches are
conditions of business rules and nodes identify audience groups, multiple
business rules may be combined into a single decision tree.” Spec. ¶ 48.
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As recited in claim 1, this concept is similar to a concept determined
to be abstract in Intellectual Ventures I LLC v. Erie Indemnity Company, 850
F.3d 1315 (Fed. Cir. 2017). There, the claims recited a method of creating
an index of tags and metafiles to search for and retrieve data in a database.
Id. at 1327. Creating such a common index to search a database involved
conduct that pre-dated the advent of computers and the Internet, namely,
library and hard-copy indexing systems that classify, organize, and cross-
reference information and resources (books, magazines, and the like) by
certain tags such as title, author, subject. Id. The claimed tags were used to
identify, organize, and locate a desired resource.
Here, the claimed decision tree identifies and organizes business rules
and audience groups into a common format of paths and nodes so users may
be classified into audience groups by comparing their summary contextual
information (e.g., online activities) to the paths and nodes.
BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281 (Fed. Cir. 2018)
held a similar method of indexing data added to a database by considering
“summary comparison usage information” about parameters and values
selected by prior users of the database recited an abstract idea. Id. at 1286.
The method presented summary comparison information to database users
before they input their data to conform their input to the existing database
structure.
It amounts to having users consider previous item descriptions
before they describe items to achieve more consistent item
descriptions. Whether labeled as a fundamental, long-prevalent
practice or a well-established method of organizing activity,
this qualifies as an abstract idea.
BSG, 899 F.3d at 1286.
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Here, claim 1 recites a method that generates a decision tree from a
plurality of business rules at a very high level of generality that includes “at
least one path comprising an intermediate node identifying a first audience
group and a leaf node identifying a second audience group.” At best, this
step involves identifying, combining, organizing, classifying business rules
in the same decision tree format with common conditions of rules placed on
the same path in at least one instance. That is what data structures do. They
organize and categorize data so it can be accessed more efficiently compared
to searching through thousands of unorganized, separate data files/records.
Like the index of tags and metafiles that facilitated database searching
in Erie Indemnity and summary comparison usage information that stored
data more consistently for easier access in BSG, the claimed decision tree
organizes business rules and audience groups into common paths and nodes
that facilitate the subsequent classification of user contextual information in
this data structure. Users are classified into audience groups by using their
online activity to traverse decision tree paths of business rules and nodes in
order to receive targeted advertising based on the audience group(s).
Such tailoring of advertising content based on user activities involving
location and time recites an abstract idea. In Intellectual Ventures I LLC v.
Capital One Bank (USA), 792 F.3d 1363 (Fed. Cir. 2015), the court held that
tailoring advertising content based on the location of an individual and time
of day during which content is viewed recites an abstract idea. Id. at 1369–
70. Claim 1 similarly recites tailoring of advertising content streamed to an
individual based on the user’s audience group, which is determined based on
a user’s contextual information, which the Specification describes as actions
that include location and time data. See Spec. ¶¶ 21, 25, 38.
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As our reviewing court held in an analogous context:
This court, however, has held that “customizing information
based on . . . information known about the user” is an abstract
idea. Intellectual Ventures I LLC v. Capital One Bank (USA),
792 F.3d 1363, 1369 (Fed. Cir. 2015). The court in the
Intellectual Ventures I case explained that tailoring of content
based on information about the user—such as where the user
lives or what time of day the user views the content—is an
abstract idea that is as old as providing different newspaper
inserts for different neighborhoods. Id.

Affinity Labs of Texas LLC v. Amazon.com Inc., 838 F.3d 1266, 1271 (Fed.
Cir. 2016); see also Bridge & Post, Inc. v. Verizon Commc’ns, Inc., 778 F.
App’x 882, 887 (Fed. Cir. 2019) (“Targeted marketing is a form of ‘tailoring
information based on [provided] data,’ which we have previously held is an
abstract idea.”) (citation omitted).
Here, claim 1 sends targeted advertisements to users for the audience
group(s) in which they are classified by a decision tree based on their online
activities. Organizing such activity for advertising recites an abstract idea.
See Revised Guidance, 84 Fed. Reg. at 52. Accordingly, we determine that
claim 1 recites an abstract idea of certain methods of organizing human
activity for advertising activities and mental processes as identified above.
Step 2A, Prong Two: Integration into a Practical Application
We next consider whether claim 1 recites additional elements that
integrate the abstract idea into a practical application. Revised Guidance, 84
Fed. Reg. at 54 (Revised Step 2A, Prong Two). Appellant argues that the
Examiner ignores most claim limitations and states in a conclusory manner
that additional elements do not amount to significantly more without any
analysis of particular claim language. Appeal Br. 11; Reply Br. 10.
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20
This argument is not persuasive because the Examiner determines that
all the limitations of claim 1 recite the abstract idea of using business rules
to organize user interactions into associations of users with audience groups
and provide advertisements targeted to those users based on their audience
group. Final Act. 3. The Examiner determines the decision tree is not an
additional element that adds significantly more to the abstract idea because
the decision tree recites an improvement to an abstract concept rather than
an improvement to the way a computer functions. Ans. 4 (“Decision trees
exist outside of computers and the Internet and are an evaluation tool that
can practically be carried out in the human mind (i.e., an abstract idea).”);
Final Act. 2–3 (“Applicant has not claimed a technical improvement, rather
they have taken a known method of reducing burden (e.g. time, effort, and
resources) and applied it to a computer environment.”). We agree.
“It has been clear since Alice that a claimed invention’s use of the
ineligible concept to which it is directed cannot supply the inventive concept
that renders the invention ‘significantly more’ than that ineligible concept.”
BSG, 899 F.3d at 1290; see id. at 1291 (“As a matter of law, narrowing or
reformulating an abstract idea does not add ‘significantly more’ to it.”); see
also RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir.
2017) (“Adding one abstract idea (math) to another abstract idea (encoding
and decoding) does not render the claim non-abstract.”); Synopsys, Inc. v.
Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“But, a
claim for a new abstract idea is still an abstract idea.”); Versata Dev. Grp.,
793 F.3d at 1335 (holding claims reciting improvements in an abstract idea
of hierarchical data structures but not reciting any of the supposed computer
improvements were not patent eligible).
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21
As discussed above, claim 1 does not recite any technical advances
that Appellant asserts to be patent eligible, namely, redundancy reduction,
improved computer processing efficiency, and/or hierarchical sorting of
business rules and conditions. See Appeal Br. 6–8. The method does not
sort hundreds or thousands of business rules to generate a decision tree with
redundancy reduction for such a large number of business rules. It generates
a decision tree from a “plurality of business rules” with one common path.
The Specification describes decision tree engine 250 being capable of
associating a user with one or more of hundreds or thousands of audience
groups (Spec. ¶¶ 48, 57), but claim 1 recites “one or more nodes” that each
identify “at least one audience group.” Only “at least one path compris[es]
an intermediate node identifying a first audience group and a leaf node
identifying a second audience group.” Appeal Br. 16 (Claims App.). Thus,
claim 1 recites a method that generates a decision tree “contain[ing] rules for
associating a user with three audience groups” as illustrated in Figure 3E
above. Spec. ¶ 48. Arguments that claim 1 recites a method that represents
a technical advance in computers and data processing (Appeal Br. 6–10) are
not commensurate with the scope of claim 1. See Two-Way Media Ltd. v.
Comcast Cable Commc’ns, LLC, 874 F.3d 1329, 1340 (Fed. Cir. 2017) (“As
with claim 1 of the ’187 patent, the problem is that no inventive concept
resides in the claims.”).
The Examiner determines the client device in claim 1 is an additional
element, but it is recited as a generic computer component that performs
generic computer functions that merely implement the abstract idea with a
computerized system. See Final Act. 4–5; Ans. 5–6. Appellant does not
dispute this finding and determination of the Examiner. Nor do we.
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22
The Specification describes the system architecture as illustrated in
Figure 1 as comprising one or more client devices 110. Spec. ¶ 15. Client
devices are computing devices that receive user input and transmit and/or
receive data via network 120. Id. ¶ 16. In one embodiment, client device
110 is a conventional computer system like a desktop or laptop computer.
Id. In other embodiments, it may be a personal digital assistant, a mobile
telephone, a smartphone, or another suitable device. Id.
Even if we treat the decision tree as an additional element beyond the
abstract idea that it represents as set forth in our analysis above, it does not
integrate the abstract idea into a practical application. It is recited at a high
level of generality without improving computers or data structures, or using
a particular machine that is integral to the claim or transforming a particular
article to a different state or thing. Revised Guidance, 84 Fed. Reg. at 55.
Although Enfish and McRO involved analysis under Alice Step One,
which is comparable to Revised Step 2A, Prong One discussed above in the
previous section, those cases illustrates why the claimed decision tree does
not recite a technical improvement to make claim 1 patent eligible.
In Enfish, the claim recited a logical table with logical rows and
columns, but each row and column also included an object identification
number (OID) to identify it. The OID acted as a pointer to the associated
row or column and therefore was an important aspect of the self-referential
table and its technical advancement over known data structures. See Enfish,
822 F.3d at 1336–37. The claimed “means for configuring” allowed new
columns to be created and appended to the table for immediate use. Id. In
short, the self-referential table, as claimed, included new data structures in a
different arrangement that performed more efficiently. Id. at 1337.
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Here, claim 1 recites a decision tree that includes generic elements
such as a root node, one or more paths connecting the root node to one or
more nodes, to include intermediate and leaf nodes that identify at least one
audience group. As illustrated in Figure 3D above (as well as Appellant’s
Figure 3A), such elements are generic. The claimed decision tree does not
change this configuration of a decision tree when it generates a decision tree
for multiple business rules. The decision tree combines business rules into a
common format that may eliminate redundancy, e.g., where business rules
share common conditions as in Figure 3E above. Nor does claim 1 change
the way a decision tree is traversed to apply data (contextual information) to
the business rules as organized in the decision tree as paths between nodes.
The decision tree is traversed from a root node through paths to intermediate
nodes and/or leaf nodes and other nodes.
Any elimination of redundancy is recited at a high level of generality
as an abstract concept as discussed in the preceding section. Combining
common business rules in such a generic manner of paths and nodes is what
databases do to organize and store data as illustrated by Erie Indemnity and
BSG also discussed above.
Placing two audience groups on nodes along the same path merely
associates different information labels with nodes, which is an abstract idea
rather than a technical improvement. The very nature of a decision tree is
that it organizes rules or information in a hierarchical, logical fashion that
can be traversed through conjunctive/disjunctive connectors. A traversal
notes each node that is traversed and whether the condition of that node is
satisfied. In the example above, did the user search web pages of Paris or
London. Did the URLs include the terms “booking” or “ticket.” Spec. ¶ 44.
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24
“Information as such is an intangible” and collecting, analyzing, and
displaying that information, without more, is an abstract idea. See Interval
Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344–45 (Fed. Cir. 2018)
(quoting Elec. Power Grp., 830 F.3d at 1353–54 and citing similar decisions
holding that displaying different types or sets of information from various
sources on a generic display is abstract absent a specific improvement to the
way computers or other technologies operate).
Here, claim 1 recites a generic traversal of a decision tree by passing
from a root node along paths to other nodes. Moving from a root node along
a path to a node may result in classification into an “audience group” which
is just a label for traversing a particular path to a particular node. Traversing
a path from an intermediate node to a leaf node may associate a user with
another “audience group.”
These limitations represent abstract ideas not technological advances
in data structures or computer technology. Even if a computer may traverse
the claimed decision tree faster than it can move through individual business
rules, this fact does not make a computer run faster or more efficiently. See
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d at 1371
(“Requiring the use of a ‘software’ ‘brain’ ‘tasked with tailoring information
and providing it to the user’ provides no additional limitation beyond
applying an abstract idea, restricted to the Internet, on a generic computer.”);
Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d
1266, 1278 (Fed. Cir. 2012) (“To salvage an otherwise patent-ineligible
process, a computer must be integral to the claimed invention, facilitating
the process in a way that a person making calculations or computations
could not.”).
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25
The claims in McRO recited a new set of rules and parameters for lip
synchronization of animated characters. McRO, 837 F.3d at 1313, 1315.
The claims were directed to the creation of something physical—the display
of lip synchronization and facial expressions of animated characters on a
display. SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir.
2018). The method obtained a first set of rules to define an output morph
weight stream as a function of phoneme sequence, obtained a timed data file
of phonemes having a plurality of sub-sequences, generated an intermediate
stream of output morph weight sets and transition parameters between two
adjacent morph weight sets by evaluating the sub-sequences against the first
set of rules, and generated a final stream of output morph weight sets at a
desired frame rate. McRO, 837 F.3d. at 1307–08, 1313.
Here, claim 1 generates a decision tree to associate users in audience
groups that receive targeted ads. Combining audience groups on a common
path is recited as an abstract idea at a high level of generality. See BSG, 899
F.3d at 1287 (“[T]he recitation of a database structure slightly more detailed
than a generic database does not save the asserted claims at step one.”).
Indeed, the Specification discloses that the decision tree engine 250
may simplify a decision tree associated with third party system 130 to reduce
a size of the decision tree and improve efficiency of traversals of it. Spec.
¶ 49. “[T]he amount of time spent traversing the decision tree may be less
than the amount of time needed to separately evaluate each business rule
associated with a third party system 130.” Id. ¶ 63 (emphasis added). The
method may include different and/or additional steps that may be performed
in a different order than described in Figure 4, which outlines the method
recited in claim 1. See id. ¶ 53.
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Thus, we are not persuaded that claim 1 recites a technical solution to
a technical problem or imposes a meaningful limit on the abstract idea. See
Reply Br. 2–7; Ericsson Inc. v. TCL Commc’n Tech. Holdings Ltd., 955 F.3d
1317, 1325 (Fed. Cir. 2020) (holding that the specification must always
yield to the claim language when identifying the true focus of a claim).
Providing advertisements to a user based on their association with an
audience group identified by traversing a decision tree does not integrate the
abstract idea into a practical application. See Voit Techs., LLC v. Del-Ton,
Inc., 757 F. App’x 1000, 1003 (Fed. Cir. 2019) (“Voit fails to explain how
employing different formats, as claimed, improves compression techniques
or the functioning of the computer. Instead, the specification demonstrates
that the Asserted Claims are directed to use of generic computer components
performing conventional compression techniques to carry out the claimed
invention.”). Even though the claims in Voit recited advanced image data
compression techniques that provided rapid transmission of higher
resolution digital images, the claims were not patent eligible.
Although Voit alleges the method of the ’412 patent “improves
the efficiency of transmitting” high resolution data, Appellant’s
Br. 10, we have recognized that claims directed to “improved
speed or efficiency inherent with applying the abstract idea on a
computer” are insufficient to demonstrate an inventive concept
. . . . Moreover, Voit has to do more than simply restate the
claim limitations and assert that the claims are directed to a
technological improvement without an explanation of the nature
of that improvement. . . . General statements of “advanced
image data compression” or faster communications will not
suffice where it is unclear how the different compression format
claim limitations actually achieve the alleged improvements.
Id. at 1003–04 (citations omitted).

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27
Here, claim 1 recites, at a high level of generality, receiving business
rules and generating a decision tree with a root node and one or more paths
connecting the root node to one or more nodes identifying an audience group
and at least one path includes an intermediate node for a first audience group
and a leaf node of a second audience group. Appeal Br. 16 (Claims App.).
If a computer can process user contextual information on a combined
decision tree faster than on individual decision trees, any efficiency increase
results from combining common paths as an abstract idea. If a technological
advance is required to sort individual decision trees to generate the claimed
decision tree with a combined path and nodes, such advance is not claimed
in contrast to the technical advances claimed in Enfish and McRO.
Stated another way, the computer is not configured to operate faster or
more efficiently. Nor is the decision tree configured to be processed more
efficiently beyond the possibility that it may comprise less paths and nodes,
which is an abstract idea of organizing human activity discussed above.
Our reviewing court recently held similar activity involving
advertising data in a similar context to be an abstract idea:
In short, by dedicating a section of the computer’s memory to
advertising data, the claimed invention ensures memory is
available for at least some advertising data. This does not,
however, improve the functionality of the computer itself.
Even if we accept Customedia’s assertions, the claimed
invention merely improves the abstract concept of delivering
targeted advertising using a computer only as a tool. This is not
what the Supreme Court meant by improving the functioning of
the computer itself nor is it consistent with our precedent
applying this concept.
Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1363 (Fed.
Cir. 2020).
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28
The claims did not enable computers to operate more quickly or
efficiently, or solve any technological problem because:
The only improvements identified in the specification are
generic speed and efficiency improvements inherent in applying
the use of a computer to any task. Therefore, the claimed
invention is at most an improvement to the abstract concept of
targeted advertising wherein a computer is merely used as a
tool. This is not an improvement in the functioning of the
computer itself.
Id. at 1365 (“They merely recite reserving memory to ensure storage space is
available for at least some advertising data.”).
Mere recitation of concrete, tangible components such as a processor
is not sufficient to make abstract ideas performed on or with that processor
patent-eligible. Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic
computer cannot transform a patent-ineligible abstract idea into a patent-
eligible invention.”). Here, the focus of claim 1 is not on a technological
advance in processors, databases, or client devices. Rather, the focus is on
performing an abstract idea for which computers are invoked as a tool. See
Enfish, 822 F.3d at 1335–36; Bancorp, 687 F.3d at 1278 (“[T]he use of a
computer in an otherwise patent-ineligible process for no more than its most
basic function––making calculations or computations––fails to circumvent
the prohibition against patenting abstract ideas and mental processes.”).
Here, claim 1 essentially automates the traversal of a decision tree generated
from individual decision trees that otherwise could be traversed as a mental
process or by a person using pen and paper.
Accordingly, we determine that claim 1 does not include additional
elements that integrate the abstract idea into a practical application.

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Step 2B: Does Claim 1 Include an Inventive Concept?
We next consider whether claim 1 recites elements, individually, or as
an ordered combination, that provide an inventive concept. Alice, 573 U.S.
at 217–18. The second step of the Alice test is satisfied when the claim
limitations involve more than performance of well-understood, routine, and
conventional activities previously known to the industry. Berkheimer v. HP
Inc., 881 F.3d 1360, 1367 (Fed. Cir. 2018); see Revised Guidance, 84 Fed.
Reg. at 56 (explaining that the second step of the Alice analysis considers
whether a claim adds a specific limitation beyond a judicial exception that is
not “well-understood, routine, conventional” activity in the field). Appellant
argues that the Examiner has not established that the additional elements are
well-understood, routine, and conventional. Appeal Br. 12–14.
Individually, the limitations of claim 1 implement the abstract idea on
generic components that perform generic functions. They receive business
rules from a third party system and contextual information from a client
device. They generate a decision tree and traverse it to associate users with
audience groups. See Final Act. 5; Ans. 5–6; MPEP ¶ 2106.05(d)(II) (listing
elements that courts have held to be well-understood, routine, conventional
activity such as receiving and transmitting data over a network, performing
repetitive calculations, and storing and retrieving information in memory).
The client device may be a conventional computer system or generic
device configured to communicate over generic networks using standard
communication technologies. Spec. ¶¶ 16, 17. Contextual information is
received from client devices. It describes user interactions with content of a
third party system. Id. ¶ 7. However, claim 1 does not recite any features of
the process by which the client devices obtain user contextual information.
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The third party system is an application provider communicating
information describing applications for execution by client device 110 or
communicating data to client devices 110 for use by an application
executing on the client device. Spec. ¶ 18. It can communicate information
to the online system such as advertisements or content. Id. Claim 1 recites
this system generically to perform a generic function of providing business
rules that specify criteria for including users in audience groups.
As discussed above, the claimed decision tree recites the abstract idea
identified in Step 2A, Prong One. Therefore, it cannot provide an inventive
concept. See BSG, 899 F.3d at 1290. Even if the steps are groundbreaking,
innovative, or brilliant, that is not enough for eligibility. See Ass’n for
Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013);
accord SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (“No matter
how much of an advance in the finance field the claims recite, the advance
lies entirely in the realm of abstract ideas, with no plausibly alleged
innovation in the non-abstract application realm. An advance of that nature
is ineligible for patenting.”).
Claim 1 recites no particulars in the way the decision tree is generated
to include plural business rules besides a generic root node, path(s), and
nodes. Associating nodes with audience groups is illustrated in the decision
tree of Figure 3D. The Specification provides no description or indication
that this process involves innovations in software or hardware architecture.
See Spec. ¶¶ 48, 49. If an inventive step is used to sort business rules and
generate a decision tree, the step is not claimed. Nor does the Specification
describe this process in a way that indicates an inventive step is involved in
generating the decision tree beyond the abstract idea identified above.
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The “ordered combination” also lacks an inventive concept. The
limitations considered together recite the abstract idea identified above,
which involves collecting business rules and generating a decision tree to
determine audience groups based on user contextual information received
from a client device. See Elec. Power Grp., 830 F.3d at 1355 (holding that
the claims interpreted in light of the specification required off-the-shelf,
conventional computer, network, and display technology to gather, send, and
present desired information and “displaying concurrent visualization”
required only readily-available displays); Ericsson, 955 F.3d at 1330 (“Even
assuming that this collection of elements led to a more efficient way of
controlling resource access, ‘our precedent is clear that merely adding
computer functionality to increase the speed or efficiency of the process
does not confer patent eligibility on an otherwise abstract idea.”) (citation
omitted); Customedia, 951 F.3d at 1366 (“Aside from the abstract idea of
delivering targeted advertising, the claims recite only generic computer
components.”).
Similar claims to systems and methods of dynamically and selectively
generating a hierarchical functional organization from a hierarchical
operating organization structure in a generic computer environment did not
provide an inventive concept. In re Morinville, 767 F. App’x 964, 969 (Fed.
Cir. 2019). The claim elements used known computer processes to perform
known abstract steps of restructuring a business organization. Id. at 970. As
such, it was not enough for subject matter eligibility that the techniques may
be novel and non-obvious over the prior art when they were an abstract idea.
See id. Here, claim 1 uses a computer to generate a decision tree of business
rules, which is recited as an abstract idea identified above.
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Accordingly, we determine that the claims do not recite any elements,
individually or as an ordered combination, that provide an inventive concept
sufficient to transform the abstract idea into patent eligible subject matter.
Thus, we sustain the Examiner’s determination that claims 1–4, 6–10,
12–17, 19, and 20 are directed to patent-ineligible subject matter.
CONCLUSION
In summary:
Claims
Rejected
35 U.S.C. § Reference(s)/
Basis
Affirmed Reversed
1–4, 6–10,
12–17, 19, 20
101 Eligibility 1–4, 6–10,
12–17, 19, 20

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