Source: https://patents.google.com/patent/US8843467B2/en
Timestamp: 2019-04-25 12:36:16+00:00

Document:
A method and system for providing information to a user of a device on a local network is provided. This involves obtaining information about current user activity on the local network, obtaining contextual information about current user activity on the local network and obtaining additional information interrelated to the contextual information and the user activity information. Then correlations are identified between the additional information, the contextual information and the user activity information. The correlations are used in forming a query to search for information related to the current user activity, to provide to the user.
The present invention relates to providing relevant information to users, and in particular to providing relevant information to users with minimal user input.
The availability of vast and rich information on the Internet has changed business and has dramatically impacted many aspects of social and home lives. As a result, searching for information on the Internet with the aid of a search engine using a browser has become one of the primary ways of obtaining information.
Meanwhile, advances in hardware and software technologies in recent years have enabled users such as home network users to equip their networks with networked consumer electronics (CE) devices, which often can store large amounts of content. User experience in searching for information can be greatly enriched by seamlessly receiving related information from the Internet while accessing content available in the home network. The related information includes information that is related to the content accessed by the user, and as a result such related information is likely of potential interest to the user.
However, searching for information using conventional technologies requires users to repeatedly enter and modify query keywords using a keyboard. As a result, the conventional searching experience is limited to computing devices with a keyboard. Further, the degree of success in finding information of interest is highly dependent on user knowledge and skill in forming a good query. Moreover, search engines often return large amounts of search results (i.e., hits). For a user, having to repeatedly modify a query and inspect numerous hits on a CE device that has limited computing resources and no convenient input devices, can be a trying and time consuming experience.
Certain Internet search engines provide both enterprise data searching using Enterprise appliance products, and personal data searching using Personal Desktop search applications. Desktop search applications (e.g., Google Desktop Search, Copernic) are extensions of Internet searches where users can now search for content on their computers. However, such search engines have several shortcomings, including: (1) requiring users to form queries; (2) requiring users to have knowledge and skills to form/refine the queries in order to obtain desired results; (3) requiring significant computing resources exceeding that provided in CE devices such as TVs, DVD player, DVRs, Set-top boxes, etc.; (4) requiring input devices such as a keyboard for entering a significant amount of text; and (5) requiring a powerful PC-type computing device to allow users to inspect a large amount of search results.
Similarly, media players, such as Windows Media Player, Real Player etc., extract related metadata information from the Internet for music CDs played using such media players. Typically, such media players maintain a standard set of metadata types that could be extracted and displayed, and rely on specific websites to obtain the required metadata. However, these media players do not allow the user to access random information related to a music CD (e.g., lyrics of a song, artist biography). This is because such random information is hot among the standard metadata information available on the specific websites pre-configured for access. Further, because such media players rely on specific websites, if those websites become inaccessible, the media players would fail to obtain the metadata information, even though the required information may be available on some other website or resource.
The present invention provides a method and system for providing information to a user of a device on a local network. In one embodiment, this involves obtaining information about current user activity on the local network, obtaining contextual information about current user activity on the local network and obtaining additional information interrelated to the contextual information and the user activity information. Then, correlations are identified between the additional information, the contextual information and the user activity information. The correlations are used in forming a query to search for information related to the current user activity.
Forming a query includes automatically forming a query without requiring user intervention. The query is executed to obtain search results including information related to the current user activity. The search results are presented to the user on a user interface in the device, such as a consumer electronics device. User interface functions are mapped to a small number of key presses on the device for minimizing the need for user involvement.
In one implementation, the present invention allows seamlessly bringing relevant Internet information to a user by data correlation, with minimal user input. This enriches the experience in using CE devices, without requiring the user to enter queries. As such, a user can utilize a CE device for searching information using a small number of keys, without a keyboard, and can obtain relevant information (i.e., information of interest) from the Internet with minimal involvement in query construction, query refinement and searching.
FIG. 1 shows an example of a network implementing a process for providing relevant information to users, according to an embodiment of the present invention.
FIG. 2 shows a flowchart of the steps of a process for providing relevant information to users to augment user experiences with minimal user input, according to an embodiment of the present invention.
FIG. 3 shows an example of a functional block diagram of a system implementing a process for providing relevant information to users utilizing data correlation, according to the present invention.
FIG. 4 shows an example of a functional block diagram of another system implementing a process for providing relevant information to users utilizing data correlation, according to the present invention.
The present invention provides a method and system for providing relevant information to users. In one embodiment, this involves seamlessly bringing relevant Internet information to a user by data correlation, with minimal user input. This enriches the experience in using CE devices, without requiring the user to enter queries. For example, the present invention enables a user to utilize a CE device for searching information using a small number of keys without a keyboard, and to obtain desired/relevant information from the Internet with minimal involvement in query construction. The present invention improves the precision in obtaining relevant search results for the user that is easily accessible to a user utilizing a resource-limited CE device. Further, the present invention suggests information of potential interest to the user based on the nature of user interaction with a CE device.
FIG. 1 shows a functional architecture of an example network 10, such as a home network, embodying aspects of the present invention. The network 10 comprises devices 20 which may include content, a PC 21, CE devices 30 (e.g., TV, VCR, STB, cell phone, PDA) which may include content, and an interface 40 that connects the network 10 to an external network 50 (e.g., another local network, the Internet). The external network 50 is connected to one or more servers 51. Though the devices 20 and 30 are shown separately, a single physical device can include one or more logical devices.
The devices 20 and 30 can implement the HTTP protocol which uses Universal Plug and Play (UPnP) for communication therebetween. Though in the example described herein the HTTP protocol is utilized by the network 10, those skilled in the art will recognize that the present invention is useful with other network communication protocols (e.g., Jini, HAVi, IEEE 1394).
6. Presenting the search results to the user as information related to the current user activity (i.e., information of interest to the user).
Identifying correlations can be performed in one or more of the following example ways: (1) identifying correlations between information about current user activity and the interrelated information obtained from local sources, (2) identifying correlations between information about current user activity and the interrelated information obtained from external sources, and (3) identifying correlations between information about current user activity and the interrelated information obtained from local and external sources.
An implementation of the above process for providing relevant information to a user of a CE device in the home network is now described in more detail. In order to minimize the number of keystrokes a user has to enter to receive information related to the current user activity, functionalities that support information searching are mapped to a small number of keys (e.g., mapping searches to a few keys of a remote control). Then, certain information is gathered about current user activity on CE devices. This includes obtaining metadata contained in media that is accessible only by content-rendering CE devices (e.g., length and type of the content contained in a CD or a DVD).
The process further involves obtaining information embedded in broadcast streams that are accessible only by a receiving/rendering CE device (e.g., subtitles and closed captions). In addition, information is gathered about content already existing on the home network (e.g., songs by Sting that are already owned by the user and the corresponding metadata). Further information is gathered about relevant structured data that exists on the Internet (e.g., gathering metadata about the songs already owned by the user from a compact disk database (CDDB)). Additional relevant information is obtained from semi-structured data that exists on the Internet (e.g., the biography of an artist from the Internet Movie Database (IMDb) and/or from the relevant web pages). Further relevant information is gathered from unstructured data that exists on the Internet (e.g., URLs of the web pages carrying the geographical, economical, political, and cultural information about the place from which main events are being reported in the news).
The gathered/obtained information defines the information at hand. Then, when a user operates a CE device, the user input to a CE device is correlated with the information at hand to automatically form queries to search for related information. This minimizes the need for the user to generate queries or use a keyboard in forming queries.
Then, from the information at hand, the data extracted from the Internet sources is correlated with the data extracted from home network content to form a query plan to refine the queries for precision searching. The query plan is then executed for searching the queries on the external network (e.g., the Internet, other resources), without requiring user intervention. The query execution results, in the form of search results, are then presented to the user. Preferably, based on the information at hand, the most relevant information from the search results is selected for presentation to the user, without requiring user intervention. Therefore, the information presented to the user includes information of potential interest to the user as related to the information at hand.
Step 210: Searching the external network based on the queries to obtain search results.
Step 212: Presenting the search results to the user. Preferably, by correlating the search results to the information at hand, the most relevant information from the search results is selected for presentation to the user.
FIG. 3 shows a functional block diagram of an example system 300 that encapsulates and implements a process for providing relevant information to a user of a CE device in a local network, according to the present invention. The system 300 comprises a client user interface 310, a correlation framework 305, a local contextual information gatherer 302, an unstructured data extractor and analyzer 317, a structured data extractor and analyzer 319, a semi-structured data extractor and analyzer 321, a broadcast data extractor and analyzer 306 and a search engine interface 324. The system further comprises home network data sources including local content sources 307 and application states 309. The system further comprises Internet sources including Internet unstructured data sources 330, Internet structured data sources 320, Internet semi-structured data sources 327, and other sources including broadcast unstructured data sources 301.
Elements/Components 310, 305, 302, 317, 319, 321, 306 and 324 in the system 300 represent processing components, each of which can typically be implemented as a software module running on electronics devices with CPU and memory. All these components can run on a single device. Alternatively, they can be partitioned and implemented so as to run on more than one device connected by one or more interconnected networks. For example, in one implementation, the devices are connected by a home local area network (LAN), in another embodiment some of the modules of the components run on the devices connected by the home LAN and others run on devices reachable through the Internet.
Further, the elements 307 and 309 in the system 300 represent in-home data sources reachable through a home LAN, and the elements 330, 320, 327 and 301 in the system 300 represent out-of-home sources reachable through a wide area network (WAN) e.g., through the Internet, a telecommunication network, or a broadcast network such as a cable network and satellite network. The arrows connecting the elements in the system 300 indicate the interactions between the elements with the arrowheads pointing towards the direction of data flowing between the elements. The various elements in the system 300 are described in more detail below.
The system 300 only requires local content sources 307, application states 309, and the Internet unstructured data sources 330. All the other data sources are optional. Although in FIG. 3 several types of data sources are shown, as those skilled in the art will recognize, the principles of the present invention are applicable to other types of data sources as well.
The local content sources 307 include information about the digital contents at home stored on, e.g., CD's, DVD's, tapes, internal hard disks and removable storage devices.
The local application states 309 include information about the current user activity using one or more devices 20 or 30, e.g., the user is listening to music using a DTV, or a media player.
The Internet unstructured data sources 330 includes data or data segments whose semantics cannot be analyzed, e.g., free text. Internet servers that host web pages typically contain this kind of data.
The Internet structured data sources 320 includes data whose semantics are closely defined. Internet servers that host XML data enclosed by semantic-defining tags, and Internet database servers such as CDDB are examples of such sources.
The Internet semi-structured data sources 327 includes data that have tags to define the free-form data without describing the semantics of the data. For example, a review section of an XML-based EPG data is semi-structured data; it is tagged as <review> . . . </review>, but without defining the semantics of the enclosed text. Most web pages contain semi-structured data. Internet servers that host this kind of data are examples of such sources.
The broadcast unstructured data sources 301 include unstructured data embedded in media streams. Cable receivers, satellite receivers, TV antennas, and radio antennas are examples of such data sources.
In the system 300, the required processing components are the client user interface 310, the correlation framework 305, the search engine interface 324, and the local contextual information gatherer 302. The remaining processing components are optional.
The client user interface (UI) 310 interacts with a user. It maps UI functions to a small number of keys, takes user input from the selected keys and passes the input to the correlation framework (CF) 305 in a predefined form. Further, the UI 310 displays the results passed back from the CF 305 when instructed by the CF 305. An example of the UI 310 includes a module that receives signals from a remote control, and a web browser that overlays on a TV screen to display search results.
The CF 305 takes input from the UI 310, the local contextual information gatherer 302, the search engine interface 324, and optionally other components, and forms an initial query based on the current activity of the user. The CF 305 is described in more detail further below.
The local contextual information gatherer (LCIG) 302 collects metadata and other contextual information about the contents on the local/home network. The LCIG 302 also derives contextual information from existing contextual information such as metadata. Examples of metadata of content include title, type, artist, time of publication, album, band, actors, and language.
The LCIG 302 also performs the following tasks: gathering metadata from local (home) sources whenever new content is added to the local collection; gathering information about current user activity on the local network based on the states of applications running on the local network devices (e.g., devices 30 in FIG. 1); accepting metadata and/or contextual information extracted from Internet sources and other external sources that describes the local content.
The LCIG 302 further derives contextual information from the available data (i.e., the data at hand). The LCIG 302 maintains a local metadata cache 303 (FIG. 4), stores the collected metadata in the cache 303, and provides an interface for other modules to add, delete, access, and modify the metadata in the cache 303. An example of the LCIG 302 is described in related U.S. patent application Ser. No. 11/633,880, filed Dec. 4, 2006, entitled “Method and Apparatus for Contextual Search and Query Refinement on Consumer Electronics Devices,” incorporated herein by reference.
The search engine interface (SEI) 324 receives a query as input (e.g., from the CF 305), and sends the query over the Internet using a predefined Internet communication protocol such as HTTP. The SEI 324 also accepts the response to the query sent by the search engine(s) on the Internet, and passes the response to the component or device that issued the query.
The unstructured data extractor and analyzer 317 receives a query from the CF 305 as input and passes the query to the SEI 324. The unstructured data extractor and analyzer 317 receives the response returned from the SEI 324, extracts highly-relevant terms therefrom that are not already in the query, and returns the terms to the CF 305.
The structured data extractor and analyzer 319 takes query input from the CF 305, uses the input to access structured data from Internet structured data sources 320 according to predefined protocols such as HTTP or proprietary remote access protocols. The structured data extractor and analyzer 319 extracts the desired metadata from the results based on the query, and returns the metadata to the CF 305.
The semi-structured data extractor and analyzer 321 takes query input from the CF 305, uses the input to access semi-structured data from Internet semi-structured data sources 327, according to predefined protocols, e.g., HTTP and SOAP. After the semi-structured data extractor and analyzer 321 receives the results and extracts the desired metadata and/or a list of terms from the results based on the query. The semi-structured data extractor and analyzer 321 may use all or a part of the extracted items to form one or more new queries to refine the quality of the list of terms. The refinement can be performed with one or more iterations, each of which may use more or less of the terms, a partially overlapped set of terms, or a different set of terms. The semi-structured data extractor and analyzer 321 then returns the final list of terms and/or metadata to the CF 305.
The broadcast data extractor and analyzer 306 takes query input from the CF 305, and uses defined interfaces of a particular media to access text data embedded in the broadcast stream, e.g., subtitles and closed captions. It extracts the desired terms from the embedded text and returns the data to the CF 305.
Based on user activity and/or user input, the CF 305 instructs the LCIG 302 to obtain contextual information about the user activity and/or user input. Based on the user activity and/or user input, the CF 305 instructs the unstructured data extractor and analyzer 317 to gather more data about the activity and/or user input. Based on the user activity and/or user input, the CF 305 instructs the structured data extractor and analyzer 319 to gather more data about the activity and/or user input. Based on the user activity and/or user input, the CF 305 instructs the semi-structured data extractor and analyzer 321 to gather more data about the activity and/or user input. Based on the user activity and/or user input, the CF 305 instructs the broadcast data extractor and analyzer 306 to gather more data about the activity and/or user input. The data gathering and correlation processes can be iterative based on defined evaluation criteria to determine the quality of the data gathered.
The data gathering process can be performed in real time or in the background and use the data when needed. For example, if a user has expressed interest in song lyrics, the CF 305 may initiate a pre-fetch for the lyrics of more songs when computing resources are available to show the user when a user issues such a request.
The CF 305 correlates the data gathered from the Internet sources (e.g., sources 330, 320, 326) and other external sources (e.g., source 301), with the data gathered locally, and with the activity and user input, if any. The correlation may be performed according to the rules defined for different types of information sources and/or for different user interests.
Based on the available metadata about the current user activity, the CF 305 instructs the UI 310 to display suggestions for related information, e.g., the biography of the artist and the lyrics of the song currently being played, and to provide a way for the user to buy more songs from the same artist.
Based on the current activity and the user input (e.g., to buy more songs), the CF 305 forms a query plan (e.g., for finding the songs already existing at home, finding the songs available on the Internet, and finding the songs that can be suggested to the user for purchase). The CF 305 then orchestrates the execution of the query plans (e.g., via the SEI 324), and receives result of the query execution (search results). The CF 305 passes the search results to the UI 310 for display.
An example of the CF 305 is described in related U.S. patent application Ser. No. 11/726,340, filed Mar. 21, 2007, entitled “A Framework for Correlating Content on a Local Network with Information on an External Network,” incorporated herein by reference. In the embodiment shown in FIG. 3, components 307 and 309 can reside on the local network, while components 320, 326 and 330 reside outside the local network. The remaining components reside on a CE device on the network.
Further, information gathering is performed by components 302, 317, 319, 321, 306, while information correlation is performed by the correlation framework 305. Further, query formation is performed by the correlation framework 305 using the local contextual information gatherer 302.
FIG. 4 shows a functional block diagram of another example system 400 that implements a process for providing relevant information to a user of a CE device in a local network, according to the present invention. The system 400 includes: a broadcast unstructured data sources 301, a local contextual information gatherer 302, a local metadata cache 303, a user profile 304, a broadcast data extractor and analyzer 306, local content sources 307, a document theme extractor 308, application states 309, a client UI 310, Internet metadata gatherer from structured sources 318, Internet structured data sources 320, a search engine interface 324, web pages 326, a snippet analyzer 328, Internet unstructured data sources 330, a scraper 331, a user profile manager 335, Internet semi-structured data sources 327, and a correlation framework 305 which includes a query execution planner 312, a correlation plan executor 314 and a correlation constructor 316.
In place of the unstructured data extractor and analyzer 317 of system 300 in FIG. 3, the system 400 in FIG. 4 includes the snippet analyzer 328 and the document theme extractor 308. A query 322 is shown as an input for the search engine interface 324, and the web pages 326 are shown as output of the search engine interface 324. In place of the semi-structured data extractor and analyzer 321 of system 300 in FIG. 3, the system 400 in FIG. 4 includes a scraper 331. The local contextual information gatherer 302 takes an additional input from the local metadata cache 303 and stores its output in the cache 303. The query 322 is searched for on the Internet, i.e., a type of encapsulation of the information needed. It is derived from the information and metadata available at the home network. The web pages 326 comprise any web page on the Internet that is returned by the search engine as a result of a query.
When a query is sent to a search engine, the search engine returns a list of URLs that are relevant to that query. For each relevant URL, most search engines also return a small piece of text (snippet) from the corresponding web page. The text is either from the web page itself, or it could be taken from the meta tags of the web page. Different search engines have different techniques for generating these snippets. The main purpose of these snippets is to give the user a brief overview of what the web page is about. The snippet analyzer 328 takes the output search results of a search engine (e.g., 330) as one input and takes a query from the CF 305 as another input. The snippet analyzer 328 analyzes the snippets from the results, extracts terms that are relevant to the query from the snippets, and passes the extracted terms to the CF 305.
Step 2: Clustering the documents returned as results by the search engine interface 324, extracting and selecting a list of terms that best summarize the themes of each cluster, and returning the lists to the CF 305.
The scraper 331 takes the query from the CF 305 as an input and sends search query to a selected Internet site (e.g., 327). After the scraper 331 receives a query response from the Internet site, the scraper 331 extracts the desired URLs and/or data, and passes the results to the CF 305. Alternatively, instead of sending the query as a search query to an Internet site, the scraper 331 may just fetch data (web pages) from an Internet site using the query, and/or once the page is retrieved, it may use the query to extract the required information from it.
The user profile store 304 stores user profiles. Examples of the information contained in a user profile include user information, recent user activity history, historical user activity, user's access patterns, user interests, etc.
The user profile manager 335 builds and maintains the user profile store 304. The user profile manager 335 provides an interface for other modules to add, delete, access and modify the user profile store 304. The user profile manager 335 further takes input from the CF 305 for accessing or modifying the user profile store 304 and returns corresponding results to the CF 305.
The query execution planner 312 provides a plan including forming a query based on correlations identified between, e.g., the additional information, the contextual information and the user activity information. The correlation plan executor 314 executes the query plan and correlates the query plan execution results so as to deliver better results to the user.
The correlation constructor 316 either works with the execution planner 312 to form the query plan by correlating data gathered from external sources and the data gathered from the home network, or forms the plan automatically through the correlation. The Internet metadata gatherer from structured sources 318 gathers metadata about local content from Internet structured data sources 320.
Accordingly, the present invention augments a user's experience by providing relevant information to a user by data correlation while requiring minimal user input. By mapping UI functions to a small number of key presses the user can obtain or select relevant information with a few key presses. User interests based on past and present user activity in the network forms contextual information. The contextual information is used in forming search queries in performing contextual searches for information relevant to the user interest, and presents the results to the user. In one implementation, the metadata related to the local content and the current application states are used to obtain the contextual information for query formation and result filtering to suggest more relevant information, essentially without user intervention.
1. Invoking one or more of the components 302, 306, 310, 324, 328, 308, 318, and passing the relevant contextual information thereto for forming a query or a query plan, executing a plan, or examining the results returned by the above components.
d. Whether any of the contextual information should be used as context of a query.
3. If new contextual terms are found from a returned list, then using all or some of the terms, and optionally passing the terms to the LCIG 302 to store for future use.
4. If a new query should be formed, then constructing the query according to the decision made and executing the query.
5. If some of the contextual information should be used for context of a query, then using such information according to the predetermined format and executing the query.
6. If a returned list of terms needs to be further refined, then processing contextual information along with the list. Essentially, the list of terms is returned by components that retrieve related information from the Internet (i.e., one or more of components 308, 328, 324). The component 318 is not included because it retrieves fixed information from a fixed external resource.
As such, a CE device is configured according to an embodiment of the present invention, forms a query using contextual information about a user activity, user environment (e.g., home network) contents, and the metadata about such contents, and thus does not require the user to be involved in the search process. Further, users need not be skilled in query formation to obtain information from the Internet. Such a configured CE device uses the contextual information to select the most relevant results returned in response to the query for presentation to the user.
In the embodiment shown in FIG. 4, components 303 and 307 can reside on the local network, while components 320, 327 and 330 reside outside the local network. The components 304 and 309 can reside on the local network or the CE device, while the remaining components reside on the CE device on the network.
The present invention enables users to use a CE device for searching information while using a small number of keys without a keyboard. Users can obtain desired information on the Internet with no or minimum involvement in query construction. Further, the precision of the search results is improved wherein that most relevant information can be easily accessed using a resource-limited CE device. In addition, the present invention suggests information from the search results based on the contextual information, to further augment user experience in using CE devices without a keyboard.
As such, the present invention provides a contextual search and query refinement process for CE devices, whereby the cognitive load of query formation is relegated to the CE device itself, freeing the user to simply enjoy the content. Using the contextual information, the CE device not only forms queries to obtain search results with relevant information, but the CE device then uses that contextual information for search result filtering to suggest those search results that are of more interest to the user in relation to the contextual information.
displaying information to the user that is based on the relevant portion and not based on other portions of the results received in response to the third search operation.
the plurality of search operations are performed in sequence and during the sequential performance of the search operations, no input is received from the user that indicates what the search operations should search for.
the media is a song.
5. A method as recited in claim 4 wherein the second search operation further includes obtaining Compact Disc Database (CDDB) information over the Internet from the CDDB.
6. A method as recited in claim 4 wherein the song includes lyrics and wherein, during the first search operation, the lyrics of the song are pre-fetched from a source stored in the local home network.
7. A method as recited in claim 2 wherein the third search involves a search of the Internet.
the contextual information received from the local home network in response to the first search is based at least in part on the profile.
displaying references to a plurality of the multiplicity of media products that helps provide an option to the user to purchase the plurality of media products wherein the plurality of media products are all of the multiplicity of media products except for the at least one media product that is already stored on the local home network.
10. A method as recited in claim 2 wherein the input received from the user indicates that the user pressed a key and wherein no input is received from the user while the plurality of search operations is being performed.
extracting relevant portions from the snippets wherein the third search operation is based on the extracted relevant portions.
12. A method as recited in claim 2 further comprising determining the correlations between the contextual information received from the local home network in response to the first search and the results received in response to the second search from the Internet.
the information displayed to the user is based on both the first and second sets of purchasable items and provides an option to purchase one of the first and second sets of purchasable items.
the contextual information obtained from local home network is based on the stream data.
17. A computing system as recited in claim 16 wherein the second search operation further includes obtaining Compact Disc Database (CDDB) information over the Internet from the CDDB.
18. A computing system as recited in claim 16 wherein the song includes lyrics and wherein, during the first search operation, the lyrics of the song are pre-fetched from a source stored in the local home network.
19. A computing system as recited in claim 14 wherein the third search involves a search of the Internet.
display references to a plurality of the multiplicity of media products that helps provide an option to the user to purchase the plurality of media products wherein the plurality of media products are all of the multiplicity of media products except for the at least one media product that is already stored on the local home network.
22. A computing system as recited in claim 14 wherein the input received from the user indicates that the user pressed a key and wherein no input is received from the user while the plurality of search operations is being performed.
extract relevant portions from the snippets wherein the third search operation is based on the extracted relevant portions.
24. A computing system as recited in claim 14 wherein the computer readable instructions, when executed by the computing system, further cause the computing system to determine the correlations between the contextual information received from the local home network in response to the first search and the results received in response to the second search from the Internet.
the contextual information received from the local home network in response to the first search is based on the stream data.
29. A computer readable storage medium as recited in claim 28 wherein the second search operation further includes obtaining Compact Disc Database (CDDB) information over the Internet from the CDDB.
30. A computer readable storage medium as recited in claim 28 wherein the song includes lyrics and wherein, during the first search operation, the lyrics of the song are pre-fetched from a source stored in the local home network.
31. A computer readable storage medium as recited in claim 26 wherein the third search involves a search of the Internet.
the contextual information received from the local home network is based at least in part on the profile.
executable computer code operable to display references to a plurality of the multiplicity of media products that helps provide an option to the user to purchase the plurality of media products wherein the plurality of media products are all of the multiplicity of media products except for the at least one media product that is already stored on the local home network.
34. A computer readable storage medium as recited in claim 26 wherein the input received from the user indicates that the user pressed a key and wherein no input is received from the user while the plurality of search operations is being performed.
executable computer code operable to extract relevant portions from the snippets wherein the third search operation is based on the extracted relevant portions.
36. A computer readable storage medium as recited in claim 26 further comprising executable computer code operable to determine the correlations between the contextual information received in response to the first search and the results received in response to the second search from the Internet.
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References: Application No. 200880016311
 Application No. 2008100826213
 Application No. 200880016311
 Application No. 2007101962371
 Application No. 200880016311
 Application No. 200880016311
 Application No. 200880009063
 Application No. 2010
 Application No. 2009
 Application No. 2009554447
 Application No. 200810082621