Facebook, Inc.Download PDFPatent Trials and Appeals BoardJan 29, 20212020003121 (P.T.A.B. Jan. 29, 2021) 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. 15/174,865 06/06/2016 Robert Oliver Burns Zeldin 108282.020540 6620 169063 7590 01/29/2021 BakerHostetler / Facebook Cira Centre 12th Floor 2929 Arch Street Philadelphia, PA 19104-2891 EXAMINER EVANS, KIMBERLY L ART UNIT PAPER NUMBER 3629 NOTIFICATION DATE DELIVERY MODE 01/29/2021 ELECTRONIC 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. Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the following e-mail address(es): eofficemonitor@bakerlaw.com PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ____________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ____________ Ex parte ROBERT OLIVER BURNS ZELDIN, NATHAN JOHN DAVIS, ANAND SUMATILAL BHALGAT, HARSH DOSHI, and HAO SONG ____________ Appeal 2020-003121 Application 15/174,8651 Technology Center 3600 ____________ Before JAMES P. CALVE, BRUCE T. WIEDER, and BRADLEY B. BAYAT, Administrative Patent Judges. WIEDER, Administrative Patent Judge. DECISION ON APPEAL This is a decision on appeal under 35 U.S.C. § 134 from the Examiner’s rejection of claims 1–21. We have jurisdiction under 35 U.S.C. § 6(b). We REVERSE. 1 We use the word “Appellant” to refer to “applicant” as defined in 37 C.F.R. § 1.42. Appellant identifies the real party in interest as Facebook Technologies, LLC. (Appeal Br. 1.) Appeal 2020-003121 Application 15/174,865 2 CLAIMED SUBJECT MATTER Appellant’s invention relates “to selecting content to online system users based on actions by the users at least a reasonable amount of time after presentation of the content.” (Spec. ¶ 1.) Claims 1 and 12 are the independent claims on appeal. Claim 1 is illustrative. It recites (emphasis added): 1. A method comprising: receiving content items at an online system for presentation to users of the online system; retrieving models maintained by the online system for determining likelihoods of users performing one or more interactions with presented content items within a threshold amount of time after presentation of the presented content items based on characteristics of the users and characteristics of content items; determining weights associated with each of the retrieved models based on prior actions by users after presentation of content items to users of the online system; generating a model for determining a latent metric describing user actions occurring after the threshold amount of time after presentation of content items by applying the weights to the retrieved models associated with the weights and combining the retrieved models after application of the weights; identifying an opportunity to present one or more content items to the user; identifying content items eligible for presentation to the user from the received content items; determining the latent metric describing user actions occurring after the threshold amount of time after presentation of an identified content item for each identified content item using the model for determining the latent metric describing user actions occurring after the threshold amount of time after presentation of content items; selecting a content item of the identified content items for presentation to the user based on the determined latent metrics; and Appeal 2020-003121 Application 15/174,865 3 providing the selected content item to a client device for presentation to the user. REJECTIONS2 Claims 10 and 21 are rejected under 35 U.S.C. § 112(b) as indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 1, 2, 4–6, 10–13, 15–17, and 21 are rejected under 35 U.S.C. § 103 as unpatentable in view of Zhang (US 2014/0068011 A1, pub. Mar. 6, 2014) and Phil Blunsom, Hidden Markov Models, Aug. 19, 2004, (available at http://tka4.org/materials/lib/Articles- Books/Speech%20Recognition/hmm-tutorial.pdf (last visited Jan. 27, 2021) (hereinafter “Blunsom”)). Claims 3 and 14 are rejected under 35 U.S.C. § 103 as unpatentable in view of Zhang, Blunsom, and El-Rafei (US 2009/0018983 A1, pub. Jan. 15, 2009). Claims 7–9 and 18–20 are rejected under 35 U.S.C. § 103 as unpatentable in view of Zhang, Blunsom, and Karande (US 2015/0206170 A1, pub. July 23, 2015). 2 The Answer states that “[p]er the Advisory Action dated 9/11/19, the rejection of claims 10 and 21 under 35 U.S.C. 112(b) has been withdrawn.” (Answer 4.) However, the Advisory Action maintains the rejection of claims 10 and 21 under § 112(b), and the § 112(b) rejection is addressed in the Answer. (See id. at 4–6.) The Advisory Action withdraws the § 101 rejection of claims 12–21. (See Advisory Action dated Sept. 11, 2019.) Moreover, the § 112(b) rejection is argued in the appeal briefs and the § 101 rejection is not argued. Therefore, we treat as erroneous the statement in the Answer that the § 112(b) rejection is withdrawn, and instead treat the § 101 rejection as withdrawn. Appeal 2020-003121 Application 15/174,865 4 ANALYSIS The § 112(b) rejection of dependent claims 10 and 21 Claim 10 recites (paragraphing and emphasis added): 10. The method of claim 1, wherein a model maintained by the online system for determining a likelihood of users performing an interaction with a presented content item within the threshold amount of time after presentation of the presented content items is selected from a group consisting of: a model determining a likelihood of users accessing the presented content item, a model determining a likelihood of users performing a specific interaction with the presented content item, a model determining a likelihood of users performing a specific interaction with an object associated with the presented content item, a model determining an amount of time users will view the presented content item, and any combination thereof. The Examiner rejects claim 10 because the “Examiner was unable to find support, either by way of example or a clear definition in the specification for the conjunctive interpretation that appellant is claiming for selecting a model.” (Answer 5.) Specifically, the Examiner finds that Appellant’s disclosure at ¶6 teaches, “... Example models maintained by the online system include: a model determining a likelihood of a user accessing content item presented to the user, a model determining a likelihood of the user performing a specific interaction with a content item presented to the user (e.g., expressing a preference for the content item, sharing the content item with another user, commenting on the content item), a model determining a likelihood of the user performing a specific interaction with an object (e.g., a page, a user, etc.) Appeal 2020-003121 Application 15/174,865 5 associated with a content item presented to the user, a model determining an amount of time the user will view a content item presented to the user, or models predicting any other suitable interaction with a content item presented to the user. The description provided in the specification fails to provide the conjunctive interpretation “and” suggested by appellant inclusive of all models and combinations. (Id. at 5–6.) Appellant argues that “claims 10 and 21 recite an acceptable form of Markush grouping (claim construction: ‘selecting from a group consisting of A, B, and C’).” (Reply Br. 3.) “A Markush group is a listing of specified alternatives of a group in a patent claim, typically expressed in the form: a member selected from the group consisting of A, B, and C.” Abbott Labs. v. Baxter Pharm. Prods., Inc., 334 F.3d 1274, 1280 (Fed. Cir. 2003). A Markush group should be closed, i.e., it should be characterized with the phrase “consisting of” rather than, e.g., comprising. Id. Claim 10 recites “a model maintained by the online system . . . is selected from a group consisting of: a model . . . , a model . . . , a model . . . , a model . . . , and any combination thereof.” Claim 10 is in proper Markush form. We do not agree that claim 10 is indefinite under § 112(b), and we will reverse this rejection of claim 10 and of claim 21, which contains similar language.3 3 The Examiner suggests that these claims, properly construed, may not have sufficient written description support under § 112(a). (See Answer 5–6.) But the Examiner does not make such a rejection, and we do not address this question in the first instance. Appeal 2020-003121 Application 15/174,865 6 The § 103 rejections Obviousness is a legal conclusion involving a determination of underlying facts. Under § 103, the scope and content of the prior art are to be determined; differences between the prior art and the claims at issue are to be ascertained; and the level of ordinary skill in the pertinent art resolved. Against this background, the obviousness or nonobviousness of the subject matter is determined. Such secondary considerations as commercial success, long felt but unsolved needs, failure of others, etc., might be utilized to give light to the circumstances surrounding the origin of the subject matter sought to be patented. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007) (quoting Graham v. John Deere Co. of Kansas City, 383 U.S. 1, 17–18 (1966)). With regard to the scope and content of the prior art, the Examiner finds that Zhang teaches content selection module 162 may use the various data in client identifier data 190 and content database 122 as inputs to a predictive model, such as a linear regression model. For example, content selection module 162 may generate a pCTR [(predicted click-through rate)] or pCVR [(predicted conversion rate4)] for third-party content eligible for selection for client device 102. Content selection service 198 may use such a metric in the content selection process. . . . Hence, Zhang teaches the intended function of appellant’s weights associated with models based on retrieved user actions. (Answer 8–9 (citing Zhang ¶ 30).) 4 “Generally, a conversion refers to any specified type of online action performed by a user after being presented the third-party content. For example, a conversion may correspond to the user clicking on the third-party content and making a purchase at the third-party content provider’s website, signing up for an online newsletter, downloading software, etc.” (Zhang ¶ 19.) Appeal 2020-003121 Application 15/174,865 7 The Examiner also finds that Blunsom teaches various models including weighted models (equations 31, 32) for generating sequences (log all the probability values and then add values (equation 31) …using scaling coefficients (weights) that keep the probability values ...that are dependent on t (equation 32) ...-pg 7) that can be characterized by an underlying process, hence, the intended function of appellant’s weighted models (applying weights to models ... combing [sic] the retrieved models). (Id. at 11.) The Examiner then determines that it would have been obvious “to modify the method/system for predicting content performance of Zhang with the Hidden Markov Models (statistical tools) as taught by Blunsom since it allows for modeling a wide range of time series data (Abstract, Introduction lines 1, 2, Figs 1-6, pgs 3-7).” (Id. at 11.) Appellant argues that Zhang determines whether an indication of a click was received in a certain amount of time, which is distinct from applying weights to received models and combining the retrieved models. Similarly, Blunsom’s use of a Hidden Markov Model does not generate a model by “applying the weights to the retrieved models associated with the weights and combining the retrieved models after application of the weights,” as claimed. (Reply Br. 7–8.) Zhang “generally relates to the selection of content for a client device.” (Zhang ¶ 2.) More particularly, Zhang teaches using “topical interest categories associated with [a] client identifier . . . as inputs to a prediction model to predict the likelihood of an online action occurring as a result of third-party content being selected.” (Id., Abstract.) Appeal 2020-003121 Application 15/174,865 8 Blunsom relates to the use of Hidden Markov Models (HMMs). The HMM “is a powerful statistical tool for modeling generative sequences that can be characterised by an underlying process generating an observable sequence. HMMs have found application in many areas interested in signal processing . . . .” (Blunsom 1.) Blunsom teaches that “[g]iven a HMM, and a sequence of observations, we’d like to be able to compute . . . the probability of the observation sequence given a model.” (Id. at 3.) One approach “is to recognize that many redundant calculations would be made by direct[] evaluat[ion].” (Id.) Blunsom implements a “cache [of calculations] as a trellis of states at each time step, [and] calculat[es] the cached valued . . . for each state as a sum over all states at the previous time step.” Blunsom uses a “forward probability variable” to “work through the trellis filling in . . . values.” (Id.) “The algorithm for this process is called the forward algorithm.” (Id.) Blunsom explains that “[w]hen implementing a HMM, floating-point underflow is a significant problem.” (Id. at 7.) And with regard to the forward algorithm, “[t]he most common solution to this problem is to use scaling coefficients that keep the probability values in the dynamic range of the [computing] machine, and that are dependent only on t [(time)].” (Id.) We agree with the Examiner that Appellant’s Specification “does not distinctly describe any software or algorithm specifying the criteria regarding how the weights are determined and/or applied to the retrieved models, and subsequently combined.” (Answer 13.) But claim 1 itself recites both “determining [the] weights associated with each of the retrieved models based on prior actions by users” and “applying the weights to the Appeal 2020-003121 Application 15/174,865 9 retrieved models associated with the weights and combining the retrieved models after application of the weights.” (See Claim 1.) The Supreme Court in KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398 . . . (2007), explained that, “because inventions in most, if not all, instances rely upon building blocks long since uncovered, and claimed discoveries almost of necessity will be combinations of what, in some sense, is already known,” “it can be important to identify a reason that would have prompted a person of ordinary skill in the relevant field to combine the elements in the way the claimed new invention does.” Id. at 418– 19. Personal Web Techs., LLC v. Apple, Inc., 848 F.3d 987, 991–92 (Fed. Cir. 2017). Even if we agree that pCTR and pCVR are weights associated with each of the retrieved models, the Examiner does not sufficiently explain why, in view of Blunsom’s teaching of using scaling coefficients to avoid floating-point underflow, it would have been obvious to generate a latent metric model to describe user actions after a threshold amount of time “by applying the weights to the retrieved models . . . and combining the retrieved models after application of the weights,” as required by claim 1. Therefore, we will reverse the rejection of claim 1. Independent claim 12 contains similar language and for similar reasons we will also reverse the rejection of claim 12, and of dependent claims 2–11 and 13–21. Appeal 2020-003121 Application 15/174,865 10 CONCLUSION The Examiner’s rejection of claims 10 and 21 under 35 U.S.C. § 112(b) is reversed. The Examiner’s rejections of claims 1–21 under 35 U.S.C. § 103 are reversed. Specifically: Claims Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed 10, 21 112(b) Indefiniteness 10, 21 1, 2, 4–6, 10–13, 15–17, 21 103 Zhang, Blunsom 1, 2, 4–6, 10–13, 15–17, 21 3, 14 103 Zhang, Blunsom, El-Rafei 3, 14 7–9, 18– 20 103 Zhang, Blunsom, Karande 7–9, 18– 20 Overall Outcome 1–21 REVERSED Copy with citationCopy as parenthetical citation