The present disclosure relates to techniques and mechanisms for retrieval and display of related content using text stream data feeds.
Identifying content of interest to a user is a difficult and imperfect task. In some examples, shopping related sites will suggest new products based on user historical viewing and purchase activity. Other sites will provide advertisements based on text entered for a query or the content being viewed. However, mechanisms for identifying content of interest are limited.
Consequently, the techniques and mechanisms of the present invention provide additional improved mechanisms for retrieval and display of related content, particularly retrieval and display of related content using text stream data feeds.
Mechanisms are provided for retrieving and presenting related content using text stream data feeds. Text stream data feeds such as caption information associated with media content or conversations associated with social networking applications are aggregated and used to retrieve related media content, text documents, and advertisements. Text stream data feeds that a user is exposed to may indicate that the user is interested or at least primed for particular types of related content. In particular examples, an inverse vector space search engine is used to determine particular pieces of related content and categories of interest. Post filtering may also be applied to the results.
These and other features of the present invention will be presented in more detail in the following specification of the invention and the accompanying figures, which illustrate by way of example the principles of the invention.
The disclosure may best be understood by reference to the following description taken in conjunction with the accompanying drawings, which illustrate particular embodiments of the present invention.
Reference will now be made in detail to some specific examples of the invention including the best modes contemplated by the inventors for carrying out the invention. Examples of these specific embodiments are illustrated in the accompanying drawings. While the invention is described in conjunction with these specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims.
For example, the techniques of the present invention will be described in the context of particular types of text feeds. However, it should be noted that the techniques and mechanisms of the present invention can be used to identify related content for a variety of types of text feeds. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be practiced without some or all of these specific details. In other instances, well known process operations have not been described in detail in order not to unnecessarily obscure the present invention.
Various techniques and mechanisms of the present invention will sometimes be described in singular form for clarity. However, it should be noted that some embodiments include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. For example, a processor is used in a variety of contexts. However, it will be appreciated that multiple processors can also be used while remaining within the scope of the present invention unless otherwise noted. Furthermore, the techniques and mechanisms of the present invention will sometimes describe two entities as being connected. It should be noted that a connection between two entities does not necessarily mean a direct, unimpeded connection, as a variety of other entities may reside between the two entities. For example, a processor may be connected to memory, but it will be appreciated that a variety of bridges and controllers may reside between the processor and memory. Consequently, a connection does not necessarily mean a direct, unimpeded connection unless otherwise noted.
Identifying content for presentation to a user is a difficult task. Some content providers will select material based on user interest, profile, and demographic information. Some retailers will identify what a user has purchased and recommend similar products, or products purchased by others who have the same purchasing pattern. Other sites will select advertising based on what a user has written in an email or what sites the user has viewed in the past. These mechanisms identify content of interest with some degree of accuracy. However, all of these mechanisms are still limited. The techniques of the present invention provide additional mechanisms for determining content of interest.
Text stream data feeds provide a wealth of information relating to what a user has viewed or may be interested in viewing. Text stream data feeds include caption information associated with media, text streams from social networking sites, and other real-time data streams. According to various embodiments, caption information is aggregated and used to search for related content of interest. In particular embodiments, caption information is used to identify particularly relevant advertising that can be presented to the user in real-time. It is recognized that in some instances, a user may not necessarily be interested in the material corresponding to the captions, but the user is at least primed by the material viewed. In still other embodiments, data from a social networking data feed is used to identify media content for presentation to a user watching a particular television program or movie on a mobile phone. The user may not necessarily be interacting on the social networking site, but the material may be relevant to user interests. In other examples, most frequently discussed topics on social networking sites or most frequently blogged topics may be used to identify media content for presentation to a user.
A “vector space search engine” (VSSE) is a tool/mechanism used in many modern search engines. In this technique, each page or document is entered into the search engine as a vector, where each unique word becomes a column in a matrix common to the entire data set represented by all of the pages and documents. Each occurrence of each unique word indexes that column, and each document entry can be considered a row. Natural language processing tricks are applied to reduce the number of columns (and corresponding dimensions) in the vector space search engine matrix. Punctuation and symbols are usually stripped, capitalization is removed, plurals/common forms of words are used and some words are even blacklisted so that they are not included in the VSSE matrix. It is often desirable to minimize the size of the matrix, as storage and processing resources required can become enormous.
Like the page and/or document vectors, a query is processed as a vector in the space defined by the data set. The actual search is performed by finding the minimal multi-dimensional distance between the search vector and the page and/or document vectors. Page and/or document vectors that are closest to the search query vector are ranked higher as a closer match. Euclidean geometry and linear algebra can be used to determine distance between vectors.
According to various embodiments, it is recognized that a VSSE can be used not only to search pages or data, but can be used to identify relevant or related content of interest. Content may include media, product information, text, etc. It is recognized that an inverse VSSE (IVSSE) can be used. Each row of an IVSSE can represent a particular piece of content or a content type. Keywords that may be associated with various categories are provided as columns. For example, row headers may include different video clips. Column headers may include celebrity names, movie titles, products, locations, sports teams, emotions, hobbies, etc. Words, phrases, and all types of data that somehow describe different types of content could be included, but keywords are used here for simplicity.
To determine the content matches for a particular text stream, information from the text stream is used to construct the search vector, which is then matched for closeness to existing content. According to various embodiments, a ranked list of content is returned, where the category vectors closest to the search vector are determined to be the most relevant content.
A final filtering or negative search of results can be applied to define explicitly inappropriate content. For example, explicit words can be defined to keep a piece of content from being suggested to children.
According to various embodiments, a streaming server receiving content from multiple sources can automatically identify content of interest for users based on text streams associated with the particular users. New content can be dynamically generated and added as text streams associated with the users are analyzed. Multiple categorize or recategorize content using an IVSSE. Content can be categorized even if no description is provided. Media streams can be categorized using metadata such as caption information or review information. New categories may be dynamically generated and added. Multiple candidate media clips for a media stream may be provided.
By supporting a passive user interface (UI), content can be suggested in an environment where a user can view program related to social media interaction or popular topics. When a friend posts a link or comments on an interesting topic, a user may be more likely to follow-up if a single click versus a separate effort is required. Streamlining these efforts is particular important on mobile devices. The scope and depth of content libraries is made available to the user without manual browsing or search, and content suggestions can be driven in real time. In other examples, content that a user is primed for is suggested after using captions in search queries. A television show having recently discussed environmentally friendly habits may prime a user for green product related commercials. A related content generator can determine what a user has just viewed in a dynamic manner and identify pertinent content including appropriate advertisements.
According to various embodiments, a variety of natural language techniques can be applied to reduce the size of a VSSE matrix. Groups of words or phrases can also be included in a single column. It is desirable to minimize the number of columns for performance, space and processing resources required can become enormous. A search query can be structured as a vector in the space defined by the data set.
The actual search is performed by finding the minimal multi-dimensional distance between the search vector and the page and/or document vectors. Page and/or document vectors that are closest to the search query vector are ranked higher as a closer match. The Pythagorean Theorem as well as optimized linear algebra techniques can be used to find the closest distance between search and document vectors.
In particular embodiments, a search query may be “who likes to eat fish.” The search vector 213 is populated with the search terms and the distance between the search vector and the various document vectors is determined. According to various embodiments, the distances between the search vector and the document vectors are determined to be 2.24, 2, 1.41, 2, 1.41, and 2 for documents 201, 203, 205, 207, 209 and 211 respectively.
In particular embodiments, an IVSSE 271 includes rows corresponding to content. Rows may include content 251, 253, 255, 257, 259, 261, and 263. Columns in the IVSSE may include keywords associated with description for media content. Keywords may include finance, baseball, cartoon, animation, car, symphony, science, and planet. In one example, a text stream is received. The text stream includes the keywords cartoon, animation, science, and planet in its description and/or captions. The keywords are used to select content 255 based on the distance between the text stream vector and the content 255 vector. Secondary and tertiary content can also be identified based on vector distances.
An encoder farm 371 is associated with the satellite feed 387 and can also be associated with media aggregation server 361. The encoder farm 371 can be used to process media content from satellite feed 387 as well as from media aggregation server 361 into potentially numerous encoding formats. According to various embodiments, file formats include open standards MPEG-1 (ISO/IEC 11172), MPEG-2 (ISO/IEC 13818-2), MPEG-4 (ISO/IEC 14496), as well as proprietary formats QuickTime™, ActiveMovie™, and RealVideo™. Some example video codecs used to encode the files include MPEG-4, H.263, and H.264. Some example audio codecs include Qualcomm Purevoice™ (QCELP), The Adaptive Multi-Narrow Band (AMR-NB), Advanced Audio coding (AAC), and AACPlus. The media content may also be encoded to support a variety of data rates. The media content from media aggregation server 361 and encoder farm 371 is provided as live media to a streaming server 375. In one example, the streaming server is a Real Time Streaming Protocol (RTSP) server 375. Media streams are broadcast live from an RTSP server 375 to individual client devices 301. A variety of protocols can be used to send data to client devices.
Possible client devices 301 include personal digital assistants (PDAs), cellular phones, smartphones, personal computing devices, personal computers etc. According to various embodiments, the client devices are connected to a cellular network run by a cellular service provider. In other examples, the client devices are connected to an Internet Protocol (IP) network. Alternatively, the client device can be connected to a wireless local area network (WLAN) or some other wireless network. Live media streams provided over RTSP are carried and/or encapsulated on one of a variety of wireless networks.
The client devices are also connected over a wireless network to a media content delivery server 331. The media content delivery server 331 is configured to allow a client device 301 to perform functions associated with accessing live media streams. For example, the media content delivery server allows a user to create an account, perform session identifier assignment, subscribe to various channels, log on, access program guide information, obtain information about media content, etc. According to various embodiments, the media content delivery server does not deliver the actual media stream, but merely provides mechanisms for performing operations associated with accessing media. In other implementations, it is possible that the media content delivery server also provides media clips, files, and streams. The media content delivery server is associated with a guide generator 351. The guide generator 351 obtains information from disparate sources including content providers 381 and media information sources 383. The guide generator 351 provides program guides to database 355 as well as to media content delivery server 331 to provide to client devices 301.
According to various embodiments, the guide generator 351 obtains viewership information from individual client devices. In particular embodiments, the guide generation 351 compiles viewership information in real-time in order to generate a most-watched program guide listing most popular programs first and least popular programs last. The client device 301 can request program guide information and the most-watched program guide can be provided to the client device 301 to allow efficient selection of video content. According to various embodiments, guide generator 351 is connected to a media content delivery server 331 that is also associated with an abstract buy engine 341. The abstract buy engine 341 maintains subscription information associated with various client devices 301. For example, the abstract buy engine 341 tracks purchases of premium packages.
The media content delivery server 331 and the client devices 301 communicate using requests and responses. For example, the client device 301 can send a request to media content delivery server 331 for a subscription to premium content. According to various embodiments, the abstract buy engine 341 tracks the subscription request and the media content delivery server 331 provides a key to the client device 301 to allow it to decode live streamed media content. Similarly, the client device 301 can send a request to a media content delivery server 331 for a most-watched program guide for its particular program package. The media content delivery server 331 obtains the guide data from the guide generator 351 and associated database 355 and provides appropriate guide information to the client device 301.
Although the various devices such as the guide generator 351, database 355, media aggregation server 361, etc. are shown as separate entities, it should be appreciated that various devices may be incorporated onto a single server. Alternatively, each device may be embodied in multiple servers or clusters of servers. According to various embodiments, the guide generator 351, database 355, media aggregation server 361, encoder farm 371, media content delivery server 331, abstract buy engine 341, and streaming server 375 are included in an entity referred to herein as a media content delivery system.
An authentication module 421 verifies the identity of mobile devices. A logging and report generation module 453 tracks mobile device requests and associated responses. A monitor system 451 allows an administrator to view usage patterns and system availability. According to various embodiments, the media content delivery server 491 handles requests and responses for media content related transactions while a separate streaming server provides the actual media streams. In some instances, a media content delivery server 491 may also have access to a streaming server or operate as a proxy for a streaming server. But in other instances, a media content delivery server 491 does not need to have any interface to a streaming server. In typical instances, however, the media content delivery server 491 also provides some media streams. The media content delivery server 491 can also be configured to provide media clips and files to a user in a manner that supplements a streaming server.
Although a particular media content delivery server 491 is described, it should be recognized that a variety of alternative configurations are possible. For example, some modules such as a report and logging module 453 and a monitor 451 may not be needed on every server. Alternatively, the modules may be implemented on another device connected to the server. In another example, the server 491 may not include an interface to an abstract buy engine and may in fact include the abstract buy engine itself. A variety of configurations are possible.
At 503, text streams are received from one or more sources and/or one or more media. In some examples, one text stream may be received from broadcast television, another text stream may be received from web content, and still another text stream may be received from a live news service. In particular embodiments, sources may include program captions, social networking feeds, search query term feeds, etc. At 505, text streams are aggregated to form search queries. In particular embodiments, natural language processing is used to identify appropriate boundaries or appropriate sentences to form search queries. In other embodiments, text streams are periodically sampled to form search queries. In still other examples, text obtained between scene changes is used to form search queries. At 507, related content is identified using aggregated text streams. In some examples, text streams from multiple sources are aggregated and fed into an IVSSE. At 509, related content is ranked based on vector distance to an aggregated text stream.
At 511, post-filtering is applied to remove select results. At 513, a user a profiled based on the most common content viewed.
Particular examples of interfaces supports include Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various very high-speed interfaces may be provided such as fast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as packet switching, media control and management.
According to various embodiments, the system 600 is a content server that also includes a transceiver, streaming buffers, and a program guide database. The content server may also be associated with subscription management, logging and report generation, and monitoring capabilities. In particular embodiments, functionality for allowing operation with mobile devices such as cellular phones operating in a particular cellular network and providing subscription management. According to various embodiments, an authentication module verifies the identity of devices including mobile devices. A logging and report generation module tracks mobile device requests and associated responses. A monitor system allows an administrator to view usage patterns and system availability. According to various embodiments, the content server 691 handles requests and responses for media content related transactions while a separate streaming server provides the actual media streams.
Because such information and program instructions may be employed to implement the systems/methods described herein, the present invention relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein. Examples of machine-readable media include hard disks, floppy disks, magnetic tape, optical media such as CD-ROM disks and DVDs; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and programmable read-only memory devices (PROMs). Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.
While the invention has been particularly shown and described with reference to specific embodiments thereof, it will be understood by those skilled in the art that changes in the form and details of the disclosed embodiments may be made without departing from the spirit or scope of the invention. It is therefore intended that the invention be interpreted to include all variations and equivalents that fall within the true spirit and scope of the present invention.
This application is a continuation of and claims priority to U.S. patent application Ser. No. 14/639,554, filed Mar. 5, 2015 by Todd Stiers, titled “Retrieval And Display Of Related Content Using Text Stream Data Feeds,” and is a continuation of and claims priority to U.S. patent application Ser. No. 12/708,350, filed Feb. 18, 2010 by Todd Stiers, titled “Retrieval And Display Of Related Content Using Text Stream Data Feeds,” now U.S. Pat. No. 8,996,496 issued on Mar. 31, 2015 both of which are incorporated herein by reference in its entirety and for all purposes.
Number | Name | Date | Kind |
---|---|---|---|
6510406 | Marchisio | Jan 2003 | B1 |
6910030 | Choi et al. | Jun 2005 | B2 |
8996496 | Stiers | Mar 2015 | B2 |
9635081 | Stiers | Apr 2017 | B2 |
20030218696 | Bagga et al. | Nov 2003 | A1 |
20030221198 | Sloo | Nov 2003 | A1 |
20060129917 | Volk et al. | Jun 2006 | A1 |
20070174340 | Gross | Jul 2007 | A1 |
20080010270 | Gross | Jan 2008 | A1 |
20080021976 | Chen et al. | Jan 2008 | A1 |
20080034058 | Korman et al. | Feb 2008 | A1 |
20080183681 | Messer et al. | Jul 2008 | A1 |
20080281832 | Pulver et al. | Nov 2008 | A1 |
20090164301 | O'Sullivan et al. | Jun 2009 | A1 |
20090300680 | Cook et al. | Dec 2009 | A1 |
20100205169 | Narayan et al. | Aug 2010 | A1 |
20110179002 | Dumiitru et al. | Jul 2011 | A1 |
20150229686 | Stiers | Aug 2015 | A1 |
Number | Date | Country |
---|---|---|
112011100612 | Jul 2013 | DE |
2009082784 | Jul 2009 | WO |
Entry |
---|
Platzer, A Vector Space Search Engine for Web Services, 2005, 9 pages. |
“U.S. Appl. No. 12/708,350, Final Office Action dated Aug. 13, 2014”, 14 pgs. |
“U.S. Appl. No. 12/708,350, Non Final Office Action dated Jan. 6, 2014”, 20 pgs. |
“U.S. Appl. No. 12/708,350, Non Final Office Action dated Nov. 23, 2012”. |
“U.S. Appl. No. 12/708,350, Notice of Allowance dated Dec. 9, 2014”, 7 pgs. |
“U.S. Appl. No. 12/708,350, filed Feb. 25, 2013 to Non Final Office Action dated Nov. 23, 2012”. |
“U.S. Appl. No. 14/639,554, Final Office Action dated Mar. 30, 2016”, 15 pgs. |
“U.S. Appl. No. 14/639,554, Non Final Office Action dated Aug. 13, 2015”, 18 pgs. |
“U.S. Appl. No. 14/639,554, Notice of Allowance dated Dec. 20, 2016”, 8 pages. |
“United Kingdom Application Serial No. 1214631.2 office Action dated Feb. 19, 2014”, 5 pgs. |
“United Kingdom Application Serial No. 1214631.2, Office Action dated Jul. 17, 2013”, 5 pgs. |
“United Kingdom Application Serial No. 1214631.2, Office Action dated Oct. 2, 2012”. |
“United Kingdom Application Serial No. 1214631.2, Response filed Dec. 3, 2012 to Office Action dated Oct. 2, 2012”. |
Platzer, Christian et al., “A Vector Space Search Engine for Web Services”, Proceedings of the Third European Conference on Web Services (ECOWS'05), 2005, 1-9. |
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20170302716 A1 | Oct 2017 | US |
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Parent | 14639554 | Mar 2015 | US |
Child | 15460131 | US | |
Parent | 12708350 | Feb 2010 | US |
Child | 14639554 | US |