Patent Publication Number: US-2023141590-A1

Title: System and method for ultrasonic sensor enhancement using lidar point cloud

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present disclosure claims the benefit of priority of co-pending U.S. Provisional Patent Application No. 63/277,678, filed on Nov. 10, 2021, and entitled “SYSTEM AND METHOD FOR ULTRASONIC SENSOR ENHANCEMENT USING LIDAR POINT CLOUD,” the contents of which are incorporated in full by reference herein. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to the automotive field. More particularly, the present disclosure relates to a system and method for ultrasonic sensor enhancement using a lidar point cloud. 
     BACKGROUND 
     In general, ultrasonic sensor (USS) readings are noisy and low resolution. They are one-dimensional (1D) and cannot describe small structures in the environment. Since the readings from a USS are distance, using them directly does not provide a map of the environment and point positions cannot be obtained. The present background provides an illustrative automotive context in which the concepts and principles of the present disclosure may be implemented. It will be readily apparent to those of ordinary skill in the art that the concepts and principles of the present disclosure may be implemented in other contexts equally. 
     SUMMARY 
     The present disclosure provides a system and method for USS reading enhancement using a lidar point cloud. This provides noise reduction and enables the generation of a two-dimensional (2D) environmental map. More specifically, the present disclosure provides a system and method for generating an enhanced environmental map using USSs, and the map is enhanced using a lidar point cloud. Using the lidar point cloud has advantages because the lidar point cloud is accurate and thus can provide accurate labels for training and the like. 
     In one illustrative embodiment, the present disclosure provides a method for ultrasonic sensor reading enhancement using a lidar point cloud, the method including: receiving an ultrasonic sensor temporal feature using an ultrasonic sensor; inputting the ultrasonic sensor temporal feature into an autoencoder system including instructions stored in a memory and executed by a processor; wherein the autoencoder system is trained using a prior inputted ultrasonic sensor temporal feature and a corresponding prior inputted lidar feature label received from a lidar system; and, using the trained autoencoder system, outputting an enhanced ultrasonic sensor environmental mapping. The ultrasonic sensor temporal feature includes a 1D environmental map with relatively more noise and the enhanced ultrasonic sensor environmental mapping includes a 2D environmental map with relatively less noise. The prior inputted ultrasonic sensor temporal feature is formed by performing ultrasonic sensor data feature extraction using inertial measurement unit data across N frames and a kinematic bicycle model to generate an ego vehicle trajectory, and, for each position in the ego vehicle trajectory, calculating a reflection point in an environment based on a yaw angle and each ultrasonic sensor reading, thereby providing one environmental mapping across the N frames for the ego vehicle trajectory. The data feature extraction further includes, for a trajectory cut based on an ultrasonic sensor&#39;s field of view, using the environmental mapping from one ultrasonic sensor, as well as a same mapping from the lidar system. The prior inputted lidar feature label is formed by performing lidar point cloud feature generation by filtering lidar points by height and by a field of view of an ultrasonic sensor. The lidar point cloud feature generation further includes finding closest lidar points to an ego vehicle by splitting the field of view of the ultrasonic sensor into angles centered at the ultrasonic sensor and, within each angle, selecting a constant number of lidar points that are closest to the ego vehicle, wherein a third dimension of the selected points is discarded, thereby providing a the lidar feature with a total number of the selected points that matches the inputted ultrasonic sensor temporal feature. The method also includes, at a vehicle control system, receiving the outputted an enhanced ultrasonic sensor environmental mapping and directing operation of a vehicle based on the outputted an enhanced ultrasonic sensor environmental mapping. 
     In another illustrative embodiment, the present disclosure provides a non-transitory computer-readable medium including instructions stored in a memory and executed by a processor to carry out steps for ultrasonic sensor reading enhancement using a lidar point cloud, the steps including: receiving an ultrasonic sensor temporal feature using an ultrasonic sensor; inputting the ultrasonic sensor temporal feature into an autoencoder system including instructions stored in a memory and executed by a processor; wherein the autoencoder system is trained using a prior inputted ultrasonic sensor temporal feature and a corresponding prior inputted lidar feature label received from a lidar system; and, using the trained autoencoder system, outputting an enhanced ultrasonic sensor environmental mapping. The ultrasonic sensor temporal feature includes a 1D environmental map with relatively more noise and the enhanced ultrasonic sensor environmental mapping includes a 2D environmental map with relatively less noise. The prior inputted ultrasonic sensor temporal feature is formed by performing ultrasonic sensor data feature extraction using inertial measurement unit data across N frames and a kinematic bicycle model to generate an ego vehicle trajectory, and, for each position in the ego vehicle trajectory, calculating a reflection point in an environment based on a yaw angle and each ultrasonic sensor reading, thereby providing one environmental mapping across the N frames for the ego vehicle trajectory. The data feature extraction further includes, for a trajectory cut based on an ultrasonic sensor&#39;s field of view, using the environmental mapping from one ultrasonic sensor, as well as a same mapping from the lidar system. The prior inputted lidar feature label is formed by performing lidar point cloud feature generation by filtering lidar points by height and by a field of view of an ultrasonic sensor. The lidar point cloud feature generation further includes finding closest lidar points to an ego vehicle by splitting the field of view of the ultrasonic sensor into angles centered at the ultrasonic sensor and, within each angle, selecting a constant number of lidar points that are closest to the ego vehicle, wherein a third dimension of the selected points is discarded, thereby providing a the lidar feature with a total number of the selected points that matches the inputted ultrasonic sensor temporal feature. 
     In a further illustrative embodiment, the present disclosure provides a system for ultrasonic sensor reading enhancement using a lidar point cloud, the system including: an ultrasonic sensor operable for generating an ultrasonic sensor temporal feature; and an autoencoder system including instructions stored in a memory and executed by a processor, the autoencoder system operable for receiving the ultrasonic sensor temporal feature from the ultrasonic sensor and outputting an enhanced ultrasonic sensor environmental mapping; wherein the autoencoder system is trained using a prior inputted ultrasonic sensor temporal feature and a corresponding prior inputted lidar feature label generated by a lidar system. The ultrasonic sensor temporal feature includes a 1D environmental map with relatively more noise and the enhanced ultrasonic sensor environmental mapping includes a 2D environmental map with relatively less noise. The prior inputted ultrasonic sensor temporal feature is formed by performing ultrasonic sensor data feature extraction using inertial measurement unit data across N frames and a kinematic bicycle model to generate an ego vehicle trajectory, and, for each position in the ego vehicle trajectory, calculating a reflection point in an environment based on a yaw angle and each ultrasonic sensor reading, thereby providing one environmental mapping across the N frames for the ego vehicle trajectory. The data feature extraction further includes, for a trajectory cut based on an ultrasonic sensor&#39;s field of view, using the environmental mapping from one ultrasonic sensor, as well as a same mapping from the lidar system. The prior inputted lidar feature label is formed by performing lidar point cloud feature generation by filtering lidar points by height and by a field of view of an ultrasonic sensor. The lidar point cloud feature generation further includes finding closest lidar points to an ego vehicle by splitting the field of view of the ultrasonic sensor into angles centered at the ultrasonic sensor and, within each angle, selecting a constant number of lidar points that are closest to the ego vehicle, wherein a third dimension of the selected points is discarded, thereby providing a the lidar feature with a total number of the selected points that matches the inputted ultrasonic sensor temporal feature. The system also includes a vehicle control system operable for receiving the outputted an enhanced ultrasonic sensor environmental mapping and directing operation of a vehicle based on the outputted an enhanced ultrasonic sensor environmental mapping. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated and described herein with reference to the various drawings, in which: 
         FIG.  1    is a schematic diagram of one illustrative embodiment of the USS lidar point cloud enhancement system and method of the present disclosure; 
         FIG.  2    is another schematic diagram of one illustrative embodiment of the USS lidar point cloud enhancement system and method of the present disclosure; 
         FIG.  3    illustrates USS data feature extraction in accordance with the USS lidar point cloud enhancement system and method of the present disclosure; 
         FIG.  4    further illustrates USS data feature extraction in accordance with the USS lidar point cloud enhancement system and method of the present disclosure; 
         FIG.  5    illustrates lidar point cloud feature generation in accordance with the USS lidar point cloud enhancement system and method of the present disclosure; 
         FIG.  6    further illustrates lidar point cloud feature generation in accordance with the USS lidar point cloud enhancement system and method of the present disclosure; 
         FIG.  7    is a network diagram of a cloud-based system for implementing the various systems and methods of the present disclosure; 
         FIG.  8    is a block diagram of a server/processing system that may be used in the cloud-based system of  FIG.  7    or stand-alone; and 
         FIG.  9    is a block diagram of a remote device that may be used in the cloud-based system of  FIG.  7    or stand-alone. 
     
    
    
     DETAILED DESCRIPTION 
     Again, the present disclosure provides a system and method for USS reading enhancement using a lidar point cloud. This provides noise reduction and enables the generation of a 2D environmental map. More specifically, the present disclosure provides a system and method for generating an enhanced environmental map using USSs, and the map is enhanced using a lidar point cloud. Using the lidar point cloud has advantages because the lidar point cloud is accurate and thus can provide accurate labels for training and the like. 
       FIG.  1    is a schematic diagram of one illustrative embodiment of the USS lidar point cloud enhancement system and method  10  of the present disclosure. The training aspect includes a USS temporal feature  12  and a labeled lidar feature  14  input to an autoencoder  16  to provide an enhanced USS environmental mapping  18 . The testing aspect includes a USS temporal feature  12  input to the trained autoencoder  16  to provide an enhanced USS environmental mapping  18 . Thus, the trained autoencoder  16 , after training using inputted USS temporal features  12  and corresponding labeled lidar features  14 , is operable for, given a subsequently inputted USS temporal feature  12 , outputting an enhanced USS environmental mapping  18 , which is a map generated using a USS that is a noise-reduced 2D environmental map. 
       FIG.  2    is another schematic diagram of one illustrative embodiment of the USS lidar point cloud enhancement system and method  10  of the present disclosure. Here, an inertial measurement unit (IMU)  20  provides an ego vehicle trajectory calculation  22  including a vehicle trajectory for T frames  24 . Methodologies for such ego vehicle trajectory calculation  22  are well known to those of ordinary skill in the art. USS data  26  is provided from which an environmental map  28  is determined. Again, methodologies for the generation of such environmental map  28  are well known to those of ordinary skill in the art. An angle-guided mapping cut  30  is performed, as well as dimension pruning  32  to determine the USS temporal feature  12 . On the lidar side, a lidar unit  34  provides corresponding points that are filtered by height  36  and field of view (FoV)  38 . Methodologies for such height and FoV filtering  36 , 38  are well known to those of ordinary skill in the art. The FoV is then split into angles and the closest points are found in each angle  42 . After dimension pruning  44 , the corresponding lidar feature  14  is determined and provided. 
       FIG.  3    illustrates USS data feature extraction  50  in accordance with the USS lidar point cloud enhancement system and method  10  ( FIGS.  1  and  2   ) of the present disclosure. In terms of generation of the trajectory  52  of the ego vehicle  54  using the IMU  20  ( FIG.  2   ), based on the IMU data across N frames, a kinematic bicycle model is used to generate the ego vehicle trajectories  52 . At each timestamp, the past N frames are used. In terms of environmental mapping based on the USS readings, for each position in the ego vehicle trajectory  52 , the reflection point  56  in the environment is calculated based on the yaw angle and the USS readings. As illustrated, the dot is this point in the environmental mapping. For each position in the vehicle trajectory  52 , such a point  56  may be calculated. Then one mapping across N frames is obtained from one trajectory  52 . 
       FIG.  4    further illustrates USS data feature extraction  50  in accordance with the USS lidar point cloud enhancement system and method  10  ( FIGS.  1  and  2   ) of the present disclosure. For the trajectory cut based on the sensor&#39;s FoV, the environmental mapping from one USS  26  ( FIG.  2   ) is used, as well as the same mapping from the lidar  34  ( FIG.  2   ). It is only necessary to use the mapping within the FoV of the USS  26 , and these points are cut. Since the speed of the vehicle  54  changes while driving, the number of points within the FoV may change. In order to keep the input feature dimension fixed, the cut mapping is padded with (−1,−1) if the length of the points is smaller than the predefined dimension. Otherwise, the points that exceed the dimension are discarded. 
       FIG.  5    illustrates lidar point cloud feature generation  60  in accordance with the USS lidar point cloud enhancement system and method  10  ( FIGS.  1  and  2   ) of the present disclosure. The lidar points  62  are filtered by height. The lidar points  62  are three-dimensional (3D) data that may include ground and tree leaves above the vehicle, for example. Those points  62  can be filtered out that are lower than the ego vehicle&#39;s body or higher than the ego vehicle&#39;s roof, for example. The lidar points  62  remaining are those that represent obstacles to the vehicle&#39;s body. The lidar points  62  are also filtered by the USS&#39;s FoV. Since the lidar points  62  will be used as labels for the USS features  12  ( FIGS.  1  and  2   ), they should match the same FoV. Those lidar points  62  that fall outside the USS&#39;s FoV are thus filtered out. 
       FIG.  6    further illustrates lidar point cloud feature generation  60  in accordance with the USS lidar point cloud enhancement system and method  10  ( FIGS.  1  and  2   ) of the present disclosure. The closest lidar points  62  to the ego vehicle  54  are found by splitting the USS&#39;s FoV into small angles centered at the USS  26  ( FIG.  2   ). Within each angle, a constant number of lidar points  62  are selected that are closest to the ego vehicle  54 . The third dimension (pointing from the ground to the sky) of the selected points  62  is discarded. If there is no lidar point  62  or less points  62  are found, they are padded with (−1,−1) points. The selected lidar points  62  form the lidar feature  14  ( FIGS.  1  and  2   ). The total number of the selected points  62  matches the USS input feature  12  ( FIGS.  1  and  2   ). 
     An autoencoder with supervised learning is used to learn the mapping between USS features  12  and lidar features  14  ( FIGS.  1  and  2   ). For the (−1,−1) points in the lidar labels, since the output will be learned towards the label, they are masked with 0 during the loss calculation. In this way, the meaningful points contribute more to the results during training. The USS feature  12  is used as input. The lidar feature  14  is used as a label. Both serve to map the environment in the enhanced USS environment mapping  18  ( FIG.  1   ). 
     It is to be recognized that, depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. It should be noted that the algorithms of the present disclosure may be implemented on an embedded processing system running a real time operating system (OS), which provides an assured degree of availability and low latency. As discussed below, processing in a cloud system may also be implemented if such availability and latency problems are addressed. 
       FIG.  7    is a network diagram of a cloud-based system  100  for implementing various cloud-based services of the present disclosure, where applicable. The cloud-based system  100  includes one or more cloud nodes (CNs)  102  communicatively coupled to the Internet  104  or the like. The cloud nodes  102  may be implemented as a server or other processing system  200  (as illustrated in  FIG.  8   ) or the like and can be geographically diverse from one another, such as located at various data centers around the country or globe. Further, the cloud-based system  100  can include one or more central authority (CA) nodes  106 , which similarly can be implemented as the server  200  and be connected to the CNs  102 . For illustration purposes, the cloud-based system  100  can connect to a regional office  110 , headquarters  120 , various individual&#39;s homes  130 , laptops/desktops  140 , and mobile devices  150 , each of which can be communicatively coupled to one of the CNs  102 . These locations  110 ,  120 , and  130 , and devices  140  and  150  are shown for illustrative purposes, and those skilled in the art will recognize there are various access scenarios to the cloud-based system  100 , all of which are contemplated herein. The devices  140  and  150  can be so-called road warriors, i.e., users off-site, on-the-road, etc. The cloud-based system  100  can be a private cloud, a public cloud, a combination of a private cloud and a public cloud (hybrid cloud), or the like. 
     Again, the cloud-based system  100  can provide any functionality through services, such as software-as-a-service (SaaS), platform-as-a-service, infrastructure-as-a-service, security-as-a-service, Virtual Network Functions (VNFs) in a Network Functions Virtualization (NFV) Infrastructure (NFVI), etc. to the locations  110 ,  120 , and  130  and devices  140  and  150 . Previously, the Information Technology (IT) deployment model included enterprise resources and applications stored within an enterprise network (i.e., physical devices), behind a firewall, accessible by employees on site or remote via Virtual Private Networks (VPNs), etc. The cloud-based system  100  is replacing the conventional deployment model. The cloud-based system  100  can be used to implement these services in the cloud without requiring the physical devices and management thereof by enterprise IT administrators. 
     Cloud computing systems and methods abstract away physical servers, storage, networking, etc., and instead offer these as on-demand and elastic resources. The National Institute of Standards and Technology (NIST) provides a concise and specific definition which states cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing differs from the classic client-server model by providing applications from a server that are executed and managed by a client&#39;s web browser or the like, with no installed client version of an application required. Centralization gives cloud service providers complete control over the versions of the browser-based and other applications provided to clients, which removes the need for version upgrades or license management on individual client computing devices. The phrase “software as a service” is sometimes used to describe application programs offered through cloud computing. A common shorthand for a provided cloud computing service (or even an aggregation of all existing cloud services) is “the cloud.” The cloud-based system  100  is illustrated herein as one example embodiment of a cloud-based system, and those of ordinary skill in the art will recognize the systems and methods described herein are not necessarily limited thereby. 
       FIG.  8    is a block diagram of a server or other processing system  200 , which may be used in the cloud-based system  100  ( FIG.  7   ), in other systems, or stand-alone, such as in the vehicle itself. For example, the CNs  102  ( FIG.  7   ) and the central authority nodes  106  ( FIG.  7   ) may be formed as one or more of the servers  200 . The server  200  may be a digital computer that, in terms of hardware architecture, generally includes a processor  202 , input/output (I/O) interfaces  204 , a network interface  206 , a data store  208 , and memory  210 . It should be appreciated by those of ordinary skill in the art that  FIG.  8    depicts the server or other processing system  200  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 202 ,  204 ,  206 ,  208 , and  210 ) are communicatively coupled via a local interface  212 . The local interface  212  may be, for example, but is not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  212  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  212  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  202  is a hardware device for executing software instructions. The processor  202  may be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server  200 , a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the server  200  is in operation, the processor  202  is configured to execute software stored within the memory  210 , to communicate data to and from the memory  210 , and to generally control operations of the server  200  pursuant to the software instructions. The I/O interfaces  204  may be used to receive user input from and/or for providing system output to one or more devices or components. 
     The network interface  206  may be used to enable the server  200  to communicate on a network, such as the Internet  104  ( FIG.  7   ). The network interface  206  may include, for example, an Ethernet card or adapter (e.g., 10BaseT, Fast Ethernet, Gigabit Ethernet, or 10 GbE) or a Wireless Local Area Network (WLAN) card or adapter (e.g., 802.11a/b/g/n/ac). The network interface  206  may include address, control, and/or data connections to enable appropriate communications on the network. A data store  208  may be used to store data. The data store  208  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  208  may incorporate electronic, magnetic, optical, and/or other types of storage media. In one example, the data store  208  may be located internal to the server  200 , such as, for example, an internal hard drive connected to the local interface  212  in the server  200 . Additionally, in another embodiment, the data store  208  may be located external to the server  200  such as, for example, an external hard drive connected to the I/O interfaces  204  (e.g., a SCSI or USB connection). In a further embodiment, the data store  208  may be connected to the server  200  through a network, such as, for example, a network-attached file server. 
     The memory  210  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory  210  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  210  may have a distributed architecture, where various components are situated remotely from one another but can be accessed by the processor  202 . The software in memory  210  may include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory  210  includes a suitable operating system (O/S)  214  and one or more programs  216 . The operating system  214  essentially controls the execution of other computer programs, such as the one or more programs  216 , and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The one or more programs  216  may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein. 
     It will be appreciated that some embodiments described herein may include one or more generic or specialized processors (“one or more processors”) such as microprocessors; central processing units (CPUs); digital signal processors (DSPs); customized processors such as network processors (NPs) or network processing units (NPUs), graphics processing units (GPUs), or the like; field programmable gate arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more application-specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured or adapted to,” “logic configured or adapted to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various embodiments. 
     Moreover, some embodiments may include a non-transitory computer-readable medium having computer-readable code stored thereon for programming a computer, server, appliance, device, processor, circuit, etc. each of which may include a processor to perform functions as described and claimed herein. Examples of such computer-readable mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, and the like. When stored in the non-transitory computer-readable medium, software can include instructions executable by a processor or device (e.g., any type of programmable circuitry or logic) that, in response to such execution, cause a processor or the device to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. as described herein for the various embodiments. 
       FIG.  9    is a block diagram of a user device  300 , which may be used in the cloud-based system  100  ( FIG.  7   ), as part of a network, or stand-alone. The user device  300  can be a vehicle, a smartphone, a tablet, a smartwatch, an Internet of Things (IoT) device, a laptop, a virtual reality (VR) headset, etc. The user device  300  can be a digital device that, in terms of hardware architecture, generally includes a processor  302 , I/O interfaces  304 , a radio  306 , a data store  308 , and memory  310 . It should be appreciated by those of ordinary skill in the art that  FIG.  9    depicts the user device  300  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 302 ,  304 ,  306 ,  308 , and  310 ) are communicatively coupled via a local interface  312 . The local interface  312  can be, for example, but is not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  312  can have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  312  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  302  is a hardware device for executing software instructions. The processor  302  can be any custom made or commercially available processor, a CPU, an auxiliary processor among several processors associated with the user device  300 , a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the user device  300  is in operation, the processor  302  is configured to execute software stored within the memory  310 , to communicate data to and from the memory  310 , and to generally control operations of the user device  300  pursuant to the software instructions. In an embodiment, the processor  302  may include a mobile optimized processor such as optimized for power consumption and mobile applications. The I/O interfaces  304  can be used to receive user input from and/or for providing system output. User input can be provided via, for example, a keypad, a touch screen, a scroll ball, a scroll bar, buttons, a barcode scanner, and the like. System output can be provided via a display device such as a liquid crystal display (LCD), touch screen, and the like. 
     The radio  306  enables wireless communication to an external access device or network. Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the radio  306 , including any protocols for wireless communication. The data store  308  may be used to store data. The data store  308  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  308  may incorporate electronic, magnetic, optical, and/or other types of storage media. 
     Again, the memory  310  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, etc.), and combinations thereof. Moreover, the memory  310  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  310  may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor  302 . The software in memory  310  can include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. In the example of  FIG.  9   , the software in the memory  310  includes a suitable operating system  314  and programs  316 . The operating system  314  essentially controls the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The programs  316  may include various applications, add-ons, etc. configured to provide end user functionality with the user device  300 . For example, example programs  316  may include, but not limited to, a web browser, social networking applications, streaming media applications, games, mapping and location applications, electronic mail applications, financial applications, and the like. In a typical example, the end-user typically uses one or more of the programs  316  along with a network, such as the cloud-based system  100  ( FIG.  7   ). 
     Although the present disclosure is illustrated and described herein with reference to illustrative embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following non-limiting claims for all purposes.