Source: http://www.google.com/patents/US8037071?dq=6,891,551
Timestamp: 2014-03-16 15:35:31
Document Index: 377679705

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 07']

Patent US8037071 - Methods and systems for dynamically rearranging search results into ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsMethods of and systems for dynamically rearranging search results into hierarchically organized concept clusters are provided. A method of searching for and presenting content items as an arrangement of conceptual clusters to facilitate further search and navigation on a display-constrained device includes...http://www.google.com/patents/US8037071?utm_source=gb-gplus-sharePatent US8037071 - Methods and systems for dynamically rearranging search results into hierarchically organized concept clustersAdvanced Patent SearchPublication numberUS8037071 B2Publication typeGrantApplication numberUS 12/423,448Publication dateOct 11, 2011Filing dateApr 14, 2009Priority dateSep 14, 2006Also published asCA2663222A1, EP2062171A2, EP2062171A4, US7536384, US20080071771, US20090198688, US20120023102, WO2008034057A2, WO2008034057A3Publication number12423448, 423448, US 8037071 B2, US 8037071B2, US-B2-8037071, US8037071 B2, US8037071B2InventorsSashikumar Venkataraman, Pankaj Garg, Pranav RajanalaOriginal AssigneeVeveo, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (102), Non-Patent Citations (41), Referenced by (2), Classifications (15), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetMethods and systems for dynamically rearranging search results into hierarchically organized concept clustersUS 8037071 B2Abstract Methods of and systems for dynamically rearranging search results into hierarchically organized concept clusters are provided. A method of searching for and presenting content items as an arrangement of conceptual clusters to facilitate further search and navigation on a display-constrained device includes providing a set of content items and receiving incremental input to incrementally identify search terms for content items. Content items are selected and grouped into sets based on how the incremental input matches various metadata associated with the content items. The selected content items are grouped into explicit conceptual clusters and user-implied conceptual clusters based on metadata in common to the selected content items. The clustered content items are presented according to the conceptual clusters into which they are grouped.
8. The system of claim 4, wherein presenting the flattened cluster is based at least in part on a combination of the one or more cluster identifiers matching the user input and the child cluster identifier. Description
CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. application Ser. No. 11/855,661 filed Sep. 14, 2007, which claims priority to U.S. Provisional Application Ser. No. 60/825,616, filed Sep. 14, 2006.
U.S. patent application Ser. No. 11/136,261, entitled Method And System For Performing Searches For Television Programming Using Reduced Text Input, filed on May 24, 2005, which claims priority to U.S. Provisional Patent Application No. 60/626,274, entitled Television Systems and Associated Methods, filed on Nov. 9, 2004, and U.S. Provisional Patent Application No. 60/664,879, entitled Method And System For Performing Searches For Television Programming Using Reduced Text Input, filed on Mar. 24, 2005; U.S. patent application Ser. No. 11/312,908, entitled Method And System For Dynamically Processing Ambiguous, Reduced Text Search Queries And Highlighting Results Thereof, filed on Dec. 20, 2005, which claims priority to U.S. Provisional Patent Application No. 60/711,866, entitled A Dynamic Highlighting Interface Of Multiword Prefixes Of Results Obtained By Incremental Search With Reduced Text Entry On Television And Mobile Devices Using A Keypad With Overloaded Keys, filed on Aug. 26, 2005, and U.S. Provisional Patent Application No. 60/716,101, entitled Method And System For Incremental Search With Reduced Text Entry Using A Reduced Keypad With Overloaded Keys, filed Sep. 12, 2005; and U.S. patent application Ser. No. 11/235,928, entitled Method And System For Processing Ambiguous, Multiterm Search Queries, filed on Sep. 27, 2005, which claims priority to U.S. Provisional Patent Application No. 60/716,101, entitled Method And System For Incremental Search With Reduced Text Entry Using A Reduced Keypad With Overloaded Keys, filed Sep. 12, 2005, and U.S. Provisional Patent Application No. 60/711,866, entitled A Dynamic Highlighting Interface Of Multiword Prefixes Of Results Obtained By Incremental Search With Reduced Text Entry On Television And Mobile Devices Using A Keypad With Overloaded Keys, filed on Aug. 26, 2005. BACKGROUND 1. Field of Invention
In addition, ambiguous text inputs can match a wide variety of results because of the nature of the ambiguous input. This is so because the ambiguous input not only represents the search input intended by the user, but can also represent other words or phrases. For example, using the well-known 12-key telephone keypad, the input �227� represents both �car� and �bar�, which can match very different results. Thus, while incremental, ambiguous text input is a convenient way to enter search input on an input constrained device, the increase in the amount of results returned can be cumbersome on a display constrained device, where only a few entries in a result set are visible.
SUMMARY OF THE INVENTION The invention provides a method of dynamically rearranging search results for an incremental search query into hierarchically organized concept clusters.
DETAILED DESCRIPTION Preferred embodiments of the invention provide methods of and systems for discovering and dynamically rearranging search results into hierarchically organized concept clusters. A concept cluster is a set of content items and/or topics that are related by one or more common themes or information types. For example, one concept cluster may be �baseball�, which can contain search results related to scores of past Major League Baseball games and/or schedules for future games. In some implementations, the concept clusters are time-sensitive (described below) and include both precomputed concept clusters and dynamically generated concept clusters. The search results can include lexical matches between the content results and the incremental input of search queries, as well as matches between the incremental input and the concept cluster identifiers. This method of generating and presenting search results significantly enhances the user experience of performing incremental search for information because the hierarchical concept-driven clustering of results provides a richer organization of results. The techniques disclosed herein enable the user to more easily find the desired information content, as all results pertaining to a particular concept have been collected together. This stands in contrast to lexical matching, where results pertaining to the same concept may be interleaved among other results, which increases the cognitive load for the user.
As mentioned above, in some embodiments, this step can be omitted, as the content items can be maintained without a hierarchy, and later organized according to metadata associated with the content items, as described in greater detail below. Thus, in some implementations, the content items are simply associated with metadata and need not be arranged in a hierarchy. In such an embodiment, the content items have a �flat� arrangement in that there is no express hierarchy to the content item collection. The metadata associated with the content items consists of metadata phrases that can have one or more terms to describe the informational content of the content item.
As mentioned above, the concept clusters can be created based on the metadata associated with the content items. However, not every metadata term may be selected to also serve as a concept cluster. For example, in one implementation, terms that occur among the metadata of the entire set of content items are used to create the concept cluster hierarchy. In a further example, the concept clusters are created based on popular categorizations of the content items. Thus, one concept cluster would be �sports�, which would have sub-clusters �baseball�, �basketball�, etc. Another set of clusters would be �movies�, which would have subsclusters �genres�, �actors�, �directors�, etc. Any meaningful organization of concept clusters can be used with the techniques disclosed herein, and the invention is not limited to any particular method of generating the clusters and the corresponding hierarchy.
FIG. 3 provides an example of the reorganization and presentation of search results. A user enters �Tom� 301 as a prefix for �Tom Cruise� into a system supporting incremental search. The prefix �Tom� is matched with concept clusters such as �TV content�, �web videos�, and �movies� by way of these clusters' relationship with the parent cluster node �Tom Cruise� 302. Thus, in this example, Tom Cruise is an explicit conceptual cluster. However, rather than presenting the TV content, web videos, and movies of Tom Cruise under a single cluster �Tom Cruise�, the system dynamically creates the �Tom Cruise . . . TV Content�, �Tom Cruise . . . Web Videos�, and �Tom Cruise . . . Movies� clusters, effectively �flattening� a portion of the cluster hierarchy associated with Tom Cruise. This facilitates the user's selection and navigation of the results related to Tom Cruise by displaying the variety of Tom Cruise content on one screen.
The input also matches other concept clusters associated with the term �Tom�, such as content related to �Tom Jones� 303, again, another example of an explicit conceptual cluster. Because Tom Jones is a singer, there are different concept sub-clusters associated with the parent cluster of �Tom Jones�, for example, CDs of his music, concert dates, etc. As above, the system dynamically flattens a portion of the Tom Jones cluster hierarchy to achieve the benefits described above. The decision of whether to flatten or not flatten portions of the predefined hierarchy can be based on the number of items that would result in the list of results to be presented. The ideal number of results can be determined based on the type of device on which the techniques are employed and user preferences.
Meanwhile, the system discovers content items based on the matching techniques described in the incorporated applications and/or lexical matches of the content items' metadata with the search input �Tom�. These search results are then presented in the concept cluster hierarchy determined according to the concept cluster match and reorganization described above. Thus, all content related to Tom Cruise is organized according to the sub-clusters that are child nodes under Tom Cruise; all content related to Tom Jones is organized in a similar manner under the sub-clusters associated with Tom Jones.
FIG. 4 illustrates employment of the techniques disclosed herein to reorganize search results from a partial prefix search input. The hierarchical reorganization in FIG. 4 is generated by performing lexical matches of the search input 401 against the content items and precomputed concept clusters (e.g., the clusters of FIG. 2) and dynamically generating new concept clusters 402 based on the matching results. The user incrementally inputs partial prefixes of two cast members 401. In this example, �Tom� for Tom Cruise and �Jac� for Jack Nicholson. The incremental input matches content items from a relatively large set of content items, some of which are arranged into new concept clusters 402 that are dynamically-formed (e.g., the user-implied conceptual clusters), while others are presented directly in the results presentation 403. In both cases, the partial prefix inputs 401 are matched against the results and the results are order by relevance (see the incorporated applications for methods of ordering by relevance).
Dynamically-created concept clusters 402 can be formed by creating a new cluster that will contain sub-clusters and content items that satisfy both prefixes of the search criteria, i.e., �Tom� and �Jac�. This aspect will be described in greater detail below. One method of naming the dynamically-created concept clusters 402 is to combine the different clusters that came together to form the new cluster. For example, dynamically-formed concept clusters 402 that are presented to the user include �Tom Cruise . . . Jack Nicholson,� �Tom Wilkinson . . . Jackie Chan,� �Tom Jones . . . Jack Nicholson,� and �Marisa Tomei . . . Jack Nicholson�, where each person's name represents a cluster associated with that person. Thus, each of clusters 402 is an example of a user-implied conceptual cluster, in that, no single metadata phrase associated with a content item contains both personalities. The user-implied conceptual cluster is formed based on a combination of two separate metadata phrases common to multiple content items of the cluster. An arrow symbol 404 associated with the various results indicate that additional child cluster nodes and/or content items are organized beneath the result presented.
Results 403 are directly presented, i.e., are not grouped into concept clusters, and include �The Cat From Outer Space,� a movie with Tom Jackman, �Nothing in Common,� a movie with Jackie Gleason and Tom Hanks, �The Pledge,� a movie with Jack Nicholson and Tom Noonan, and �Sliders:Eggheads� a TV show with Tom Jackson. These results 403 are not organized into dynamic concept clusters because (1) the content item contains metadata matching both partial prefix terms (i.e., an explicit conceptual cluster) and/or (2) only one result is found having the specific terms which caused the content item result to be presented. For example, �The Cat From Outer Space� appears as a match because both search terms, �Tom� and �Jac� appeared in the metadata �Tom Jackman� associated with that movie. Whereas the result �The Pledge� appears as a match because the first term �Tom� matches the metadata item �Tom Noonan� associated with the movie �The Pledge� and the second term �Jac� matches a separate metadata item �Jack Nicholson� associated with the same movie. However, in this example, no other content items are associated with both metadata terms �Tom Noonan� and �Jack Nicholson�. Had other content items been discovered that also shared those two metadata, a �Tom Noonan . . . Jack Nicholson� dynamic cluster would have been created. This cluster would have contained the content item �The Pledge� as well as the other content items associated with both of these metadata terms. An arrow symbol 405 shown next to the result �Nothing in Common� indicates that that result has child nodes, such as video clips, commentaries, and/or links to vendors that sell a DVD of the movie.
One distinction of the techniques disclosed herein over other search and/or presentation methods is the non-lexical nature of concept clusters. The combination of Tom Cruise and Jack Nicholson can itself form a concept cluster. With such a concept match, the user is presented with a single result for �Tom Cruise . . . Jack Nicholson�. This result can be hierarchical and contain result items, such as particular movies with both actors, and/or sub-clusters, such as lists of movies, lists of TV shows, and/or links to other content with both actors. This dynamic aggregation of results into concept clusters greatly enhances the user experience in contrast to other incremental search systems, where the match is purely lexical in nature. For example, a purely lexical-based search might return results with multiple items matching Tom Cruise and Jack Nicholson where the results of intersecting the sets of content items associated with these two persons may be mixed within other results from other lexical matches, e.g., Tom Wilkinson and Jackie Chan. Furthermore, the ordering of the mixed results may be cumbersome due to the different popularities of the individual results of this intersection.
FIG. 5 illustrates the user's discovery of information, by expanding a concept cluster. In this example, the user has incrementally entered �RE� as a search term 501. The user can continue to type more text to further refine the search or navigate into one of the results returned from the incremental search. Here the concept cluster �Red Sox� 502 is one of the results currently matching the incremental text input �RE� 501. If the user navigates 503 into the �Red Sox� concept cluster (an explicit conceptual cluster), the sub-clusters within the hierarchy are displayed 504. These sub-clusters include the sub-clusters �Red Sox live games,� �Red Sox TV schedule,� �Red Sox past games,� and �Red Sox web videos�, which, in one implementation, contains only content items associated with the Red Sox in some way. The �Red Sox live games,� �Red Sox TV schedule,� and �Red Sox past games� sub-clusters are time-sensitive clusters 505, whose contents are dynamically adjusted with time. The �Red Sox web videos� sub-cluster is not time sensitive and does not need to be dynamically adjusted with time. A content item �Blue Jays @Red Sox� 506 is also presented among the results.
FIG. 6 illustrates the user's discovery of information, by expanding a concept cluster. In this example, the user has incrementally entered �YAN� as a search term 601. As with the previous example, the user can continue to type more text to further refine the search or navigate into one of the results returned from the incremental search. Here the concept cluster �New York Yankees� 602 is one of the results currently matching the incremental text input �YAN� 601. If the user navigates 603 into the �New York Yankees� concept cluster (an explicit conceptual cluster), the sub-clusters within the hierarchy are displayed 604. These sub-clusters include the sub-clusters �New York Yankees live games,� �New York Yankees TV schedule,� �New York Yankees past games,� �New York Yankees web videos,� and �Baseball web videos.� Note, that in this example, in addition to content items associated with the New York Yankees in some way, the list includes an item associated with a related concept, namely, �Baseball web videos� 606, which is associated with the more general concept �baseball�. The �New York Yankees live games,� �New York Yankees TV schedule,� and �New York Yankees past games� sub-clusters are time-sensitive clusters 605, whose contents are dynamically adjusted with time. The �New York Yankees web videos� and �Baseball web videos� sub-clusters are not time sensitive and do not need to be dynamically adjusted with time. A content item �Yankees @Royals� 607 is also presented.
FIG. 7 illustrates the presentation output by one implementation of the embodiment, where the information reorganization of a dynamic concept cluster is based on the cluster hierarchy associated with clusters that are common to matches of multiple terms in the user's incremental partial prefix input. In this example, the user has incrementally entered �RE YAN� as a search input 701. Again, the user can continue to type more text to further refine the search or navigate into one of the results returned from the incremental search. In response to the input, the concept cluster �Red Sox . . . New York Yankees� 702 is one of the results currently matching the incremental text input �RE YAN� 701. The �Red Sox . . . New York Yankees� cluster 702 is dynamically created by intersecting the two concepts �Red Sox� and �New York Yankees� (thus, forming a user-implied conceptual cluster). During the pre-computation step (step 101 of FIG. 1), the concept �Red Sox� was related to the concept �baseball�, as was the concept �New York Yankees.�
Because both the concept �Red Sox� and the concept �New York Yankees� are related to the concept �baseball�, the dynamic, user-implied, concept cluster �Red Sox . . . . New York Yankees� 702 is created and content associated with matches of the two input terms, �RE� and �YAN�, are organized according to the hierarchy of the shared parent concept �baseball� and presented to the user. Similar to previous examples, if the user selects the �Red Sox . . . New York Yankees� concept cluster 702, the sub-clusters from the intersection of the two concepts are displayed 704. In this case, the dynamically-formed intersection clusters are �Live Games,� �TV schedule,� �web videos,� and �past games.� Again, this organization is governed by the information hierarchy associated with the parent concept �baseball�, which can be determined during the precomputation step described above. Thus, �Live Games,� �TV schedule,� �web videos,� and �past games� are selected as clusters because they are common types of content items associated with the broader concept �baseball�. Note, the content item �Blue Jays @ Red Sox� 506 of FIG. 5, the content item �Yankees @ Royals� 607 of FIG. 6, and concept cluster �Baseball Web Videos� 606 of FIG. 6 are not included in the newly formed concept cluster structure presented in FIG. 7. This is so because those content items and clusters did not match both inputs �RE� and �YAN�.
The dynamic intersection of concepts is also performed if the user first entered �RE� and then selected the �Red Sox� concept (as described in connection with FIG. 5) and then typed �YAN� while in the �Red Sox� concept cluster. Similarly, the user can browse a tree arrangement of information nodes to arrive at a similar result. Thus, the user could browse to a top-level node �Sports�, followed by selection of the child node �Major League Baseball�, further followed by selection of the �Red Sox� node. Once in the �Red Sox� cluster, the user could enter the search term �YAN� to complete the dynamic intersection of the concept clusters �Red Sox� and �New York Yankees�. In the alternate, the user could indicate through the interface that the �Red Sox� cluster is to be part of a dynamic intersection query and browse up the tree to find the �New York Yankees� cluster and add that cluster to the intersection.
A system implementing such a search can be configured to enable this type of search method by maintaining the query state of the user's search session, e.g., the system tracks that the user is current browsing within the �Red Sox� concept. Thus, when the user begins to enter text after having browsed to the concept cluster �Red Sox�, the system would use the new text entry along with the current cluster to form the completed query rather than take the new text entry as a standalone query entry. Such a system can also be configurable to not track the state of the user, in which case, the new text entry would be treated as a standalone query. Similarly, a device implementing such a system can provide an �escape� key that would allow the user to reset the query state, providing the ability to enter a new standalone query regardless of the user's location in the content hierarchy.
The description above illustrates how the precomputed cluster hierarchy can be flattened and/or merged to form a new hierarchy into which content items are organized for presentation. Concept clusters can also be combined to form new, conflated concept clusters, which contain an aggregation of content items that are otherwise organized in different clusters. For example, FIG. 8 illustrates another possible concept cluster hierarchy 800 and an example of the formation of a dynamically-formed, conflated concept cluster 801. In this hierarchy, a Tom Jones cluster 802 is organized under the singers cluster 803. However, there is also a Tom Jones cluster 804 under the actors cluster 805 because he has appeared in a movie, there are web videos about him, and some of his concerts have been televised. Thus, when the user enters the incremental search text �TO JO� 806, the content items under the Tom Jones singer cluster 802 and Tom Jones actor cluster 804 will be returned because �TO� incrementally matches �Tom� and �JO� incrementally matched �Jones�. This is another example of an explicit conceptual cluster. In addition, content items for other personalities matching the search text may be returned, such as content items for composer �Tom Johnson�, baseball player �Todd Jones�, and other matches. Each of these personalities can have corresponding concept clusters.
In order to assist the user in finding the desired content items, the system can organize the content items according to the associated personality concept clusters 807. Thus, the system will dynamically create a general concept cluster for Tom Jones 808 and combine the sub-clusters under the Tom Jones actor cluster 804 and the sub-clusters under the Tom Jones singer cluster 802 so they are grouped under the dynamically-formed general Tom Jones cluster 808. Thus, the user can first select the personality Tom Jones 809 in which he or she is interested, and then further browse into the specific type of content he or she is seeking 810. The dynamically-formed concept cluster Tom Jones 808 can contain sub-clusters as well as content items, e.g., �She's a lady�.
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