Patent Publication Number: US-2023141160-A1

Title: Systems and methods for application programming interface analysis

Description:
TECHNICAL FIELD 
     The present invention relates generally to systems and methods for analyzing software applications, including systems and methods for analyzing and identifying software applications using a crawler. 
     BACKGROUND 
     Most medium and large enterprise computing environments rely on a wide variety of software applications to provide the necessary functionality to internal users and/or external customers. Due to the variation in technical infrastructure, hosting environments, non-functional requirements, and dependencies of such software applications, many organizations have turned to the implementation of application programming interfaces (APIs) as a flexible and efficient method for integrating software applications together into a cohesive framework of functionality. 
     However, every software application may not be a candidate for conversion or integration into an API paradigm. In many cases, certain software applications may be hosted and/or developed internally while other applications may be hosted externally. In addition, some applications may be managed by external vendors. Furthermore, internally-developed applications may host and/or expose multiple services—which may result in a monolith-type application that may be more effectively implemented if broken down into a plurality of micro services. Finally, certain applications may have other functional and/or non-functional requirements like traffic management, monitoring, security access and the like. All of these application characteristics can have a critical effect on whether software applications can be considered as viable candidates for API development and/or conversion. 
     SUMMARY 
     Accordingly, an object of the invention is to provide systems and methods for analyzing and identifying software applications as candidates for API adoption using an automated crawler that receives application-specific data from a plurality of data sources, including an application repository and an application image store, and analyzes the data to generate one or more lists of candidate applications that can be reviewed to determine which application(s) may be potentially integrated into an API framework. The techniques described herein beneficially enable the rapid and efficient identification of software applications that may be suitable for API development and delivery using programmatic logic implemented in one or more computing devices. The methods and systems advantageously enable product managers and software developers to focus on creating or enhancing API functionality for software applications that can benefit from such efforts. 
     The invention, in one aspect, features a computerized method for analyzing and identifying applications using a crawler. A server computing device receives first data from an application registry and second data from an application image store corresponding to a plurality of applications. The server computing device identifies a first set of candidate applications of the plurality of applications based on the first data. The server computing device identifies a second set of candidate applications of the plurality of applications based on the second data. The server computing device stores the first set of candidate applications and the second set of candidate applications in a database. The server computing device generates for display on a user interface a first list of the first set of candidate applications and a second list of the second set of candidate applications. The server computing device receives third data from a user via the user interface. The server computing device identifies a third set of candidate applications of the plurality of applications based on the first data and the third data. 
     The invention, in another aspect, features a system for analyzing and identifying applications using a crawler. The system comprises a server computing device communicatively coupled to a user device and a database over a network. The server computing device receives first data from an application registry and second data from an application image store corresponding to a plurality of applications. The server computing device identifies a first set of candidate applications of the plurality of applications based on the first data. The server computing device identifies a second set of candidate applications of the plurality of applications based on the second data. The server computing device stores the first set of candidate applications and the second set of candidate applications in the database. The server computing device generates for display on a user interface a first list of the first set of candidate applications and a second list of the second set of candidate applications. The server computing device receives third data from a user via the user interface. The server computing device identifies a third set of candidate applications of the plurality of applications based on the first data and the third data. 
     Any of the above aspects can include one or more of the following features. In some embodiments, the server computing device identifies a fourth set of candidate applications of the plurality of applications based on the second data and the third data, and generates for display on the user interface a third list of the third set of candidate applications and a fourth list of the fourth set of candidate applications. In some embodiments, identifying the first set of candidate applications comprises determining, for each of the applications of the plurality of applications, whether the application is externally hosted and managed by a vendor. In some embodiments, the first set of candidate applications comprises determining, for each of the applications of the plurality of applications, whether the application is managed by a vendor. 
     In some embodiments, identifying the second set of candidate applications comprises determining, for each of the applications of the plurality of applications, whether the application hosts multiple services. In some embodiments, the user interface retrieves the first set of candidate applications and the second set of candidate applications from the database. In some embodiments, the server computing device is configured to receive a portion of the third data from the application registry corresponding to the plurality of applications. In some embodiments, the third data comprises traffic management characteristics. In some embodiments, the third data comprises application monitoring requirements. In some embodiments, the third data comprises security access requirements. 
     Other aspects and advantages of the invention can become apparent from the following drawings and description, all of which illustrate the principles of the invention, by way of example only. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The advantages of the invention described above, together with further advantages, may be better understood by referring to the following description taken in conjunction with the accompanying drawings. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. 
         FIG.  1    is a block diagram of an exemplary data communications network, according to embodiments of the technology described herein. 
         FIG.  2    is a block diagram of an exemplary server computing device and an exemplary user device, according to embodiments of the technology described herein. 
         FIG.  3    is a block diagram of an exemplary system for analyzing and identifying applications using a crawler, according to embodiments of the technology described herein. 
         FIG.  4    is a flow diagram of a computerized method of analyzing and identifying applications using a crawler, according to embodiments of the technology described herein. 
         FIG.  5    is a diagram of an exemplary user interface generated by user interface module of server computing device for display. 
         FIG.  6    comprises exemplary programmatic logic used by application crawler of server computing device. 
     
    
    
     DETAILED DESCRIPTION 
     The systems and methods described herein can be implemented using a data communications network, server computing devices, and mobile devices. For example, referring to  FIGS.  1  and  2   , an exemplary communications system  100  includes data communications network  150 , exemplary server computing devices  200 , and exemplary user devices  250 . In some embodiments, the system  100  includes one or more server computing devices  200  and one or more user devices  250 . Each server computing device  200  can include a processor  202 , memory  204 , storage  206 , and communication circuitry  208 . Each user device  250  can include a processor  252 , memory  254 , storage  256 , and communication circuitry  258 . In some embodiments, communication circuitry  208  of the server computing devices  200  is communicatively coupled to the communication circuitry  258  of the user devices  250  via data communications network  150 . Communication circuitry  208  and communication circuitry  258  can use Bluetooth, Wi-Fi, or any comparable data transfer connection. The user devices  250  can include personal workstations, laptops, tablets, mobile devices, or any other comparable device. 
       FIG.  3    is a block diagram of an exemplary system  300  for analyzing and identifying applications using a crawler, according to embodiments of the technology described herein. As can be appreciated, system  300  can comprise any or all of the devices and/or components described above with respect to  FIGS.  1  and  2   . 
     As shown in  FIG.  3   , system  300  comprises application registry  302   a,  application image store  302   b,  client computing device  303 , communications network  304 , server computing device  306  that includes user interface module  308   a  and application crawler  308   b,  and candidate database  312 . In some embodiments, application registry  302   a,  application image store  302   b,  and/or candidate database  312  are located on one or more server computing devices that are communicably coupled to server computing device  306  via communications network  304 . 
     As will be explained in greater detail below, application registry  302   a,  application image store  302   b,  and candidate database  312  are each configured to receive, generate, and store specific segments of data relating to the process of analyzing and identifying applications using a crawler as described herein. In some embodiments, all or a portion of application registry  302   a,  application image store  302   b,  and candidate database  312  can be integrated with the server computing device  306  or be located on a separate computing device or devices. In some embodiments, one or more of application registry  302   a,  application image store  302   b,  and candidate database  312  are located in a cloud storage infrastructure comprising one or more nodes accessible by server computing device  306 . 
     Application registry  302   a  includes data relating to one or more applications that may be deployed in a production computing environment, such as an enterprise computing system that provides application functionality to internal and/or external users. In some embodiments, application registry  302   a  includes a dedicated section that contains specialized data used by the other components of the system  300  to perform at least a portion of the process of analyzing and identifying applications using a crawler as described herein. Generally, application registry  302   a  comprises data and metadata associated with one or more applications, including but not limited to application hosting information—such as whether an application is hosted internally or externally and/or whether the application is managed by a vendor. An exemplary application registry that can be used with system  300  is the Now™ platform available from ServiceNow, Inc. of Santa Clara, Calif. (www.servicenow.com). 
     Application image store  302   b  includes data relating to one or more applications that may be deployed in a production computing environment, such as an enterprise computing system that provides application functionality to internal and/or external users. In some embodiments, application image store  302   b  includes a dedicated section that contains specialized data used by the other components of the system  300  to perform at least a portion of the process of analyzing and identifying applications using a crawler as described herein. Generally, application image store  302   b  comprises data and metadata associated with one or more applications, including but not limited to data corresponding to application binaries, libraries, images, and dependencies. As can be appreciated, an application can be considered ‘monolithic’—meaning that it hosts and/or exposes multiple services and thus it may be desirable to break down the application into micro services in order to provide a more flexible and adaptive technology infrastructure. As such, application image store  302   b  can comprise data relating to whether the application hosts and/or exposes multiple services. An exemplary application image store  302   b  that can be used with system  300  is the JFrog™ Artifactory available from JFrog Ltd. of Sunnyvale, Calif. (jfrog.com). 
     An exemplary client computing device  303  includes but are not limited to desktop computers, laptop computers, tablets, mobile devices, smartphones, smart watches, Internet-of-Things (IoT) devices, and internet appliances. It should be appreciated that other types of computing devices that are capable of connecting to the components of the system  300  can be used without departing from the scope of invention. Although  FIG.  3    depicts a single client computing device  303 , it should be appreciated that the system  300  can include any number of client computing devices. 
     Network  304  enables application registry  302   a,  application image store  302   b,  candidate database  312  and server computing device  306  to communicate with each other. In some embodiments, client computing device  303  is similarly connected to network  304  in order to communicate with server computing device  306 . Network  304  is typically a wide area network, such as the Internet and/or a cellular network. In some embodiments, network  304  is comprised of several discrete networks and/or sub-networks (e.g., cellular to Internet). 
     In some embodiments, server computing device  306  is a device including specialized hardware and/or software modules that execute on a processor and interact with memory modules, to receive data from other components of the system  300 , transmit data to other components of the system  300 , and perform functions for analyzing and identifying applications using a crawler as described herein. Server computing device  306  includes several computing modules  308   a,    308   b  that execute on one or more processors of server computing device  306 . In some embodiments, modules  308   a,    308   b  are specialized sets of computer software instructions programmed onto one or more dedicated processors in server computing device  306  and can include specifically-designated memory locations and/or registers for executing the specialized computer software instructions. 
     Although modules  308   a,    308   b  are shown in  FIG.  3    as executing within the same server computing device  306 , in some embodiments the functionality of modules  308   a,    308   b  can be distributed among a plurality of server computing devices. As shown in  FIG.  3   , server computing device  306  enables modules  308   a,    308   b  to communicate with each other in order to exchange data for the purpose of performing the described functions. It should be appreciated that any number of computing devices, arranged in a variety of architectures, resources, and configurations (e.g., cluster computing, virtual computing, cloud computing) can be used without departing from the scope of the invention. Exemplary functionality of modules  308   a,    308   b  is described in detail below. 
       FIG.  4    is a flow diagram of a computerized method  400  of analyzing and identifying applications using a crawler, according to embodiments of the technology described herein. The computerized method  400  of  FIG.  4    can be implemented using the system  300  of  FIG.  3   . 
     Application crawler  308   b  of server computing device  306  connects to application registry  302   a  via network  304  to receive (step  402 ) first data from application registry  302   a  that corresponds to a plurality of applications. In some embodiments, application crawler  308   b  receives data from application registry  302   a  that is associated with (i) whether one or more of the plurality of applications is externally or internally hosted and/or (ii) whether one or more of the plurality of applications is managed by a vendor. Application crawler  308   b  generally comprises programmatic logic that analyzes application hosting information stored in application registry  302   a  and receives the above-referenced data from application registry  302   a.  Exemplary application hosting information can include, but is not limited to, host address data (e.g., physical and/or logical IP addresses), network connectivity and infrastructure data, server and/or platform identifier(s), host device identifiers, vendor identifiers, and the like. 
     Application crawler  308   b  also connects to application image store  302   b  via network  304  to receive (step  402 ) second data from application image store  302   b  that corresponds to a plurality of applications. In some embodiments, application crawler  308   b  receives data from application image store  302   b  that is associated with whether one or more of the plurality of applications hosts and/or exposes multiple services. Application crawler  308   b  generally comprises programmatic logic that analyzes application image information stored in application image store  302   b  and receives the above-referenced data from application image store  302   b.  In some embodiments, application image store  302   b  may not comprise information whereby application crawler  308   b  can directly determine whether one or more of the plurality of applications hosts and/or exposes multiple services. Instead, in these embodiments application crawler  308   b  can execute logic that deciphers available information in application image store  302   b  to understand whether one or more of the plurality of applications hosts and/or exposes multiple services. For example, in an implementation that uses JFrog™ Artifactory as application image store  302   b,  application crawler  302   b  can execute the JFrog Xray™ tool (described at jfrog.com/xray) on one or more application images stored in application image store  302   b  to receive data that is used to determine whether one or more of the plurality of applications hosts and/or exposes multiple services. 
     When application crawler  308   b  receives the first data from application registry  302   a  and the second data from application image store  302   b,  application crawler  308   b  identifies (step  404 ) a first set of candidate applications of the plurality of applications based on the first data and identifies (step  406 ) a second set of candidate applications of the plurality of applications based on the second data. In some embodiments, application crawler  308   b  executes programmatic logic on the first data and the second data in a predetermined sequence in order to identify the first set of candidate applications and the second set of candidate applications. For example, as an initial step, application crawler  308   b  can analyze the first data received from application registry  302   a  to determine whether one or more of the plurality of applications is hosted internally or externally and whether one or more of the plurality of applications is managed by a vendor. Application crawler  308   b  identifies applications that are managed by a vendor and that are hosted externally for inclusion in the first set of candidate applications of the plurality of applications. 
     In some embodiments, between step  402  and step  404 , one or more users of client computing device  303  can review the applications in the first set of candidate applications and provide input on certain features, such as non-functional features like traffic management characteristics (e.g., quota, rate limiting), monitoring requirements, security access requirements and the like, of one or more of the applications in the first list. In some embodiments, applications with certain identified non-functional features can be removed or pruned from the first set of candidate applications. 
     Then, for applications that are not included in the first set of candidate applications, application crawler  308   b  can analyze the second data received from application image store  302   b  to determine whether one or more of the applications hosts and/or exposes multiple services. Application crawler  308   b  identifies applications that host and/or expose multiple services for inclusion in the second set of candidate applications of the plurality of applications. 
     When application crawler  308   b  generates the first set of candidate applications and the second set of candidate applications as described above, application crawler  308   b  stores (step  408 ) the first set of candidate applications and the second set of candidate applications in candidate database  312 . In some embodiments, application crawler  308   b  can supplement data associated with the first set of candidate applications and/or the second set of candidate applications with additional information from application repository  302   a —such as authentication method(s) used by the candidate applications and/or other non-functional requirements of the candidate applications—for use in generating the user interface as described below. 
     User interface module  308   a  of server computing device  306  generates (step  410 ) for display on a user interface a first list of the first set of candidate applications and a second list of the second set of candidate applications. In some embodiments, user interface module  308   a  generates a user interface comprising the first list and the second list and communicates the user interface to client computing device  303  for display to one or more users. As can be appreciated, client computing device  303  can comprise or be coupled to a display screen (not shown) that renders and displays the user interface. One or more users at client computing device  303  can view the first list of the first set of candidate applications and the second list of the second set of candidate applications and provide input relating to aspects or features of one or more applications in the first list and/or the second list. As mentioned above, in some embodiments one or more users of client computing device  303  can review the applications in the first list and/or the second list and provide input on certain features, such as non-functional features like traffic management characteristics (e.g., quota, rate limiting), monitoring requirements, security access requirements and the like, of one or more of the applications in the first list and/or the second list. As mentioned above, application crawler  308   b  can supplement the requirements with data received from application repository  308   a  (e.g., authentication methods, security access requirements) and display the data to a user of client computing device  303 . It should be appreciated that in some embodiments user interface module  308   a  can be located on client computing device  303 , such that application crawler  308   b  provides data that enables client computing device  303  to generate the user interface locally. 
       FIG.  5    is a diagram of an exemplary user interface  500  generated by user interface module  308   a  of server computing device  306  for display on client computing device  303 . User interface  500  includes a table comprised of first list  502  and second list  504 , wherein first list  502  includes the first set of candidate applications of the plurality of applications as determined by application crawler  308   a  using the first data, and second list  504  includes the second set of candidate applications of the plurality of applications as determined by application crawler  308   a  using the second data. A user at client computing device  303  can provide input relating to other functional or non-functional requirements of one or more applications in first list  502 . For example, a user can indicate that application A4275 requires traffic management service-level architecture (SLA) (e.g., by typing into the Other Requirements field for that application, by selecting from a drop-down menu, etc.). When the user interacts with the submit button, client computing device  303  transmits the user input to application crawler  308   b  of server computing device  306 . 
     Application crawler  308   b  receives (step  412 ) the above-referenced input provided by the one or more users of client computing device  303  as third data. Application crawler  308   b  identifies (step  412 ) a third set of candidate applications of the plurality of applications based on the first data and the third data. For example, certain applications may be considered as candidates for application programming interface integration and/or adoption only when (i) the applications are vendor-managed and externally hosted (as determined using the first data) and (ii) the applications have certain non-functional requirements (as determined using the third data). In one example, as mentioned above a non-functional requirement can be when the application requires traffic management service-level architecture (SLA). One or more users at client computing device  303  can indicate that one or more applications in the first set of candidate applications requires traffic management SLA and provide that indication as third data to application crawler  308   b.  Application crawler  308   b  then uses the third data to identify a third set of candidate applications based on the first data and the third data. 
       FIG.  6    comprises exemplary programmatic logic  600  used by application crawler  308   b  to perform steps  404 ,  406 , and  412  of  FIG.  4   . As shown in  FIG.  6   , application crawler  308   b  executes lines  602  to  604  to identify a first set of candidate applications of the plurality of applications based on the first data (e.g.., whether the application is managed by a vendor and hosted externally or internally, as received from application registry  302   a )—which corresponds to step  404  of  FIG.  4   . With the first set of candidate applications, application crawler  308   b  executes lines  605  to  607  to analyze the third data received from one or more users via the user interface displayed on client computing device  303  (e.g., traffic management characteristics or monitoring requirements) to identify a third set of candidate applications of the plurality of applications based on the first data and the third data—which corresponds to step  412  of  FIG.  4   . 
     For applications that are not identified in the first set of candidate applications or the third set of candidate applications, application crawler  308   b  executes lines  610  to  616  to identify a second set of candidate applications of the plurality of applications based on the second data (e.g., whether the application hosts and/or exposes multiple services). As mentioned above, in some embodiments application crawler  308   b  analyzes application images stored in application image store  302   b  (e.g., using a tool like JFrog Xray) to determine the second data. 
     In some embodiments, application crawler  308   b  can identify a fourth set of candidate applications of the plurality of applications based on the second data and fourth data, where the fourth data can comprise (i) input provided by a user of client computing device  303  (e.g., using user interface  500 ) that relates to functional and/or non-functional requirements of the plurality of applications and/or (ii) data received from application registry  302   a  that relates to functional and/or non-functional requirements of the plurality of applications. For example, a user at client computing device  303  can provide input into user interface  500  relating to one or more applications in second list  504  and when the user interacts with the submit button, user interface  500  transmits the input to application crawler  308   b  of server computing device  306  as fourth data. Application crawler  308   b  can identify a fourth set of candidate applications of the plurality of applications using the second data and the fourth data. For example, application crawler  308   b  can determine that an application exposes multiple services (using the second data) and that the application has certain requirements (e.g., security access requirements, traffic management requirements, monitoring requirements) using the fourth data, as input by user of client computing device  303  and/or received from application registry  302   a.  In some embodiments, user interface module  308   a  can generate for display on the user interface a third list of the third set of candidate applications and a fourth list of the fourth set of candidate applications. For example, a user at client computing device  303  can view the user interface comprising the third list and the fourth list to, e.g., set a product roadmap, schedule delivery of one or more APIs for application(s) in the third list and/or the fourth list, or perform other types of software development activities based upon the information contained in the user interface. 
     In some embodiments, once a final list of candidate applications of the plurality of applications is generated by application crawler  308   b,  the final list can be provided to client computing device  303  so that a user at client computing device  303  can validate the final list (e.g., to confirm the final list of candidate applications is complete, accurate, correct, etc.). 
     The above-described techniques can be implemented in digital and/or analog electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The implementation can be as a computer program product, i.e., a computer program tangibly embodied in a machine-readable storage device, for execution by, or to control the operation of, a data processing apparatus, e.g., a programmable processor, a computer, and/or multiple computers. A computer program can be written in any form of computer or programming language, including source code, compiled code, interpreted code and/or machine code, and the computer program can be deployed in any form, including as a stand-alone program or as a subroutine, element, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one or more sites. The computer program can be deployed in a cloud computing environment (e.g., Amazon® AWS, Microsoft® Azure, IBM®). 
     Method steps can be performed by one or more processors executing a computer program to perform functions of the invention by operating on input data and/or generating output data. Method steps can also be performed by, and an apparatus can be implemented as, special purpose logic circuitry, e.g., a FPGA (field programmable gate array), a FPAA (field-programmable analog array), a CPLD (complex programmable logic device), a PSoC (Programmable System-on-Chip), ASIP (application-specific instruction-set processor), or an ASIC (application-specific integrated circuit), or the like. Subroutines can refer to portions of the stored computer program and/or the processor, and/or the special circuitry that implement one or more functions. 
     Processors suitable for the execution of a computer program include, by way of example, special purpose microprocessors specifically programmed with instructions executable to perform the methods described herein, and any one or more processors of any kind of digital or analog computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and/or data. Memory devices, such as a cache, can be used to temporarily store data. Memory devices can also be used for long-term data storage. Generally, a computer also includes, or is operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. A computer can also be operatively coupled to a communications network in order to receive instructions and/or data from the network and/or to transfer instructions and/or data to the network. Computer-readable storage mediums suitable for embodying computer program instructions and data include all forms of volatile and non-volatile memory, including by way of example semiconductor memory devices, e.g., DRAM, SRAM, EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and optical disks, e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memory can be supplemented by and/or incorporated in special purpose logic circuitry. 
     To provide for interaction with a user, the above described techniques can be implemented on a computing device in communication with a display device, e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display) monitor, a mobile device display or screen, a holographic device and/or projector, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse, a trackball, a touchpad, or a motion sensor, by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, and/or tactile input. 
     The above-described techniques can be implemented in a distributed computing system that includes a back-end component. The back-end component can, for example, be a data server, a middleware component, and/or an application server. The above described techniques can be implemented in a distributed computing system that includes a front-end component. The front-end component can, for example, be a client computer having a graphical user interface, a Web browser through which a user can interact with an example implementation, and/or other graphical user interfaces for a transmitting device. The above described techniques can be implemented in a distributed computing system that includes any combination of such back-end, middleware, or front-end components. 
     The components of the computing system can be interconnected by transmission medium, which can include any form or medium of digital or analog data communication (e.g., a communication network). Transmission medium can include one or more packet-based networks and/or one or more circuit-based networks in any configuration. Packet-based networks can include, for example, the Internet, a carrier internet protocol (IP) network (e.g., local area network (LAN), wide area network (WAN), campus area network (CAN), metropolitan area network (MAN), home area network (HAN)), a private IP network, an IP private branch exchange (IPBX), a wireless network (e.g., radio access network (RAN), Bluetooth, near field communications (NFC) network, Wi-Fi, WiMAX, general packet radio service (GPRS) network, HiperLAN), and/or other packet-based networks. Circuit-based networks can include, for example, the public switched telephone network (PSTN), a legacy private branch exchange (PBX), a wireless network (e.g., RAN, code-division multiple access (CDMA) network, time division multiple access (TDMA) network, global system for mobile communications (GSM) network), and/or other circuit-based networks. 
     Information transfer over transmission medium can be based on one or more communication protocols. Communication protocols can include, for example, Ethernet protocol, Internet Protocol (IP), Voice over IP (VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol (HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway Control Protocol (MGCP), Signaling System #7 (SS7), a Global System for Mobile Communications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT over Cellular (POC) protocol, Universal Mobile Telecommunications System (UMTS), 3GPP Long Term Evolution (LTE) and/or other communication protocols. 
     Devices of the computing system can include, for example, a computer, a computer with a browser device, a telephone, an IP phone, a mobile device (e.g., cellular phone, personal digital assistant (PDA) device, smart phone, tablet, laptop computer, electronic mail device), and/or other communication devices. The browser device includes, for example, a computer (e.g., desktop computer and/or laptop computer) with a World Wide Web browser (e.g., Chrome™ from Google, Inc., Microsoft® Internet Explorer® available from Microsoft Corporation, and/or Mozilla® Firefox available from Mozilla Corporation). Mobile computing device include, for example, a Blackberry® from Research in Motion, an iPhone® from Apple Corporation, and/or an Android™-based device. IP phones include, for example, a Cisco® Unified IP Phone 7985G and/or a Cisco® Unified Wireless Phone 7920 available from Cisco Systems, Inc. 
     The above-described techniques can be implemented using supervised learning and/or machine learning algorithms. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. Each example is a pair consisting of an input object and a desired output value. A supervised learning algorithm or machine learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. 
     Comprise, include, and/or plural forms of each are open ended and include the listed parts and can include additional parts that are not listed. And/or is open ended and includes one or more of the listed parts and combinations of the listed parts. 
     One skilled in the art will realize the subject matter may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting of the subject matter described herein.