Patent Abstract:
A method for comprehensive user/event matching or recommendations is described. The method includes a network environment which receives one or more pieces of user data, event data, or social data from users or third party data sources, determining the relevance of the data for users, and displaying the identified data to user in the form of recommendations.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present application incorporates by reference U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013 for “Comprehensive User/Event Matching or Recommendations Based on Awareness of Entities, Activities, Interests, Desires, Location.” 
     
    
     BACKGROUND 
     Field of the Invention 
       [0002]    The invention relates generally to social networking and, in particular, to systems and methods for serving meaningful “real-world” recommendations. 
       Description of the Related Art 
       [0003]    Social networking services include social utilities that track and enable connections between users (including people, businesses, and other entities), which have become prevalent in recent years. In particular, social networking services allow users to communicate more efficiently information that is relevant to their friends or other connections in the social networking service. Social networking services typically incorporate a system for connecting users to content that is likely to be relevant to each user. For example, users may be grouped according to one or more common attributes in their profiles, such as geographic location, employer, job type, age, music preferences, page likes, followers, or other attributes. Users of the social networking service and/or external parties can then use these groups to customize or target information delivery so that information that might be of particular interest to a group can be communicated to that group. 
         [0004]    Furthermore, advertisers wishing to use members&#39; affinities, or common attributes, as targeting criteria for advertisements have difficulty placing their ads in contextually relevant areas, a problem called “ad blindness.” As a result, members are inundated with advertisements for products unrelated to the context of what the members are currently viewing. Thus, these ads are largely ignored by members of a social network. 
         [0005]    Despite prior art designed to increase the relevance of this information, the systems and methods of the prior art do not achieve the desired outcome, and continue to overwhelm the user with large amounts of non-relevant information. Additionally, methods for indicating user interests including page likes and followers on social networks today are largely comprised of non-useful and non-meaningful data for real-world (offline) interactions, thus the power for directing content, recommendations, and advertising that promotes real world interactions is greatly limited. 
       SUMMARY 
     Brief Summary of Invention 
       [0000]    
       
         
           
             A recommendation engine that connects users with the things that they find important. This creates a real-world social network promoting the connection of people in meaningful interactions based on the context of their interests and behavior. 
             Useful matches or recommendations of other users (1 or more) and events (one-time, repeating, deal-based, or general), including any combinations (users with users, users with events, events with users, events with events). 
             Based on interest (as indicated by a user or friend, inferred, or profiled/guessed) or intent (indicated or guessed), location (current place, or a remote place, where one plans to be or would like to be), compatibility. 
             Towards the promotion of “real world”/offline activities or connections. 
             Connecting brand/businesses/advertisers&#39; events with the users who, based on a set of algorithms, may find the recommendation of interest 
             Data received or gathered from external systems, public information, or from the invention itself. 
             In one embodiment, these matches or recommendations are displayed in a feed on the web, a mobile device, by text message, or in an email. 
             In another embodiment in an augmented reality application, such as a pair of glasses or contact lenses projecting a data layer on top of real objects in the field of vision. 
             Recommendations can be conveyed in any means whereby such data can be relayed, such as over internet/intranet, via a dedicated or within a separate mobile/web/tablet application, via augmented reality layers, email, bluetooth, in-car audio/video/display systems, mobile tethering, mobile hotspots, near-field communication, or any transmission that offers relevant real-world content with regard to any of the items indicated hereto. 
           
         
       
     
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]      FIG. 1  illustrates an exemplary environment for generating relevant recommendations in a social network environment; 
           [0016]      FIG. 2  is a block diagram of an exemplary recommendation engine; 
           [0017]      FIG. 3  is an exemplary screen shot of one embodiment of a feed of recommendations on a mobile device; 
           [0018]      FIG. 4  is a flow diagram of an exemplary process, for the importing/classification of data, display of relevant recommendations, and an optional feedback loop to improve future recommendations; and 
           [0019]      FIG. 5 - FIG. 9  are additional embodiments of the present invention as described in U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013 and noted as  FIGS. 1-2, and 6-8  respectively therein. 
           [0020]      FIG. 5  is an exemplary screen shot of one embodiment of a feed of recommendations on a mobile device 
           [0021]      FIG. 6  is an exemplary screen shot of one embodiment of a details view for one recommendation, on a mobile device 
           [0022]      FIG. 7  is an early mockup screen shot of one embodiment of a view allowing users to specify interest preferences 
           [0023]      FIG. 8  is an early mockup screen shot of one embodiment of a feed of recommendations on a mobile device 
           [0024]      FIG. 9  is an early mockup screen shot of one embodiment of a settings menu allowing users to specify general preferences for recommendations. 
       
    
    
       [0025]    The figures depict various embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein. 
       DETAILED DESCRIPTION 
     Detailed Description of Invention 
       [0000]    
       
         
           
             A comprehensive matching system for matching or recommending users and events, to create a “real-world” social network—the content delivered from this system could be outputted through any means whereby electronic data is delivered, for example in a feed on the web, a mobile device, by text message, in an email, through augmented reality in the field of vision, or in any means whereby such data can be relayed, such as over internet/intranet, via a dedicated—or within a separate—mobile/web/tablet application, via augmented reality layers; email, bluetooth, in-car audio/video/display systems, mobile tethering, mobile hotspots, near-field communication, other outputs as known in the art, or any transmission that offers relevant real-world content with regard to any of the items indicated hereto. 
             To create a novel and meaningful experience, in one embodiment of the system, multiple steps of information gathering and contextualization occur including:
           User interest or intent data through explicit input by self or friend, inferred by interactions within the system, and/or profiled/guessed based on similarities to other users or other available data.   Current, future, or desired location, based on user provided input, inferred using data provided by a front-end system/application, or guessed based on other information, such as IP address.   Entity and event/activity/experience awareness to understand possibilities of things to do, and the entities associated with those activities to understand which user(s) should receive the specific recommendation.
               Involves an ontology/graph/model of several layers of possible interests and interest relations, in one embodiment using multi-step natural language contextualization system to take diverse inputs from disparate sources online and offline (external systems, public information, or from the invention itself) to understand and “tag” accordingly.   
               
         
             This information is used to recommend or match, in one embodiment of the system using matching algorithms, statistical models, or other data analysis techniques the following combinations: users with users, users with events, events with users, events with events toward the promotion of “real world”/offline activities or connections.
           Users could be a single or group of users, potentially with compatibility considerations from user data provided or inferred/guessed.   Events could be one-time events, such as a concert, repeating such as a weekly special, a deal-based event such as an expiring coupon/special promotion, or a general event such as “hike in the park.”   Certain embodiments include (each user-provided, inferred, or profiled/guessed): varying importance of weighting for recommendations to receive more/less and nearer/farther recommendations, user importance ratings as positives (likes) and as negatives (dislikes).   Automatic awareness and changes to system parameters based on new habits, user located in a new place (one they do not typically go to), actively moving, and others.   Different modes (either user or system induced) to receive different numbers/types of recommendations for events and users, as well as times and places when the system will be on/off automatically.   Subscription-like recommendations or matches such that “tastemaker” or popular users curate and can be followed for their feeds/playlists/activity.   
         
             Connecting brands&#39;/businesses&#39;/advertisers&#39; events with the users who, based on a set of algorithms, may find the recommendation of interest.
           Could occur on a one-time basis, similar to a notification, or a recurring/continual basis, similar to an advertising campaign.   
         
               FIG. 1  illustrates an exemplary system for generating relevant recommendations in a network environment  104 . One or more users, such as users  101  at user devices  102 , are coupled to a network environment  104 . The network environment  104  can query data from social data provider  105  and/or event data provider  106 . All data received by the network environment  104  is then processed by recommendation engine  107 , which may filter the information according to thresholds set on the inputs to recommendation engine  107  (as more fully described in  FIG. 2 ), and generally the guidance of self-stated and/or discovered/inferred interests. It sends recommendations believed that user  101  will find relevant given aforementioned interests, to user device  102 . Recommendations provided may include events, activities, experiences, people, deals, specials, desire matchups, and/or advertisements for user  101  alone, or include friends/people in said recommendations who may have a corresponding interest. This mutual interest could be realized directly by users  101  and  103 , not necessarily friends, because of their direct involvement on the same network environment  104 , or explicitly between users  103  and “ghost” user  103   a —a friend who has not subscribed or directly provided information to network environment  104 , but whose data was obtained because of user  103 &#39;s engagement with the network environment  104  or social data provider  105 .
           One or more users such as user  101  and user  103 , connect to network environment  104  through user device  102 . User device  102  may comprise of a web, mobile/tablet application, augmented reality layers, email, bluetooth, in-car audio/video/display systems, mobile tethering, mobile hotspots, near-field communication, other outputs as known in the art, or any transmission that offers relevant real-world content with regard to any of the items indicated hereto.   Network environment  104  consists of one or more servers, with one or more hard drives, an amount of memory, and other hardware specifications known in the art necessary to receive and transmit information appropriately to user device  102 . Network environment  104  may be accessed via the internet, a wireless or wired network such as a mobile device carrier network or any other network that can be used for communication between a server and client.   Social data provider  105  may comprise any user or entity that provides social relationship or interest data, communication services, dating services, company intranets, and so forth. Event data provider  106  may comprise any user or entity that provides structured or unstructured data about events, activities, experiences, people, deals, specials, desire matchups, and/or advertisements for recommendation engine  107  to recommend. This may occur via Application Programming Interfaces (API&#39;s), data scraping, user additions, or inferred by the network environment using methods such as locations/interests of users, and so forth.   
         
             Referring now to  FIG. 2 , a block diagram of an exemplary recommendation engine  107 , such as the recommendation engine  107  shown in  FIG. 1 . A user-interests database  202  is provided for storing interest and other user data associated with each of the users, such as the user  101  associated with user device  102 . When a user  101  subscribes to services provided by the network environment  104 , a user profile may be generated for user  101 . For example, user  101  may specify interests explicitly, and through engagement with the service, implicitly specify other interests. For example, the user  101  may select interests ‘electronic music’, ‘yoga’, and ‘sushi’. Furthermore by frequently viewing and attending events/experiences associated with ‘yoga’ and ‘electronic music’, the user also provides meaningful data about true interests, and provides meaningful data about ‘sushi’ a potentially less relevant interest. For each input to recommendation engine  107 , thresholds may exist to define and refine recommendations  209 . For example, if event data  201  contained an event ‘Sushi and Yoga Night’, this may trigger a threshold for user to receive that event. However, one or more user or system-defined criterion may limit or further refine the threshold. For example, a certain number of ghost users may be necessary to show the event, or the event may have to be within a certain distance of the user&#39;s location.
           According to some embodiments, the user profile is created, outside of the network environment  104  and provided to or accessed by the network environment  104 . Alternatively, the user interests  202  may be located remotely and accessed by the network environment  104 .   Optionally, if user  103  engages network environment  104  in such a manner where social relationship data is available, ghost users  103   a  may be created. These ghost users  103   a  may contain information about user  103 ′s relationships with these ghost users and their interests, known as ghosts&#39; interests  204 , so that recommendation engine  107  may recommend relevant ghosts using social thresholds, along with events/activities/experiences recommended by recommendation engine  107  through network environment  104 .   Significance analysis  206  may be performed to determine which people or friends are most relevant to the potential recommendation, and to rank this relevancy in order to show most relevant people/friends before others. This analysis may consider factors such as user location, significance to each of the users of the overlapping interests for the particular recommendation, or any other thresholds regarding the inputs considered by the recommendation engine  107  to arrive at  209  recommendation.   Engagement data with the service—clicks, views, etc may also factor into recommendations  209  provided by recommendation engine  107  to network environment  104 .   Categorized event data  201  can be considered by recommendation engine  107 , narrowing down for the most relevant events, activities, experiences, people, deals, specials, desire matchups, and/or advertisements using relevancy popularity analysis  203 . This relevancy analysis may include known or guessed information of the attendance/engagement numbers of a particular recommendation, the distance of the potential recommendation to the user&#39;s current, estimated, and/or anticipated location, the significance to the user of the interests belonging to the potential recommendation, or any other thresholds regarding the inputs considered by the recommendation engine  107  to arrive at  209  recommendation.   Relevant context-aware specials/advertisements  205  may be shown—to users nearby, potentially interested in the entity hosting a particular special/advertisement, and optionally including social recommendations with a particular special/advertisement.   Finally, previous attendance data  207  may be used to further determine user-interest relevancy for whether a specific recommendation should appear through network environment  104  to user  101 / 103  via user device  102 .   The output of the recommendations engine  209  is then relayed to the network environment  104 , where it exists for any amount of time before being sent to user  101  and/or  103  via user device  102 . Recommendations  209  offered may be one or more, are not final, and can be further modified/refined at any time as a result of additional input received by the network environment  104  and thus recommendation engine  107 . Recommendations  209  may include events, activities, experiences, people, deals, specials, desire matchups, and/or advertisements with/without corresponding people/friend recommendations, nearby the users current and/or future known/anticipated location.   
         
               FIG. 3  is an exemplary screen shot of one embodiment of a ‘feed’  302  of recommendations  209  from recommendation engine  107 , displaying through the network environment  104  on a user device  102  to the viewing user  103 . The user device  102  in this instance is a mobile phone. The exemplary screen shot  302  represents a display page showing four recommendations  209  to user  103 , and including both ghost users  103   a  and network environment users  101 . However, more or fewer than four items with or without ghost/network environment users may be displayed. The total number displayed may be limited by the significance analysis  206 .
           Interest indication  301  is one embodiment of a method whereby user  101  and/or  103  may provide user-interests  202  explicitly to network environment  104 .   Various, types of recommendations—events, activities, experiences, people, deals, specials, desire matchups, advertisements and/or other content may be displayed in the feed  302 . In the exemplary screen shot shown in feed  302 , nearby events for the weekend are displayed.   Interests relevant for the events displayed may be specified explicitly along subcategories of those shown in interests  301 . For example ‘dogs’ and ‘yoga’ may be explicitly indicated by the user, which results in  2  of the events shown in feed  302 —a Pup Crawl&#39; and ‘Free Sunset Yoga in the Park’.   In this exemplary screen shot  302 , relevant interests to the user are filtered and shown  305  as part of the recommendation. Additionally, optional relevant person/friend recommendations may be displayed  306  if the users data allows for such recommendations to be made by recommendation engine  107 . Finally, specials/relevant advertisements  304  are shown in this exemplary screen shot in-line with the event recommendation. They may also appear as separate notifications, separate recommendations, or using any of the methods of data transmission known in the art or described hereto.   Exemplary screen shot  303  depicts additional details about the specific event recommendation, including the names of relevant friend recommendations  307 , non-truncated special/advertisement details  308 , and full list of interests for the event  309 . These details and the others depicted in the exemplary screen shot  303  represent one embodiment, however many modifications and variations are also in the spirit of the invention as defined hereto.   
         
             Referring now to  FIG. 4 , a flow diagram of an exemplary process for the importing/classification of data, display of relevant recommendations, and an optional feedback loop to improve future recommendations.
           Multiple layers: events, users, and ghosts, can be processed in this exemplary process.   At step  406 , event data can be imported from event data provider  106 . This may occur via Application Programming Interfaces (API&#39;s), data scraping, user additions, or inferred by the network environment using methods such as locations/interests of users.   In this exemplary diagram, a user  103  explicitly provides user-interest info  202  using a mechanism similar to  301 . Additional data may be optionally gathered, if permitted, from external social data provider(s)  105 . Similarly, data from social data provider  105  generates ghost profiles  103   a  as relevant to user  103 .   Matching/classification of events to real-world interests on the network environment  403  can occur via natural language processing, text classification, and/or be manually assisted. One skilled in the art will be aware of many software implementations to reach this goal.   Refinement and exclusion of events  404  also takes: place, using thresholds—which can be user or system defined, as described herein, to ensure the most meaningful events can be displayed to users  101  and  103 . Factors considered by the exclusion analysis may include event size, guessed popularity, distance, and what the user  101  or  103  has attended or engaged with previously.   At optional step  407 , network environment  104  creates relationships between user-interests on the network environment  104 , and the ghost&#39;s guessed or explicitly stated interests via natural language processing, text classification, and or manual assistance. Once these associations are made, recommendation  107  can adequately map single outputs from event data provider  106 , and their associated user-interests  202 , with relevant ghosts who may be interested in also attending a particular event;
               For example user  103  provides an interest in ‘sushi’ explicitly to network environment  104 . When she engages with the service, ghost user  103   a  is also created, and it is explicitly determined this user has an interest in ‘Japanese food’. When the event ‘Sushi and Wine on the Beach’ is upcoming, user  103  will see a recommendation  209  that may include ghost friend  103   a  based on the association of Japanese food with sushi, which occurred in process  407 .   
               Other items depicted in  FIG. 4  prior to step  401  can be handled by recommendation engine  107 . Once recommendations  209  are synthesized, they can be displayed for the user or otherwise sent to the user according to methods of data transmission known in the art.   Optionally, other implicit or explicit feedback is gathered (click, attendance data, changing/updating user-interests, or other data points) to refine and improve recommendations  209  in the future.   
         
             Referring now to  FIG. 5 , similar to  FIG. 3  herein, and as noted as  FIG. 1  in U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013, illustrates one embodiment of a ‘feed’  502  of recommendations  209  from recommendation engine  107 , displaying through the network environment  104  on a user&#39;device  102  to the viewing user  103 . The user device  102  in this instance is also a mobile phone. The total number displayed may be limited by the significance analysis  206 . For readability, the same descriptions and numbering as  FIG. 3  are used.
           This exemplary embodiment shows an optional expanded ‘details’ view  503  within the feed  502 .   Included are the elements from  FIG. 3  in a different visual arrangement: specials/relevant advertisements  508 , relevant person/friend recommendations  507 , and event relevancy filtering  509 .   
         
               FIG. 6 , as noted as  FIG. 2  in U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013, illustrates another embodiment of an expanded ‘details’ view  603  of one recommendation  209 . For readability, the same descriptions and numbering as  FIG. 3  are used.
           Included are the elements from  FIG. 3  in a different visual arrangement: specials/relevant advertisements  608 , relevant person/friend recommendations  607 , and event relevancy filtering  609 .   Interest slider  610  highlights one embodiment of user importance ratings, an ingredient of event relevancy/popularity  203  as described herein.   
         
             Referring now to  FIG. 7 , as noted as  FIG. 6  in U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013, illustrates an early mockup screen shot of an alternative embodiment of a view allowing users to specify interest preferences  202 .
           Embodiment depicts a map overlay with recommendations  209  displayed on the map, as the user indicates interest preferences  202 .   Embodiment depicts interest slider feature  610  to specify importance of a particular interest.   
         
               FIG. 8 , as noted as  FIG. 7  in U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013, illustrates an early mockup screen shot of an alternative embodiment of a ‘feed’  302  of recommendations  209  from recommendation engine  107 , displaying through the network environment  104  on a user device  102  to the viewing user  103 . The user device  102  in this instance is also a mobile phone. The total number displayed may be&#39; limited by the significance analysis  206 .
           Included are each of the elements from  FIG. 3  in a different visual arrangement.   
         
               FIG. 9 , as noted as  FIG. 8  in U.S. Provisional Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013, illustrates an early mockup screen shot of a settings menu intended to be inputs for user interest relevancy  202  and engagement data  208  for recommendation engine  107  to arrive at recommendations  209 . 
             Invention uses an improvement in the quality of data and the manner in which it&#39;is contextualized and joined, to offer a new, useful, and non-obvious improvement over the currently available user &amp; event matching, recommendations, and/or real-life interactions fostered in current social networking solutions. 
           
         
       
     
         [0083]    The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure. 
         [0084]    Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art: These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof. 
         [0085]    Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. 
         [0086]    Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible computer readable storage medium or any type of media suitable for storing electronic instructions, and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. 
         [0087]    Embodiments of the invention may also relate to a computer data signal embodied in a carrier wave, where the computer data signal includes any embodiment of a computer program product or other data combination described herein. The computer data signal is a product that is presented in a tangible medium or carrier wave and modulated or otherwise encoded in the carrier wave, which is tangible, and transmitted according to any suitable transmission method. 
         [0088]    Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description; but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Technology Classification (CPC): 6