Ex Parte Epstien et al

Patent Trial and Appeal Board

Sep 22, 2014

10429230 (P.T.A.B. Sep. 22, 2014)

UNITED STATES PATENT AND TRADEMARKOFFICE
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.
10/429,230 05/01/2003 Jeremy Epstien RSW9201251US1/0968.
0013
2163
106784 7590 09/23/2014
Edell Shapiro & Finnan LLC
9801 Washingtonian Blvd.
Suite 750
Gaithersburg, MD 20878
EXAMINER
PATS, JUSTIN
ART UNIT PAPER NUMBER
3624
MAIL DATE DELIVERY MODE
09/23/2014 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 JEREMY EPSTIEN,
LOUIS ROEHRS,
JAMES RICHARDS,
WILLIAM LE CLAIR,
and KRISHNA VENKATRAMAN
___________

Appeal 2012-000278
Application 10/429,2301
Technology Center 3600
___________

Before ANTON W. FETTING, BIBHU R. MOHANTY, and
NINA L. MEDLOCK, Administrative Patent Judges.
FETTING, Administrative Patent Judge.

DECISION ON APPEAL

1 The real party in interest, identified by Appellants, is DemandTec, Inc.
Appeal Br. 3.
Appeal 2012-000278
Application 10/429,230

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STATEMENT OF THE CASE2

Jeremy Epstien, Louis Roehrs, James Richards, William Le Clair, and
Krishna Venkatraman (Appellants) seek review under 35 U.S.C. § 134 of a
final rejection of claims 1–22, the only claims pending in the application on
appeal. We have jurisdiction over the appeal pursuant to 35 U.S.C. § 6(b).
The Appellants invented a user interface for a rules engine useful in
association with price optimization systems. (Specification para. 3).
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
for defining optimization rules
useful in association with a rules engine
of a price optimization system,
comprising:
[1] selecting an optimization rule from a plurality of rule types;
[2] selecting an attribute consistent with the rule type,
wherein the selected attribute is modeled as a first state
map of a plurality of state maps,
and
wherein each state map includes possible state transitions
for the attribute,
and

2 Our decision will make reference to the Appellants’ Appeal Brief (“App.
Br.,” filed February 16, 2011) and the Examiner’s Answer (“Ans.,” mailed
June 28, 2011).
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wherein each possible state transitions for the attribute
includes a coding for a subsequent state map of the
plurality of state maps;
[3] generating,
using the plurality of state maps
and
utilizing at least one state machine,
at least one valid choice for the attribute,
wherein the at least one valid choice is a subset of a
plurality of attribute choices,
and
wherein each of the at least one valid choice corresponds
to one of the subsequent state maps;
[4] displaying
on a computer screen
the at least one valid choice for the attribute to the user;
[5] adjusting,
using the plurality of state maps,
the at least one valid choice for the attribute
if
a previous attribute of the at least one valid
choice for the attribute
becomes invalid;
[6] displaying the adjusted at least one valid choice
for the attribute to the user;
[7] receiving
on the computer screen
a user selection
of the adjusted at least one valid choice for the
attribute;
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[8] inputting the user selection into the rules engine,
wherein the inputted user selection defines the
optimization rule;
and
[9] outputting the defined optimization rule
to the optimization system
for use in pricing optimization
of a plurality of products.
The Examiner relies upon the following prior art:
Delurgio US 2002/0165834 A1 Nov. 7, 2002
Sharangpani US 7,085,918 B2 Aug. 1, 2006
Bayer US 2008/0243645 A1 Oct. 2, 2008

Claims 1–22 stand rejected under 35 U.S.C. § 103(a) as unpatentable
over Delurgio, Bayer, and Sharangpani.
ISSUES
The issues of obviousness turn on whether there is an articulated reason
for applying Sharangpani’s state models to Delurgio’s optimization
strategies and paramters.

FACTS PERTINENT TO THE ISSUES
The following enumerated Findings of Fact (FF) are believed to be
supported by a preponderance of the evidence.

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Facts Related to the Prior Art
Delurgio
01. Delurgio is directed to determining optimum prices for a set of
products within a product category, where the optimum prices are
determined to maximize a merchandising figure of merit such as
revenue, profit, or sales volume. Delurgio para. 3.
02. What is needed is a technique that enables a user to configure and
execute optimization scenarios within a model that determines
optimized prices for products within a product category, where the
model considers the cost of the products as well as the demand for
those products and other related products. Delurgio para. 11.
03. Delurgio enables a user to determine optimum prices of products
for sale. The interface includes a scenario/results processor that
enables a user to prescribe an optimization scenario, and that
presents the optimum prices to the user. The optimum prices are
determined by execution of the optimization scenario, where the
optimum prices are determined based upon estimated product
demand and calculated activity based costs. The input/output
processor acquires data corresponding to the optimization scenario
from the user, and distributes optimization results to the user. The
scenario controller controls acquisition of the data and the
distribution of the optimization results in accordance with a price
optimization procedure. Delurgio para. 15.
04. In operation, each of the customers maintains a protected data set
within the customer data base. Point of sale data is uploaded over
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the data network from files on the customer computers into
corresponding data sets within the data base. The scenario/results
processor controls the timing and sequence of customer activities
for uploading data, configuring optimization scenarios, setting
rules and constraints, and downloading optimization results for
display on the client computers. Delurgio para. 62.
05. Configured optimization scenarios are executed by the
optimization engine. Using scenario configuration parameters
provided by users, the optimization engine directs the demand
engine to extract data from the customer data set that applies to
the optimization scenario that is being executed. The demand
engine predicts sales and market share of products as a function of
price according to rules and constraints of the optimization
scenario and the activity based cost engine calculates variable and
fixed costs for products at specific store locations according to
parameters of the optimization scenario. Delurgio para. 64.
06. The optimization engine executes the optimization scenario that
clients configure using the scenario/results processor. Using
estimated sales and market share data provided by the demand
engine, along with fixed and variable activity based costs
calculated by the activity based cost engine, in a price
optimization embodiment, the optimization engine determines
optimum prices for selected products within one or more demand
groups across a product category as constrained by rules and
constraints provided by clients. Some of the rules/constraints set
by the client include constraints to the overall weighted price
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advance or decline of products, branding price rules, size pricing
rules, unit pricing rules, line pricing rules, and cluster (i.e., groups
of stores) pricing rules. In addition, the client provides overall
constraints for optimization scenarios that include specification of
figures of merit that optimum prices are determined to maximize.
Delurgio para. 68.
07. Operationally, through a subset of the new scenario templates, a
user on a client machine selects to perform one of a plurality of
available optimizations. The data collection logic prescribes client
data that is required to execute the selected optimization. The
rules generator selects a rules logic element that comports with the
selected optimization. And the results export logic identifies
results templates and/or file designations that are required to
present results of the selected optimization. Template
designations for additional data that are required from the user are
provided to the input/output processor and the selected rules logic
element provides rules configuration parameters for the
optimization scenario to the optimization engine via bus.
Delurgio para. 75.
08. The system acquires data that are required to perform an
optimization according to the selection provided. The data
include rules and constraints that the user specifies concerning
product categories and demand groups for optimization, selection
of stores for optimization, grouping of stores for imputation of
data where insufficient sales history exists, swing constraints
(i.e., maximum and/or minimum change limits for parameters
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such as volume, price change, etc.), front end parameters for an
activity based cost engine (e.g., labor rates, cost of capitol, etc.),
merchandising figure of merit to maximize, and user preference
for presentation of results (i.e., list, graph, downloadable file, etc.).
Delurgio para. 80.
09. In FIG. 15, a diagram is presented portraying a strategy template
that is part of the new scenario wizard. The strategy template
indicates parameters that are presently being configured and those
that have been configured. The strategy window provides overall
optimization strategy buttons that enable the user to prescribe an
optimization to maximize profit, volume, or revenue. In addition,
the strategy template provides a volume max decline/min increase
field and a volume min decline/max increase field that allow the
user to enter values constraining the allowable volumetric swing
for the optimization. In addition buttons are provided that enable
the user to use both limits specified in the fields, no limits, only
the lower limit, or only the upper limit. A scenario name field
enables the user to assign a name to the configured scenario and a
save scenario button allows the user to save the configured
scenario and exit the new scenario wizard. Delurgio para. 102.
10. Selecting the rules tab enables the rules/constraints menu, which
provides a plurality of options that enable the user to prescribe
optimization rules and constraints according to product classes as
well as across store rules and group-to-group rules. Such rules,
being at levels much lower than those specified according to the
at-large rules template, are more readily prescribed by selecting a
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configured scenario and then enabling the rules/constraints menu.
Delurgio para. 124.
Bayer
11. Bayer is directed to conducting product configuration research
over a computer-based network by respondents at their computers
to enable each respondent to configure a product and then provide
information about the configuration of the product to a server
computer over the network. This is useful in collecting
information regarding consumer preferences about a product
efficiently and rapidly to multiple respondents at their computers
over a network, such as the Internet. Bayer para. 1.
Sharangpani
12. Sharangpani is directed to the field of content analytics and
processing. Sharangpani col. 1:15–17.
13. Initially, the nodes are in a certain state. With each evaluation
cycle, an input is entered to the state transition dynamic trigger
computation, which compares the input to the state transition
evaluation symbols contained in register 202. The comparison
information is input to the state transition interconnections and
next state evaluation logic 215. Then, based on the nodal
connections contained in register 203, the next state is computed
and latched and then becomes the current state. That is, the next
states are calculated using triggers, connectivity controls, and
current state bits. Sharangpani col. 6:7–20.
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ANALYSIS
We are not persuaded by the Appellants’ argument that Bayer is non-
analogous art and, therefore, inappropriate for use as prior art because
“Bayer does not appear to discuss state machines or price optimization.”
App. Br. 11. Delurgio is directed to determining optimum prices for a set of
products. Bayer is directed to conducting product configuration research.
Pricing optimization would need to consider such product configuration
analysis as to price elasticity with respect to consumer product preferences.
Sharangpani is directed to the field of content analytics and processing used
in fields that require high levels of content analysis and processing. Price
optimization based on product analysis requires a high level of product
demand content analysis and processing.
Thus, although Bayer is not in the same field of endeavor as the claimed
invention, we find that Bayer is analogous prior art because it is “reasonably
pertinent” to the price optimization issues addressed in Appellants’
application. See In re Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004) (Even
when prior art is not in the same field of endeavor as the claimed invention,
it is still analogous prior art if it is “reasonably pertinent” to the particular
problem with which the inventor is involved.).
This notwithstanding, we are persuaded by the Appellants’ argument
that the Examiner provides no theory for how to combine the cited
references (App. Br. 12) and that
there is no indication within Sharangpani that said states
correspond to “attributes consistent with [an optimization] rule
type” as claimed. Further, there is no teaching or suggestion
that these attributes are modeled as state maps. Further, even
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though transitions from one state to another are disclosed by
Sharangpani, there does not appear to be any indication that
said transitions are in any way related to changes in the
attributes of an optimization rule, as claimed. Id. Lastly, there
does not appear to the Appellants that the states of Sharangpani,
in any way, correspond to valid choices for the attribute, as in
Claims 1 and 16.
App. Br. 14.
The Examiner found it would have been obvious to a person of ordinary
skill in the art to apply Sharangpani to Delurgio to achieve a predictable
result, i.e., an improved system that is more efficient due to improved and
more reliable data management and organization. Ans. 7, 8.
Although such end results may be laudatory if achieved, their
achievement is only reached if the claimed invention can be pieced together
from the descriptions in the references. We find no articulated reason for
selecting an attribute consistent with the rule type, wherein the selected
attribute is modeled as a first state map of a plurality of state maps.
The Examiner finds that Delurgio has a user select the optimization
strategy and parameters. The Examiner also finds that Delurgio displays
various optimizations underway. This in itself does not provide a reason for
modeling the strategy and parameters as state maps. Although it may be that
the optimization calculation process follows a state map model, Delurgio is
silent as to the actual optimization algorithms employed.
The Examiner apparently finds it sufficient that applying these known
techniques to known elements would have been obvious to one having
ordinary skill in the art at the time of the invention so as to achieve a
predictable result. Ans. 7. The references are not known variations of a
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common subject as in the KSR International Co. v. Teleflex Inc., 550 U.S.
398 (2007) commonality of a mechanical acceleration panel with an
electronic acceleration pedal. There must be some reason articulated for
using Sharangpani’s state model to implement the data structures of
Delurgio’s optimization strategies and parameters.
As the Examiner has provided no such reason, we find no prima facie
case as to obviousness. Both independent claims have comparable
limitations.
CONCLUSIONS OF LAW
The rejection of claims 1–22 under 35 U.S.C. § 103(a) as unpatentable
over Delurgio, Bayer, and Sharangpani is improper.
DECISION
The rejection of claims 1–22 is reversed.

REVERSED

rvb