Ex Parte Dao et al

Board of Patent Appeals and Interferences

Jul 10, 2012

10139503 (B.P.A.I. Jul. 10, 2012)

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.
10/139,503 05/03/2002 Fu-Tak Dao 94591 5271
22242 7590 07/10/2012
FITCH EVEN TABIN & FLANNERY, LLP
120 SOUTH LASALLE STREET
SUITE 1600
CHICAGO, IL 60603-3406
EXAMINER
HAIDER, FAWAAD
ART UNIT PAPER NUMBER
3627
MAIL DATE DELIVERY MODE
07/10/2012 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 1
___________ 2
3
BEFORE THE BOARD OF PATENT APPEALS 4
AND INTERFERENCES 5
___________ 6
7
Ex parte FU-TAK DAO, 8
RICARDO MARTIJA, 9
THOMAS SPACEK, 10
and SAMARADASA WEERAHANDI 11
___________ 12
13
Appeal 2011-002013 14
Application 10/139,503 15
Technology Center 3600 16
___________ 17
18
19
Before ANTON W. FETTING, JOSEPH A. FISCHETTI, and 20
MEREDITH C. PETRAVICK, Administrative Patent Judges. 21
FETTING, Administrative Patent Judge. 22
DECISION ON APPEAL 23
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STATEMENT OF THE CASE1 1

1 Our decision will make reference to the Appellants’ Appeal Brief (“App.
Br.,” filed June 23, 2010) and Reply Brief (“Reply Br.,” filed October 21,
2010), and the Examiner’s Answer (“Ans.,” mailed September 14, 2010).
Fu-Tak Dao, Ricardo Martija, Thomas Spacek, and, Samaradasa 2
Weerahandi (Appellants) seek review under 35 U.S.C. § 134 (2002) of a 3
final rejection of claims 1-9 and 20-41, the only claims pending in the 4
application on appeal. We have jurisdiction over the appeal pursuant to 5
35 U.S.C. § 6(b) (2002). 6
Although Appellants state at Appeal Brief 4, 7
[t]here are no related appeals or interferences known to 8
appellant, the appellant's legal representative, or assignee that 9
will directly affect, or be directly affected by or have a bearing 10
on the Board's decision in the pending appeal[,] 11
we find that Appeal 2011-002371 is for Divisional Application 11/975,533 12
from this application as parent, and both appeals had their Notices of 13
Appeals filed on April 14, 2010, their Appeal Briefs filed on June 23, 2010, 14
and their Reply Briefs filed on October 21, 2010. Both applications were 15
examined by the same Examiner and rejected over the same art in 16
Examiner’s Answers entered in the record on October 13, 2010. Thus, we 17
treat these appeals as being related. 18
The Appellants invented a way of analytically determining the revenue 19
of certain types of Internet companies using Web based and equipment 20
based metrics related to Internet companies for analytically determining the 21
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current revenue and statistically projecting the future revenue of these 1
companies (Specification 1:12-15). 2
An understanding of the invention can be derived from a reading of 3
exemplary claim 1, which is reproduced below [bracketed matter and some 4
paragraphing added]. 5
1. A method for estimating current revenue of a company, said 6
method comprising: 7
[1] a computing device 8
obtaining from network resources 9
data representing an actual revenue performance of 10
the company 11
for a plurality of prior time periods, 12
[2] the computing device obtaining data points 13
for an Internet metric, 14
not including sales data, 15
over the plurality of prior time periods, 16
[3] the computing device generating a revenue model 17
for the company 18
using 19
the obtained actual revenue performance 20
and 21
the obtained Internet metric data points, 22
[4] the computing device obtaining one or more current data 23
points 24
for the Internet metric, 25
and 26
[5] the computing device estimating the current revenue of the 27
company 28
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by applying the obtained current Internet metric data 1
points 2
to the generated revenue model. 3
The Examiner relies upon the following prior art: 4
Singh US 2002-0169657 A1 Nov. 14, 2002
Rotman US 2003/0018550 A1 Jan. 23, 2003
Claims 1-9 and 20-41 stand rejected under 35 U.S.C. § 103(a) as 5
unpatentable over Rotman and Singh. 6
ISSUES 7
The issue of obviousness turns primarily on whether the references may 8
be combined, which in turn depends on whether Singh’s forecasting 9
techniques are applicable to Rotman’s revenue forecast. 10
FACTS PERTINENT TO THE ISSUES 11
The following enumerated Findings of Fact (FF) are believed to be 12
supported by a preponderance of the evidence. 13
Facts Related to the Prior Art 14
Rotman 15
01. Rotman is directed to provide near real-time market information 16
predictions based on money flow maps derived from payment 17
transaction information. Payment system operators, such as credit 18
card issuers, have transactional data that is representative at a 19
statistically significant level of general market trends of individual 20
companies and broader econometric data, and payment system 21
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operators have access to transactional data on a real-time basis. 1
The data can be manipulated to best fit corporate revenue trends, 2
giving equity analysts more information on how a company is 3
doing. More specifically, the embodiment can process the 4
transactional data to produce revenue predictions for a company 5
over the next month or quarter. Rotman ¶ 0024. 6
02. Rotman uses transaction data from credit cardholders to project 7
corporate revenues for companies traded on any of the major 8
exchanges (e.g., NYSE, NASDAQ, AMEX, etc.). Rotman ¶ 9
0053. 10
03. For each company of interest, the dollar value of all 11
transactions can be accumulated over a predetermined period of 12
time, for example from the first day of current business quarter 13
until the current day of business quarter. Rotman ¶ 0054. 14
04. The accumulated transaction data is then processed by an issuer 15
system. For example, the issuer system may normalize the data. 16
After normalizing the data, the issuer system scales the data. The 17
normalized data may be scaled using a number of different 18
techniques such as using linear regression or pattern recognition 19
via a neural network. Rotman ¶ 0055. 20
05. Depending on the particular parameter, different scaling 21
methods may be required to obtain a good prediction. For 22
example, to predict corporate revenue trends, some information 23
about a company's accounting practices may be necessary. An 24
airline may, for example, obtain payment for a July flight in 25
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January, when a traveler plans her trip. However, the airline may 1
not actually book the revenue until the ticket is used. Therefore, 2
there may be some delay between the time when a payment 3
transaction occurs and when the transaction is reflected in 4
revenue. Rotman ¶ 0056. 5
06. Finally, the issuer system applies the data to provide financial 6
information to end users. This may involve either providing the 7
scaled and normalized sales information in its raw form or 8
applying the scaled and normalized information to known or 9
newly created models for predicting financial metrics, such as 10
stock price, interest rates, or commodity supplies. One example is 11
the application of sales information to predict a company stock 12
performance. In another example, near real-time predictions may 13
be made about stored supplies of a commodity (such as gasoline). 14
Periodically, government agencies report information about 15
aggregate stored supply levels of particular commodities. Thus, 16
the last published report of commodity storage levels may be used 17
in conjunction with normalized and scaled information about 18
recent sales of the commodity to make near real-time predictions 19
of the actual storage levels, before the next report becomes 20
available. Rotman ¶ 0057. 21
07. A scale factor is useful for scaling normalized transaction data 22
(N) to produce a prediction of monthly or quarterly corporate 23
revenue. For example, to scale the data, a historical ratio can be 24
used that compares the normalized data to the actual reported 25
revenue of a company. Rotman ¶ 0071. 26
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08. For example, to scale the data, the value N is calculated which 1
corresponds to the result of normalizing the data. Next, the 2
number of past periods X that will be considered is determined. 3
Then, the ratio of normalized results to actual revenue for each of 4
the past X periods is calculated. Thereafter, the ratios for the past 5
X periods are averaged to determine the average ratio of 6
normalized transaction amounts to actual corporate revenue. 7
Finally, N is divided by the average ratio to produce a revenue 8
prediction. Rotman ¶ 0072. 9
09. As an example using a regression analysis to predict actual 10
revenue, a number X of past periods to consider in a regression 11
analysis is determined. Next, the normalized data corresponding to 12
the X periods is calculated . Then the actual revenue 13
corresponding to the relevant past periods is obtained. Next, based 14
on the normalized data and the actual revenue figures, a regression 15
equation is formulated using, for example, a "least squares" 16
method. Next, a normalized transaction figure is calculated 17
corresponding to a period for which revenue is to be predicted. 18
Finally, predicted revenue is calculated based on the formulated 19
regression equation. Rotman ¶ 0073. 20
10. Rotman describes a process for applying data using industry 21
trends. Tracking indices for specialized industries can be created 22
using the predicted revenue data. Examples of industries include 23
retail sales, transportation, airlines, mail order, Internet, etc. Singh 24
¶ 0086. 25
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Singh 1
11. Singh is directed to accurately forecasting future demand for 2
many products and product types in many markets. Singh enables 3
organizations to produce and compare alternative models of 4
forecasted demand in order to constantly improve demand-5
forecasting capabilities, producing and identifying optimal 6
demand forecasts that take into account independent causal 7
factors, such as new competitive products and price promotions, 8
that will impact upon future demand, so users can easily account 9
for current demand trends without having to produce an entirely 10
new forecast. Singh ¶’s 0014-0018. 11
12. In the manufacturing and distributing industries, supplying 12
products in response to the current level of customer demand with 13
a minimum of overstocking reduces stocking costs and 14
distribution expenses and thus leads to a reduction of the sales unit 15
price of products. Typically, this also leads to an enhancement of 16
profit margins. It is therefore necessary for sellers to forecast 17
product demand precisely such that they then can determine a 18
sales plan, production plan, and distribution plan according to an 19
accurate forecast of the demand trend of customers. Singh ¶ 0004. 20
13. Conventional methods of forecasting demand by analyzing the 21
trend of past sales results are performed with the goal of the 22
forecaster being to apply the most accurate statistical analysis 23
techniques and econometric modeling to provide the most 24
accurate forecast possible. In these conventional methods, time 25
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series forecasting is performed which develops and uses various 1
forecasting algorithms that attempt to describe the knowledge of 2
the business and fluctuation trend of sales results as evidenced by 3
past history in the form of a rule. Singh ¶ 0005. 4
14. Unfortunately it is common for product demand trends to 5
change in a short lifecycle. When this is the case, the data used in 6
providing a forecast rapidly becomes old and the precision of 7
forecasting lowers. Therefore, in order to keep a high precision in 8
forecasting, algorithms (and historic data points used to generate 9
those algorithms) must be maintained on an ongoing basis as well 10
as be able to adjust their forecasts relatively easily. Singh ¶ 0006. 11
15. Singh provides a multiple model framework that allows 12
multiple alternative forecasting algorithms, including well known 13
statistical algorithms such as Fourier and multiple linear 14
regression ("MLR") algorithms and proprietary algorithms useful 15
for modeling the causal factors relating to various products and/or 16
industries, to be associated with various data streams of demand 17
history to product advanced forecasting models. The present 18
invention helps users to determine the forecast algorithm that best 19
suits a given problem/industry/business from the available history 20
streams of demand data by enabling the production of various 21
forecasts for comparison with one another and, eventually, with 22
incoming data relating to actual demand. Singh ¶ 0020. 23
16. To generate a particular forecast, Singh calculates a variety of 24
forecasts according to alternative models and then selects the 25
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results of a particular forecast model that one feels is most 1
accurate and would like to submit (or "publish") for subsequent 2
supply planning. Algorithms as utilized in the multiple model 3
framework (MMF) are composed of a series of mathematical 4
calculations that are executed based upon history stream data in 5
order to create a statistical forecast. Preferred embodiments enable 6
users to achieve a balance between strictly statistical forecasts and 7
judgment forecasts by controlling the use of history data. 8
Statistical forecasts assume that products generate stable demand. 9
As such, statistical forecasts are typically more useful to reflect 10
the characteristics of mature products that are produced and 11
maintain long life cycles with many customers. These forecasts 12
rely upon a base history (i.e., history not containing such 13
anomalous or unusual data points) for predictable and repeatable 14
demand. Singh ¶ 0050. 15
17. Demand history data often includes data points (such as spikes 16
or valleys in demand) that can cause problems if they are included 17
within calculations to predict future demand. Examples of such 18
problematic data include variation in demand caused by unusual 19
market conditions, decreases in demand due to obsolete or 20
superseded products, general data errors, and the inability of the 21
user to distinguish between base and non-base data. In order to 22
account for such problematic data in base or non-base history, the 23
user is provided the opportunity to adjust history to overcome 24
such problems. Mechanisms by which a user can adjust history 25
data include manually editing extremely high or low values, 26
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controlling the time period of the relative history used to ignore 1
periods of sporadic and/or otherwise unreliable history, and 2
masking specific periods of unreliable and/or sporadic history. 3
Singh ¶ 0081. 4
ANALYSIS 5
Independent claims 1, 20, and 31 are method, computer media, and 6
system variants of forecasting revenue by applying some past and present 7
Internet metric data to a past revenue stream. The Examiner found that Sing 8
generally describes forecasting revenue by applying some demand data to a 9
past revenue stream in those instances where past revenue streams alone 10
might prove insufficient for accurate forecasting. This left the choice of 11
using some Internet metric data as for such scaling or normalizing as the sole 12
issue. In resolving this, the Examiner found that Singh described Internet 13
commerce demand data among other industry options. 14
Before even taking up the Examiner’s findings regarding Singh, we 15
initially find that the only calculations claimed are described by Rotman. 16
The nature of the particular factors used to normalize or scale are perceptible 17
only to the human mind. 18
In a recent non-precedential decision, our reviewing court reminded us 19
of the applicability of the precedential In re Gulack, 703 F.2d 1381 (Fed. 20
Cir. 1983), In re Bernhart, 417 F.2d 1395 (CCPA 1969) and In re Lowry, 21
32 F.3d 1579 (Fed. Cir. 1994) decisions. 22
We have held that patent applicants cannot rely on printed 23
matter to distinguish a claim unless “there exists [a] new and 24
unobvious functional relationship between the printed matter 25
and the substrate.” In re Gulack, 703 F.2d 1381, 1386 (Fed. 26
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Cir. 1983). [] [T]he Board did not create a new “mental 1
distinctions” rule in denying patentable weight []. On the 2
contrary, the Board simply expressed the above-described 3
functional relationship standard in an alternative formulation—4
consistent with our precedents—when it concluded that any 5
given position label’s function [] is a distinction “discernable 6
only to the human mind.” []; see In re Lowry, 32 F.3d 1579, 7
1583 (Fed. Cir. 1994) (describing printed matter as “useful and 8
intelligible only to the human mind”) (quoting In re Bernhart, 9
417 F.2d 1395, 1399 (CCPA 1969)). 10
In re Xiao, 2011 WL 4821929 (Fed Cir 2011)(Non-precedential). Thus 11
non-functional descriptive material, being useful and intelligible only to the 12
human mind, is given no patentable weight. See also In re Ngai, 367 F.3d 13
1336, 1339 (Fed. Cir. 2004). That is, we give weight to using some factor to 14
adjust prior revenue for modeling a forecast, but not to the particular factor 15
actually used, which is no more than arbitrary binary data as actually 16
employed. 17
The Examiner found that even giving such weight, however, the 18
independent claims were at least predictable. The Examiner found that 19
Singh adjusted revenue by essentially normalizing with respect to demand 20
changes. In any distribution model which relied on e-commerce, such 21
demand statistics would naturally be an Internet metric, being a metric of 22
demand communicated over the Internet. As Rotman describes the bulk of 23
the independent claims and Singh shows the predictability of using an 24
Internet metric for the one limitation not in Rotman, the independent claims 25
are predictable and therefore obvious. 26
We are not persuaded by the Appellants’ argument that Rotman teaches 27
away from using revenue data. Appeal Br. 11-13. Appellants cite Rotman ¶ 28
0018, which describes background art including providing merchant 29
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rankings instead of sales data as having limited utility. Appellants take this 1
to be discouraging the use of non-sales data. First, Rotman does admit that 2
such rankings have some utility. Second, rankings are but one form of 3
metric and that paragraph does not discourage using other non-sales metrics. 4
Indeed Rotman describes using transaction data generally, and not just sales 5
data. More to the point, the claims also use sales data – they just also use 6
something else as well – which both Rotman and Singh do too. 7
We are not persuaded by the Appellants’ argument that Singh’s history 8
streams do not contain Internet metric data. Appeal Br. 13-14. Singh’s 9
history data would represent the nature of the transaction history. One of the 10
exemplary contexts for such history is in Internet commerce. FF 10. 11
We are not persuaded by the Appellants’ argument that one cannot 12
combine the references. Appeal Br. 14-15. Both references describe 13
forecasting techniques. Such forecasting techniques are mathematical in 14
nature, and thus, the practicality of combining techniques is not bounded by 15
the specific examples described by the references. Singh describes 16
adjustments that might be applied to Rotman’s revenue forecasts using 17
demand data to improve forecasts. As revenue is clearly dependent on 18
demand, the applicability of Singh to Rotman is all too apparent. 19
CONCLUSIONS OF LAW 20
The rejection of claims 1-9 and 20-41 under 35 U.S.C. § 103(a) as 21
unpatentable over Rotman and Singh is proper. 22
DECISION 23
The rejection of claims 1-9 and 20-41 is affirmed. 24
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No time period for taking any subsequent action in connection with this 1
appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R. 2
§ 1.136(a)(1)(iv) (2011). 3
4
AFFIRMED 5
6
7
8
JRG 9