Patent Publication Number: US-11657443-B2

Title: Data mesh based environmental augmentation

Description:
RELATED APPLICATIONS 
     This application is a continuation of and claims the benefit of priority of U.S. application Ser. No. 16/391,852, filed Apr. 23, 2019, which is a continuation of and claims the benefit of priority of U.S. application Ser. No. 14/449,126, filed Jul. 31, 2014, which claims the priority benefit of U.S. Provisional Application No. 61/970,263, filed Mar. 25, 2014, each of which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to mobile device technology and, more particularly, but not by way of limitation, to data mesh based environmental augmentation. 
     BACKGROUND 
     In recent years mobile devices, wearable devices, smart devices, and the like have pervaded nearly every aspect of modern life. Such devices are increasingly incorporating sensors to monitor everything from the moisture level of houseplants to the dribbling of a basketball. Network connected devices like these are capable of providing a near real-time and constant data feed. These trends have provided a vast amount of rich, constantly updated data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope. 
         FIG.  1    is a block diagram illustrating a networked system, according to some example embodiments. 
         FIG.  2 A  is a block diagram illustrating an example embodiment of a data mesh system, according to some example embodiments. 
         FIG.  2 B  is a block diagram illustrating an example embodiment of an augmentation system, according to some example embodiments. 
         FIG.  3    is a flow diagram illustrating an example method for authenticating a user and augmenting a user activity, according to some example embodiments. 
         FIGS.  4  and  5    are flow diagrams illustrating further example operations of the method of  FIG.  3   , according to some example embodiments. 
         FIG.  6    depicts communication between a device of the user and the data mesh system, according to some example embodiments. 
         FIGS.  7 - 10    are flow diagrams illustrating further example operations of the method of  FIG.  3   , according to some example embodiments. 
         FIG.  11    illustrates augmenting an example user activity, according to some example embodiments. 
         FIG.  12    depicts an example user interface to facilitate augmentation of a user activity, according to some example embodiments. 
         FIG.  13    is a flow diagram illustrating various communications to facilitate the method of  FIG.  3   , according to some example embodiments. 
         FIGS.  14 A and  14 B  depict example configurations for communicatively coupling attribute sources, according to some example embodiments. 
         FIG.  15    depicts various example attribute sources, according to some example embodiments. 
         FIG.  16    depicts various components that provide attribute data, according to some example embodiments. 
         FIG.  17    is a block diagram of an example data structure for example attribute data associated with a user, according to some example embodiments. 
         FIG.  18    is a block diagram of an example data structure for example attribute data associated with a device, according to some example embodiments. 
         FIG.  19    is a block diagram illustrating an example of a software architecture that may be installed on a machine, according to some example embodiments. 
         FIG.  20    illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. 
     
    
    
     The headings provided herein are merely for convenience and do not necessarily affect the scope or meaning of the terms used. 
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those skilled in the art, that embodiments of the inventive subject matter may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail. 
     In various example embodiments, attribute data, including real-time data, may be received from a plurality of attribute sources, a user may be authenticated based on an analysis of the attribute data, a user activity may be identified based on the attribute data, and the user activity may be augmented according to a user setting. The attribute data may include data received from a broad gamut of attribute sources such as, for example, from mobile devices, smart devices, smart homes, social network services, user profiles, browsing histories, purchase histories, and so forth. 
     Subsequent to receiving the attribute data, a portion of the real-time data indicative of an identity of the user may be identified. For example, the real-time data may include a location of a mobile device of the user, sensor data from a device of the user, and so on. In an example embodiment, the identity of the user may be authenticated based on an analysis of the identified portion of the real-time data. For instance, the location of the user mobile device may be indicative of the location of the user, and if the location of the user device is within a distance of an Internet device currently being used, it may be inferred that the user may be currently using the Internet device. Many other indications of the user&#39;s identity may be employed to authenticate the identity of the user. In further example embodiments, an identity likelihood metric may be calculated based on an analysis of the attribute data. In some example embodiments, the identity of the user may be authenticated when the identity likelihood metric exceeds a threshold. 
     In still further example embodiments, based on authenticating the identity of the user, a user activity being performed by the user may be identified based on the real-time data. For example, the user may be using a website, jogging, entering a room (e.g., walking from a living room to a kitchen), and so on. The user activity may be augmented according to the user setting. In a specific example, the user may be using a website that implements a login, and based on the authentication of the user identity, the security for the website login may be reduced or the user is automatically logged in. In another specific example, the user may be streaming media in the living room to an Internet-connected device (e.g., a media entertainment system presenting to a display), and the streaming may be continued in the kitchen (e.g., to a smart refrigerator that includes a display) when the user moves from the living room to the kitchen. Many other user activities may be augmented in a variety of ways. 
     With reference to  FIG.  1   , an example embodiment of a high-level client-server-based network architecture  100  is shown. A networked system  102  provides server-side functionality via a network  104  (e.g., the Internet or wide area network (WAN)) to a client device  110 . A user (e.g., user  106 ) may interact with the networked system  102  using the client device  110 .  FIG.  1    illustrates, for example, a web client  112  (e.g., a browser, such as the Internet Explorer® browser developed by Microsoft® Corporation of Redmond, Wash. State), client application(s)  114 , and a programmatic client  116  executing on the client device  110 . The client device  110  may include the web client  112 , the client application(s)  114 , and the programmatic client  116  alone, together, or in any suitable combination. Although  FIG.  1    shows one client device  110 , multiple client devices may be included in the network architecture  100 . 
     The client device  110  may comprise a computing device that includes at least a display and communication capabilities that provide access to the networked system  102  via the network  104 . The client device  110  may comprise, but is not limited to, a remote device, work station, computer, general purpose computer, Internet appliance, hand-held device, wireless device, portable device, wearable computer, cellular or mobile phone, personal digital assistant (PDA), smart phone, tablet, ultrabook, netbook, laptop, desktop, multi-processor system, microprocessor-based or programmable consumer electronic, game consoles, set-top box, network PC, mini-computer, and the like. In further example embodiments, the client device  110  may comprise one or more of a touch screen, accelerometer, gyroscope, biometric sensor, camera, microphone, global positioning system (GPS) device, and the like. 
     The client device  110  may communicate with the network  104  via a wired or wireless connection. For example, one or more portions of the network  104  may be an ad hoc network, an intranet, an extranet, a Virtual Private Network (VPN), a Local Area Network (LAN), a wireless LAN (WLAN), a Wide Area Network (WAN), a wireless WAN (WWAN), a Metropolitan Area Network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a Wireless Fidelity (Wi-Fi®) network, a Worldwide Interoperability for Microwave Access (WiMax) network, another type of network, or a combination of two or more such networks. 
     The client device  110  may include one or more of the applications (also referred to as “apps”) such as, but not limited to, web browsers, book reader apps (operable to read e-books), media apps (operable to present various media forms including audio and video), fitness apps, biometric monitoring apps, messaging apps, electronic mail (email) apps, e-commerce site apps (also referred to as “marketplace apps”), and so on. The client application(s)  114  may include various components operable to present information to the user and communicate with networked system  102 . In some embodiments, if the e-commerce site application is included in the client device  110 , then this application may be configured to locally provide the user interface and at least some of the functionalities with the application configured to communicate with the networked system  102 , on an as needed basis, for data or processing capabilities not locally available (e.g., access to a database of items available for sale, to authenticate a user, to verify a method of payment). Conversely, if the e-commerce site application is not included in the client device  110 , the client device  110  may use its web browser to access the e-commerce site (or a variant thereof) hosted on the networked system  102 . 
     In various example embodiments, the users (e.g., the user  106 ) may be a person, a machine, or other means of interacting with the client device  110 . In some example embodiments, the users may not be part of the network architecture  100 , but may interact with the network architecture  100  via the client device  110  or another means. For instance, the users may interact with client device  110  that may be operable to receive input information from (e.g., using touch screen input or alphanumeric input) and present information to (e.g., using graphical presentation on a device display) the users. In this instance, the users may, for example, provide input information to the client device  110  that may be communicated to the networked system  102  via the network  104 . The networked system  102  may, in response to the received input information, communicate information to the client device  110  via the network  104  to be presented to the users. In this way, the user may interact with the networked system  102  using the client device  110 . 
     An Application Program Interface (API) server  120  and a web server  122  may be coupled to, and provide programmatic and web interfaces respectively to, one or more application server(s)  140 . The application server(s)  140  may host one or more publication system(s)  142 , payment system(s)  144 , and a data mesh system  150 , each of which may comprise one or more modules or applications and each of which may be embodied as hardware, software, firmware, or any combination thereof. The application server(s)  140  are, in turn, shown to be coupled to one or more database server(s)  124  that facilitate access to one or more information storage repositories or database(s)  126 . In an example embodiment, the database(s)  126  are storage devices that store information to be posted (e.g., publications or listings) to the publication system(s)  142 . The database(s)  126  may also store digital goods information in accordance with some example embodiments. 
     Additionally, a third party application  132 , executing on a third party server  130 , is shown as having programmatic access to the networked system  102  via the programmatic interface provided by the API server  120 . For example, the third party application  132 , utilizing information retrieved from the networked system  102 , may support one or more features or functions on a website hosted by the third party. The third party website may, for example, provide one or more promotional, marketplace, or payment functions that are supported by the relevant applications of the networked system  102 . 
     The publication system(s)  142  may provide a number of publication functions and services to the users that access the networked system  102 . The payment system(s)  144  may likewise provide a number of functions to perform or facilitate payments and transactions. While the publication system(s)  142  and payment system(s)  144  are shown in  FIG.  1    to both form part of the networked system  102 , it will be appreciated that, in alternative embodiments, each system  142  and  144  may form part of a payment service that is separate and distinct from the networked system  102 . In some example embodiments, the payment system(s)  144  may form part of the publication system(s)  142 . 
     The data mesh system  150  may provide functionality to receive, retrieve, or store a broad spectrum of data associated with the user. It will be noted that the collective, aggregated attribute data may be referred to as a “data mesh.” The data mesh system  150  may store, for example, received data in storage devices such as the database(s)  126 . In an example embodiment, the data mesh system  150  may include an augmentation system  152  that authenticates the user based on the attribute data, identifies a user activity being performed by the user, and augments the user activity according to a user preference. In some example embodiments, the data mesh system  150  may communicate with the client device  110 , the third party server(s)  130 , the publication system(s)  142  (e.g., retrieving listings), and the payment system(s)  144  (e.g., purchasing a listing). In an alternative example embodiment, the data mesh system  150  may be a part of the publication system(s)  142 . 
     Further, while the client-server-based network architecture  100  shown in  FIG.  1    employs a client-server architecture, the present inventive subject matter is, of course, not limited to such an architecture, and may equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various systems of the applications server(s)  140  (e.g., the publication system(s)  142  and the payment system(s)  144 ) may also be implemented as standalone software programs, which do not necessarily have networking capabilities. 
     The web client  112  may access the various systems of the networked system  102  (e.g., the publication system(s)  142 ) via the web interface supported by the web server  122 . Similarly, the programmatic client  116  and client application(s)  114  may access the various services and functions provided by the networked system  102  via the programmatic interface provided by the API server  120 . The programmatic client  116  may, for example, be a seller application (e.g., the Turbo Lister application developed by eBay® Inc., of San Jose, Calif.) to enable sellers to author and manage listings on the networked system  102  in an off-line manner, and to perform batch-mode communications between the programmatic client  116  and the networked system  102 . 
       FIG.  2 A  is a block diagram of the data mesh system  150 , which may provide functionality to receive, retrieve, or access attribute data from attribute sources, analyze the attribute data, manage the attribute data, and so forth. In an example embodiment, the data mesh system  150  may include a presentation module  210 , a communication module  215 , an attribute module  220 , a characteristic module  225 , a management module  230 , and the augmentation system  152 .  FIG.  2 B  is a block diagram of the augmentation system  152 , which may provide functionality to authenticate the identity of the user, identify the user activity, and augment the user activity. The augmentation system  152  may include an authentication module  250 , an activity module  255 , and a settings module  260 . All, or some, of the modules  210 - 260  of  FIGS.  2 A and  2 B , may communicate with each other, for example, via a network coupling, shared memory, and the like. It will be appreciated that each module of modules  210 - 260  may be implemented as a single module, combined into other modules, or further subdivided into multiple modules. It will further be appreciated that the modules or functionality of the augmentation system  152  may be implemented in the data mesh system  150  and the modules or functionality of the data mesh system  150  may be implemented in the augmentation system  152 . Other modules not pertinent to example embodiments may also be included, but are not shown. 
     Referring to  FIG.  2 A , the presentation module  210  may provide various presentation and user interface functionality operable to interactively present and receive information from users. For example, the presentation module  210  may cause presentation of various user interfaces to the user to facilitate augmenting the user activity. The presentation module  210  may present or cause presentation of information using a variety of means including visually displaying information and using other device outputs (e.g., acoustic, haptic). Interactively presenting is intended to include the exchange of information between a device and a user. The user may provide input to interact with the user interface in a variety of ways including alphanumeric input, cursor input, tactile input, or other input (e.g., one or more touch screen, camera, tactile sensors, light sensors, infrared sensors, biometric sensors, microphone, gyroscope, accelerometer, or other sensors). It will be appreciated that the presentation module  210  may provide many other user interfaces to facilitate functionality described herein. Further, it will be appreciated that “presenting” as used herein is intended to include communicating information to another device with functionality operable to perform presentation using the communicated information. 
     The communication module  215  may provide various communications functionality and web services. For example, network communication such as communicating with the networked system  102 , the client device  110 , and the third party server(s)  130  may be provided. In various example embodiments, the network communication may operate over wired or wireless modalities. Web services are intended to include retrieving information from the third party server(s)  130 , the database(s)  126 , and the application server(s)  140 . Information retrieved by the communication module  215  may comprise data associated with the user (e.g., user profile information from an online account, social network service data associated with the user), data associated with one or more items listed on an e-commerce website (e.g., images of the item, reviews of the item, item price), or other data to facilitate the functionality described herein. 
     The attribute module  220  may receive, access, or retrieve a wide variety of attribute data from many different attribute sources. For example, the attribute module  220  may receive, retrieve, or access the attribute data from user devices or machines (e.g., the client device  110 ), social network services, the third party server(s)  130 , the publication system(s)  142 , the payment system(s)  144 , other applications servers, or other attribute sources. The attribute data, as used herein, is intended to include raw data such as sensor data, profile data, social network content, and so on. 
     In some example embodiments, the attribute module  220  may extract the attribute data from various sources. For instance, a payment history log of the user may include a tremendous amount of extraneous data. The attribute module  220  may extract purchase information such as item purchased, time, purchase price, seller, location, brand, and so forth from the payment history log of the user. 
     In further example embodiments, the attribute module  220  may perform various functions to prepare or condition the attribute data for analysis. For instance, the attribute module  220  may standardize the attribute data to facilitate analysis of the attribute data (e.g., determine a normal form for the data to allow for comparison and other mathematical analysis). The attribute module  220  may perform many other functions to prepare the attribute data for analysis. 
     In various example embodiments, the attribute module  220  may store the attribute data in association with the user for subsequent analysis. For example, the attribute module  220  may store the attribute data in the database(s)  126 . The attribute data may be stored in conjunction with a user identifier such that the attribute module  220  may subsequently use the user identifier to access the attribute data corresponding to a particular user. The attribute module  220  may access the stored attribute data using other schemes. For instance, the attribute module  220  may access a portion of the attribute data associated with a time, an item, a user, a type of user, a particular attribute source, and so forth. In this way, the attribute module  220  may access a portion of attribute data according to various parameters from among a large quantity of the attribute data to access, identify, or find pertinent and relevant data. 
     The characteristic module  225  may infer a user characteristic or multiple user characteristics corresponding to the user based on an analysis of at least a portion of the attribute data. Many schemes and techniques may be employed to infer the characteristic from the attribute data. For example, a particular user characteristic may be a work location of the user. The attribute data may include a plurality of locations (e.g., as determined by a GPS component of a user device used by the user) that include time stamps. The work location of the user may be inferred based on the consistency and timing of the locations included in the attribute data (e.g., during normal working hours, the user is typically at a particular office building). Many different portions of attribute data and combinations of portions of attribute data may be analyzed to infer a wide variety of characteristics. 
     In various example embodiments, characteristics (e.g., the user characteristics), as used herein, are intended to include traits, qualities, actions, activities, attitudes, habits, behaviors, and the like pertaining to a person or people. Inasmuch as the attribute data may not necessarily pertain to a person (e.g., raw data such as coordinates of a particular location), a characteristic (e.g., current location of the user, disliking spicy food, having young children, being a Star Trek fanatic) may be distinct from the attribute data. 
     The management module  230  may provide management functions associated with the attribute data. For example, the management module  230  may provide the user with functionality to edit, modify, update, or otherwise control the attribute data. For instance, the user may remove undesired attribute data via the functionality provided by the management module  230 . In a further instance, the user may specify permissions for portions of the attribute data using the functionality provided by the management module  230 . The permissions may allow or prohibit certain access or uses for the attribute data (e.g., the permission may prohibit access to the attribute data by third parties). The user may grant various levels of access and abilities. In some example embodiments, the permissions may persist for a period of time, and after expiration of the time period, the management module  230  may revoke the permissions. 
     In further example embodiments, the management module  230  may request consent from the user to access portions of the attribute data or to request permission for certain uses of the attribute data. For example, the management module  230  may request consent from the user to allow third parties to access portions of the attribute data. The management module  230  may request a variety of other consents associated with various actions corresponding to the attribute data. 
     In still further example embodiments, the management module  230  may provide functionality to allow third parties to access the attribute data or the user characteristics. For example, the management module  230  may provide a set of APIs that may be invoked by third parties to access the attribute data or the user characteristics. As discussed above, in some example embodiments, the management module  230  may determine permission or consent of the user prior to providing access to the attribute data. 
     Referring now to  FIG.  2 B , the authentication module  250  in the augmentation system  152  may provide functionality to facilitate authenticating a user&#39;s identity. For example, the authentication module  250  may identify a portion of the attribute data indicative of the identity of the user. Subsequently, the authentication module  250  may authenticate the identity of the user by analyzing the identified portion of the attribute data. In further example embodiments, the authentication module  250  may calculate an identity likelihood metric based on real-time data included in the attribute data. The identity likelihood metric may indicate the likelihood of authenticating the identity of the user (e.g., a higher identity likelihood metric may indicate a strong probability that the user&#39;s identity may be authenticated). The authentication module  250  may analyze a variety of portions of the attribute data using many different schemes and techniques to authenticate the identity of the user. 
     The activity module  255  may provide functionality associated with user activities. For example, the activity module  255  may identify a user activity being performed by the user based on the attribute data included in the real-time data. In a further example, the activity module  255  may facilitate augmentation of the identified user activity according to a user setting or user preference. For instance, the user setting may be associated with presenting information to the user (e.g., the user may have a desire to use a larger screen for presenting information if available). In this instance, the activity module  255  may augment presentation of information to the user based on the user setting. 
     The settings module  260  may provide functionality to access or determine one or more user settings. For example, the settings module  260  may determine the user setting based on the attribute data and the identified user activity. In some example embodiments, the settings module  260  may access the user setting from a storage device (e.g., the database(s)  126 ). In further example embodiments, the settings module  260  may determine the user setting based on an analysis of the user characteristics, similar users, a augmentation result from augmenting the user activity, and so forth. 
       FIG.  3    is a flow diagram illustrating an example method  300  for authenticating the user and augmenting the user activity, according to some example embodiments. The operations of the method  300  may be performed by components of the data mesh system  150  and the augmentation system  152 . At operation  310 , the attribute module  220  may receive the attribute data associated with the user from a plurality of attribute sources. In various example embodiments, at least a portion of the attribute data may include real-time data or near real-time data. The term “real-time data,” as used herein, is intended to include data associated with an event currently happening. For example, the real-time data may include user input data or sensor data communicated to the attribute module  220  after a delay interval (e.g., due to transmission delay or other delays such as being temporarily stored at an intermediate device) between capturing the data and the attribute module  220  receiving the data. 
     As will be discussed in connection with  FIGS.  15  and  16   , the attribute data may be received from a broad spectrum of attribute sources (e.g., devices, sensors, servers, databases, and other sources). Additionally, the attribute module  220  may receive the attribute data via many pathways resulting from an assortment of configurations of the attribute sources as further discussed in connection with  FIGS.  14 A and  14 B . In an example embodiment, the attribute module  220  may receive the attribute data directly from the attribute sources. In other example embodiments, the attribute module  220  may receive the attribute data from a central device that receives attribute data from a plurality of user devices. In still other example embodiments, various user devices may be communicatively coupled in a decentralized device-to-device mesh, and the attribute module  220  may receive the attribute data corresponding to a particular device in the mesh from any of the devices in the mesh. The attribute module  220  may receive the attribute data from the attribute sources in many other configurations including various suitable combinations of configurations. 
     In various example embodiments, the attribute module  220  may store the attribute data in association with the user (e.g., indexed based on a user identifier) for subsequent analysis. The attribute module  220  may store the attribute data in a storage device such as the database(s)  126 , for example. The attribute module  220  may access the stored attribute data using a variety of search or find schemes. For instance, the attribute data associated with a particular user may be accessed using a user identifier that corresponds to the particular user. It will be noted that the collective, aggregated attribute data may be referred to as a “data mesh.” 
     At operation  320 , the authentication module  250  may identify a portion of the real-time data indicative of an identity of the user. The attribute data including the real-time data may comprise a vast amount of data associated with the user. All or various portions (e.g., segments or pieces) of the real-time data may be indicative of the identity of the user. The authentication module  250  may identify, extract, parse, or otherwise obtain data from the real-time data that is pertinent, relevant, or otherwise useful in authenticating the identity of the user. 
     In an example embodiment, various devices that provide the real-time data to the attribute module  220  may correspond to the user. For instance, one or more user devices (e.g., mobile device, wearable device) may provide at least a portion of the real-time data. The user devices and the real-time data provided by the user device may be identified via a device identifier such as an Internet Protocol (IP) address, a Media Access Control (MAC) address, other unique identifies, an International Mobile Station Equipment Identity (IMEI), a Mobile Equipment Identifier (MEID), and so forth. In various example embodiments, the authentication module  250  may identify portions of the real-time data indicative of the identity of the user by matching device identifiers corresponding to the user with respective device identifiers associated with the real-time data. In a specific example, location data (e.g., as determined by a GPS component of a mobile device) may be identified by the authentication module  250  as indicative of the identity of the user if the location data originated from a device having a device identifier corresponding to the user (e.g., the location data originated from the user&#39;s mobile device). To be clear, data that originates from a device corresponding to the user may merely be indicative of the identity of the user rather than identify the user as another user may be operating the device. The identity of the user may be authenticated with respect to the real-time data in subsequent operations discussed below. 
     In various example embodiments, the attribute data and the real-time data may include sensor data. In an example embodiment, the authentication module  250  may identify portions of the sensor data that may be indicative of the identity of the user. For instance, the sensor data may include biometric data such as a fingerprint scan, vocal sample, electroencephalogram, or retinal scan (refer to  FIG.  16    for additional sensor data). In this instance, the authentication module  250  may identify the biometric data as being indicative of the identity of the user (e.g., matching fingerprints included in the sensor data with fingerprints of the user or matching another sensor signature with a sensor signature of the user). In these particular example embodiments and the following example embodiments, the real-time data need not necessarily have originated from a device corresponding to the user. 
     In further example embodiments, the authentication module  250  may identify portions of the attribute data indicative of the identity of the user based on various analyses or patterns. For example, a particular device may provide location data (e.g., as determined by a GPS component of a mobile device). The authentication module  250  may determine that the location data may be indicative of the user identity based on past location data of the user. In a specific example, if the user has, in the past, exhibited a particular pattern of travel or has frequently visited a particular location (e.g., a particular route home, or spending a particular amount of time in certain locations) then the authentication module  250  may identify the real-time data corresponding to the particular device providing the location data as indicative of the user identity. 
     In still further example embodiments, the authentication module  250  may employ many other analyses to identify the attribute data indicative of the identity of the user. For example, the user may be a member of various websites (e.g., e-commerce websites, social website.). If the user logs into a particular website using a particular device, the authentication module  250  may identify the particular device and the attribute data received from the particular device as indicative of the user identity. 
     At operation  330 , the authentication module  250  may authenticate the identity of the user with respect to the real-time data by analyzing the identified portion of the real-time data and the attribute data. In some example embodiments, authenticating the identity of the user with respect to the real-time data may establish that the real-time data resulted from an action of the user. For instance, if the authentication module  250  authenticates the identity of the user with respect to location data included in the real-time data, the location data may indicate the current location of the user. The authentication module  250  may analyze many different portions of the attribute data using a variety of analysis schemes and techniques to authenticate the identity of the user. The following discussion merely provides non-limiting examples of the authentication module  250  authenticating the identity of the user based on the attribute data. 
     In an example embodiment, the authentication module  250  may identify a portable device (e.g., mobile device, wearable device) corresponding to the user from the real-time data, and use the identified portable device as a basis for authenticating the identity of the user with respect to the real-time data. For example, the authentication module  250  may calculate the identity likelihood metric based on the identified portable device. The identity likelihood metric may indicate the likelihood that the identified portion of the attribute data identifies the user. In a specific example, the user may be operating a computer, carrying a mobile device, and wearing a smart watch. In this example, the attribute module  220  may receive the real-time data from each of the devices. If the authentication module  250  determines that the mobile device and the smart watch correspond to the user (e.g., by matching respective device identifiers with device identifiers corresponding to the user), then the authentication module  250  may authenticate the identity of the user with respect to the real-time data received from those devices. The reasoning being that if a person is determined to be carrying one or more devices belonging to the user, the person may be the user (e.g., the identified portable devices implicate or suggest the person may be the user). The more devices of the user that the person may be carrying, the stronger the basis may be that the person is the user. 
     In continuing with the above example, the authentication module  250  may authenticate the identity of the user with respect to the real-time data corresponding to the computer based on the authentication with respect to the mobile device and wearable device. The authentication module  250  may perform this authentication, for example, based on the location of the computer relative to the location of the portable devices of the user. For example, if the location of the computer is the same or within a short distance (e.g., an arms length) of the location of the portable devices of the user, the authentication module  250  may infer that the user is using the computer. The location of the computer may be established based on sensor data included in the real-time data such as near field, Bluetooth®, or other interactions between the portable devices and the computer. In other words, the location of a particular portable device may be known based on a GPS component of the particular portable device, and the location of a device in communication with the particular portable device may be inferred based on short-range communications that operate over short distances. Thus, in this example, the authentication module  250  may authenticate the identity of the user with respect to the real-time data received from the computer. The above is merely a non-limiting example and many other techniques may be employed by the authentication module  250  to authenticate the identity of the user with respect to the real-time data of various devices. 
     In further example embodiments, the authentication module  250  may use other indications of the identity of the user to authenticate the identity of the user with respect to the real-time data. For example, the authentication module  250  may use sensor data, at least in part, to authenticate the identity of the user. For instance, the sensor data may include biometric data that the authentication module  250  may use to authenticate the identity of the user. In a specific example, the biometric data may include biometric identification data such as fingerprint scans, vocal samples, retinal scans, facial scans, or electroencephalogram data (see  FIG.  16    for additional biometric identification data). The authentication module  250  may match, correlate, or otherwise determine that the biometric data corresponds to the user to authenticate the identity of the user with respect to the real-time data. 
     In still further example embodiments, the authentication module  250  may use location data to authenticate the identity of the user with respect to the real-time data. For example, the location data (e.g., as determined by a GPS component of a mobile device) may indicate a location pattern that the authentication module  250  may use to authenticate the identity of the user. In this example, the location pattern may comprise being at a particular location at a particular time or a particular route at a particular time. In a specific example, the location data may indicate a location that may be the home of the user. Given that the location may be the home of the user, it may be probable that the real-time data provided by the mobile device may correspond to the user. Thus, in some example embodiments, the authentication module  250  may authenticate the identity of the user with respect to the real-time data based on a location corresponding to the real-time data that may be indicative of the user (e.g., a location that may be typically access restricted for which the user has access such as a home or office). 
     In continuing with the discussion of the operation  330 ,  FIG.  4    is a flow diagram illustrating further example operations of the method  300  of  FIG.  3   , according to some example embodiments. As described above, subsequent to the operation  320 , at the operation  330 , the authentication module  250  may authenticate the identity of the user by analyzing the identified portion of the real-time data. In addition, at operation  410 , the authentication module  250  may calculate the identity likelihood metric based on the identified portion of the attribute data. The identity likelihood metric may indicate the likelihood that the identified portion of the attribute data identifies the user (e.g., the probability that the identified portion of the real-time data identifies the user). 
     The authentication module  250  may calculate the identity likelihood metric using a variety of schemes and techniques. In an example embodiment, the authentication module  250  may weight various portions of the real-time data indicative of the identity of the user. For example, the authentication module  250  may more heavily weight the real-time data that strongly indicates the identity of the user (e.g., a fingerprint scan that matches a fingerprint scan of the user). Conversely, the authentication module  250  may weight less heavily the real-time data that does not strongly indicate the identity of the user (e.g., real-time data indicating a user device in a particular location at a particular time may be indicative of the identity of the user but may not as strongly implicate the identity of the user as biometric identification data). In some example embodiments, the authentication module  250  may use a combination of the real-time data to calculate the identity likelihood metric. 
     Subsequent to calculating the identity likelihood metric, at decision  420 , the authentication module  250  may determine whether the identity likelihood metric exceeds an authentication threshold. In an example embodiment, when the identity likelihood metric exceeds the authentication threshold, the authentication module  250  may authenticate the identity of the user. Conversely, if the identity likelihood metric does not exceed the threshold, the authentication module  250  may not authenticate the identity of the user, and no further operations may be performed in method  300 . 
     At operation  430 , the authentication module  250  may authenticate the identity of the user. As discussed above, the authentication module  250  may use the identity likelihood metric exceeding the authentication threshold as a factor in authenticating the identity of the user with respect to the real-time data. The authentication module  250  may use the identity likelihood metric alone or in conjunction with other factors to authenticate the identity of the user. Once the identity of the user is authenticated with respect to the real-time data, subsequent operations of the method  300  may be performed. 
     In further discussing the operation  330 ,  FIG.  5    is a flow diagram illustrating a further embodiment for authenticating the identity of the user, according to some example embodiments. Subsequent to the operation  320 , at the operation  330 , the authentication module  250  may authenticate the identity of the user by analyzing the identified portion of the attribute data. In addition, at operation  510 , the authentication module  250  may derive, extract, or otherwise obtain past identification indications from past attribute data. For example, if the past attribute data includes location data, the authentication module  250  may extract favorite or frequent locations corresponding to the user. 
     At operation  520 , the authentication module  250  may derive, extract or otherwise obtain real-time identification indications from the real-time data. For instance, the real-time data may include location data. In some example embodiments, the real-time identification indications may be derived. For example, the authentication module  250  may derive location information based on short-range communications with devices of a known or fixed location. 
     At operation  530 , the authentication module  250  may calculate the identity likelihood metric by correlating, matching, or otherwise comparing the real-time identification indications with the past identification indications. For example, if the real-time data indicates a particular location, the authentication module  250  may match the particular location with a frequent location of the user to authenticate the identity of the user. Although the discussion of  FIG.  5    is mostly directed to location data, the authentication module  250  may, in a similar manner, use many other types of data included in the attribute data to calculate the identity likelihood metric. 
     Referring back to  FIG.  3   , at operation  340 , the activity module  255  may identify or infer a user activity being performed by the user based on the real-time data. In other words, the activity module  255  may identify a user objective (e.g., logging into a website) being advanced by the user based on the real-time data. The user activity may include a wide variety of activities such as, for example, operating a computer (e.g., logging into a website), jogging in the park, walking toward the refrigerator, and streaming video or other media content. In an example embodiment, the activity module  255  may identify the user activity based on sensor data and status data received from one or more user devices. The status data may indicate activity associated with a particular device. For example, the user devices may include a mobile device that provides location data and a variety of other user devices that may provide the status data (e.g., a smart TV that indicates current operating status such as streaming a particular piece of media). In this example, the activity module  255  may infer the user activity by analyzing the location data in conjunction with the status data (e.g., the user may be proximate to the smart TV based on the location data and the smart TV may indicate that it is streaming a video). 
     In another example, the activity module  255  may infer, extract, or derive the status data for a particular device based on an analysis of sensor data corresponding to the particular device. For example, a mobile device may be equipped with accelerometers that measure motion and provide motion data. If the motion data indicates that the device is not moving, the activity module  255  may infer that a person may not be carrying the particular device. In this scenario, the activity module  255  may not infer the user activity based on a device that is not currently being carried by the user. The above examples are merely non-limiting examples of the activity module  255  identifying or inferring the user activity based on the real-time data. The activity module  255  may use many other portions of the real-time data in a variety of schemes to identify or infer the user activity. 
     At operation  350 , the activity module  255  may augment, adapt, or otherwise modify the user activity according to a user setting or user preference. In other words, the activity module  255  may augment an environment of the user based on a user preference to facilitate the user&#39;s advancement towards the user objective. The environment of the user is intended to include, for example, user devices within the vicinity of the user. For example, if the user activity includes an authorization task (e.g., logging into a website), the activity module  255  may augment the user activity by adjusting a security level of the authorization task. In some example embodiments, the activity module  255  may adjust the security level of the authorization task based on the identity likelihood metric. For example, if the identity likelihood metric indicates strongly that the identity of the user is authenticated, then the security level may be reduced more than if the identity likelihood metric did not strongly indicate the identity of the user is authenticated. In some example embodiments, adjusting the security level of the authorization task may include automatically performing the authorization task on behalf of the user. For example, if the authentication module  250  has authenticated the identity of the user and the user activity includes accessing a particular website, then the activity module  255  may automatically log the user into the particular website. 
     In a further specific example, the identified user activity may comprise making a payment (e.g., an electronic payment to an e-commerce website corresponding to an item listing listed on the e-commerce website or a electronic payment to a merchant at a physical store). The activity module  255  may augment the user activity associated with making a particular payment by facilitating the payment between the user and the payee. For instance, based on the authentication of the identity of the user, the user may not need to provide security credential or provide fewer security credentials to make the payment. 
     To assist in illustrating the above discussion,  FIG.  6    depicts communication between a device of the user and the data mesh system  150 , according to some example embodiments. In the illustration of  FIG.  6   , a user  610  may be wearing one or more smart devices such as a smart watch  620 . The smart watch  620  may be communicatively coupled to the network  104  via various modalities. For example, the smart watch  620  may be communicatively coupled to a network interface  640 , which, in turn, is communicatively coupled to the network  104 . For instance, the smart watch  620  may send signals  630  that are received at the network interface  640 . In another example, the smart watch  620  may be communicatively coupled to the network  104  without the network interface  640 . In addition, the networked system  102 , including the data mesh system  150  and the augmentation system  152 , may be communicatively coupled to the network  104 . 
     Thus, the smart watch  620  of the user  610  may be communicatively coupled to the data mesh system  150  including the augmentation system  152 . The data mesh system  150 , including the augmentation system  152 , may receive or access the attribute data corresponding to the smart watch  620  via the network  104 . Similarly, the data mesh system  150 , including the augmentation system  152 , may communicate or exchange data with the smart watch  620  to facilitate augmentation of the user activity such as communicating instructions to present a user interface. Although the example of  FIG.  6    depicts an example smart watch, it will be appreciated that a wide variety of other devices may similarly be configured to interact with the data mesh system  150 . 
       FIGS.  7 - 10    are flow diagrams illustrating further example operations of the method  300  of  FIG.  3   , according to some example embodiments. Subsequent to the operation  340 , at the operation  350 , the activity module  255  may augment the user activity according to the user setting. Each of the flow diagrams of  FIGS.  7 - 10    illustrates additional operations of the operation  350 . The additional operations of the operation  350  include various example embodiments of augmenting the user activity according to the user setting. The following discussion merely describes non-limiting examples and many other schemes and techniques may be employed by the augmentation system  152  to augment the user activity using the user setting. 
     In the flow diagram of  FIG.  7   , subsequent to the operation  340 , the settings module  260  may determine the user setting based on the attribute data and the user activity at operation  710 . For example, the settings module  260  may store a plurality of user settings in a storage device such as the database(s)  126 . Subsequent to determining the user activity, the settings module  260  may determine the user setting associated with or pertaining to the user activity. For instance, the user activity may include the user streaming a movie to a particular user device. Based on the activity module  255  identifying the activity of streaming the movie, the settings module  260  may determine the user setting associated with augmenting the user activity of streaming the movie such as, for example, automatically pausing the streaming when the user leaves a vicinity of the particular user device presenting the movie to the user. 
     At operation  720 , the activity module  255  may augment the user activity according to the determined user setting. In continuing with the example above, the user activity may include streaming the movie. The activity module  255  may augment the user activity according to the determined user setting. For example, the activity module  255  may automatically pause or otherwise halt the movie based on a trigger (e.g., the user leaving the vicinity or answering a phone call), present the streaming movie to another display based on the user setting and a location of the user, and so on. Although the above discussion is directed to streaming the movie, the activity module  255  may augment the user activity that includes many other activities and based on many different types or kinds of user settings. 
     In the flow diagram of  FIG.  8   , subsequent to the operation  340 , the characteristic module  225  may infer or measure directly user characteristics based on an analysis of at least a portion of the attribute data at operation  810 . In some example embodiments, the characteristic module  225  may store the inferred user characteristics for subsequent analysis, for example, in a storage device such as database(s)  126 . The characteristic module  225  may infer a vast spectrum of the user characteristics from the attribute data. A few specific examples of user characteristics may include demographic data (e.g., age, gender, marital status, number of children), user preferences (e.g., being a morning person, favorite locations, enjoying spicy food), idiosyncrasy (e.g., being forgetful, such as draining the battery on a mobile device; or being impatient, such as a line breaker that will leave a store if the line is too long), qualities (e.g., being athletic, being tall, having a large vocabulary), personality traits (e.g., being a risk taker), actions, activities (e.g., working for a non-profit), attitudes, habits (e.g., being a coffee drinker), behaviors, beliefs, biases, demeanor, and physical characteristics of the user (e.g., height, weight, garment sizes, eye color, hair color). The specificity of the characteristics may range from very narrow (e.g., drinks a particular brand of soda) to very broad (e.g., being generally philanthropic). To illustrate inferring the user characteristic from the attribute data, the attribute data may include user location data that may indicate frequent visits to a local school, local soccer fields, and the like. In this example, the characteristic module  225  may infer that the user has children based on the types of locations the user may be frequently visiting. 
     In some instances, the characteristic module  225  may perform varying degrees of inferential analysis of the attribute data to derive the user characteristics. For example, the characteristic module  225  may infer the user&#39;s wake-up time based on user device activity or other activity (e.g., connected alarm clock settings, logins to accounts, and various other user activities that may indicate a wake-up time). In this example, the characteristic module  225  may infer a particular user characteristic that may be of a larger inferential jump such as the user being a morning person or a person that likes to sleep in. The degree of inferential jump may be configurable. In some example embodiments, the characteristic module  225  may employ various techniques to minimize or otherwise control incorrect inferences (e.g., machine learning, other learning algorithms). 
     In further example embodiments, the characteristic module  225  may learn or evolve as more of the attribute data is received (e.g., via machine learning techniques or other learning algorithms). For example, the attribute data may include location data of the user. The characteristic module  225  may infer a favorite location of the user based on a pattern (e.g., frequently visited locations) in the location data. However, the characteristic module  225  may subsequently receive employment data of the user that may indicate a current employer including an employer location. The characteristic module  225  may learn, update, or otherwise adapt to account for the new attribute data. Thus, in this example, the characteristic module  225  may not infer a favorite location of the user if the location is a work location of the user. In some instance, the user may provide input directly (e.g., via a user interface configured to receive inferential guidance from the user) to facilitate the characteristic module  225  in inferring characteristics from the attribute data (e.g., user input indicating that a particular inferred characteristic is incorrect or providing input to be used as a basis for future inferences). 
     In other instances, the characteristic module  225  may perform very little or no analysis to derive the user characteristic from the attribute data. For example, the attribute data may include an alarm time setting from a connected alarm clock (e.g., a smart phone with an alarm clock app). The alarm time setting may directly indicate a wake-up time. Since the attribute data directly relates to a particular user characteristic, the characteristic module  225  need not perform analysis to derive the user characteristic. 
     In some example embodiments, the user characteristic may comprise predefined characteristics or dynamically determined characteristics. For instance, a particular set of characteristics may be predefined (e.g., work location, home location, marital status, socio-economic level). The characteristic module  225  may determine that particular predefined characteristics are associated with the user based on an analysis of the attribute data. In other instances, the characteristic module  225  may dynamically determine characteristics based on the attribute data. For example, the attribute data may indicate that the user owns a particular exotic pet. Although there may not be a predefined characteristic associated with the particular exotic pet, the characteristic module  225  may determine the user characteristic of owning an exotic pet from the attribute data. 
     At operation  820 , the settings module  260  may determine the user setting based on the inferred user characteristics and the user activity. For example, the inferred user characteristics may indicate the user has an affinity to view user interfaces on a largest screen available. In this example, the settings module  260  may determine that the user setting includes presenting user interfaces to the largest screen available. 
     In further example embodiments, the settings module  260  may identify similar users to the user based on the attribute data or the user characteristics. For instance, the settings module  260  may identify users associated with the same or similar demographic data as that of the user. In an example embodiment, the settings module  260  may determine the user setting based on attribute data or characteristics corresponding to the similar users. For example, the settings module  260  may access the user characteristics corresponding to other users and correlate, match, or otherwise compare the user characteristics of the user with the user characteristics of other users to identify the similar users. 
     At operation  830 , the activity module  255  may augment the user activity according to the determined user setting. For example, if the user is viewing a particular user interface on a mobile device and the user is within a distance of a larger screen, such as a computer or smart TV, the activity module  255  may augment the user activity by presenting the particular user interface on the larger screen. 
     In the flow diagram of  FIG.  9   , subsequent to the operation  340 , the activity module  255  may determine that the user activity includes a presentation of a user interface to the user at operation  910 . For example, the presentation of the user interface may include the user viewing a movie, using a website, or reading an email. The activity module  255  may determine that the user activity includes the presentation of the user interface based on the real-time data. For instance, the status data of a particular device of the user may indicate that the particular device may be presenting the user interface to the user. 
     At operation  920 , the activity module  255  may identify presentation devices available to the user based on the attribute data that are capable of presenting the user interface to the user. For instance, the user may be in a room that includes several user devices such as a smart TV, a laptop computer, and a mobile device. The activity module  255  may identify these devices based on the real-time data. For example, if the smart TV is active and connected to a network, the activity module  255  may query the smart TV to determine whether it may be capable of presenting the user interface. 
     At operation  930 , the activity module  255  may determine an alternate presentation device from among the identified presentation devices based on the user setting. The activity module  255  may determine the alternate presentation device based on a number of factors. For example, the user setting may indicate the user has an affinity to view a larger screen when available. Based on this user setting, the activity module  255  may identify a presentation device with a larger display. In another example, the activity module  255  may determine that the alternate presentation device should be within a close vicinity of the user. For example, a particular presentation device that is out of view of the user may not be the best choice for the alternate presentation device. The activity module  255  may make this determination based on location data included in the real-time data. In still another example, the activity module  255  may identity a presentation device that is portable. For example, the activity module  255  may determine that the user is viewing a movie and that the user is leaving the vicinity, and may determine that a portable device to continue playing the movie is a desirable choice for the alternate presentation device. 
     At operation  940 , the activity module  255  may cause presentation of the user interface to the user with the alternate presentation device. In a specific example, the user may be viewing a live sporting event on a smart TV. When the user leaves the vicinity of the smart TV, the activity module  255  may cause presentation of the live sporting event to the alternate presentation device such as another smart TV that is viewable to the user or a portable device of the user to allow the user to continue view the live sporting event despite being out of view of the initial presentation device. 
     In the flow diagram of  FIG.  10   , subsequent to the operation  340 , the activity module  255  or the characteristic module  225  may determine a current location of the user based on the real-time data at operation  1010 . For example, the real-time data may include location data as determined by a GPS component of a mobile device, near field beacon detections, and other location services. 
     At operation  1020 , the activity module  255  or the characteristic module  225  may access device location data included in the attribute data that includes the real-time data. Similar to the operation  1010 , the activity module  255  may access, receive, or otherwise obtain the device location data based on GPS, near field beacon detections, and other location services. Although  FIG.  10    illustrates performing the operation  1010  prior to operation  1020 , in alternative example embodiments, the operation  1010  may be performed concurrently or after the operation  1020 . For instance, the current location of the user and the device location data may be received, access, retrieved, derived, or otherwise obtained by the activity module  255  at the same time in in any order and the subsequent operations illustrated in  FIG.  10    performed. 
     At operation  1030 , the activity module  255  may identify user devices within an operation distance of the user based on the current location of the user and the device location data. The operation distance may be configurable or dynamically determined. In some example embodiments, the operation distance may vary from device to device. For example, the operation distance corresponding to a smart TV may be a reasonable distance such that the user may view the smart TV. In other examples, the operation distance corresponding to a mobile device may be a reasonable distance (e.g., arm&#39;s length) such that he user may touch of the mobile device. 
     At operation  1040 , the activity module  255  may augment the operation of the identified user devices according to the user setting. In some example embodiments, the activity module  255  may augment the operation of the identified user device according to the user setting and the user activity. For example, if the user activity includes the user moving into a smart kitchen, the user activity module  255  may augment the operation of various user devices according to the user setting based on moving into the smart kitchen. For instance, relevant notifications may be pushed to a mobile device of the user based on the user moving into or being in the smart kitchen (e.g., a notification regarding the status of a smart kitchen appliance). In another instance, the activity module  255  may cause the smart appliance of the smart kitchen to perform a task on behalf of the user automatically based on the user moving into the smart kitchen (e.g., automatically brew a cup of coffee). 
     To help illustrate the concepts described above,  FIG.  11    illustrates non-limiting examples of augmenting the user activity, according to some example embodiment. A scene  1100  depicts a living room attached to an open kitchen. The scene  1100  may include a media entertainment device  1110 , a smart TV  1120 , a lamp  1130 , a mobile computer  1140 , a mobile device  1150 , a user  1160 , a smart refrigerator  1170 , and a kitchen display  1180 . Each of the devices  1110 - 1150 ,  1170 , and  1180  may be attribute sources coupled to a network (e.g., the network  104 ) and operable to communicate with the data mesh system  150 . In various example embodiments, the real-time data may include location data corresponding to the user. For instance, the user  1160  may be carrying a mobile device or another smart device (e.g., smart watch, NFC enable smart ring) that may provide real-time location data (e.g., as determined by a GPS component, beacon location detect, or other location services). In this way, the location of the user  1160  may be tracked, monitored, or observed via a particular device the user is wearing or the location of the user  1160  may be derived from various real-time data associated with the user&#39;s location included in the attribute data (e.g., Bluetooth® handshakes between a device the user is wearing and a another device with a known or fixed location). The activity module  255  may infer the user activity based the real-time data and augment the user activity based on the user setting. 
     In an example embodiment, the activity module  255  may determine that the user  1160  may be streaming media content to the smart TV  1120  and may be moving away from the smart TV  1120  towards the kitchen. In this example, the activity module  255  may identify the mobile computer  1140  and the display  1180  as alternate presentation devices. The activity module  255  may further determine that the kitchen display  1180  may be a desirable alternate presentation device as it may be within viewing distance of the user. The activity module  255  may then augment the activity of the user by presenting the streaming media content on the kitchen display  1180  and halting the presentation to the smart TV  1120 . In further example embodiments, the activity module  255  may further determine that the user  1160  has opened the smart refrigerator  1170  and may pause the streaming of the media content while the user  1160  may be using the smart refrigerator  1170 . 
     In another example embodiment, the authentication module  250  may authentication the identity of the user  1160  by detecting a mobile device or wearable the user  1160  may be carrying in proximity to the kitchen display  1180  (e.g., detected from the attribute data that may include a data feed from the mobile device, the wearable device, and the kitchen display  1180 ). Subsequently, the activity module  255  may detect that the user is walking within an operating distance (e.g., a reasonable distance such as a distance that would place the user  1160  within the same room a particular device) of a particular user device such as the kitchen display  1180 . The activity module  255  may detect the user  1160  within the operating distance of the kitchen display  1180  via a GPS component of a mobile device the user may be carrying, short range communication detections (e.g., Bluetooth® or near field communication handshakes between the mobile device of the user and the kitchen display  1180 ), and so on. The activity module  255  may then present a personalized message to the user  1160  on the kitchen display  1180  that may be pertinent or relevant to the user  1160  with respect to the context or environment of the user  1160  as determine by the settings module  260  via an analysis of the attribute data (e.g., the attribute data may include calendar information corresponding to the user  1160  or food inventory information as provided by the smart refrigerator  1170 ). For instance, the personalized message may be a reminder regarding an upcoming appointment or a reminder to purchase a particular product the user may be running low on. For a particular user other than the user  1160 , the activity module  255  may present a different personalized message or perform a different augmentation of the user activity. Thus, in this example, the augmentation system  152  has received the attribute data associated with the user (e.g., the user  1160 ), authenticated the identity of the user with respect to the real-time data (e.g., detecting a portable device corresponding to the user  1160 ), identified the user activity (e.g., walking near the kitchen display  1180 ), and augmented the activity of the user according to the user setting (e.g., presenting the personalized message to the user  1160  on the kitchen display  1180 ). 
     In yet another example embodiment, the activity module  255  may detect the user may be in a vicinity of the lamp  1130  (e.g., the user  1160  in the same room as the lamp  1130 ) via location tracking of the user  1160  (e.g., a wearable device on the user&#39;s person). In response to the activity module  255  detecting the user  1160  near the lamp  1130 , the activity module  255  may augment the environment of the user by switching on the lamp or changing a brightness of the lamp (e.g., the lamp  1130  may be a smart lamp operable to execute various commands or the lamp  1130  may be coupled to a smart outlet operable to control various functions of the lamp  1130 ). The activity module  255  may adjust the operation of the lamp  1130  according to the user setting corresponding to the lamp  1130  as determined by the settings module  260  (e.g., adjusting the brightness of the lamp according to historical brightness of the lamp corresponding to the user). 
       FIG.  12    depicts an example user interface to facilitate augmentation of a user activity, according to another embodiment. In example embodiments, the activity module  255  may identify the user activity, such as, the user moving into a particular location (e.g., the kitchen). In some embodiments, the activity module  255  may augment the activity of the user by presenting a user interface to the user. An example mobile device  1200  displays an example notification  1210  is shown. The activity module  255  may present notifications, such as the notification  1210 , to the user that are relevant within the context of the user activity. For instance, if the user has just entered the kitchen, it may be a good time to provide the user with information relevant to the kitchen, such as, kitchen supplies. In an example embodiment, the attribute data may include data received from a smart refrigerator (e.g., smart refrigerator  1150 ) indicating a food supply. The activity module  255  may augment the activity of the user by presenting a notification to the user regarding the food supply. In this example embodiment, the user may interact with the notification using user interface elements  1220  (e.g., place an order for the item or dismiss the notification). 
       FIG.  13    is a flow diagram  1300  illustrating various communications to facilitate the method of  FIG.  3   , according to example embodiments. At operation  1310 , attribute sources  1302  may communicate attribute data to the data mesh system  150 . As described above with reference to  FIG.  3   , the data mesh system  150  may receive the attribute data at operation  310 , authenticate the identity of the user at the operation  330 , identify the user activity at operation  340 , and augment the user activity at the operation  350 . 
     The data mesh system  150  may augment the user activity by communicating with a user device  1306 . At operation  1320 , the user device  1306  may facilitate augmentation of the user activity. For instance, the data mesh system  150  may communicate instructions to the user device  1306  to present a particular user interface (e.g., a notification), and the user device  1306  may present the user interface in response. 
     At operation  1330 , the user device  1306  may communicate data associated with a user action responsive to the user activity. The data associated with the user action responsive to the augmentation of the user activity may indicate whether the augmentation is desired by the user. For instance, if the augmentation included presenting a notification to the user and the user dismisses the notification, then the settings module  260  may use that as a basis for determining the user setting in subsequent analysis. 
     Similarly, at operation  1340 , the attribute source  1302  may communicate attribute data associated with the user action responsive to the augmentation of the user activity. For instance, the data mesh system  150  may request, retrieve, or otherwise obtain the attribute data associate with the user action from the attribute sources  1302  responsive to the augmentation of the user. The attribute source  1302  may communicate the attribute data that indicates whether the augmentation was desired by the user. 
     At operation  1350 , the settings module  260  may infer an augmentation result from the data received from the user device  1306  and the attribute source  1302 . The settings module  260  or characteristic module  225  may identify a user action of the user based on the real-time data, the user action being in response to the augmented user activity or environment. For instance, the user action may include dismissing a notification or interacting with a user interface. The augmentation result may indicate whether the augmentation was desired by the user. For instance, the attribute data may include engagement data that indicates the user&#39;s engagement in a particular activity, such as, clicking on user interface elements. The settings module  260  may infer the augmentation result based on the engagement data or other data. In further example embodiments, the activity module  255  may store the augmentation result in a storage device such as the databases  126  to be used subsequently to determine the user setting. Thus, the settings module  260  may evolve the user settings to better fit the user over time as more data associated with augmentations is received. 
       FIGS.  14 A and  14 B  depict example configurations for communicatively coupling attribute sources, according to some example embodiments. The example embodiments described herein may access a vast and rich “Internet of Things” (IoT) dataset that is predominantly provided via communicatively connected, interconnected, or otherwise communicatively coupled machines and devices that may include a multitude of sensors. In example embodiments, devices and machines that provide the attribute data, such as the attribute sources, may be communicatively coupled in many different configurations. For instance, each attribute source may be communicatively coupled to the networked system  102  independently to provide the networked system  102  access to the attribute data corresponding to each of the communicatively coupled attribute sources.  FIGS.  14 A and  14 B  depict alternative example attribute source configurations. It will be appreciated that  FIGS.  14 A and  14 B  are merely non-limiting examples of attribute source configurations and many other configurations or suitable combinations of configurations may be employed. 
       FIG.  14 A  depicts an example embodiment that may include attribute sources  1410  communicatively coupled in a decentralized device-to-device mesh. In this example embodiment, the attribute data corresponding to a particular device in the mesh may be received from any one or more of the devices in the mesh. For instance, the networked system  102  may access the attribute data corresponding to attribute source E via attribute source H or a combination of attribute sources H and I in  FIG.  14 A . In an example embodiment, the attribute source H or I may aggregate and store the attribute data corresponding to attribute sources A-F in  FIG.  14 A . In some example embodiments, the networked system  102  may access the attribute data associated with attribute source E by communicating with attribute source H or I in  FIG.  14 A . 
       FIG.  14 B  depicts another example embodiment that may include attribute sources  1420  communicatively coupled to a central attribute source (e.g., attribute source H in  FIG.  14 B ). The networked system  102  may access the attribute data associated with attribute sources A-G via the central attribute source in  FIG.  14 B . In some embodiments, the central attribute source may aggregate and store the attribute data received or accessed from the attribute sources A-G and provide a centralized access point for the attribute data associated with all, or some, of the communicatively coupled attribute sources A-G in  FIG.  14 B . 
       FIG.  15    depicts example sources  1500  including attribute sources  1510 , according to some example embodiments. In various example embodiments, the attribute data may include data received, retrieved, or accessed from the attribute sources  1510 . For example, the attribute sources  1510  may provide data including everything from a moisture level of a houseplant to a dribbling rhythm of a basketball. In some embodiments, the attribute data corresponding to the attribute sources  1510  may be received or accessed in real-time or near real-time. For instance, the attribute sources  1510  may communicate or otherwise provide access to the attribute data as it becomes available. In example embodiments, the attribute sources  1510  may include user device sources  1520 , user data sources  1530 , transportation sources  1540 , materials sources  1550 , third party sources  1560 , home sources  1570 , and a variety of other sources. As will be discussed in connection with  FIG.  16    the attribute sources  1510  may be associated with a wide variety of sensors, gauges, measurement components, and other components. 
     In an example embodiment, the attribute data may include data corresponding to the user device sources  1520 . The user device sources  1520  may include such non-limiting examples as a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), and other smart devices. As will be discussed further in connection with  FIG.  16   , the attribute data corresponding to the user device sources  1520  may include data associated with sensors, gauges, and other measurement components such as environmental sensor data (e.g., ambient temperature), biometric sensor data (e.g., heart rate), detection data (e.g., detection of a Near Field Communication (NFC) beacon), motion data (e.g., acceleration), position data (e.g., location as determined by a GPS of a mobile device), and so forth. 
     In further example embodiments, the attribute data corresponding to the user device sources  1520  may include data such as device type, device model, device name, a unique device identifier, and other device parameters. In some example embodiments, the device type data may provide a basis for an inference associated with the attribute data. For instance, if the device type data indicates that the device is a mobile device of the user, location data corresponding to the mobile device may indicate the location of the user. Similarly, if the device type is a media entertainment system, the attribute data corresponding to the media entertainment system may be associated with a home of the user. 
     The user data sources  1530  may include calendars (e.g., user calendar events such as birthdays, trips, exams), user profiles (e.g., demographic information such as age, gender, income level), purchase histories, browse histories (e.g., search terms), social media content (e.g., checkins, posts, connections), other user data (e.g., bookmarked websites, preferences or settings for various applications, application usage data such as time spent using a particular application), and the like. The attribute data corresponding to the user data sources  1530  may be stored, for example, by the user device sources  1520  (e.g., a mobile device that includes a mobile browser with browse history of the user), application server(s)  140  (e.g., payment history of the user stored in payment system(s)  144 , user profiles stored by an e-commerce website), the third party server(s)  130  (e.g., social media data stored in a social networking service), and so on. For example, the attribute data corresponding to the user device sources  1520  may include device resource data. The device resource data may include files stored on the devices or metadata associated with the files. For instance, the device resources may include digital media files (e.g., MP3 formatted songs) or apps (e.g., pedometer app). The metadata associated with the device resources may include usage data such as number of times a song has been played, amount of time using a particular app, and so forth. 
     As cars and other forms of transportation become increasingly equipped with sensors and the ability to communicate, a vast amount of data may be provided by the transportation sources  1540 . For example, the attribute data corresponding to the transportation sources  1540  may include acceleration data, velocity data, and other sensors data (e.g., brake pad wear data, gear shifting data). In this example, the attribute data corresponding to the transportation sources  1540  may provide indications of a user&#39;s driving patterns and styles (e.g., comes to a complete stop at a stop sign, speeds, or finicky use of the brakes). 
     The materials sources  1550 , such as clothing and structures, are also increasingly gaining the ability to capture data. In various example embodiments, the attribute data may include data corresponding to the materials sources  1550 . For example, clothing may be embedded with sensors to detect motion. Data from these sensors may provide indications of whether the user is agile or inactive. In another example, clothing may be embedded with biometric sensors that may provide continuous feed of biometric data corresponding to the user. The biometric data may provide indications of the user&#39;s health, athletic ability, and many other characteristics corresponding to the user. Similarly, structures may be equipped with sensors to passively or actively monitor the surrounding environment (e.g., street cameras, traffic cameras, and other sensors). 
     In example embodiments, the attribute data may include data associated with the third party sources  1560 . The third party sources  1560  may also provide an abundance of data associated with the user. For instance, the attribute data may include data accessed from government websites or other public records that may provide criminal histories, civil citation histories, credit histories, or other publicly available information. 
     Nearly every facet of a smart home may be capable of providing data associated with the user. The attribute data may include data corresponding to the home sources  1570 . For instance, the home sources  1570  may include smart appliances, consumables, utilities, and many other smart home devices. In a few specific instances, the attribute data may include consumable inventories and consumption rates of various consumable goods (e.g., milk, bread) tracked or monitored by smart refrigerators. In another instance, the attribute data may include utility usage data (e.g., electricity, water). Analysis of the utility usage data may indicate patterns or status of the user, such as, the user being on vacation, the user being ill (e.g., increasing house thermostat set temperature to cope with a cold), the user being an energy conscious consumer, and so on. 
       FIG.  16    depicts non-limiting example components  1600  that may provide attribute data according to some example embodiments. In example embodiments, I/O components  1610  may include input components  1620 , output components  1630 , environmental components  1640 , motion components  1650 , position components  1660 , biometric components  1670 , communication components  1680 , detection components  1690 , and a wide gamut of other sensors, gauges, and measurement components. The I/O components  1610  or a suitable combination of the I/O components  1610  may be included in any suitable device or machine such as those included in the attribute sources  1510  depicted in  FIG.  15    to facilitate the functionality described herein. In various example embodiments, the attribute data provided by the/O components  1610  may be accessible to all or some of the modules described above on a real-time or near real-time basis. The components  1600  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. 
     The input components  1620  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), tactile input components (e.g., a physical button, a touch screen that provide location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. The input components  1620  may receive input from the user to facilitate the functionalities described herein. For instance, the user may interact with a user interface using the input components  1620 . 
     The output components  1630  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor), other signal generators, and so forth. The output components  1630  may present information to the user. For example, the output components  1630  may present a user interface to the user or present media files to the user. 
     The environmental components  1640  may include illumination sensors (e.g., photometer), temperature sensors (e.g., one or more thermometers that detect ambient temperature), humidity sensors, pressure sensors (e.g., barometer), acoustic sensors (e.g., one or more microphone that detects background noise), proximity sensors (e.g., an infrared sensor that detects nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), and so on. The environmental components  1640  may measure various physical parameters to provide an indication or signal corresponding to the physical environment surrounding the environmental components  1640 . 
     The motion components  1650  may include acceleration sensors (e.g., accelerometer), gravitation sensors, rotation sensors (e.g., gyroscope), and so forth. The motion components  1650  may provide motion data such as velocity, acceleration, or other force measurements along an x, y, and z axes. The motion data may be provided at a regular update rate (e.g.,  10  updates per second) that may be configurable. 
     The position components  1660  may include location sensors (e.g., a Global Position System (GPS) receiver component), altitude sensors (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensors (e.g., magnetometers that provide magnetic field strength along the x, y, and z axes), and the like. In an example embodiment, the position components  1660  may provide position data such as latitude, longitude, altitude, and a time stamp. Similar to the motion components  1650 , the position components  1660  may provide the motion data at a regular update rate that may be configurable. 
     The biometric components  1670  may include components to detect expressions, measure biosignals, or identify people, among other functions. For example, the biometric components  1670  may include expression components to detect expressions (also referred to as “kinesics”) such as hand gestures (e.g., an optical component to detect a hand gesture or a Doppler component to detect hand motions), vocal expressions (e.g., a microphone to detect changes in voice pitch that may indicate tension), facial expressions (e.g., a camera to detect expressions or micro-expressions of a person such as a smile), body gestures, and eye tracking (e.g., detecting the focal point of a person&#39;s eyes or patterns in eye movement). The biometric components  1670  may also include, for example, biosignal components to measure biosignals such as blood pressure, heart rate, body temperature, perspiration, and brain waves (e.g., as determined by a electroencephalogram). In further examples, the biometric components  1670  may include identification components to identify people such as retinal scanners (e.g., a camera component), vocal detectors (e.g., a microphone to receive audio data for voice identification), facial detectors, fingerprint detectors, and electroencephalogram sensors (e.g., to identify a person via unique brain wave patterns). 
     Communication may be implemented using a wide variety of technologies. The I/O components  1610  may include communication components  1680  operable to communicatively couple machines or devices. For example, the communication components  1680  may include a network interface component or other suitable device to interface with a network (e.g., the network  104 ). In further examples, the communication components  1680  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. In addition, a variety of information may be derived using the communication components  1680  such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth. 
     The/O components  1610  may include detection components  1690  that may detect a variety of identifiers. For example, the detection components  1690  may include Radio Frequency Identification (RFID) tag reader components, Near Field Communication (NFC) smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), and acoustic detection components (e.g., microphones to identify tagged audio signals). 
       FIG.  17    is a block diagram  1700  of an example data structure for the attribute data associated with a particular user according to example embodiments. In example embodiments, the attribute data may be associated with a plurality of users such as user  1702 ,  1704 ,  1706 ,  1708 ,  1710 ,  1712 , and  1714 . In an example embodiment, the attribute data may be accessed for a particular user by using a user identifier. The attribute data may include profile data  1720 , device data  1722 , calendar data  1724 , list data  1726 , list type data  1728 , interest data  1730 , fitment data  1732 , garment type data  1734 , preference data  1736 , measured dimension data  1738 , fitness goal data  1740 , reward data  1742 , location data  1744 , and other data not shown. In some example embodiments, the attribute data may be structured such that various portions of the attribute data are associated with other portions of the attribute data via relationships. For instance, the calendar data  1724  may include a calendar event associated with an event name, an event data, and an event location for the calendar event. 
       FIG.  18    is a block diagram  1800  of an example data structure for data associated with a device according to some example embodiments. In an example embodiment, the device data  1722  of  FIG.  17    may include a device identifier, a device name, device resources data (e.g., files stores on the devices such as browser cookies, media files), I/O component data, and so forth. In example embodiments, the device identifier may, for example, comprise an Internet Protocol (IP) address, a Media Access Control (MAC) address, other unique identifies, an International Mobile Station Equipment Identity (IMEI), or a Mobile Equipment Identifier (MEID). In an example embodiment, the U/O component data may include standard device parameters  1802 , position data  1804 , location data  1806 , motion data  1808 , environmental data  1810 , biometric data  1812 , and other data.  FIG.  18    merely depicts example attribute data that may correspond to a particular device, and a variety of other data not shown may be included in the device data. The standard device parameters  1802  may include parameters that are standard across multiple devices included in the IoT. In various example embodiments, standardized parameters and protocols may facilitate access and utilization of the attribute data corresponding to such devices. For example, the attribute data available on an unknown device may be accessed and utilized without the need to discover or otherwise determine which parameters are available and which units of measure are associated with the parameters. Many other schemes may be employed to discover or otherwise determine available parameters accessible on a particular device. 
     Modules, Components, and Logic 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software encompassed within a general-purpose processor or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware modules) at different times. Software may accordingly configure a particular processor or processors, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)). 
     The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented modules may be distributed across a number of geographic locations. 
     Software Architecture 
       FIG.  19    is a block diagram  1900  illustrating an architecture of software  1902 , which may be installed on any one or more of devices described above.  FIG.  19    is merely a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software  1902  may be executing on hardware such as machine  2000  of  FIG.  20    that includes processors  2010 , memory  2030 , and I/O components  2050 . In the example architecture of  FIG.  19   , the software  1902  may be conceptualized as a stack of layers where each layer may provide particular functionality. For example, the software  1902  may include layers such as an operating system  1904 , libraries  1906 , frameworks  1908 , and applications  1910 . Operationally, the applications  1910  may invoke application programming interface (API) calls  1912  through the software stack and receive messages  1914  in response to the API calls  1912 . 
     The operating system  1904  may manage hardware resources and provide common services. The operating system  1904  may include, for example, a kernel  1920 , services  1922 , and drivers  1924 . The kernel  1920  may act as an abstraction layer between the hardware and the other software layers. For example, the kernel  1920  may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services  1922  may provide other common services for the other software layers. The drivers  1924  may be responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  1924  may include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth. 
     The libraries  1906  may provide a low-level common infrastructure that may be utilized by the applications  1910 . The libraries  1906  may include system  1930  libraries (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  1906  may include API libraries  1932  such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries  1906  may also include a wide variety of other libraries  1934  to provide many other APIs to the applications  1910 . 
     The frameworks  1908  may provide a high-level common infrastructure that may be utilized by the applications  1910 . For example, the frameworks  1908  may provide various graphic user interface (GUI) functions, high-level resource management, high-level location services, and so forth. The frameworks  1908  may provide a broad spectrum of other APIs that may be utilized by the applications  1910 , some of which may be specific to a particular operating system or platform. 
     The applications  1910  include a home application  1950 , a contacts application  1952 , a browser application  1954 , a book reader application  1956 , a location application  1958 , a media application  1960 , a messaging application  1962 , a game application  1964 , and a broad assortment of other applications such as third party application  1966 . In a specific example, the third party application  1966  (e.g., an application developed using the Android™ or iOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as iOS™, Android™, Windows® Phone, or other mobile operating systems. In this example, the third party application  1966  may invoke the API calls  1912  provided by the mobile operating system  1904  to facilitate functionality described herein. 
     Example Machine Architecture and Machine-Readable Medium 
       FIG.  20    is a block diagram illustrating components of a machine  2000 , according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG.  20    shows a diagrammatic representation of the machine  2000  in the example form of a computer system, within which instructions  2016  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  2000  to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine  2000  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  2000  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  2000  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  2016 , sequentially or otherwise, that specify actions to be taken by machine  2000 . Further, while only a single machine  2000  is illustrated, the term “machine” shall also be taken to include a collection of machines  2000  that individually or jointly execute the instructions  2016  to perform any one or more of the methodologies discussed herein. 
     The machine  2000  may include processors  2010 , memory  2030 , and I/O components  2050 , which may be configured to communicate with each other via a bus  2002 . In an example embodiment, the processors  2010  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, processor  2012  and processor  2014  that may execute instructions  2016 . The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (also referred to as “cores”) that may execute instructions contemporaneously. Although  FIG.  20    shows multiple processors, the machine  2000  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core process), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory  2030  may include a main memory  2032 , a static memory  2034 , and a storage unit  2036  accessible to the processors  2010  via the bus  2002 . The storage unit  2036  may include a machine-readable medium  2038  on which is stored the instructions  2016  embodying any one or more of the methodologies or functions described herein. The instructions  2016  may also reside, completely or at least partially, within the main memory  2032 , within the static memory  2034 , within at least one of the processors  2010  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  2000 . Accordingly, the main memory  2032 , static memory  2034 , and the processors  2010  may be considered as machine-readable media  2038 . 
     As used herein, the term “memory” refers to a machine-readable medium  2038  able to store data temporarily or permanently and may be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium  2038  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions  2016 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions  2016 ) for execution by a machine (e.g., machine  2000 ), such that the instructions, when executed by one or more processors of the machine  2000  (e.g., processors  2010 ), cause the machine  2000  to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more data repositories in the form of a solid-state memory (e.g., flash memory), an optical medium, a magnetic medium, other non-volatile memory (e.g., Erasable Programmable Read-Only Memory (EPROM)), or any suitable combination thereof. The term “machine-readable medium” specifically excludes non-statutory signals per se. 
     The I/O components  2050  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. It will be appreciated that the I/O components  2050  may include many other components that are not shown in  FIG.  20   . The I/O components  2050  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components  2050  may include output components  2052  and input components  2054 . The output components  2052  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor), other signal generators, and so forth. The input components  2054  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  2050  may include biometric components  2056 , motion components  2058 , environmental components  2060 , or position components  2062  among a wide array of other components. For example, the biometric components  2056  may include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components  2058  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  2060  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometer that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  2062  may include location sensor components (e.g., a Global Position System (GPS) receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  2050  may include communication components  2064  operable to couple the machine  2000  to a network  2080  or devices  2070  via coupling  2082  and coupling  2072  respectively. For example, the communication components  2064  may include a network interface component or other suitable device to interface with the network  2080 . In further examples, communication components  2064  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  2070  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)). 
     Moreover, the communication components  2064  may detect identifiers or include components operable to detect identifiers. For example, the communication components  2064  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  2064 , such as, location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth. 
     Transmission Medium 
     In various example embodiments, one or more portions of the network  2080  may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, the network  2080  or a portion of the network  2080  may include a wireless or cellular network and the coupling  2082  may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling  2082  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology. 
     The instructions  2016  may be transmitted or received over the network  2080  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  2064 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  2016  may be transmitted or received using a transmission medium via the coupling  2072  (e.g., a peer-to-peer coupling) to devices  2070 . The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions  2016  for execution by the machine  2000 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Furthermore, the machine-readable medium  2038  is non-transitory (in other words, not having any transitory signals) in that it does not embody a propagating signal. However, labeling the machine-readable medium  2038  as “non-transitory” should not be construed to mean that the medium is incapable of movement; the medium should be considered as being transportable from one physical location to another. Additionally, since the machine-readable medium  2038  is tangible, the medium may be considered to be a machine-readable device. 
     Language 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Although an overview of the inventive subject matter has been described with reference to specific example embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or inventive concept if more than one is, in fact, disclosed. 
     The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. 
     As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, modules, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the example configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.