PATENT DOCUMENT

Publication Number: US-10565262-B2
Application Number: US-201615274802-A
Country: US
Kind Code: B2

Title: Methods for refining search results in an application

Abstract:
Disclosed herein is a technique for refining search results presented in a graphical user interface of a search application operating at a user device. A scrollable category list is presented in the graphical user interface, where the scrollable category list includes a number of categories related to a search query provided by a user of the user device. Categories that are available within a geographic region associated with a location of the user device are included in the scrollable category list.

Claims:
What is claimed is: 
     
       1. A computing device comprising:
 a processor; and 
 a memory configured to store instructions that, when executed by the processor, 
 cause the computing device to perform operations that include: 
 sending, to a server device, input data corresponding to a selection of a category from a first list of categories displayed at an interface of a search application that provides search results for a geographic region; and 
 receiving, from the server device in response to the input data, a list of suggestions that is associated with the input data and the geographic region and ordered according to a relevance metric, wherein the list of suggestions includes a second list of categories associated with the first list of categories; 
 receiving a selection of a second category from the second list of categories; 
 receiving a selection of a search result from a subset of search results displayed at the interface of the search application, the subset of search results associated with the selection of the second category; 
 modifying a usage log stored in the memory to indicate a correspondence between the search result and the second category; and 
 sending the modified usage log to the server device to facilitate derivation of the relevance metric using the modified usage log. 
 
     
     
       2. The computing device of  claim 1 , wherein the relevance metric is computed based on a number of times a particular suggestion of the list of suggestions has been previously selected at one or more computing devices associated with different users. 
     
     
       3. The computing device of  claim 1 , wherein the operations further include:
 modifying the interface of the application to include the subset of search results associated with the selection of the second category. 
 
     
     
       4. The computing device of  claim 3 , wherein the operations further include:
 modifying the usage log stored in the memory to include an entry that indicates the correspondence between the search result and the second category. 
 
     
     
       5. The computing device of  claim 4 , wherein the search application is a map-based search application. 
     
     
       6. The computing device of  claim 5 , wherein the operations further include:
 simultaneously displaying a map of the geographic region and a scrollable category list that includes the list of suggestions, wherein the second list of categories within the list of suggestions includes sub-categories of a selected category within the input data sent to the server device, the sub-categories determined by the server device. 
 
     
     
       7. The computing device of  claim 6 , wherein the scrollable category list is responsive to a swipe gesture at the interface of the application and the scrollable category list includes icons corresponding to the first sub-category and the second sub-category. 
     
     
       8. A method for refining search results based on a geographic region, the method comprising:
 by a computing device:
 sending location data and a search query over a network connection, wherein the search query comprises first category data associated with a search within the geographic region that is defined by the location data; 
 receiving, via the network connection, search results corresponding to the first category data, and second category data associated with the first category data and the geographic region, wherein the second category data includes a first sub-category and a second sub-category for the first category data; 
 sending, via the network connection, a second search query that comprises the first sub-category and the second sub-category; 
 receiving, via the network connection, a first subset of the search results associated with the first sub-category and a second subset of search results associated with the second sub-category; and 
 displaying, at a graphical interface of a search application, the first subset of the search results when the first sub-category is selected and the second subset of search results when the second sub-category is selected. 
 
 
     
     
       9. The method of  claim 8 , further comprising:
 simultaneously displaying, at the graphical interface, a first icon corresponding to the first sub-category and a second icon corresponding to the second sub-category. 
 
     
     
       10. The method of  claim 8 , further comprising:
 receiving, at the graphical interface, a selection of an entity in the first subset of the search results subsequent to the first sub-category being selected; and 
 updating a usage log to include an entry that associates the entity with the first sub-category. 
 
     
     
       11. The method of  claim 10 , further comprising:
 sending the usage log over the network connection to a server after updating the usage log. 
 
     
     
       12. The method of  claim 8 , further comprising:
 in response to receiving the second category data, simultaneously displaying icons corresponding to the first sub-category and the second sub-category in a scrollable portion of the graphical interface. 
 
     
     
       13. The method of  claim 12 , further comprising:
 sending different location data over the network connection in response to adjusting a portion of the graphical interface that includes a map; 
 receiving additional category data that includes a third category; and 
 incorporating an additional icon into the scrollable portion of the graphical interface, the additional icon corresponding to the third category. 
 
     
     
       14. The method of  claim 8 , wherein the second category data is dynamically updated based on the location data. 
     
     
       15. A non-transitory computer readable medium configured to store instructions that when executed by a processor of a computing device, cause the computing device to perform operations that include:
 receiving, from a mobile device that is operating a search application, location data corresponding to a geographic region; 
 receiving, from the mobile device, search data for identifying locations of interest within the geographic region; 
 analyzing aggregated search logs stored at the computing device based on the search data to identify at least one category associated with the search data; 
 sending, to the mobile device, suggestion information that includes the at least one category; 
 receiving, from the mobile device, input data indicating that the at least one category was selected at an application of the mobile device; and 
 incrementing a value corresponding to a number of times the at least one category was selected at the application. 
 
     
     
       16. The non-transitory computer readable medium of  claim 15 , wherein the operations further include:
 receiving a usage log from the mobile device, the usage log comprising the input data indicating that the at least one category was selected at an application of the mobile device, the usage log including an entry that associates the at least one category with an entity selected at the application on the mobile device. 
 
     
     
       17. The non-transitory computer readable medium of  claim 16 , wherein the operations further include:
 storing the usage log in association with a portion of the aggregated search logs that correspond to the geographic region. 
 
     
     
       18. The non-transitory computer readable medium of  claim 17 , wherein the operations further include:
 generating the aggregated search logs by aggregating data from the usage log with other usage logs associated with the geographic region, the other usage logs received from other mobile devices. 
 
     
     
       19. The non-transitory computer readable medium of  claim 18 , wherein the suggestion information includes a plurality of categories that are ordered according to the value. 
     
     
       20. The non-transitory computer readable medium of  claim 15 , wherein the suggestion information includes a subset of search results corresponding to the at least one category.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of U.S. Provisional Application No. 62/348,822, entitled “METHODS FOR REFINING SEARCH RESULTS IN AN APPLICATION” filed Jun. 10, 2016, the content of which is incorporated herein by reference in its entirety for all purposes. 
    
    
     FIELD 
     The described embodiments set forth a technique for dynamically refining search results presented in a graphical user interface of an application operating at a computing device. 
     BACKGROUND 
     Typically, in response to a search query, a user is provided with a broad list of search results that may or may not be entirely relevant to the search query. Also, even when the search results are relevant to the search query, the user generally has to perform the tedious task of going through the search results to find the particular result in which he or she is interested. This deteriorates the searching experience for the user. Therefore, there is a need for a system that is capable of intelligently and dynamically refining the search results related to the search query to allow the user to quickly and efficiently locate particular search results of interest. 
     SUMMARY 
     Representative embodiments set forth herein disclose various techniques for refining search results presented in a graphical user interface of a search application operating at a user device. In particular, the embodiments set forth various techniques for determining suggestion information that can be presented to a user in response to search queries. The user device sends, to a server device, location data including a current location of the user device and input data corresponding to a search query. The input data includes a selection of a category (e.g., restaurants) from a first list of categories (e.g., restaurants, drinks, travel, etc.) presented in graphical user interface. In response to receiving the input data, the server device dynamically determines suggestion information corresponding to the input data. The suggestion information includes a second list of categories (e.g., “Popular” restaurants, “American” restaurants, “Italian” restaurants, etc.) that includes sub-categories for restaurants that are available in a geographic region defined by the location data. The suggestion information also includes a subset of search results that corresponds to each category in the second list of categories. This suggestion information, when presented to the user at the user device, can assist the user in rapidly filtering through the search results and locating particular search results of interest. 
     This Summary is provided merely for purposes of summarizing some example embodiments so as to provide a basic understanding of some aspects of the subject matter described herein. Accordingly, it will be appreciated that the above-described features are merely examples and should not be construed to narrow the scope or spirit of the subject matter described herein in any way. Other features, aspects, and advantages of the subject matter described herein will become apparent from the following Detailed Description, Figures, and Claims. 
     Other aspects and advantages of the embodiments described herein will become apparent from the following detailed description taken in conjunction with the accompanying drawings that illustrate, by way of example, the principles of the described embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The included drawings are for illustrative purposes and serve only to provide examples of possible structures and arrangements for the disclosed inventive apparatuses and methods for providing wireless computing devices. These drawings in no way limit any changes in form and detail that may be made to the embodiments by one skilled in the art without departing from the spirit and scope of the embodiments. The embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings, wherein like reference numerals designate like structural elements. 
         FIG. 1  illustrates a block diagram of different components of a system configured to implement the various techniques described herein, according to some embodiments. 
         FIG. 2  illustrates a sequence diagram of a method for refining search results displayed at the user device of  FIG. 1 , according to some embodiments. 
         FIGS. 3A-3E  illustrate conceptual diagrams of example graphical user interfaces that enable a user to input search queries and receive suggestion information at the user device of  FIG. 1 , according to one embodiment. 
         FIG. 4  illustrates a method that is carried out at the user device of  FIG. 1 , according to one embodiment. 
         FIG. 5  illustrates a method that is carried out at the server device of  FIG. 1 , according to one embodiment. 
         FIG. 6  illustrates a detailed view of a computing device that can be used to implement the various components described herein, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Representative applications of apparatuses and methods according to the presently described embodiments are provided in this section. These examples are being provided solely to add context and aid in the understanding of the described embodiments. It will thus be apparent to one skilled in the art that the presently described embodiments can be practiced without some or all of these specific details. In other instances, well known process steps have not been described in detail in order to avoid unnecessarily obscuring the presently described embodiments. Other applications are possible, such that the following examples should not be taken as limiting. 
     The embodiments described herein set forth techniques for providing a user with multiple suggestions in a graphical user interface of a search application in response to a search query. The suggestions include categories that are related to the search query and are dynamically generated based on availability of the categories in a geographic region associated with a location of a user device operating the search application. The suggestions include selectable icons presented in a scrollable portion of the graphical user interface. Selection of a particular icon causes a narrower set of search results corresponding to the selected icon to be displayed in the graphical user interface. 
     Accordingly, the techniques described herein provide a mechanism for refining search results presented in a search application. A more detailed discussion of these techniques is set forth below and described in conjunction with  FIGS. 1, 2, 3A-3E, and 4-6 , which illustrate detailed diagrams of systems and methods that can be used to implement these techniques. 
       FIG. 1  illustrates a block diagram of different components of a system  100  that is configured to implement the various techniques described herein, according to some embodiments. More specifically,  FIG. 1  illustrates a high-level overview of the system  100 , which, as shown, includes a user device  110  and a server device  120 . The user device  110  can represent a computing device (e.g., a smartphone device, a tablet device, a laptop computer, a desktop computer, a wearable device, a vehicle infotainment system that communicates with a smartphone or other device, etc.). A processor (or CPU)  112 , in conjunction with the memory  114 , can implement an operating system (OS)  116  configured to execute various applications  118  (e.g., native OS applications and user applications) and other processes/services on the user device  110 . Similarly, the server device  120  can include a processor  122 /memory  124  to execute an OS/different applications for carrying out the various techniques described herein. 
     According to some embodiments, applications  118  can include a search application that is configured to receive search queries as input (e.g., from a user or another application  118 ) and provide search results based on the search queries. In one embodiment, the search application can include a map-based search application that is configured to: (i) receive, display, and store map data (e.g., data related to various points of interest at or within a particular geographical region, driving directions, and/or other location-based data), and (ii) receive search queries pertaining to particular categories (e.g., food, drinks, restaurants, etc.) of interest and provide location-specific search results for the queries. In some embodiments, the map-based search application is configured to: (i) store map data, and (ii) receive a user command to select a category (e.g., restaurants, drinks, etc.) from a list. In some embodiments, in response to receiving either search queries or a user command to select a category, a graphical user interface (GUI) of the map-based search application can display a list of restaurants that are located within a particular geographical region (e.g., near the current location of the user device  110 ). In particular, the GUI can display a combined map/listing view where a listing of the various restaurants in the particular geographical region is displayed in one portion of the GUI (e.g., second portion  314  of GUI  300  depicted in  FIG. 3B ) and a map that highlights the locations of the restaurants in the listing is displayed in another portion of the GUI (e.g., first portion  312  of GUI  300  depicted in  FIG. 3B ). For example, the map can display icons at locations on the map that correspond to the locations of the restaurants. In some embodiments, the GUI can display a list of restaurants based on a user search query that is mutually exclusive to the current location of the user device  110 . For example, even if the user device  110  is currently located in Cupertino, Calif., the application can receive a search query for “New York City” and subsequently provide a list of restaurants in New York City. 
     According to some embodiments, the user device  110  can communicate with a server device  120  via a network  130 . The server device  120  can represent a computing device that is configured to (i) receive location data from the user device  110 , (ii) receive search data in response to search queries, (iii) determine a number of search results that are based on the search queries, (iv) provide a number/list of suggestions that can assist the user in filtering/refining the search results, and (v) provide the search results and the suggestions to the map-based application of the user device  110 . The network  130  can include one or more of the Internet, an intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a MAN (Metropolitan Area Network), a wireless communication network, and other networks or combination of networks. 
       FIG. 2  illustrates a sequence diagram of a method  200  for refining search results associated with a particular geographic region, according to some embodiments. As shown in  FIG. 2 , at step  202 , the user device  110  obtains location data that includes a current location of the user device  110 . The location data can be received or originate from a Global Positioning System (GPS), nearby WiFi base stations, and/or nearby cell towers. In some embodiments, upon receiving the location data, the user device  110  can automatically transmit the location data to the server device  120 . At step  204 , the user device  110  receives a search query (e.g., from the map-based search application as shown in  FIG. 3A ). In some embodiments, the search query can include an input by a user in a search box  302  provided in the GUI. In some embodiments, the search query can include a selection of a particular category from a first list of categories  304  that is displayed within the GUI of the application. For example, the user can select the “restaurants” category  306  from the first list of categories  304  including but not limited to restaurants (R), drinks (D), shopping (S), travel (T), etc. The selection can also include a user gesture (e.g., a finger tap on a category icon associated with the particular category). The search query can also include other forms of input, e.g., voice input. 
     Referring back to  FIG. 2 , at step  206 , the user device  110  provides the location data and search data (also referred to herein as “input data”) to the server device  120 . The search data includes the search query input by the user (via the search box  302 , the selection of the category  306  via the category icon, or other forms of input). At step  208 , the server device  120  receives the search data and the location data. The server device  120  can then determine search results that are relevant to the location data and the search data. Subsequently, the server device  120  generates a list of the search results that satisfies at least one of the location data or the search data. For example, when the user selects the “restaurants” category  306 , the server device  120  generates a list of restaurants that are located within a particular geographical region defined by the location data. The geographical region can include a point or area at or near the current location of the user device  110 . In some embodiments, the server device  120  can transmit the list of the search results to the application on the user device  110 . 
     At step  210 , the server device  120  determines suggestion information associated with the search query. Any approach can be used to determine the suggestion information, e.g., keyword-based matching, location-based matching, sponsored advertisement-based matching, user-preference-based matching, and the like. The suggestion information can include a number/list of suggestions/options related to the search query that can assist the user in filtering/refining the search results. In some embodiments, the suggestion information can include a second list of categories associated with the search query and/or category selected from the first list of categories. For example, when the “restaurants” category  306  is selected, the second list of categories can include different types of cuisines (e.g., American, Italian, Mexican, Burgers, etc.) that are available within the geographical region defined by the location data. In other words, the server device  120  generates intelligent suggestions/options that enable the user to efficiently narrow down the search results and rapidly locate particular search results of interest. Also, the suggestions/options are dynamically generated based on the search data and the location data. In some implementations, the suggestion information can include the second list of categories and a subset of search results that corresponds to each category in the second list of categories. For example, the suggestion information can include categories for the different types of cuisines (e.g., American, Italian, etc.) and a subset of search results associated with each cuisine category (e.g., a first subset of search results for American cuisine, a second subset of search results for Italian cuisine, and so on). 
     At step  212 , the server device  120  provides the search results and the suggestion information to the user device  110 . At step  214 , the user device  110  displays the search results and the suggestion information within the GUI  300  of the map-based search application. In some embodiments, the search results and the suggestion information is presented in a combined map/list view  310  as shown in  FIG. 3B , for example. As shown in  FIG. 3B , the map is displayed in a first portion  312  of the GUI  300 . In other words, the first portion  312  of the GUI displays an image representing the geographic region defined by the location data. The listing of the search results (e.g., listing of restaurants within the geographic region) is displayed in a second portion  314  of the GUI  300 . The first portion  312  of the GUI  300  also displays icons corresponding to the location of the restaurants. Also depicted in  FIG. 3B  is a scrollable category list  316  that includes the second list of categories (i.e., cuisine categories). The scrollable category list  316  includes selectable icons  318 - 1 ,  318 - 2 ,  318 - 3  (also referred to herein as selectable icon/icons  318 ) for each category (e.g., Popular, Italian, Mexican, etc.) in the second list of categories. While the disclosure depicts the scrollable category list  316  as a scrollable bar with selectable icons or buttons, other graphical forms of scrollable category lists that are responsive to a swipe gesture or other gesture at the GUI  300  can be utilized without departing from the scope of this disclosure. In some implementations, the selectable icons  318 - 1 ,  318 - 2 , and  318 - 3  of the scrollable category list  316  can be scrolled through horizontally. However, the selectable icons  318 - 1 ,  318 - 2 , and  318 - 3  can be manipulated in various other ways (e.g., by scrolling through vertically, by providing an expanded selectable area, etc.) without departing from the scope of this disclosure. By scrolling through the scrollable category list  316 , the application can display a full list of selectable icons for each category. 
     According to some embodiments, when a selectable icon  318 - 1 ,  318 - 2 , or  318 - 3  is selected, the user device  110  can generate a new/refined search query in accordance with the selected icon/category and transmit the new/refined search query to the server device  120 . Subsequently, the server device  120  can utilize a filter to narrow the number of search results to be relevant to the new/refined search query. For example, if a user selects “Italian”  318 - 2 , then the user device  110  can transmit the search query “Italian.” Thereafter, the server device  120  can determine suggestions for restaurants that are categorized as Italian, and provide those suggestions to the user device  110  to be displayed within the application. In some implementations, the server device  120  performs a new search for a subset of search results that is relevant to the current location of the user device  110  and that corresponds to the selected icon/category. 
     In some implementations, prior to selection of any selectable icon  318  in the scrollable category list  316 , the second portion  314  of the GUI  300  displays a broad list of search results (i.e., a number of restaurants) in the geographic region. Thereafter, when the user selects a particular selectable icon  318  in the scrollable category list  316 , the user device  110  sends a new search query that includes the sub-category associated with the selected icon  318  to the server device  120 . In response, the server device  120  generates a subset of search results corresponding to the selected sub-category and sends the subset of search results to the user device  110 . The subset of search results is displayed in the second portion  314  of the GUI  300 . For example, when the user selects an icon  318 - 2  corresponding to “Italian” cuisine sub-category, the second portion  314  of the GUI  300  is updated to display Italian restaurants within the particular geographic region. Similarly, when the user selects an icon  318 - 3  corresponding to “Mexican” cuisine sub-category, the second portion  314  of the GUI  300  is updated to display Mexican restaurants, and so on. In some implementations, the map in the first portion  312  of the GUI is also updated to display icons corresponding to the locations of the Italian restaurants and Mexican restaurants, respectively, according to the selections. In other words, the geographic region/map view depicted in the first portion of GUI  300  remains the same across the various category selections and only the icons corresponding to the location of the restaurants changes based on the selections. 
     In some implementation, when the user moves the viewport on the map to a different location (for example, move the view to New York from San Francisco), the suggestion information and the associated search results are updated to reflect the new geographic region. In other words, the selectable icons  318  are updated to correspond to categories that are available in the new geographic region. 
       FIGS. 3C-3E  illustrates example GUIs  300  that depict different scrollable category lists  316  based on different types of search queries input by the user. For example,  FIG. 3C  depicts a scrollable category list  316  that is generated for the search query “coffee”. The scrollable category list  316  includes selectable icons  320 - 1 ,  320 - 2 ,  320 - 3  for each category (e.g., Popular, Small Business, Ed&#39;s Coffee, etc.) related to the search query “coffee”. When the user selects icon  320 - 1 , the second portion  314  of the GUI  300  is updated to display popular coffee shops in the particular geographic region that is defined by the location data. Similarly, when the user selects icon  320 - 2 , the second portion  314  is updated to display small coffee shops (e.g., non-franchise or non-chain based businesses), and when the user selects the icon  320 - 3 , the second portion  314  is updated to display all Ed&#39;s Coffee shops (i.e., chain-based coffee shops) in the particular geographic region. 
     In some implementations, when a user inputs ambiguous search queries, the server device  120  intelligently generates the suggestion information/list of suggestions (that are presented in the scrollable category list  316 ) that can help disambiguate the queries. For example, as shown in  FIG. 3D , when the user enters the query “Paris” in search box  315 , the user could be searching for the location “Paris” or businesses that have the word “Paris” in their names. Accordingly, a scrollable category list  316  is presented that includes a first selectable icon  330 - 1  for displaying locations and a second selectable icon  330 - 2  for displaying businesses within the geographic region that have the word “Paris” in their name. Similarly, when the user enters the query “Union”, the user can be provided with the scrollable category list  316  that includes a first selectable icon  340 - 1  for locations and a second selectable icon  340 - 2  for businesses within the geographic region that have the word “Union” in their name. For example,  FIG. 3E  depicts street names with “Union” being presented in the second portion  314  of the GUI  100  when the selectable icon  340 - 1  is selected. As shown in  FIGS. 3D and 3E , the first selectable icons  330 - 1  and  340 - 1 , respectively, are highlighted or bolded indicating that the “locations” category is selected. Moreover, as depicted in  FIGS. 3C-3E , the suggestion information can include different types of suggestions instead of or in addition to categories or sub-categories (e.g., brands/chains, locations, businesses, etc.). 
     In this manner, the scrollable category list  316 , depicted in  FIGS. 3B-3E , allows the user to rapidly locate search results of interest without having to use the keyboard to specifically type time consuming multi-word search queries, thereby considerably enhancing the user&#39;s search experience. Moreover, the second list of categories included in the scrollable category list  316  is dynamically generated based on categories (related to the search query in the search box  315 , for example) that are available within the geographical region defined by the location data. In other words, the scrollable category list  316  is dynamic and changes across different search queries and different locations. For example, if the user is searching for restaurants in a particular geographic region, the server device  120  may determine that American and Italian restaurants are available in the region (but no Mexican restaurants are available) and may generate the second list of categories which includes only categories for American cuisine and Italian cuisine. In some embodiments, the second list of categories can include sub-categories (e.g., American cuisine, Italian cuisine, etc.) associated with the main category included in the search query (e.g., restaurants). In some embodiments, the second list of categories can include related categories/suggestions (e.g., locations, businesses, etc.) that are related to the keyword included in the search query (e.g., Union), and/or other categories or suggestions. As such, the scrollable category list  316  provides the users with a number of intelligent options to choose from regardless of whether the search query is ambiguous or whether the user&#39;s intent is known. 
       FIG. 4  illustrates a method  400  that is carried out by the user device  110  of  FIG. 1 , according to one embodiment. As shown, the method  400  begins at step  402 , where the user device  110  obtains location data that includes a current location of the user device  110 . At step  404 , the user device  110  receives input data corresponding to a search query. For example, the input data can include a keyword typed by the user in the search box  315  presented in the GUI  300 . The input data can include a selection of a category (e.g., restaurants) from a first list of categories  304  (e.g., restaurants, drinks, travel, etc.) presented in the GUI  300 . In some implementations, the first list of categories  304  can include a static list of predefined categories. 
     At step  406 , the user device  110  provides the input data and the location data to the server device  130 . At step  408 , the user device  110  receives suggestion information from the server device  120 . The suggestion information can include the second list of categories (e.g., Popular, American, Italian, etc.) and the subset of search results that corresponds to each category in the second list of categories. For example, the subset of search results can include search results associated with each cuisine category (e.g., a first subset of search results for American cuisine, a second subset of search results for Italian cuisine, and so on). 
     At step  410 , the user device displays the suggestion information in the GUI  300  of the map-based search application, for example. The second list of categories are displayed as the scrollable category list  316  that includes icons  318  corresponding to the different cuisines available in the geographic region defined by the location data. In response to a selection of a particular category/icon from the scrollable category list  316 , the second portion  314  of the GUI is modified/updated to display the subset of search results corresponding to the selection. 
     In some implementations, a usage log is maintained at the user device  110  (e.g., in memory  114 ) that tracks information regarding the user&#39;s interactions with the map-based search application within the particular geographic region. The tracked information can include, but is not limited to, queries input by the user, category selections made by the user (e.g., category selections from the first list of categories or the second list of categories), selections of search results (from the subset of search results displayed in the second portion  314  of the GUI  300 ), and the like. In some implementations, when the user selects a particular search result from the subset of search results, the usage log is modified to include an entry that provides a correspondence between the search result and the associated category/query. 
     In some embodiments, the icons  318  in the scrollable category list  316  are ordered according to a relevance metric. In some implementations, the user device  110  sends the usage log to the server device  120  to facilitate derivation of the relevance metric. 
       FIG. 5  illustrates a method  500  carried out by the server device  120  of  FIG. 1 , according to one embodiment. As shown, the method  500  begins at step  502 , where the server device  120  receives, from the user device  110 , location data associated with the user device  110  and search data for identifying locations of interest within a geographic region corresponding to the location data. The location data can include the current location of the user device. The search data can include the search query input by the user via the search box  315  in GUI  300  or the selection of the category (e.g. “restaurants” category  306 ) from the first list of categories  304 ). At step  504 , the server device  120  generates search results that satisfy the search data. For example, the server device  120  can generate a broad list of search results for the search data. 
     At step  506 , the server device  120  determines suggestion information associated with the search data. The suggestion information includes the second list of categories associated with the search data. The suggestion information can include the second list of categories (e.g., American, Italian, etc.) and the subset of the search results (i.e., subset of the broad list of search results generated in step  504 ) that corresponds to each category in the second list of categories. For example, the subset of search results can include search results associated with each cuisine category (e.g., a first subset of search results for American cuisine, a second subset of search results for Italian cuisine, and so on). 
     In some implementations, the suggestion information is determined by analyzing aggregated search logs that correspond to the geographic region. The aggregated search logs are stored at the server device  120  and include usage logs collected from a number of user devices associated with a number of users. In other words, usage logs associated with the geographic region are collected from a number of users and aggregated to form aggregated search logs. For example, when the usage log associated with user device  110  is received, the server device  120  stores the usage log in association with a portion of the aggregated search logs that correspond to the geographic region (i.e., the region the user is performing the search in). The usage logs collected from the number of users provide insight into the importance of certain categories or businesses within the geographic region based on the users&#39; interaction with the search application at the respective user devices. For example, when a number of users select a particular search result (e.g., category or business) within the geographic region, a determination can be made that the search result is important for the associated search query. Thus, the usage logs provide information about categories that the various users are searching for or have selected in the geographic region, businesses that have been selected in the geographic region, and the like. In some implementations, the usage logs can be used to determine a list of top businesses in the geographic region based on user interaction, where the businesses in the list can be divided by category, geographical regions (e.g., based on the Geohash geocoding system), popularity, and/or other metrics. 
     In some implementations, the server device  120  can implement machine learning algorithms for the purpose of providing intelligent suggestions. The server device  120  can generate a model based on training data, where the training data includes information from the aggregated search logs. For example, the training data can include the list of top businesses in the geographic region based on user interaction (from the aggregated search logs) and a total number of business in the geographic region (from a separate set of all businesses in the geographic region). 
     In some implementations, when the server device  120  receives the location data and the search data from the user device  110 , the server device  120  analyzes the information obtained/determined from the aggregated search logs to intelligently determine the suggestion information (e.g., the second list of categories) that is relevant to the search data. In some implementations, the location data and the search data are provided as input to the machine learning algorithm that, in turn, generates the suggestion information (i.e., second list of categories). In some implementations, the server device  120  can implement a rating system for rating the relevance of the generated categories. The rating can be provided by human raters, where the rating is used as feedback to improve the model. In some implementations, in addition to or instead of the human rating, live feedback (in the form of usage logs) can be received from the users of the search application (across multiple devices) to continuously improve the model so that better suggestions can be provided. In some implementations, a weight-based system can be utilized where the weight assigned to the human rating can be lower than the weight assigned to the live feedback. In some implementations, the rating/feedback is applied until a certain threshold is reached implying that sufficient information has been gathered for the particular search query/category. 
     Referring back to  FIG. 5 , at step  508 , the server device  120  provides the suggestion information to the user device  110 . The suggestion information is presented via the scrollable category list  316  in the GUI  300  of the search application. In some implementations, the suggestions/categories associated with the suggestion information (e.g., the second list of categories) are ordered according to a relevance metric. The relevance metric is tracked by the server device  120  based on the users&#39; interaction information obtained from the usage logs/aggregated search logs. The relevance metric is computed for a particular query and suggestion/category (i.e., individual item in the second list of categories or suggestions) combination. The relevance metric is computed based on a number of factors, such as, (1) historical popularity based on a number of times a particular category of the second list of categories has been previously selected/interacted with by the number of users for the particular query, (2) density of businesses in the geographic region for the particular category, (3) query reformulations from the aggregated search logs, and (4) category popularity in the geographic region (e.g., popularity of a particular chain of coffee shops in the geographic region). For example, when a number of users select (i.e. tap on) the particular category in the geographic area in comparison to other categories, a determination is made the particular category is more popular than the other categories and a value corresponding to the number of times the particular category has been selected is incremented. In some implementations, the position of the category in the scrollable category list  316  is determined based on the value. For example, when the value/relevance metric indicates that the particular category is very popular, the category can be positioned at or near the beginning of the scrollable category list  316 . In other words, the position of the category in the scrollable category list  316  can dynamically change based on the relevance metric associated with the category. In one example, the server device  120  can learn by taking into account how accurate the server device  120  was in providing category suggestions in response to search queries. For example, if everyone picks Maria&#39;s Tacos in response to a search query for “Mexican”, the server device  120  can learn that users in the geographic region prefer Maria&#39;s Tacos and associate that restaurant listing with a high ranking value so that the restaurant is displayed as a top suggestion within the list. 
       FIG. 6  illustrates a detailed view of a computing device  600  that can be used to implement the various components described herein, according to some embodiments. In particular, the detailed view illustrates various components that can be included in the first device  110  or second device  120  illustrated in  FIG. 1 . As shown in  FIG. 6 , the computing device  600  can include a processor  602  that represents a microprocessor or controller for controlling the overall operation of computing device  600 . The computing device  600  can also include a user input device  608  that allows a user of the computing device  600  to interact with the computing device  600 . For example, the user input device  608  can take a variety of forms, such as a button, keypad, dial, touch screen, audio input interface, visual/image capture input interface, input in the form of sensor data, etc. Still further, the computing device  600  can include a display  610  (screen display) that can be controlled by the processor  602  to display information to the user (for example, email interface described herein). A data bus  616  can facilitate data transfer between at least a storage device  640 , the processor  602 , and a controller  613 . The controller  613  can be used to interface with and control different equipment through and equipment control bus  614 . The computing device  600  can also include a network/bus interface  611  that couples to a data link  612 . In the case of a wireless connection, the network/bus interface  611  can include a wireless transceiver. 
     The computing device  600  also include a storage device  640 , which can comprise a single disk or a plurality of disks (e.g., hard drives), and includes a storage management module that manages one or more partitions within the storage device  640 . In some embodiments, storage device  640  can include flash memory, semiconductor (solid state) memory or the like. The computing device  600  can also include a Random Access Memory (RAM)  620  and a Read-Only Memory (ROM)  622 . The ROM  622  can store programs, utilities or processes to be executed in a non-volatile manner. The RAM  620  can provide volatile data storage, and stores instructions related to the operation of the computing device  600 . 
     The various aspects, embodiments, implementations or features of the described embodiments can be used separately or in any combination. Various aspects of the described embodiments can be implemented by software, hardware or a combination of hardware and software. The described embodiments can also be embodied as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data that can thereafter be read by a computer system. Examples of the computer readable medium include read-only memory, random-access memory, CD-ROMs, DVDs, magnetic tape, hard disk drives, solid state drives, and optical data storage devices. The computer readable medium can also be distributed over network-coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. 
     The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the described embodiments. However, it will be apparent to one skilled in the art that the specific details are not required in order to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It will be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings.

Metadata:
Filing Date: 20160923
Publication Date: 20200218
Grant Date: 20200218
Priority Date: 20160610
Inventors: LOBO, PHILOMENA F.
MANTRIPRAGADA, UDAY M.
RAMAN, VIJAY
TENNETI, TEJASWI
SHELBY, RYAN D.
CHANG, JAE WOO
ROMAN, PETER W.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F3/0485", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/90328", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/9537", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/285", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04817", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/9537", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0482", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0488", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04817", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0485", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0485", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04817", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/285", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/9537", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0482", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F16/90328", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/0482", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/0488", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 60573950