Patent Publication Number: US-11380303-B2

Title: System and method for call classification

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims benefit of priority under 35 U.S.C. 119(a)-(d) to a Russian Application No. 2020108161 filed on Feb. 26, 2020, which is incorporated by reference herein. 
     FIELD OF TECHNOLOGY 
     The invention relates to the field of information security, and, more specifically, to system and method for voice call classification. 
     BACKGROUND 
     At present, practically every owner of a universal mobile device (such as a mobile telephone, a smartphone, a tablet, and so forth) is dealing with unwanted calls from banks, medical centers, insurance offices, beauty salons, and other companies using telemarketing in their work. In the United States, legislation is in effect that legally prohibits certain telemarketer calls without the consent of the user. However, the majority of companies implicitly stipulate such a clause in their contracts and get around the legislation. 
     In some cases, the companies providing telephone marketing services make active use of intelligent bots (robots), which in the process of communication may imitate people. The robots determine the level of irritability or interest of a person and often tailor the conversation in such a manner as to avoid a marketing tone. Intelligent bots are also often used by hackers to obtain confidential information of the user (such as data related to credit cards, social security numbers, and so forth). 
     Currently there are mobile applications which determine telephone numbers (such as TrueCaller) that are used to deal with such nuisance calls. In the majority of cases, incoming calls are checked against a database of spam numbers, which is constantly updated, and if the number of a calling spammer or hacker is present in this database the user is notified that the calling party has been detected in intrusive communication. 
     However, at present, it is possible to make a telephone call anonymous or to mask a telephone number with the aid of number swapping technology. Increasingly often, hackers and other malicious entities use this technology to call bank customers from the numbers of credit organizations and request the information needed to make withdrawals. 
     Conventional systems are well equipped to deal well with the problem of identifying robotized calls, but typically are not able to classify a call from various malicious entities. Moreover, in new hacking schemes the changing of the number discredits the legal subscriber instead of the hacker. The blocking of such numbers, for example, may result in banks losing the ability to get through to their customers. 
     Thus, there is a need to solve the problem of call classification, including hacking calls with swapped numbers. 
     SUMMARY 
     Disclosed are systems and methods for classifying voice calls by analysis of the recording and certain attributes of the voice call, and providing the user with information on a particular class of call by means of call classification. 
     In one aspect, a method is proposed for voice call analysis and classification, wherein the method involves steps in which: a voice call session between an initiating device and a recipient device is intercepted. Voice call data exchanged between the initiating device and the recipient device during the voice call session is transformed into a predefined data format. The transformed voice call data is analyzed to determine one or more attributes of the intercepted voice call. One or more features associated with the intercepted voice call session are identified based on the determined one or more attributes. The intercepted voice call is classified using the identified one or more features. 
     In one aspect, transforming the voice call data further includes recording the voice call data. In one aspect, the predefined data format includes formatted text. 
     In one aspect, classification type of the intercepted voice call includes at least one of: a regular call and unwanted call. In one aspect, the one or more identified features includes at least one of: a call category, emotional component of the voice call data, presence of robotized speech and call duration. 
     In one aspect, the call category includes at least one of: telemarketing, social survey, offer of services, fraud. 
     In one aspect, the call category is determined using one or more call categorization attributes based on the recording. In one aspect, the call categorization attributes include at least one of: words, n-grams, word-embedding, bag-of-words. 
     In one aspect, the emotional component of the intercepted voice call is positive or negative based on at least one of: (i) rules in which text is broken up into words or sequences of words having a previously assigned positive or negative evaluation, (ii) glossaries in which the number of positive and negative words from a previously compiled glossary are counted, (iii) machine learning. 
     In one aspect, the presence of the robotized speech is determined using a set of previously selected strings of phonemes. 
     In one aspect, classifying the intercepted voice call further includes using a trained classification model based on a set of previously selected voice calls having features. 
     In one aspect, the trained classification model is based on at least one of: naive Bayesian classifier, logistic regression, MRF (Markov Random Field) classifier, SVM (support vector machine), k-nearest neighbor, and/or decision tree. 
     In one aspect, the method further includes sending a notification to the recipient device. In one aspect, the notification indicates a classification category of the intercepted voice call. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate one or more example aspects of the present disclosure and, together with the detailed description, serve to explain their principles and implementations. 
         FIG. 1  shows a block diagram of a communication network, in accordance with aspects of the present disclosure. 
         FIG. 2  shows a block diagram of an intelligent communication network, in accordance with aspects of the present disclosure. 
         FIG. 3  illustrates a call classification system, in accordance with aspects of the present disclosure. 
         FIG. 4  illustrates a method for analyzing and classifying a voice call, in accordance with aspects of the present disclosure. 
         FIG. 5  shows an example of a general-purpose computer system. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary aspects are described herein in the context of a system, method, and computer program product for voice call classification. Those of ordinary skill in the art will realize that the following description is illustrative only and is not intended to be in any way limiting. Other aspects will readily suggest themselves to those skilled in the art having the benefit of this disclosure. Reference will now be made in detail to implementations of the example aspects as illustrated in the accompanying drawings. The same reference indicators will be used to the extent possible throughout the drawings and the following description to refer to the same or like items. 
       FIG. 1  shows a block diagram of a communication network. The communication network  100  may include an initiating device  101 , a recipient device  102 , a switching node  110 , and media data  111 . The communication network  100  provides multiple physical communication channels and at least one switching node  110 . The switching node  110  provides services for transmission of media data  111  (such as voice call data) between the initiating device  101  and the recipient device  102 . Examples of communication networks  100  may include, but are not limited to: a computer network; a telephone network, and/or a cellular communication network. 
     The term “device” (such as initiating device and recipient device) refers to equipment which transforms user information into media data  111  (such as voice call data) for transmission by communication channels and which performs the back transformation. 
     The term “call-up” refers to an attempt to establish a connection with the recipient device  102 , initiated by the initiating device  101 . 
     The term “call” refers to the process of transmitting media data  111  between the initiating device  101  and the recipient device  102  within an established voice call session. 
     The media data  111  may comprise: a media file; and/or streaming data (media data sent in streaming mode). 
     For example, the initiating device  101  and the recipient device  102  may be mobile telephones of subscribers of a cellular communication network, where the term “call” refers to an ordinary voice call. 
       FIG. 2  shows a block diagram of an intelligent network, in accordance with aspects of the present disclosure. An example of an intelligent communication network  200  may include a switching node  110 , a service switching point  220 , a service control point  230 , and a branching point  240 . The intelligent network  200 , by modernizing at least one switching node  110  of the communication network  100 , may facilitate additional communication services. It should be noted that the architecture of the intelligent network  200  is independent of the type of switching network  100 . 
     In this example, the switching node  110  retains the functions for providing the basic services, but it may also be equipped with a service switching point  220 . The service switching point  220  may be configured to provide for the initiatalizion of various call handling algorithms, may carry out instructions arriving from the service control point  230 , and may monitor the process of call handling of the intelligent network  200 . 
     The service control point  230  may facilitate services in the entire intelligent network  200 , providing protocols for interaction between its elements. 
     In one variant aspect, the intelligent network  200  may contain a branching point  240  that may be configured to provide for the transmission of a copy of the media data  111  to other auxiliary elements of the intelligent network  200  (for example, a speech recognition device), making the services of the intelligent network  200  more convenient to the user. 
     The services provided by intelligent network  200  may include but are not limited to: a toll-free call service, providing directory information free of charge; televoting, providing voting capabilities by calling up a particular number; and/or providing communications by prepaid cards. 
       FIG. 3  illustrates a call classification system, in accordance with aspects of the present disclosure. The call classification system  300  may include a communication network  100 , an initiating device  101 , a recipient device  102 , a call handling engine  310 , media data  111  (such as voice call data), call attributes  312 , a recording unit  320 , a call recording  321 , a classification unit  330 , a recognition module  331 , an analysis module  332 , a classification module  333 , a security unit  335 , a classification model  340 , and a training unit  350 . 
     The call handling engine  310  may be configured to intercept a voice call from the initiating device  101  to the recipient device  102  in the communication network  100 , to determine the characteristics of the call  312 . The call handling engine  310  may be further configured to send the media data  111  to the recording unit  320 . The characteristics of the call determined at this stage may include, but are not limited to: identifier of the initiating device  101 , identifier of the recipient device  102 , a timestamp indicating time of the intercepted call, status of the recipient device  102 . 
     In one aspect, the communication network  100  may comprise an intelligent network  200  (shown in  FIG. 2 ), utilizing resources of public telephone networks. 
     For example, the initiating device  101  may initiate a voice call. After receiving the voice call, the service switching point  220  of the switching node  110  may determine that the connection requires switching with the service control point  230 . The switching node  110  may establish the corresponding voice call session and may send a signal containing the characteristics of the call  312 . 
     The call handling engine  310  may be configured to intercept the characteristics of the call  312  and to notify the switching node  110  as to the need for routing the intercepted voice call through the branching point  240 . The branching point  240  may establish a connection using two different channels: a first channel to the recipient device  102  and a second channel to the recording unit  320 . 
     After the connection has been established, the voice call data  111  from each of the respective devices are sent through the branching point  240 , where the data may be duplicated. In other words, one data stream may be directed to the receiving party, another data stream may be directed to the recording unit  320 . Such duplication may continue until the voice call is completed. 
     In one aspect, the switching node  110  may not be able to establish a connection with the recipient device  102 , at least because: the recipient device  102  responds with a “busy” signal; the recipient device  102  is unavailable; an error has occurred in establishing the connection; and/or the recipient device  102  responds with a refusal to receive the connection. 
     In these scenarios, the call handling engine  310  may send a signal to the switching node  110  indicating the need to route the intercepted voice call to the recording unit  320 . 
     The recording unit  320  may be configured to record the media data  111  being sent within an established voice call session during a call and to direct the produced recording of the call  321  to the classification unit  330 . The recording of the call  321  may comprise a media file, for example. 
     In one aspect, the recording unit  320  may produce a recording of the voice call data  111  in a plurality of fragments. The recording unit  320  may send a fragment recording of the voice call  321  to the classification unit  330  prior to the actual completion of the call, thusly making it possible to determine its classification in advance. 
     As shown in  FIG. 3 , the classification unit  330  may include a recognition module  331 , an analysis module  332 , and a classification module  333 . 
     The recognition module  331  may be configured to transform the recording of the call  321  received from the recording unit  320  into a predefined format suitable for analysis, such as formatted text. 
     In one aspect, the recognition module  331  may divide up the media file into a plurality of fragments. Each fragment of the media file may be subjected to a number of transformations, as a result of which coefficients are obtained which describe frequency characteristics of the corresponding fragment. On the basis of this data, the recognition module  331  may determine with a certain probability which phoneme the corresponding fragment belongs to. 
     The recognition module  331  may also be additionally trained using a set of previously selected texts to recognize strings of probable phonemes. If necessary, the recognition module  331  may reconstruct unrecognized words by meaning, based on the context and available statistics. The data obtained in the course of the recognition of phonemes and the recognition of strings of phonemes may be combined and the recognition module  331  may determine the most likely sequence of words. 
     For example, for two equally probable phonemes “m” and “t” in the word “make”, the recognition module  331  will conclude that it is more likely that the phoneme “m” is used in the word, since during its training it often encountered the sequence “make money”, and more seldom “take money”. 
     In one variant aspect, in the concluding phase, the recognition module  331  may transform numerals into numbers and certain punctuation marks (such as hyphens) may be put in place. This transformed text is the final result of the recognition, which may be sent to the analysis module  332  by the recognition module  331 . 
     In an aspect, the recognition module  331  may be further trained using a set of previously selected strings of phonemes to recognize robotized speech in a call recording  321 . 
     In an aspect, the recognition module  331  may identify the features of the call  312 . 
     The identified call features may include, but are not limited to: a call category, emotional component of the voice call data, presence of robotized speech and call duration. 
     The analysis module  332  may be configured to pronounce a verdict as to whether a call recording  321  pertains to at least one of the categories based on an analysis of the content of the call recording  321  processed by the recognition module  331 . 
     The categories of the call recording  321  may include, but are not limited to: fraud; spam; offer of services; and/or regular call. 
     In an aspect, the analysis module  332  may transform the call recording  321  processed by the recognition module  331  into a set of attributes suitable for categorization. The attributes may include, but are not limited to: words; a sequence of words (n-grams); a vectorial representation of words (word embedding); and/or a multiset of words not counting grammar or order (bag-of-words). 
     Next, using a machine learning algorithm, the analysis module  332  may determine a type of the call recording  321  processed by the recognition module  331 . The machine learning classification algorithm may include, but is not limited to: Bayesian classifiers (naive Bayesian classifier); logistic regression; MRF classifier; the method of support vectors (SVM, support vector machine); nearest neighbor methods (k-nearest neighbor); and/or decision tree. 
     For example, the machine learning algorithm utilized by the analysis module  332  may be trained with a large number of fraudulent call recordings, where hackers under a variety of pretexts (checking data, financial transactions) ask for certain personal data, such as bank account numbers or passwords. Each fraudulent call recording may be represented as a set of attributes. As a non-limiting example, the phrase “send password from SMS”, present in the call recording  321  processed by the recognition module  331  in the form of one of the attributes, may allow the analysis module  332  to assign the call recording  321  processed by the recognition module  331  to the classification type of “fraud”. 
     In one aspect, the analysis module  332  may utilize the plurality of attributes to make an emotional assessment of the call recording  321 . Such analysis enables the analysis module  332  to determine whether the emotional content of the intercepted voice call was one of the following: positive; negative; and/or neutral. 
     The analysis module  332  may be configured to determine the emotional content of the intercepted voice call using one of the following methods:
         based on rules, in which text is broken up into words or sequences of words having a previously assigned positive or negative evaluation;   based on glossaries, in which the number of positive and negative words from a previously compiled glossary are counted; such glossaries may include the particles “not” or “non”;   based on machine learning; and/or   based on a hybrid method involving the use of some or all of the classifiers in a particular sequence.       

     The classification module  333  may be configured to provide a verdict as to whether the call belongs to at least one of the types of voice calls by using a trained classification model  340 . The classification categories of voice calls may include, but are not limited to: unwanted call and/or regular call. 
     In one aspect, the characteristics of a call pertaining to an already known class of voice calls may be first collected. On the basis of the collected training data, the classification model  340  may be trained so that voice calls having similar characteristics can be classified by that classification model  340  with an improved accuracy. 
     The classification model  340  may be trained using at least the following training data: the category of the call recording (such as telemarketing, social survey, offer of services, fraud and the like); the emotional components of the voice call data (positive, negative, neutral); presence of the robotized speech in the call recording and/or the duration of the call. 
     For example, if the analysis module  332  has classified a call recording  321  of the intercepted voice call as offering services and has determined the emotional content of the call as being negative, the classification module  333  may classify the intercepted voice call as unwanted, and pertaining to spam. 
     The classification algorithm employed by the classification module  333  may include at least one of the following:
         Bayesian classifiers (naive Bayesian classifier);   logistic regression;   MRF classifier;   the method of support vectors (SVM, support vector machine);   nearest neighbor methods (k-nearest neighbor); and/or   decision tree.       

     The security unit  335  may be configured to inform the user of the recipient device  102  as to a certain class of the voice call via a notification message. The notification message may include, but is not limited to: an SMS message; a local notification (push notification); and/or a pop-up window. 
     In one aspect, the notification message sent to the recipient device  102  may include the recording  321  of the voice call data processed by the recognition module  331 . 
     For example, if during the call-up the switching node  110  was unable to establish a connection with the recipient device  102 , the classification unit  330  may determine the class of the voice call based on the recording of media data (such as voice call data)  111  sent by the initiating device  101 . The security unit  335  may be configured to inform the user of the recipient device  102  as to the determined class of the missed voice call with a local notification, for example. 
     In an aspect, the security unit  335  may send a command to the switching node  110  in advance to terminate a call-up if the type of the call has been determined at least as fraud. 
     The training unit  350  may be configured to train the classification model  340  on the basis of new training data. 
     For example, if the decision of the classification model  340  has proved to be wrong, the user of the recipient device  102  may redefine the type of the corresponding voice call. In other words, the training unit  350  may train the classification model  340 , based on the feedback of the user of the recipient device  102 , so that the probability of a correct determination of the call class is heightened during further use of the classification model  340 . 
       FIG. 4  illustrates a method for analyzing and classifying a voice call, in accordance with aspects of the present disclosure. 
     At step  410 , the call handling engine  310  may intercept a voice call from the initiating device  101  to the recipient device  102  in the communication network  100 . 
     At step  420 , the recognition module  331  may transform the recording of the call  321  received from the recording unit  320  into a predefined format suitable for analysis, such as formatted text. 
     At step  430 , the call handling engine  310  may be used to determine the attributes of the intercepted voice call  312 . In one aspect, the call categorization attributes may include at least one of: words, n-grams, word-embedding, bag-of-words. 
     At step  440 , the analysis module  332  may transform the call recording  321  processed by the recognition module  331  into a set of features suitable for classification. As noted above, the features may include, but are not limited to: a call category, emotional component of the voice call data, presence of robotized speech and call duration. 
     At step  450 , the classification module  333  may be configured to provide a verdict as to whether the call belongs to at least one of the classification categories of voice calls by using a trained classification model  340 . 
     The classification categories of voice calls may include, but are not limited to: fraud; spam; telemarketing; unwanted call; and/or regular call. 
     In addition, in an aspect, at step  460  the training unit  350  may be used to train the classification model  340  so that the accuracy of the classification is increased in the next iteration. 
     In addition, in an aspect, at step  470  the security unit  335  may be used to provide the user of the recipient device  102  with information about the call class which has been determined. 
       FIG. 5  is a block diagram illustrating a computer system  20  on which aspects of systems and methods for call classification may be implemented in accordance with an exemplary aspect. The computer system  20  may represent a call classification system of  FIG. 3  and can be in the form of multiple computing devices, or in the form of a single computing device, for example, a desktop computer, a notebook computer, a laptop computer, a mobile computing device, a smart phone, a tablet computer, a server, a mainframe, an embedded device, and other forms of computing devices. 
     As shown, the computer system  20  includes a central processing unit (CPU)  21 , a system memory  22 , and a system bus  23  connecting the various system components, including the memory associated with the central processing unit  21 . The system bus  23  may comprise a bus memory or bus memory controller, a peripheral bus, and a local bus that is able to interact with any other bus architecture. Examples of the buses may include PCI, ISA, PCI-Express, HyperTransport™, InfiniBand™, Serial ATA, I2C, and other suitable interconnects. The central processing unit  21  (also referred to as a processor) can include a single or multiple sets of processors having single or multiple cores. The processor  21  may execute one or more computer-executable code implementing the techniques of the present disclosure. The system memory  22  may be any memory for storing data used herein and/or computer programs that are executable by the processor  21 . The system memory  22  may include volatile memory such as a random access memory (RAM)  25  and non-volatile memory such as a read only memory (ROM)  24 , flash memory, etc., or any combination thereof. The basic input/output system (BIOS)  26  may store the basic procedures for transfer of information between elements of the computer system  20 , such as those at the time of loading the operating system with the use of the ROM  24 . 
     The computer system  20  may include one or more storage devices such as one or more removable storage devices  27 , one or more non-removable storage devices  28 , or a combination thereof. The one or more removable storage devices  27  and non-removable storage devices  28  are connected to the system bus  23  via a storage interface  32 . In an aspect, the storage devices and the corresponding computer-readable storage media are power-independent modules for the storage of computer instructions, data structures, program modules, and other data of the computer system  20 . The system memory  22 , removable storage devices  27 , and non-removable storage devices  28  may use a variety of computer-readable storage media. Examples of computer-readable storage media include machine memory such as cache, SRAM, DRAM, zero capacitor RAM, twin transistor RAM, eDRAM, EDO RAM, DDR RAM, EEPROM, NRAM, RRAM, SONOS, PRAM; flash memory or other memory technology such as in solid state drives (SSDs) or flash drives; magnetic cassettes, magnetic tape, and magnetic disk storage such as in hard disk drives or floppy disks; optical storage such as in compact disks (CD-ROM) or digital versatile disks (DVDs); and any other medium which may be used to store the desired data and which can be accessed by the computer system  20 . 
     The system memory  22 , removable storage devices  27 , and non-removable storage devices  28  of the computer system  20  may be used to store an operating system  35 , additional program applications  37 , other program modules  38 , and program data  39 . The computer system  20  may include a peripheral interface  46  for communicating data from input devices  40 , such as a keyboard, mouse, stylus, game controller, voice input device, touch input device, or other peripheral devices, such as a printer or scanner via one or more I/O ports, such as a serial port, a parallel port, a universal serial bus (USB), or other peripheral interface. A display device  47  such as one or more monitors, projectors, or integrated display, may also be connected to the system bus  23  across an output interface  48 , such as a video adapter. In addition to the display devices  47 , the computer system  20  may be equipped with other peripheral output devices (not shown), such as loudspeakers and other audiovisual devices. 
     The computer system  20  may operate in a network environment, using a network connection to one or more remote computers  49 . The remote computer (or computers)  49  may be local computer workstations or servers comprising most or all of the aforementioned elements in describing the nature of a computer system  20 . Other devices may also be present in the computer network, such as, but not limited to, routers, network stations, peer devices or other network nodes. The computer system  20  may include one or more network interfaces  51  or network adapters for communicating with the remote computers  49  via one or more networks such as a local-area computer network (LAN)  50 , a wide-area computer network (WAN), an intranet, and the Internet. Examples of the network interface  51  may include an Ethernet interface, a Frame Relay interface, SONET interface, and wireless interfaces. 
     Aspects of the present disclosure may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. 
     The computer readable storage medium can be a tangible device that can retain and store program code in the form of instructions or data structures that can be accessed by a processor of a computing device, such as the computing system  20 . The computer readable storage medium may be an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. By way of example, such computer-readable storage medium can comprise a random access memory (RAM), a read-only memory (ROM), EEPROM, a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), flash memory, a hard disk, a portable computer diskette, a memory stick, a floppy disk, or even a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon. As used herein, a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or transmission media, or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network interface in each computing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing device. 
     Computer readable program instructions for carrying out operations of the present disclosure may be assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language, and conventional procedural programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a LAN or WAN, or the connection may be made to an external computer (for example, through the Internet). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure. 
     In various aspects, the systems and methods described in the present disclosure can be addressed in terms of modules. The term “module” as used herein refers to a real-world device, component, or arrangement of components implemented using hardware, such as by an application specific integrated circuit (ASIC) or FPGA, for example, or as a combination of hardware and software, such as by a microprocessor system and a set of instructions to implement the module&#39;s functionality, which (while being executed) transform the microprocessor system into a special-purpose device. A module may also be implemented as a combination of the two, with certain functions facilitated by hardware alone, and other functions facilitated by a combination of hardware and software. In certain implementations, at least a portion, and in some cases, all, of a module may be executed on the processor of a computer system. Accordingly, each module may be realized in a variety of suitable configurations, and should not be limited to any particular implementation exemplified herein. 
     In the interest of clarity, not all of the routine features of the aspects are disclosed herein. It would be appreciated that in the development of any actual implementation of the present disclosure, numerous implementation-specific decisions must be made in order to achieve the developer&#39;s specific goals, and these specific goals will vary for different implementations and different developers. It is understood that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking of engineering for those of ordinary skill in the art, having the benefit of this disclosure. 
     Furthermore, it is to be understood that the phraseology or terminology used herein is for the purpose of description and not of restriction, such that the terminology or phraseology of the present specification is to be interpreted by the skilled in the art in light of the teachings and guidance presented herein, in combination with the knowledge of those skilled in the relevant art(s). Moreover, it is not intended for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such. 
     The various aspects disclosed herein encompass present and future known equivalents to the known modules referred to herein by way of illustration. Moreover, while aspects and applications have been shown and described, it would be apparent to those skilled in the art having the benefit of this disclosure that many more modifications than mentioned above are possible without departing from the inventive concepts disclosed herein.