Patent Publication Number: US-2018053055-A1

Title: Integrating augmented reality content and thermal imagery

Description:
TECHNICAL FIELD 
     The subject matter disclosed herein generally relates to integrating augmented reality content with thermal imagery and, in particular, to leveraging acquired thermal imagery to display augmented reality content relating to the objects associated with the acquired thermal imagery. 
     BACKGROUND 
     Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are augmented (or supplemented) by computer-generated sensory input such as sound, video, graphics or Global Positioning System (GPS) data. With the help of advanced AR technology (e.g., adding computer vision and object recognition) the information about the surrounding real world of the user becomes interactive. Device-generated (e.g., artificial) information about the environment and its objects can be overlaid on the real world. 
     Typically, a user uses a computing device to view the augmented reality. Conventional computing devices often show a view of the user&#39;s environment as it appears to the user (e.g., within the light wavelengths perceivable by the human eye). However, in some circumstances, a user may need information about his or her environment that he or she cannot perceive (e.g., outside of the light wavelengths perceivable by the human eye). For example, where a surface does not change within the visible light spectrum according to temperature, the user may inadvertently come into contact with such surface and injure himself or herself. Thus, augmented reality within the visible light spectrum may be insufficient to ensure the safety of the user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limited to the figures of the accompanying drawings. 
         FIG. 1  is a block diagram illustrating an example of a network environment suitable for a wearable computing device, according to an example embodiment. 
         FIG. 2  is a block diagram of the wearable computing device of  FIG. 1 , according to an example embodiment 
         FIG. 3  is a block diagram illustrating different types of sensors used by the wearable computing device of  FIG. 1 , according to an example embodiment. 
         FIGS. 4A-4B  illustrate an example of displaying selected thermal imagery with augmented reality content, according to an example embodiment. 
         FIG. 5  illustrates a further example of displaying thermal imagery with augmented reality content, according to an example embodiment. 
         FIGS. 6A-6B  illustrate a method, according to an example embodiment, implemented by the wearable computing device of  FIG. 1  for identifying objects and acquiring their corresponding thermal images. 
         FIG. 7  is a block diagram illustrating components of a machine, 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. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure provides for a wearable computing device that identifies objects using thermal imagery and displays augmented reality content in response to an analysis of the obtained thermal imagery. In one embodiment, the wearable computing device obtains thermal imagery for a recognized object. The obtained thermal imagery is then communicated to a server in communication with the wearable computing device. The server then performs a comparison of the obtained thermal imagery for the recognized object with baseline thermal imagery for the recognized object. Based on the comparison, the server communicates instructions and/or information to the wearable computing device to display as augmented reality content to the wearer. Such instructions and/or information may include whether the obtained thermal imagery indicates a problem with the recognized object or whether the recognized object is operating outside of normal operating parameters (e.g., according to the baseline thermal imagery). 
     Accordingly, in one embodiment, the disclosed wearable computing device includes a machine-readable memory storing computer-executable instructions and at least one hardware processor in communication with the machine-readable memory that, when the computer-executable instructions are executed, configures the wearable computing device to perform a plurality of operations. The plurality of operations includes detecting one or more objects in an environment in which the wearable computing device is being worn, and acquiring, by one or more cameras of the wearable computing device, one or more thermal images of at least one object of the detected one or more objects. The plurality of operations also includes obtaining one or more instructions relating to the at least one object of the detected one or more objects based on the one or more acquired thermal images, generating augmented reality content having the obtained one or more instructions, and displaying, by a display of the wearable computing device, the generated augmented reality content with one or more of the acquired thermal images of the at least one object of the detected one or more objects. 
     In another embodiment of the wearable computing device, the plurality of operations further comprises comparing one or more of the acquired one or more thermal images with one or more previously obtained thermal images of the at least one object of the detected one or more objects, and obtaining the one or more instructions comprises generating instructions based on the comparison. 
     In a further embodiment of the wearable computing device, the one or more previously obtained thermal images relate to an operating state of the at least one object of the detected one or more objects, and the instructions comprise a notification that the at least one object of the detected one or more objects is operating outside expected operating parameters. 
     In yet another embodiment of the wearable computing device, obtaining the one or more instructions comprise communicating the acquired one or more thermal images to a server in communication with the wearable computing device, and receiving the one or more instructions in response to the communication of the acquired one or more thermal images 
     In yet a further embodiment of the wearable computing device, the plurality of operations further comprises obtaining a three-dimensional model of the at least one object of the one or more detected objects, and displaying the acquired one or more thermal images as a texture applied to one or more surfaces of the three-dimensional model. 
     In another embodiment of the wearable computing device, the acquired one or more thermal images are associated with a first set of coordinates indicating a location of each of the acquired one or more thermal images, the at least one object is associated with a second set of coordinates indicating a location of the at least one object, and the plurality of operations further comprises determining a third set of coordinates for displaying the acquired one or more thermal images as the texture based on aligning the second set of coordinates with the first set of coordinates. 
     In a further embodiment of the wearable computing device, the plurality of operations further comprises identifying the at least one object of the detected one or more objects, and the one or more instructions are obtained in response to a comparison of the acquired one or more thermal images with a baseline thermal imaging profile associated with the identified at least one object. 
     This disclosure further describes a computer-implemented method for providing augmented reality images of an environment in which the wearable computing device is worn. In one embodiment, the computer-implemented method includes detecting, by a wearable computing device, one or more objects in an environment in which the wearable computing device is being worn, and acquiring, by one or more cameras of the wearable computing device, one or more thermal images of at least one object of the detected one or more objects. The computer-implemented method also includes obtaining one or more instructions relating to the at least one object of the detected one or more objects based on the one or more acquired thermal images, generating augmented reality content having the obtained one or more instructions, and displaying, by a display of the wearable computing device, the generated augmented reality content with one or more of the acquired thermal images of the at least one object of the detected one or more objects. 
     In another embodiment of the computer-implemented method, the method includes comparing one or more of the acquired one or more thermal images with one or more previously obtained thermal images of the at least one object of the detected one or more objects, and obtaining the one or more instructions comprises generating instructions based on the comparison. 
     In a further embodiment of the computer-implemented method, the one or more previously obtained thermal images relate to an operating state of the at least one object of the detected one or more objects, and the instructions comprise a notification that the at least one object of the detected one or more objects is operating outside expected operating parameters. 
     In yet another embodiment of the computer-implemented method, obtaining the one or more instructions comprises communicating the acquired one or more thermal images to a server in communication with the wearable computing device, and receiving the one or more instructions in response to the communication of the acquired one or more thermal images. 
     In yet a further embodiment of the computer-implemented method, the computer-implemented method includes obtaining a three-dimensional model of the at least one object of the one or more detected objects, and displaying the acquired one or more thermal images as a texture applied to one or more surfaces of the three-dimensional model. 
     In another embodiment of the computer-implemented method, the acquired one or more thermal images are associated with a first set of coordinates indicating a location of each of the acquired one or more thermal images, the at least one object is associated with a second set of coordinates indicating a location of the at least one object, and the computer-implemented method further comprises determining a third set of coordinates for displaying the acquired one or more thermal images as the texture based on aligning the second set of coordinates with the first set of coordinates. 
     In a further embodiment of the computer-implemented method, the computer-implemented method includes identifying the at least one object of the detected one or more objects, and the one or more instructions are obtained in response to a comparison of the acquired one or more thermal images with a baseline thermal imaging profile associated with the identified at least one object. 
     This disclosure also describes a machine-readable medium having computer-executable instructions stored thereon that, when executed by at least one hardware processor, cause a wearable computing device to perform a plurality of operations comprising detecting one or more objects in an environment in which the wearable computing device is being worn, acquiring, by one or more cameras of the wearable computing device, one or more thermal images of at least one object of the detected one or more objects, obtaining one or more instructions relating to the at least one object of the detected one or more objects based on the one or more acquired thermal images, generating augmented reality content having the obtained one or more instructions, and displaying, by a display of the wearable computing device, the generated augmented reality content with one or more of the acquired thermal images of the at least one object of the detected one or more objects. 
     In another embodiment of the machine-readable medium, the plurality of operations further comprises comparing one or more of the acquired one or more thermal images with one or more previously obtained thermal images of the at least one object of the detected one or more objects, and obtaining the one or more instructions comprises generating instructions based on the comparison. 
     In a further embodiment of the machine-readable medium, the one or more previously obtained thermal images relate to an operating state of the at least one object of the detected one or more objects, and the instructions comprise a notification that the at least one object of the detected one or more objects is operating outside expected operating parameters. 
     In yet another embodiment of the machine-readable medium, obtaining the one or more instructions comprises communicating the acquired one or more thermal images to a server in communication with the wearable computing device, and receiving the one or more instructions in response to the communication of the acquired one or more thermal images. 
     In yet a further embodiment of the machine-readable medium, the plurality of operations further comprises obtaining a three-dimensional model of the at least one object of the one or more detected objects, and displaying the acquired one or more thermal images as a texture applied to one or more surfaces of the three-dimensional model. 
     In another embodiment of the machine-readable medium, the acquired one or more thermal images are associated with a first set of coordinates indicating a location of each of the acquired one or more thermal images, the at least one object is associated with a second set of coordinates indicating a location of the at least one object, and the plurality of operations further comprises determining a third set of coordinates for displaying the acquired one or more thermal images as the texture based on aligning the second set of coordinates with the first set of coordinates. 
     Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details. 
       FIG. 1  is a block diagram illustrating an example of a network environment  102  suitable for a wearable computing device  104 , according to an example embodiment. The network environment  102  includes the wearable computing device  104  and a server  112  communicatively coupled to each other via a network  110 . The wearable computing device  104  and the server  112  may each be implemented in a computer system, in whole or in part, as described below with respect to  FIG. 7 . 
     The server  112  may be part of a network-based system. For example, the network-based system may be or include a cloud-based server system that provides additional information, such as three-dimensional ( 3 D) models or other virtual objects, to the wearable computing device  104 . 
     The wearable computing device  104  may be implemented in various form factors. In one embodiment, the wearable computing device  104  is implemented as a helmet, which the user  120  wears on his or her head, and views objects (e.g., physical object(s)  106 ) through a display device, such as one or more lenses, affixed to the wearable computing device  104 . In another embodiment, the wearable computing device  104  is implemented as a lens frame, where the display device is implemented as one or more lenses affixed thereto. In yet another embodiment, the wearable computing device  104  is implemented as a watch (e.g., a housing mounted or affixed to a wrist band), and the display device is implemented as a display (e.g., liquid crystal display (LCD) or light emitting diode (LED) display) affixed to the wearable computing device  104 . 
     A user  120  may wear the wearable computing device  104  and view one or more physical object(s)  106  in a real world physical environment. The user  120  may be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the wearable computing device  104 ), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user  120  is not part of the network environment  102 , but is associated with the wearable computing device  104 . For example, the wearable computing device  104  may be a computing device with a camera and a transparent display. In another example embodiment, the wearable computing device  104  may be hand-held or may be removably mounted to the head of the user  120 . In one example, the display device may include a screen that displays what is captured with a camera of the wearable computing device  104 . In another example, the display may be transparent or semi-transparent, such as lenses of wearable computing glasses or the visor or a face shield of a helmet. 
     The user  120  may be a user of an augmented reality (AR) application executable by the wearable computing device  104  and/or the server  112 . The AR application may provide the user  120  with an AR experience triggered by one or more identified objects (e.g., physical object(s)  106 ) in the physical environment. For example, the physical object(s)  106  may include identifiable objects such as a two-dimensional (2D) physical object (e.g., a picture), a 3D physical object (e.g., a factory machine), a location (e.g., at the bottom floor of a factory), or any references (e.g., perceived corners of walls or furniture) in the real-world physical environment. The AR application may include computer vision recognition to determine various features within the physical environment such as corners, objects, lines, letters, and other such features or combination of features. 
     In one embodiment, the objects in an image captured by the wearable computing device  104  are tracked and locally recognized using a local context recognition dataset or any other previously stored dataset of the AR application. The local context recognition dataset may include a library of virtual objects associated with real-world physical objects or references. In one embodiment, the wearable computing device  104  identifies feature points in an image of the physical object  106 . The wearable computing device  104  may also identify tracking data related to the physical object  106  (e.g., GPS location of the wearable computing device  104 , orientation, or distance to the physical object(s)  106 ). If the captured image is not recognized locally by the wearable computing device  104 , the wearable computing device  104  can download additional information (e.g., 3D model or other augmented data) corresponding to the captured image, from a database of the server  112  over the network  110 . 
     In another example embodiment, the physical object(s)  106  in the image is tracked and recognized remotely by the server  112  using a remote context recognition dataset or any other previously stored dataset of an AR application in the server  112 . The remote context recognition dataset may include a library of virtual objects or augmented information associated with real-world physical objects or references. 
     The network environment  102  also includes one or more external sensors  108  that interact with the wearable computing device  104  and/or the server  112 . The external sensors  108  may be associated with, coupled to, or related to the physical object(s)  106  to measure a location, status, and characteristics of the physical object(s)  106 . Examples of measured readings may include but are not limited to weight, pressure, temperature, velocity, direction, position, intrinsic and extrinsic properties, acceleration, and dimensions. For example, external sensors  108  may be disposed throughout a factory floor to measure movement, pressure, orientation, and temperature. The external sensor(s)  108  can also be used to measure a location, status, and characteristics of the wearable computing device  104  and the user  120 . The server  112  can compute readings from data generated by the external sensor(s)  108 . The server  112  can generate virtual indicators such as vectors or colors based on data from external sensor(s)  108 . Virtual indicators are then overlaid on top of a live image or a view of the physical object(s)  106  (e.g., displayed on the display device  114 ) in a line of sight of the user  120  to show data related to the physical object(s)  106 . For example, the virtual indicators may include arrows with shapes and colors that change based on real-time data. Additionally and/or alternatively, the virtual indicators are rendered at the server  112  and streamed to the wearable computing device  104 . 
     The external sensor(s)  108  may include one or more sensors used to track various characteristics of the wearable computing device  104  including, but not limited to, the location, movement, and orientation of the wearable computing device  104  externally without having to rely on sensors internal to the wearable computing device  104 . The external senor(s)  108  may include optical sensors (e.g., a depth-enabled 3D camera), wireless sensors (e.g., Bluetooth, Wi-Fi), Global Positioning System (GPS) sensors, and audio sensors to determine the location of the user  120  wearing the wearable computing device  104 , distance of the user  120  to the external sensor(s)  108  (e.g., sensors placed in corners of a venue or a room), the orientation of the wearable computing device  104  to track what the user  120  is looking at (e.g., direction at which a designated portion of the wearable computing device  104  is pointed, e.g., the front portion of the wearable computing device  104  is pointed towards a player on a tennis court). 
     Furthermore, data from the external senor(s)  108  and internal sensors (not shown) in the wearable computing device  104  may be used for analytics data processing at the server  112  (or another server) for analysis on usage and how the user  120  is interacting with the physical object(s)  106  in the physical environment. Live data from other servers may also be used in the analytics data processing. For example, the analytics data may track at what locations (e.g., points or features) on the physical object(s)  106  or virtual object(s) (not shown) the user  120  has looked, how long the user  120  has looked at each location on the physical object(s)  106  or virtual object(s), how the user  120  wore the wearable computing device  104  when looking at the physical object(s)  106  or virtual object(s), which features of the virtual object(s) the user  120  interacted with (e.g., such as whether the user  120  engaged with the virtual object), and any suitable combination thereof. To enhance the interactivity with the physical object(s)  106  and/or virtual objects, the wearable computing device  104  receives a visualization content dataset related to the analytics data. The wearable computing device  104 , via the display device  114 , then generates a virtual object with additional or visualization features, or a new experience, based on the visualization content dataset. 
     Any of the machines, databases, or devices shown in  FIG. 1  may be implemented in a general-purpose computer modified (e.g., configured or programmed) by software to be a special-purpose computer to perform one or more of the functions described herein for that machine, database, or device. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect to  FIG. 7 . As used herein, a “database” is a data storage resource and may store data structured as a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. Moreover, any two or more of the machines, databases, or devices illustrated in  FIG. 1  may be combined into a single machine, and the functions described herein for any single machine, database, or device may be subdivided among multiple machines, databases, or devices. 
     The network  110  may be any network that facilitates communication between or among machines (e.g., server  112 ), databases, and devices (e.g., the wearable computing device  104  and the external sensor(s)  108 ). Accordingly, the network  110  may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network  110  may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof. 
       FIG. 2  is a block diagram of the wearable computing device  104  of  FIG. 1 , according to an example embodiment. The wearable computing device  104  includes various different types of hardware components. In one embodiment, the wearable computing device includes one or more processor(s)  202 , a display  204 , a communication interface  206 , and one or more sensors  208 . The wearable computing device  104  also includes a machine-readable memory  210 . The various components  202 - 210  communicate via a communication bus  234 . 
     The one or more processors  202  may be any type of commercially available processor, such as processors available from the Intel Corporation, Advanced Micro Devices, Qualcomm, Texas Instruments, or other such processors. Further still, the one or more processors  202  may include one or more special-purpose processors, such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC). The one or more processors  202  may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. Thus, once configured by such software, the one or more processors  202  become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. 
     The display  204  may include a display surface or lens configured to display AR content (e.g., images, video) generated by the one or more processor(s)  202 . In one embodiment, the display  204  is made of a transparent material (e.g., glass, plastic, acrylic, etc.) so that the user  120  can see through the display  204 . In another embodiment, the display  204  is made of several layers of a transparent material, which creates a diffraction grating within the display  204  such that images displayed on the display  204  appear holographic. The processor(s)  202  are configured to display a user interface on the display  204  so that the user  120  can interact with the wearable computing device  104 . 
     The communication interface  206  is configured to facilitate communications between the wearable computing device  104 , the user  120 , the external sensor(s)  108 , and the server  112 . The communication interface  206  may include one or more wired communication interfaces (e.g., Universal Serial Bus (USB), an I 2 C bus, an RS-232 interface, an RS-485 interface, etc.), one or more wireless transceivers, such as a Bluetooth® transceiver, a Near Field Communication (NFC) transceiver, an 802.11x transceiver, a 3G (e.g., a GSM and/or CDMA) transceiver, a 4G (e.g., LTE and/or Mobile WiMAX) transceiver, or combinations of wired and wireless interfaces and transceivers. In one embodiment, the communication interface  206  interacts with the sensors  208  to provide input to the wearable computing device  104 . In this embodiment, the user  120  may engage in gestures, eye movements, speech, or other physical activities that the wearable computing device  104  interprets as input (e.g., via the AR application  214  and/or input detection module  218 ). 
     To detect the movements of the user  120 , the wearable computing device  104 , and/or other objects in the environment, the wearable computing device  104  includes one or more sensors  208 . The sensors  208  may generate internal tracking data of the wearable computing device  104  to determine a position and/or an orientation of the wearable computing device  104 . In addition, the sensors  208  cooperatively operate so as to assist the wearable computing device  104  in identifying objects and obtaining thermal imagery for objects within the environment where the wearable computing device  104  is located. 
     The position and the orientation of the wearable computing device  104  may be used to identify real-world objects in a field of view of the wearable computing device  104 . For example, a virtual object may be rendered and displayed in the display  204  when the sensors  208  indicate that the wearable computing device  104  is oriented towards a real-world object (e.g., when the user  120  looks at one or more physical object(s)  106 ) or in a particular direction (e.g., when the user  120  tilts his head to watch his wrist). 
     The wearable computing device  104  may display a virtual object in response to a determined geographic location of the wearable computing device  104 . For example, a set of virtual objects may be accessible when the user  120  of the wearable computing device  104  is located in a particular building. In another example, virtual objects, including sensitive material, may be accessible when the user  120  of the wearable computing device  104  is located within a predefined area associated with the sensitive material and the user  120  is authenticated. Different levels of content of the virtual objects may be accessible based on a credential level of the user  120 . For example, a user who is an executive of a company may have access to more information or content in the virtual objects than a manager at the same company. The sensors  208  may be used to authenticate the user  120  prior to providing the user  120  with access to the sensitive material (e.g., information displayed in as a virtual object such as a virtual dialog box in a transparent display). Authentication may be achieved via a variety of methods such as providing a password or an authentication token, or using sensors  208  to determine biometric data unique to the user  120 . 
     The wearable computing device  104  is further configured to display thermal imagery corresponding to objects detected by the wearable computing device  104 . In one embodiment, the wearable computing device  104  detects objects within its environment and retrieves a three-dimensional model corresponding to the detected object. The wearable computing device  104  also obtains thermal imagery corresponding to the detected object. The wearable computing device  104  then maps the obtained thermal imagery as a texture to the three-dimensional model of the detected object. The wearable computing device  104  then displays the thermal imagery on the display  204 . In this manner, the wearable computing device  104  displays the thermal imagery as augmented reality content, which helps the user to visualize the thermal output of the object being viewed through the wearable computing device  104 . 
       FIG. 3  is a block diagram illustrating different types of sensors  208  used by the wearable computing device  104  of  FIG. 1 , according to an example embodiment. For example, the sensors  208  may include an external camera  302 , an inertial measurement unit (IMU)  304 , a location sensor  306 , an audio sensor  308 , an ambient light sensor  310 , and one or more forward looking infrared (FLIR) camera(s)  312 . One of ordinary skill in the art will appreciate that the sensors illustrated in  FIG. 3  are examples, and that different types and/or combinations of sensors may be employed in the wearable computing device  104 . 
     The external camera  302  includes an optical sensor(s) (e.g., camera) configured to capture images across various spectrums. For example, the external camera  302  may include an infrared camera or a full-spectrum camera. The external camera  302  may include a rear-facing camera(s) and a front-facing camera(s) disposed in the wearable computing device  104 . The front-facing camera(s) may be used to capture a front field of view of the wearable computing device  104  while the rear-facing camera(s) may be used to capture a rear field of view of the wearable computing device  104 . The pictures captured with the front- and rear-facing cameras may be combined to recreate a 360-degree view of the physical environment around the wearable computing device  104 . 
     The IMU  304  may include a gyroscope and an inertial motion sensor to determine an orientation and/or movement of the wearable computing device  104 . For example, the IMU  304  may measure the velocity, orientation, and gravitational forces on the wearable computing device  104 . The IMU  304  may also measure acceleration using an accelerometer and changes in angular rotation using a gyroscope. 
     The location sensor  306  may determine a geolocation of the wearable computing device  104  using a variety of techniques such as near field communication (NFC), the Global Positioning System (GPS), Bluetooth®, Wi-Fi®, and other such wireless technologies or combination of wireless technologies. For example, the location sensor  306  may generate geographic coordinates and/or an elevation of the wearable computing device  104 . 
     The audio sensor  308  may include one or more sensors configured to detect sound, such as a dynamic microphone, condenser microphone, ribbon microphone, carbon microphone, and other such sound sensors or combinations thereof. For example, the microphone may be used to record a voice command from the user (e.g., user  120 ) of the wearable computing device  104 . In other examples, the microphone may be used to measure an ambient noise (e.g., measure intensity of the background noise, identify specific type of noises such as explosions or gunshot noises). 
     The ambient light sensor  310  is configured to determine an ambient light intensity around the wearable computing device  104 . For example, the ambient light sensor  314  measures the ambient light in a room in which the wearable computing device  104  is located. Examples of the ambient light sensor  310  include, but are not limited to, the ambient light sensors available from ams AG, located in Oberpremstatten, Austria. 
     The one or more FLIR camera(s)  312  are configured to capture and/or obtain thermal imagery of objects being viewed by the wearable computing device  104  (e.g., by the external camera  302 ). One of ordinary skill in the art will appreciate that the FLIR camera(s)  312  illustrated in  FIG. 3  and described below are examples, and that different types and/or combinations of infrared imaging devices may be employed in the wearable computing device  104 . 
     The FLIR camera(s)  312  may be affixed to different parts and/or surfaces of the wearable computing device  104  depending upon its implementation. For example, where the wearable computing device  104  is implemented as a head-mounted device, one or more of the FLIR camera(s)  312  may be affixed or mounted in a forward-looking or rearward-looking position on an exterior or interior surface of the wearable computing device  104 . As another example, where the wearable computing device  104  is implemented as a wrist-mounted device (e.g., a watch), one or more of the FLIR camera(s)  312  may be affixed or disposed on a surface perpendicular to a surface having the display  204 . In either examples, the one or more FLIR camera(s)  312  are arranged or disposed within the wearable computing device  104  such that the FLIR camera(s)  312  obtain thermal imagery within the environment of the wearable computing device  104 . 
     In one embodiment, the FLIR camera(s)  312  are configured to capture thermal imagery of objects detected by the wearable computing device  104  and/or designated by the user  120 . In this embodiment, the FLIR camera(s)  312  may operate so as to capture the thermal energy being emitted by the designated object(s). In another embodiment, the FLIR camera(s)  312  are configured to capture thermal imagery without the explicit designation of object(s) by the user, in which case, the wearable computing device  104  and/or the server  112  then selectably modifies to correspond to object(s) detected by the wearable computing device  104  and/or the server  112 . Further still, the wearable computing device  104  and/or the server  112  may leverage the obtained thermal imagery to detect and/or identify object(s). As discussed below with reference to  FIG. 2 , the obtained thermal imagery may then be projected on the display  204  to be viewed by the user  120 . 
     Referring back to  FIG. 2 , the machine-readable memory  210  includes various modules  212  and data  214  for implementing the features of the wearable computing device  104 . The machine-readable memory  210  includes one or more devices configured to store instructions and data temporarily or permanently and may include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or any suitable combination thereof. The term “machine-readable memory” 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 the modules  212  and the data  214 . Accordingly, the machine-readable memory  210  may be implemented as a single storage apparatus or device, or, alternatively and/or additionally, as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. As shown in  FIG. 2 , the machine-readable memory  210  excludes signals per se. 
     In one embodiment, the modules  212  are written in a computer-programming and/or scripting language. Examples of such languages include, but are not limited to, C, C++, C#, Java, JavaScript, Perl, Python, Ruby, or any other computer programming and/or scripting language now known or later developed. 
     The modules  212  include one or more modules  216 - 224  that implement the features of the wearable computing device  104 . In one embodiment, the modules  212  include an AR application  216 , an object recognition module  218 , a thermal imaging module  220 , and an object model retrieval module  222 . The data  214  includes one or more different sets of data  226 - 232  used by, or in support of, the modules  212 . In one embodiment, the data  214  includes AR application data  226 , object recognition data  228 , thermal imaging data  230 , and object model data  232 , and thermal occlusion data  234 . 
     The AR application  216  is configured to provide the user  120  with an AR experience triggered by one or more of the physical object(s)  106  in the user&#39;s  120  environment. Accordingly, the machine-readable memory  210  also stores AR application data  222  which provides the resources (e.g., sounds, images, text, and other such audiovisual content) used by the AR application  216 . In response to detecting and/or identifying physical object(s)  106  in the user&#39;s  120  environment, the AR application  216  generates audiovisual content (e.g., represented by the AR application data  222 ) that is displayed on the display  204 . To detect and/or identify the physical object(s)  106 , the AR application  216  may employ various object recognition algorithms and/or image recognition algorithms. 
     The AR application  216  may further generate and/or display interactive audiovisual content on the display  204 . In one embodiment, the AR application  214  generates an interactive graphical user interface that the user  120  may use to interact with the AR application  216  and/or control various functions of the wearable computing device  104 . In addition, the wearable computing device  104  may translate physical movements and/or gestures, performed by the user  120 , as input for the graphical user interface. 
     The object recognition module  218  is configured to identify and/or detect objects within the environment of the wearable computing device  104 . In one embodiment, the object recognition module  218  communicates with one or more of the sensors  208  to identify the objects within the environment. For example, and with reference to  FIG. 3 , the external camera  302  may communicate one or more images to the object recognition module  218 , which then performs one or more object recognition algorithms on the received images. The objects identified and/or detected by the object recognition module  218  are then stored as the object recognition data  228 . The object recognition module  218  may perform object recognition on the images for previously unidentified objects and may also perform the object recognition on the images according to predefined fiducial markers. With previously unidentified objects, the object recognition module  218  may reference a database of objects and/or a classifier (e.g., via the server  112 ) to classify the unidentified objects and, with fiducial markers, may reference a database of fiducial markers to identify the object to which the fiducial marker is affixed. 
     In an alternative embodiment, the object recognition module  218  communicates the images obtained by the sensors  208  to the server  112 , which performs the object recognition and/or detection algorithms on the received images. The server  112  communicates the detected objects to the object recognition module  218  via the communication interface  206 , which then stores the detected objects as the object recognition data  228 . 
     The object recognition data  228  may store a variety of information about a given object. In one embodiment, such information may include the type of object, whether the object is assigned a formal or informal name, the location of the object relative to the Earth (e.g., via latitude, longitude, and elevation coordinates), the location of the object relative to the wearable computing device  104  (e.g., distance, elevation, etc.), a three-dimensional model associated with the object (e.g., a three-dimensional graphical object that can be displayed via the AR application  216 ), and a thermal imaging profile associated with the detected object. In one embodiment, the thermal imaging profile includes various thermal images (or representations of such thermal images) that represent the object operating a different states (where applicable). For example, where the detected objects include one or more water pipes, the thermal imaging profile may include thermal images associated with the water pipes that indicate the various temperatures emitted by the water pipes depending on the temperature of the water being carried. As another example, where the detected objects include one or more steam pipes, the thermal imaging profile may include thermal images that indicate the temperatures emitted by the steam pipes at various stages of their operation (e.g., off, warming up, steam-filled, etc.). As discussed below, this thermal imaging profile can be used by the server  112  and/or the wearable computing device  104  to inform the user  120  whether there is a potential problem with the object being viewed by the wearable computing device  104  that would otherwise be difficult to see with the naked eye. 
     The thermal imaging module  220  is configured to acquire one or more thermal images of the environment and/or selected objects being viewed by the wearable computing device  104 . In one embodiment, the thermal imaging module  220  communicates with one or more sensors  208  (e.g., the FLIR camera(s)  312 ) to acquire the one or more thermal images. The thermal images acquired by the thermal imaging module  220  are then stored as the thermal imaging data  230 . In one embodiment, the thermal imaging module  220  acquires the thermal imaging data  230  at a framerate equal to, or higher than, the framerate perceivable by the human eye (e.g., 30 frames per second, 23.97 frames per second, 29.97 frames per second, etc.). In this embodiment, the thermal imaging data  230  may be displayed by the AR application  216 , which appears as a video of thermal imagery to the user  120 . 
     In addition, the thermal imaging data  230  may be mapped to the objects detected by the object recognition module  218  and stored as the object recognition data  228 . As discussed previously, an object recognized by the wearable computer device  104  (e.g., via the object recognition module  218 ) may be associated with a three-dimensional model. In one embodiment, an object model retrieval module  222  is configured to retrieve the three-dimensional model, e.g., from the server  112  via the communicate interface  206 . The object model retrieval module  222  may then store the retrieved three-dimensional model as the object model  232 . By having a three-dimensional model (e.g., the object model  232 ) of a recognized object, the AR application  216  can apply the thermal imaging data  230  as a texture to one or more surfaces of the object model  232 . Using the various sensors  208  of the wearable computing device  104 , the AR application  216  may apply various graphical transformations to the thermal imaging data  230  (e.g., scaling, skewing, rotations, etc.) to align with one or more surfaces of the object model  232 . In this manner, the AR application  216  can display the thermal imaging data  230  as augmented reality content via the display  204 , with or without the associated object model  232 . Thus, when the user  120  is viewing the object within view of the wearable computing device  104 , the user  120  can perceive the thermal energy being emitted by the object as if the thermal energy was perceptible within the light wavelengths detectable by the human eye. 
     In some instances, the thermal imaging data  230  is not mapped to a particular object model (e.g., object model  232 ), but is displayed along with other augmented reality content on the display  204 . In this regard, the AR application  216  is configured to display thermal images (e.g., the thermal imaging data  230 ) as it is acquired by the one or more sensors  208  without performing the texture mapping operation. In this regard, the user  120  of the wearable computing device  104  views the thermal imaging data  230  as it would appear to the sensors  208  rather than being graphically transformed to map to a particular object model  232 . 
       FIGS. 4A-4B  illustrate an example of displaying thermal imagery with augmented reality content, according to an example embodiment. In  FIG. 4A , a scene  402  includes one or more objects  406  that the object recognition module  218  identifies and/or detects. In particular, the one or more objects  406  include various pipes and boilers for a heating system. In one embodiment, the object recognition module  218  may retrieve a three-dimensional model corresponding to the detected one or more objects  406  via the communication interface  206 , which the object recognition module  218  stores as the object model  232 . Additionally and/or alternatively, the object recognition module  218  may execute one or more object recognition algorithms to identify, detect, and/or distinguish the various one or more objects  406  present in the scene  402 . 
     In  FIG. 4B , the wearable computing device  104  invokes the thermal imaging module  220  to obtain thermal imagery  408  of the one or more detected objects  406 . As explained above, the AR application  216  may map the obtained thermal imagery  408  to the detected one or more objects  406 . Thus, when the user  120  requests that the AR application  216  display the obtained thermal imagery  408 , the AR application  216  displays the obtained thermal imagery  408  as augmented reality content. In one embodiment, the wearable computing device  104  displays thermal imagery  408  for the one or more detected objects  406  that are within view of the user  120 . Thus, in this embodiment, the thermal imagery for objects that are in view of the user  120  may not be displayed. This approach ensures that the user  120  is shown thermal imagery for objects that are within view, while not displaying the thermal imagery for objects that are not within view. In an alternative embodiment, the wearable computing device  104  displays thermal imagery for one or more objects regardless of whether such objects are within view of the user  120 . 
     Additionally, and/or alternatively, the obtained thermal imagery  408  may be textured map to a three-dimensional model of the one or more objects  406  (e.g., the object model  232 ) such that the user  120  can view the thermal imagery  408  from different angles as he or she moves about the environment where the wearable computing device  104  is located. 
       FIG. 5  illustrates a further example of displaying thermal imagery with augmented reality content, according to an example embodiment. In  FIG. 5 , the user  120  views a scene  502  via the display  204  that includes augmented reality content  506  derived from one or more thermal images obtained by the thermal imaging module  220  and mapped to the one or more objects  504  identified and/or detected by the object recognition module  218 . The augmented reality content  506  includes the direction of flow for fluid within the pipes (e.g., the one or more objects  504 ) identified and/or detected by the object recognition module  218 . In one embodiment, the flow direction is determined by performing a gradient differential analysis on the obtained thermal imagery, which indicates in which direction the warmer (or hotter) fluid is traveling. 
     In addition, as shown within the boxed portion  508 , augmented reality content  508 A associated with a first pipe appears overlaid augmented reality content  508 B associated with a second pipe. In this manner, the wearable computing device  104  provides a real-time, or near real-time, view of thermal imagery for objects within view as those objects spatially relate to one another and the wearable computing device  104 . This functionality allows the user  120  to readily discern and identify particular elements of thermal imagery (e.g., a specific pipe) from a set of thermal imagery that appears nominally similar 
       FIGS. 6A-6B  illustrate a method  602 , according to an example embodiment, implemented by the wearable computing device  104  of  FIG. 1  for identifying objects and acquiring their corresponding thermal images. The method  602  may be implemented by one or more components of the wearable computing device  104  and is discussed by way of reference thereto. 
     Initially, the wearable computing device  104  may be thermally calibrated (Operation  604 ). Thermally calibrating the wearable computing device  104  may include exposing the one or more FLIR camera(s)  312  to ambient environment temperatures such that the FLIR camera(s)  312  can better identify objects from the surrounding environment. Alternatively, calibrating the wearable computing device  104  may include adjusting one or more thermal sensitivity thresholds of the FLIR camera(s)  312  to adjust the range (e.g., increase and/or decrease) of sensitivity the FLIR camera(s)  312  have to thermal energy. By calibrating the wearable computing device  104  prior to imaging an environment, the wearable computing device  104  can be configured to better detect objects that emit thermal energy. 
     The wearable computing device  104  then detects one or more objects (e.g., physical objects  106 ) within its environment (Operation  606 ). As explained above, the wearable computing device  104  may invoke or execute an object recognition module  218  that detects one or more objects within the environment of the wearable computing device  104 . In one embodiment, the object recognition module  218  performs the object detection and/or identification. Additionally, and/or alternatively, the object recognition module  218  communicates with the server  112  via the communication interface  234  to detect and/or identify the one or more objects. For example, the object recognition module  218  may communicate one or more images to the server  112 , which then performs the object identification and/or recognition. In this implementation, the server  112  then communicates the results of the object identification and/or recognition to the object recognition module  218 . Information pertaining to the detected and/or identified one or more objects is then stored as the object recognition data  228 . 
     The wearable computing device  104  next obtains thermal imagery from one or more of the detected objects (Operation  608 ). As explained with reference to  FIGS. 2-3 , the thermal imaging module  220  communicates with one or more FLIR camera(s)  312  to acquire thermal images of objects within the environment the wearable computing device  104 . The acquired thermal images may then be stored as thermal imaging data  230 . In addition, the thermal imaging module  220  may then associate the thermal imaging data  230  with one or more detected objects stored as the object recognition data  228  (Operation  610 ). For example, the thermal imaging module  220  may store one or more identifiers with the thermal imaging data  230 , and use the identifiers as references for the object recognition data  228 . In this manner, the thermal imaging module  220  can associate detected objects with their respective thermal images. In some instances, the thermal images may be stored as the thermal imaging data  230  without references in the object recognition data  228 , such as where the object recognition module  218  is unable to identify and/or detect the object from which the thermal images were acquired. 
     The AR application  216  may then display the thermal imaging data  230  as augmented reality content and/or as part of the wearable computing device  104  operating in a user-selected thermal imaging mode (Operation  612 ). Where the thermal imaging data  230  is displayed as augmented reality content, the AR application  216  may correlate the thermal imaging data  230  with the object recognition data  228  such that, when the thermal imaging data  230  is displayed as the augmented reality content, the thermal imaging data  230  appears with its real-world counterpart. In one embodiment, correlating the thermal imaging data  230  with the object recognition data  228  may include one or more graphical transformations (e.g., translations, rotations, resizing, etc.) to align the coordinate system of the thermal imaging data  230  with the coordinate system of the object recognition data  228 . Having aligned the thermal imaging data  230  with the object recognition data  228 , the AR application  216  then displays the one or more thermal images on the display  204  via the communication bus  234 . Alternatively, the thermal imaging data  230  may not be aligned with the object recognition data  228 , such as where there is no correlating object for the thermal imaging data  230  in the object recognition data  228 . 
     Using the thermal imaging data  230 , the AR application  216  is further configured to inform the user  120  whether the displayed thermal images conform to expected thermal images (e.g., the thermal images are associated with one or more temperature values within an expected range of temperature values). Accordingly, and referring to  FIG. 6B , the AR application  216  may communicate one or more of the thermal images to the server  112  via the communication interface  206  (Operation  614 ). In one embodiment, the AR application  216  communicates a sampled set of thermal images to the server  112  for analysis. In another embodiment, the AR application  216  continuously streams the thermal images to the server  112 , such that the server  112  processes the received thermal images within a short period of time of the wearable computing device  104  having acquired them. 
     In addition, and to facilitate the processing of the communicated thermal images, the wearable computing device  104  may also communicate the detected object(s) associated with the thermal images communicated to the server  112  (Operation  616 ). By communicating the detected object(s) to the server  112  (e.g., the object recognition data  228 ), the wearable computing device  104  effectively informs the server  112  of the objects associated with the communicated thermal images. Alternatively, and/or additionally, the server  112  may perform one or more image recognition algorithms on the received thermal images to determine the type of objects associated with the thermal images. 
     Although the wearable computing device  104  may communicate the object recognition data  228  and/or the thermal imaging data  230  to the server  112  for further processing, the wearable computing device  104  may be configured to perform the analysis of the object recognition data  228  and/or the thermal imaging data  230 . In this embodiment, the wearable computing device  104  may not communicate the object recognition data  228  and/or the thermal imaging data  230  to the server  112  but, instead, locally perform the processing of the data  228 , 230 . 
     In one embodiment, the server  112  (or the wearable computing device  104 ) determines whether the thermal imaging data  230  indicates whether its corresponding object(s), or portions thereof, are operating within expected temperatures. As explained previously, the server  112  (or the wearable computing device  104 ) may store a baseline thermal imaging profile for one or more objects, where the baseline thermal imaging profile indicates the expected temperatures for an object operating under various conditions (e.g., one or more operating states). Using one or more image comparison techniques (e.g., machine learning, classification and/or categorizing, neural network training etc.), the server  112  and/or the wearable computing device  104  determines whether the received thermal imaging data  230  conforms to the baseline thermal imaging profile. Should the server  112  determine that there is a discrepancy in the thermal imaging data  230 , the server  112  may then communicate instructions to and/or information to the wearable computing device  104  to be displayed to user  120  via the display  204  (Operation  618 ). 
     The wearable computing device  104  then, if applicable, acts upon the information received from the server  112 , such as by displaying information about the objects associated with the thermal imaging data  230  (Operation  620 ). Such information and/or instructions may include whether the object(s) are emitting temperatures within a range associated with the operating condition of the object, whether there is a malfunction or damage to the object causing the temperatures associated with the thermal imaging data  230 , how to repair or fix the object(s) 
     associated with the thermal imaging data  230 , and other such information and/or instructions. In one embodiment, the information and/or instructions are displayed as augmented reality content within the field of view of the user  120  via the display  204 . 
     Thus, this disclosure provides for a wearable computing device  104  configured to acquire thermal imagery for objects within an environment, including determining whether the acquired thermal imagery indicates whether one or more of the objects are experiencing a problem or are operating outside of expected parameters. In addition, the acquired thermal imagery may be displayed as augmented reality content such that the acquired thermal imagery appears overlaid on corresponding objects. As the wearable computing device  104  can use the acquired thermal energy to compare with previously obtained thermal imagery and/or a baseline thermal imaging profile, the wearable computing device  104  is configured to inform the user  120  whether an object associated with the acquired thermal energy is emitting thermal energy outside of expected parameters (e.g., via a comparison with the object&#39;s thermal imaging profile). 
     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 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 executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware modules become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. 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 accordingly configures 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. 
     Example Machine Architecture and Machine-Readable Medium 
       FIG. 7  is a block diagram illustrating components of a machine  700 , 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. 7  shows a diagrammatic representation of the machine  700  in the example form of a computer system, within which instructions  716  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  700  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions may cause the machine to execute the method illustrated in  FIGS. 6A-6B . Additionally, or alternatively, the instructions may implement one or more of the modules  212  illustrated in  FIG. 2  and so forth. The instructions transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  700  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  700  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  700  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  716 , sequentially or otherwise, that specify actions to be taken by machine  700 . Further, while only a single machine  700  is illustrated, the term “machine” shall also be taken to include a collection of machines  700  that individually or jointly execute the instructions  716  to perform any one or more of the methodologies discussed herein. 
     The machine  700  may include processors  710 , memory  730 , and I/O components  750 , which may be configured to communicate with each other such as via a bus  702 . In an example embodiment, the processors  710  (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  712  and processor  714  that may execute instructions  716 . The term “processor” is intended to include multi-core processor that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 7  shows multiple processors, the machine  700  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/storage  730  may include a memory  732 , such as a main memory, or other memory storage, and a storage unit  736 , both accessible to the processors  710  such as via the bus  702 . The storage unit  736  and memory  732  store the instructions  716  embodying any one or more of the methodologies or functions described herein. The instructions  716  may also reside, completely or partially, within the memory  732 , within the storage unit  736 , within at least one of the processors  710  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  700 . Accordingly, the memory  732 , the storage unit  736 , and the memory of processors  710  are examples of machine-readable media. 
     As used herein, “machine-readable medium” means a device able to store instructions and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or any suitable combination thereof. 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  716 . 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  716 ) for execution by a machine (e.g., machine  700 ), such that the instructions, when executed by one or more processors of the machine  700  (e.g., processors  710 ), cause the machine  700  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” excludes signals per se. 
     The I/O components  750  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  750  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  750  may include many other components that are not shown in  FIG. 7 . The I/O components  750  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  750  may include output components  752  and input components  754 . The output components  752  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, resistance mechanisms), other signal generators, and so forth. The input components  754  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/or 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  750  may include biometric components  756 , motion components  758 , environmental components  760 , or position components  762  among a wide array of other components. For example, the biometric components  756  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  758  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  760  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  762  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  750  may include communication components  764  operable to couple the machine  700  to a network  780  or devices  770  via coupling  782  and coupling  772  respectively. For example, the communication components  764  may include a network interface component or other suitable device to interface with the network  780 . In further examples, communication components  764  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  770  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  764  may detect identifiers or include components operable to detect identifiers. For example, the communication components  764  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  764 , 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  780  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  780  or a portion of the network  780  may include a wireless or cellular network and the coupling  782  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  782  may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1xRTT), 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  716  may be transmitted or received over the network  780  using a transmission medium via a network interface device (e.g., a network interface component included in the communication components  764 ) and utilizing any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  716  may be transmitted or received using a transmission medium via the coupling  772  (e.g., a peer-to-peer coupling) to devices  770 . The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions  716  for execution by the machine  700 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     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.