Ex Parte GrossDownload PDFPatent Trial and Appeal BoardMar 16, 201510856579 (P.T.A.B. Mar. 16, 2015) 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. 10/856,579 05/28/2004 John N. Gross JNG.2004_09 9830 40280 7590 03/16/2015 STEVEN VOSEN 1563 SOLANO AVENUE #206 BERKELEY, CA 94707 EXAMINER POUNCIL, DARNELL A ART UNIT PAPER NUMBER 3621 MAIL DATE DELIVERY MODE 03/16/2015 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 JOHN N. GROSS ___________ Appeal 2012–004490 Application 10/856,579 Technology Center 3600 ___________ Before ANTON W. FETTING, JOSEPH A. FISCHETTI, and PHILIP J. HOFFMANN, Administrative Patent Judges. FETTING, Administrative Patent Judge. DECISION ON APPEAL STATEMENT OF THE CASE 1 John N. Gross (Appellant) seeks review under 35 U.S.C. § 134 of a final rejection of claims 12–19 and 21–30, 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 Appellant’s Appeal Brief (“App. Br.,†filed July 1, 2011) and Reply Brief (“Reply Br.,†filed January 9, 2012), and the Examiner’s Answer (“Ans.,†mailed November 8, 2011). Appeal 2012-004490 Application 10/856,579 2 The Appellant invented a way of correlating and coordinating operations of electronic recommendation systems and online advertising systems employed by online content service providers. Specification 1:11–13. An understanding of the invention can be derived from a reading of exemplary claim 12, which is reproduced below [some paragraphing added]. 12. A method of delivering advertising from a third party through presentation of preference items embodied in recommendations provided by a recommender computing system to users of an online content service provider, the method comprising the steps of: (a) automatically causing the recommender computing system to identify a set of recommended items to be submitted to a user in response to a request for a recommendation on an item; (b) automatically determining whether any of said set of recommended items includes or should include one of the preference items associated with a preference to be given to the preference items based on a request provided by the third party Appeal 2012-004490 Application 10/856,579 3 which is an entity other than said user making the request for the recommendation; (c) based on the results of step (b) automatically causing the recommender computing system to give said preference to said third party and advertise said preference items by at least one of: 1) altering said set of recommended items to be shown to said user to include one of the preference items; and 2) altering a presentation format of said set of recommended items when it includes one of the preference items; (d) automatically computing a compliance rate achieved by the recommender computing system for presenting third party preference items in response to recommendation requests by users. The Examiner relies upon the following prior art: Gatto US 6,681,211 B1 Jan. 20, 2004 Smith US 6,853,982 B2 Feb. 8, 2005 Bezos US 6,963,850 B1 Nov. 8, 2005 Greenstein US 2006/0064304 A1 Mar. 23, 2006 Kazuhiro Kohara, Yoshimi Fukuhara, and Yukihiro Nakamura , Selectively Intensive Learning To Improve Large Change Prediction by Neural Networks, NTT Information and Communication Systems Laboratories, 463–466 (1996), last visited June 2010. (Hereinafter referred to as “Koharaâ€). Appeal 2012-004490 Application 10/856,579 4 Claims 12–19 and 21–30 stand rejected under 35 U.S.C. § 112, first paragraph, as lacking a supporting written description within the original disclosure. Claims 12–19 and 21–30 stand rejected under 35 U.S.C. § 112, second paragraph, as failing to particularly point out and distinctly claim the invention. Claims 12–16 and 21–27 stand rejected under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, and Gatto. 2 Claims 17, 18, 29, and 30 stand rejected under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, Gatto, and Kohara. Claims 19 and 28 stand rejected under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, Gatto, and Bezos. ISSUES The issues of written description turn primarily on whether there is adequate support for each of several limitations in the originally filed disclosure. The issues of definiteness turn primarily on whether the scope of the claims is ascertainable. The issues of obviousness turn primarily on whether the limitations of the independent claims are taught or otherwise made predictable by the prior art. 2 Claims 17–19 and 28depend from claims 12 and 25. Accordingly the rejections of claims 17–19 and 28 necessarily also are based on Greenstein and Gatto. The omission of these references from those dependent claim rejections is treated as inadvertent harmless error. Appeal 2012-004490 Application 10/856,579 5 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 Claim Construction 01. The disclosure contains no lexicographic definition of “preference item.†Facts Related to the Prior Art Smith 02. Smith is directed to monitoring activities of online users, and for recommending items to users based on such activities by providing personalized recommendations that are relevant to a current browsing session of a user. Smith 1:7–12. 03. A recommendation service is a computer-implemented service that recommends items from a database of items. The recommendations are customized to particular users based on information known about the users. One common application for recommendation services involves recommending products to online customers. Smith 1:15–20. 04. One technique commonly used by recommendation services is known as content-based filtering. Pure content-based systems operate by attempting to identify items which, based on an analysis of item content, are similar to items that are known to be of interest to the user. Smith 1:28–32. Appeal 2012-004490 Application 10/856,579 6 05. Another common recommendation technique is known as collaborative filtering. In a pure collaborative system, items are recommended to users based on the interests of a community of users, without any analysis of item content. Collaborative systems commonly operate by having the users explicitly rate individual items from a list of popular items. Smith 4:33–37. 06. Smith describes viewing histories of users to identify, for each product, a set of additional products that are deemed related to the first product. Smith 1:47–53. 07. Smith describes using the current and/or recent contents of the user’s shopping cart as inputs to the Recommendation Service. For example, if the user currently has three items in his or her shopping cart, these three items can be treated as the items of known interest for purposes of generating recommendations, in which case the recommendations may be generated and displayed automatically when the user views the shopping cart contents. This method of generating recommendations can also be used within other types of recommendation systems, including content- based systems and systems that do not use item-to-item mappings. Smith 8:25–40. 08. Smith describes how an item that is related to two or more of the items of known interest will generally be ranked more highly than (and thus recommended over) an item that is related to only one of the items of known interest. Smith 13:62–65. Appeal 2012-004490 Application 10/856,579 7 09. Smith describes how captured search terms/phrases may be used for a variety of purposes, such as filtering or ranking the personal recommendations returned. Smith 27:29–31. 10. Smith describes how the related items list for a given product is generated by retrieving the corresponding similar items list, optionally filtering out items falling outside the product category of the product, and then extracting the N top-rank items. Once this related items list 64 has been generated for a particular product, it may be re-used (e.g., cached) until the relevant similar items table 60 is regenerated. Smith 29:63–30:5. 11. Smith describes selecting a set of additional products to present to the user by taking into consideration a frequency with which a candidate additional product co-occurs with a viewed product within product viewing histories of users. Smith 3:56–60. 12. Smith describes combining sets of related items to form a ranked set of related items, in which a ranking of a related item reflects whether the ranked item is included within more than one of the plurality of sets of related items, and during the browsing session, generating and providing to the user a customized page that includes representations of recommended items selected from the ranked set. Smith 32:12–41. Greenstein 13. Greenstein is directed to collecting, monitoring and directing data from individual consumers and processing such against indicative Appeal 2012-004490 Application 10/856,579 8 data collected with respect to a plurality of consumer purchased and/or leased products/services. Greenstein para. 2. 14. Greenstein identifies as being needed a more accessible and easily utilized purchasing/leasing decisions making process provides a more customized purchasing/leasing decision solution based on information and/or preferences of the purchaser/lessor or the person who will utilize the products or services, which could include providing information and preferences that are particular to the persons who will utilize the products or services Greenstein para. 10. 15. Greenstein describes ranking of the products and/or services by ordering the optimal products or services, which would include the recommendation or selection of a single product or service, based on the unique facts and circumstances of each individual consumer. Greenstein para. 22. 16. Greenstein describes generating a recommendation and/or a comparison of services for a client in accordance with the preference data for the client. Greenstein para. 37. 17. Greenstein describes providing input concerning the products and or services that the third party expert will take into account and even as to the formulae upon which products and/or services will be recommended or sold. The purchaser then chooses which products or services and selects the parameters/criteria used to select or recommend the products and/or services. Greenstein paras. 64–65. Appeal 2012-004490 Application 10/856,579 9 18. Greenstein describes how a recommendation or selection can be precisely calculated to the need of and therefore unique to the individual purchaser that can also reflect the preferences of the participant and/or vendors. Greenstein para. 80. Gatto 19. Gatto is directed to managing and viewing historical data including security analysts’ predictions and actual reported data; measuring, analyzing, and tracking the historical performance of security analysts’ predictions; and creating, managing, backtesting, and using models that use such historical and performance data, attributes and other information to automatically produce better predictors of future events. Gatto 1:16–25. 20. Gatto describes how a user may create a model by identifying various factors to be taken into account in the model. For each factor, a user specifies rules by which each non-excluded analyst is assigned an N-score (normalized) according to the rules. Such factors may include, for example, accuracy, rating, broker list, experience, estimate age, and other factors. Each factor is assigned a weight to enable a user to place greater emphasis on one or more factors for a given model. For each model, the analyst's N-score for each factor is multiplied by the factor weight and those weighted N-scores are summed for each analyst. The actual emphasis placed on an analyst's current estimate is determined by taking the sum of the analyst's weighted factor Appeal 2012-004490 Application 10/856,579 10 scores divided by the sum of the weighted factor scores for all analysts. Gatto 4:50–65. Kohara 21. Kohara is directed to selective intensive learning techniques for improving the ability of back-propagation heuristic networks to learn and predict large changes. Gatto, Abstract. 22. Kohara describes reducing the learning rate for small change data as compared to large change data to increase the effectiveness in training a neural network. ANALYSIS Claims 12–19 and 21–30 rejected under 35 U.S.C. § 112, first paragraph, as lacking a supporting written description within the original disclosure We are persuaded by the Appellant’s argument that the Specification provides support for automating the steps in the claims. We are not persuaded by the Appellant’s argument that the Specification’s use of the word “preference†shows possession of “possession items†as used in the claims. The phrase “preference item†is not lexicographically defined and does not occur in the originally filed disclosure. The word “preference†in the originally filed disclosure refers to policy rather than items of goods or services. Claims 12 and 25 recite determining whether the recommended items includes preference items. This domain equivalence of preference and recommended items differs from the use in the originally field disclosure of preferences as policies. Appeal 2012-004490 Application 10/856,579 11 We are persuaded by the Appellant’s argument that the Specification supports the “compliance rate†limitation. App. Br. 16. We are not persuaded by the Appellant’s argument that the preference items of claim 13 are supported for the same reasons as claim 12, supra. We are persuaded by the Appellant’s argument that the bias determination recited in claim 19 is supported by the Specification. App. Br. 18. We are not persuaded by the Appellant’s argument that the originally filed disclosure supports the “learning rate†of claims 29 and 30. Appellant’s argument shows only that it might have been obvious, not that it was disclosed. One shows that one is “in possession†of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious. Id. (“[T]he applicant must also convey to those skilled in the art that, as of the filing date sought, he or she was in possession of the invention. The invention is, for purposes of the ‘written description’ inquiry, whatever is now claimed.â€) (emphasis in original). One does that by such descriptive means as words, structures, figures, diagrams, formulas, etc., that fully set forth the claimed invention. Although the exact terms need not be used in haec verba, see Eiselstein v. Frank, 52 F.3d 1035, 1038 . . . (Fed.Cir.1995) (“[T]he prior application need not describe the claimed subject matter in exactly the same terms as used in the claims . . . .â€), the specification must contain an equivalent description of the claimed subject matter. Lockwood v. Am. Airlines, Inc., 107 F.3d 1565, 1572 (Fed. Cir. 1997) Appeal 2012-004490 Application 10/856,579 12 Claims 12–19 and 21–30 rejected under 35 U.S.C. § 112, second paragraph, as failing to particularly point out and distinctly claim the invention We are persuaded by the Appellant’s arguments that the claims are definite in scope. App. Br. 20. Claims 12–16 and 21–27 rejected under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, and Gatto Claim 12 has four steps. The first step simply provides product or service recommendations. Appellant admits that Smith, as a generic reference describing such recommender systems, describes this step. The second and third steps determine whether the recommendations contain a preference item and (1) if not, show a preference item, or (2) if so, somehow alter the display of recommended items. The Examiner applies Greenstein to Smith primarily for this. Appellant contends these limitations are not described by either of the references. The last step is essentially an audit step reporting compliance. The Examiner applies Gatto to Smith for this, and again Appellant contends this is not described. As Smith describes both providing recommendations and altering the presentation based on some preference items, the issues extant are whether the types of preferences in the art differ from the claim, if so, then whether weight should be afforded to such difference, and whether it was predictable to report some compliance rate. Appeal 2012-004490 Application 10/856,579 13 The phrase “preference item†is not lexicographically defined and indeed does not even occur in the Specification. The only limitation in claim 12 concerning a preference item is that it be associated in some manner with some preference to be given to the preference items based on a request provided by the third party. There is no step or operation that forms such an association or bases the giving on a third party request. Thus, although Appellant contends that preference items are not merely labels, in the claims as drafted, the designation of data as preference items is no more than a label, as there are no operations that directly use or produce such preference items. In any event, the Examiner found that Greenstein describes using vendor preferences in making recommendations that are tailored based on those preferences. See FF 18. These recommendations and displays also rely on third party experts. See FF 17. Thus Greenstein shows it was predictable to use preferences from third party vendors or experts in altering the presentation from recommender systems in showing those preference items. Gatto describes managing and viewing historical data and tracking the historical performance. FF 19. Thus, Gatto shows it was at least predictable to track actual transaction history and rates providing statistical measures of that history. Gatto also shows it was known to normalize results and track accuracy and other factors for model evaluation. FF 20. As compliance is just a measure of accuracy in meeting policy, Gatto shows it was at least predictable to track a compliance rate. As to claim 17, reciting “identifying a learning rate for new items by the recommender system,†the Examiner found that Kohara discloses the Appeal 2012-004490 Application 10/856,579 14 computation of a learning rate. Ans. 13. Kohara does describe explicitly reducing the learning rate in some circumstances to train an expert system. As a recommender system is a form of expert system and therefore is predictably implemented with a neural network, Kohara’s description of tweaking learning rates to more effectively train the net is predictable in implementing Smith. As to claim 22, reciting “adjusting advertising for said preference items,†the Examiner found that Smith discloses selecting additional products to present. Ans. 12. CONCLUSIONS OF LAW The rejection of claims 12–19 and 21–30 under 35 U.S.C. § 112, first paragraph, as lacking a supporting written description within the original disclosure for automation and compliance rate limitations is improper. The rejection of claims 12–19 and 21–30 under 35 U.S.C. § 112, first paragraph, as lacking a supporting written description within the original disclosure for the preference items and learning rate limitations is proper. The rejection of claims 12–19 and 21–30 under 35 U.S.C. § 112, second paragraph, as failing to particularly point out and distinctly claim the invention is improper. The rejection of claims 12–16 and 21–27 under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, and Gatto is proper. The rejection of claims 17, 18, 29, and 30 under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, Gatto, and Kohara is proper. Appeal 2012-004490 Application 10/856,579 15 The rejection of claims 19 and 28 under 35 U.S.C. § 103(a) as unpatentable over Smith, Greenstein, Gatto, and Bezos is proper. DECISION The rejection of claims 12–19 and 21–30 is 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)(1)(iv) (2011). AFFIRMED rvb Copy with citationCopy as parenthetical citation