Ex Parte WeinerDownload PDFPatent Trial and Appeal BoardMay 8, 201813562733 (P.T.A.B. May. 8, 2018) Copy Citation UNITED STA TES p A TENT AND TRADEMARK OFFICE APPLICATION NO. FILING DATE 13/562,733 07 /31/2012 86738 7590 05/10/2018 MCCARTER & ENGLISH, LLP BOSTON 265 Franklin Street Boston, MA 02110 FIRST NAMED INVENTOR Steven Weiner 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 ATTORNEY DOCKET NO. CONFIRMATION NO. 121439-00301 6420 EXAMINER ORTIZ SANCHEZ, MICHAEL ART UNIT PAPER NUMBER 2658 NOTIFICATION DATE DELIVERY MODE 05/10/2018 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): docket@mccarter.com PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE BEFORE THE PATENT TRIAL AND APPEAL BOARD Ex parte STEVEN WEINER Appeal2017-006620 Application 13/562,733 1 Technology Center 2600 Before ROBERT E. NAPPI, MARC S. HOFF, and NORMAN H. BEAMER, Administrative Patent Judges. BEAMER, Administrative Patent Judge. DECISION ON APPEAL STATEMENT OF THE CASE Appellant appeals under 35 U.S.C. § 134(a) from the Examiner's final rejection of claims 1, 3-11, 13-21, and 23-30. Claims 2, 12, and 22 are cancelled. We have jurisdiction over the pending rejected claims under 35 U.S.C. § 6(b). We affirm. 1 Appellant identifies SRI International as the Real Party in Interest. (App. Br. 1.) Appeal2017-006620 Application 13/562,733 THE INVENTION Appellant's disclosed and claimed invention is directed to personalizing a voice user interface of a remote multi-user service, using a language model specific to an identified user. (Abstract.) Independent claim 1, reproduced below, is illustrative of the subject matter on appeal: 1. A computer-implemented method for personalizing a voice user interface of a single remote multi-user service, the method comprising: providing a voice user interface for the single remote multi-user service; receiving voice information from an identified user at the multi-user service through the voice user interface; retrieving from memory, in at least one server of the multi-user service, a language model specific to the identified user and specific to the multi-user service, which models one or more language elements relating to content stored by the multi- user service and associated with a personal account of the identified user, wherein the one or more language elements comprise metadata that describes content stored by the remote service in association with the personal account of the identified user and that are associated with at least one prior instance of voice information from the identified user; applying the retrieved language model, with a processor, to interpret the received voice information; responding to the interpreted voice information; and responsive to a change in the content maintained by the multi-user service and associated with the user, updating by the processor the language model specific to the identified user and the multi-user service so as to reflect said change. 2 Appeal2017-006620 Application 13/562,733 REJECTION The Examiner rejected claims 1, 3-11, 13-21, and 23-30 under 35 U.S.C. § 103(a) as being unpatentable over Van Kommer (US 2007/0124134 Al, pub. May 31, 2007) (hereinafter "Kommer"), and Zavaliagkos et al. (US 2013/0030804 Al, pub. Jan. 31, 2013) (hereinafter "Zavaliagkos"). (Final Act. 3-18.) ISSUE ON APPEAL Appellant's arguments in the Appeal Brief present the following issue: 2 Whether the Examiner erred in finding the combination of Kommer and Zavaliagkos teaches or suggests the independent claim 1 limitation: wherein the one or more language elements comprise metadata that describes content stored by the remote service in association with the personal account of the identified user and that are associated with at least one prior instance of voice information from the identified user .... and the commensurate limitations of independent claims 11 and 21. (Hereinafter, "the metadata limitation"). (App. Br. 9-22.) ANALYSIS We have reviewed the Examiner's rejections in light of Appellant's arguments that the Examiner erred. We disagree with Appellant's 2 Rather than reiterate the arguments of Appellant and the positions of the Examiner, we refer to the Appeal Brief (filed Sept. 15, 2016) (herein, "App. Br."); the Reply Brief (filed Mar. 13, 2017) (herein, "Reply Br."); the Final Office Action (mailed Mar. 18, 2016) (herein, "Final Act."); and the Examiner's Answer (mailed Jan. 12, 2017) (herein, "Ans.") for the respective details. 3 Appeal2017-006620 Application 13/562,733 arguments, and we adopt as our own ( 1) the pertinent findings and reasons set forth by the Examiner in the Action from which this appeal is taken (Final Act. 3-18) and (2) the corresponding findings and reasons set forth by the Examiner in the Examiner's Answer in response to Appellant's Appeal Brief. (Ans. 17-29.) We concur with the applicable conclusions reached by the Examiner, and emphasize the following. In finding that Kommer and Zavaliagkos teach or suggest the claimed subject matter, the Examiner relies on the disclosure in Kommer of a "multimodal business channel between users, service providers and network operators," in which "user dependent language models" are generated by speech recognition systems and are centrally stored in a database, as well as on user devices. The language models are continuously adapted, through user interactions and input, to improve the quality of speech recognition. Users may verbally interact with the system to obtain services - for example, to order travel packages, interact with call centers, or automatically select music played to the user. The language models used for access to the various services are updated on a daily basis, and the information used to update the models includes users' metadata. (Final Act. 3-5; Kommer Abstract, Fig. 3, i-fi-f 11,12, 16, 17, 40, 54, 70, 76-83, 91-102.) The Examiner also relies on the disclosure in Zavaliagkos of a method for improving the accuracy of transcription generated by an automatic speech recognition engine, which uses personal data of a client, such as metadata including the client's phone number or electronic address, and web-based sources such as social networking or other service providers, to improve the language models used by a central server, and send the improved models back to the server to facilitate more accurate future 4 Appeal2017-006620 Application 13/562,733 transcriptions. (Final Act. 3-5; Zavaliagkos Abstract, Figs. 3, 4B, i-fi-f 17, 19, 23, 25, 31, 37, 41, 48, claim 24.) Appellant argues the combination of Kommer and Zavaliagkos does not teach or suggest the metadata limitation at issue. (App. Br. 12-15.) Although the Specification provides no definition of "metadata" as used in the claims, it discloses an example: As one example, a user's media content can include music, videos, and images, as well as metadata associated with the music, videos, and images. Metadata for music can include, for example, artist names, album titles, song titles, playlists, music genres, and/or any other information related to the music. Metadata for videos can include, for example, video titles (e.g., movie names), actor names, director names, movie genres, and/or any other information related to the videos. (Spec. i1 42.) The Specification further discloses the use of the metadata as the basis for language models for speech recognition: "The personalized language model can be constructed for the user based on user information accessible by the user, such as, for example, the content of the user's multi- user service account and/or the metadata associated therewith." (Spec. i1 47 .) In this context, we are not persuaded the Examiner erred in finding that the combination of Kommer and Zavaliagkos teaches or suggests the metadata limitation. In particular, for this limitation the Examiner relies on the disclosure in Zavaliagkos of the use of a client's personal data- "such as address book information, SMS and email messages, web-based sources, etc."; or data "gathered from a social networking service or another service provider" - as the basis for language models used for speech recognition. (Final Act. 5-6; Ans. 19-22; Zavaliagkos i-fi-f 17, 19, 25, 34.) As the 5 Appeal2017-006620 Application 13/562,733 Examiner points out, this personal data includes metadata including a "phone number or electronic address of a person." (Ans. 21; Zavaliagkos claim 24, see also i-f 38 ("the term 'personal data' is intended to also include all supplemental data the system uses to create the vocabulary or improve the accuracy of the transcription").) Appellant argues that "Zavagliagkos' personal data stored on a client is simply not the 'content stored by the remote service in association with the personal account of the identified user' recited in claim 1," because the personal data of Zavaliagkos is stored on the client and not available to the server. (App. Br. 14.) However, the claims do not require the personal data, i.e., metadata, itself to be stored on a remote server - rather, the claims require the "language model ... which models" the metadata to be stored in a server memory. As the Examiner finds, Zavaliagkos discloses the speech recognition engine may be "locally running on the personal device or otherwise accessible by the personal device if stored/executed elsewhere." (Ans. 23; Zavaliagkos i-f 33.) In particular, Zavaliagkos discloses that after the client "rescores" the results of the speech recognition engine, it "sends the results back to the server," which results include updated language models of metadata encountered during speech recognition. (Zavaliagkos i-f 17, see also Figs. 3 (elements 345, 350), 4B (element 482), 6 (element 635), i-fi-131, 37, 41, 48, 72, 74.) The results are sent back to the server in order "to be considered during future transcriptions." (Zavaliagkos i-f 41.) Appellant further argues the Examiner errs in combining the references because "Zavaliagkos and Kommer teach away from each other in that they stem from opposite paradigms." (App. Br. 16.) Appellant 6 Appeal2017-006620 Application 13/562,733 argues Zavaliagkos is devoted to improving transcription accuracy and is premised on users protecting privacy and avoiding sharing personal information, while Kommer does not mention transcription, and is premised on users giving up privacy for particular benefits. (Id.) These arguments are unpersuasive. As discussed above, both references are directed to improvements in automatic speech recognition, and in particular to the use of language models personalized in accord with individual user information. Also as discussed above, although Zavaliagkos confines user information to the local users devices, it shares the language models based on the user information with the central server, similar to the approach of Kommer and the preferred embodiment of Appellant's Specification. Moreover, "[t]he test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference .... Rather, the test is what the combined teachings of those references would have suggested to those of ordinary skill in the art." In re Keller, 642 F.2d 413, 425 (CCPA 1981). "Combining the teachings of references does not involve an ability to combine their specific structures." In re Nievelt, 482 F.2d 965, 968 (CCPA 1973). We therefore are not persuaded that the Examiner erred in combining Zavaliagkos and Kommer based on the motivation to improve the capabilities of Kommer using the teachings of Zavaliagkos "for the benefit of improving the transcription[] accuracy by rescoring using the personalized data." (Final Act. 6; see also Ans. 26-27.) As the Examiner further finds, and we agree: In regards to the motivation to combine both references are within the same field of endeavor Kommer teaches methods for personalizing services in a telecommunications network 7 Appeal2017-006620 Application 13/562,733 which involves generating user dependent language models in a speech recognition system and Zavaliagkos teaches methods for improving accuracy of a transcription in an automatic speech recognition engine. The Zavaliagkos invention would improve on the Kommer speech recognizer by using a personal profile of a user which is what is taught in the claims. (Ans. 19.) Appellant does not point to any evidence of record that the resulting combination would be "uniquely challenging or difficult for one of ordinary skill in the art" or "represented an unobvious step over the prior art." Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1162 (Fed. Cir. 2007) (citing KSR Int'! Co. v. Teleflex Inc., 550 U.S. 398, 418-19 (2007)). The Examiner's findings are reasonable because the skilled artisan would "be able to fit the teachings of multiple patents together like pieces of a puzzle" because the skilled artisan is "a person of ordinary creativity, not an automaton." KSR, 550 U.S. at 420-21. We are persuaded the claimed subject matter exemplifies the principle that "[ t ]he combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results." KSR, 550 U.S. at 416. Accordingly, we sustain the Examiner's obviousness rejection of independent claims 1, 11, and 21. 8 Appeal2017-006620 Application 13/562,733 CONCLUSION For the reasons stated above, we sustain the Examiner's obviousness rejection of independent claims 1, 11, and 21 over Kommer and Zavaliagkos. We also sustain the obviousness rejections of claims 3-10, 13- 20, and 23-30 over Kommer and Zavaliagkos, which rejections are not argued separately with particularity. (App. Br. 17, 19, 21-22.) DECISION We affirm the Examiner's decision rejecting claims 1, 3-11, 13-21, and 23-30. No time period for taking any subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a)(l )(iv). See 37 C.F.R. § 41.50(f). AFFIRMED 9 Copy with citationCopy as parenthetical citation