Patent Publication Number: US-2022215049-A1

Title: Query Categorization Based on Image Results

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of, and claims priority to, U.S. patent application Ser. No. 12/649,160, titled “Query Categorization Based on Image Results,” filed on Dec. 29, 2009. The disclosure of the foregoing application is incorporated herein by reference in its entirety for all purposes. 
    
    
     BACKGROUND 
     Internet search engines provide information about Internet accessible resources (e.g., Web pages, images, text documents, multimedia content) that are responsive to a user&#39;s search query by returning, in the case of image searching, a set of image search results in response to the query. A search result includes, for example, a Uniform Resource Locator (URL) of an image or of a document containing the image, and a snippet of information. The search results can be ranked (e.g., in an order) according to scores assigned to them by a scoring function. The scoring function ranks the search results according to various signals, for example, where (and how often) query text appears in document text surrounding an image, an image caption, or in alternative text for the image, and how common the query terms are in the search results indexed by the search engine. 
     SUMMARY 
     In general, one aspect of the subject matter described in this specification can be embodied in a method that includes obtaining images from first image results responsive to a first query, wherein a plurality of the obtained images are associated with scores and user behavior data that indicates user interaction with the obtained images when the obtained images were presented as search results for the query; selecting a plurality of the obtained images each having respective behavior data that satisfies a threshold; and associating the selected first images with a plurality of annotations based on analysis of the selected images&#39; content. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products. 
     These and other aspects can optionally include one or more of the following features. The first query can be associated with one or more categories based on the annotations. The category and annotation associations can be stored for future use. Second image results responsive to a second query that is the same or similar to the first query can be received and wherein each of the second images is associated with a score; and one or more of the second images can be modified based on the categories associated with the first query. One of the categories can indicate that the first query is a single person query, and increasing the scores of one or more of the second images whose annotations indicate that the set of second images contain a single face. One of the categories can indicate that the first query is a diverse query, and increasing the scores of one or more of the second images whose annotations indicate that the set of second images are diverse. One of the categories can indicate that the first query is a text query, and increasing the scores of one or more of the second images whose annotations indicate that the set of second images contain text. The first query can be provided to a trained classifier to determine a category in the categories. Analysis of the selected first images&#39; content can include clustering the selected first image results to determine an annotation in the annotations. User behavior data can be a number of times users select the image in search results for the first query. 
     Particular embodiments of the subject matter described in this specification can be implemented so as to realize one or more of the following advantages. The image result set is analyzed in order to derive image annotations and query categories. User interaction with image search results can be used to derive categories for queries. Query categories can, in turn, improve the relevance, quality and diversity of image search results. Query categorization can also be used as part of query processing or in an off-line process. Query categories can be used to provide automated query suggestions such as, “show only images with faces,” or “show only clip art.” 
     The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  illustrates an example system that uses query categorization to improve the set of results returned for a query. 
         FIG. 1B  illustrates an example query categorizor engine. 
         FIG. 2  is an example system for improving search results based on query categories. 
         FIG. 3A  illustrates example components of an information retrieval system. 
         FIG. 3B  illustrates another example information retrieval system. 
         FIG. 4  is a flow chart of an example technique for categorizing queries. 
         FIG. 5  illustrates an architecture of an example system. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG. 1A  illustrates an example system  100  that uses query categorization to improve the set of results returned for a query. In various implementations, a client  102 , such as a web browser or other process executing on a computing device, submits an input query  104  to a search engine  106 , and the search engine  106  returns image search results  128  to the client  102 . In some implementations, a query comprises text such as one or more characters in a character set (e.g., “red tomato”). In other implementations, a query comprises one or more images, sounds, videos, or combinations of these. Other query types are possible. In some implementations, the search engine  106  searches for results based on one or more alternate versions of the query that are equivalent to, conceptually broader than, or conceptually more specific than the input query  104 . 
     The image search results  128  are an ordered or ranked list of documents, or links to such, determined to be responsive to the input query  104 , with the documents determined to be most relevant having the highest rank. A document is web page, an image, or other electronic file, for example. In the case of image search, the search engine  106  determines an image&#39;s relevance based, at least in part, on the image&#39;s content, text surrounding the image, an image caption, or in alternative text for the image, for example. In producing the image search results  128 , the search engine  106  in some implementations submits a request  108  for categories associated with the query  104 . The search engine  106  can use the associated categories  126  to re-order the image search results  128  by, for example, increasing the rank of image results that are determined to belong to the associated categories and, in some cases, decreasing the rank of image results that are determined not belong to the associated categories, or both. The search engine  106  can also use the categories of the results to determine how they should be ranked in the finalized set of results  128  in combination with or independently of the query category. 
     To derive categories for the query  104 , a categorizer engine  110  or other process employs image results  128  retrieved for the query and (optionally) a user behavior data repository  112 . The repository  112  stores user behavior data. In some implementations, repository  112  indicates the number of times one or more populations of users selected an image result for a given query. Image selection can be accomplished in a variety of ways including selection using the keyboard, a computer mouse or a finger gesture, a voice command, or other ways. In other implementations, user behavior data includes “click data”. Click data indicates how long a user views or “dwells” on an image result after selecting it in a results list for the query. For example, a longer time spent dwelling on an image (e.g., greater than 1 minute), termed a “long click”, can indicate that a user found the image to be relevant for the user&#39;s query. A brief period viewing an image (e.g., less than 30 seconds), termed a “short click”, can be interpreted as a lack of image relevance. Other types of user behavior data are possible. 
     By way of illustration, user behavior data can be generated by a process that creates a record for result documents that are selected by users in response to a specific query. Each record can be represented as a tuple: &lt;document, query, data&gt;) that includes a query submitted by users or a query reference indicating the query, a document reference indicating a document selected by users in response to the query, and an aggregation of click data (e.g., a count of each click type) for all users or a subset of all users that selected the document reference in response to the query. In some implementations, extensions of this tuple-based approach to user behavior data are possible. For instance, the user behavior data can be extended to include location-specific (e.g., country or state) or language-specific identifier. With such identifiers included, a country-specific tuple would include the country from where the user query originated from and a language-specific tuple would include the language of the user query. 
     For simplicity of presentation, the user behavior data associated with documents A-CCC for the query  104  is depicted in a table  114  as being either a “high,” “med,” or “low” amount of favorable user behavior data (e.g., user behavior data indicating relevance between the document and the query  104 ). Favorable user behavior data for a document can indicate that the document is frequently selected by users when it is viewed in the results for the query  104 , or when a users view the document after selecting it from the results for the query  104 , the users view the document for a relatively long period of time (e.g., the user finds the document to be relevant to the query  104 ). The categorizer engine  110  works in conjunction with the search engine  106  using returned results and optionally user behavior data to determine query categories and then re-rank the results before they are returned to the user. 
     In general, for the query (e.g., query  104  or an alternate form of the query  104 ) specified in the query category request  108 , the categorizer engine  110  analyzes image results for the query in order to determine if the query belongs to one or more categories. Image results that are analyzed in some implementations are those that have been selected by users as a search result for the query a total number of times that is above a threshold (e.g., selected at least ten times). In other implementations, the categorizer engine  110  analyzes all image results retrieved by the search engine for a given query. In further implementations, the categorizer engine  110  analyzes image results for the query where a metric (e.g., the total number of selections or other measure) for the click data is above a threshold. The image results can be analyzed using computer vision techniques in a variety of ways, either off-line or on-line during the scoring process. Images are then annotated with information extracted from their visual content. For example, image annotations can be stored in annotation store  120 . Each analyzed image (e.g., image  1 , image  2 , and so on) is associated with one or more annotations (e.g., A1, A2, and so on) in an image to annotation association  122 . The annotations can include, for example: a number of faces in the image, a size of each face, dominant colors of the image, whether an image contains text or a graph, and whether an image is a screen-shot. Additionally, each image can be annotated with a fingerprint which can be then used to determine if two images are identical or nearly identical. 
     Next, the categorizer engine  110  analyzes image results for a given query together with their annotations in order to determine query categories  116 . Associations  118  of query categories (e.g., C1, C2, and so on) for a given query (e.g., query 1, query 2, and so on) can be determined in many ways, such as using a simple heuristic or using an automated classifier. As an example, a simple query categorizer based on a heuristic can be used determine the desired dominant color for the query (and whether there is one). The heuristic can be, for example, that if out of the top 20 most often clicked images for the query, at least 70% have a dominant color red (based on the image annotations  120 ), then the query can be categorized as “red query”. For such queries, the search engine can re-order the retrieved results to increase the rank of all images that are annotated with red as a dominant color. The same categorization can be used with all other common colors. An advantage of this approach over analyzing the text of the query, is that it works for all languages without the need for translation (e.g., it will promote images with dominant red color for query “red apple” in any language) and is more robust (e.g., it will not increase the rank of red images for query “red sea”). 
       FIG. 1B  illustrates an example categorizer engine  110 . The categorizer engine  110  can work in an on-line mode (as shown in  FIG. 1A ) or in an off-line mode in which query category associations are stored ahead of time (e.g., in table  118 ) for use by the search engine  106  during query processing. The engine  110  receives query image results  130  for a given query, and provides the image results  130  to one or more image annotators  132   a - e . Each image annotator analyzes image results and extracts information about the visual content of the image, which is then stored as an image annotation (e.g., image annotations  120 ) for the image. By way of illustration, a face image annotator  132   a  determines how many faces are in an image and the size of each face, a fingerprint image annotator  132   b  extracts image visual features in a condensed form (fingerprint) which then can be compared with the fingerprint of another image to determine if the two images are similar, a screenshot image annotator  132   c  determines if an image is a screenshot, a text image annotator  132   d  determines if an image contains text, a graph/chart image annotator  132   e  determines if an image contains graphs or charts (e.g., bar graphs), and a dominate color annotator  132   e  determines if an image contains a dominant color. Other image annotators can also be used. For example, several image annotators are described in a paper entitled “Rapid Object Detection Using a Boosted Cascade of Simple Features,” by Viola, P.; Jones, M., Mitsubishi Electric Research Laboratories, TR2004-043 (May 2004). 
     Next, the categorizer engine  110  analyzes image results for a given query together with their annotations to determine query categories (e.g., query categories  116 ). In some implementations, query categories are determined using a classifier. A query classifier can be realized with a machine learning system. By way of illustration, AdaBoost, short for Adaptive Boosting, is a machine learning system that can be used in conjunction with other learning algorithms to improve their performance. AdaBoost is used to generate a query classifier. (Additional learning algorithms are possible, however.) AdaBoost invokes a “weak” image annotator repeatedly in a series of rounds. By way of illustration, the single person query classifier  133   a  can be based on a learning machine algorithm that has been trained to determine whether a query calls for images of a single person or not. By way of illustration, such a query classifier can be trained with data sets comprising a query, a set of feature vectors representing result images for the query that have zero or more faces, and the correct categorization for the query (i.e., faces or not). For each call the query classifier updates a distribution of weights that indicates the importance of examples in the training data set for the classification. On each round, the weights of each incorrectly classified training example are increased (or alternatively, the weights of each correctly classified training example are decreased), so that the new query classifier focuses more on those examples. The resulting trained query classifier  133   a  is able to take as input a query and output a probability that the query calls for images containing single persons. 
     A diverse/homogeneous query classifier  133   b  takes as input a query and outputs a probability that the query is for an image that is diverse. In some implementations, the classifier  133   b  uses a clustering algorithm to cluster image results  130  according to their fingerprints based on a measure of distance from each other. Each image is associated with a cluster identifier. The image cluster identifier is used to determine the number of clusters, the size of the clusters and the similarity between clusters formed by images in the result set. This information is used to associate a probability that the query is specific (or inviting duplicates) or not, for example. The classifier  133   b  can also be used to associate queries with canonical meanings and representations. For example, if there is a single large cluster, or several large clusters, then the probability that the query is associated with duplicate image results is high. If there are many, smaller clusters, then the probability that the query is associated with duplicate image results is low. Duplicates of images are usually not very useful as they provide no additional information, so they should be demoted as query results. However, there are exceptions. For example, if there are many duplicates in initial results (a few, large clusters), this indicates that the query is very specific and duplicates should not be demoted, for example. 
     A screenshot/non-screenshot query classifier  133   c  takes as input a query and outputs a probability that the query calls for images that are screenshots. A text/non-text query classifier  133   d  takes as input a query and outputs a probability that the query calls for images that contain text. A graph/non-graph query classifier  133   e  takes an input a query and outputs a probability that the query calls for images that contain a graph or a chart. A color query classifier  133   f  takes an input a query and outputs a probability that the query calls images that are dominated by a single color. Other query classifiers are possible. 
       FIG. 2  shows an example system  200  for improving the relevance of image results based on query categories. A user  202  ( 202   a ,  202   b ,  202   c ) can interact with the system  200  through a client device  204  ( 204   a ,  204   b ,  204   c ) or other device. For example, the client device  204  can be a computer terminal within a local area network (LAN) or wide area network (WAN). In another example, the client device  204  can be a mobile device (e.g., a mobile phone, a mobile computer, a personal desktop assistant, etc.) that is capable of communicating over a LAN, a WAN, or some other network (e.g., a cellular phone network). The client device  204  can include a random access memory (RAM)  206  (or other memory and/or a storage device) and a processor  208 . The processor  208  is structured to process instructions and data within the system  200 . In some implementations, the processor  208  is a single-threaded or multi-threaded micro-processor having one or more processing cores. The processor  208  is structured to execute instructions stored in the RAM  206  (or other memory and/or a storage device included with the client device  204 ) to render graphical information for a user interface. 
     A user  202   a  can connect to the search engine  230  within a server system  214  to submit an input query  215 . In some implementations, the search engine  214  is an image search engine or a generic search engine which can retrieve images and other types of content such as documents (e.g., HTML pages). When the user  202   a  submits the input query  215  through an input device attached to a client device  204   a , a client-side query  210   a  is sent into a network  212  and is forwarded to the server system  214  as a server-side query  210   b . Server system  214  can be one or more server devices in one or more locations. A server device  214  includes a memory device  216 , which can include the search engine  230  loaded therein. A processor  218  is structured to process instructions within the device  214 . These instructions can implement one or more components of the search engine  230 . The processor  218  can be a single-threaded processor or a multi-threaded processor, and can include multiple processing cores. The processor  218  can process instructions stored in the memory  216  related to the search engine  230  and can send information to the client device  204 , through the network  212 , to create a graphical presentation in a user interface of the client device  204  (e.g., a search results web page displayed in a web browser). 
     The server-side query  210   b  is received by the search engine  230 . The search engine  230  uses the information within the input query  215  (e.g. query terms) to find relevant documents. The search engine  230  can include an indexing engine  220  that actively searches a corpus (e.g., web pages on the Internet) to index the documents found in that corpus, and the index information for the documents in the corpus can be stored in an index database  222 . This index database  222  can be accessed to identify documents related to the user query  215 . Note that, an electronic document (which for brevity will simply be referred to as a document) does not necessarily correspond to a file. A document can be stored in a portion of a file that holds other documents, in a single file dedicated to the document in question, or in multiple coordinated files. Moreover, a document can be stored in a memory without having first been stored in a file. 
     The search engine  230  can include a ranking engine  252  to rank the documents related to the input query  215 . The ranking of the documents can be performed using traditional techniques for determining an Information Retrieval (IR) score for indexed documents in view of a given query. The relevance of a particular document with respect to a particular search term or to other provided information may be determined by any appropriate technique. For example, the general level of back-links to a document that contains matches for a search term may be used to infer a document&#39;s relevance. In particular, if a document is linked to (e.g., is the target of a hyperlink) by many other relevant documents (e.g., documents that also contain matches for the search terms), it can be inferred that the target document is particularly relevant. This inference can be made because the authors of the pointing documents presumably point, for the most part, to other documents that are relevant to their audience. 
     If the pointing documents are in turn the targets of links from other relevant documents, they can be considered more relevant, and the first document can be considered particularly relevant because it is the target of relevant (or even highly relevant) documents. Such a technique may be the determinant of a document&#39;s relevance or one of multiple determinants. Appropriate techniques can also be taken to identify and eliminate attempts to cast false votes so as to artificially drive up the relevance of a page. 
     To further improve such traditional document ranking techniques, the ranking engine  252  can receive an additional signal from a rank modifier engine  256  to assist in determining an appropriate ranking for the documents. The rank modifier engine  256 , in conjunction with image annotators  260  and query classifiers  258  as described above, provides one or more measures of relevance for the documents, which can be used by the ranking engine  252  to improve the search results&#39; ranking provided to the user  202 . The rank modifier engine  256  can perform operations to generate the one or more measures of relevance. In some implementations, whether an image result&#39;s score is increased or decreased depends on whether the image&#39;s visual content (as represented in image annotations) matches the query&#39;s category. In some implementations, each image category is considered separately. For example, if the query&#39;s category is “single person”, then an image result that is classified both as a “screenshot” and “single face” would first have its score decreased because of the “screenshot” category, and then have its score increased because of the “single face” category. The search engine  230  can forward the final, ranked result list within a server-side search results  228   a  through the network  212 . Exiting the network  212 , a client-side search results  228   b  can be received by the client device  204   a  where the results can be stored within the RAM  206  and/or used by the processor  208  to display the results on an output device for the user  202   a.    
       FIG. 3A  illustrates example components of an information retrieval system. These components include an indexing engine  3010 , a scoring engine  3020 , a ranking engine  3040 , and a rank modifier engine  3070 . The indexing engine  3010  functions as described above for the indexing engine  220 . The scoring engine  3020  generates scores for document results based on many different features, including content-based features that link a query to document results, and query-independent features that generally indicate the quality of documents results. Content based features for images include, for example, aspects of the document that contains the image, such as query matches to the document&#39;s title or the image&#39;s caption. The query-independent features include, for example, aspects of document cross-referencing of the document or the domain, or image dimensions. Moreover, the particular functions used by the scoring engine  3020  can be tuned, to adjust the various feature contributions to the final IR score, using automatic or semi-automatic processes. 
     The ranking engine  3040  produces a ranking of document results  3040  for display to a user based on IR scores received from the scoring engine  3020  and one or more signals from the rank modifier engine  3070 . The rank modifier engine  3070  provides one or more measures of relevance for the documents, which can be used by the ranking engine  3040  to improve the search results&#39; ranking provided to the user. A tracking component  3050  is used to record information regarding user behavior such as individual user selections of the results presented in the ranking  3040 . In some implementations, the tracking component  3050  is embedded JavaScript code included in a web page ranking  3040  that identifies user selections of individual document results and also identifies when the user returns to the results page, thus indicating the amount of time the user spent viewing the selected document result. In other implementations, the tracking component  3050  is a proxy system through which user selections of the document results are routed. The tracking component can also include pre-installed software at the client (e.g., a toolbar plug-in to the client&#39;s operating system). Other implementations are also possible, for example, an implementation that uses a feature of a web browser that allows a tag/directive to be included in a page, which requests the browser to connect back to the server with message(s) regarding link(s) clicked by the user. 
     The recorded information is stored in result selection logs  3060 . In various implementations, the recorded information includes log entries that indicate user interaction with each result document presented for each query submitted. For each user selection of a result document presented for a query, the log entries indicate the query (Q), the document (D), the user&#39;s dwell time (T) on the document, the language (L) employed by the user, and the country (C) where the user is likely located (e.g., based on the server used to access the IR system) and a region code (R) identifying the metropolitan area of the user. The log entries also records negative information, such as the fact that a document result was presented to a user, but was not selected. Other information such as position(s) of click(s) (i.e., user selection(s)) in the user interface, information about the session (e.g., existence and type of previous clicks, and post-click session activity), IR scores of clicked results, IR scores of all results shown before click, the titles and snippets shown to the user before the click, the user&#39;s cookie, cookie age, IP (Internet Protocol) address, user agent of the browser, and so on, can also be recorded. 
     In various implementations, the time (T) between the initial click-through to the document result and the user&#39;s returning to the main page and clicking on another document result (or submitting a new search query) is also recorded. An assessment is made about the time (T) regarding whether this time indicates a longer view of the document result or a shorter view of the document result, since longer views are generally indicative of quality or relevance for the clicked through result. This assessment about the time (T) can further be made in conjunction with various weighting techniques. 
     The components shown in  FIG. 3A  can be combined in various manners and implemented in various system configurations. For example, the scoring engine  3020  and the ranking engine  3040  can be merged into a single ranking engine, such as the ranking engine  352  of  FIG. 3 . The rank modifier engine  3070  and the ranking engine  3040  can also be merged, and in general, a ranking engine includes any software component that generates a ranking of document results after a query. Moreover, a ranking engine can be included in a client system in addition to (or rather than) in a server system. 
       FIG. 3B  illustrates another example information retrieval system. In this system, a server system  3150  includes an indexing engine  3160  and a scoring/ranking engine  3170 . A client system  3100  includes a user interface  3110  for presenting a ranking, a tracking component  3120 , result selection logs  3121  and a ranking/rank modifier engine  3140 . For example, the client system  3100  can include a company&#39;s enterprise network and personal computers, in which a browser plug-in incorporates the ranking/rank modifier engine  3140 . When an employee in the company initiates a search on the server system  3150 , the scoring/ranking engine  3170  can return the search results along with either an initial ranking or the actual IR scores for the results. The browser plug-in then re-ranks the results locally based on tracked page selections for the company-specific user base. 
       FIG. 4  is a flow chart of an example technique  400  for categorizing queries. This technique can be performed in an on-line fashion (as part of query processing), or in an off-line fashion. First image results responsive to a first query are received (step  402 ). Each of the first images is associated with an order (e.g., an IR score) and a respective user behavior data (e.g., click data). A plurality of the first images are selected where a metric for the respective behavior data for each selected image satisfies a threshold (step  404 ). The selected first images are associated with a plurality of annotations based on analysis of the selected first images&#39; content (step  406 ; see, e.g.,  FIG. 1B  and accompanying discussion). The image annotations can be persisted in image annotations  120 , for example. One or more categories are then associated with the first query based on the annotations (step  408 ; see, e.g.,  FIG. 1B  and accompanying discussion). The query category associations can be persisted in query categories  116 . Second image results responsive to a second query that is the same or similar to the first query are then received. (If the second query is not found in the query categories  116 , the second query can be transformed or “rewritten” to determine if an alternate form of the query matches a query in the query categories  116 .) In this example, the second query is the same as, or is able to be rewritten as, the first query. The second image results are re-ordered based on the categories previously associated with the first query (step  410 ). 
       FIG. 5  illustrates an architecture of an example system  500 . The system generally consists of a server  502 . The server  502  is optionally connected to one or more user computers through a network. The server  502  consists of one or more data processing apparatus. While only one data processing apparatus is shown in  FIG. 5 , multiple data processing apparatus can be used. The server  502  includes various modules, e.g. executable software programs, including an optional result selector  504 , image annotators  506  which determine annotations for images, query classifiers  508  which use image annotations to determine categories for the given query, and a rank modifying engine  510 . Each module runs as part of the operating system on the server  502 , runs as an application on the server  502 , or runs as part of the operating system and part of an application on the server  502 , for instance. 
     The server  502  can also include hardware or firmware devices including one or more processors  512 , one or more additional devices  514 , computer readable medium  516 , a communication interface  518 , and one or more user interface devices  520 . Each processor  508  is capable of processing instructions for execution within the server  502 . In some implementations, the processor  508  is a single or multi-threaded processor. Each processor  508  is capable of processing instructions stored on the computer readable medium  516  or on a storage device such as one of the additional devices  514 . The server  502  uses its communication interface  518  to communicate with one or more computers, for example, over a network. Examples of user interface devices  520  include a display, a camera, a speaker, a microphone, a tactile feedback device, a keyboard, and a mouse. 
     The server  502  can store instructions that implement operations associated with the modules described above, for example, on the computer readable medium  516  or one or more additional devices  514 , for example, one or more of a floppy disk device, a hard disk device, an optical disk device, or a tape device. In some implementations, the server  502  also stores additional data, for example, the query-category associations table  118 . 
     The systems  100 ,  200  and  500  are example data processing apparatus in which the systems, components and techniques described herein can be implemented. Although several software components are illustrated in each system, there may be fewer or more software components. Moreover, the software components can be distributed on one or more computing devices connected by one or more networks or other suitable communication mediums. 
     Embodiments of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). 
     The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources. 
     The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures. 
     A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. 
     To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user&#39;s client device in response to requests received from the web browser. 
     Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.