Systems and methods for providing online event tracking

Abstract

A system notifies a user of the detection of data that is relevant to an event of interest. The system obtains a user profile that includes one or more example documents that define the event. The system receives data that corresponds to multimedia information and determines the relevance of the data to the event based on the one or more example documents. The system notifies the user when the data is determined to be relevant.

Description

BACKGROUND OF THE INVENTION

[0004] 1. Field of the Invention

[0005] The present invention relates generally to multimedia environments and, more particularly, to systems and methods for tracking events and providing notification to users when events of interest are detected.

[0006] 2. Description of Related Art

[0007] With the ever-increasing number of data producers throughout the word, such as audio broadcasters, video broadcasters, news stream sources, etc., it is getting harder to determine when information relating to events of interest are created. One reason for this is that the data exists in many different formats and in many different languages.

[0008] The need for tracking events takes many forms. For example, government agencies may desire all information relating to a particular event, such as the World Trade Center attacks. Stock brokers and fund managers may desire all information regarding a company scandal or takeover. Lawyers may desire all information regarding a controversial Supreme Court decision. Concerned parents might desire all information regarding a particular disaster at their son's college. These are but a few examples of the need for event tracking.\

[0009] A conventional approach to event tracking requires that the individual desiring the information, or someone associated with this individual, spend time searching for it. For example, the individual might search the Internet and visit different web sites to read news articles relating to the event. The individual might also watch the news or news channels on television for information relating to the event.

[0010] There are several problems with this approach. For example, it is a time consuming process to search out and peruse different types of media to find information relevant to an event of interest. To consistently monitor a wide range of media sources, any of which can broadcast information of interest at any time of the day and any day of the week, would require a rather large work force.

[0011] As a result, there is a need for an automated event tracking system that monitors multimedia broadcasts and alerts one or more users when information relating to an event of interest is detected.

SUMMARY OF THE INVENTION

[0012] Systems and methods consistent with the present invention address this and other needs by providing event tracking that monitors multimedia broadcasts against a user-provided profile to identify information relating to an event of interest. The systems and methods alert one or more users using one or more alerting techniques when information relating to the event is identified.

[0013] In one aspect consistent with the principles of the invention, a system notifies a user of the detection of data that is relevant to an event of interest. The system obtains a user profile that includes one or more example documents that define the event. The system receives data that corresponds to multimedia information and determines the relevance of the data to the event based on the one or more example documents. The system notifies the user when the data is determined to be relevant.

[0014] In another aspect of the invention, an event tracking system includes collection logic and tracking logic. The collection logic receives data items that include textual representations of multimedia information. The tracking logic obtains a user profile that includes one or more example documents that define an event for which a user desires data and determine the relevance of the data items received by the collection logic to the event based on the user profile. The tracking logic sends an alert to the user when at least one of the data items is determined to be relevant.

[0015] According to yet another aspect of the invention, an event tracking system is provided. The event tracking system includes one or more indexers and alert logic. The one or more indexers are configured to capture data that includes audio data, video data, and/or text data, and transcribe the data when the data is the audio data or the video data to create text data. The alert logic is configured to receive at least one example document that defines an event for which a user desires information and receive the text data from the one or more indexers. The alert logic is further configured to determine the relevance of the text data to the event based on the at least one example document and alert the user when the text data is determined to be relevant.

[0016] According to a further aspect of the invention, a method for notifying a user of documents that are relevant to an event of interest is provided. The method includes receiving one or more example documents that define the event and obtaining different types of media documents. The method further includes using a model-based approach to determine the relevance of the media documents to the event based on the one or more example documents and alerting the user when one or more of the media documents are determined to be relevant.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate the invention and, together with the description, explain the invention. In the drawings,

[0018] FIG. 1 is a diagram of a system in which systems and methods consistent with the present invention may be implemented;

[0019] FIGS. 2A-2C are exemplary diagrams of the multimedia sources of FIG. 1 according to an implementation consistent with the principles of the invention;

[0020] FIG. 3 is an exemplary diagram of an audio indexer of FIG. 1;

[0021] FIG. 4 is a diagram of a possible output of the speech recognition logic of FIG. 3;

[0022] FIG. 5 is a diagram of a possible output of the story segmentation logic of FIG. 3;

[0023] FIG. 6 is an exemplary diagram of the alert logic of FIG. 1 according to an implementation consistent with the principles of the invention;

[0024] FIGS. 7 and 8 are flowcharts of exemplary processing for providing information relating to an event of interest according to an implementation consistent with the principles of the invention;

[0025] FIG. 9 is an exemplary diagram of a graphical user interface according to an implementation consistent with the principles of the invention;

[0026] FIG. 10 is an exemplary diagram of a graphical user interface once example documents have been provided by a user according to an implementation consistent with the principles of the invention;

[0027] FIG. 11 is an exemplary diagram of a graphical user interface that presents relevant documents to a user according to an implementation consistent with the principles of the invention; and

[0028] FIG. 12 is an exemplary diagram of an entry in the list of FIG. 11 according to an implementation consistent with the principles of the invention.

DETAILED DESCRIPTION

[0029] The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims and equivalents.

[0030] Systems and methods consistent with the present invention provide mechanisms for monitoring multimedia broadcasts against a user-provided profile to identify information relating to an event of interest. The systems and methods may use a model-based approach to find the relevance of information to the user profile. The systems and methods may alert one or more users upon detection of relevant information.

[0031] It may be useful to begin with a definition of an event. An event is an occurrence that is specific to time, a place, or a person. By contrast, a topic or subject is a broad level description of happenings. Examples of events may include Sonny Bono being killed in a skiing accident or the crash of American Airlines flight 587. Examples of topics may include skiing accidents or airplane disasters.

Exemplary System

[0032] FIG. 1 is a diagram of an exemplary system 100 in which systems and methods consistent with the present invention may be implemented. System 100 may include multimedia sources 110, indexers 120, alert logic 130, database 140, and servers 150 and 160 connected to clients 170 via network 180. Network 180 may include any type of network, such as a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a public telephone network (e.g., the Public Switched Telephone Network (PSTN)), a virtual private network (VPN), or a combination of networks. The various connections shown in FIG. 1 may be made via wired, wireless, and/or optical connections.

[0033] Multimedia sources 110 may include audio sources 112, video sources 114, and text sources 116. FIGS. 2A-2C are exemplary diagrams of audio sources 112, video sources 114, and text sources 116, respectively, according to an implementation consistent with the principles of the invention.

[0034] FIG. 2A illustrates an audio source 112. In practice, there may be multiple audio sources 112. Audio source 112 may include an audio server 210 and one or more audio inputs 215. Audio input 215 may include mechanisms for capturing any source of audio data, such as radio, telephone, and conversations, in any language. There may be a separate audio input 215 for each source of audio. For example, one audio input 215 may be dedicated to capturing radio signals; another audio input 215 may be dedicated to capturing conversations from a conference; and yet another audio input 215 may be dedicated to capturing telephone conversations. Audio server 210 may process the audio data, as necessary, and provide the audio data, as an audio stream, to indexers 120. Audio server 210 may also store the audio data.

[0035] FIG. 2B illustrates a video source 114. In practice, there may be multiple video sources 114. Video source 114 may include a video server 220 and one or more video inputs 225. Video input 225 may include mechanisms for capturing any source of video data, with possibly integrated audio data in any language, such as television, satellite, and a camcorder. There may be a separate video input 225 for each source of video. For example, one video input 225 may be dedicated to capturing television signals; another video input 225 may be dedicated to capturing a video conference; and yet another video input 225 may be dedicated to capturing video streams on the Internet. Video server 220 may process the video data, as necessary, and provide the video data, as a video stream, to indexers 120. Video server 220 may also store the video data.

[0036] FIG. 2C illustrates a text source 116. In practice, there may be multiple text sources 116. Text source 116 may include a text server 230 and one or more text inputs 235. Text input 235 may include mechanisms for capturing any source of text, such as e-mail, web pages, newspapers, and word processing documents, in any language. There may be a separate text input 235 for each source of text. For example, one text input 235 may be dedicated to capturing news wires; another text input 235 may be dedicated to capturing web pages; and yet another text input 235 may be dedicated to capturing e-mail. Text server 230 may process the text, as necessary, and provide the text, as a text stream or file, to indexers 120. Text server 230 may also store the text.

[0037] Returning to FIG. 1, indexers 120 may include one or more audio indexers 122, one or more video indexers 124, and one or more text indexers 126. Each of indexers 122, 124, and 126 may include mechanisms that receive data from multimedia sources 110, process the data, perform feature extraction, and output analyzed, marked up, and enhanced language metadata. In one implementation consistent with the principles of the invention, indexers 122-126 include mechanisms, such as the ones described in John Makhoul et al., “Speech and Language Technologies for Audio Indexing and Retrieval,” Proceedings of the IEEE, Vol. 88, No. 8, August 2000, pp. 1338-1353, which is incorporated herein by reference.

[0038] Audio indexer 122 may receive an input audio stream from audio sources 112 and generate metadata therefrom. For example, indexer 122 may segment the input stream by speaker, cluster audio segments from the same speaker, identify speakers by name or gender, and transcribe the spoken words. Indexer 122 may also segment the input stream based on topic and locate the names of people, places, and organizations. Indexer 122 may further analyze the input stream to identify when each word was spoken (possibly based on a time value). Indexer 122 may include any or all of this information in the metadata relating to the input audio stream.

[0039] Video indexer 124 may receive an input video stream from video sources 122 and generate metadata therefrom. For example, indexer 124 may segment the input stream by speaker, cluster video segments from the same speaker, identify speakers by name or gender, identify participants using face recognition, and transcribe the spoken words. Indexer 124 may also segment the input stream based on topic and locate the names of people, places, and organizations. Indexer 124 may further analyze the input stream to identify when each word was spoken (possibly based on a time value). Indexer 124 may include any or all of this information in the metadata relating to the input video stream.

[0040] Text indexer 126 may receive an input text stream or file from text sources 116 and generate metadata therefrom. For example, indexer 126 may segment the input stream/file based on topic and locate the names of people, places, and organizations. Indexer 126 may further analyze the input stream/file to identify when each word occurs (possibly based on a character offset within the text). Indexer 126 may also identify the author and/or publisher of the text. Indexer 126 may include any or all of this information in the metadata relating to the input text stream/file.

[0041] FIG. 3 is an exemplary diagram of indexer 122. Indexers 124 and 126 may be similarly configured. Indexers 124 and 126 may include, however, additional and/or alternate components particular to the media type involved.

[0042] As shown in FIG. 3, indexer 122 may include audio classification logic 310, speech recognition logic 320, speaker clustering logic 330, speaker identification logic 340, name spotting logic 350, topic classification logic 360, and story segmentation logic 370. Audio classification logic 310 may distinguish speech from silence, noise, and other audio signals in an input audio stream. For example, audio classification logic 310 may analyze each thirty second window of the input stream to determine whether it contains speech. Audio classification logic 310 may also identify boundaries between speakers in the input stream. Audio classification logic 310 may group speech segments from the same speaker and send the segments to speech recognition logic 320.

[0043] Speech recognition logic 320 may perform continuous speech recognition to recognize the words spoken in the segments that it receives from audio classification logic 310. Speech recognition logic 320 may generate a transcription of the speech. FIG. 4 is an exemplary diagram of a transcription 400 generated by speech recognition logic 320. Transcription 400 may include an undifferentiated sequence of words that corresponds to the words spoken in the segment. Transcription 400 contains no linguistic data, such as punctuation and capitalization.

[0044] Returning to FIG. 3, speaker clustering logic 330 may identify all of the segments from the same speaker in a single document (i.e., a body of media that is contiguous in time (from beginning to end or from time A to time B)) and group them into speaker clusters. Speaker clustering logic 330 may then assign each of the speaker clusters a unique label. Speaker identification logic 340 may identify the speaker in each speaker cluster by name or gender.

[0045] Name spotting logic 350 may locate the names of people, places, and organizations in the transcription. Name spotting logic 350 may extract the names and store them in a database. Topic classification logic 360 may assign topics to the transcription. Each of the words in the transcription may contribute differently to each of the topics assigned to the transcription. Topic classification logic 360 may generate a rank-ordered list of all possible topics and corresponding scores for the transcription.

[0046] Story segmentation logic 370 may change the continuous stream of words in the transcription into document-like units with coherent sets of topic labels and other document features generated or identified by the components of indexer 122. This information may constitute metadata corresponding to the input audio stream. FIG. 5 is a diagram of exemplary text 500 that includes representations of metadata that may be output from story segmentation logic 370. Text 500 may include linguistic data, such as punctuation and capitalization. The metadata text may also include other information not shown in FIG. 5, such as data that identifies the type of media involved, data that identifies the source of the input stream, data that identifies relevant topics, data that identifies speaker name or gender, data that identifies names of people, places, or organizations, and data that identifies the start and duration of each word spoken. Story segmentation logic 370 may output the metadata in the form of documents to alert logic 130, where a document corresponds to a body of media that is contiguous in time (from beginning to end or from time A to time B).

[0047] Returning to FIG. 1, alert logic 130 determines the relevance of the documents from indexers 120 to one or more user profiles. In an implementation consistent with the principles of the invention, a single alert logic 130 corresponds to multiple indexers 120 of a particular type (e.g., multiple audio indexers 122, multiple video indexers 124, or multiple text indexers 126) or multiple types of indexers 120 (e.g., audio indexers 122, video indexers 124, and text indexers 126). In another implementation, there may be multiple alert logic 130, such as one alert logic 130 per indexer 120.

[0048] FIG. 6 is an exemplary diagram of alert logic 130 according to an implementation consistent with the principles of the invention. Alert logic 130 may include collection logic 610 and tracking logic 620. Collection logic 610 may manage the collection of documents from indexers 120. Collection logic 610 may store the documents in database 140. Collection logic 610 may also provide the documents to tracking logic 620.

[0049] Tracking logic 620 may determine the relevance of the documents to one or more user profiles. A user profile may include one or more example documents that define an event of interest. A user may identify an event of interest by giving the event a descriptive title and providing example documents, such as four example documents. The user may also identify the manner by which the user desires to be notified of documents relevant to the event.

[0050] Tracking logic 620 may use the example documents to create a statistical language model. The statistical language model may assign probabilities to the words in the example documents, as well as, all of the words in the lexicon. Tracking logic 620 may use the statistical language model to determine the similarity of documents collected by collection logic 610 to the example documents. The statistical language model may identify documents that contain words that are grammatically similar to the words in the example documents, but may not necessarily use the same words as the example documents.

[0051] The statistical language model may build a uni-gram model for use in determining document similarity. For the uni-gram model to succeed, as many different types of words (i.e., words that describe the event in different ways) or enough words to cover the event in detail are needed. In practice, two thousand to four thousand words may be sufficient to make a decent statistical language model. These words may be located within a single example document or multiple example documents.

[0052] Tracking logic 620 may use the statistical language model to determine the relevance of newly received documents (i.e., documents newly received by collection logic 610). Tracking logic 620 may also score the documents based on their determined relevance. For example, a document may be scored based on its similarity to the example documents.

[0053] When a relevant document is detected, tracking logic 620 may generate an alert notification and send it to notification server(s) 160. Alternatively, tracking logic 620 may wait until a predetermined number of relevant documents are detected before generating the alert notification. The particular number of relevant documents that tracking logic 620 identifies before generating the alert notification may be specified in the user profile.

[0054] Returning to FIG. 1, database 140 may store the documents received by alert logic 130. Database 140 may, thereby, store a history of the information seen by alert logic 130. Database 140 may also store some or all of the original media (audio, video, or text) relating to the documents. In order to maintain adequate storage space in database 140, it may be practical to expire (i.e., delete) documents after a certain time period.

[0055] Server 150 may include a computer or another device that is capable of interacting with alert logic 130 and clients 170 via network 180. Server 150 may obtain user profiles from clients 170 and provide them to alert logic 130. Server 150 may also gather information from alert logic 130 and send it to one or more of clients 170. Clients 170 may include personal computers, laptops, personal digital assistants, or other types of devices that are capable of interacting with server 150 to provide user profiles and, possibly, receive alerts and other information. Clients 170 may present information to users via a graphical user interface, such as a web browser window.

[0056] Notification server(s) 160 may include one or more servers that transmit alerts regarding one or more documents that relate to an event of interest. A notification server 160 may include a computer or another device that is capable of receiving notifications from alert logic 130 and notifying users of the alerts. Notification server 160 may use different techniques to notify users. For example, notification server 160 may place a telephone call to a user, send an e-mail, page, instant message, or facsimile to the user, or use other mechanisms to notify the user. In an implementation consistent with the principles of the invention, notification server 160 and server 150 are the same server. In another implementation, notification server 160 is a knowledge base system.

[0057] The notification sent to the user may include a message that indicates that one or more relevant documents have been detected. Alternatively, the notification may include a portion or all of a relevant document, possibly in its original format. For example, an audio or video signal may be streamed to the user or a text document may be sent to the user.

Exemplary Processing

[0058] FIGS. 7 and 8 are flowcharts of exemplary processing for providing information relating to an event of interest according to an implementation consistent with the principles of the invention. Processing may begin with a user generating a user profile. To do this, the user may access server 150 in a conventional manner using, for example, a web browser on client 170. The user may interact with server 150 to provide a title and one or more example documents that describe an event for which the user would be interested in receiving information. In other words, the user desires to know when future information is created or broadcast that relates to the event described by the title and the example document(s).

[0059] As described above, it may be beneficial to provide sufficient words (e.g., two to four thousand words) that describe the event. These words may be located in one or more example documents. The user may obtain the example documents by whatever means and provide them in electronic form (or links to them) to server 150. For example, the user may copy a news article from a newspaper's web site, scan a magazine article, or type in or otherwise input text. It may also be possible for the user to provide an audio or video clip to server 150. In this case, server 150 may send the clip through the appropriate indexers 120 to obtain a transcription of the clip.

[0060] To facilitate the user's interaction with server 150, client 170 may present a graphical user interface (GUI) to the user. FIG. 9 is an exemplary diagram of a GUI 900 according to an implementation consistent with the principles of the invention. GUI 900 may include buttons 910 associated with creating an event, adding examples to an event, and removing an event. GUI 900 may also include information describing existing events 920 (i.e., events for which the user has already provided a title and/or example document(s)). Each of existing events 920 may be identified by the title provided by the user for the event.

[0061] If the user wants to create an event, the user may select the create an event button. The user may, thereafter, provide a title for the event and add example documents that describe the event. The title may be used for later retrieval of information relating to the event. If the user wants to remove an event, the user may select one of existing events 920 and then select the remove an event button. If the user wants to add example documents to an already existing event, the user may select one of existing events 920 and then select the add examples button. The user may, thereafter, add one or more documents that define the event.

[0062] FIG. 10 is an exemplary diagram of GUI 900 once example documents have been provided by a user according to an implementation consistent with the principles of the invention. In this example, the user provided four example documents 1010-1040 that describe the event, which in this case relates to U.S. Special Forces in the Philippines. Each of documents 1010-1040 includes information that identifies the type of document, the name of the document, and the source of the document. Document 1010, for example, is a text document, entitled “Opinion Piece,” taken from the Guardian newspaper of the United Kingdom. Document 1020 is an audio document, entitled “Support of Troop Deployment,” taken from National Public Radio (NPR). Document 1030 is a text document taken from Reuters and document 1040 is a text document taken from the Washington Post.

[0063] Alert logic 130 receives the user profile from server 150 (act 710) (FIG. 7). From the example document(s) in the user profile, alert logic 130 may build a statistical language model using conventional techniques. The statistical language model may assign probabilities to the words in the example documents, as well as other words in the lexicon.

[0064] Alert logic 130 continuously receives documents from indexers 120 in near real time (i.e., in real time subject to minor processing delays by indexers 120) (act 720). In the implementation where there is one alert logic 130 per indexer 120, then alert logic 130 may operate upon documents from a single indexer 120. In the implementation where there is one alert logic 130 for multiple indexers 120, then alert logic 130 may concurrently operate upon documents from multiple indexers 120. In either case, alert logic 130 may store the documents in database 140.

[0065] Alert logic 130 may also determine the relevance of the documents to the event defined in the user profile (act 730). For example, alert logic 130 may use the statistical language model to find similarities between words in the documents and the words in the example documents. Because the statistical language model looks for similarities based on specific words or word synonyms, a document may be determined relevant even if it does not have the same words as the example documents.

[0066] If alert logic 130 determines that the documents are not relevant to the event (act 740), then alert logic 130 awaits receipt of the next document(s) from indexers 120. If one or more of the documents are relevant (act 740), however, alert logic 130 may generate a relevance score for the document(s) (act 750). The score may be based on the degree of similarity between the document and the example documents.

[0067] Alternatively, a document's relevance may be determined based on the score. For example, alert logic 130 may generate a relevance score for each of the documents and determine that documents with scores above a certain threshold are relevant and documents with scores below the threshold are not relevant.

[0068] In any event, alert logic 130 may then generate an alert notification (act 760). Alternatively, alert logic 130 may wait until a sufficient number of relevant documents have been identified before generating the alert notification. In any event, the alert notification may identify the relevant document(s) (audio, video, or text) and/or the event to which the alert pertains. This permits the user to obtain more information regarding the document(s) if desired. Alert logic 130 may send the alert notification to notification server(s) 160. Alert logic 130 may identify the particular notification server 160 to use based on information in the user profile.

[0069] Notification server 160 may generate a notification based on the alert notification from alert logic 130 and send the notification to the user (act 770). For example, notification server 160 may place a telephone call to the user, send an e-mail, page, instant message, or facsimile to the user, or otherwise notify the user. In one implementation, the notification includes a portion of or the entire relevant document, possibly in its original format.

[0070] At some point, the user may desire additional information regarding an event. In this case, the user may provide some indication to client 170 of the desire for additional information. For example, the user may select a button on a graphical user interface, such as GUI 900 (FIG. 9), that indicates that information regarding a particular alert or event is desired. In the example of FIG. 9, the user may double click or otherwise select an existing event 920. Client 170 may send this indication to alert logic 130 via server 150.

[0071] Alert logic 130 may receive the indication that the user desires additional information regarding an event (act 810) (FIG. 8). In response, alert logic 130 may retrieve the relevant documents, possibly including metadata relating to the event, from database 140 (act 820). Alert logic 130 may then provide a list of the relevant documents and/or the documents themselves to client 170 (act 830). Client 170 may provide a list of the relevant documents to the user.

[0072] FIG. 11 is an exemplary diagram of a graphical user interface (GUI) 1100 that presents relevant documents to a user according to an implementation consistent with the principles of the invention. GUI 1100 includes a list 1110 of relevant documents detected by alert logic 130. List 1110 may include a number of entries corresponding to the number of relevant documents identified by alert logic 130.

[0073] FIG. 12 is an exemplary diagram of an entry 1200 in list 1110 according to an implementation consistent with the principles of the invention. Entry 1200 includes a score 1210, a date 1220, and a title 1230. Score 1210 may include a visual indication of the score of the document. In this example, score 1210 includes a scale. In other cases, score 1210 may include a numerical value or some other type of visual indication of a score. Date 1220 may include the time and/or date on which the document was created. Title 1230 may include the title of the document. In some cases, the document title may be generated by indexers 120.

[0074] Returning to FIG. 11, the relevant documents may be presented to the user in a number of ways. For example, the documents may be presented in order of score (e.g., highest score first) and date (newest document first). In one implementation, score takes precedents over date. GUI 1100 may also visually distinguish documents that have already been seen by the user from documents that have not yet been seen. In the example of FIG. 11, documents that have not yet been seen by the user are italicized and bolded.

[0075] If the user desires additional information regarding one of the documents, the user may indicate so by selecting (e.g., double clicking) the document. In this case, client 170 may present the document to the user within a graphical user interface, such as GUI 1100. If the user desires, the user may retrieve the original media corresponding to the document. The original media may be stored in database 140 along with the metadata, stored in a separate database possibly accessible via network 180, or maintained by one of servers 210, 220, or 230 (FIG. 2). If the original media is an audio or video document, the audio or video document may be streamed to client 170. If the original media is a text document, the text document may be provided to client 170.

[0076] If the user identifies a document that is of particular relevance, the user may add the document to the set of example documents in the user profile. To do this, the user may select the document by, for example, clicking or double clicking on it. The user may then select the add examples button (see FIG. 11). The document would then be added to the set of example documents in the user profile.

CONCLUSION

[0077] Systems and methods consistent with the present invention permit a user to define an event of interest by supplying a set of documents that describe the event in a way that the user is interested in the event. The systems and methods then determine the relevance of all incoming documents to the event defined by the user. The systems and methods notify the user when one or more relevant documents are identified.

[0078] The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. For example, while series of acts have been described with regard to the flowcharts of FIGS. 7 and 8, the order of the acts may differ in other implementations consistent with the principles of the invention.

[0079] Also, systems and methods have been described as acting upon newly-created information. In other implementations consistent with the principles of the invention, the systems and methods may also identify information that has already been created. In this case, database 140 may be searched for relevant information.

[0080] In the implementations described above, an event is identified by a title and defined by one or more example documents. In another implementation, the title may be used as one of the example documents defining the event.

[0081] Certain portions of the invention have been described as “logic” that performs one or more functions. This logic may include hardware, such as an application specific integrated circuit or a field programmable gate array, software, or a combination of hardware and software.

[0082] No element, act, or instruction used in the description of the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Where only one item is intended, the term “one” or similar language is used. The scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for identifying documents that are relevant to an event of interest, comprising: receiving, from a user, one or more example documents that define the event; obtaining documents in real time that correspond to information created in a plurality of media formats; determining relevance of the documents to the event based on the one or more example documents; and alerting the user when one or more of the documents are determined to be relevant.

2. The method of claim 1, wherein the one or more example documents include at least one of text documents, audio documents, and video documents.

3. The method of claim 1, wherein the one or more example documents include a total of at least approximately two thousand words.

4. The method of claim 1, wherein the information includes at least two of real time audio broadcasts, real time video broadcasts, and text streams or files.

5. The method of claim 1, further comprising: building a statistical language model using the one or more example documents.

6. The method of claim 5, wherein the determining relevance of the documents includes: finding similarities between words in the documents and words in the one or more example documents, and identifying one of the documents as relevant when the words in the document are similar to the words in the one or more example documents.

7. The method of claim 1, wherein the determining relevance of the documents includes: determining similarities between the documents and the one or more example documents, and identifying one of the documents as relevant when the document is similar to at least one of the one or more example documents.

8. The method of claim 1, wherein the determining relevance of the documents includes: generating scores for the documents, and determining that ones of the documents with scores above a threshold are relevant.

9. The method of claim 8, further comprising: providing the ones of the documents to the user based on the scores.

10. The method of claim 8, wherein the generating scores includes: determining scores based on degrees of similarities between the documents and the one or more example documents.

11. The method of claim 1, wherein the alerting the user includes at least one of: placing a telephone call to the user, sending an e-mail to the user, sending a page to the user, sending an instant message to the user, and sending a facsimile to the user.

12. The method of claim 1, wherein the alerting the user includes: sending an alert to the user after a predetermined number of the documents are determined to be relevant.

13. The method of claim 1, further comprising: receiving, from the user, a request for additional information relating to the event, and sending the additional information to the user.

14. The method of claim 13, wherein the additional information includes the one or more documents that are determined to be relevant.

15. The method of claim 13, wherein the additional information includes the information, corresponding to the one or more documents that are determined to be relevant, in one of the media formats in which the information was created.

16. A system for identifying data that is relevant to an event of interest, comprising: means for obtaining, from a user, a user profile that includes one or more example documents that define the event; means for receiving real-time data that corresponds to multimedia information; means for determining relevance of the data to the event based on the one or more example documents; and means for notifying the user when the data is determined to be relevant.

17. An event tracking system, comprising: collection logic configured to: receive data items that include textual representations of multimedia information; and tracking logic configured to: obtain a user profile that includes one or more example documents that define an event for which a user desires data, determine relevance of the data items received by the collection logic to the event based on the user profile, and send an alert to the user when at least one of the data items is determined to be relevant.

18. The system of claim 17, wherein the one or more example documents include at least one of text documents, audio documents, and video documents.

19. The system of claim 17, wherein the one or more example documents collectively include at least approximately two thousand words.

20. The system of claim 17, wherein the multimedia information includes at least one of real time audio broadcasts, real time video broadcasts, text streams, and text files.

21. The system of claim 17, wherein the tracking logic is further configured to: build a statistical language model using the one or more example documents.

22. The system of claim 21, wherein when determining relevance of the data items, the tracking logic is configured to: determine similarities between words in the data items and words in the one or more example documents, and identify one of the data items as relevant when the words in the data item are similar to the words in the one or more example documents.

23. The system of claim 17, wherein when determining relevance of the data items, the tracking logic is configured to: determine similarities between the data items and the one or more example documents, and identify one of the data items as relevant when the data item is similar to at least one of the one or more example documents.

24. The system of claim 17, wherein when determining relevance of the data items, the tracking logic is configured to: generate scores for the data items, and determining that ones of the data items with scores greater than a threshold are relevant.

25. The system of claim 24, wherein the tracking logic is further configured to: provide the ones of the data items to the user based on the scores.

26. The system of claim 24, wherein when generating scores, the tracking logic is configured to: determine scores based on a degree of similarity between the data items and the one or more example documents.

27. The system of claim 17, wherein when sending an alert, the tracking logic is configured to cause at least one of a telephone call to be placed to the user, an e-mail to be sent to the user, a page to be sent to the user, an instant message to be sent to the user, and a facsimile to be sent to the user.

28. The system of claim 17, wherein when sending an alert, the tracking logic is configured to: wait until a predetermined number of the data items are determined to be relevant before sending the alert to the user.

29. The system of claim 17, wherein the tracking logic is further configured to: receive, from the user, a request for additional information relating to the event, and send the additional information to the user.

30. The system of claim 29, wherein the additional information includes the at least one data item that is determined to be relevant.

31. The system of claim 29, wherein the additional information includes the multimedia information corresponding to the at least one data item that is determined to be relevant.

32. A computer-readable medium that stores instructions which when executed by a processor cause the processor to perform a method for notifying a user of documents that are relevant to an event of interest, the computer-readable medium comprising: instructions for obtaining at least one example document that defines the event; instructions for acquiring real-time documents corresponding to information created in a plurality of media formats; instructions for determining relevance of the real-time documents to the event based on the at least one example document; and instructions for notifying the user when one or more of the real-time documents are determined to be relevant.

33. An event tracking system, comprising: one or more indexers configured to: capture data, the data including at least one of audio data, video data, and text data, and transcribe the data when the data is the audio data or the video data to create text data; and alert logic configured to: receive at least one example document that defines an event for which a user desires information, receive the text data from the one or more indexers, determine relevance of the text data to the event based on the at least one example document, and alert the user when the text data is determined to be relevant.

34. A method for notifying a user of documents that are relevant to an event of interest, comprising: receiving one or more example documents that define the event; obtaining a plurality of types of media documents; using a model-based approach to determine relevance of the media documents to the event based on the one or more example documents; and alerting the user when one or more of the media documents are determined to be relevant.