Taiger Spain SL

Patent Trials and Appeals Board

Mar 26, 2021

2020000186 (P.T.A.B. Mar. 26, 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.
15/199,609 06/30/2016 Sinuhé Arroyo 148336.00013 1235
26710 7590 03/26/2021
QUARLES & BRADY LLP
ATTN: IP DOCKET
411 E. WISCONSIN AVENUE
SUITE 2400
MILWAUKEE, WI 53202-4428
EXAMINER
DAVANLOU, SOHEILA
ART UNIT PAPER NUMBER
2153
NOTIFICATION DATE DELIVERY MODE
03/26/2021 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):
pat-dept@quarles.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte SINUHÉ ARROYO, JOSÉ MANUEL LÓPEZ COBO,
GUILLERMO ALVARO REY, and SILVESTRE LOSADA ALONSO
Appeal 2020-000186
Application 15/199,609
Technology Center 2100

Before JEAN R. HOMERE, JAMES B. ARPIN,
and IRVIN E. BRANCH, Administrative Patent Judges.

BRANCH, Administrative Patent Judge.

DECISION ON APPEAL
STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject claims 1–5 and 7–20, all of the pending
claims. See Final Act. 1. Claim 6 is cancelled. 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 the real party in interest as TAIGER SPAIN. Appeal Br. 1.
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CLAIMED SUBJECT MATTER
The claims are directed to “enabling a user to query a database using a
natural language query.” Spec. ¶ 2. Claim 1, reproduced below, is
illustrative:
1. An information retrieval system, comprising:
a knowledge model database configured to store a
knowledge model for a knowledge domain, the knowledge
model defining a plurality of entities and interrelationships
between one or more of the plurality of entities;
a knowledge base identifying a plurality of items, each of
the plurality of items being associated with at least one
annotation identifying at least one of the entities in the
knowledge model database; and
a query processing server configured to:
receive, from a client computer, a natural language
query using a computer network,
analyze the natural language query to identify a
plurality of terms,
identify a pair of terms in the plurality of terms,
determine a type of a first term in the pair of terms
and a type of a second term in the pair of terms, where the
type of the first term is one of an instance type, a concept
type, and a relationship type and the type of the second
term is one of an instance type, a concept type, and a
relationship type,
identify a first entity in the knowledge model
database that is related to at least one term in the pair of
terms selected from the plurality of terms, wherein the first
entity is determined by the type of the first term in the pair
of terms and the type of the second term in the pair of
terms,
construct a triple statement including the pair of
terms and the first entity in the knowledge model database,
execute a query against the knowledge base using
the triple statement to generate a set of results, and
transmit, to the client computer, the set of results.
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REFERENCES
The Examiner relies on the following references:
Name Reference Date
Wical US 6,460,034 B1 Oct. 1, 2002
Ranganathan US 2008/0040308 A1 Feb. 14, 2008
REJECTIONS2
Claims Rejected 35 U.S.C. § Reference(s)/Basis
1–3, 7–10, 13–16, 19, 20 102(b) Ranganathan
4, 5, 11, 12, 17, 18 103(a) Ranganathan, Wical
OPINION
We have reviewed the Examiner’s rejections in light of Appellant’s
arguments. We have considered in this Decision only those arguments
Appellant actually raised in the Brief. Arguments not made are forfeited.3
To the extent consistent with our analysis herein, we adopt as our own
the findings and reasons set forth by the Examiner in (1) the action from
which this appeal is taken (Final Act. 2–12) and (2) the Examiner’s Answer
in response to Appellant’s Appeal Brief (Ans. 3–8) and concur with the

2 Appellant presents essentially the same arguments for each of the
independent claims, 1, 8, and 15, and challenges the rejections of the
dependent claims based on the alleged shortcomings in the rejection of the
independent claims. Accordingly, we review claim 1, and, except for our
Decision Summary, do not further address any other claims.
3 See In re Google Tech. Holdings LLC, 980 F.3d 858, 862 (Fed. Cir. 2020)
(“We interpret the Patent Office to be arguing that Google’s failure to raise
its lexicography arguments, inadvertent or not, compels a finding of
forfeiture.”); 37 C.F.R. § 41.37(c)(1)(iv) (2018) (“Except as provided for in
§§ 41.41, 41.47 and 41.52, any arguments or authorities not included in the
appeal brief will be refused consideration by the Board for purposes of the
present appeal.”).
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Application 15/199,609
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conclusions reached by the Examiner. We highlight the following for
emphasis.
Appellant presents five arguments:
A) Ranganathan does not disclose or read on a knowledge base
identifying a plurality of items, each of the plurality of items
being associated with at least one annotation identifying at least
one of the entities in the knowledge model database. Appeal
Br. 8–9.
B) Ranganathan does not disclose or read on executing a query
against the knowledge base. Id. at 9–10.
C) Ranganathan does not disclose or read on receiving a natural
language query. Id. at 10–11.
D) “Ranganathan does not disclose or read on identifying a pair of
terms in the plurality of terms or determining a type of a first
term in the pair of terms . . . an instance type, a concept type,
and a relationship type and the type of the second term.” Id. at
11.
E) Ranganathan does not disclose or read on identifying a first
entity in the knowledge model database that is related to at least
one term in the pair of terms, wherein the first entity is
determined by the type of the first term in the pair of terms and
the type of the second term in the pair of terms. Id. at 11–12.
We address Appellant’s arguments in turn.
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A.
Appellant first argues as follows:
The specification defines the knowledge base as “items [that]
include many different types of content (e.g., documents, data,
multimedia, [text documents, marked up text, images, audio,
programs, executable files,] and the like) that a user may wish to
search . . . With the items in the knowledge base catalogued
using information described in the knowledge model, the
knowledge model becomes an index or table of contents by
which to navigate the contents of the knowledge base.”
By contrast, Ranganathan teaches nothing more than using
SPARQL queries to navigate databases using domain knowledge
contained in ontologies, and is completely silent regarding a
knowledge base comprising items storing digital content
including annotations/metadata identifying entities in a
knowledge model database. Specifically, paragraph [0031]
defines the ontologies used by Ranganathan as defining
“hierarchies or taxonomies of concepts describing the different
kinds of entities that may appear in a particular domain,” but is
silent regarding a knowledge base as defined in the specification.
Paragraph [0046] specifies a data-modeling ontology, using
terms defined in different ontologies and describing a semantic
model of data loosely based on a schema of a database, but is
silent regarding a knowledge base as defined in the specification.
Paragraph [0127] describes loading the relevant ontologies into
a Minerva system, but is silent regarding a knowledge base as
defined in the specification. Thus, the rejection must be reversed.
Appeal Br. 9.
The Examiner responds as follows:
The instant specification in paragraph [0007] defines knowledge
base as “the system includes a knowledge model database
configured to store a knowledge model for a knowledge domain.
The knowledge model defines a plurality of entities and
interrelationships between one or more of the plurality of entities.
The system includes a knowledge base identifying a plurality of
items. Each of the plurality of items is associated with at least
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one annotation identifying at one of the entities in the knowledge
model.”
Ranganathan in paragraph [0024-0025] teaches
“ontologies describe semantic information about the terms
(entities and relations) that may be present in the database. This
semantic information includes the concepts that the term belongs
to and its relations to other terms. In general, ontologies describe
hierarchies of different kinds of concepts, the properties of these
concepts, individuals that belong to the concepts and the relations
between individuals. A system, in accordance with at least one
embodiment of the present invention, preferably makes use of
such ontologies to enrich the knowledge present in the database
and thus reduce the semantic gap between the user's query and
the database.
Besides describing the semantics of terms, ontologies also
help in describing a semantic model of the data. They allow for
the representing of complex inter-relations among different data
elements at a higher, semantic level, and provide a layer of
abstraction for the user that shields him/her from the specific E-
R model used to store the data in the database. This makes it
easier for users to browse the ontologies and frame queries using
the terms (e.g., concepts, properties and individuals) in the
ontology. The user is, thus, shielded from the actual layout of the
data in different tables in the database and he can frame queries
based on a semantic model of the data.”
Ranganathan also defines knowledge base / semantic
information as “the system includes a knowledge model
database configured to store a knowledge model for a
knowledge domain [e.g. the system includes a semantic model
tables in the database configured to store a knowledge model for
a semantic concept].
The knowledge model defines a plurality of entities and
interrelationships between one or more of the plurality of
entities [e.g. the semantic model defines a plurality of entities
terms (entities and relations) that may be present in the database].
The system includes a knowledge base identifying a
plurality of items. Each of the plurality of items is associated
with at least one annotation identifying at one of the entities
in the knowledge model [e.g. the system includes a semantic
information identifying a plurality of terms. Each of the plurality
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of terms is associated with at least one concept identifying at one
of the terms in the semantic model].”
Therefore as shown above Ranganathan teaches knowledge base
(e.g. semantic information) as define in the instant specification.
Note: The terms/entities derived from the semantic
information concept corresponding to the searched items are the
identified entities.
Ans. 5–6.
We are not persuaded of error because we see no distinction between
the recited knowledge base and that described in Ranganathan. Specifically,
the Specification defines “knowledge base” as follows: “[t]he knowledge
base is a database including many items (or references to many items) where
the items can include many different types of content (e.g., documents, data,
multimedia, and the like) that a user may wish to search.” Spec. ¶ 30. We are
persuaded the claimed knowledge base reads on Ranganathan’s disclosure as
follows:
This semantic information includes the concepts that the term
belongs to and its relations to other terms. In general, ontologies
describe hierarchies of different kinds of concepts, the properties
of these concepts, individuals that belong to the concepts and the
relations between individuals. A system, in accordance with at
least one embodiment of the present invention, preferably makes
use of such ontologies to enrich the knowledge present in the
database and thus reduce the semantic gap between the user’s
query and the database.
Ranganathan ¶ 24 (emphasis added).
B.
Appellant next argues as follows:
Furthermore, without disclosing a knowledge base, it is
logically impossible for Ranganathan to disclose, read on, teach,
or suggest “execut[ing] a query against the knowledge base,”
explicitly included in the instant claims.
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Thus, not only does Ranganathan fail to disclose the
“plurality of items” explicitly claimed, but the cited paragraphs,
and in fact the entire Ranganathan reference, fail to disclose the
additional limitation of “each of the plurality of items being
associated with at least one annotation identifying at least one of
the entities in the knowledge model database.” Paragraphs
[0031], [0046], and [0127], as well as the entire Ranganathan
reference, are silent regarding this explicitly claimed limitation.
The Final OA, therefore, fails to show anticipation by
demonstrating all of the recited limitations arranged or combined
in the same way as recited in the claim in a single prior art
reference. For at least this reason, the Final OA is legally and
factually deficient, and Appellants respectfully request the
allowance of all pending claims. Thus, the rejection must be
reversed.
Appeal Br. 10.
The Examiner responds as follows:
Ranganathan teaches executing a query [e.g. handle
semantic queries] against the knowledge base [e.g. semantic
information] ([0024] ontologies describe semantic information
about the terms (entities and relations) that may be present in the
database[)]. This semantic information includes the concepts that
the term belongs to and its relations to other terms. In general,
ontologies describe hierarchies of different kinds of concepts, the
properties of these concepts, individuals that belong to the
concepts and the relations between individuals. A system,
preferably makes use of such ontologies to enrich the knowledge
present in the database and thus reduce the semantic gap between
the user’s query and the database.
[0029] semantic queries can help reduce the semantic
mismatch between the database and the user. For example, let it
be assumed that a database regarding disaster-relief aid workers
has detailed information about workers’ professions (e.g.,
carpenter, civil engineer, gynecologist, surgeon, etc.). If a user
has a query about all healthcare practitioners, then a conventional
database would not be able to infer that a gynecologist and a heart
surgeon are in fact medical workers and should be part of the
answer. If, however, the database management system can
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handle semantic queries and there is a job ontology that describes
different classes of professions as a hierarchy, then the system
can indeed make the inference and return all semantically
relevant results).
Ans. 6.
We are not persuaded of error in Appellant’s argument for the reasons
stated by the Examiner, with which we agree and adopt.
C.
Appellant next argues as follows:
The Final OA argues that Ranganathan paragraphs [0018,
0020, 0021, and 0022], [0123], and [0147] disclose and/or read
on receiving and analyzing a natural language query. In fact,
these paragraphs teach away from the concept of a natural
language query. For example, paragraph [0020] “illustrates a
system architecture which allows posing semantic [rather than
natural language] queries in SPARQL.” Paragraph [0123]
demonstrates that the example language query is less effective
than a semantic query such as those in SPARQL, because
generalizing the query “progress[es] farther from the original
query . . . there is a decrease in the similarity of results,” and
therefore teaches away from the use of a natural language query.
Paragraph [0147] teaches that “SPARQL allows for imparting
more structure [as opposed to the looser structure provided in a
natural language query . . . ] to the query . . . it takes greater effort
to specify a SPARQL query rather than a [natural language]
keyword-based query.” Thus, Ranganathan teaches away from
the claimed invention, and fails to disclose, read on, teach, or
suggest receiving or analyzing a natural language query. The
Final OA, therefore, fails to show anticipation by demonstrating
all of the recited limitations arranged or combined in the same
way as recited in the claim in a single prior art reference.
Appeal Br. 10.
The Examiner responds as follows:
Ranganathan teaches receiving a natural language
query [e.g. queries users want to express] ([0010] herein, in
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essence, is a system that bridges the semantic gap between the
queries users want to express and the queries that can be
answered by the database using domain knowledge contained in
ontologies.
[0123] FIG. 4 shows the queries represented in natural
language. [¶ 23 (“FIG. 4 shows the queries represented in natural
language, as opposed to SPARQL, for the purposes of
readability.”]
[0147] in the information retrieval area, various query
expansion techniques (such as in [18, 20, 21, 22]) are commonly
used to add extra search terms to a query to return more useful
results. Some of these techniques also consider the semantics of
query terms, semantic queries expressed in SPARQL. SPARQL
allows for imparting more structure to the query, such as
referring to terms defined in an ontology and describing the
relationships between terms).
Ans. 6–7.
For the reasons stated by the Examiner, we are unpersuaded
Ranganathan fails to disclose a natural language query. As to Appellant’s
teaching away argument, “[o]bviousness may be defeated if the prior art
indicates that the invention would not have worked for its intended purpose
or otherwise teaches away from the invention.” Meiresonne v. Google, Inc.,
849 F.3d 1379, 1382 (Fed. Cir. 2017) (citing DePuy Spine, Inc. v. Medtronic
Sofamor Danek, Inc., 567 F.3d 1314, 1326 (Fed. Cir. 2009)). Teaching away
is an argument against obviousness. This is an anticipation rejection.
D.
Appellant further argues as follows:
The Final OA argues that Ranganathan paragraphs [0076]-
[0077], [0004], [0010], and [0058] disclose and/or read on
determining a type (instance type, concept type, and relationship
type) of a first term of a pair of terms identified in a plurality of
terms, and identifying a type of a second term in the pair of terms.
Paragraphs [0076]-[0077] disclose making use of constraints in
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the SPARQL query, as well as making use of the concept
hierarchy in the ontology, and are silent regarding identifying a
pair of terms, or an instance type, concept type, or relationship
for each of the pair of terms. Paragraph [0004] discloses a
problem statement with existing SQL queries, and is silent
regarding identifying a pair of terms, or an instance type, concept
type, or relationship for each of the pair of terms. Paragraph
[0010] discloses a solution statement in response to paragraph
[0004] of using SPARQL to overcome the problems in typical
SQL queries, and is silent regarding identifying a pair of terms,
or an instance type, concept type, or relationship for each of the
pair of terms. The Ranganathan reference is equally silent
regarding these claim limitations. The Final OA, therefore, fails
to show anticipation by demonstrating all of the recited
limitations arranged or combined in the same way as recited in
the claim in a single prior art reference. For at least this reason,
the Final OA is legally and factually deficient, and Appellants
respectfully request the allowance of all pending claims. Thus,
the rejection must be reversed.
Appeal Br. 11.
The Examiner finds as follows:
Ranganathan teaches identifying a pair of terms [e.g. aid
workers, Gulf Coast] in the plurality of terms [e.g. aid workers,
Gulf Coast, United States] or determining a type [e.g. instance]
of a first term [e.g. aid workers] in the pair of terms . . . an
instance type [e.g. instance/person], a concept type [e.g.
class/profession], and a relationship type [e.g. properties/ID
and age] and the type of the second term [e.g. Gulf Coast,
United States] ([0004] a user may have a query about which aid
workers are located in the Gulf Coast region of the United States.
[0030] ontologies describe a semantic model of data,
expressed in the form of concepts (or classes), properties (or
relations) and individuals (or instances). Depending on the kind
of logic used to specify the ontologies, they may be able to
specify the model of the data using different constructs and with
different levels of expressivity.
[0046] a data-modeling ontology, in a particularly
preferred embodiment of the present invention, uses terms
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defined in the different ontologies and describes a semantic
model of the data that is loosely based on the schema of the
database. This semantic model is shown in FIG. 3. Some of
the concepts in the semantic model are foaf:Person.
labor:Profession, loc:City, loc:State, per:Sex,
dr:DisasterAidWorker, dr:DisasterExperienceLevel and
dr:DisasterPreparednessCourse. The concept
dr:DisasterAidWorker is defined to be a sub class of
foaf:Person, and hence inherits all the properties and
restrictions defined on t. The concepts in the semantic data
model are related by various object properties like
per:hasProfession, loc:locatedln, etc. The concept foaf:Person
also has two datatype properties defined on it, per:haslD and
per:hasAge, whose ranges are integers. It also defines
individuals of some classes; for instance, it defines two
individuals of the class per:Sex, viz. per:Male and per:Female).
Ans. 7.
We understand the Examiner to identify “aid worker” and “Gulf
Coast” as the pair of terms and to determine that aid worker is an “instance
type” and Gulf Coast is a “relationship type.” These findings are not
persuasively rebutted. Accordingly, on this record, we are not persuaded of
error for the reasons stated by the Examiner.4
E.
Appellant lastly argues as follows:
The Final OA argues that Ranganathan paragraphs [0085]-
[0061], and [0123] disclose and/or read on identifying a first
entity in the knowledge model database that is related to at least
one term in the pair of terms selected from the plurality of terms,
wherein the first entity is determined by the type of the first term
in the pair of terms, and the type of the second term in the pair of
terms. As a preliminary matter, and as demonstrated above,

4 Appellant did not file a Reply Brief responsive to the Examiner’s Answer,
which include additional explanation and reasoning.
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Ranganathan fails to disclose identifying the pair of terms, or
identifying a type for each of the pair of terms. It would therefore
be logically impossible for Ranganathan to disclose identifying
an entity in a knowledge model database according to the
instance, concept, and/or relationship types in identified in the
first and second of the pair of terms. Furthermore, paragraphs
[0058]-[0061] (which Appellants assume is the intended
meaning of the Final OA) disclose nothing more than an example
of a SPARQL query for disaster aid workers, which arguably
includes a plurality of terms, but fails to disclose identifying a
pair of terms in the plurality of terms, or a type for a first and
second term in the pair of terms. Paragraph [0123] discloses
nothing more than a query tree including the phrase "Male
Disaster Aid Relief workers with profession Surgeon, located in
Baton Rouge. As with paragraph [0058]-[0061], at best, this
paragraph discloses a plurality of terms, but is completely silent
regarding identifying a pair of terms within the plurality of terms,
or identifying an instance, concept, and/or relationship type of
the first or second terms in the pair of terms. As such, it would
further be logically impossible for Ranganathan to disclose
identifying a first entity in a knowledge model database that is
related to at least one term in the pair of terms selected from the
plurality of terms, and the type of the second term in the pair of
terms. The Final OA, therefore, fails to show anticipation by
demonstrating all of the recited limitations arranged or combined
in the same way as recited in the claim in a single prior art
reference. For at least this reason, the Final OA is legally and
factually deficient, and Appellants respectfully request the
allowance of all pending claims.
Appeal Br. 11–12.
The Examiner responds as follows:
Ranganathan teaches identifying a first entity [e.g. disaster
aid workers] in the knowledge model database [e.g. semantic
model tables in the database] that is related to at least one term
in the pair of terms [“DisasterAidWorker”], wherein the first
entity is determined by the type [e.g. individual] of the first term
[e.g. disaster aid workers] in the pair of terms and the type [e.g.
location] of the second term [e.g. Gulf Coast] in the pair of terms
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([0085-0091] for example, a SPARQL query for disaster aid
workers who are located in the Gulf Coast region could appear
as follows:
SELECT ?x WHERE
(?x rdf:type dr:DisasterAidWorker)
(?x loc:locatedln loc:GulfCoast)).
[0100] For example, if the original semantic query is for
disaster aid workers who are located in Baton Rouge, then the
generalized semantic query is for disaster aid workers who are
located in the state of LA (Louisiana), which is the individual
immediately above BatonRouge in the locatedln lattice (FIG. 2).
The generalized query returns aid workers who are located in any
town in the state of Louisiana, and not just Baton Rouge).
Ans. 8.
Again, on this record, we are not persuaded of error for the reasons
stated by the Examiner, which we adopt and which Appellant does not
persuasively rebut.
Accordingly, we are not persuaded of error in the rejection of claim 1.
DECISION SUMMARY
In summary:
Claim(s)
Rejected
35 U.S.C.
§
Reference(s)/Basis Affirmed Reversed
1–3, 7–10,
13–16, 19, 20
102(b) Ranganathan 1–3, 7–10,
13–16, 19, 20

4, 5, 11, 12,
17, 18
103(a) Ranganathan,
Wical
4, 5, 11, 12,
17, 18

Overall
Outcome
1–5, 7–20
TIME PERIOD FOR RESPONSE
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
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AFFIRMED