Source: http://www.google.com/patents/US7991770?dq=5,815,488
Timestamp: 2017-01-22 03:59:03
Document Index: 263137547

Matched Legal Cases: ['Application No. 200680051559', 'art 1', 'art 1', 'art 2', 'art 2', 'Application No. 06838486', 'Application No. 06838488']

Patent US7991770 - Detecting repeating content in broadcast media - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsSystems, methods, devices, and computer program products provide social and interactive applications for detecting repeating content in broadcast media. In some implementations, a method includes: generating a database of audio statistics from content; generating a query from the database of audio statistics;...http://www.google.com/patents/US7991770?utm_source=gb-gplus-sharePatent US7991770 - Detecting repeating content in broadcast mediaAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS7991770 B2Publication typeGrantApplication numberUS 11/563,665Publication dateAug 2, 2011Filing dateNov 27, 2006Priority dateNov 29, 2005Fee statusPaidAlso published asCA2631151A1, CA2631151C, CA2631270A1, EP1955458A2, EP1955458A4, EP1955458B1, EP1958362A2, EP1958362A4, EP1986145A1, US8442125, US8479225, US8700641, US20070124756, US20070130580, US20070143778, US20120059845, US20150156542, WO2007064640A2, WO2007064640A3, WO2007064641A2, WO2007064641A3Publication number11563665, 563665, US 7991770 B2, US 7991770B2, US-B2-7991770, US7991770 B2, US7991770B2InventorsMichele Covell, Shumeet Baluja, Michael FinkOriginal AssigneeGoogle Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (46), Non-Patent Citations (60), Referenced by (36), Classifications (89), Legal Events (3) External Links: USPTO, USPTO Assignment, EspacenetDetecting repeating content in broadcast media
This application is related to U.S. patent application Ser. No. 11/563,653, for “Determining Popularity Ratings Using Social and Interactive Applications For Mass Media,” filed Nov. 27, 2006, and U.S. patent application Ser. No. 11/563,661, for “Social and Interactive Applications For Mass Media,” filed Nov. 27, 2006. Each of these patent applications is incorporated by reference herein in its entirety.
Conventional television and interactive television systems lack the ability to detect rebroadcasts of advertising embedded in television programming. Conventional recording devices allow users to store television programs (including commercials) for rebroadcast at a later date or time. A common complaint among broadcasters is their inability to profit from these rebroadcasts, which from the broadcasters' perspective amounts to “free” advertising for the advertisers who bought space on the show's original airing.
In “Audio Identification Based on Signatures,” an implementation of a retrieval process includes the following steps: 1) given the audio spectra of an audio snippet, extract spectral images of, for example, 11.6*w ms duration, with random spacing averaging d-ms apart. For each spectral image: 2) compute wavelets on the spectral image; 3) extract the top-t wavelets; 4) create a binary representation of the top-t wavelets; 5) use min-hash to create a sub-fingerprint of the top-t wavelets; 6) use LSH with b bins and 1 hash tables to find sub-fingerprint segments that are close matches; 7) discard sub-fingerprints with less than v matches; 8) compute a Hamming distance from the remaining candidate sub-fingerprints to the query sub-fingerprint; and 9) use dynamic programming to combined the matches across time.
The process 300 begins when a client-side interface (e.g., client-side interface 102) monitors and records snippets of ambient audio of a mass media broadcast in a broadcast environment (302). The recorded ambient audio snippets are encoded into descriptors (e.g., compressed statistical summaries), which can be sent to an audio database server (304) as queries. The audio database server receives the queries from the client (306). The audio database server compares the queries against a database of reference descriptors computed from mass media broadcast statistics to determine candidate descriptors that best match the query (308). The candidate descriptors are sent to a social application server or other network resource, which uses the candidate descriptors to aggregate personalized information for the user (310). For example, if the user is watching the television show “Seinfeld,” then query descriptors generated from the show's ambient audio will be matched with reference descriptors derived from previous “Seinfeld” broadcasts. Thus, the best matching candidate descriptors are used to aggregate personalized information relating to “Seinfeld” (e.g., news stories, discussion groups, links to ad hoc social peer communities or chat rooms, advertisements, etc.). In some implementations, the matching procedure is efficiently performed using hashing techniques (e.g., direct hashing or locality sensitive hashing (LSH)) to achieve a short list of candidate descriptors, as described with respect to FIG. 4. The candidate descriptors are then processed in a validation procedure, such as described in Ke et al.
If a “summary” (i.e., shorter version) of non-repeated program material is needed, one way to get that is to remove the advertisements (as detected by repeated material) and to take segments from the material just preceding and just following the advertisement location. On broadcast television, these positions in the program typically contain “teasers” (before the ads) and “recaps” (just after the ads). If a summary is to be made of a news program that includes a mix of non-repeated and repeated non-advertisement material, typically the repeated non-advertisement material corresponds to a sound bite. These segments generally contribute less information than the anchorperson's narration of the news story and are good candidates for removal. If a summary is to be made of a narrative program (e.g. a movie or a serial installment), repeated audio tracks typically correspond to theme sounds, mood music, or silence. Again, these are typically good segments to remove from a summary video. The process 700 described below provides a way of detecting these repeated audio tracks so they can be removed from the summary video.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS4811399Jun 14, 1988Mar 7, 1989Itt Defense Communications, A Division Of Itt CorporationApparatus and method for automatic speech recognitionUS5706364Apr 28, 1995Jan 6, 1998Xerox CorporationMethod of producing character templates using unsegmented samplesUS6023693Oct 6, 1995Feb 8, 2000Fujitsu LimitedLearning system for a data processing apparatusUS6236758Oct 22, 1999May 22, 2001Sarnoff CorporationApparatus and method for encoding wavelet trees by backward predictive coding of wavelet transformed coefficientsUS6494720Nov 14, 1997Dec 17, 2002Jan MeyrowitschMethods for objectification of subjective classificationsUS6563909Dec 20, 2000May 13, 2003Koninklijke Philips Electronics N.V.X-ray examination apparatusUS6585521Dec 21, 2001Jul 1, 2003Hewlett-Packard Development Company, L.P.Video indexing based on viewers' behavior and emotion feedbackUS6704920Oct 30, 2001Mar 9, 2004Nova Measuring Instruments Ltd.Process control for micro-lithographyUS6751601Jul 20, 2001Jun 15, 2004Pablo ZegersMethod and a system for solving dynamic problems using the dynamical system architectureUS6763339Jun 25, 2001Jul 13, 2004The Regents Of The University Of CaliforniaBiologically-based signal processing system applied to noise removal for signal extractionUS6766523Nov 27, 2002Jul 20, 2004Microsoft CorporationSystem and method for identifying and segmenting repeating media objects embedded in a streamUS6773266Jun 18, 2002Aug 10, 2004Athenium, L.L.C.Method for implementing collaborative training and online learning over a computer network and related techniquesUS6782186Dec 23, 1996Aug 24, 2004Interval Research Corp.System and method for selective recording of informationUS6879967Mar 24, 2000Apr 12, 2005Ricoh Co., Ltd.Method and apparatus for open data collectionUS6892191Feb 7, 2000May 10, 2005Koninklijke Philips Electronics N.V.Multi-feature combination generation and classification effectiveness evaluation using genetic algorithmsUS6895514Jun 25, 1999May 17, 2005Lucent Technologies Inc.Method and apparatus for achieving secure password accessUS6944632Apr 21, 2003Sep 13, 2005Prn CorporationMethod and apparatus for gathering statistical information about in-store content distributionUS7103801Nov 1, 2002Sep 5, 2006AlcatelMethod and apparatus for analyzing alarms coming from a communications networkUS7107207Jun 19, 2002Sep 12, 2006Microsoft CorporationTraining machine learning by sequential conditional generalized iterative scalingUS7266492Aug 16, 2006Sep 4, 2007Microsoft CorporationTraining machine learning by sequential conditional generalized iterative scalingUS7281219Dec 5, 2003Oct 9, 2007International Business Machines CorporationBlended learning experience tool and methodUS7375304Dec 6, 2004May 20, 2008Lincoln Global, Inc.System and method providing automated welding notificationUS7472096Dec 16, 2005Dec 30, 2008Microsoft CorporationTraining a learning system with arbitrary cost functionsUS7617164Mar 17, 2006Nov 10, 2009Microsoft CorporationEfficiency of training for ranking systems based on pairwise training with aggregated gradientsUS20020023020Jul 13, 2001Feb 21, 2002Kenyon Stephen C.Audio identification system and methodUS20020133499Aug 20, 2001Sep 19, 2002Sean WardSystem and method for acoustic fingerprintingUS20030033223 *Aug 8, 2002Feb 13, 2003Yamaha CorporationContent sales site and programUS20030093790Jun 8, 2002May 15, 2003Logan James D.Audio and video program recording, editing and playback systems using metadataUS20030101144Nov 29, 2001May 29, 2003Compaq Information Technologies Group, L.P.System and method for detecting repetitions in a multimedia streamUS20040025174May 29, 2003Feb 5, 2004Predictive Media CorporationMethod and system for the storage, viewing management, and delivery of targeted advertisingUS20040031058May 8, 2003Feb 12, 2004Richard ReismanMethod and apparatus for browsing using alternative linkbasesUS20040128682Dec 31, 2002Jul 1, 2004Kevin LigaTechniques for reinsertion of local market advertising in digital video from a bypass sourceUS20040199387Apr 26, 2004Oct 7, 2004Wang Avery Li-ChunMethod and system for purchasing pre-recorded musicUS20050009620Aug 9, 2004Jan 13, 2005Hodgetts George W.Golf club shaft with adjustable flexUS20050066352Nov 12, 2004Mar 24, 2005Microsoft CorporationSystem and method for providing user control over repeating objects embedded in a streamUS20050086682 *Oct 15, 2003Apr 21, 2005Burges Christopher J.C.Inferring information about media stream objectsUS20050147256Dec 30, 2003Jul 7, 2005Peters Geoffrey W.Automated presentation of entertainment content in response to received ambient audioUS20050193016 *Feb 17, 2005Sep 1, 2005Nicholas SeetGeneration of a media content database by correlating repeating media content in media streamsUS20050283792 *Aug 26, 2005Dec 22, 2005Swix Scott RSystems, methods and products for assessing subscriber content accessUS20060080356 *Oct 13, 2004Apr 13, 2006Microsoft CorporationSystem and method for inferring similarities between media objectsUS20060174348Jan 11, 2006Aug 3, 2006Rhoads Geoffrey BWatermark-based personal audio applianceUS20070130580Nov 27, 2006Jun 7, 2007Google Inc.Social and Interactive Applications for Mass MediaUS20070143778Nov 27, 2006Jun 21, 2007Google Inc.Determining Popularity Ratings Using Social and Interactive Applications for Mass MediaUS20080090551 *Nov 27, 2007Apr 17, 2008Yoad GidronRule-based system and method for managing the provisioning of user applications on limited-resource and/or wireless devicesUS20080263041 *Nov 14, 2006Oct 23, 2008Mediaguide, Inc.Method and Apparatus for Automatic Detection and Identification of Unidentified Broadcast Audio or Video SignalsEP1524857A2Aug 23, 2004Apr 20, 2005Microsoft CorporationInferring information about media stream objects* Cited by examinerNon-Patent CitationsReference1"Community" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/50045241?single=1&query-type=word&queryword=community.2"Community" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/50045241?single=1&query—type=word&queryword=community.3"Compression" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/50045890?single=1&query-type=word&queryword=compression.4"Compression" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/50045890?single=1&query—type=word&queryword=compression.5"Database" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/50057772?single=1&query-type=word&queryword=database.6"Database" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/50057772?single=1&query—type=word&queryword=database.7"Encrypt" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/00292459?single=1&query-type=word&queryword=encrypt.8"Encrypt" definition. Oxford English Dictionary. Accessed Apr. 27, 2009. http://dictionary.oed.com/cgi/entry/00292459?single=1&query—type=word&queryword=encrypt.9‘Shazam Experience Music’ [online]. [retrieved on May 30, 2007]. Retrieved from the Internet: <URL: www.shazam.com/music/portal/sp/s/media-type/html/user/anon/page/default/template/Myhome/music.html>, 2 pages.10A Multi-layer Adaptive Function Neural Network (MADFUNN) for Analytical Function Recognition, Miao Kang; Palmer-Brown, D.; Neural Networks, 2006, IJCNN '06. International Joint Conference on Digital Object Identifier 10.1109/IJCNN.2006.246895 Publication Year 2006, pp. 1784-1789.11An adaptive function neural network ADFUNN) for phrase recognition, Miao Kang; Palmer-Brown, D.; Neural Networks, 2005. IJCNN '05. Proceedings 2005 IEEE International Joint Conference on vol. 1, Digital Object Identifier: 10.1109/IJCNN.2005.1555898 Publication Year 2005, pp. 593-597, vol. 1.12An all-phoneme ergodic HMM for unsupervised speaker adaptation, Miyazawa, Y.; Acoustics, Speech, and Signal Processing, 1993, ICASSP-93, 1993 IEEE International Conference on vol. 2, Digital Object Identifier 10.1109/ICASSP. 1993.319372, Publication Year 1993, pp. 574-577, vol. 2.13An information theoretic approach to adaptive system training using unlabeled data Kyu-Hwa Jeong; Jian-Wu Xu; Principe, J.C.; Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on vol. 1, Jul. 31-Aug. 4, 2005, pp. 191-195, vol. 1 Digital Object Identifier 10.1109/IJCNN.2005.1555828.14Burges et al., "Duplicate Detection and Audio Thumbnails with Audio Fingerprinting" [online]. 2004, [retrieved on Nov. 21, 2006]. Retrieved on the Internet: , 5 pages.15Burges et al., "Duplicate Detection and Audio Thumbnails with Audio Fingerprinting" [online]. 2004, [retrieved on Nov. 21, 2006]. Retrieved on the Internet: <URL: www.research.microsoft.com/˜cburges/tech—reports/tr-2004-19.pdf>, 5 pages.16Burges et al., "Using Audio Fingerprinting for Duplicate Detection and Thumbnail Generation," Mar. 2005, 4 pages.17Cano et al., "A Review of Algorithms for Audio Fingerprinting" [online]. 2002, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: , 5 pages.18Cano et al., "A Review of Algorithms for Audio Fingerprinting" [online]. 2002, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: <URL: www.iua.upf.es/mtg/publications/MMSP-2002-pcano.pdf>, 5 pages.19Chinese Patent Office Action for Application No. 200680051559.0 dated Jan. 22, 2010, 14 pages.20Cohen et al., "Finding Interesting Associations without Support Pruning," 2001, Retrieved from the Internet: , 12 pages.21Cohen et al., "Finding Interesting Associations without Support Pruning," 2001, Retrieved from the Internet: <URL: www.dbis.informatik.huberlin.de/dbisold/lehre/WS0405/kDD/paper/CDFG—00.pdf>, 12 pages.22Connectionist training of non-linear hidden Markov models for speech recognition, Zhao, Z.; Neural Networks, 1991. 1991 IEEE International Joint Conference on Nov. 18-21, 1991, pp. 1647-1652, vol. 2, Digital Object Identifier 10.1109/IJCNN.1991.170645.23Ding, et al., Robust Technologies towards Automatic Speech Recognition in Car Noise Environments, Signal Processing, 2006 8th International Conference on vol. 1, Digital Object Identifier: 10.11 09/ICOSP.2006.345538 Publication Year: 2006.24EP Search Report dated Sep. 26, 2008, pp. 1-8.25Gauch, J. M. et al., "Identification of New Commercials Using Repeated Video Sequence Detection," Sep. 11, 2005, Image Processing, 2005, ICIP 2005, IEEE International Conference on Genova, Italy Sep. 11-14, 2005, Piscataway, NJ, USA, IEEE, pp. 1252-1255.26Google, Inc., International Search Report and Written Opinion of the Corresponding PCT Application No. PCT/US06/45551 dated Jul. 21, 2008, 8 pages.27Haitsma and Kalker, "A Highly Robust Audio Fingerprinting System" [online]. 2002,[retrieved on Nov. 16, 2006]. Retrieved from the Internet: , 9 pages.28Haitsma and Kalker, "A Highly Robust Audio Fingerprinting System" [online]. 2002,[retrieved on Nov. 16, 2006]. Retrieved from the Internet: <URL: www.ismir2002.ismir.net/proceedings/02-FP04-2.pdf>, 9 pages.29International Preliminary Report on Patentability, Application No. PCT/US06/45549 mailed Jun. 12, 2008, 7 pages.30International Preliminary Report on Patentability, Application No. PCT/US06/45551 mailed Apr. 2, 2009, 11 pages.31Jacobs et al., "Fast Multiresolution Image Querying" [online]. 1995, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: , 10 pages.32Jacobs et al., "Fast Multiresolution Image Querying" [online]. 1995, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: <URL: www.grail.cs.washington.edu/projects/query/mrquery.pdf>, 10 pages.33Ke et al., "Computer Vision for Music Identification" [online]. 2005, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: , 8 pages.34Ke et al., "Computer Vision for Music Identification" [online]. 2005, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: <URL: www.cs.cmu.edu/˜yke/musicretrieval/cvpr2005-mr.pdf>, 8 pages.35Lin, et at., Input Data Representation for Self-Organizing Map in Software Classification, Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on vol. 2, Digital Object Identifier: 10.11 09/KAM.2009.151 Publication Year: 2009 , pp. 350-353.36On adaptive acquisition of language, Gorin, AL.; Levinson, S.E.; Miller, L.G.; Gertner, A.N.; Ljolje, Al.; Goldman, E.R.; Acoustics, Speech, and Signal Processing, 1990. ICASSP-90, 1990 International Conference on Digital Object Identifier: 10.1109/ICASSP.1990.115784 Publication Year 1990, pp. 601-604, vol. 1.37PCT International Search Report in corresponding PCT application #PCT/US06/45549 dated Oct. 9, 2007, 2 pages.38Sadlier et al., "Automatic TV Advertisement Detection from MPEG Bitstream," 2001, Pattern Recognition, vol. 35, Issue 12, pp. 2719-2726.39'Shazam Experience Music' [online]. [retrieved on May 30, 2007]. Retrieved from the Internet: <URL: www.shazam.com/music/portal/sp/s/media-type/html/user/anon/page/default/template/Myhome/music.html>, 2 pages.40Shazazm, "Shazam Entertainment Brings Music Recognition to Windows Mobile 5.0 Powered Smartphones" [online]. 2006, [retrieved on Nov. 16, 2006]. Retrieved from the Internet: <URL: www..shazam.com/music/portal/sp/s/media-type/html/user/anon/page/default/template/pages/p/company-release30.html>, 1 page.41Shazazm, "Shazam Entertainment Brings Music Recognition to Windows Mobile 5.0 Powered Smartphones" [online]. 2006, [retrieved on Nov. 16, 2006]. Retrieved from the Internet: <URL: www..shazam.com/music/portal/sp/s/media-type/html/user/anon/page/default/template/pages/p/company—release30.html>, 1 page.42Stanford, "CS276 Information Retrieval and Web Mining" [online]. 2005, [retrieved on Nov. 16, 2006]. Retrieved from the Internet: , 8 pages.43Stanford, "CS276 Information Retrieval and Web Mining" [online]. 2005, [retrieved on Nov. 16, 2006]. Retrieved from the Internet: <URL: www.stanford.edu/class/cs276/handouts/lecture19.pdf>, 8 pages.44Stanford, "Data Mining: Associations" [online]. 2002, [retrieved on Nov. 16, 2006]. Retrieved from the Internet: , 11 pages.45Stanford, "Data Mining: Associations" [online]. 2002, [retrieved on Nov. 16, 2006]. Retrieved from the Internet: <URL: www.stanford.edu/class/cs206/cs206-2.pdf>, 11 pages.46Stollnitz et al., "Wavelets for Computer Graphics: A Primer, Part 1" [online]. 1995, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: , 8 pages.47Stollnitz et al., "Wavelets for Computer Graphics: A Primer, Part 1" [online]. 1995, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: <URL: www.grail.cs.washington.edu/pub/stoll/wavelet1.pdf>, 8 pages.48Stollnitz et al., "Wavelets for Computer Graphics: A Primer, Part 2" [online]. 1995, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: , 9 pages.49Stollnitz et al., "Wavelets for Computer Graphics: A Primer, Part 2" [online]. 1995, [retrieved on Nov. 21, 2006]. Retrieved from the Internet: <URL: www.grail.cs.washington.edu/pub/stoll/wavelet2.pdf>, 9 pages.50Supplemental EP Search Report for EP Application No. 06838486.6 dated Feb. 16, 2010, 8 pages.51Supplemental EP Search Report for EP Application No. 06838488.2 dated Feb. 16, 2010, 7 pages.52Training neural networks with additive noise in the desired signal Chuan Wang: Principe, J.C.; Neural Networks, IEEE Transactions on vol. 10, Issue 6, Nov. 1999, pp. 1511-1517.53Training neural networks with additive noise in the desired signal Chuan Wang; Principe, J.C.; Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on vol. 2, May 4-9, 1998, pp. 1084-1089, vol. 2, Digital Object Identifier 10.1109/IJCNN.1998.685923.54U.S. Appl. No. 11/468,265, Covell et al.55U.S. Appl. No. 11/563,653, filed Nov. 27, 2006, Covell et al.56U.S. Appl. No. 11/563,661, filed Nov. 27, 2006, Covell et al.57Viola and Jones, "Robust Real-Time Object Detection," Int. J. Computer Vision, 2002.58Wang, "The Shazam Music Recognition Service," Communications of the ACM, Aug. 2006, 49(8): 5 pages.59Weixin, et al., Learning to Rank Using Semantic Features in Document Retrieval, Intelligent Systems, 2009. GCIS '09. WRI Global Congress on vol. 3 Digital Object Identifier: 10.11 09/GCIS.2009.148, Publication Year: 2009, pp. 500-504.60Yang, C. "MACS: Music Audio Characteristic Sequence Indexing for Similarity Retrieval", Oct. 21-24, 2001, New Paltz, New York.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS8229219 *Aug 6, 2009Jul 24, 2012Google Inc.Full-length video fingerprintingUS8290918Sep 29, 2009Oct 16, 2012Google Inc.Robust hashing of digital media dataUS8380564 *Jul 30, 2008Feb 19, 2013At&T Intellectual Property I, LpSystem and method for internet protocol television product placement dataUS8411977Aug 29, 2006Apr 2, 2013Google Inc.Audio identification using wavelet-based signaturesUS8442125 *Nov 27, 2006May 14, 2013Google Inc.Determining popularity ratings using social and interactive applications for mass mediaUS8494851 *Sep 13, 2010Jul 23, 2013International Business Machines CorporationSystem and method for contextual social network communications during phone conversationUS8532382 *Jun 21, 2012Sep 10, 2013Google Inc.Full-length video fingerprintingUS8612517 *Feb 9, 2012Dec 17, 2013Google Inc.Social based aggregation of related media contentUS8645485 *Jan 30, 2012Feb 4, 2014Google Inc.Social based aggregation of related media contentUS8732739Jul 18, 2012May 20, 2014Viggle Inc.System and method for tracking and rewarding media and entertainment usage including substantially real time rewardsUS8819714 *May 19, 2010Aug 26, 2014Cisco Technology, Inc.Ratings and quality measurements for digital broadcast viewersUS8977067Apr 1, 2013Mar 10, 2015Google Inc.Audio identification using wavelet-based signaturesUS9020415May 4, 2011Apr 28, 2015Project Oda, Inc.Bonus and experience enhancement system for receivers of broadcast mediaUS9026034 *May 4, 2011May 5, 2015Project Oda, Inc.Automatic detection of broadcast programmingUS9027048 *Nov 14, 2012May 5, 2015Bank Of America CorporationAutomatic deal or promotion offering based on audio cuesUS9113202Sep 21, 2011Aug 18, 2015Google Inc.Inverted client-side fingerprinting and matchingUS9123330 *May 1, 2013Sep 1, 2015Google Inc.Large-scale speaker identificationUS9143742 *Jan 30, 2012Sep 22, 2015Google Inc.Automated aggregation of related media contentUS9210119Mar 31, 2014Dec 8, 2015Garret J. LoPortoAutomated triggering of a broadcastUS9265458Dec 4, 2012Feb 23, 2016Sync-Think, Inc.Application of smooth pursuit cognitive testing paradigms to clinical drug developmentUS9313593 *Dec 15, 2011Apr 12, 2016Dolby Laboratories Licensing CorporationRanking representative segments in media dataUS9317561Dec 15, 2011Apr 19, 2016Dolby Laboratories Licensing CorporationScene change detection around a set of seed points in media dataUS9380976Mar 11, 2013Jul 5, 2016Sync-Think, Inc.Optical neuroinformaticsUS9542203Dec 6, 2010Jan 10, 2017Microsoft Technology Licensing, LlcUniversal dock for context sensitive computing deviceUS20070143778 *Nov 27, 2006Jun 21, 2007Google Inc.Determining Popularity Ratings Using Social and Interactive Applications for Mass MediaUS20100030642 *Jul 30, 2008Feb 4, 2010Att Knowledge Ventures L.P.System and method for internet protocol television product placement dataUS20110275312 *May 4, 2011Nov 10, 2011Kai BuehlerAutomatic detection of broadcast programmingUS20110289538 *May 19, 2010Nov 24, 2011Cisco Technology, Inc.Ratings and quality measurements for digital broadcast viewersUS20120065969 *Sep 13, 2010Mar 15, 2012International Business Machines CorporationSystem and Method for Contextual Social Network Communications During Phone ConversationUS20120136466 *Nov 28, 2011May 31, 2012Aron WeissSystem and method for identifying a broadcast source of ambient audioUS20120226706 *Feb 28, 2012Sep 6, 2012Samsung Electronics Co. Ltd.System, apparatus and method for sorting music files based on moodsUS20130254803 *Feb 14, 2013Sep 26, 2013James HuffmanSystem and method for internet protocol television product placement dataUS20130289756 *Dec 15, 2011Oct 31, 2013Barbara ReschRanking Representative Segments in Media DataUS20150032876 *Jul 26, 2013Jan 29, 2015Opentv, Inc.Measuring response trends in a digital television networkUS20150222950 *Aug 21, 2013Aug 6, 2015Omnifone Ltd.Method of identifying media contentUS20160127423 *Jan 12, 2016May 5, 2016Microsoft Technology Licensing, LlcDirect connection with side channel control* Cited by examinerClassifications U.S. Classification707/722, 707/758, 704/256International ClassificationH04H60/06, H04H60/66, H04H60/45, H04H60/64, H04H60/87, H04H60/65, H04H1/00, H04H60/80, H04H60/58, H04H60/37, G06F17/30, G06Q30/00, H04H60/59, H04N7/173Cooperative ClassificationH04N21/858, H04N21/8133, H04N21/812, H04N21/8106, H04N21/4828, H04N21/4788, H04N21/4782, H04N21/4756, H04N21/4722, H04N21/4542, H04N21/44213, H04N21/440236, H04N21/44016, H04N21/4394, H04N21/42203, H04N21/4143, H04N21/41407, H04N21/26603, H04N21/252, H04N7/17336, H04N7/17318, H04N5/445, H04H2201/90, H04H60/87, H04H60/80, H04H60/66, H04H60/65, H04H60/64, H04H60/59, H04H60/58, H04H60/45, H04H60/375, H04H60/372, H04H60/37, H04H60/06, H04H20/14, G11B27/28, G11B27/034, G06Q30/02, G06F17/30761, G06F17/30743European ClassificationH04N7/173B2, H04N7/173B4, H04H60/58, H04H60/64, H04H60/37, H04N21/4143, H04N21/422M, H04N21/4788, H04N21/4782, H04N21/44S, H04N21/858, H04N21/482S, H04N21/414M, H04N21/454B, H04N21/81C, H04N21/4722, H04N21/475R, H04N21/81D1, H04N21/25A1, H04N21/439D, H04N21/81A, G06Q30/02, H04H60/59, G11B27/28, H04N21/266D, H04N21/4402M, H04H60/37B, G11B27/034, H04H60/37A, G06F17/30U1, G06F17/30U3FLegal EventsDateCodeEventDescriptionApr 5, 2007ASAssignmentOwner name: GOOGLE INC., CALIFORNIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:COVELL, MICHELE;BALUJA, SHUMEET;FINK, MICHAEL;REEL/FRAME:019123/0733;SIGNING DATES FROM 20070130 TO 20070404Owner name: GOOGLE INC., CALIFORNIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:COVELL, MICHELE;BALUJA, SHUMEET;FINK, MICHAEL;SIGNING DATES FROM 20070130 TO 20070404;REEL/FRAME:019123/0733Dec 20, 2011CCCertificate of correctionFeb 2, 2015FPAYFee paymentYear of fee payment: 4RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services