Patent Publication Number: US-10776467-B2

Title: Establishing personal identity using real time contextual data

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates generally to the field of user identification, and more particularly to automated user identification using biometric sensors. 
     Computer vision is an interdisciplinary field that aims for computers to analyze and extract, and interpret contents of images and videos. In other words, computer vision attempts to describe the reality (e.g., the identity of a person) based on analysis of one or more images. Computer vision is used widely for tasks such as optical character recognition (OCR), object recognition in retail industry, medical imaging, motion capture, and security (e.g., monitoring pools, highway traffic, etc.). Computer vision is further used for applications such as face detection and visual authentication for computer users. 
     SUMMARY 
     Embodiments of the present invention disclose a method, a computer program product, and a system for establishing personal identity using real-time contextual data. The method may include one or more processors receiving a registration information for a first user. The method may further include one or more processors receiving a first biometric data for the first user and determining a first user behavior based on the first biometric data and the registration information for the first user. The method may additionally include one or more processors storing the registration information, the first biometric data, and the first user behavior in a first user profile for identification of the first user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram illustrating a distributed data processing environment, in accordance with an embodiment of the present invention; 
         FIG. 2  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for receiving identification for a user and analyzing user behavior captured via site sensors in order to identify the user in the future, in accordance with an embodiment of the present invention; 
         FIG. 3  illustrates operational steps of a user identification program, on a client device within the distributed data processing environment of  FIG. 1 , for receiving identification for a user, including social media profiles, and analyzing online user behavior in order to identify the user in the future, in accordance with an embodiment of the present invention; 
         FIG. 4  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for analyzing user behavior based on received biometric data and comparing the biometric data and the user behavior with stored user profiles, in accordance with an embodiment of the present invention; 
         FIG. 5  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for determining one or more subsets of user profiles and comparing contextual data and user insights with the subsets of user profiles, in accordance with an embodiment of the present invention; 
         FIG. 6  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for receiving a set of input images and selecting the best images for user identification based on a quality score, in accordance with an embodiment of the present invention; 
         FIG. 7  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for receiving a set of input images and selecting the best images for user identification based on a set of attributes, in accordance with an embodiment of the present invention; 
         FIG. 8  shows an exemplary set of input images, in accordance with an embodiment of the present invention; 
         FIG. 9  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for creating and managing temporary user profiles for users in a specific location, in accordance with an embodiment of the present invention; 
         FIG. 10  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for matching temporary user profiles for users in a specific location with a stored user profile, in accordance with an embodiment of the present invention; 
         FIG. 11  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for matching temporary user profiles for users in a specific location with a stored user profile, in accordance with an embodiment of the present invention; 
         FIG. 12  shows an exemplary comparison between a set of input images and a set of profile images, in accordance with an embodiment of the present invention; 
         FIG. 13  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for storage of customer characteristics and behaviors for a passively managed customer loyalty program through the use of social media profiles, in accordance with an embodiment of the present invention; 
         FIG. 14  is a functional block diagram and flowchart illustrating a distributed data processing environment for a passively managed customer loyalty program through the use of social media profiles, in accordance with an embodiment of the present invention; 
         FIG. 15  is a functional block diagram and flowchart illustrating a distributed data processing environment for a passively managed customer loyalty program through the use of local profiles in a retailer database, in accordance with an embodiment of the present invention; 
         FIG. 16  is a functional block diagram and flowchart illustrating a distributed data processing environment for a passively managed customer loyalty program through the use of local profiles replicated from a social network, in accordance with an embodiment of the present invention; 
         FIG. 17  is a functional block diagram and flowchart illustrating a distributed data processing environment for a passively managed customer loyalty program through the use of local profiles, in accordance with an embodiment of the present invention; 
         FIG. 18  depicts a block diagram of a multi-tenant database storage for a passively managed customer loyalty program, in accordance with an embodiment of the present invention; 
         FIG. 19  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for storage of customer characteristics and behaviors for a passively managed customer loyalty program through the use of shadow profiles, in accordance with an embodiment of the present invention; 
         FIG. 20  is a functional block diagram and flowchart illustrating a distributed data processing environment for storage of customer characteristics and behaviors for a passively managed customer loyalty program through the use of shadow profiles, in accordance with an embodiment of the present invention; 
         FIG. 21  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for storage of point of sale transactions related to a passively managed customer loyalty program, in accordance with an embodiment of the present invention; 
         FIG. 22  is a functional block diagram and flowchart illustrating a distributed data processing environment for storage of customer characteristics and behaviors for storage of point of sale transactions related to a passively managed customer loyalty program, in accordance with an embodiment of the present invention; 
         FIG. 23  is a flowchart depicting operational steps of a user identification program, on a server computer within the distributed data processing environment of  FIG. 1 , for data analytics related to a passively managed customer loyalty program, in accordance with an embodiment of the present invention; 
         FIG. 24  is a functional block diagram and flowchart illustrating a distributed data processing environment for storage of customer characteristics and behaviors for data analytics related to a passively managed customer loyalty program, in accordance with an embodiment of the present invention; and 
         FIG. 25  depicts a block diagram of components of the server computer executing the user identification program within the distributed data processing environment of  FIG. 1 , in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Identifying users walking into locations (e.g., stores, restaurants, or other similar locations) by comparing user biometric data (e.g., data captured with video cameras) with a plurality of social media profiles may be inefficient based on the large number social media profiles available for inspection. In general, passive management of customer loyalty programs using biometric data has been unfeasible due to the difficulty of identifying users by capturing biometric data. 
     Some embodiments of the present invention provide improved techniques for user identification by: (1) building subsets of social profiles to match contextual, real-time social media data, and static social media data; (2) applying local filtering to a set of input images to improve selection of images for user identification; and (3) matching multiple sub-optimal images of the user to multiple photos in a social media profile. 
     Some embodiments of the present invention further disclose a passively managed customer loyalty program through the use of social media profiles. In some embodiments, the loyalty program includes the following characteristics: (1) using a shadow profile for customers for which no social media profile is found; (2) using social media profile data at the point of sale (POS); (3) managing customer data online and/or on premise at the retailer using data isolation; (4) performing analytics on the social media profiles; and (5) generating target advertisements. 
     Implementation of embodiments of the present invention may take a variety of forms, and exemplary implementation details are discussed subsequently with reference to the Figures. 
       FIG. 1  is a functional block diagram illustrating a distributed data processing environment, generally designated  100 , in accordance with one embodiment of the present invention. The term “distributed” as used in this specification describes a computer system that includes multiple, physically distinct devices that operate together as a single computer system.  FIG. 1  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. 
     Distributed data processing environment  100  includes server computer  104 , mobile device  110 , site sensors  116 , and social media sources  118 , all interconnected over network  102 . 
     In general, network  102  can be any combination of connections and protocols that will support communications between server computer  104 , mobile device  110 , site sensors  116 , and social media sources  118 , and other computing devices (not shown) within distributed data processing environment  100 . Network  102  can be, for example, a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, or a combination of the three, and can include wired, wireless, or fiber optic connections. Network  102  can include one or more wired and/or wireless networks that can receive and transmit data, voice, and/or video signals, including multimedia signals that include voice, data, and video information. 
     Server computer  104  can be a standalone computing device, a management server, a content service, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data. In other embodiments, server computer  104  can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In another embodiment, server computer  104  can be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any other programmable electronic device capable of communicating with mobile device  110 , site sensors  116 , social media sources  118 , and other computing devices (not shown) within distributed data processing environment  100  via network  102 . In another embodiment, server computer  104  represents a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within distributed data processing environment  100 . Server computer  104  may include internal and external hardware components, as depicted and described in further detail with respect to  FIG. 25 . 
     Mobile device  110  enables a user to access social media sources  118  and other websites and applications (not shown). For instance, mobile device  110  can be a laptop computer, a tablet computer, a smart phone, or any programmable electronic mobile device capable of communicating with various components and devices within distributed data processing environment  100 , via network  102 . In general, mobile device  110  represents any programmable electronic mobile device or combination of programmable electronic mobile devices capable of executing machine readable program instructions and communicating with other computing devices (not shown) within distributed data processing environment  100  via a network, such as network  102 . Mobile device  110  includes an instance of user interface  112 . Mobile device  110  may include internal and external hardware components, as depicted and described in further detail with respect to  FIG. 25 . 
     User interface  112  provides an interface to social media sources  118  for a user of mobile device  110 . In some embodiments of the present invention, user interface  112  may be a graphical user interface (GUI) or a web user interface (WUI) and can display text, documents, web browser windows, user options, application interfaces, instructions for operation, and include the information (such as graphic, text, and sound) that a program presents to a user and the control sequences the user employs to control the program. In other embodiments, user interface  112  may also be mobile application software that provides a connection between data generated by a user of mobile device  110  and server computer  104 . Mobile application software, or an “app,” is a computer program designed to run on smart phones, tablet computers and other mobile devices. In other embodiments, user interface  112  may be an external device operatively connected to mobile device  110  via near field communication or other types of wired and/or wireless technologies. 
     Mobile device  110  may also provide mobile sensors  114  useful to identify users. Mobile sensors  114  may include cameras, global position system (GPS) sensors, and near-field communication sensors, among others. 
     Site sensors  116  may include any device from which an identification of a user may be obtained. For example, site sensors  116  may include biometric devices such as video cameras, depth sensors, and other similar devices. Site sensors  116  may also include near-field communication sensors to identify a user via mobile device  110 . 
     Social media sources  118  may include one or a combination of social media websites, focused chat sites or applications, blogs, and wikis, or other content sites. Social media sources  118  may include text, graphics, pictures, and other content posted by users. 
     Database  108  is a repository for data used by user identification program  106 . Data used by user identification program  106  may include user data such as images, identification information, user behaviors, social network data, profile data, and other information useful to determine user identification or behavior. In the depicted embodiment, database  108  resides on server computer  104 . In another embodiment, database  108  may reside elsewhere within distributed data processing environment  100  provided user identification program  106  has access to database  108 . 
     Server computer  104  includes user identification program  106  for real-time identification of users. Some embodiments of the present invention use real-time contextual data (e.g., user location) as well as real-time generated insights for a user (e.g., user behavior with respect to products or services), to compare with stored social profiles based on real-time dynamic social data (e.g., user activity in social media) and static social data (e.g., user profile data) for purposes of user identification. Other embodiments of the present invention build subsets of social profiles to match contextual, real-time social media data, and static social media data in order to improve user identification. These aspects of the disclosure are discussed subsequently with reference to  FIGS. 2-5 . 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 2 . Referring to flowchart  200 , user identification program  106  receives identification for a user and analyzes user behavior captured via site sensors  116  in order to identify the user in the future. 
     Processing begins at operation  255 , where user identification program  106  receives a registration information for a user. In some embodiments of the present invention, the registration information may be received via a registration process where the user provides biometric data that allows high confidence identification of the user by the system. In some embodiments, biometric data includes images received from a photographic or video camera, data received from a depth sensing camera, data received from temperature sensors, or any other data that may be used to identify the user and the user behavior in a physical location such as a retail store. In some embodiments, the biometric data may be obtained via site sensors  116 . In other embodiments, the user may provide biometric data as part of the registration process. In an example embodiment, a department store runs user identification program  106  to provide marketing services for the users. In this exemplary embodiment, a user named Ben registers in the user identification program  106  by providing one or more pictures coupled with personal information (e.g., address, telephone, and other similar information). 
     Processing continues at operation  260 , where user identification program  106  monitors a user by receiving biometric data corresponding to the user. In some embodiments of the present invention, biometric data may be received from site sensors  116  placed at a specific location (e.g., a store or restaurant). In an example embodiment, Ben walks into the department store a week after registering in user identification program  106 . Site sensors  116  begin to monitor Ben by obtaining his biometric data. 
     Processing proceeds at operation  265 , where user identification program  106  determines user behavior based on the biometric data for the user. In some embodiments of the present invention, user identification program  106  performs sensor based methods (e.g., cameras, depth sensors, temperature sensors, among others) for identifying the users and detecting interactions between the user and the site location using one or more site sensors  116 . In some embodiments, user behavior is determined by factors such as the time spent looking at a specific product, product characteristics (e.g., clearance or sales products) or product categories (e.g., clothes for 3-5 years old babies), time spent at a specific aisle, identification of products for which the user has performed a price check, path taken in the store, among others. 
     In some embodiments of the present invention, user identification program  106  continuously learns and stores the user behavior. In some embodiments, user information and behaviors are added or modified in the user profile as they are discovered. In some embodiments, user information and behaviors are removed or weighted relatively lower for identification purpose as the user information is found to be no longer relevant. In some embodiments, changes to the user information and behaviors (e.g., adding, modifying, removing, or weighting user information and behaviors) may be determined based using existing methods, such as aging of data, and trends, among other similar factors. In some embodiments, user information and behaviors may be determined by probability over a period of time. For example, if a user looked for clothes for a 3-year old baby 2 years ago, user identification program  106  may identify the user by associating behavior based on clothes for a 5-year old baby. 
     In some embodiments of the present invention, user identification program  106  associates the user behavior captured at different times. The association can be done using a path tracking method, where the user is tracked from location A to location B. This path tracking method will ensure it is the same user at location A and location B, and therefore the behavior belongs to the same user. 
     In our exemplary embodiment, Ben looks for products and services inside the department store while user identification program  106  analyzes his behavior in order to identify Ben in a future visit to the store. 
     Processing continues at operation  270 , where user identification program  106  stores the user information for identification of the user. In some embodiments of the present invention, the user information, comprising the high confidence identification and the user behavior, is stored in database  108  for future identification of the user. Continuing our exemplary embodiment, after Ben walks out of the department store, user identification program  106  stops collecting user information for Ben, creates a user profile for Ben, and stores the collected information in the user profile. 
     Another aspect of user identification program  106  is depicted and described in further detail with respect to  FIG. 3 . Referring to flowchart  300 , user identification program  106  receives identification for a user, including social media profiles, and analyzes online user behavior in order to identify the user in the future. 
     Processing begins at operation  355 , where user identification program  106  receives registration information for a user. In some embodiments of the present invention, the registration information for the user may be received via a registration process where the user provides biometric data for high confidence identification by the system. In an example embodiment, Ben registers in the user identification program  106  by providing one or more pictures coupled with personal information (e.g., address, telephone, and other similar information). 
     Processing continues at operation  360 , where user identification program  106  receives social network information for the user. In some embodiments of the present invention, the user provides links for his/her social media profiles. In other embodiments, user identification program  106  obtains authorization to access data from the social media profiles provided by the user. In our exemplary embodiment, Ben provides a link to his social media profiles and provides user identification program  106  with the pertinent authorization to access the social media profiles. 
     Processing proceeds at operation  365 , where user identification program  106  receives online shopping history for the user. In some embodiments of the present invention, user identification program  106  traverses the social media profiles of the user to determine products that the user has recently bought, expressed sentiment (e.g., liked or followed products), browsed, discussed, or searched for. In other embodiments, user identification program  106  traverses the social media profiles of the user to determine the shopping preferences of the user (e.g., favorite brands). Continuing our exemplary embodiment, user identification program  106  receives information about the shopping history and preferences for Ben. 
     Processing proceeds at operation  370 , where user identification program  106  receives additional personal data for the user. In some embodiments of the present invention, user identification program  106  further receives additional personal data for the user such as calendar information. Continuing our exemplary embodiment, Ben provides a link to his cloud-based calendar and provides user identification program  106  with the pertinent authorization to access the social media profiles. 
     Processing continues at operation  375 , where user identification program  106  creates a profile for the user and stores the user information for identification. In an exemplary embodiment, user identification program  106  stores the identification and collected information for Ben in database  108 . 
     A further aspect of user identification program  106  is depicted and described in further detail with respect to  FIG. 4 . Referring to flowchart  400 , user identification program  106  receives a low confidence identification for a user. User identification program  106  further receives biometric data from site sensors  116  and identifies user behavior based on the received biometric data. User identification compares the biometric data and the user behavior with stored user profiles in database  108 . 
     Processing begins at operation  455 , where user identification program  106  receives a biometric data for an unknown user. In some embodiments of the present invention, user identification program  106  receives a set of images for an unknown user. In this context, unknown user means a user that has not yet been identified either because there is no profile associated with the user or because there is not enough information to identify the user yet. In some embodiments, user identification program  106  captures biometric data for a user from site sensors  116 . In an exemplary embodiment, User A walks into a department store where site sensors  116  begin to capture his biometric data while User A is inside the store. In this exemplary embodiment, user identification program  106  cannot determine the identity of User A based on the information available. 
     Processing proceeds at operation  460 , where user identification program  106  determines a behavior of the unknown user based on the biometric data. In some embodiments of the present invention, user behavior is determined by factors such as the time spent looking at a specific product, time spent at a specific aisle, identification of products for which the user has performed a price check, among others. In our exemplary embodiment, User A visits the children toy section and later the sports clothing section. 
     Processing continues at operation  465 , where user identification program  106  searches for a matching user in a database based on the biometric data of the user and the behavior of the user. In some embodiments of the present invention, user identification program  106  searches for a matching user profile in database  108 . In some embodiments of the present invention, a subset of the user behavior can be used to calculate the confidence score when matching against a user profile. In other embodiments, the percentages or probability from multiple user behaviors can be combined using in a linear combination or any other suitable formulation. In these embodiments, the formulation and appropriateness of the user behavior can be determined through continuous verification of whether a user was correctly identified or not. In some embodiments, the percentages or probability of a behavior can be measured, projected, and/or determined by other existing methods. In other embodiments, the relationship between each behavior from multiple users (both online and in store behavior) can be determined using existing analytics methods including clustering, simulation, and/or other existing methods. 
     In our exemplary embodiment, the user profile for Ben states  5  different categories that Ben is interested in as follows: (i) Ben stops by the food court 95% of the time, (ii) Ben visits 2-3 categories each visit, (iii) Ben visits the children toy section 40% of the time, (iv) Ben visits the sports clothing section 70% of the time, and (v) Ben visits both the children toy section and the sport clothing section 80% of the time. In our exemplary embodiment, user identification program  106  identifies the user profile for Ben based on the biometric data and user behavior for User A. 
     An additional aspect of user identification program  106  is depicted and described in further detail with respect to  FIG. 5 . Referring to flowchart  500 , user identification program  106  receives contextual data about a user. User identification program  106  additionally determines one or more subsets of user profiles and compares the contextual data and the user insights with the subsets of user profiles. 
     Processing begins at operation  555 , where user identification program  106  receives user contextual data including biometric data and associated metadata for a user. Examples of user contextual data include GPS position, location name, date, and others. In an exemplary embodiment, User A walks into a department store. User identification program  106  identifies User A as an unknown user. In our exemplary embodiment, User A looks for products and services inside the department store while user identification program  106  analyzes his behavior in order to identify User A. 
     Processing proceeds at operation  560 , where user identification program  106  determines a first subset of social media profiles by comparison of the user contextual data and real-time social media updates. In some embodiments of the present invention, user identification program  106  determines a first subset of social media profiles by comparison of the contextual data with the social media updates, such as geotagging of digital pictures, videos, and postings on social media profiles. Other examples of real-time social updates include mention of locations (e.g., mentioning the name of a store, restaurant, and/or shopping mall), check-in to location in specialized mobile applications, among others. In some embodiments, a list of selection criteria (e.g., geotags, posts about particular products, and others) are assigned a weight based on their relative importance to match users. For example, a list of possible matches for user profiles may be ranked in accordance to each of the selection criteria (e.g., ranking=criteria_1*weight_1+criteria_2*weight_2+ . . . +criteria_n &amp; weight_n). In these and some other embodiments, the user with the highest overall ranking above a predetermined threshold will be selected. In our exemplary embodiment, User A checks-in to the location using a mobile application. 
     Processing continues at operation  565 , where user identification program  106  compares the biometric data with the first subset of social media profiles. In some embodiments of the present invention, user identification program  106  searches for a matching user profile in database  108 . 
     If a match is not found (operation  570 , “no” branch), processing continues at operation  575 , where user identification program  106  determines a second subset of social media profiles by comparison of the user contextual data with static social media data. In some embodiments of the present invention, user identification program  106  determines a second subset of social media profiles by comparison of the user contextual data with static social media, such as the pages that a user follows in social media profiles. In some embodiments, operation  560  and operation  575  may be combined by determining rankings of profiles based on predetermined criteria and relative weights. In these and some other embodiments, the list of selection criteria may comprise real-time and static social network data. In our exemplary embodiment, User A follows the page of the department store where he is shopping. 
     Processing proceeds at operation  580 , where user identification program  106  compares the biometric data with the second subset of social media profiles. If a match is not found (operation  585 , “no” branch), processing continues at operation  590 , where user identification program  106  sends a notification that no match was found for the user. 
     Conversely, if a match is found (operations  570  and  585 , “yes” branch), processing continues at operation  595 , where user identification program  106  sends the matched identity of the user for the matching social profile. In our exemplary embodiment, user identification program  106  identifies the user profile for Ben based on his contextual data and user identification program  106  sends the user profile. 
     Server computer  104  includes user identification program  106  for real-time identification of users. Some embodiments of the present invention apply local filtering to a set of input images to improve selection of images for user identification. These aspects of the disclosure are discussed subsequently with reference to  FIGS. 6-8 . 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 6 . Referring to flowchart  600 , user identification program  106  receives a set of input images and selects the best images for user identification based on a quality score. 
     Processing begins at operation  655 , where user identification program  106  receives a set of input images. In some embodiments of the present invention, user identification program  106  captures a set of input images for a user from site sensors  116 . In our exemplary embodiment, user identification program  106  receives a set of input images (shown in  FIG. 8 ) for Ben. The set of input images includes a plurality of images of varying quality. 
     Processing continues at operation  660 , where user identification program  106  determines a quality score for each image in the set of input images. The attributes affecting the score may include: (a) resolution; (b) luminance and chroma image noise; (c) focus; (d) user identity; (e) angle; and (f) obstruction. Continuing our exemplary embodiment, user identification program  106  analyzes each image as follows: (i) the quality score for image  802  is 50 based on face angle and poor resolution of the image; (ii) the quality score for image  804  is 65 based on high image noise; (iii) the quality score for image  806  is 50 based on the focus of the image; (iv) the quality score for image  808  is 85 based on the face angle of the image; (v) the quality score for image  810  is 0 based on the subject of the image; (vi) the quality score for image  812  is 70 based on an obstruction of the face of the user; (vii) the quality score for image  814  is 50 based on the face angle of the image; and (viii) the quality score for image  816  is 70 based on the face angle of the image. 
     Processing proceeds at operation  665 , where user identification program  106  determines a set of output images based on the quality score for each image in the set of input images. In some embodiments of the present invention, if there are a plurality of images having similar quality scores and characteristics, user identification program  106  may include only a single representative image to the set of output images in order to improve storage and bandwidth utilization. In our exemplary embodiment, user identification program  106  determines a set of user images based on quality score threshold of 70. In this example, user identification program  106  selects images  808 ,  814 , and  816 . User identification program  106  excludes image  812  based on an obstruction in the image. Furthermore, user identification program  106  excludes image  808  based on its similarity with image  814 . 
     Processing continues at operation  670 , where user identification program  106  sends the set of output images. In some embodiments of the present invention, user identification program  106  sends the set of output images to another computer (not shown) through network  102 . In other embodiments, user identification program  106  stores the set of output images in database  108 . In our exemplary embodiment, user identification program  106  sends the set of output images to a server of a department store and stores the set of output images in database  108  for future access. 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 7 . Referring to flowchart  700 , user identification program  106  receives a set of input images and selects the best images for user identification based on a set of attributes. 
     Processing begins at operation  755 , where user identification program  106  receives the set of input images. In our exemplary embodiment, user identification program  106  receives a set of input images (shown in  FIG. 8 ) for Ben. The set of input images includes a plurality of images of varying attributes. 
     Processing proceeds at operation  760 , where user identification program  106  receives a set of image attributes for filtering the set of input images. The set of attributes may include: (a) whether the image includes a body; (b) whether the image includes a face; (c) whether the image includes hair; (d) whether the image includes a face in a predetermined angle (e.g., facing front, facing left, etc.); and (e) whether the image includes a person interacting with an object, among others. The attributes may further include static rules (e.g., face at an angle of 30 degrees or all images with a quality score higher than a predetermined threshold). The attributes may also be determined dynamically by user identification program  106  (e.g., determining a scoring mechanism). 
     In some embodiments of the present invention, the set of image attributes may be determined by server computer  104  and received by site sensors  116 . In these and some other embodiments, site sensors  116  perform the analysis of input images based on the image attributes determined by server computer  104 . In an exemplary embodiment, user identification program  106  receives a set of image attributes including face angle (facing at an angle higher than 0) and interaction with objects. In another exemplary embodiment, server computer  104  determine image attributes based on the captured images for a user. For example, images for unknown User A is captured. Server computer  104  identified two known users, Alice and Ann potentially matching this user based on existing image attributes (e.g. long hair, blood hair, 5′10″ height). Based on Alice and Ann, server computer  104  determines that Alice wears a necklace 95% of time, while Ann never wears a necklace. In this exemplary embodiment, server computer  104  instructs site sensor  116  to send only images of User A exposes the neck. In a further exemplary embodiment, other similar user attributes can be identified based on information available at the server computer or using computing resources available at the server computer. In these embodiments, the server computer can determine images attributes (e.g., images of users without necklaces) that does not require significant processing resources and in-depth knowledge of the user. In some embodiments, image attributes may also be selected based on the computing resources available at one or more site sensors  116 . In these and other embodiments, each site sensor of site sensors  116  may perform different types of pattern recognition. For example, a first site sensor may be configured to recognize body parts based on relative position from the head. In this example, a second site sensor may recognize the shape of hands or feet in relation to a body and a third site sensor may recognize clothing or clothing accessories. In other embodiments, server computer  104  dynamically communicates the image attributes to site sensors  116 . 
     Processing continues at operation  765 , where user identification program  106  determines a set of output images by analyzing which images in the set of input images matches one or more attributes in the set of image attributes. Continuing our exemplary embodiment, user identification program  106  analyzes each image and determines a set of output images as follows: (i) image  808  is selected based on a face angle of 15 degrees; (ii) image  812  is selected based on additional object in the image; (iii) image  714  is selected based on face angle of 45 degrees; and (iv) image  816  is selected based on face angle of 90 degrees. 
     Processing continues at operation  770 , where user identification program  106  sends the set of output images. In some embodiments of the present invention, user identification program  106  sends the set of output images to another computer (not shown) through network  102 . In other embodiments, user identification program  106  stores the set of output images in database  108 . In our exemplary embodiment, user identification program  106  sends the set of output images to a server of a department store and stores the set of output images in database  108  for future access. 
     Some embodiments of the present invention match multiple sub-optimal images of the user to multiple photos in a social media profile or improve confidence score of identifying an individual by using additional sub-optimal images. These aspects of the disclosure are discussed subsequently with reference to  FIGS. 9-12 . 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 9 . Referring to flowchart  900 , user identification program  106  creates and manages temporary user profiles for users in a specific location (e.g., a store or a restaurant). 
     Processing begins at operation  955 , where user identification program  106  receives a set of input images. In some embodiments of the present invention, user identification program  106  captures a set of input images for a user from site sensors  116 . In an exemplary embodiment, User A enters a department store and user identification program  106  receives a set of input images (shown in  FIG. 12 ) corresponding to User A. 
     Processing continues at operation  960 , where user identification program  106  determines whether the user is entering or exiting the premises. If the user is entering the premises (operation  965 , “enter” branch”), processing proceeds at operation  970 , where user identification program  106  determines an identity pattern for a user based on the set of input images. Examples of identity patterns include: (a) body shape; (b) clothing color; (c) clothing patterns; (d) physical appearance; (e) gait; and (f) accessories (e.g., necklace, watch, and similar accessories), among other identity patterns. In our exemplary embodiment, User A enters the department store and user identification program  106  identifies a pinstriped shirt (see  FIG. 12 , profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210 ). 
     In some embodiments of the present invention, the identity patterns are unique for each user currently within the premises (i.e., users that have entered the premises but have not yet exited). In these embodiments, user identification program  106  determines a set of identify patterns for the user when a user enters the premises. These identity patterns are compared with identity patterns of other users within the premises, such that unique identity patterns can be determined. For example, User X entered the premises with a red t-shirt. User identification program  106  determined that the red t-shirt is a unique identity pattern for User X because there are no other users with red t-shirts currently in the premises. Later, User Y enters the premises with a red t-shirt. In response, user identification program  106  determines that User X and User Y have an overlapped identity pattern (i.e., the identity pattern of red t-shirt) and re-evaluates the identity patterns for User X and User Y, such that the chosen identity patterns allow unique identification of User X and User Y within all the users within the premises. For example, if User X has black hair and User Y has blonde hair, user identification program  106  may determine an identity pattern of black hair with red t-shirt for User X and an identity pattern of blonde hair with red t-shirt for User Y. In some embodiments, users are advised of the identity activities performed before entering the location to minimize privacy concerns. In other embodiments, users consent to the identity activities before entering the location, for example, as part of a registration process. 
     In some embodiments of the present invention, the identity patterns for a user are selected based on the usability of the identity pattern. For example, a unique shoe might not be usable all the time because the shoe might be blocked by obstacle between the camera and the user. Instead, patterns from the upper body might be preferred. In an alternative embodiment, multiple identity patterns might be used for a user, while a subset of the pattern is required for uniquely identifying a user. Continuing the example above, User X might have shoes in orange color, which is unique among all users in the store. In this example, the identity pattern for User X could have three components: orange shoe, black hair, and red t-shirt, where User X can be identified as long as one of the components of the identity pattern matches. In other embodiments, user identification program  106  might select an identity pattern to use based on needs, efficiency, or any other criteria. For example, if the upper body for a user is blocked, user identification program  106  can use lower body components of the identity pattern for identification of the user (e.g., shoes in orange color). In the case of a full body image, user identification program  106  can use the component which is easier to process (e.g., shoes in orange color) to identify the user instead of comparing “black hair and red t-shirt,” which involves multiple components or more complicated pattern matching. 
     Processing proceeds at operation  975 , where user identification program  106  determines an unknown user behavior based on the set of input images. Examples of user behavior include: (a) interaction with objects, and (b) time at specific locations. Continuing our exemplary embodiment, user identification program  106  tracks the user behavior of User A throughout the store in order to identify his user profile in database  108 . User A spends twenty minutes in the electronics department looking for flat-screen television sets. 
     Processing continues at operation  980 , where user identification program  106  creates a temporary profile for the user based on the identity pattern and the user behavior. Continuing our exemplary embodiment, user identification program  106  user identification program  106  creates a temporary profile including the images and user behavior for User A. 
     If the user is exiting the premises (operation  960 , “exit” branch”), processing proceeds at operation  985 , where user identification program  106  stores the temporary user profile in database  108  and deletes the local temporary profile. In our exemplary embodiment, User A exits the store and user identification program  106  stores the temporary user profile for User A in database  108  and deletes the local temporary file. 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 10 . Referring to flowchart  1000 , user identification program  106  matches temporary user profiles for users in a specific location (e.g., a store or a restaurant) with a stored user profile. 
     Processing begins at operation  1055 , where user identification program  106  receives a set of input images. In some embodiments of the present invention, user identification program  106  captures a set of input images for a user from site sensors  116 . In an exemplary embodiment, User A enters a department store and user identification program  106  receives a set of input images (shown in  FIG. 12 ) for Ben. 
     Processing continues at operation  1060 , where user identification program  106  determines an identity pattern for a user based on the set of input images. Examples of identity patterns include: (a) body shape; (b) clothing color; (c) clothing patterns; (d) physical appearance; (e) gait; and (f) accessories (e.g., necklace, watch, and similar accessories), among other identity patterns. In our exemplary embodiment, User A enters the department store and user identification program  106  identifies a pinstriped shirt (see  FIG. 12 , profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210 ). 
     Processing proceeds at operation  1065 , where user identification program  106  determines a user behavior based on the set of input images. Examples of user behavior include: (a) interaction with objects, and (b) time at specific locations. Continuing our exemplary embodiment, user identification program  106  tracks the user behavior of User A throughout the store in order to identify his user profile in database  108 . User A spends twenty minutes in the electronics department looking for flat-screen television sets. 
     Processing continues at operation  1070 , where user identification program  106  matches the set of input images and the user behavior to a user profile. In our exemplary embodiment, user identification program  106  matches each profile image  1202 ,  1204 ,  1206 ,  1208 , and  1210  in the set of input images with the profile images  1212 ,  1214 , and  1216  associated with a user profile for Ben. Profile images  1212 ,  1214 , and  1216  associated with the user profile may have been chosen in accordance to one or more embodiments of the present invention based on the quality score and/or the attributes of the image. The results of the image comparisons are as follows: (i) profile image  1212  matches the profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210  with confidence of 0, 0.6, 0.4, 0.2, and 0.7, respectively; (ii) profile image  1214  matches the profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210  with confidence of 0, 0.2, 0.5, 0.3, and 0.6, respectively; and (iii) profile image  1212  matches the profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210  with confidence of 0, 0.3, 0.4, 0.2, and 0.6, respectively. Based on these results, user identification program  106  identifies Ben as User A. Ben also matches User A&#39;s behavior with high confidence (0.8) based on a user profile with an average fifteen minutes spent in the electronics department per visit. Thus, user identification program  106  identifies matches the temporary user profile of User A to the user profile of Ben. 
     If a match is not found (operation  1075 , “no” branch), execution continues at operation  1085 , where user identification program  106  may generate a temporary user profile in accordance with the embodiment described in  FIG. 9 . If a match is found (operation  1075 , “yes” branch), processing proceeds at operation  1080 , where user identification program  106  stores the set of input images and the user behavior under the user profile. In our exemplary embodiment, the set of input images and the user behavior for User A is stored in the user profile for User A in database  108 . 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 11 . Referring to flowchart  1100 , user identification program  106  matches a temporary user profile for users in a specific location (e.g., a store or a restaurant) with a stored user profile. 
     Processing begins at operation  1155 , where user identification program  106  receives a set of input images. In some embodiments of the present invention, user identification program  106  captures a set of input images for a user from site sensors  116 . In an exemplary embodiment, User A enters a department store and user identification program  106  receives a set of input images (shown in  FIG. 12 ) for User A. 
     Processing continues at operation  1160 , where user identification program  106  determines a user behavior based on the set of input images. Examples of user behavior include: (a) interaction with objects, and (b) time at specific locations. Continuing our exemplary embodiment, user identification program  106  tracks the user behavior of User A throughout the store in order to identify his user profile in database  108 . User A spends twenty minutes in the electronics department looking for flat-screen television sets. 
     Processing continues at operation  1165 , where user identification program  106  selects a new user profile for matching. In some embodiments of the present invention, selection of the user profile for matching may be based on one or more embodiments described in this disclosure (e.g.,  FIG. 2-5 ). 
     Processing continues at operation  1170 , where user identification program  106  compares the set of input images and the user behavior to the user profile. In our exemplary embodiment, user identification program  106  matches each profile image  1202 ,  1204 ,  1206 ,  1208 , and  1210  in the set of input images with the profile images  1212 ,  1214 , and  1216  stored in a user profile for User A. Profile images  1212 ,  1214 , and  1216  stored in the user profile may have been chosen in accordance to one or more embodiments of the present invention based on the quality score and/or the attributes of the image. The results of the image comparisons are as follows: (i) profile image  1212  matches the profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210  with confidence of 0, 0.6, 0.4, 0.2, and 0.7, respectively; (ii) profile image  1214  matches the profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210  with confidence of 0, 0.2, 0.5, 0.3, and 0.6, respectively; and (iii) profile image  1212  matches the profile images  1202 ,  1204 ,  1206 ,  1208 , and  1210  with confidence of 0, 0.3, 0.4, 0.2, and 0.6, respectively. User A also matches the user behavior with high confidence (0.8) based on a user profile with an average fifteen minutes spent in the electronics department per visit. 
     Processing continues at operation  1175 , where user identification combines multiple confident scores from all profile images and user behavior. In some embodiments of the present invention, the confidence for each profile photo can be combined by any suitable function (e.g., maximum confidence for each image weighted average of the confidence for each image, weighted average of highest score matching for each profile image, or dynamic weight that is learned over time). In our exemplary embodiment, user identification program  106  combines the confidence scores for the images using a max function. In this case, the maximum confidence is 0.7. In the exemplary embodiment, user identification program  106  combines the confidence score for the user behavior (0.8) and the confidence scores for the image (0.7) by a weighted average function. In this case, the combined confidence score is 0.7. 
     In some embodiments of the present invention, combining the confidence scores for each profile photo and behavior can improve over time and a profile photo can be added or removed based on its effectiveness of providing accurate matching based on machine learning. For example, a 90% match with frontal facial image typically means a very good match of captured image and profile photo for User A. If images with 45 degrees of the frontal face are also captured for User A, these images can be compared with all the profile images belonging to the user and profile images belonging to different users. If these captured images match the profile image of User A with 95% confidence while the highest confidence for profile images for other users is 60%, then these captured images will be assigned to User A with a very high weight. In another example, if the captured image for the user has a very low confidence score when compared with profile images for other users, then this captured image can be added as a profile image for the user because no one else matches it. In some embodiments, profile images that are added to a user profile will have a high weight only if the confidence score is high with respect to the matching user. In some embodiments, if the confidence score of matching is low (e.g., less than 60% confident), the profile image will not affect the combined confidence score. In another example, a profile image can be compared with profile images of other users. In some embodiments, the weight for a profile image may be decreased, removed, or remove when weight is below a threshold, if the profile image provides relatively high confidence score of matching other users (e.g., 70% or more). 
     Processing continues at operation  1180 , where user identification determines the combined confidence score is higher than or equal to a threshold. In some embodiments of the present invention, the threshold may be a predetermined threshold or an adjustable threshold based on machine learning patterns. If the combined threshold is higher than or equal to the threshold (operation  1180 , “yes” branch), processing continues at operation  1185 , where user identification program  106  receives the matched user profile. In some embodiments, user identification program  106  receives a user profile ID for the matched user profile. In our exemplary embodiment, the predetermined threshold is 0.7 and the combined confidence score is equal to the predetermined threshold (0.7). Therefore, user identification program  106  receives the user profile for Ben. 
     Processing proceeds at operation  1190 , where user identification program  106  stores the set of input images and the user behavior under the matched user profile. In our exemplary embodiment, the set of input images and the user behavior for User A is stored in the user profile for Ben in database  108 . 
     If the combined threshold is below the threshold (operation  1180 , “no” branch), processing continues at operation  1165 , where user identification program  106  selects a new user profile for matching. 
     Some embodiments of the present invention provide a passively managed loyalty program based on user attributes and behaviors. Some embodiments of the present invention may also provide additional functionality to traditional loyalty programs and/or actively managed loyalty programs. These aspects of the disclosure are discussed subsequently with reference to  FIGS. 13-24 . 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 13 . Referring to flowchart  1300 , user identification program  106  provides a passively managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on a database of user profiles (e.g., social network profiles, profiles within the brick and mortar retail store database, profiles from the online retailer database, profiles from a distributed and/or shared ledger, among others). 
     Processing begins at operation  1355 , where user identification program  106  receives user attributes and user behaviors. In some embodiments of the present invention, user identification program  106  captures a set of input images for a user from site sensors  116  and determines user attributes and user behaviors from the set of input images. In some embodiments of the present invention, receiving the user attributes (e.g., identity patterns) and the user behaviors for a user may be based on one or more embodiments described in this disclosure. 
     Processing continues at operation  1360 , where user identification program  106  matches a user profile based on the user attributes and the user behaviors. In some embodiments of the present invention, user identification program  106  queries one or more user profiles successively to find a matching user. In some embodiments, querying for a user profile may be based on one or more embodiments described in this disclosure. 
     Processing proceeds at operation  1365 , where user identification program  106  returns a user profile ID. In some embodiments of the present invention, the user profile ID is a numeric or alphanumeric unique identifier (UID) guaranteed to be unique for the matched user profile within a specific client (e.g., a database for a specific retail store). In other embodiments, the user profile ID is a numeric or alphanumeric universally unique identifier (UUID) guaranteed to be unique for the matched user profile for all stored user profiles. 
     Processing continues at operation  1370 , where user identification program  106  stores the user attributes and user behaviors in the user profile. In some embodiments of the present invention, user identification program  106  uses the user profile ID to access and store the user attributes and user behaviors in the matched user profile. 
       FIG. 14  shows an embodiment of a passively managed loyalty program and a social network managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on social network profiles. Referring to diagram  1400 , processing begins at operation  1455 , where retail store  1402  provides customer characteristics (i.e., user attributes, images of the customer, etc.) and customer behaviors (i.e., user behaviors) for processing by retailer backend office  1404 . In some embodiments of the present invention, one or more site sensors  116  at retail store  1402  capture a set of input images and determine user attributes and user behaviors for one or more users. In some embodiments of the present invention, receiving the user attributes (e.g., identity patterns) and the user behaviors for a user may be based on one or more embodiments described in this disclosure. 
     Processing continues at operation  1460 , where retailer backend office  1404  authenticates to social network  1406  using a client ID. In some embodiments of the present invention, a second retailer backend office authenticates to social network  1406  using a second client ID. In some embodiments, the retailer backend office  1404  authenticates to social network  1406  with a client ID via an application programming interface (API). In some embodiments, social network  1406  includes virtual multi-tenant profiles corresponding to different clients (e.g., retail store  1402 ) each virtual multi-tenant profile providing a separate client configuration and data access configuration. These virtual multi-tenant profiles may map to physical storage located on premise, in the cloud, or as part of social network storage (e.g., social network database  1408 ). Data stored in this way can be provided with different access control mechanisms, such as crowd-based anonymization, and other mechanisms. 
     In an exemplary embodiment shown in  FIG. 18 , diagram  1800 , social network database  1408  may include virtual multi-tenant profiles  1802 ,  1804 , and  1806  grouped and isolated from each other in  1808  and  1810 . Virtual multi-tenant profiles  1802 ,  1804 , and  1806  may include user  1812  named Patrick and user  1814  named Mark. In this exemplary embodiment, user  1812  represents a social network profile while user  1814  represents a shadow (i.e., temporary) profile. 
     Processing proceeds at operation  1465 , where retailer backend office  1404  provides the customer characteristics (i.e., user attributes) and customer behaviors (i.e., user behaviors) for processing by social network  1406 . In some embodiments of the present invention, the retailer backend office  1404  sends the customer characteristics and customer behaviors to social network  1406  with a client ID via an application programming interface (API). In some embodiments, retailer backend office  1404  sends the customer characteristics and customer behaviors to social network  1406  to determine whether the there is a social network profile associated to the user. 
     Processing continues at operation  1470 , where social network  1406  queries social network database  1408  to match a user based on the customer characteristics (i.e., user attributes) and the customer behaviors (i.e., user behaviors). In some embodiments of the present invention, social network  1406  performs operation  1470  at the request of the retailer backend office  1404  by providing its client ID via the application programming interface (API). In some embodiments, social network database  1408  resides on the same computer system as social network  1406 . In some embodiments, social network database  1408  includes social network profiles for users. Social network profiles for users include images, videos, text, URL links, and user behaviors, among other types of data. Social network  1406  may query social network  1408  based on the customer characteristics and the customer behaviors in accordance with one or more embodiments of the present invention. 
     Processing proceeds at operation  1475 , where social network  1406  receives a user profile ID. In some embodiments of the present invention, the user profile ID is a numeric or alphanumeric identification used to identify the user profile in social network  1406  and social network database  1408 . 
     Processing continues at operation  1480 , where social network  1406  queries for a unique ID specific to the client ID based on the user profile ID. In some embodiments of the present invention, social network  1406  performs operation  1470  at the request of the retailer backend office  1404  by providing its client ID via the application programming interface (API). In some embodiments, the user profile ID is a numeric or alphanumeric universally unique identifier (UUID) guaranteed to be unique for the matched user profile for all stored user profiles. In these and other embodiments, the social network profile for a user may be accessed via an API call using the unique ID after authenticating to social network  1406  using a corresponding client ID. Continuing the exemplary embodiment (see  FIG. 18 ), user  1812  named Patrick has UUID A for virtual multi-tenant profile  1802 , UUID B for virtual multi-tenant profile  1804 , and UUID C for virtual multi-tenant profile  1806 . Similarly, user  1814  named Mark has UUID X for virtual multi-tenant profile  1802 , UUID Y for virtual multi-tenant profile  1804 , and UUID Z for virtual multi-tenant profile  1806 . 
     In some embodiments of the present invention, social network database  1408  and social network  1406  may be responsible to store and manages user data belong to the retailer store  1402 . In these and other embodiments, social network  1406  may as part of or on behalf of retailer store  1402 . In some embodiments, retailer store  1402  may be owned or controlled to a chain of stores corresponding to a parent company. In some embodiments, social network database  1408  may merge the user data collected from each store belonging to a chain of stores, or track the user data from each store independently and provide grouping for the chain of stores. For example, using customizable access control, both Retail Store X and Retail Store Y belonging to the same parent company may have access to the same data corresponding to User A. 
     In some embodiments of the present invention, the data belonging to a client in social network profile  1812  can be used by a different client for selected purposes. For example, Client  1  submits images and behavior corresponding to User A captured by site sensor  116  to social network  1406 . In this exemplary embodiment, social network  1406  can use the data and identity patterns from Client  2  and Client  3  to identify User A. However, Client  1  may not be allowed to retrieve specific customer behavior or purchases from Client  2  or Client  3 . 
     Processing proceeds at operation  1485 , where social network  1406  receives the unique ID and sends the unique ID to retailer backend office  1404 . In some embodiments of the present invention, retailer backend office  1404  and social network  1406  may access the social network profile for a user via an API call using the user profile ID or the unique ID. 
       FIG. 15  shows an alternative embodiment of the embodiment shown in  FIG. 14  for a passively managed loyalty program and a social network managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on social network profiles. Referring to diagram  1500 , upon completion of operation  1485  (see  FIG. 14 ), processing continues at operation  1555 , where retailer backend office  1404  receives the unique ID and stores the customer characteristics and the customer behaviors in retailer database  1502 . In some embodiments of the present invention, the customer characteristics and customer behaviors are stored locally for improved querying of users that have already been identified at retail store  1402 . In some embodiments, the customer characteristics and customer behaviors are protected to ensure they are not available to third parties for security reasons. In some embodiments, retailer database  1502  may be any database within any server computer for storing user profiles such as social network profiles, profiles within the brick and mortar retail store database, profiles from the online retailer database, profiles from a distributed or shared ledger, among others. In some embodiments, the customer characteristics and customer behaviors are stored in a shadow (i.e., temporary) profile database  1504  temporarily while retailer backend office  1404  has not yet received a unique ID for a user (see  FIG. 19 ). In these embodiments, the shadow profile corresponding to a user is merged with the user profile to retailer database  1502  when a unique ID is received. In some embodiments of the present invention, the user profile may be stored in single-tenant storage based on a single retail store  1402  or multi-tenant fashion based on one or more retail stores, where each retail store  1402  is treated as a different client (see  FIG. 18 ). 
       FIG. 16  shows yet another alternative embodiment of the embodiment shown in  FIG. 14  for a passively managed loyalty program and a social network managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on social network profiles and local customer profiles. Referring to diagram  1600 , upon completion of operation  1485  (see  FIG. 14 ), processing continues at operation  1655 , where retailer backend office  1404  creates a local customer profile at retailer database  1502  after receiving the unique ID for a user. In some embodiments of the present invention, the local customer profile is used for local identification of users in retail store  1402  (see  FIG. 17 ). 
     Processing proceeds at operation  1660 , where retailer backend office  1404  stores data associated to the unique ID for a user in retailer database  1502 . In some embodiments of the present invention, retailer backend office  1404  accesses social network  1406  via an API call and downloads the user profile associated to the unique ID. 
     Processing continues at operation  1665 , where retailer backend office  1404  performs a periodic sync up of the images and user attributes from social network database  1408 . In some embodiments of the present invention, retailer backend office  1404  periodically accesses social network  1406  via an API call and updates the user profile associated to the unique ID. 
       FIG. 17  shows still another embodiment of the embodiment shown in  FIG. 16  for a passively managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on social network profiles stored in a retailer database. Referring to diagram  1700 , processing begins at operation  1755 , where retail store  1402  provides customer characteristics (i.e., user attributes) and customer behaviors (i.e., user behaviors) for processing by retailer backend office  1404 . In some embodiments of the present invention, one or more site sensors  116  at retail store  1402  capture a set on input images and determine user attributes and user behaviors for one or more users. In some embodiments of the present invention, receiving the user attributes (e.g., identity patterns) and the user behaviors for a user may be based on one or more embodiments described in this disclosure. 
     Processing continues at operation  1760 , where retailer backend office  1404  queries a retailer database  1502  for a user based on customer characteristics and customer behaviors. Retailer backend office  1404  may query retailer database  1502  based on the customer characteristics and the customer behaviors in accordance with one or more embodiments of the present invention. 
     Processing proceeds at operation  1765 , where retailer backend office  1404  receives a user ID from retailer database  1502 . In some embodiments of the present invention, the user profile ID is a numeric or alphanumeric identification used to identify the user profile in retailer backend office  1404  and retailer database  1502 . 
     Processing continues at operation  1770 , where retailer backend office  1404  stores customer behaviors and customer characteristics on retailer database  1502 . In some embodiments of the present invention, the customer characteristics and customer behaviors are stored locally for improved querying of users that have already been identified at retail store  1402 . In some embodiments, the customer characteristics and customer behaviors are protected to ensure they are not available to third parties for security reasons. In some embodiments, the customer characteristics and customer behaviors are stored locally for improved querying of users that have already been identified at retail store  1402 . In some embodiments, retailer database  1502  may be any database within any server computer for storing user profiles such as social network profiles, profiles within the brick and mortar retail store database, profiles from the online retailer database, profiles from a distributed digital ledger, among others. In some embodiments, the customer characteristics and customer behaviors are stored in a shadow (i.e., temporary) profile database  1504  temporarily while retailer backend office  1404  has not yet received a unique ID for a user (see  FIG. 19 ). In these embodiments, the shadow profile corresponding to a user is merged with the user profile to retailer database  1502  when a unique ID is received (i.e., the shadow profile collected sufficient information and the identity of the user can be determined). In some embodiments of the present invention, the user profile may be stored in single-tenant storage based on a single retail store  1402  or multi-tenant fashion based on one or more retail stores, where each retail store  1402  is treated as a different client (see  FIG. 18 ). 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 19 . Referring to flowchart  1900 , user identification program  106  provides a passively managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on shadow (i.e., temporary) profiles while existing user profiles are identified in a social network. In other embodiments, the customer characteristics and customer behaviors are stored in a shadow (i.e., temporary) profile while the corresponding customer has not been identified or associated with a user profile. In these embodiments, the shadow profile corresponding to a user is merged with the user profile when the customer is identified. 
     Processing begins at operation  1360 , where user identification program  106  queries for a user profile based on the user attributes and the user behaviors. In some embodiments of the present invention, user identification program  106  queries one or more user profiles successively to find a matching user. In some embodiments of the present invention, querying for a user profile may be based on one or more embodiments described in this disclosure. 
     If a match is found (operation  1955 , “yes” branch), processing proceeds at operation  1365 , where user identification program  106  returns the user profile ID matching the user attributes and the user behavior. If a match is not found (operation  1955 , “no” branch), processing continues at operation  1960 , where user identification program  106  creates a shadow profile for the user. In some embodiments of the present invention, the shadow profile is a temporary profile for a user that does not have a stored user profile in database  108  or social media sources  118 . In other embodiments, the shadow profile is a temporary profile for a user that has not yet been matched with a stored user profile based on the user attributes and the user behaviors. 
     Processing proceeds at operation  1965 , where user identification program  106  receives user attributes and user behaviors. In some embodiments of the present invention, user identification program  106  continues receiving user attributes and user behaviors corresponding to the shadow profile. In some embodiments of the present invention, receiving the user attributes (e.g., identity patterns) and the user behaviors for a user may be based on one or more embodiments described in this disclosure. 
     Processing continues at operation  1970 , where user identification program  106  determines if the shadow profile matches an existing user profile. In some embodiments of the present invention, determining if the shadow profile matches an existing user profile may be performed in accordance to one or more embodiments of the present disclosure. If a match is not found (operation  1975 , “no” branch), processing proceeds at operation  1965 , where user identification program  106  further receives user attributes and user behaviors. If a match is found (operation  1975 , “yes” branch), processing proceeds at operation  1980 , where user identification program  106  merges the shadow profile with the existing user profile and returns the profile ID. In some embodiments of the present invention, the user attributes and user behaviors are stored in the existing user profile. In other embodiments, the user attributes and user behaviors in the shadow profile are compared to the user attributes and user behaviors in the existing user profile. In yet other embodiments, selection of the user attributes and user behaviors that will be stored in the existing user profile may be performed in accordance to one or more embodiments described in this disclosure. 
       FIG. 20  shows still another alternative embodiment of the embodiment shown in  FIG. 14  for a passively managed loyalty program for users in a specific location (e.g., a store or a restaurant) based on shadow profiles. Referring to diagram  2000 , upon completion of operation  1470  (see  FIG. 14 ), processing continues at operation  2055 , where social network  1406  creates a shadow profile at social network database  1408  if no user matches the customer characteristics and the customer behaviors. In some embodiments of the present invention, the customer characteristics and customer behaviors are stored in the shadow (i.e., temporary) profile while the corresponding customer has not been identified or associated with a user profile. In some embodiments, the shadow profile is created for users that have no known identity and/or users without a shadow profile. 
     Processing proceeds at operation  2060 , where social network  1406  receives the shadow profile ID from social network database  1408 . In some embodiments of the present invention, the shadow profile ID is a numeric or alphanumeric unique identifier (UID) guaranteed to be unique for the matched user profile within a specific client (e.g., a database for a specific retail store) or a numeric or alphanumeric universally unique identifier (UUID) guaranteed to be unique for the matched user profile. In some embodiments, the shadow profile ID corresponds to a new shadow profile created for a user that without a known identity and for which a shadow profile has not yet been created. In other embodiments, the shadow profile ID corresponds to an existing shadow profile for which there is no known user identity. 
     Processing continues at operation  2065 , where social network  1406  stores the customer characteristics and the customer behaviors under the shadow profile ID and the client ID. In some embodiments of the present invention, social network  1406  stores the customer characteristics and customer behaviors in the shadow profile for future identification of users that do not currently have an associated user profile in social network  1406 . In these embodiments, the user will continue to be identified based the customer characteristics and customer behaviors. 
     Processing proceeds at operation  2070 , where social network  1406  periodically evaluates all shadow profiles to find a matching social network profile. In some embodiments of the present invention, once the user has been associated with a user profile in social network  1406  (e.g., the user registers an account in the social network  1406  or signs up for the loyalty program at retail store  1402 ) all the customer characteristics and customer behaviors are copied to the user profile and the shadow profile is deleted. In other embodiments, the shadow profile and the user profile are compared for selection of customer characteristics and customer behaviors that help identify the user more efficiently. In some embodiments, selecting the customer characteristics (e.g., identity patterns) and the customer behaviors for a user may be based on the user profile or the shadow profile and/or one or more embodiments described in this disclosure. 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 21 . Referring to flowchart  2100 , user identification program  106  provides a passively managed loyalty program including storing transactions made at a point of sale (POS) system. 
     Processing begins at operation  2155 , where user identification program  106  receives customer identity for a user. In some embodiments of the present invention, the customer identity may include customer characteristics and customer behaviors. In other embodiments, receiving the customer identity for a user may be based on one or more embodiments described in this disclosure. 
     Processing continues at operation  2160 , where user identification program  106  receives a transaction data for the user from a point of sale (POS) system. In some embodiments of the present invention, any transactions performed at the POS are logged at a backend service for determining award points and/or advertisements for the user. In some embodiments, the transaction data may include the following data, among others: (a) list of items; (b) price of items; (c) coupons or offers applied, if any; (d) total price of sale; (e) method of payment; (f) time and date. 
     Processing proceeds at operation  2165 , where user identification program  106  associates the transaction data to a temporary user ID. In some embodiments of the present invention, a temporary user ID is assigned to the transaction data to log the transaction data for the user while the user is identified in asynchronous form. 
     Processing continues at operation  2170 , where user identification program  106  receives a social network user ID associated to the user. In some embodiments of the present invention, requests and asynchronously receives the social network user ID associated to the user. In these embodiments, the transaction data is associated to a temporary user ID while the social network user ID has not yet been received. 
     Processing proceeds at operation  2175 , where user identification program  106  replaces the temporary user ID with the social network user ID. In some embodiments of the present invention, the temporary user ID is replaced with the social network user ID when it becomes available. 
     Processing continues at operation  2180 , where user identification program  106  stores the transaction data for user ID. In some embodiments of the present invention, once the transaction data is associated to the social network user ID, user identification program  106  may store the transaction in database  108  as part of the customer characteristics and the customer behaviors used to identify the user and/or provide analytics for the user. 
       FIG. 22  shows an embodiment for a passively managed loyalty program and a social network managed loyalty program for users in a specific location (e.g., a store or a restaurant) that associates transactions made at a point of sale device with a social network profile. Referring to diagram  2200 , processing begins at operation  2255 , where retailer backend office  1404  receives a customer identity from a retail store camera  2202 . In some embodiments of the present invention, receiving the customer identity for a user may be based on one or more embodiments described in this disclosure. 
     Processing continues at operation  2260 , where retailer backend office  1404  sends the customer identity to social network  1406  for identification of the user. In some embodiments of the present invention, social network  1406  may require authentication of retailer backend office  1404  via an API call. In these and other embodiments, retailer backend office  1404  asynchronously requests the social network user ID from social network  1406  and continues to process the transaction data at POS  2204 . 
     Processing proceeds at operation  2265 , where retailer backend office  1404  receives POS data, including a transaction ID, from POS  2204 . In some embodiments of the present invention, the transaction data may include the following data, among others: (a) list of items; (b) price of items; (c) coupons or offers applied, if any; (d) total price of sale; (e) method of payment; (f) time and date. 
     Processing continues at operation  2270 , where retailer backend office  1404  associates the transaction ID to a temporary user ID. In some embodiments of the present invention, a temporary user ID is assigned to the transaction data to log the transaction data for the user while the user is identified in asynchronous form. 
     Processing proceeds at operation  2275 , where retailer backend office  1404  receives a social network user ID for the user from social network  1406 . In some embodiments of the present invention, requests and asynchronously receives the social network user ID associated to the user. In these embodiments, the transaction data is associated to a temporary user ID while the social network user ID has not yet been received. 
     Processing continues at operation  2280 , where retailer backend office  1404  replaces the temporary user ID with the social network user ID for the transaction ID. In some embodiments of the present invention, the temporary user ID is replaced with the social network user ID when it becomes available. In these embodiments, replacing the temporary user ID with the social network user ID allows identification of the transactions made by the user. 
     Processing proceeds at operation  2285 , where retailer backend office  1404  stores a transaction ID for the user ID in retailer database  1502 . In some embodiments of the present invention, once the transaction data is associated to the social network user ID, user identification program  106  may store the transaction in retailer database  1502  as part of the customer characteristics and the customer behaviors used to identify the user and/or provide analytics for the user. 
     User identification program  106  is depicted and described in further detail with respect to  FIG. 23 . Referring to flowchart  2300 , user identification program  106  provides a passively managed loyalty program including analytics of retail data and social network data for users. 
     Processing begins at operation  2355 , where user identification program  106  receives a request for analytics on retail data from a retail store and social network data from a social network for a user. In some embodiments of the present invention, user identification program  106  performs one or more analytics of the customer characteristics and customer behaviors associated to the users to determine one or more advertisement strategies. 
     Processing continues at operation  2360 , where user identification program  106  receives retail data and social network data. In some embodiments of the present invention, user identification program  106  receives data stored at database  108  related to retail data (e.g., customer characteristics and customer behaviors at one or more retail stores) and social network data (e.g., followed brands, photographed items, items placed in shopping carts, among others). 
     Processing proceeds at operation  2365 , where user identification program  106  performs analytics on retail data and social network data to determine an advertisement strategy for a user. In some embodiments of the present invention, analytics may be performed based on: (i) time spent at specific departments; (ii) time spent looking at specific items; (iii) items bought; (iv) items returned; (v) items placed in shopping cart; (vi) items liked or searched for in a social network; (vii) items saved or photographed in a social network; (viii) specific brands followed in social networks; among other factors. 
     Processing continues at operation  2370 , where user identification program  106  sends the advertisement strategy to the retail store and the social network. In some embodiments of the present invention, the advertisement strategy is sent to one or more retail stores, one or more mobile devices for the user, among other devices. In some embodiments, the advertisement strategy may include text, picture, videos, coupons, buy more and save campaigns, subscription for future shipments, installation of retailer mobile apps, additional offerings such as delivery to home, and/or other marketing and advertisement mechanism. In some embodiments, the advertisement strategy may be a real-time strategy (e.g., by text messaging or instant notification on mobile device) or it may be a delayed strategy (e.g., by showing ads on a search engine at a future time). 
       FIG. 24  shows an embodiment of a passively managed loyalty program for users in a specific location (e.g., a store or a restaurant) that provides advertisement strategies based on social network profiles. Processing begins at operation  2455 , where retailer backend office  1404  sends a request for analytics of retail data to social network  1406 . In some embodiments of the present invention, user identification program  106  performs one or more analytics of the customer characteristics and customer behaviors associated to the users to determine one or more advertisement strategies. 
     Processing proceeds at operation  2465 , where social network  1406  receives retail data and social network data from social network database  1408 . In some embodiments of the present invention, social network  1406  may also receive retail data from retailer database  1502 . In some embodiments, retail data may include, for example, customer characteristics and customer behaviors at one or more retail stores, and social network data may include, for example, followed brands, photographed items, items placed in shopping carts, among others. 
     Processing continues at operation  2470 , where social network  1406  performs analytics to develop an advertising strategy based on the retail data and the social network data. In some embodiments of the present invention, analytics may be performed based on: (i) time spent at specific departments; (ii) time spent looking at specific items; (iii) items bought; (iv) items returned; (v) items placed in shopping cart; (vi) items liked or searched for in a social network; (vii) items saved or photographed in a social network; (viii) specific brands followed in social networks; among other factors. 
     Processing proceeds at operation  2475 , where social network  1406  sends the analytics results to retailer backend office  1404 . In some embodiments of the present invention, the analytics results are directed to determine an advertising strategy based on the retail data and the social network data. 
     Processing continues at operation  2480 , where retailer backend office  1404  determines an advertising strategy. In some embodiments of the present invention, the advertising strategy comprises items and/or offers that the user may be interested in based on past behavior. 
     Processing continues at operation  2485 , where retailer backend office  1404  requests advertising to the user on social network  1406 . 
     Processing continues at operation  2490 , where retailer backend office  1404  requests advertising at retail store  1402 . In some embodiments of the present invention, retailer backend office  1404  may also request advertising at mobile device  110  (see  FIG. 1 ). In some embodiments, advertising at retail store  1402  may include advertisements or offers in a receipt or similar manner. 
       FIG. 25  depicts block diagram  2500  of components of server computer  104  within distributed data processing environment  100  of  FIG. 1 , in accordance with an embodiment of the present invention. It should be appreciated that  FIG. 25  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     Computing device  2505  and server computer  104  include communications fabric  2502 , which provides communications between computer processor(s)  2504 , memory  2506 , persistent storage  2508 , communications unit  2510 , and input/output (I/O) interface(s)  2512 . 
     Communications fabric  2502  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  2502  can be implemented with one or more buses. 
     Memory  2506  and persistent storage  2508  are computer-readable storage media. In this embodiment, memory  2506  includes random access memory (RAM)  2514  and cache memory  2516 . In general, memory  2506  can include any suitable volatile or non-volatile computer-readable storage media. 
     User identification program  106  is stored in persistent storage  2508  for execution by one or more of the respective computer processors  2504  via one or more memories of memory  2506 . In this embodiment, persistent storage  2508  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  2508  can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer-readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  2508  may also be removable. For example, a removable hard drive may be used for persistent storage  2508 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer-readable storage medium that is also part of persistent storage  2508 . 
     Communications unit  2510 , in these examples, provides for communications with other data processing systems or devices, including resources of distributed data processing environment  100 . In these examples, communications unit  2510  includes one or more network interface cards. Communications unit  2510  may provide communications through the use of either or both physical and wireless communications links. User identification program  106  may be downloaded to persistent storage  2508  through communications unit  2510 . 
     I/O interface(s)  2512  allows for input and output of data with other devices that may be accessible to computing device  2505  and server computer  104 , such as mobile device  110 , site sensors  116 , social media sources  118 , and other computing devices (not shown). For example, I/O interface  2512  may provide a connection to external devices  2518  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  2518  can also include portable computer-readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., user identification program  106  can be stored on such portable computer-readable storage media and can be loaded onto persistent storage  2508  via I/O interface(s)  2512 . I/O interface(s)  2512  also connect to a display  2520 . 
     Display  2520  provides a mechanism to display data to a user and may be, for example, a computer monitor. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be any tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general-purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.