Patent Publication Number: US-2020286180-A1

Title: Method and system for computing portfolio allocation recommendations

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The application claims priority to U.S. Provisional Application 62/815,531 (Attorney Docket No. 72167.001634), filed Mar. 8, 2019, the contents of which are incorporated herein in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The invention relates generally to a system and method for computing portfolio allocation recommendations and specifically to a portfolio insight observation engine that financial advisors may access to analyze portfolios relative to a benchmark. 
     BACKGROUND OF THE INVENTION 
     Current systems approach portfolio allocation evaluation by developing a risk profile and a target for what that kind of volatility might look like. Advisors may then fine tune investments to have different target volatility. Other solutions break down a portfolio using factor analysis for a portion of the portfolio and determine how those factors might impact returns over times. However, such solutions are limited to a risk return profile and fail to provide a comprehensive solution to portfolio allocation evaluation. 
     These and other drawbacks exist. 
     SUMMARY OF THE INVENTION 
     According to one embodiment, the invention relates to a system for computing portfolio allocation recommendations. The system comprises: a memory component that stores portfolio data and observations data; an interactive interface that communicates with a user via a network communication; and a processor coupled to the memory component and the interactive interface, the processor configured to perform the steps of: identifying a portfolio; identifying one or more goals and concerns specific to the portfolio; selecting a benchmark model portfolio; identifying a set of metrics for comparison between the portfolio and the benchmark model portfolio; evaluating one or more deviations relative to the set of metrics associated with the benchmark model portfolio; applying the one or more goals and concerns to the deviations; generating observations based on the benchmark portfolio; ranking the observations based on risk; identifying a subset of ranked observations; and providing, via the interactive interface, customized insights and solutions for each of the ranked observations. 
     According to another embodiment, the invention relates to a method for computing portfolio allocation recommendations. The method comprises the steps of: identifying, via an observation engine comprising a computer processor, a portfolio; identifying, via the observation engine, one or more goals and concerns specific to the portfolio; selecting, via the observation engine, a benchmark model portfolio; identifying, via the observation engine, a set of metrics for comparison between the portfolio and the benchmark model portfolio; evaluating, via the observation engine, one or more deviations relative to the set of metrics associated with the benchmark model portfolio; applying, via the observation engine, the one or more goals and concerns to the deviations; generating, via the observation engine, observations based on the benchmark portfolio; ranking, via the observation engine, the observations based on risk; identifying, via the observation engine, a subset of ranked observations; and providing, via an interactive interface, customized insights and solutions for each of the ranked observations. 
     The computer implemented system and method described herein provide unique advantages to financial institutions, financial advisors, asset management professionals, clients, portfolio managers and other users, according to various embodiments of the invention. An embodiment of the present invention is directed to analyzing a multitude of different elements of an asset allocated model investment portfolio for a client and identifying differences in allocation relative to a benchmark. An embodiment of the present invention further presents potential risks and/or opportunities and accordingly highlights the most relevant and interesting issues to the financial adviser. An embodiment of the present invention uniquely leverages risk management and model portfolio allocation to fine-tune a model relative to the benchmark based on goals of the portfolio, which may include income increase, growth increase, capital preservation, risk mitigation as well as other concerns such as rising interest rates, portfolio volatility and equity markets volatility. These and other advantages will be described more fully in the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order to facilitate a fuller understanding of the present invention, reference is now made to the attached drawings. The drawings should not be construed as limiting the present invention, but are intended only to illustrate different aspects and embodiments of the invention. 
         FIG. 1  is an exemplary application flow, according to an embodiment of the present invention. 
         FIG. 2  is an exemplary flow diagram of digital portfolio insights and benchmark, according to an embodiment of the present invention. 
         FIG. 3  is an exemplary flow diagram of an Observation Engine, according to an embodiment of the present invention. 
         FIG. 4  is an exemplary user interface, according to an embodiment of the present invention. 
         FIG. 5  is an exemplary user interface, according to an embodiment of the present invention. 
         FIG. 6  is an exemplary user interface, according to an embodiment of the present invention. 
         FIG. 7  is an exemplary system diagram, according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S) 
     The following description is intended to convey an understanding of the present invention by providing specific embodiments and details. It is understood, however, that the present invention is not limited to these specific embodiments and details, which are exemplary only. It is further understood that one possessing ordinary skill in the art, in light of known systems and methods, would appreciate the use of the invention for its intended purposes and benefits in any number of alternative embodiments, depending upon specific design and other needs. 
     An embodiment of the present invention is directed to implementing an Insights Observation Engine. According to an embodiment of the present invention, the Observation Engine may employ a sophisticated algorithm to dynamically evaluate an investment portfolio relative to a benchmark/model identifying and prioritizing risks and/or opportunities based observations. For example, observations may be based on a multitude of metrics including relative asset allocation deviations, relative and risk adjusted performance comparisons, thematic allocation opportunities, market and economic stress tests, as well as potential performance risk and asset allocation risk evaluations. The Observation Engine&#39;s algorithm may customize the evaluation and the resulting observations by incorporating user selected (or user based) investment goals and risk appetite (e.g., market or event) into its assessment process. The Observation Engine may further dynamically leverage aggregated holdings and returns based data driven from the relative weights of the portfolio&#39;s underlying investments and contrasts relative to a benchmark/model. The benchmark/model employed in the evaluation may be user selected and/or Observation Engine suggested, based on a holdings and returns based assessment of the portfolio relative to a known universe of risk adjusted benchmarks/models. Other considerations from various sources may be applied. The Observation Engine may prioritize the resulting observations by risk and/or opportunity revealing a set of items to review, e.g., the most critical items to review. Further, the Observation Engine may provide suggested actions to resolve the identified risks and uncaptured opportunities while integrating market, economic and/or investment outlook data to support conclusions. 
     Based on a model portfolio, an embodiment of the present invention may suggest a relevant asset management view on asset allocation and/or benchmark. A benchmark may represent a standard or measure that may be used to analyze the allocation, risk and return of a given portfolio. An originating entity (e.g., financial institution, etc.) may have access to various levels of insights and analytics for the benchmark portfolio. In addition, an embodiment of the present invention may leverage benchmark portfolios generated from external sources, e.g., another financial institution. Depending on the source and quality of data available from the benchmark portfolio, the observations provided may vary. 
     An embodiment of the present invention is directed to a robust digital portfolio construction tool that provides diagnostics, observations and/or supporting content to help advisors build stronger portfolios and communicate their rationale with clarity and ease. 
     The system may provide diagnostics and other analytics that financial advisors can understand and use. The system further helps identify risk and return drivers, uncover new investment opportunities, fine-tune portfolio allocations and perform other analytics and actions. In addition, the system provides integrated insights to support rationale via an interactive user interface or other output. 
     The system provides portfolio observations and explanations (e.g., high level and detailed) and further uses risk and portfolio management systems. The system may be web based and accessible from any browser via a device (e.g., mobile device, etc.) for various users, including Financial Advisors. 
     An embodiment of the present invention may further provide fully integrated market insights and retirement insights “case for” packs to support rationale; portfolio export functionality. “Case for” may represent market insights and retirement insights program content. This may include observation content to add education information about the asset class, economics or investing behavior to further explain why the observation makes sense to address. The system may further provide factor analysis, investment product analysis (e.g., Fund v. Fund) and investment heatmaps and/or other interactive features. In addition, the system may provide white label offering, internationalization and artificial intelligence (AI). An embodiment of the present invention may allow for integration into partner firms websites, allowing non U.S. investments and users to leverage the system as well as provide ways to add artificial intelligence to the system for a more interactive and guided experience. 
     According to an embodiment of the present invention, the Observation Engine may apply machine learning to adapt and improve the ability to identify relevant observations about portfolios to financial advisors. The Observation Engine may consider real time analyst data as well as metrics and inputs from various sources, including internal to a financial entity as well as external sources. Accordingly, the Observation Engine may apply machine learning to refine and fine-tune observations for an optimal and highly personalized view. For example, Observation Engine may apply machine learning to determine an optimal or most appropriate benchmark (or benchmark factors/characteristics) and further determine observations based on the identified benchmark. 
     According to an embodiment of the present invention, an output of the Observation Engine may be presented to a user, e.g., human analyst, asset management professionals, etc. In this example, the user may perform various actions, which may include altering an order of observations, creating customized observations, modifying existing observations, etc. A collective set of human-guided observations may then become “data labeling,” which may be used for supervised machine learning to further improve future Observation engine outputs. An embodiment of the present invention may implement various learning models, including human-in-the-loop learning (HitL) model. 
       FIG. 1  is an exemplary application flow, according to an embodiment of the present invention. At step  110 , a portfolio may be uploaded. At step  112 , goals and concerns may be identified. At step  114 , a benchmark and/or model portfolio may be selected. For example, a most relevant benchmark may be suggested based on a model portfolio. At step  116 , observations, supporting content and/or investment ideas may be received. At step  118 , the system may provide robust diagnostics including contribution to risk and/or return. At step  120 , a dashboard may provide past analysis and client-friendly reports as well as other outputs and interfaces. The order illustrated in  FIG. 1  is merely exemplary. While the process of  FIG. 1  illustrates certain steps performed in a particular order, it should be understood that the embodiments of the present invention may be practiced by adding one or more steps to the processes, omitting steps within the processes and/or altering the order in which one or more steps are performed. 
     An embodiment of the present invention may consider a mix of various different holdings and returns base matrix to evaluation differences between the model portfolio and the benchmark and further customize the evaluation based on goals and/or concerns in the marketplace. An embodiment of the present invention is directed to a combination of returns-based and holdings-based information on underlying products. The system may further customize outputs of the evaluation based on the goals of the portfolio and concerns around risk in the portfolio. In addition, the system may adapt to changes and/or modifications in terms of goals and concerns as the environment is constantly changing, e.g., interest rates, etc. 
       FIG. 2  is an exemplary flow diagram of digital portfolio insights and benchmark, according to an embodiment of the present invention. An embodiment of the present invention is directed to providing guidance to advisors to select an appropriate benchmark for their portfolio. Additionally, the innovative tool offers the ability for the advisor to build a customized benchmark or model portfolios to contrast their advisor portfolio. As shown in  FIG. 2 , an Advisor Portfolio may be identified at  210 . At  212 , a percentage of alternatives in a portfolio may be determined. For example, a predetermined percentage threshold (e.g., +/−15%) may be used to determine with or without alternatives benchmark suite. At  214 , a percentage of equity in the portfolio may be determined. This may involve using a holdings look through to select a range of relevant benchmarks. For example, the system may select benchmarks with +/−15% portfolio equity exposure. Other percentages or calculations may be applied. The system may then evaluate portfolio correlation to benchmarks, as shown by  216 . According to an exemplary scenario, the system may return a top number of benchmarks, represented by  218 , that the portfolio has a highest R2. R-squared (R2) may represent a statistical measure of how close the data are to the fitted regression line. R-squared may be interpreted as the percentage of a portfolio or security&#39;s movements that may be explained by movements in a benchmark. This may be used in an embodiment of the present invention to guide the user to the best fit benchmark. As shown in  FIG. 2 , the system identifies three representative benchmarks. R2 is one exemplary measure; other statistical measurements and calculations may be applied. 
     An embodiment of the present invention is directed to an Observation Engine that custom tailors evaluation and resulting observations based on portfolio goals and/or volatility concerns. For example, current goal choices may include increase income, improve growth, preserve capital, etc. Current volatility concerns may include minimize impact of rising interest rates and minimize impact of equity market volatility. Other goals and concerns may be applied. The Observation Engine may further evaluate the advisor&#39;s portfolio across various holdings and risk/return based metrics relative to a benchmark/model and provide a top number of actionable opportunities. For example, holding based metrics may include: Equity U.S. vs. International Exposure; Equity Region—European vs. Asian Exposure; Equity Developed vs. Emerging Markets Exposure; Equity Style—Value vs. Growth; Equity Market Capitalization—Large vs. Small; Equity Sector Balance; Exposure to Real Estate; Fixed Income U.S. vs International Exposure; Fixed Income Duration Profile; Fixed Income Sector Balance; Fixed Income Credit Quality Dispersion; and Cash Exposure. 
     Risk/Return based metrics may include: Stress Test Sensitivity (e.g., currently: Equity Market Volatility (e.g., performance when equity markets sold off) and Rising Interest Rate Volatility (e.g., performance when interest rates rose)); Returns; Sharpe Ratio; Up Market vs Down Market Capture; and Yield. 
       FIG. 3  is an exemplary flow diagram of an Observation Engine, according to an embodiment of the present invention. An Advisor Portfolio may be contrasted relative to a chosen benchmark or model. As shown in  FIG. 3 , the process may evaluate deviations relative to benchmark at  310 . An embodiment of the present invention may identify differences (e.g., asset allocation differences, etc.) between the Advisor Portfolio  312  and Benchmark  314  with respect to a set of metrics. The set of metrics shown in  FIG. 3  is illustrative and may include other considerations including e.g., deviation evaluations, scenario, analysis evaluations, stress factor test evaluations, etc. The comparison may be used to identify Baseline Observations  316 . At  320 , goals and/or concerns may modify outcomes. For example, goals and concerns may include increase income, improve growth, preserve capital, interest rates and market volatility, as shown by  322 . In addition,  322  may include other goals and concerns including asset class relevant tilt, for example. Other goals and/or concerns may be applied to refine and customize the observations. An embodiment of the present invention may then identify a set of relevant observations based on the goals and/or concerns, as shown by  324 . At  330 , the process may rank relevant observations based on a factor, such as risk. Other rankings and/or factors may be applied in accordance with the various embodiments of the present inventions. An embodiment of the present invention is directed to custom tailoring portfolio observations and to identify a set of important/relevant observations for additional analysis and exploration. At  340 , actionable observations may be provided. In the example of  FIG. 3 , three top observations may include Equity Market Volatility (Eq Vol), International Equity Exposure (Intl Eq) and Emerging Markets Equity (Emerg Eq) as shown by  342 . The number of observations and details may vary. These observations may be presented to facilitate portfolio alignment with the benchmark based on a specific set of goals and/or concerns. As shown by  344 , customized insights and solutions may be provided. For each observation, a corresponding set of insights may include asset class views, product solutions, economic insight, asset class insights, for example. 
     According to an embodiment of the present invention, Observation Engine considers risks, such as equity market volatility, as compared to observations, such as the amount of real estate exposure associated with a portfolio. Another observation may include having too many high-quality government bonds in the portfolio. This may be measured as bonds above a predetermined threshold. An embodiment of the present invention is directed to considering the composition of the portfolio. This may be represented by the metrics illustrated in 310. For example, in an exemplary portfolio that is more risk comfortable and looking to drive more growth, the portfolio may include 80% in equities and 20% in fixed income. Another portfolio for someone in retirement may have a composition that is driven toward fixed income. The risk of fixed income in the exemplary portfolio may be more driven by equity exposure and goals towards accumulation. An embodiment of the present invention is directed to differentiating risks and determining that a certain risk, e.g., equity type risk, is more important to a particular portfolio. An embodiment of the present invention is directed to improving observations by incorporating an enhanced understanding of the composition of the portfolio to better fine tune the observations for improved relevancy. Accordingly, an embodiment of the present invention may be driven by the risks and the goals that may be selected or suggested to the client and further customized to be more relevant and highly aligned with the composition of the portfolio. For example, when a portfolio is more orientated towards an equity accumulation, an embodiment of the present invention may provide observations relevant to equities. For a portfolio that is more driven towards an income oriented portfolio with more bonds that, an embodiment of the present invention may tailor the analysis to bonds. 
       FIG. 4  is an exemplary user interface, according to an embodiment of the present invention. An embodiment of the present invention is directed to identifying opportunities, including insights and potential solutions (e.g., insights, product ideas, economic information, etc.). As shown in  FIG. 4 , Portfolio Insights Interface  410  may provide customized observations based on Goal  412 , Concern  414  and Date Range  416 . Other considerations may be applied.  FIG. 4  illustrates an exemplary set of observations to build a stronger portfolio. The observations include insights relating to Stress Tests  420 , Fixed Income Credit Quality  430  and Equity Style  440  with corresponding details and data. Other metrics and data may be made available to the user. For example, a user may view additional data, such as domestic stock market declines  422 , credit quality exposure  432 , fixed income credit quality  434  and equity style tilt  442 . 
       FIG. 5  is an exemplary user interface, according to an embodiment of the present invention. As shown in  FIG. 5 , the system illustrates credit quality exposure based on allocation percentage at  510 . Other illustrations and graphics may be applied.  FIG. 5  also illustrates key observations  520 . This may include details concerning Stress Tests and Fixed Income Credit Quality Exposure, for example. Additional details may include views, insights and funds to explore. 
       FIG. 6  is an exemplary user interface, according to an embodiment of the present invention. According to an embodiment of the present invention, an output of the Observation Engine may be presented to a user, e.g., human analyst, asset management professionals, etc. In this example, the user may provide inputs to an output generated by the Observation Engine. This may include altering an order of observations, modifying an observation, deleting an observation, creating a customized observation, etc. An embodiment of the present invention may identify the user inputs and then apply various learning models to further refine and optimize the Observation Engine. This may involve applying a human-in-the-loop learning (HitL) model. 
       FIG. 6  illustrates an exemplary Observation Scenario  610 . As shown in  FIG. 6 , a user may start a new analysis, create report and/or perform other actions. In this example, a user may select Goal  612  and Concern  614 , via a drop down window or other user input. The user may also add observations at  616 , modify the identified observations, re-rank or change order of observations (e.g., drag and drop) as well as perform other actions and changes. Observations  620  may include Fixed Income Sector Exposure  622  and Equity Style Box  624 . Included Observations  630  may include Domestic Stock Market Declines  632 , Fixed Income Credit Quality Exposure  634 , Fixed Income Regional Exposure  636  and Equity Style Box  638 . Interface  640  may provide a summary of Total Observations  642  and details concerning Observations in Report  644  and Invalid Observations  646 . 
     As shown in  FIG. 6 , a user may provide an input to adjust the provided observations. The user may represent an asset management professional or other qualified user. An embodiment of the present invention may enable the user to evaluate the observation output, as shown in  FIG. 6 , and then refine the observations based on experience, personal insights and/or other information. For example, the user may observe a set of highest ranked observations generated by the Observation Engine and then adjust the findings, which may include reordering the observations, adding an observation, removing an observation, modifying/changing an observation and/or other modification. This may involve fine-tuning the observations to better align with a financial or other goal, e.g., refine observations to be more equity-oriented as opposed to fixed income based. An embodiment of the present invention may then apply the captured user inputs to refine the Observation Engine. 
     User input and adjustment may be received and considered in various stages of the present invention. As shown in  FIG. 3 , user input and adjustment may be applied at various stages of the process, including Baseline Observations  316 , Relevant Observations  324  and Rank Relevant Observations  330 . For example, a user input may relate to an adjustment in ranking the relevant observation as shown by  330 . User input and adjustment may be applied to a single instance and/or multiple instances throughout the process. Other inputs, adjustment and/or variations may be applied. 
       FIG. 7  is an exemplary system diagram, according to an embodiment of the present invention. As shown in  FIG. 7 , a user may access System  710  through a client device or system. Users  730 ,  732 ,  734  may include financial advisors, asset management professionals, analysts and/or other users. System  710  may represent a portfolio allocation recommendation system with various processing components represented by User Interface  712 , Input Capture  714 , Observation Engine  716  and Portfolio  718 . Other components, modules and/or interfaces may be implemented in System  710 . The components illustrated in  FIG. 7  are merely exemplary, other devices may be represented in various applications. While a single component is illustrated, each component may represent multiple components and multiple components may be combined and/or integrated. 
     Users, represented by  730 ,  732 ,  734 , of an embodiment of the present invention may communicate with System  710  via Network  740  through a User Interface, represented by  712 . Communication may be performed using any mobile or computing device, such as a laptop computer, a personal digital assistant, a smartphone, a smartwatch, smart glasses, other wearables or other computing devices capable of sending or receiving network signals. 
     User Interface  712  may represent an interface user interface or dashboard that receives user initiation and/or other actions and further provides observations. Via a user interface, Observation Engine  714  may prioritize the resulting observations by risk and opportunity revealing high priority items to review. Further, the Observation Engine may provide suggested actions to resolve the identified risks and uncaptured opportunities while integrating market, economic and/or investment outlook data to support conclusions. According to an embodiment of the present invention, Input Capture  714  may receive input from an Asset Management Professional represented by  734  to refine and adjust observations provided by Observation Engine  716  at various stages. 
     Observation Engine  716  may dynamically evaluate an investment portfolio relative to a benchmark/model and further identify and prioritize risks and/or opportunities based observations. According to an exemplary scenario, observations may be based on various metrics and considerations including relative asset allocation deviations, relative and risk adjusted performance comparisons, thematic allocation opportunities, market and economic stress tests, as well as potential performance risk and asset allocation risk evaluations. In addition, Observation Engine  716  is directed to customizing the evaluation and observations by incorporating investment goals and risk considerations. Observation Engine  716  may further dynamically leverage aggregated holdings and returns based data driven from the relative weights of the portfolio&#39;s underlying investments and contrasts relative to a benchmark/model. 
     Portfolio and Benchmark/Model  718  may represent and manage portfolio and benchmark/model data. For example, the benchmark/model employed in an evaluation may be user selected and/or Observation Engine suggested, based on a holdings and returns based assessment of the portfolio relative to a known universe of risk adjusted benchmarks/models. 
     An embodiment of the present invention may communicate, via Network  742 , with internal and external sources of data, represented by Source  760 . 
     An entity, such as a Financial Institution  750 , may host System  710  according to an embodiment of the present invention. The entity may support portfolio allocation recommendations and observation engine functionality as an integrated feature or system. According to another example, portfolio allocation recommendations and observation engine services may be offered by a third party service provider. Other scenarios and architectures may be implemented. An embodiment of the present invention may send and/or receive data from various other sources represented by databases  720 ,  722 . Databases may be internal or external to a host entity. Data may be stored and managed in storage components via one or more networks. Databases may include any suitable data structure to maintain the information and allow access and retrieval of the information. The storage may be local, remote, or a combination thereof with respect to Databases. Communications with Databases may be over a network, or communications may involve a direct connection between Databases and other participants, as depicted in  FIG. 7 . Databases may also represent cloud or other network based storage or an application presenting a data source via an API. 
     The system  700  of  FIG. 7  may be implemented in a variety of ways. Architecture within system  700  may be implemented as hardware components (e.g., module) within one or more network elements. It should also be appreciated that architecture within system  700  may be implemented in computer executable software (e.g., on a tangible, non-transitory computer-readable medium) located within one or more network elements. Module functionality of architecture within system  700  may be located on a single device or distributed across a plurality of devices including one or more centralized servers and one or more mobile units or end user devices. The architecture depicted in system  700  is meant to be exemplary and non-limiting. For example, while connections and relationships between the elements of system  700  are depicted, it should be appreciated that other connections and relationships are possible. The system  700  described below may be used to implement the various methods herein, by way of example. Various elements of the system  700  may be referenced in explaining the exemplary methods described herein. 
     The various features of an embodiment of the present invention may be applied to other applications, uses and scenarios. For example, an embodiment of the present invention may be applied to procurement decisions and other hardware, configuration and infrastructure decisions. An embodiment of the present invention may be applied to identify underlying trends and patterns to address various automation incompatibilities and other issues. 
     The foregoing examples show the various embodiments of the invention in one physical configuration; however, it is to be appreciated that the various components may be located at distant portions of a distributed network, such as a local area network, a wide area network, a telecommunications network, an intranet and/or the Internet. Thus, it should be appreciated that the components of the various embodiments may be combined into one or more devices, collocated on a particular node of a distributed network, or distributed at various locations in a network, for example. As will be appreciated by those skilled in the art, the components of the various embodiments may be arranged at any location or locations within a distributed network without affecting the operation of the respective system. 
     As described above, the various embodiments of the present invention support a number of communication devices and components, each of which may include at least one programmed processor and at least one memory or storage device. The memory may store a set of instructions. The instructions may be either permanently or temporarily stored in the memory or memories of the processor. The set of instructions may include various instructions that perform a particular task or tasks, such as those tasks described above. Such a set of instructions for performing a particular task may be characterized as a program, software program, software application, app, or software. 
     It is appreciated that in order to practice the methods of the embodiments as described above, it is not necessary that the processors and/or the memories be physically located in the same geographical place. That is, each of the processors and the memories used in exemplary embodiments of the invention may be located in geographically distinct locations and connected so as to communicate in any suitable manner. Additionally, it is appreciated that each of the processor and/or the memory may be composed of different physical pieces of equipment. Accordingly, it is not necessary that the processor be one single piece of equipment in one location and that the memory be another single piece of equipment in another location. That is, it is contemplated that the processor may be two or more pieces of equipment in two or more different physical locations. The two distinct pieces of equipment may be connected in any suitable manner. Additionally, the memory may include two or more portions of memory in two or more physical locations. 
     In the system and method of exemplary embodiments of the invention, a variety of “user interfaces” may be utilized to allow a user to interface with the mobile devices or other personal computing device. As used herein, a user interface may include any hardware, software, or combination of hardware and software used by the processor that allows a user to interact with the processor of the communication device. A user interface may be in the form of a dialogue screen provided by an app, for example. A user interface may also include any of touch screen, keyboard, voice reader, voice recognizer, dialogue screen, menu box, list, checkbox, toggle switch, a pushbutton, a virtual environment (e.g., Virtual Machine (VM)/cloud), or any other device that allows a user to receive information regarding the operation of the processor as it processes a set of instructions and/or provide the processor with information. Accordingly, the user interface may be any system that provides communication between a user and a processor. The information provided by the user to the processor through the user interface may be in the form of a command, a selection of data, or some other input, for example. 
     The software, hardware and services described herein may be provided utilizing one or more cloud service models, such as Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS), and/or using one or more deployment models such as public cloud, private cloud, hybrid cloud, and/or community cloud models. 
     Although the embodiments of the present invention have been described herein in the context of a particular implementation in a particular environment for a particular purpose, those skilled in the art will recognize that its usefulness is not limited thereto and that the embodiments of the present invention can be beneficially implemented in other related environments for similar purposes.