Ex Parte Kenkre et al

Patent Trial and Appeal Board

Nov 21, 2016

13655880 (P.T.A.B. Nov. 21, 2016)

UNITED STA TES p A TENT AND TRADEMARK OFFICE
APPLICATION NO. FILING DATE
13/655,880 10/19/2012
89885 7590 11/21/2016
FERENCE & ASSOCIATES LLC
409 BROAD STREET
PITTSBURGH, PA 15143
FIRST NAMED INVENTOR
Sreyash Kenkre
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
ATTORNEY DOCKET NO.
IN920120l46US1
(790.168)
CONFIRMATION NO.
1515
EXAMINER
AZIZ, ABDULMAJEED
ART UNIT PAPER NUMBER
3694
MAIL DATE DELIVERY MODE
11/21/2016 PAPER
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.
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte SREY ASH KENKRE and RAGHURAM KRISHNAPURAM
Appeal2014--006916
Application 13/655,880
Technology Center 3600
Before ANTON W. PETTING, BIBHU R. MOHANTY, and
BRADLEY B. BAY AT, Administrative Patent Judges.
PETTING, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE 1
Sreyash Kenkre and Raghuram Krishnapuram (Appellants) seek
review under 35 U.S.C. § 134 of a final rejection of claims 1, 5, 7-12, 16,
and 18-21, the only claims pending in the application on appeal. We have
jurisdiction over the appeal pursuant to 35 U.S.C. § 6(b ).
1 Our decision will make reference to the Appellants' Appeal Brief ("App.
Br.," filed January 27, 2014) and Reply Brief ("Reply Br.," filed May 23,
2014), and the Examiner's Answer ("Ans.," mailed March 26, 2014), and
Final Action ("Final Act.," mailed August 29, 2013).
Appeal2014-006916
Application 13/655,880
The Appellants invented a way of facilitating the detection of
prominent transactions in complex networks, such as money laundering
transactions. Spec., para. 19.
An understanding of the invention can be derived from a reading of
exemplary claim 1, which is reproduced below (bracketed matter and some
paragraphing added).
1. A method comprising:
utilizing one or more processors to execute a program of
instructions configured to:
[ 1] identify a locality
comprising a set of nodes in a graph of nodes and edges
via identifying nodes within a predetermined connective
distance of a core node,
wherein the edges represent financial transactions
between nodes;
[2] identify, in the locality, at least one target source-destination
node pair for monitoring;
[3] generate, with respect to the monitoring, at least one rule
relating to money laundering;
[ 4] flag interactions upon detected violations of at least one
rule·
'
and
[5] update, in response to the detected rule violations:
the identified locality;
the at least one target source-destination pair;
and
the at least one rule.
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Application 13/655,880
The Examiner relies upon the following prior art:
Reiter
Steier
Kolhatkar
US 2002/0156747 Al
US 2005/0222929 Al
US 2013/0018796 Al
Oct. 24, 2002
Oct. 6, 2005
Jan. 17.2013
Claims 11, 12, 16, and 18-20 stand rejected under 35 U.S.C. § 101 as
directed to non-statutory subject matter.
Claims 1, 5, 7-9, 11, 12, 16, 18, 19, and 21 stand rejected under 35
U.S.C. § 103(a) as unpatentable over Steier and Kolhatkar.
Claims 10 and 20 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Steier, Kolhatkar, and Reiter.
ISSUES
The issues of obviousness tum primarily on the predictability of
updating data in response to detecting an underlying problem identified by
some rule.
FACTS PERTINENT TO THE ISSUES
The following enumerated Findings of Fact (FF) are believed to be
supported by a preponderance of the evidence.
Facts Related to the Prior Art:
Kolhatkar
01. Kolhatkar is directed to detecting electronic payment card
money laundering. Kolhatkar describes receiving real-time
payment card transaction data from ingress channels and egress
channels of at least one payment card system through a first
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Application 13/655,880
application programming interface (API); generating transactional
profiles for each of at least payment cards, the ingress channel, the
egress channels, and funding sources of the payment cards; in
response to receiving transaction data for a current payment card
transaction, evaluating the transaction data using a predictive
algorithm that compare the transaction data to the transactional
profiles to calculate a probabilistic money laundering score for the
current transaction; evaluating the probabilistic money laundering
score and current transaction data based on a set of rules to
generate a suspicious activity report that recommends whether to
approve or report the current transaction; and transmitting the
suspicious activity report back to the payment card system and
transmitting the suspicious activity report to an identified
regulatory body. Kolhatkar, para. 12.
02. An exemplary system for identifying money laundering allows
for real-time monitoring of funds moving into and exiting an
electronic payment card across multiple channels. This system
has the capacity for dynamically updating an Anti-Money
Laundering monitoring (AML) system to reflect current AML
trends with (a) the use of real-time feedback events that notify the
AML system of known money laundering behavior, and (b) self-
leaming money laundering prediction algorithms that use this real-
time feedback to modify the algorithms to account for current
patterns in money laundering behavior. This AML system has the
capability of tracking fund movements to both other electronic
payment cards and other exit channels. The system has the ability
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to use every transaction to build profiles of users, payment cards,
ingress and egress channels, and funding sources across multiple
dimensions. The system has the ability to evaluate every
transaction in a payment system in real-time using internal and
external data to predict money laundering risk with a dynamic
rules engine and/or predictive models to recommend a decision to
approve or file a suspicious activity report (SAR) for the card
holder. Kolhatkar, paras. 25-26.
03. The anti-money laundering rules component may use multiple
predictive algorithms in general as well as within a given use case.
The predictive algorithms may include, but are not limited to,
regression, decision trees, neural networks, random forest, and
genetic algorithms. The result of the anti-money laundering rules
component is a probabilistic score assigned to a transaction and/or
to the parties involved in the transaction. The anti-money
laundering rules component is designed to be self-learning with
the ability to incorporate new anti-money laundering behaviors
into the predictive algorithms. Kolhatkar, para. 40.
Steier
04. Steier is directed to financial accounting and auditing, and more
particularly to systems and methods of identifying risks of
material misstatement due to fraudulent financial reporting in
connection with a financial audit, and to systems and methods of
investigating financial fraud with regard to forensic and
investigative accounting. Steier, para. 2.
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05. A way to examine and analyze transactions is to find rules that
can be applied to the characteristics of the transactions to
distinguish transactions that result in anomalous account values
from those that result in non-anomalous account values. The
transactions are divided into two sets, anomalous transactions and
non-anomalous transactions, depending on whether the
transactions are linked to anomalous account activity or other
anomalies, as determined above. The two sets of transactions are
then input into a decision tree algorithm or a rule induction
algorithm to construct a set of rules that describes each set. For
example, the decision tree algorithm processes the set of
transactions linked to anomalous account activity or other
anomalies. In processing this set, the decision tree identifies a set
of rules, such that each transaction meets at least one of the rules.
This set of rules is then outputted. A similar set of rules is
generated for the transactions linked to non-anomalous account
activity or other non-anomalous data. The rules that are output are
similar to the common characteristics identified in the descriptions
of the clusters above. Once generated, these rules may be more
succinct and easier to use, because the rules include only the
characteristics relevant to the operation of the rules. Steier, para.
88.
06. Once the clustering algorithms have identified the common
characteristics of the anomalous data points, such as the
transactions known to generate the anomalies in the activity, or the
decision tree algorithms have identified the set of rules that
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Application 13/655,880
describe the characteristics of the anomalous data points, then the
common characteristics of each cluster are compared with
characteristics predictive of risks of material misstatement due to
fraud, such as the characteristics of clusters of transactions or the
set of rules generated from analyses of companies known to be
fraudulent. For example, data retrieved from a company where
fraud is already known to have existed is analyzed to identify
anomalous account activity and then identify the common
characteristics or set of rules of the underlying transactions which
contributed to the anomalous account activity. Alternatively, the
financial data from known fraudulent companies may be analyzed
using other methods, such as the classical forensic investigative
techniques to identify such predictive characteristics or sets of
rules. Steier, para. 89.
07. At a high level, one way to determine which subsets to use in
the multivariate regression analysis follows the method of Figure
11. A structural equivalence profiling is applied to the money
flow graph. The results of the structural equivalence profiling are
analyzed to identify structurally similar accounts or account
clusters, based on the money flows between accounts. These
account clusters are subjected to further analysis. The flow of
money amongst the accounts of the company can be depicted as a
graph, with each account being represented by a node in the graph,
and each transfer of money between accounts being represented
by a line (known as an edge) connecting a pair of nodes in the
graph. The nodes of the graph in Figure 12 are derived from the
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account data, by associating one node with each account in the
financial accounting system for XYZ Company. The edges of the
graph are derived from the transaction data from XYZ Company's
financial accounting system, over a given time period. An edge
between two account nodes is created if the two accounts appear
in the same transaction. The arrows on the edges between each
pair of nodes in the graph indicate which direction the money is
flowing in the graph. The edges of the money flow graph may
depict simple flow paths between accounts during the time period,
or alternatively the edges may include additional data, such as the
number of transactions, the average dollar value of the
transactions, the total dollar value of the transactions, or other
such data. The nodes of the money flow graph may represent
accounts within the company, or alternatively they may represent
other aggregations of transaction or other financial information,
such as financial statement line items, consolidated spreadsheet
entries, account category aggregations, sub-accounts, or any other
aggregation of transaction information useful to the analysis.
Steier, paras. 113-117.
08. Principal component analysis is applied to the collection of time
series derived from the changes to each account in the general
ledger over time. The anomaly detection algorithms are then
applied, to only the first few principal components to detect dates
on which there are sudden changes in coefficients of the terms.
These dates are then flagged as anomalies and are then used as
inputs by the algorithms that compare the entries on the
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anomalous dates to the entries on the previous dates, as well as the
other algorithms used to process the anomalous data, such as to
determine potential reasons for the anomalies, common
characteristics of the anomalies, or compare the anomalous data to
fraud predictive data. Use of the smaller number of principal
components instead of the larger underlying collection of time
series data streamlines the anomaly detection process
significantly, because the anomaly detection algorithms are
processing significantly less data, without losing significant levels
of accuracy. Steier, para. 135.
ANALYSIS
Claims 11, 12, 16, and 18-20 rejected under 35 US. C. § 1 OJ as directed to
non-statutory subject matter
We summarily affirm this uncontested rejection.
Claims 1, 5, 7-9, 11, 12, 16, 18, 19, and21 rejectedunder35 USC
§ 103(a) as unpatentable over Steier and Kolhatkar
Claims 10 and 20 rejected under 35 US. C. § 103 (a) as unpatentable over
Steier, Kolhatkar, and Reiter
We are not persuaded by Appellants' argument that
the applied references whether considered alone or in
combination, at the very least fail to teach (as presently broadly
claimed by independent Claims 1, 11, 12 and 21) updating, in
response to detected rule violations: an identified locality; at
least one target source-destination pair; and at least one rule ....
To the extent that Kolhatkar alone is relied upon for allegedly
showing updating of any sort, it falls far short of teaching or
suggesting all three types of presently claimed updates.
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Application 13/655,880
App. Br. 15-16. The limitation at issue is "update, in response to the
detected rule violations: the identified locality; the at least one target
source-destination pair; and the at least one rule." The manner and
implementation of such an update is neither recited nor narrowed. As the
recited locality, target source-destination pair, and rule refers to rules, and
graph elements themselves are abstractions of accounts and their usage, the
recited update is metaphoric rather than literal. Some representational data
is the implied target of such update. The nature of this representational data
is similarly neither recited nor narrowed.
As the Examiner finds,
Kolhatkar alone is not relied upon for all three types of
'updates'. Rather, "the identified locality" and "the at least one
target source-destination pair" are taught by Steier. Whereas
"the at least one rule" is taught by Kolhatkar.
Ans. 3. Kolhatkar in particular describes its operation as including self-
leaming, which inherently incorporates updates of its data as learning
implies memory of changes, i.e. updates. Also, Steier describes rules that
are output, and, once generated, these rules may be more succinct and easier
to use, because the rules include only the characteristics relevant to the
operation of the rules. This explicitly describes an update for easier use.
Steier describes analysis of source and destination nodes and
transactions between them. The path between them is within the scope of
the recited identified locality and the source and destination are within the
scope of a target source-destination pair. As the edges may include
additional data, such as the number of transactions, the average dollar value
of the transactions, the total dollar value of the transactions or other such
data, the information describing the identified locality and source-destination
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pair are updated to include such information. Having also identified
potential rule violations, the association of those particular nodes and edges
forming an identified locality and source destination pair combined with the
identification of such a violation are then updates in response to the rule
violations.
CONCLUSIONS OF LAW
The rejection of claims 11, 12, 16, and 18-20 under 35 U.S.C. § 101
as directed to non-statutory subject matter is proper.
The rejection of claims 1, 5, 7-9, 11, 12, 16, 18, 19, and21 under35
U.S.C. § 103(a) as unpatentable over Steier and Kolhatkar is proper.
The rejection of claims 10 and 20 under 35 U.S.C. § 103(a) as
unpatentable over Steier, Kolhatkar, and Reiter is proper.
DECISION
The rejections of claims 1, 5, 7-12, 16, and 18-21 are affirmed.
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) (2011).
AFFIRMED
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