FIELD OF INVENTION:

[0001] The present disclosure generally relates to digital banking and electronic payment systems.

BACKGROUND OF THE INVENTION:

[0002] Point-of-sale (PoS) refers to a place where the payment for goods or services is executed. It may be present at a physical store, where POS terminals and systems are used to process card payments or at a virtual sales point such as a computer or mobile electronic device.

[0003] PoS devices are provided by banks to merchants for sales of goods and services. On every transaction, bank charges a merchant with a fee that generates revenue for the bank. However, the PoS devices come with certain restrictions to comply with banks risk policy e.g., one such restriction is transaction limit. The transaction limit is usually set by banks on terminals at the time of onboarding. There are no set intervals for re-visiting or changing the limits and on most occasions, these are changed only when there is an explicit request made from merchants to the bank.

[0004] Often merchants face transaction limit issues due to increase in business, festive season, sudden growth etc. This leads to opportunity loss for merchants as well as banks because bank’s revenue is directly proportional to merchant’s transaction amount. Further, if the transaction limit is set to a very large amount, it may attract or increase the scope for misuse by way of making high value fraudulent transactions at the terminals by the merchants or their associated parties and increase the chances of fraud.

[0005] Thus, there exists a need in the art to reduce opportunity loss and at the same time limit chances of fraud by setting an appropriate transaction limit and the duration for which this is required.

OBJECTS OF THE INVENTION:

[0006] An object of the present invention is to provide transaction limit setting for merchants (both new and existing) to reduce opportunity loss and reduce the chances of fraud. [0007] Another object of the present invention is to generate an accurate revenue forecast for sales and planning team, thereby improving the operation efficiency of a business.

SUMMARY OF THE INVENTION:

[0008] The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

[0009] In one non-limiting embodiment of the present disclosure, a method to detect at least one changepoint time for a business is disclosed. The method comprises retrieving application data associated with a target merchant from a merchant device of the target merchant and collecting transaction data, suspicious activities data, accounts data, and geographical data of plurality of existing merchants present near the target merchant, the plurality of existing merchants are segmented based on the application data of the target merchant. The method further comprises determining a merchant risk score for each of the of plurality of existing merchants, based on a merchant suspicious class, number of suspicious transactions, and the suspicious activities data, categorizing the plurality of existing merchants based on the merchant risk score as risky or non- risky, and detecting the at least one changepoint time for the business based at least on the transaction data of the non-risky merchants.

[0010] In still non-limiting embodiment of the present disclosure, the method further comprises determining a germination time period of the target merchant based on the at least one changepoint time and generating a revenue forecast of the business based on the germination time period. The application data comprises at least one of location information of the target merchant, target merchant profile and target merchant business data.

[0011] In yet another non-limiting embodiment of the present disclosure, the method further comprises setting a transaction limit for the target merchant based on the germination time period. The detecting the at least one changepoint time of the target merchant comprises aggregating transaction data of each of the non-risky merchants, determining a transaction growth rate of each of the non-risky merchants based on the aggregated transaction data, clustering the non-risky merchants based on the determined transaction growth rate, identifying a cluster based on a number of the non-risky merchants, and analyzing the transaction data of each of the non- risky merchants in the identified cluster for detecting the at least one changepoint time of the target merchant.

[0012] In yet another non-limiting embodiment of the present disclosure, the suspicious activities data at least comprises transaction using a pickup or lost or stolen card, invalid merchant, invalid account number, transaction at invalid terminal, consecutive transaction leading to insufficient fund and consecutive attempt of transaction above daily limit. The categorizing the plurality of existing merchants comprises normalizing the merchant risk score for the plurality of existing merchants and categorizing a merchant as risky or non-risky based on the normalized merchant risk score of the plurality of existing merchants.

[0013] In yet another non-limiting embodiment of the present disclosure, a system to determine at least one changepoint time for a business is disclosed. The system comprising a memory and a processor communicatively coupled to the memory. The processor configured to retrieve application data associated with a target merchant from a merchant device of the target merchant, collect transaction data, suspicious activities data, accounts data, and geographical data of plurality of existing merchants present near the target merchant, the plurality of existing merchants are segmented based on the application data of the target merchant. The processor is further configured to determine a merchant risk score for each of the of plurality of existing merchants, based on a merchant suspicious class, number of suspicious transactions, and the suspicious activities data, categorize the plurality of existing merchants based on the merchant risk score as risky or non-risky, and detect the at least one changepoint time for the business based at least on the transaction data of the non-risky merchants.

[0014] In yet another non-limiting embodiment of the present disclosure, the processor is further configured to determine a germination time period of the target merchant based on the at least one changepoint time and set a transaction limit for the target merchant based on the germination time period.

[0015] In yet another non-limiting embodiment of the present disclosure, the application data comprises at least one of location information of the target merchant, target merchant profile and target merchant business data and to detect the at least one changepoint time of the target merchant, the processor is configured to aggregate transaction data of each of the non-risky merchants, determine a transaction growth rate of each of the non-risky merchants based on the aggregated transaction data, cluster the non-risky merchants based on the determined transaction growth rate, identify a cluster based on a number of the non-risky merchants, and analyze the transaction data of each of the non-risky merchants in the identified cluster for detecting the at least one changepoint time of the target merchant.

[0016] In yet another non-limiting embodiment of the present disclosure, the suspicious activities data at least comprises transaction using a pickup or lost or stolen card, invalid merchant, invalid account number, transaction at invalid terminal, consecutive transaction leading to insufficient fund and consecutive attempt of transaction above daily limitand to categorize the plurality of existing merchants, the processor is configured to normalize the merchant risk score for the plurality of existing merchants and categorize a merchant as risky or non-risky based on the normalized merchant risk score of the plurality of existing merchants.

[0017] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF DRAWINGS:

[0018] The features, nature, and advantages of the present disclosure will become more apparent from the detailed description set forth below when taken conjunction with the drawings in which like reference characters identify correspondingly throughout. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

[0019] Fig. 1 shows an exemplary environment illustrating a scenario of providing an analytics service for a target merchant, in accordance with an embodiment of the present disclosure; [0020] Fig. 2 illustrates a block diagram of service provider system for providing an analytics service for a target merchant, in accordance with another embodiment of the present disclosure;

[0021] Fig. 3 illustrate a flowchart of an exemplary method for detecting at least one changepoint time for a business, in accordance with another embodiment of the present disclosure;

[0022] Fig. 4 illustrates shape based time series clustering, in accordance with another embodiment of the present disclosure;

[0023] Fig. 5 illustrates a block diagram of system for detecting at least one changepoint time for a business, in accordance with another embodiment of the present disclosure;

[0024] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION OF DRAWINGS:

[0025] In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0026] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.

[0027] The terms “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus proceeded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[0028] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0029] Fig. 1 shows an exemplary environment 100 illustrating a scenario of providing an analytics service for a target merchant, in accordance with an embodiment of the present disclosure.

[0030] In one embodiment of the present disclosure, the environment 100 may comprise a target merchant application 101, a service provider system 103, payment network 107, bank or financial institution 109, database 111. The payment network 107 may provide transaction between a plurality of merchant (Mi, M2, ... M _n ) and customers (Ci, C2, ... C _n ). The plurality of merchants may be associated with a specific business type and may belong to a specific geographical location. The database 111 may comprise merchant’s data including transaction data, merchant profile, suspicious transaction data, geo-location data associated with the respective merchant.

[0031] The bank or financial institution 109 may request an analytics service from the service provider system 103 for one or more target merchants. The service provider system 103 may retrieve application data from target merchant application 101 present on the merchant device of the target merchant. The service provider system 103 may collect transaction data, suspicious activities data, accounts data, and geographical data of plurality of existing merchants from the database 111. [0032] The service provider system 103 may segment the plurality of existing merchants based on the collected and retrieved data. The service provider system 103 may then determine a merchant risk score for each of the existing merchant based on the determination of merchant suspicious class, number of suspicious transactions and suspicious activities observed at merchant location. The service provider system 103 may categorize the merchant as risky or non-risky based on the merchant risk score.

[0033] The service provider system 103 may determine the output function for the target merchant based on the transaction data of the non-risky merchant. The output function may comprise at least one changepoint time and germination time period. In one non-limiting embodiment of the present invention, the output function may also comprise revenue forecast of business of the target merchant. The service provider system 103 may also set the transaction limit for the target merchant based on the at least one changepoint time and germination time period.

[0034] The service provider system 103 by setting an appropriate transaction limit for a particular time interval may facilitate reduction in opportunity loss for merchant and reduction in chances of fraud due to merchant’s fraudulent transactions. The service provider system 103 may further facilitate accurate revenue forecast for sales and planning team for a particular type of business.

[0035] Fig. 2 illustrates a block diagram of service provider system 200 for providing an analytics service for a target merchant, in accordance with another embodiment of the present disclosure.

[0036] In one embodiment of the present disclosure, the service provider system 200 may comprise data pre-processing module 201, category and location analysis module 203, risk scoring module 205, merchant segmenting module 207, and germination time estimator module 209 in communication with each other. According to one embodiment, the modules 201-209 may be dedicated hardware units capable of performing various operations of the service provider system 200. However, according to other embodiments, the modules 201-209 may a processor or an application-specific integrated circuit (ASIC) or any circuitry capable of executing instructions stored in the memory (not shown) of the service provider system 200. [0037] In an embodiment of the present disclosure, the service provider system 200 address the technical challenge of how to reduce the opportunity loss and the chances of fraud, and accurately generate revenue forecast for sales and planning team for a particular type of business. The input data to the service provider system 200 may comprise transaction data of the existing merchants, application data of the target merchant, geo-location data of the existing merchants, and the fraud report of existing merchants present near the target merchant. The fraud report may comprise transaction using a pickup or lost or stolen card, invalid merchant, invalid account number, transaction at invalid terminal, consecutive transaction leading to insufficient fund and consecutive attempt transaction above daily limit. The existing merchants may be located at a predetermined distance from the target merchant.

[0038] The data pre-processing module 201 may be configured to process or analyze the transaction data of the existing merchant to generate the aggregated transaction data. The data preprocessing module 201 may apply ETL (extract/transform/load) process to the transaction data of the existing merchant. The transaction data of the existing merchant may be historical transaction data or the pre-stored previous transaction details of the existing merchant. The data pre-processing module 201 may generate the aggregated transaction data as shown in table 1 below.

Table 1: Example of aggregated transaction data

[0039] The transaction data may be aggregated based on the merchant id, transaction id, transaction amount, and transaction date using the ETL process. The category and location analysis module may be configured to apply location intelligence on the location data of existing merchants and categorize the existing merchant. Then, the merchant segmenting module 207 may be configured to segment the merchant based on the application data of the target merchant, location data of existing merchants, and merchant category code/ business type of existing merchant. The application data of the target merchant may comprise location data of the target merchant, target merchant profile and target merchant business data. The merchant segmenting module 207 may segment the merchants as shown in table 2 below:

Table 2: Example of Merchant segmentation

[0040] In the next step, risk scoring module 205 may be configured to calculate risk score or merchant risk score based on the merchant suspicious class, number or frequency of suspicious transactions, and suspicious activities observed at the merchant location. In one non-limiting embodiment of the present disclosure, the class and frequency of suspicious transaction is determined with at least one genuine transaction prior to those suspicious transaction in a sequence at a given terminal.

[0041] The risk score may be a function denoted by:

Risk score = fn(Fc, SA) (1) where,

Fc = Number of risky transactions based on suspicious class (RC codes of the transaction response code), SA = Number of suspicious activities observed.

[0042] In an embodiment of the present disclosure, every transaction may have a response code. The successful transaction may be denoted by response code “00” and unsuccessful transaction may be denoted by response code other than “00”. In an exemplary embodiment, the response code and suspicious class may be assigned as shown in table 3 below.

Table 3: Example of response code and suspicious class assignment

[0043] From the example shown in table 3, F _c (Number of risky transactions based on suspicious class) is 6. In one non-limiting embodiment of the present disclosure, the determination of number of risky transactions and suspicious class is not limited to above example and a person skilled in the art may determine the number of risky transactions and suspicious class using a different method.

[0044] In an embodiment of the present invention the determination of the suspicious activities may be determined based on the table 4 shown below:

Table 4: Determination of the suspicious activities

Table 6: Example 2

[0045] From the example shown in table 4, 5, and 6, SA (Number of suspicious merchant activities) is 5. In one non-limiting embodiment of the present disclosure, the determination of number of suspicious merchant activities is not limited to above example and a person skilled in the art may determine the number of risky transactions and suspicious class using a different method.

[0046] The merchant risk score may be calculated based on the equation 1 mentioned above. The merchant risk score may then be normalized. The merchant risk score may be normalized as shown in table 7 below:

[0047] The risk score module 205 may also be configured to categorize the merchant as risky or non-risky based on the normalized score as shown in table 7 above. The risky merchants may be removed from further analysis. In one non-limiting embodiment of the present disclosure, the merchant risk score calculation is not limited to above example and a person skilled in the art may calculate the merchant risk score by feeding ( c, SA) of all existing merchants into learning algorithms like regression and classification algorithms to automatically get risk score (Probability) and class of merchant.

[0048] The germination time estimator module 209 may be configured to receive the aggregated transaction data of each of the non-risky merchants from the data pre-processing module 201 and determine a transaction growth rate of each of the non-risky merchants based on the aggregated transaction data. The germination time estimator module 209 may comprise a merchant clustering module not shown that may be configured to use a time series-based clustering method to cluster merchants based on similar transaction growth patterns as shown in fig. 4.

[0049] The merchant clustering module may process the daily transaction of merchants in similar category code, identify the optimal number of clusters based on Silhouette analysis and apply dynamic time warping distance measures for time series clustering. The merchants time series data depends on location and merchant category (e.g. all the merchants in the target location may not be of same age). Thus, clustering method considers merchant time series data with varied length. The clustering method may cluster merchants based on growth shape for similarity as shown in fig. 4. However, the clustering method is not limited to above exemplary embodiment. In another exemplary embodiment, the clustering may be performed using other time series-based distance measures.

[0050] The germination time estimator module 209 may then be configured to identify a cluster based on a number of the non-risky merchants and analyze the transaction data of each of the non-risky merchants in the identified cluster for detecting the at least one changepoint time of the target merchant. In one non-limiting embodiment, the identified cluster comprises highest number of non-risky merchants. The germination time estimator module 209 may determine a germination time period of the target merchant based on the at least one changepoint time. The germination time may be determined based on the equation shown below:

Germination time period = Changepoint time - Start time of business . (2)

[0051] In one non-limiting embodiment of the present disclosure, the germination time estimator module 209 may be configured to set a transaction limit for the target merchant based on the germination time period. In one another non-limiting embodiment of the present disclosure, the germination time estimator module 209 may be configured to generate a revenue forecast of the business based on the germination time period. This facilitates reduction in opportunity loss and chances of suspicious, and accurate revenue forecast for the business.

[0052] Fig. 3 illustrate a flowchart of an exemplary method 300 for detecting at least one changepoint time for a business, in accordance with another embodiment of the present disclosure.

[0053] At block 301 , application data associated with a target merchant may be retrieved from a merchant device of the target merchant. The application data may comprise location information of the target merchant, target merchant profile and target merchant business data. In one non-limiting embodiment of the present disclosure, the application data associated with the target merchant is extracted from a local database of a system.

[0054] At block 303, transaction data, suspicious activities data, accounts data, and geographical data, and business data of plurality of existing merchants present near the target merchant may be collected from a database of the system. At block 305, the plurality of existing merchants may be segmented based on the transaction data, the suspicious activities data, the accounts data, and the geographical data, the business data and the retrieved application data. The plurality of existing merchants may be segmented as discussed above. [0055] At block 307, a merchant risk score for each of the of the existing merchants present near the target merchant may be determined based on the merchant suspicious class, number of suspicious transactions, and the suspicious activities data. The suspicious activities data may comprise transaction using a pickup or lost or stolen card, invalid merchant, invalid account number, transaction at invalid terminal, consecutive transaction leading to insufficient fund and consecutive attempt of transaction above daily limit. The merchant risk score for each of the existing merchants may be determined based on the method discussed above.

[0056] At block 309, the existing merchants may be categorized as risky or non-risky based on the merchant risk score. The categorization of the existing merchants may comprise normalizing of the merchant risk score for the plurality of existing merchants and categorizing a merchant as risky or non-risky based on the normalized merchant risk score. The categorization of the existing merchants may be done according to the example discussed above. The risky merchants may be removed from further analysis.

[0057] At block 311, one or more changepoint time for the business is detected based at least on the transaction data of the non-risky merchants. The changepoint time detection may comprise further steps of aggregating transaction data of each of the non-risky merchants, determining a transaction growth rate of each of the non-risky merchants based on the aggregated transaction data, clustering the non-risky merchants based on the determined transaction growth rate. The merchant clustering may be done according to the procedure discussed above. The changepoint time detection may further comprise identifying a cluster with greater number of the non-risky merchants and analyzing the transaction data of each of the non-risky merchants in the identified cluster for detecting the changepoint time of the target merchant.

[0058] The changepoint time may be used to determine the germination time period. The germination time may be determined based on the equation (2) mentioned above. The method 300 may further comprise setting a transaction limit for the target merchant based on the germination time period. The setting of transaction limit may comprise increasing or decreasing the present or current transaction limit based on the germination time period. The method 300 may further comprise generating a revenue forecast of the business based on the germination time period. This facilitates reduction in opportunity loss and chances of fraud, and accurate revenue forecast for the business. [0059] In an embodiment of the present disclosure, the steps of method 300 may be performed in an order different from the order described above.

[0060] Fig. 4 illustrates shape based time series clustering, in accordance with another embodiment of the present disclosure.

[0061] In an embodiment of the present disclosure, the non-risky merchant are clustered together based on the transaction data. The merchants having the similar shape are clustered using shape based time series clustering. The cluster having the highest number of merchants is used for estimating or determining the at least one changepoint time. In one non-limiting embodiment, the transaction data of each of the non-risky merchants of cluster 3 shown in fig. 4 may be analysed for detecting the changepoint time of the target merchant.

[0062] Fig. 5 illustrates a block diagram of system for detecting at least one changepoint time for a business, in accordance with another embodiment of the present disclosure.

[0063] In an embodiment of the present invention, the system 500 may comprise a processor 501 , a database 503, an input interface 505 and an output interface 507 in communication with each other. The input interface 505 may comprise a transmitter and the output interface may comprise a receiver. The processor 501 may be configured to retrieve application data associated with a target merchant from a merchant device of the target merchant via the input interface 505. The application data may comprise location information of the target merchant, target merchant profile and target merchant business data. In one non-limiting embodiment of the present disclosure, the application data associated with the target merchant is extracted from the memory 503 of a system 500.

[0064] The processor 501 may be then configured to collect transaction data, suspicious activities data, accounts data, and geographical data, and business data of plurality of existing merchants present near the target merchant from the memory 503 of the system. In one nonlimiting embodiment of the present disclosure, the data associated with the existing merchants may be received via the input interface 505.

[0065] The plurality of existing merchants may be segmented based on the transaction data, the suspicious activities data, the accounts data, and the geographical data, the business data and the retrieved application data. The plurality of existing merchants may be segmented as discussed above.

[0066] The processor 501 may be then configured to determine a merchant risk score for each of the of the existing merchants present near the target merchant based on the merchant suspicious class, number of suspicious transactions, and the suspicious activities data. The suspicious activities data may comprise transaction using a pickup or lost or stolen card, invalid merchant, invalid account number, transaction at invalid terminal, consecutive transaction leading to insufficient fund and consecutive attempt of transaction above daily limit. The merchant risk score for each of the existing merchants may be determined based on the procedure discussed above.

[0067] The processor 501 may be then configured to categorize the existing merchants as risky or non-risky based on the merchant risk score. To categorize the existing merchants the processor 501 may be configured to normalize the merchant risk score for the plurality of existing merchants and categorize a merchant as risky or non-risky based on the normalized merchant risk score. The categorization of the existing merchants may be done according to the example discussed above. The risky merchants may be removed from further analysis.

[0068] The processor 501 may be configured to detect one or more changepoint time for the business based at least on the transaction data of the non-risky merchants. To detect the changepoint time the processor 501 may be configured to aggregate transaction data of each of the non-risky merchants, determine a transaction growth rate of each of the non-risky merchants based on the aggregated transaction data, cluster the non-risky merchants based on the determined transaction growth rate. The merchant clustering may be done according to the procedure discussed above. The processor 501 may be configured to identify a cluster with greater number of the non-risky merchants and analyze the transaction data of each of the non-risky merchants in the identified cluster for detecting the changepoint time of the target merchant.

[0069] The processor 501 may be configured to determine the germination time period based on the detected changepoint time. The germination time may be determined based on the equation (2) mentioned above. The processor 501 may be configured to set a transaction limit for the target merchant based on the germination time period. The setting of transaction limit may comprise increasing or decreasing the present or current transaction limit based on the germination time period. The processor 501 may also be configured to generate a revenue forecast of the business based on the germination time period. This facilitates reduction in opportunity loss and chances of fraud, and accurate revenue forecast for the business.

[0070] The memory 503 may maintain software organized in loadable code segments, modules, applications, programs, etc., which may be referred to herein as software modules. Each of the software modules may include instructions and data that, when installed or loaded on the processor 501 and executed by the processor 501, contribute to a run-time image that controls the operation of the processors 501. When executed, certain instructions may cause the processor 501 to perform functions in accordance with certain methods, algorithms and processes described herein.

[0071] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

[0072] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer- readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., are non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[0073] Suitable processors include, by way of example, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), and/or a state machine.