Ex Parte Marvasti et al

Patent Trial and Appeal Board

Apr 28, 2017

13417933 (P.T.A.B. Apr. 28, 2017)

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.
13/417,933 03/12/2012 Mazda A. MARVASTI A815.CIP 6628
36378 7590
VMWARE, INC.
DARRYL SMITH
3401 Hillview Ave.
PALO ALTO, CA 94304
05/02/2017 EXAMINER
MAHMOOD, REZWANUL
ART UNIT PAPER NUMBER
2164
NOTIFICATION DATE DELIVERY MODE
05/02/2017 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):
ipteam @ vmware. com
ipadmin@vmware.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte MAZDA A. MARVASTI,
ARNAK V. POGHOSYAN, ASHOT N. HARUTYUNYAN,
and NAIRA M. GRIGORYAN
Appeal 2017-000467
Application 13/417,9331
Technology Center 2100
Before DEBRA K. STEPHENS, KEVIN C. TROCK, and
JESSICA C. KAISER, Administrative Patent Judges.
TROCK, Administrative Patent Judge.
DECISION ON APPEAL
Introduction
Appellants seek review under 35 U.S.C. § 134(a) from a Final
Rejection of claims 1—38. We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.
1 According to Appellants, the real party in interest is VMware, Inc. App.
Br. 1.
Appeal 2017-000467
Application 13/417,933
Invention
The claims are directed to a system for analyzing unstructured data by
parsing the data and incorporating the data into graphs to identify data
patterns and characteristics. Abstract.
Exemplary Claim
Claim 1, reproduced below, is illustrative of the claimed subject
matter with disputed limitations emphasized:
1. A data-analysis system comprising:
one or more processors;
an electronic memory; and
a data-analysis component that executes on the one or
more processors to analyze digitally encoded unstructured data
stored in one or more of the electronic memory and one or more
mass-storage devices by
generating a set of attribute-associated events from
the unstructured data,
carrying out a data reduction of the attribute-
associated events by removing low-information-
containing attributes,
coalescing similar events into nodes,
extracting patterns and characteristics from edge-
reduced graphs that include the nodes, and
storing the extracted patterns and characteristics in
the electronic memory.
REFERENCES
The prior art relied upon by the Examiner in rejecting the claims on
appeal is:
Menard US 2002/0173997 A1 Nov. 21,2002
2
Appeal 2017-000467
Application 13/417,933
Nolan US 2012/0323558 A1 Dec. 20, 2012
REJECTIONS
Claim 38 stands rejected under 35U.S.C. § 101 as being directed to
non-statutory subject matter. Final Act. 7—8.
Claims 1—22, 24—36, and 38 stand rejected under 35 U.S.C. § 102(e)
as being anticipated by Nolan. Id. at 8-46.
Claims 23 and 37 stand rejected under 35 U.S.C. § 103(a) as being
unpatentable over Nolan and Menard. Id. at 47-49.
ANALYSIS
We disagree with Appellants’ conclusions and adopt as our own:
(1) the findings and reasons set forth by the Examiner in the action from
which this appeal is taken (Final Act. 2-49); and (2) the reasons set forth by
the Examiner in the Answer in response to the Appeal Brief (2—13). With
respect to the claims argued by Appellants, we highlight and address specific
findings and arguments for emphasis as follows.
Non-Statutory Subject Matter
Independent claim 38 is directed to a “physical data-storage device
encoded with computer instructions.” The Examiner concludes that claim
38 is broad enough to encompass non-statutory subject matter such “signals
or other forms of transitory” carriers, and therefore claim 3 8 does not satisfy
35 U.S.C. § 101. Final Act. 7—8; Ans. 2—3; see In reNuitjen, 500 F.3d 1346
(Fed. Cir. 2007).
3
Appeal 2017-000467
Application 13/417,933
Appellants argue the Examiner erred in finding claim 38 is directed to
non-statutory subject matter because the “physical data storage device”
recited by the claims cannot “be reasonably interpreted to include signals or
other forms of transitory embodiments.” App. Br. 5—6; Reply Br. 3.
We are not persuaded. The Examiner finds, and we agree, the
Specification provides an exemplary list of possible physical data-storage
devices, rather than providing any definition excluding transitory media
from the scope of physical data-storage devices. Final Act. 8 (citing Spec.
135); Reply Br. 2—3. Furthermore, Appellants’ Specification teaches
“physical data-storage devices, such as . . . physical, tangible, data-storage
devices and media” (Spec. 135 (emphasis added)); that is, Appellants’
Specification discloses that a physical data-storage device can be physical,
tangible media. We determine media, even when modified as physical and
tangible, includes transitory signals. See Ex parte Mewherter, 107 USPQ2d
1857, 1862 (PTAB 2013) (precedential) (holding that where a specification
does not limit the term “machine readable storage medium” expressly to
exclude signals, carrier waves, and other transitory media, the term
encompasses transitory propagating signals); see also U.S. Patent &
Trademark Office, Evaluating Subject Matter Eligibility Under 35 U.S.C.
§ 101 (August 2012 Update) (pp. 11-14), available at
http://www.uspto.gOv/patents/law/exam/l 0 l_training_aug2012.pdf (noting
that while the recitation “non-transitory” is a viable option for overcoming
the presumption that those media encompass signals or carrier waves,
merely indicating that such media are “physical” or tangible” will not
overcome such presumption). Accordingly, we find that the broadest
reasonable interpretation of the term “physical data-storage device encoded
4
Appeal 2017-000467
Application 13/417,933
with computer instructions,” in view of Appellants’ Specification, includes
transitory propagating signals. Therefore, we sustain the Examiner’s
rejection of claim 38 as directed to non-statutory subject matter under 35
U.S.C. § 101.
Anticipation
“a data-analysis component ”
Appellants contend the Examiner erred in finding Nolan discloses “a
data-analysis component that executes on the one or more processors,” as
recited in claim 1 and similarly recited in claims 24 and 38. App. Br. 26—31;
Reply Br. 4—12. Specifically, Appellants argue “all of the claimed
operations carried out by this component of the claimed system are
automated operations carried out by a machine,” but Nolan “involves, in
each and every step, human subject matter experts.” App. Br. 26; Reply Br.
10. Further, Appellants argue “[tjhere is nothing in Figure 1 . . . [,] Figure
13,” or paragraph 51 of Nolan “that is labeled as, or described to be, a data-
analysis component” analyzing unstructured data (Reply Br. 5—6; App. Br.
30).
We are not persuaded. The Examiner finds (Final Act. 9), and we
agree, Nolan discloses a “system . . . capable of automatically discovering
relationships, patterns and connections between diverse sources of geospatial
and/or temporal data” using algorithms which “allow for rapid, semi-
automated preparation of. . . unstructured . . . textual and numeric data.”
Nolan || 56, 58. The Examiner further finds (Ans. 5), and we agree,
Nolan’s system is a “computer system” such as “a Dual Quad core, 32-GB
RAM, 64-bit OS” (Nolan | 51; see Nolan Fig. 1).
5
Appeal 2017-000467
Application 13/417,933
Appellants’ arguments that Nolan’s system is not “fully automated”
and involves human interaction (Reply Br. 6—7, 9-12; App. Br. 26—28, 30-
31) are not commensurate with the scope of the claim. The claim does not
recite language requiring full automation nor does the claim include
language precluding human interaction with the computer system for data
analysis. Further, Appellants’ Specification does not preclude human
interaction with the data-analysis component and, instead, includes human
interaction with the data-analysis component. Specifically, Appellants’
Specification teaches “human analysis and human-generated input to the
data-analysis and data-processing methods.” Spec. |40. While Nolan’s
system includes human input and refinement for its computer-executed data-
analysis process (see Figs. 1—3), human interaction with the data-analysis
component is not precluded from the scope of the claim.
Further, Appellants’ arguments that paragraph 51, Figure 1, and
Figure 13 of Nolan do not themselves disclose a data-analysis component or
unstructured data (Reply Br. 5—6, 8; App. Br. 30) do not address the
Examiner’s finding (Ans. 5; Final Act. 9) that Nolan discloses a system
which performs data-analysis for “unstructured and structured textual and
numeric data” by “automatically discovering relationships, patterns and
connections between diverse sources of geospatial and/or temporal data”
(Nolan || 56, 58; see Nolan | 80). Furthermore, we determine one of
ordinary skill in the art would have understood that paragraph 51, Figure 1,
and Figure 13 describe the computing system which performs Nolan’s data-
analysis features on unstructured data. Indeed, Nolan’s Figure 1 discloses an
overview of Nolan’s data-analysis system including a computer providing
“discover/search algorithms,” and paragraph 51 discloses one embodiment
6
Appeal 2017-000467
Application 13/417,933
of social network analysis and modeling data-analysis detailed throughout
Nolan.
Accordingly, we are not persuaded the Examiner erred in finding
Nolan discloses “a data-analysis component that executes on the one or more
processors” within the meaning of claims 1, 24, and 38.
“a set of attribute-associated events ”
Appellants contend the Examiner erred in finding Nolan discloses
“generating a set of attribute-associated events from the unstructured data,”
as recited in claim 1 and similarly recited in claims 24 and 38. App. Br. 31—
34; Reply Br. 12. Specifically, Appellants argue an “attribute-associated
event” is a “a set of values for a corresponding set of attributes” (App. Br. 32
(citing Spec H 37, 39, Fig. 2)), but the “prediction models, historical events,
or any of the other various types of entities” in Nolan are not “a set of
attribute/attribute-value pairs that [are] generated from unstructured data”
(App. Br. 32-33 (citing Nolan H 56, 72, 75, 80, 100-106, 112); Reply Br.
12). Further, Appellants argue the “Examiner’s methodology in attempting
to craft claim rejections and arguments is to simply mischaracterize a large
number of disparate paragraphs and then make assertions that do not
logically follow, are not supported by, and are unsupportable by the
mischaracterized paragraphs.” Reply Br. 12; App. Br. 34.
We are not persuaded. The Examiner finds (Final Act. 9; Ans. 6), and
we agree, Nolan discloses the “preparation of both unstructured and
structured textual and numeric data” for data-analysis by extracting
attributes from that unstructured data (Nolan | 58; see Nolan || 56, 72, 75,
80, 100-105).
7
Appeal 2017-000467
Application 13/417,933
Appellants do not persuade us that Nolan does not generate a set of
attribute-associated events as recited by the claims or “a set of values for a
corresponding set of attributes” as argued by Appellants. App. Br. 32—33.
Nolan generates attributes and values from unstructured data; in particular,
the “Discovery portion of [Nolan’s] workflow . . . aids in the extraction of
entities, attributes, behaviors, and themes from large scale structured and
unstructured historic text corpora” (Nolan | 80). Furthermore, while
Appellants argue a set of attribute-associated events is “a set of values for a
corresponding set of attributes” (App. Br. 32), we note that the claim does
not include language requiring that attribute-associated events are “a set of
values for corresponding attributes” and the Specification provides
examples, not definitions, of attribute-associated events (see Spec. ]Hf 37
(“illustrates”), 39 “In the general case”). Even applying Appellants’ overly-
limiting definition, we agree with the Examiner’s finding that (Final Act.
11), Table 2 of Nolan associates attributes, e.g., “Attribute 1,” with
“value [s].”
Further, we are not persuaded that the Examiner relies upon
“mischaracterize[d] . . . disparate paragraphs” that are unrelated, as asserted
by Appellants throughout the Reply Brief and the Appeal Brief. Reply Br.
10, 12—15; App. Br. 34, 36, 38. The Examiner relies on Nolan’s predictive
modeling system which has a workflow of interrelated features and
processes. See Final Act. 3—4, 9; see also Ans. 5—6. The predictive
modeling system workflow starts with unstructured data, which is then
analyzed to discover content and associated attributes, which is then formed
into prediction models. See Nolan Figs. 2—3. The processes in the workflow
are logically provided in multiple corresponding sections, those sections
8
Appeal 2017-000467
Application 13/417,933
elaborating upon respective features in the workflow and building upon
previous workflow processes. Dependent claim 14 of Nolan, provides an
example of the interrelationship between the features in Nolan; dependent
claim 14, incorporating the subject matter of claims 10—13, recites:
converting structured and unstructured text into behavioral
predictive models; and, utilizing a probabilistic modeling
algorithm to generate entity- level probability models converting
extracted entities and attributes into sets of entity-attribute-value
and entity-entity-relationship; automatically determining the
number of kinds of objects contained in the entity-attribute-value
set; assigning attributes to the kind of object described.
The Examiner necessarily cites to different passages in Nolan because
Nolan’s system is the aggregation of a number of interrelated processes,
building upon previous processes.
Accordingly, we are not persuaded the Examiner erred in finding
Nolan discloses “generating a set of attribute-associated events from the
unstructured data,” within the meaning of claims 1, 24, and 38.
“data reduction ”
Appellants contend the Examiner erred in finding Nolan discloses
“carrying out a data reduction of the attribute-associated events by removing
low-information-containing attributes,” as recited in claim 1 and similarly
recited in claims 24 and 38. App. Br. 34—35; Reply Br. 12—13. Specifically,
Appellants argue “removing attributes from categories has nothing at all to
do with a data reduction process.” App. Br. 34—35 (citing Nolan || 56, 63,
68, 74—75, 80, 84). Appellants further argue Nolan does not “remov[e]
events associated with attributes corresponding to geospatial and/or temporal
data.” Reply Br. 12.
9
Appeal 2017-000467
Application 13/417,933
We are not persuaded. The Examiner finds, and we agree, Nolan
“[r]emov[es] relationships between events and entities and associated
attributes corresponding to data unrelated to an area of interest.” Ans. 8
(citing Nolan || 74—76, 80, 84, 88).
Appellants’ arguments that the cited portions of Nolan have
“absolutely nothing at all to do with any type of data reduction” and
“removing attributes from categories has nothing at all to do with a data
reduction process” (App. Br. 34—35), are without elaboration and are
unpersuasive. In particular, Appellants have not proffered persuasive
reasoning or evidence why “the removal of documents from the [data]
corpora that are unrelated to [an] area of interest” (Nolan 174) or why
“removing] entities, attributes, and relationships from specific categories”
(Nolan | 84) does not remove low-information-containing-attributes.
Furthermore, while the portions of Nolan disclosing the removal of data or
attributes, do not specify what data or attributes are removed, i.e., geospatial
or temporal data (see Nolan || 74, 84), those removal processes are
performed on the geospatial and temporal data Nolan’s predictive modeling
system analyzes (see Nolan || 56, 58). Accordingly, we are not persuaded
the Examiner erred in finding Nolan discloses “carrying out a data reduction
of the attribute-associated events by removing low-information-containing
attributes,” within the meaning of claims 1, 24, and 38.
“coalescing similar events ”
Appellants contend the Examiner erred in finding Nolan discloses
“coalescing similar events into nodes,” as recited in claim 1 and similarly
recited in claims 24 and 38. App. Br. 35—36; Reply Br. 13. Specifically,
10
Appeal 2017-000467
Application 13/417,933
Appellants argue “Nolan discusses a statistical technique that separates
entities into distinct groups based on their relationships and attributes” but
“[tjhere are no relationships anywhere mentioned in the claim” and Nolan
“does not appear to group together entities that are considered equal.” App.
Br. 36 (citing Nolan 162); see Reply Br. 13. Further, Appellants argue
Nolan’s coalesced “entities are not events” and instead are “people,
locations, and organizations.” App. Br. 36 (citing Nolan || 60-63).
The Examiner finds (Ans. 9), and we agree, Nolan’s system
“combine[s] extracted attributes that provide similar information” (Nolan
179) and “reorganize[s] data into clusters of entities with common attribute
and relationship sets” {id. 162; see id. 1 84 (“merge categories”)).
Appellants’ argument that Nolan’s system coalesces based on
relationships rather than coalescing in the manner described by the
Specification (App. Br. 36; Reply Br. 13) is not commensurate with the
scope of the claim. Specifically, the claim does not recite language limiting
how coalescing is performed, nor does it recite language precluding
coalescing using relationships. Furthermore, the Specification does not
provide an explicit definition of coalescing to preclude coalescing using
relationships; rather, the Specification provides non-limiting examples of
coalescing. Indeed, the Specification teaches the “method illustrated in
Figure 9 is but one of many different possible implementations of the event-
to-node coalescing operation.” Spec. 1 51; see Spec. 149-50, Figs. 8—9.
Accordingly, Nolan’s clustering based on common attributes and
relationship sets (Nolan || 62, 79, 84) coalesces within the meaning of the
claim.
11
Appeal 2017-000467
Application 13/417,933
Further, we are not persuaded by Appellants’ argument that Nolan’s
coalesced entities are not events. App. Br. 36. Nolan coalesces data to
analyze data (Nolan 162); the data that Nolan’s system analyzes “can
assume the form of. . . event documentation” (Nolan 1 80 (emphasis
added)). Furthermore, Nolan’s combination of attributes (Nolan 179)
coalesces events because those attributes include “suspicious phone calls”
and “observed with a weapon,” i.e., events (Nolan 1150).
Accordingly, we are not persuaded the Examiner erred in finding
Nolan discloses “coalescing similar events into nodes,” within the meaning
of claims 1, 24, and 38.
“edge-reduced graphs ”
Appellants contend the Examiner erred in finding Nolan discloses
“extracting patterns and characteristics from edge-reduced graphs that
include the nodes,” as recited in claim 1 and similarly recited in claims 24
and 38. App. Br. 36—37; Reply Br. 13—14. Specifically, Appellants argue
“[e]dge reduction is a process that removes edges from a graph,” but “[tjhere
is no mention of edge-reduced graphs or anything related to extracting
patterns and characteristics from edge-reduced graphs anywhere” in the
Examiner’s cited portions of Nolan. App. Br. 37; Reply Br. 14.
We are not persuaded. As discussed supra, the Examiner finds, and
we agree, Nolan discloses “removing, and/or merging categories and
adding/removing entities, attributes, and relationships from specific
categories,” i.e., a reduction of data. Ans. 10 (citing Nolan 1 84); see Nolan
11 62, 79. The Examiner further finds, and we agree, based on that reduced
data, Nolan “express[es] entities and different relationships between entities
12
Appeal 2017-000467
Application 13/417,933
from graph data comprising nodes connected with edges.” Ans. 11 (citing
Nolan 11 86, 142-144, 158).
Appellants’ arguments (see App. Br. 37; see also Reply Br. 14) do not
persuade us the Examiner’s finding — that Nolan’s graph, created from
reduced data, is an edge-reduced graph (Ans. 10—11) — is unreasonable.
We agree with the Examiner’s broad, but reasonable interpretation that
“[gjraphs representing removed entities, attributes, and relationships by
removed nodes and/or edges, are edge-reduced graphs.” Ans. 11. Here,
Nolan creates a graph based on Nolan’s processes of “content discovery”
and “entity and attribute association” (see Nolan Fig. 2); those processes
remove entities (i.e., nodes) and relationships (i.e., edges) from the data to
be graphed (id. at H 84, 158), resulting in an edge-reduced graph.
Accordingly, we are not persuaded the Examiner erred in finding Nolan
discloses “extracting patterns and characteristics from edge-reduced graphs
that include the nodes,” within the meaning of claims 1, 24, and 38.
“storing... in the electronic memory ”
Appellants contend the Examiner erred in finding Nolan discloses
“storing the extracted patterns and characteristics in the electronic memory,”
as recited in claim 1. App. Br. 37—39; Reply Br. 14. Specifically,
Appellants argue “[sjtoring key individual’s behaviors and attributes has
nothing at all to do with storing patterns and characteristics extracted from
edge-reduced graphs.” App. Br. 38 (citing Nolan || 66, 123). Further,
Appellants argue the Examiner’s cited paragraphs “do[] not mention
electronic memories” or how information is stored. App. Br. 38 (citing
Nolan || 66, 123); Reply Br. 14.
13
Appeal 2017-000467
Application 13/417,933
We are not persuaded. As discusses supra, we agree with the
Examiner’s finding that Nolan’s predictive modeling system is provided by
a computer system, which includes electronic memory. Ans. 12 (citing
Nolan | 51, Figure 13). The Examiner further finds {id.), and we agree,
Nolan creates an “Entity Attribute/Value (EAV) table” which “stores
different attributes of the entities” (Nolan 1123).
Appellants’ argument, that storing behaviors and attributes “has
nothing at all to do with storing patterns and characteristics extracted from
edge-reduced graphs” (App. Br. 38 (citing Nolan || 66, 123)), does not
address the Examiner’s finding (Ans. 12) that Nolan’s system creates a
Bayesian Network, i.e., a graph, from reduced data to provide information
predicting “behaviors and actions” (Nolan || 80, 84, 86; see Nolan Figs. 3,
5).
Further, Appellants’ argument that Nolan’s information is not stored
in electronic memory {see App. Br. 37—39; see also Reply Br. 14) is not
persuasive because one of ordinary skill in the art would understand that
Nolan’s system, performed by a computer, would store data it manipulates
and analyzes in memory.
Nolan’s EAV table is the output of Nolan’s “Entity & Attribute
Association” stage of processing. Nolan || 120-123. Nolan’s “Entity &
Attribute Association” stage is an algorithm performed by a computer. See
Nolan Fig. 2 (showing “automated algorithm” processes include entity and
attribute association); see also Nolan || 110-111 (“Algorithm2: Entity &
Attribute Association”). As such, the EAV table output is stored in
electronic memory because the EAV table is created by the computer
performing the “Entity & Attribute Association” process.
14
Appeal 2017-000467
Application 13/417,933
Accordingly, we are not persuaded the Examiner erred in finding
Nolan discloses “storing the extracted patterns and characteristics in the
electronic memory,” within the meaning of claims 1, 24, and 38.
Remaining Claims 2—23 and25—37
Appellants do not argue separate patentability for dependent claims 2—
23 and 25—37, which depend directly or indirectly from claims 1 and 24.
See App. Br. 8-40. For the reasons set forth above, therefore, we are not
persuaded the Examiner erred in rejecting these claims. See In re Lovin, 652
F.3d 1349, 1356 (Fed. Cir. 2011) (“We conclude that the Board has
reasonably interpreted Rule 41.37 to require applicants to articulate more
substantive arguments if they wish for individual claims to be treated
separately”). Accordingly, we sustain the Examiner’s decision to reject
claims 2—23 and 25—37. See 37 C.F.R. § 41.37(c)(l)(iv).
DECISION
We AFFIRM the Examiner’s non-statutory subject matter rejection of
claim 38.
We AFFIRM the Examiner’s 35 U.S.C. § 102(e) rejections of claims
1-22, 24—36, and 38.
We AFFIRM the Examiner’s 35 U.S.C. § 103 rejections of claims 23
and 37.
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)(l)(iv).
AFFIRMED
15