Ex Parte Dao et alDownload PDFBoard of Patent Appeals and InterferencesJul 10, 201210139503 (B.P.A.I. Jul. 10, 2012) Copy Citation 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 Appeal 2011-002013 Application 10/139,503 2 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 Appeal 2011-002013 Application 10/139,503 3 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 Appeal 2011-002013 Application 10/139,503 4 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 Appeal 2011-002013 Application 10/139,503 5 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 Appeal 2011-002013 Application 10/139,503 6 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 Appeal 2011-002013 Application 10/139,503 7 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 Appeal 2011-002013 Application 10/139,503 8 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 Appeal 2011-002013 Application 10/139,503 9 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 Appeal 2011-002013 Application 10/139,503 10 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 Appeal 2011-002013 Application 10/139,503 11 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 Appeal 2011-002013 Application 10/139,503 12 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 Appeal 2011-002013 Application 10/139,503 13 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 Appeal 2011-002013 Application 10/139,503 14 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 Copy with citationCopy as parenthetical citation