Abstract:
Methods and apparatus, including computer program products, are provided that include techniques for searching a database. One method includes receiving one or more input search terms and providing a database. The database maps at least one search term to at least one coupon. The method further includes identifying one or more coupons in the database based on the received input search terms. The method further includes selecting one or more coupons from the identified coupons. A given coupon can be selected based on a number of times the given coupon has been previously printed. A given coupon can also be selected based on a number of times the given coupon has been previously redeemed. The method further includes presenting one or more coupons to the user where the presented one or more coupons include the selected one or more coupons.

Description:
PRIORITY CLAIM 
     This application claims benefit under 35 U.S.C. §120 as a Continuation of application Ser. No. 11/134,048, filed May 19, 2005 now U.S. Pat. No. 7,734,621, the entire contents of which is hereby incorporated by reference as if fully set forth herein. Applicants hereby rescind any disclaimer of claim scope in the parent application or the prosecution history thereof and advise the USPTO that the claims in this application may be broader than any claim in the parent application. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to data processing. 
     BACKGROUND 
     A typical database search engine receives a search query and identifies entries in the database that have some relation to the search query. As the database grows, particular limits exist as to tradeoffs between the amount of time it takes to search the database and the quality of the results returned. 
     SUMMARY 
     Methods and apparatus, including computer program products, are provided that include techniques for searching a database. In one aspect, a method is provided that includes receiving one or more input search terms. A database is provided that maps at least one search term to at least one coupon. One or more coupons in the database are identified based on the received input search terms. One or more coupons from the identified coupons are selected. A given coupon can be selected based on a number of times the given coupon has been previously printed. A given coupon can also be selected based on a number of times the given coupon has been previously redeemed. Finally, one or more coupons are presented to the user. The presented one or more coupons include the selected one or more coupons. 
     Aspects can include one or more of the following features. A given coupon can be selected based on at least one of a number of times the given coupon has been previously printed in response to the input search term, or a number of times the given coupon has been previously redeemed in response to the input search term. 
     The method can further include determining a category for the input search terms, and identifying one or more coupons in the database based on a search term within the determined category other than the input search terms. The method can further include determining a category for one of the selected one or more coupons and identifying one or more coupons in the database based on a search term within the determined category other than the input search terms. 
     Selecting one or more coupons from the identified coupons can include selecting one or more coupons based on demographical information of the user. Selecting one or more coupons from the identified coupons can further include selecting one or more coupons based on behavioral information of the user. 
     The method can further include assigning a priority to each of the identified coupons and presenting the coupons to the user in accordance with the assigned priorities of the coupons. A priority assigned to a given coupon can be based on a number of times the given coupon has been previously printed in response to the input search term. A priority assigned to a given coupon can also be based on a number of times the given coupon has been previously redeemed in response to the input search term. 
     Presenting coupons to the user can include presenting a reference to a location where the coupons can be printed or redeemed. Presenting coupons to the user can also include printing at least one of the coupons. The presented coupons can include one or more coupons selected at random from the database. 
     Receiving search terms can include receiving search terms from a user. Receiving search terms can also include deriving search terms from content of a web page. 
     The invention can be implemented to realize one or more of the following advantages. A term-coupon database is provided that links search terms to coupons that can be redeemed by the end user. The term-coupon database can include information about the number of times a given coupon has been selected, printed, and/or redeemed in the past by other users. The term-coupon database can be searched, and results filtered to allow coupons to be presented to users in accordance with the preferences of the users. The term-coupon database can be updated every time the users select, print, and/or redeem coupons. The term-coupon database can be adapted to the changing preferences of the users. One implementation of the invention provides all of the above advantages. 
     Details of one or more implementations of the invention are set forth in the accompanying drawings and in the description below. Further features, aspects, and advantages of the invention will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a computing system according to one implementation. 
         FIG. 2  is a block diagram of a computing system implemented as a client connected to a server through a network. 
         FIG. 3  is a block diagram of a search engine according to one implementation. 
         FIG. 4  is a flowchart illustrating a process for searching a database. 
         FIG. 5  is a block diagram of a term-coupon database. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , a computing system  100 , in one implementation, can include a bus  102 , a processor  104 , a main memory  106 , a read only memory (ROM)  108 , a storage device  110 , an input device  112 , an output device  114 , and a communication interface  116 . The computing system  100  can be embodied in, for example, a PDA, a desktop computer, a cellular telephone, and so on. 
     The bus  102  can include one or more conventional buses that permit communication among the components of the computing system  100 . The processor  104  can include any type of conventional processor or microprocessor that interprets and executes instructions. The main memory  106  can include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by the processor  104 . The ROM  108  can include a conventional ROM device or another type of static storage device that stores static information and instructions for use by the processor  104 . The storage device  110  can include a magnetic and/or optical recording medium and its corresponding drive. 
     The input device  112  can include one or more conventional mechanisms that permit a user to input information to the computing system  100 , such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. The output device  114  can include one or more conventional mechanisms that output information to the user, including a display, a printer, a speaker, and so on. The communication interface  116  enables the computing system  100  to communicate with other devices and/or systems. For example, the communication interface  116  can include mechanisms for communicating with another device or system through a network. 
     As will be described in detail below, the computing system  100 , in one implementation, can perform certain searching-related operations. The computing system  100  can perform these operations in response to processor  104  executing software instructions contained in a computer-readable medium, such as memory  106 . A computer-readable medium can be defined as one or more memory devices and/or carrier waves. The software instructions can be read into memory  106  from another computer-readable medium, such as the data storage device  110 , or from another device through the communication interface  116 . The software instructions contained in memory  106  can cause processor  104  to perform search-related activities described below. Alternatively, hardwired circuitry can be used in place of or in combination with software instructions to implement the functionality of the computing system  100 . Thus, the computing system  100  is not limited to any specific combination of hardware circuitry and software. 
     Referring to  FIG. 2 , the computing system  100 , in one implementation, can be divided between a client device  202  and a server device  204  connected through a network  206 . The client device  202  and the server device  204  can be configured in a manner similar to that described above in reference to  FIG. 1 . 
     The network  206  can include a local area network (LAN), a wide area network (WAN), a telephone network, such as the Public Switched Telephone Network (PSTN), an intranet, the Internet, or a combination of networks. One client device  202  and one server device  204  are illustrated as connected to the network  206  for simplicity. However, there can be multiple client devices  202  and multiple server devices  204 . 
     In one implementation, the client device  202  can include devices, such mainframes, minicomputers, personal computers, laptops, personal digital assistants, or the like, capable of connecting to the network  206 . The client devices  202  can transmit data over the network  206  or receive data from the network  206  through a wired, wireless, or optical connection. 
     A client device  202 , in one implementation, can include a client application that allows a user to enter a search query. A server device  204 , in one implementation, can include a server application that receives the user&#39;s search query from the client device  202  and searches a database (e.g., a term-coupon database) to identify data (e.g., coupons) that have some relation to the search query. The server application can further filter the identified data (e.g., coupons) to determine which of the identified data are most relevant to the search query and present to the user the filtered data (e.g., only relevant coupons). 
     Referring to  FIG. 3 , computing system  100  of  FIG. 1  or  2 , in one implementation, can include a search engine  150 . The search engine  150  can be an application executing on the computing system  100 . 
     In one implementation, the search engine  150  can include multiple components, including a term evaluator  302 , an abstraction engine  304 , a generic search engine  306 , a prioritization engine  308 , and a presentation engine  310 . The search engine  150  can further include one or more databases (e.g., a term-coupon database)  312  that are either internal or external to search engine  150 . 
     The term evaluator  302 , in one implementation, can receive a search query and extract one or more search terms from the search query. The term evaluator  302  can receive a search query in a variety of ways. For example, the search query can be typed by a user in a graphical interface, or provided over a voice interface. The search query can also be derived, e.g., from the content of a web page accessed by the user. For example, if the user opens a web page with an article, the term evaluator can extract terms from the content of the article. 
     The term evaluator  302  can include a parser and/or a natural language processor to extract search terms from a search query. For example, the term evaluator  302  can extract input search terms “waffles” and “pancakes” from the search query “some kind of waffles or pancakes”. The term evaluator  302  can further include a semantic interpreter and extract search terms for the received search query that do not appear verbatim in the received search query. For example, the term evaluator  302  can extract input search terms “low-fat”, “low-calorie”, “low-sodium”, and “breakfast” from the search query “healthy breakfast”. Alternatively, the term evaluator  302  can be used to abstract the received search query producing one or more characterizations of the search terms provided. The characterization information can then be used to generate alternative search terms. Further, the term evaluator  302  can modify search terms identified to allow for ease of search. The term evaluator  302  can make use of wild cards and other paradigms to ensure better results. 
     The abstraction engine  304 , in one implementation, can receive search terms (e.g., from the term evaluator  302 , or directly from the search user interface) and determine one or more categories for each search term. If the abstraction engine  304  determines a category for one or more search terms that have been provided, the abstraction engine  304  can further identify other search terms within the determined category. For example, the abstraction engine  304  can determine that “breakfast staples” is a category that may be associated with the search term “waffles” and consequently identify other search terms within the “breakfast staples” category, such as “pancakes” and “breakfast meats”. The abstraction engine  304  can also be used to abstract at the level of the data returned as a result of the searching performed by the generic search engine  306 . For example, once data is located that relates to the particular search terms utilized, the abstraction engine  304  can be used to abstract the returned data (e.g., characterize the returned data) so that other data (e.g., located by using new search terms generated from the characterization) can be presented. Abstraction at the return data level is discussed below. 
     The generic search engine  306 , in one implementation, can receive search terms (e.g., from the term evaluator  302  or from the abstraction engine  304 ) and use the received search terms to search for and locate data in a database. The generic search engine  306 , can use any conventional searching techniques (e.g., string matching) to locate data in the database using the received search terms. Moreover, the generic search engine  306  can search a variety of different kinds of databases. In one implementation, the generic search engine  306  can use the received search terms to search for and locate coupons in a term-coupon database  312 . 
     Referring to  FIG. 5 , in one implementation, a term-coupon database  312  is provided and associated with search engine  150  and can include mappings between search terms  502  and coupons  504 . Coupons, for example, can include certificates presented at retail in exchange for a discount (e.g., cents-off, percent discount, and so on), cash value, or other benefit, vouchers for a discount or the like. Coupons can also include rebates, tickets, admittance tickets, certificates, gift certificates, promotions or the like. Accordingly, the term-coupon database  312  is not limited to any particular kind of coupons. In one implementation, the term-coupon database  312  can include both terms and coupon identifiers (e.g., a pointer to an actual coupon in another potentially separate database). Other databases may be used by the generic search engine  306  and the prioritization engine  308  as discussed below. 
     As discussed above, once data is located in the database using the generic search engine  306 , the abstraction engine  304  can use the located data to determine a characterization about the located data (e.g., categories for the data) and assist in the further location (e.g., by generating more search terms) of other relevant data in the determined category. Accordingly, instead of, or in addition to determining categories based on search terms, the abstraction engine  304  can determine categories based on identified/located data (e.g., based on located coupons). 
     The prioritization engine  308 , in one implementation, can receive data (e.g., from the generic search engine  306 ) and prioritize the data. For example, the prioritization engine  308  can assign higher priority to data that is more relevant to the search query and assign lower priority to data that is less relevant to the search query. The prioritization engine  308  can prioritize the data based on one or more prioritization criteria, as will be described later. In one implementation, the prioritization engine  308  can further filter the received data, e.g., by only selecting data with relatively high priority. 
     The presentation engine  310  can present the resulting data to the user. For example, the presentation engine  310  can provide a user with one or more references to locations where the data can be retrieved. In one implementation, the presentation engine  310  can present the resulting data to the user in accordance with the priorities of the data (e.g., present data in a list, sorted by decreasing priority), using a pass-through device (e.g., a link) to allow for the presentation of multiple located items in a predefined manner. The presentation engine  310  can also present to the user an offer to print a coupon. In one implementation, the presentation engine  310  can present to the user a description of the coupon, and allow the user to print the coupon. 
     Referring to  FIGS. 3 and 4 , one implementation of a process  400  that the search engine  150  uses to search for data (e.g., coupons) will be described. 
     User search terms are received, either explicitly or implicitly (step  402 ). For example, a user can manually enter a search query, including one or more input search terms. Specific search terms can be extracted from the search query. In one implementation, input search terms can be extracted using the term evaluator  302 . 
     Once the input search terms are received, categories optionally can be determined for the input search terms (step  404 ). In one implementation, categories for the input search terms can be determined by the abstraction engine  304 . If categories are determined for the input search term, other search terms in the determined category can be located and used to search for data (e.g., coupons). 
     Once the appropriate search terms are identified, the search terms can be used to search a database (e.g., using the generic search engine  306 ). In one implementation, searching the database can include identifying data in the database that is associated with the search terms. In one implementation, a search engine uses the search terms in conjunction with a term-coupon database to identify coupons associated with the search terms (step  406 ). For example, if the term-coupon database is similar to the term-coupon database  312  discussed in reference to  FIG. 5 , identifying coupons associated with the search terms can be as simple as identifying all coupons in the term-coupon database  312  that the search terms map to. 
     Once coupons associated with the search terms are identified, a priority can be assigned to each of the identified coupons (e.g., by the prioritization engine  308 ) based on one or more prioritization criteria and some of the coupons can be selected (step  408 ). In one implementation, a higher assigned priority indicates a closer link between the corresponding coupon and the search query. 
     The identified coupons can be filtered (i.e., only a subset of the identified coupons can be selected) based on the assigned priorities of the identified coupons. In one implementation, only coupons with relatively high assigned priorities are selected. 
     The assigning of priorities to the identified coupons and, consequently, the filtering of the identified coupons, can be based on a variety of prioritization criteria. Each coupon can be given a score based on criteria. The criteria can be weighted. 
     In one implementation, a priority can be assigned to an identified coupon based on the number of times that coupon has been printed in the past by users of the search engine. In particular, a priority can be assigned to a coupon that is identified in response to a given input search term based on the number of times that coupon has been printed in the past in response to the same input search term. For example, a lower priority can be assigned to a coupon that has been printed fewer number of times in response to that search term. 
     Likewise, in one implementation, a priority can be assigned to an identified coupon based on the number of times that coupon has been redeemed in the past by other users of the search engine. In particular, a priority can be assigned to a coupon that is identified in response to a given input search term based on the number of times that coupon has been redeemed in the past in response to the same input search term. For example, a lower priority can be assigned to a coupon that has been redeemed fewer number of times in response to that search term. 
     Also, in one implementation, a priority can be assigned to an identified coupon based on the number of times that coupon has been selected or viewed in the past by users of the search engine. In particular, a priority can be assigned to a coupon that is identified in response to a given input search term based on the number of times that coupon has been selected or viewed in the past in response to the same input search term. For example, a lower priority can be assigned to a coupon that has been selected fewer number of times in response to that search term. 
     Other methods for assigning priorities can include methods based on revenue (e.g., to the search engine for redemption), weighting functions (e.g., weighting the provided search terms differently than derived search terms), specific user behavior, group behavior, performance criteria or combinations of these. In one specific implementation, specific user behavior is not considered, and instead, only group behavior is considered when providing filtered results. In one implementation, priorities can depend on the advertisers advertising the coupons (e.g., a higher priority can be assigned to coupons advertised by a particular advertiser). Also, priorities can be assigned to coupons based on the number of coupon prints needed on a campaign or the number of coupon prints remaining on the campaign. In one implementation, the assigning of priorities to the identified coupons can be based on provided demographical information of the user. Likewise, the assigning of priorities to the identified coupons can be based on the behavioral information of the user (e.g., the number of times a particular user printed a given coupon in response to a given input search term in the past). 
     Once priorities are assigned to the identified coupons (and the identified coupons are filtered) the coupons can be presented to the user, e.g., by the presentation engine  310  (step  410 ). In one implementation, hyperlinks to web pages where the coupons can be redeemed and printed can be displayed on a computer screen. The coupons may or may not be displayed directly (e.g., only printed) 
     Statistical information about the number of times a given coupon has been printed, redeemed, and/or selected by users of the search engine  150  can be stored in the term-coupon database  312  or in another database internal or external to search engine  150 . As the users of the search engine  150  print, redeem, and/or select coupons, an engine (e.g., the search engine  150 ) can collect and update the statistical information in the appropriate database (e.g., the term-coupon database  312 ). As a result, the search engine results can reflect the changing preferences of the users of the search engine  150 . A system for collecting print, redemption and selection results in association with electronic coupons is described in co-pending and commonly owned application entitled “Electronic Coupon Distribution System”, filed Nov. 30, 1999, and assigned Ser. No. 09/451,160, the contents of which are expressly incorporated herein by reference. 
     In one implementation, in order to improve the accuracy of the database, a user can sometimes be presented with additional data (e.g., coupons) that were not identified based on the user&#39;s search query (i.e., the results identified in step  406  of  FIG. 4 ). For example, additional coupons can be selected at random from the database (e.g., the term-coupon database  312 ) to check if a user will select, print, and/or redeem a coupon that has not been selected, printed, or redeemed by users of the search engine  150  in the past with respect to this particular search. 
     The invention has been described in terms of particular embodiments, but other embodiments can be implemented and are within the scope of the following claims.