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New Google Patent would use media keywords to trigger social network connections

Published just this morning, a new Google Patent application entitled Social and Interactive Applications for Mass Media would trigger instant connections to user's social networks based on a type of audio recognition of key phrases the user encounters in broadcast or multimedia applications they are watching or listening to.
Written by Russell Shaw, Contributor on

Published just this morning, a new Google Patent application entitled Social and Interactive Applications for Mass Media would trigger instant connections to user's social networks based on a type of audio recognition of key phrases the user encounters in broadcast or multimedia applications they are watching or listening to.

By mapping the current path of how a user would respond to, say, an event on a television program by then accessing his or her favorite messaging program or social network, Google makes the case for the Patent in the Background section of the Patent app:

Another social and interactive television application that is lacking with conventional interactive television systems is the ability to dynamically link a viewer with an ad hoc social peer community (e.g., a discussion group, chat room, etc.) in real-time. Imagine that you are watching the latest episode of "Friends" on television and discover that the character "Monica" is pregnant.

You want to chat, comment or read other viewers' responses to the scene in real-time. One option would be to log on your computer, type in the name of "Friends" or other related terms into a search engine, and perform a search to find a discussion group on "Friends."

Such required action by the viewer, however, would diminish the passive experience offered by mass media and would not enable the viewer to dynamically interact (e.g., comment, chat, etc.) with other viewers who are watching the program at the same time.

Now, I will show you a Figure and provide the relevant descriptors that will help you understand just what Google is proposing.

FIG. 1 is a block diagram of a mass personalization system 100 for providing mass personalization applications. The system 100 includes one or more client-side interfaces 102, an audio database server 104 and a social application server 106, all of which communicate over a network 108 (e.g., the Internet, an intranet, LAN, wireless network, etc.).

A client interface 102 can be any device that allows a user to enter and receive information, and which is capable of presenting a user interface on a display device, including but not limited to: a desktop or portable computer; an electronic device; a telephone; a mobile phone; a display system; a television; a computer monitor; a navigation system; a portable media player/recorder; a personal digital assistant (PDA); a game console; a handheld electronic device; and an embedded electronic device or appliance. The client interface 102 is described more fully with respect to FIG. 2.

In some implementations, the client-interface 102 includes an ambient audio detector (e.g., a microphone) for monitoring and recording the ambient audio of a mass media broadcast in a broadcast environment (e.g., a user's living room). One or more ambient audio segments or "snippets" are converted into distinctive and robust statistical summaries, referred to as "audio fingerprints" or "descriptors." In some implementations, the descriptors are compressed files containing one or more audio signature components that can be compared with a database of previously generated reference descriptors or statistics associated with the mass media broadcast.

A technique for generating audio fingerprints for music identification is described in Ke, Y., Hoiem, D., Sukthankar, R. (2005), Computer Vision for Music Identification, In Proc. Computer Vision and Pattern Recognition, which is incorporated herein by reference in its entirety. In some implementations, the music identification approach proposed by (hereinafter "Ke et al.") is adapted to generate descriptors for television audio data and queries, as described with respect to FIG. 4.

A technique for generating audio descriptors using wavelets is described in U.S. Provisional Patent Application No. 60/823,881, for "Audio Identification Based on Signatures." That application describes a technique that uses a combination of computer-vision techniques and large-scale-data-stream processing algorithms to create compact descriptors/fingerprints of audio snippets that can be efficiently matched. The technique uses wavelets, which is a known mathematical tool for hierarchically decomposing functions.

Now here is a paragraph that gets to the heart of the facilitating technology:

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.

In some implementations, the descriptors and an associated user identifier ("user id") for identifying the client-side interface 102 are sent to the audio database server 104 via network 108. The audio database server 104 compares the descriptor to a plurality of reference descriptors, which were previously determined and stored in an audio database 110 coupled to the audio database server 104. In some implementations, the audio database server 104 continuously updates the reference descriptors stored in the audio database 110 from recent mass media broadcasts.

The audio database server 104 determines the best matches between the received descriptors and the reference descriptors and sends best-match information to the social application server 106. The matching process is described more fully with respect to FIG. 4.

In some implementations, the social application server 106 accepts web-browser connections associated with the client-side interface 102. Using the best-match information, the social application server 106 aggregates personalized information for the user and sends the personalized information to the client-side interface 102. The personalized information can include but is not limited to: advertisements, personalized information layers, popularity ratings, and information associated with a commenting medium (e.g., ad hoc social peer communities, forums, discussion groups, video conferences, etc.).

In some implementations, the personalized information can be used to create a chat room for viewers without knowing the show that the viewers are watching in real time. The chat rooms can be created by directly comparing descriptors in the data streams transmitted by client systems to determine matches. That is, chat rooms can be created around viewers having matching descriptors. In such an implementation, there is no need to compare the descriptors received from viewers against reference descriptors.

In some implementations, the social application server 106 serves a web page to the client-side interface 102, which is received and displayed by a web browser (e.g., Microsoft Internet Explorer.TM.) running at the client-side interface 102. The social application server 106 also receives the user id from the client-side interface 102 and/or audio database server 104 to assist in aggregating personalized content and serving web pages to the client-side interface 102.

It should be apparent that other implementations of the system 100 are possible. For example, the system 100 can include multiple audio databases 110, audio database servers 104 and/or social application servers 106. Alternatively, the audio database server 104 and the social application server 106 can be a single server or system, or part of a network resource and/or service. Also, the network 108 can include multiple networks and links operatively coupled together in various topologies and arrangements using a variety of network devices (e.g., hubs, routers, etc.) and mediums (e.g., copper, optical fiber, radio frequencies, etc.). Client-server architectures are described herein only as an example. Other computer architectures are possible.

Next, what is described as an Ambient Audio Identification System is illustrated and discussed.

FIG. 2 illustrates an ambient audio identification system 200, including a client-side interface 102 as shown in FIG. 1. The system 200 includes a mass media system 202 (e.g., a television set, radio, computer, electronic device, mobile phone, game console, network appliance, etc.), an ambient audio detector 204, a client-side interface 102 (e.g., a desktop or laptop computer, etc.) and a network access device 206. In some implementations, the client-side interface 102 includes a display device 210 for presenting a user interface (UI) 208 for enabling a user to interact with a mass personalization application, as described with respect to FIG. 5.

In operation, the mass media system 202 generates ambient audio of a mass media broadcast (e.g., television audio), which is detected by the ambient audio detector 204. The ambient audio detector 204 can be any device that can detect ambient audio, including a freestanding microphone and a microphone that is integrated with the client-side interface 102. The detected ambient audio is encoded by the client-side interface 102 to provide descriptors identifying the ambient audio. The descriptors are transmitted to the audio database server 104 by way of the network access device 206 and the network 108.

In some implementations, client software running at the client-side interface 102 continually monitors and records n-second (e.g., 5 second) audio files ("snippets") of ambient audio. The snippets are then converted into m-frames (e.g., 415 frames) of k-bit encoded descriptors (e.g., 32-bit), according to a process described with respect to FIG. 4. In some implementations, the monitoring and recording is event based. For example, the monitoring and recording can be automatically initiated on a specified date and at a specified time (e.g., Monday, 8:00 P.M.) and for a specified time duration (e.g., between 8:00-9:00 P.M.).

Alternatively, the monitoring and recording can be initiated in response to user input (e.g., a mouse click, function key or key combination) from a control device (e.g., a remote control, etc.). In some implementations, the ambient audio is encoded using a streaming variation of the 32-bit/frame discriminative features described in Ke et al. In some implementations, the client software runs as a "side bar" or other user interface element. That way, when the client-side interface 102 is booted up, the ambient audio sampling can start immediately and run in the "background" with results (optionally) being displayed in the side bar without invoking a full web-browser session.

In some implementations, the ambient audio sampling can begin when the client-side interface 102 is booted or when the viewer logs into a service or application (e.g., email, etc.)

The descriptors are sent to the audio database server 104. In some implementations, the descriptors are compressed statistical summaries of the ambient audio, a described in Ke et al. By sending statistical summaries, the user's acoustic privacy is maintained because the statistical summaries are not reversible, i.e., the original audio cannot be recovered from the descriptor.

Thus, any conversations by the user or other individuals monitored and recorded in the broadcast environment cannot be reproduced from the descriptor. In some implementations, the descriptors can be encrypted for extra privacy and security using one or more known encryption techniques (e.g., asymmetric or symmetric key encryption, elliptic encryption, etc.).

In some implementations, the descriptors are sent to the audio database server 104 as a query submission (also referred to as a query descriptor) in response to a trigger event detected by the monitoring process at the client-side interface 102. For example, a trigger event could be the opening theme of a television program (e.g., opening tune of "Seinfeld") or dialogue spoken by the actors. In some implementations, the query descriptors can be sent to the audio database server 104 as part of a continuous streaming process. In some implementations, the query descriptors can be transmitted to the audio database server 104 in response to user input (e.g., via remote control, mouse clicks, etc.).

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