Patent Application
20170255999
In some cases, a performance value associated with an enterprise system may depend at least in part on how resources are allocated in connection with that enterprise system. For example, the performance value might change depending on a total number of associations with the enterprise system (as reflected by electronic records and associated sets of attribute variables) and that total number might, in turn, depend at least in part on how resources are allocated. Moreover, an accurate prediction of the performance value may be desired. Manually predicting the performance value, however, can be a time consuming and error prone process, especially when a substantial number of electronic records and/or attribute variables may influence the behavior of the system. Similarly, a large and diverse number of ways of allocating resources may further complicate such tasks. Note that improving the performance of the system and/or accuracy of performance predictions may result in substantial improvements to the operation of a network (e.g., by reducing an overall number of electronic messages that need to be created and transmitted via the network).
It would be desirable to provide systems and methods to automatically improve the prediction of one or more performance values in a way that provides faster, more accurate results and that allows for flexibility and effectiveness when responding to those results.
According to some embodiments, systems, methods, apparatus, computer program code and means are provided to improve the prediction of one or more performance values. In some embodiments, an automated resource allocation interface may receive, for a plurality of resource allocation categories, a selected sub-category from a set of potential sub-categories. Based on the selected sub-categories, a resource allocation score may be calculated, and the enterprise system may be assigned to a resource allocation level. A back-end application computer server may access electronic records representing a plurality of potential associations and, for each potential association, a set of attribute variables. Based on the set of attribute variables, a present total number of associations may be generated. Based on the assigned resource allocation level and the set of attribute variables, a future change to the total number of associations may be forecast. Based on the forecasted future change to the total number of associations, a predicted performance value associated with the enterprise system may be calculated and transmitted to generate a user interface display.
Some embodiments comprise: means for receiving, by an automated resource allocation interface computer for a plurality of resource allocation categories, a selected sub-category from a set of potential sub-categories; means for, based on the selected sub-categories, calculating a resource allocation score for the enterprise system; means for, based on the resource allocation score, assigning the enterprise system to one of a pre-determined number of resource allocation levels; means for automatically accessing, by the back-end application computer server, electronic records from a data store containing electronic records representing a plurality of potential associations and, for each potential association, a set of attribute variables; means for, based on the set of attribute variables, automatically generating a present total number of associations; means for receiving the assigned resource allocation level for the enterprise system; means for, based on the assigned resource allocation level and the set of attribute variables, automatically forecasting a future change to the total number of associations; means for, based at least in part on the forecasted future change to the total number of associations, automatically calculating the predicted performance value associated with the enterprise system; and means for transmitting an indication of the predicted performance value associated with the enterprise system to generate an interactive user interface display.
In some embodiments, a communication device associated with a back-end application computer server exchanges information with remote devices. The information may be exchanged, for example, via public and/or proprietary communication networks.
A technical effect of some embodiments of the invention is an improved and computerized ways to automatically improve the prediction of one or more performance values to provide faster, more accurate results and that allow for flexibility and effectiveness when responding to those results. With these and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.
The present invention provides significant technical improvements to facilitate electronic messaging and dynamic data processing. The present invention is directed to more than merely a computer implementation of a routine or conventional activity previously known in the industry as it significantly advances the technical efficiency, access and/or accuracy of communications between devices by implementing a specific new method and system as defined herein. The present invention is a specific advancement in the area of one or more performance values predictions by providing benefits in data accuracy, data availability and data integrity and such advances are not merely a longstanding commercial practice. The present invention provides improvement beyond a mere generic computer implementation as it involves the processing and conversion of significant amounts of data in a new beneficial manner as well as the interaction of a variety of specialized client and/or third party systems, networks and subsystems. For example, in the present invention information may be processed, forecast, and/or predicted via a back-end application server and results may then be analyzed accurately to evaluate the accuracy of various results, thus improving the overall performance of the system associated with message storage requirements and/or bandwidth considerations (e.g., by reducing the number of messages that need to be transmitted via a network). Moreover, embodiments associated with predictive models might further improve performance values, predictions of performance values, resource allocation decisions, etc.
A performance value associated with an enterprise system may depend at least in part on how resources are allocated in connection with that enterprise system. For example, the performance value might change depending on a total number of associations with the enterprise system (as reflected by electronic records and associated sets of attribute variables) and that total number might, in turn, depend at least in part on how resources are allocated. Moreover, an accurate prediction of the performance value may be desired. Manually predicting the performance value, however, can be a time consuming and error prone process, especially when a substantial number of electronic records and/or attribute variables may influence the behavior of the system. Similarly, a large and diverse number of ways of allocating resources may further complicate such tasks. Note that improving the performance of the system and/or accuracy of performance predictions may result in substantial improvements to the operation of a network.
It would be desirable to provide systems and methods to automatically improve the prediction of one or more performance values in a way that provides faster, more accurate results and that allows for flexibility and effectiveness when responding to those results.
The back-end application computer server 150 and/or resource allocation interface computer 140 might be, for example, associated with a Personal Computer (“PC”), laptop computer, smartphone, an enterprise server, a server farm, and/or a database or similar storage devices. According to some embodiments, an “automated” back-end application computer server 150 and/or resource allocation interface computer 140 may facilitate the prediction of a performance value based on electronic records in the computer store 110. As used herein, the term “automated” may refer to, for example, actions that can be performed with little (or no) intervention by a human.
As used herein, devices, including those associated with the back-end application computer server 150 and any other device described herein may exchange information via any communication network which may be one or more of a Local Area Network (“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network (“WAN”), a proprietary network, a Public Switched Telephone Network (“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (“IP”) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.
The back-end application computer server 150 may store information into and/or retrieve information from the computer store 110. The computer store 110 might, for example, store electronic records representing a plurality of potential associations, each electronic record having a set of attribute values. The computer store 110 may also contain information about past and current interactions with parties, including those associated with remote communication devices. The computer store 110 may be locally stored or reside remote from the back-end application computer server 150. As will be described further below, the computer store 110 may be used by the back-end application computer server 150 to predict one or more performance values. Although a single back-end application computer server 150 is shown in
According to some embodiments, the system 100 may automatically predict a performance value via the automated back-end application computer server 150. For example, at (1) the remote administrator computer 160 may provide inputs to the resource allocation interface computer 140. At (2), the resource allocation interface computer 140 might transmit information to the back-end application computer server 150 (e.g., indicating a resource allocation level). The performance value prediction platform 155 may then access information in the computer store at (3) and transmit a predicted performance value to the administrator computer at (4).
Note that the system 100 of
At S210, an automated resource allocation interface computer programmed may receive, for a plurality of resource allocation categories, a selected sub-category from a set of potential sub-categories. For example, at least one potential sub-category might be associated with a passive association processes, an active association process, and/or a negative association process. Based on the selected sub-categories, at S220 the system may calculate a resource allocation score for an “enterprise system.” As used herein, the phrase “enterprise system” might refer to, for example, an employer, trade association, etc. At S230, based on the resource allocation score, the enterprise system may be assigned to one of a pre-determined number of resource allocation levels.
According to some embodiments, the automated resource allocation interface comprises a voluntary group insurance benefit enrollment strategy interface, the “resource allocation levels” are associated with a marketing budget allocation (e.g., a “gold,” “silver,” or “bronze” level), and the resource allocation categories include an employee election style, an enrollment method, an employee access and communications process, a marketing strategy, a producer status, and/or a pricing event. Moreover, the potential sub-categories might include a passive election style, an active election style, a negative election style, generic paper enrollment, personized paper enrollment, online and mixed enrollment, benefit fair and group optional communications, email and telephonic communications, one-on-one communications, a generic marketing strategy, a one or two touch marketing strategy, a three or more touch marketing strategy, a very important person producer status indication, a new pricing event, and/or an in-force pricing event.
At S240, an automated back-end application computer server may access electronic records that represent a plurality of potential “associations” and, for each potential association, a set of attribute variables. As used herein, the term “associations” might refer to risk sharing associations such as those associated with potential voluntary benefit insurance policies. The voluntary benefit insurance policies might be associated with, for example, life insurance, accidental death and dismemberment insurance, short term disability insurance, long-term disability insurance, critical illness insurance, voluntary accident insurance, dental insurance, vision insurance, automotive insurance, and/or home insurance. Moreover, the set of attribute variables might include, for example, an enrollment type, gender, an age or date of birth value or band of values, an income value or band of values, demographic information, socio-economic status data, third party data (e.g., third party credit score data), and/or medical data (e.g., associated with employee medical history costs).
Based on the set of attribute variables, at S250 the system may automatically generate a present total number of associations (e.g., how many employees have already enrolled in a group benefit program). At S260, the back-end application computer server may receive the assigned resource allocation level for the enterprise system. Based on the assigned resource allocation level and the set of attribute variables, at S270 the system may automatically forecast a future change to the total number of associations. For example, the system might forecast that a 3% increase in participation is expected (e.g., based on the marketing budget dedicated to educating employs about a particular benefit). Note that the future change to the total number of associations might include, for example, new employees predicted to be hired in the future, employees predicted to retire in the future employee movement between age bands in the future, employee movement between income bands in the future.
At S280, based at least in part on the forecasted future change to the total number of associations, the system may automatically calculate the predicted performance value associated with the enterprise system. According to some embodiments, risk information is calculated for the automatic forecast of the future change to the total number of associations, and the predicted performance value is further based on the risk information.
According to some embodiments, the automatic forecast of the future change to the total number of associations is based at least in part on an association type, at least one time-related attribute variable (e.g., employee ages), and/or elements of the present total number of associations within bands of time periods for the time-related attribute value. According to some embodiments, the automatic forecast of the future change to the total number of associations is further based at least in part on characteristics associated with individual elements within each time period band (e.g., employees who are between 40 years old and 45 years old). According to still other embodiments, the predicted performance value associated with an employer comprises at least one of: a predicted price, a predicted loss ratio, and/or a predicted combined loss ratio. Moreover, according to some embodiments, risk information is calculated for the automatic forecast of the future change to the total number of associations, and the predicted performance value is further based on the risk information. The risk information might comprise or be associated with, for example, an overall number of insurance claims, and (ii) an overall insurance claim value amount. At S290, an indication of the predicted performance value associated with the enterprise system may be transmitted to generate an interactive user interface display.
score=Σi=1i=NWisub_categoryi
where wi represents a scoring weight for each sub-categoryi (e.g., as defined in a resource allocation scoring table). The resource allocation scoring platform 350 might, according to some embodiments, then assign a resource allocation level based on the overall score that was calculated (e.g., a “gold,” “silver,” or “bronze” level).
According to some embodiments, the resource allocation level determined in
In general, when forecasting the system might not only focus on the number of new lives, but instead also look at the age and socio-economic status of the forecasted lives (employees). Individuals who are young and have a relatively low salaries may be less likely to purchase life insurance because they have less disposable income to purchase insurance (or may not be well informed about insurance options and benefits). Older individuals and individuals with relatively higher salaries may also be less likely to purchase life insurance because they have often already researched the subject (and have already purchased supplemental life insurance through the employer or have made a decision to purchase insurance elsewhere).
The illustrations described with respect to
Note that it is possible to see slightly different loads even when the same needed percentage is input (that is, the exact same load might not result every time 2% is entered for the needed forecast). This might occur, for example, when the tool re-solves for a penetration level when the overall needed forecast pick is adjusted. This might cause the iterations of the forecasted distribution to differ slightly because the penetration levels that are being solved for can have many decimal places (allowing more than one solution to be correct).
The example of
The embodiments described herein may be implemented using any number of different hardware configurations. For example,
The processor 1110 also communicates with a storage device 1130. The storage device 1130 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device 1130 stores a program 1115 and/or a risk evaluation tool or application for controlling the processor 1110. The processor 1110 performs instructions of the program 1115, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 1110 may receive, for a plurality of resource allocation categories, a selected sub-category from a set of potential sub-categories. Based on the selected sub-categories, a resource allocation score may be calculated by the processor 1110, and the enterprise system may be assigned to a resource allocation level. The processor 1110 may access electronic records representing a plurality of potential associations and, for each potential association, a set of attribute variables. Based on the set of attribute variables, a present total number of associations may be generated by the processor 1110. Based on the assigned resource allocation level and the set of attribute variables, the processor 1110 may forecast a future change to the total number of associations. Based on the forecasted future change to the total number of associations, a predicted performance value associated with the enterprise system may be calculated and transmitted by the processor 1110 to generate a user interface display.
The program 1115 may be stored in a compressed, uncompiled and/or encrypted format. The program 1115 may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor 1110 to interface with peripheral devices.
As used herein, information may be “received” by or “transmitted” to, for example: (i) the back-end application computer server 1100 from another device; or (ii) a software application or module within the back-end application computer server 1100 from another software application, module, or any other source.
In some embodiments (such as shown in
Referring to
The quote identifier 1202 may be, for example, a unique alphanumeric code identifying an insurer, employer, and/or insurance quote. The voluntary coverages 1204 might illustrate a type of insurance (e.g., life, Short Term Disability (“STD”), Long Term Disability (“LTD”), etc.). The employee election style 1206 (e.g., active, passive, or negative) and enrollment method 1208 (online, personalized paper, etc.) may be values that are entered by a user. The score 1210 may then be calculated by the system (e.g., based on the employee election style 1206 and enrollment method 1208). Once the score 1210 is calculated, the level 1212 may be automatically determined (e.g., with quotes receiving a score 1210 from “70” to “90” receiving a “silver” level 1212 rating) and used to forecast future changes in participation.
Referring to
The employee identifier 1302 may be, for example, a unique alphanumeric code identifying an employee who works for an employer. The coverage 1304 may indicate a type of voluntary insurance. The age band 1306, income band 1308, and gender 1310 (e.g., male or female) may define demographic characteristics associated with the employee that can be used to forecast future changes in participation.
Referring to
The quote identifier 1402 may be, for example, a unique alphanumeric code identifying an insurer, employer, and or insurance quote and may be based on, or associated with, the quote identifier 1202 in the resource allocation database 1200. The voluntary coverages 1404 may describe the types of insurance being offered, and the level 1406 might reflect the resources allocated in the resource allocation database 1200. The existing enrollment 1408 might reflect a number of employees who are currently enrolled and purchasing the voluntary coverages 1404. The forecast enrollment 1410 may represent a forecast change to the existing enrollment 1408. The initial loss ratio 1412 and forecast ratio 1414 may then be calculated in accordance with any of the embodiments described herein.
At 1510, a voluntary sales manager might submit the voluntary enrollment analysis form to an analytic consulting team for national accounts. For regional accounts, the voluntary sales manager might execute a voluntary enrollment analysis automated tool. At 1512, the account executive may complete an initial enrollment strategy document (e.g., implemented via the MICROSOFT® EXCEL® spreadsheet application). According to some embodiments, some or all of this information may be further processed via a Customer Relationship Management (“CRM”) cloud-based platform, such as SALESFORCE.COM®. At 1514, the voluntary sales manager might seek approval of an enrollment strategy and provide insights and observations related to participation. At 1516, the underwriter may assess the impacts of enrollment strategy as documented in the enrollment strategy document (and consult with a compliance/legal department as needed) and consider willingness to offer open or modified open enrollment.
At 1518, the compliance/legal department may review for issues and determine if there are issues with filing, rebating or other regulations/laws. At 1520, pricing and updates may be determined and the underwriter may ensure that the enrollment strategy document includes comments on participation and pricing, and a gold, silver, or bronze marketing package might be selected for the quote. At 1522, the proposal may be sent to the broker or client by the account executive, and it may be confirmed that the broker or client agrees to the terms of the enrollment strategy document.
According to some embodiments, one or more predictive models may be used to predict or forecast future events. Features of some embodiments associated with a predictive model will now be described by first referring to
The computer system 1800 includes a data storage module 1802. In terms of its hardware the data storage module 1802 may be conventional, and may be composed, for example, by one or more magnetic hard disk drives. A function performed by the data storage module 1802 in the computer system 1800 is to receive, store and provide access to both historical transaction data (reference numeral 1804) and current transaction data (reference numeral 1806). As described in more detail below, the historical transaction data 1804 is employed to train a predictive model to provide an output that indicates an identified performance metric and/or an algorithm to score performance factors, and the current transaction data 1806 is thereafter analyzed by the predictive model. Moreover, as time goes by, and results become known from processing current transactions (e.g., audit results), at least some of the current transactions may be used to perform further training of the predictive model. Consequently, the predictive model may thereby appropriately adapt itself to changing conditions.
Either the historical transaction data 1804 or the current transaction data 1806 might include, according to some embodiments, determinate and indeterminate data. As used herein and in the appended claims, “determinate data” refers to verifiable facts such as the an age of a business; an automobile type; a policy date or other date; a driver age; a time of day; a day of the week; a geographic location, address or ZIP code; and a policy number.
As used herein, “indeterminate data” refers to data or other information that is not in a predetermined format and/or location in a data record or data form. Examples of indeterminate data include narrative speech or text, information in descriptive notes fields and signal characteristics in audible voice data files.
The determinate data may come from one or more determinate data sources 1808 that are included in the computer system 1800 and are coupled to the data storage module 1802. The determinate data may include “hard” data like a claimant's name, date of birth, social security number, policy number, address, an underwriter decision, etc. One possible source of the determinate data may be the insurance company's policy database (not separately indicated).
The indeterminate data may originate from one or more indeterminate data sources 1810, and may be extracted from raw files or the like by one or more indeterminate data capture modules 1812. Both the indeterminate data source(s) 1810 and the indeterminate data capture module(s) 1812 may be included in the computer system 1800 and coupled directly or indirectly to the data storage module 1802. Examples of the indeterminate data source(s) 1810 may include data storage facilities for document images, for text files, and digitized recorded voice files. Examples of the indeterminate data capture module(s) 1812 may include one or more optical character readers, a speech recognition device (i.e., speech-to-text conversion), a computer or computers programmed to perform natural language processing, a computer or computers programmed to identify and extract information from narrative text files, a computer or computers programmed to detect key words in text files, and a computer or computers programmed to detect indeterminate data regarding an individual.
The computer system 1800 also may include a computer processor 1814. The computer processor 1814 may include one or more conventional microprocessors and may operate to execute programmed instructions to provide functionality as described herein. Among other functions, the computer processor 1814 may store and retrieve historical insurance transaction data 1804 and current transaction data 1806 in and from the data storage module 1802. Thus the computer processor 1814 may be coupled to the data storage module 1802.
The computer system 1800 may further include a program memory 1816 that is coupled to the computer processor 1814. The program memory 1816 may include one or more fixed storage devices, such as one or more hard disk drives, and one or more volatile storage devices, such as RAM devices. The program memory 1816 may be at least partially integrated with the data storage module 1802. The program memory 1816 may store one or more application programs, an operating system, device drivers, etc., all of which may contain program instruction steps for execution by the computer processor 1814.
The computer system 1800 further includes a predictive model component 1818. In certain practical embodiments of the computer system 1800, the predictive model component 1818 may effectively be implemented via the computer processor 1814, one or more application programs stored in the program memory 1816, and computer stored as a result of training operations based on the historical transaction data 1804 (and possibly also data received from a third party). In some embodiments, data arising from model training may be stored in the data storage module 1802, or in a separate computer store (not separately shown). A function of the predictive model component 1818 may be to determine appropriate audit techniques for a set of insurance policies. The predictive model component may be directly or indirectly coupled to the data storage module 1802.
The predictive model component 1818 may operate generally in accordance with conventional principles for predictive models, except, as noted herein, for at least some of the types of data to which the predictive model component is applied. Those who are skilled in the art are generally familiar with programming of predictive models. It is within the abilities of those who are skilled in the art, if guided by the teachings of this disclosure, to program a predictive model to operate as described herein.
Still further, the computer system 1800 includes a model training component 1820. The model training component 1820 may be coupled to the computer processor 1814 (directly or indirectly) and may have the function of training the predictive model component 1818 based on the historical transaction data 1804 and/or information about potential insureds. (As will be understood from previous discussion, the model training component 1820 may further train the predictive model component 1818 as further relevant data becomes available.) The model training component 1820 may be embodied at least in part by the computer processor 1814 and one or more application programs stored in the program memory 1816. Thus, the training of the predictive model component 1818 by the model training component 1820 may occur in accordance with program instructions stored in the program memory 1816 and executed by the computer processor 1814.
In addition, the computer system 1800 may include an output device 1822. The output device 1822 may be coupled to the computer processor 1814. A function of the output device 1822 may be to provide an output that is indicative of (as determined by the trained predictive model component 1818) particular performance metrics, insurance claim losses, etc. The output may be generated by the computer processor 1814 in accordance with program instructions stored in the program memory 1816 and executed by the computer processor 1814. More specifically, the output may be generated by the computer processor 1814 in response to applying the data for the current simulation to the trained predictive model component 1818. The output may, for example, be a numerical estimate and/or likelihood within a predetermined range of numbers. In some embodiments, the output device may be implemented by a suitable program or program module executed by the computer processor 1814 in response to operation of the predictive model component 1818.
Still further, the computer system 1800 may include performance prediction module 1824. The performance prediction module 1824 may be implemented in some embodiments by a software module executed by the computer processor 1814. The performance prediction module 1824 may have the function of rendering a portion of the display on the output device 1822. Thus, the performance prediction module 1824 may be coupled, at least functionally, to the output device 1822. In some embodiments, for example, the performance prediction module 1824 may report results and/or predictions by routing, to an administrator 1828 via a performance prediction platform 1826, a results log and/or automatically generated loss ratios generated by the predictive model component 1818. In some embodiments, this information may be provided to an administrator 1828 who may also be tasked with determining whether or not the results may be improved (e.g., by further adjusting models).
In some embodiments described herein, a predictive model may use information from an enrollment strategy document (e.g., allocating a marketing resources budget within various categories) to predict future changes in employee enrollment (and those future changes may be used to adjust a loss ratio, price an insurance policy, etc.). Note, however, that a predictive model may receive other inputs and/or generate other embodiments in accordance with embodiments described herein. For example, a predictive model might receive a desired loss ratio or a desired change in future employee enrollment and use that information to populate the enrollment strategy document (e.g., automatically suggesting marketing resources budget allocations among the various categories). According to some embodiments, the predictive model might be run using several different alternate sets of input values and generate predication for each of those scenarios). As another example, an operator or administrator might select one or more values that should be optimized (e.g., a combined loss ratio) and the system may generate results to facilitate the optimization of that value.
Thus, embodiments may provide an automated and efficient way to address the need for a consistent and objective determination of how to deploy scarce marketing dollars for employee benefit enrollment campaigns. Embodiments may also address the need for a consistent and objective determination of how the potential enrollment will impact the risk profile of insurance coverage and translate that into a case-level price. The resource allocation approaches described herein may utilize an algorithm that scales a marketing budget to offer the most robust tactics to cases with the best potential to bring in voluntary premium. The algorithm may take the case-specific voluntary enrollment marketing strategy and assign it a score which results in a corresponding level rating: Gold (highest resource deployment), Silver (managed resource deployment), or Bronze (standardized support).
Moreover, embodiments may encourage terms that cross-functionally communicate anticipated participation outcomes. Still further, algorithms may process resource allocation level information along with case specific demographics (and pricing) and calculate an expected change in employee participation and risk profile. This may then be used to develop an appropriate price for the case.
The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.
Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the displays described herein might be implemented as a virtual or augmented reality display and/or the databases described herein may be combined or stored in external systems). Moreover, although embodiments have been described with respect to particular types of communication addresses, embodiments may instead be associated with other types of communications (e.g., chat implementations, web-based messaging, etc.). Similarly, although a certain number of resource allocation levels were described in connection some embodiments described herein, other numbers of resource allocations levels might be used instead (e.g., a system might automatically assign a quote to one of ten possible resource allocation levels). Still further, the displays and devices illustrated herein are only provided as examples, and embodiments may be associated with any other types of user interfaces. For example,
The present invention has been described in terms of several embodiments solely for the purpose of illustration. Persons skilled in the art will recognize from this description that the invention is not limited to the embodiments described, but may be practiced with modifications and alterations limited only by the spirit and scope of the appended claims.
