CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and is a non-provisional of U.S. Patent Application 62/914,594 (filed October 14, 2019), the entirety of which is incorporated herein by reference.

STATEMENT OF FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [0002] This invention was made with Government support under grant numbers 1648780 and 1831214 awarded by the National Science Foundation. The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

[0003] The total cost of health care services reported by the Center for Disease Control (CDC) in 2012 was $2.7 trillion. Of these expenditures, 86% were attributed to patients with chronic disease. Approximately 50 percent of the US population has one or more chronic disease. Chronic disease is the single largest burden to the health care system, accounting for 81% of hospital admissions, 91% of all prescriptions and 76% of physician visits. In a recent CDC National Diabetes Statistics Report, 30.2 million people in the United States are afflicted with chronic diabetes. Most of these people suffer from either obesity, high blood pressure, high cholesterol, physical inactivity, smoking or a combination of these conditions. The direct and indirect cost of diabetes on the health care system amounted to $245 billion, with each patient costing the system $13,700 per year, which is 2.3 times the average of all patients.

[0004] Research has shown time and again that patient engagement leads to better care outcome and reduces cost burden on the healthcare system. However, patient engagement relies on the readiness, and willingness, to take ownership on self-health management. Yet there is a lack of quantitative models to assist on understanding the alignment between the delivery of digital health service and motivation indicators to engage an individual in self-management of chronic diseases.

[0005] Recently, Theory of Planned Behavior Model, Trans-theoretical Model of Behavior Change, Health Belief Model, and IMB (Information Motivation and Behavior Skill) Model have been applied to specific intervention of chronic diseases, and have shown clinical efficacy. It was suggested that individuals perceiving risk of a condition are more likely to engage in behavior change to reduce risk. Thus perceived health risk, resulting in change of attitude and behavior are proponents for stronger intentions to be physically active and to maintain a healthy diet.

[0006] A system that utilizes behavioral prediction techniques would be helpful in managing patient self-care. Prediction techniques such as linear regression and PCA (Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with R. Chapman and Hall/CRC, ISBN 9781138495685) rely on the linearity of the data of Real in the dimensions that the data reside. These techniques work well when the data distribution exhibits linearity. Unfortunately, the relationship among the behavior constructs (motivation, intention, attitude, and ownership) are not necessarily linear.

[0007] On the other hand, information-theoretic based techniques such as ID3 (Quinlan, J.R. (1986), Induction of Decision Trees . Machine Learning, 9(1):81-106, 1986), utilize entropy reduction concept for deriving a decision tree that maximizes information gain in each traversal step of the decision tree. Such a technique does not rely on linearity assumption. However, it is exponential in nature with respect to the number of type enumeration of the multi-dimensional data of finite discrete type. It could be effective when the data distribution lends itself to rapid pruning of impossible cases, or when an association pattern fails (1) a threshold test, and/or (2) non-linear information- theoretic criteria such as the asymptotic convergence of mutual information measure towards Chi-square. [0008] Manifold clustering provides a means to discover data subsets that could be projected to hyperplanes embedded in low dimensions. In other words, a hyperplane is defined by a cluster of a data subset, which is not necessarily linear. In contrast to techniques such as PCA, manifold clustering does not rely on the linearity of the data subset.

[0009] Manifold clustering techniques such as Spectral Clustering (Kak A., (2018). Low Dimensional Manifold in a High-Dimensional Measurement Space. Data on Manifolds Tutorial, Purdue University). Low Dimensional Manifold in a High- Dimensional Measurement Space. Data on Manifolds Tutorial, Purdue University), however, suffer from two limitations. First, it is sensitive to the initial seeding for clustering and often requires a 2-phase approach (Luxburg, V. (2007). U. Stat Comput, 17: 395). Second, it could not handle a data set composed of data with mixed data types, for example, data of Real and data of finite discrete type. Even if these limitations could be overcome, manifold clusters are often difficult to interpret without incorporating information-theoretic perspective. An improved method is therefore desired.

[0010] The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE INVENTION

[0011] A method for enhancing patient compliance with a medical therapy plan. The method compiles the information from multiple medical records into a single consolidated medical record, and stores it on a single mobile device. Medical devices supply supplemental data directly to the mobile device. Surveys, that help a patient to identify behavioral indicators, are distributed before and after the supplemental data is accumulated. Comparison of the surveys quantifies changes in key disposition values of the patient. A customized message is displayed that addresses changes in dispositions that are key to compliance with the medical therapy plan. [0012] This disclosure addresses a real-world problem on assisting patients, such as diabetes patients, to better self-manage their disease condition using behavioral predictive analytics. The disclosure provides a behavioral predictive analytics technique based on manifold clustering of mixed data type. Statistically significant association patterns for inducing an initial partition of data for deriving manifolds is used. Manifolds are hyperplanes embedded in low dimensions. The advantage of this method is a bootstrap on data clusters that reveal statistical associations from the information-theoretic perspective. The disclosed technique is applied to a real data set of diabetes patients. An assessment on the effectiveness of the disclosed method is performed to show the effect of bootstrapping based on association patterns.

[0013] This brief description of the invention is intended only to provide a brief overview of subject matter disclosed herein according to one or more illustrative embodiments, and does not serve as a guide to interpreting the claims or to define or limit the scope of the invention, which is defined only by the appended claims. This brief description is provided to introduce an illustrative selection of concepts in a simplified form that are further described below in the detailed description. This brief description is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] So that the manner in which the features of the invention can be understood, a detailed description of the invention may be had by reference to certain embodiments, some of which are illustrated in the accompanying drawings. It is to be noted, however, that the drawings illustrate only certain embodiments of this invention and are therefore not to be considered limiting of its scope, for the scope of the invention encompasses other equally effective embodiments. The drawings are not necessarily to scale, emphasis generally being placed upon illustrating the features of certain embodiments of the invention. In the drawings, like numerals are used to indicate like parts throughout the various views. Thus, for further understanding of the invention, reference can be made to the following detailed description, read in connection with the drawings in which:

[0015] FIG. 1 is flow diagram showing workflow for a method of optimizing patient engagement on self-health management.

DETAILED DESCRIPTION OF THE INVENTION

[0016] In order to gauge how effectively a patient could be engaged in self-health management including self-monitoring, an assessment tool for RPM ideally should determine (1) the level of readiness in terms of motivation and skill, (2) the likelihood of behavior change overtime, and (3) the underlying relationship linking motivation, attitude and intention to behavior change.

[0017] A quantitative model grounded on a behavior theory has already been applied and shown efficacy in clinical studies. See International Patent Publication WO20 19/068086 entitled “Method for enhancing Patient Compliance with a Medical Therapy Plan and Mobile Device” to SIPPA Solutions, LLC, the content of which is hereby incorporated by reference. More specifically, such a quantitative model reveals the linkage among behavior constructs, and should provide inference power to gain insights into not just the level of readiness in terms of motivation and skill for self management, but the underlying relationship linking motivation, attitude, and intention to behavior change affected by the digital health services delivered in a mobile platform. Towards this end, this disclosure uses the Theory of Planned Behavior as a starting point for the development of a quantitative model just mentioned.

[0018] The Theory of Planned Behavior (TPB) provides a model to manifest the relationship among attitude, subjective norm, perceived behavioral control, intention and behavior. TPB is modeled through expectancy-value and assumes the best single predictor of an individual’s behavior is an intention to perform that behavior. The intention in turn depends on the attitude of an individual (positive or negative evaluation of performing a behavior); the subjective norm (perception of whether relevant others think one should or should not perform the behavior); and perceived behavioral control (perception of the ease or difficulty of carrying out a behavior).

[0019] This disclosure presents a manifold clustering approach based on the concept of statistically significant association patterns. A study based on real world data was applied to better understand the disclosed approach for predicting hidden nonlinear sub population of diabetes patients. While this disclosure demonstrates the feasibility of applying the disclosed approach to real world data, the order sequence effect of merging special case of a cluster of singleton is not yet well understood. Furthermore, dimension reduction based on the ratio of incremental change of the total reconstruction errors, in addition to the membership function, is just one out of other possible criteria for deriving the manifold clusters.

[0020] This disclosure addresses at least some of the prior art’s limitations via the following approach: (1) Data of Real (i.e. continuous real numbers representing a patient’s motivation, intention, attitude and ownership as defined according to the Theory of Planned Behavior) is discretized via an entropy approach that optimizes the trade-off between information loss and the granularity of the discrete representation of the information carried by the data of Real. The discrete representation of the data of Real then enables the discovery of statistically significant association patterns, which are detailed elsewhere in this specification (2) each of the statistically significant association patterns then induces an initial cluster for aggregating data within the proximity that characterizes the hyperplane embedded in the low dimension. This initial cluster then serves as initial seeding for clustering when applying techniques such as spectral clustering, and allows a semantic interpretation from the information-theoretic perspective. (Sy, B. (2019). “Incorporating Association Patterns into Manifold Clustering for Enabling Predictive Analytics,” 2019 International Symposium on Data Science, Las Vegas, Dec 5-7, 2019).

[0021] In this disclosure the manifold clustering based on association patterns is applied to personalized health coaching for understanding its effectiveness. A pilot study on engaging individuals on self-health management using SIPPA Health Informatics Platform was conducted to understand the feasibility of affecting behavior change towards a healthy lifestyle (Sy, B., (2018) STTR Phase II: Self-Health Management Informatics Platform: Improving Patient Engagement in Care Delivery. Award Abstract #1831214, NSF). In this pilot study, a validated survey instrument (Sy, B., (2017). SEM Approach for TPB: Application to Digital Health Software and Self-Health Management, 2017 International Symposium on Health Informatics and Biomedical Systems, Las Vegas, Dec 14 16, 2017 ) is used to discover the behavior readiness measure of an individual to be engaged in actionable health activities. Behavior readiness measure is a vector of Real characterizing four behavior attributes [motivation, intention, attitude, ownership]. On a daily basis actionable health recommendations, which range from daily advice on healthy diet, setting goals on physical activities, to self-monitoring of vitals such as blood glucose/pressure readings, were sent to an individual.

[0022] A preliminary experiment was conducted to evaluate the feasibility of applying the disclosed manifold clustering for predictive analytics to identify behavior characteristics accounting for compliance to daily messages. In the pilot study, daily messages are sent via push notifications, or a phone call scheduled and routed through a telephone exchange system PBX, to a subject’s mobile device. A subject is then asked to provide feedback on each message in terms of “like” (equivalent to useful), “dislike” (not useful), and “dismiss” (neutral). If a daily message is actionable such as self-monitoring of glucose level, the subject is expected to carry out the self-monitoring activities. FIG. 1 illustrates the workflow of the method.

[0023] In step 1, a web questionnaire is sent to a patient and the answers are received. In step 2, behavior readiness is measured. See International Patent Publication WO20 19/068086 for details. In one embodiment, behavior readiness is measured and used by the manifold cluster process described below to identify subgroups of patients with statistically significant association patterns of behavior. By segmenting patients into subgroups that exhibit similar behavior readiness patterns, customized self-care plans are produced that are specific to a given subgroup. In step 3, the patient engages in self monitoring. As discussed elsewhere in this specification, self-monitoring may include monitoring physiological parameters using devices such as glucose meters, continuous glucose meters, thermometers, pulse oximetry meters, weight scales, blood pressure meters and the like. Self-monitoring may also include recording exercise sessions and/or food consumption and diet.

[0024] In step 4, the activity data is recorded in a mobile computing device. For example, the physiological parameters obtained in step 3 are recorded in a mobile computing device.

[0025] In step 5, the activity data is encrypted and sent to the behavior readiness measurement module (i.e. to step 2). The behavior readiness measurement module returns, in step 6, personalized recommendations to the mobile computing device.

Activity and self-monitoring data shared from the mobile device are rendered into standard format such as CCD or FHIR in step 7, and returned in conjunction with personalized recommendations to the mobile computing device. The patient’s compliance with self-monitoring can thus be altered. The personalized recommendations may be sent to, for example, a smart phone that is associated with the specific patient who completed the web questionnaire. The personalized recommendations may be sent through a Private Branch Exchange (PBX) system and may be in the form of a text message, an audio voice mail, or other similar communication. Advantageously smart phones, unlike computers, are associated with specific patients and this permits communications to directly target individuals. In on embodiment, the smart phone is configured to provide further medical assistance, such as providing video chat with a healthcare provider. [0026] The significance of the disclosed manifold clustering is the ability to provide a semantic meaning on the clusters based on the concept of association patterns. Specifically, each cluster is a collection of data that are “closest” to a statistically significant association pattern in terms of semantic similarity as measured by membership function. By referencing the definition of statistically significant association pattern (Sy, B., & Gupta A. (2004). Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics. eBook ISBN: 978-1-4419-9001-3, DOI: 10.1007/978-1-4419-9001-3, Springer), such a pattern manifests a frequent occurrence as defined by the support measure exceeding a predefined threshold, as well as an inter relationship among the underlying variables of the pattern that deviates from independence as measured by mutual information from the perspective of information theory. In this manner patients who, based on their answers to the survey question, have statistically significant association patterns are grouped into like clusters. Customized self-monitoring coaching that is specific to a given cluster can thus target specific clusters of patients for maximum effect.

[0027] Manifold clustering based on association patterns is comprised of four tasks; first deriving the corresponding discrete data representation of a given set of data of Real. Second, identifying the statistically significant association patterns of the discrete data representation. Third, assigning each data point of real to a cluster based on the evaluation of the membership function of its corresponding discrete data representation against every statistically significant association pattern; fourth, deriving the data clustering on manifold by minimizing reconstruction error.

[0028] This disclosure focuses on illustrating the predictive analytics based on disclosed manifold clustering to identify non-trivial subgroups of pilot participants who are responsive to the daily messages. The testbed for this preliminary study was a sample collection of data from 53 individuals for the behavior attributes, and among them eight have participated in the pilot for almost three months. The average number of days of participation among the eight is 96.43 days. [0029] A compliance index was derived for measuring the average responsiveness of a subject to the push notifications over a subject participation period. The self-monitoring compliance index is defined as the (average) number of self-monitoring per day divided by the number of self-monitoring per day recommended by a physician according to the clinical guidelines and the diabetes condition of an individual. Similarly, the daily wisdom compliance index is defined as the number of responses to daily wisdom divided by the number of daily wisdom sent over to the subject during the subject’s participation period. Daily wisdom consists of healthy tips from a pool of over 100 messages; e.g., “Getting enough sleep is critical to keeping stress under control.” This is in addition to a push notification that could carry an actionable message such as “It’s time to self-monitor your glucose level and sync the reading to your personal health record.”

[0030] The training data set just mentioned is used to derive the manifold spaces. The task of predictive analytics is to identify the manifolds in the embedded subspace that are induced by statistically significant association patterns and define the clusters of the pilot participants. In other words, the spanning space is a 6-dimensional space composed of behavior constructs [motivation, intention, attitude, ownership], together with self monitoring compliance index and daily wisdom compliance index.

[0031] Notation, Definition, and Problem Formulation

[0032] Let be a data set of Real. For example, a given patient may have a data set of [0.76, 0.69, 0.86, 0.81] for motivation, intention, attitude and ownership (N=4) based on that patient’s answers to the survey.

[0033] Let be a data set of finite discrete non-negative Integer.

[0034] Let

[0035] Let F: be a one-on-one bijective mapping function that defines the discretization of the multivariate data set X ⁿ . [0036] Let order statistically significant association pattern for j = 1 ... m }; whereas is a statistically significant association pattern (Sy, B., & Gupta A. (2004). Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics. eBook ISBN: 978-1-4419-9001-3, DOI: 10.1007/978-1 - 4419-9001-3, Springer) when

N is the sample size

X ² is the Pearson chi-square defined as is the expected entropy measure E' is the maximum possible entropy

[0038] Definition 1

[0039] The scope coverage of a pattern represented by is defined as a subset of Y in which the logical interpretation of every element in the subset is true.

[0040] Example [0043] The scope coverage of P is {[dl: 1, d2: 0, d3: 0 d4: 0], [dl: 1, d2: 0, d3: 0, d4: 1], [dl: 1, d2: 1, d3: 0, d4: 0] [dl: 1, d2: 1, d3: 0, d4: 1]}

[0044] Definition 2

[0045] The membership function is defined by the geometric mean measure below:

[0047] is a member of the k ^th manifold induced by when k =

[0048] Example

[0050] The following terms are derived based on the definitions:

[0051]

[0052]

[0053]

[0056] is member of the manifold induced by because

[0057] Algorithm for deriving discrete data representation of data of Real

[0058] Consider a discrete variable Y of N possible states, the entropy of a system defined by is

[0059] It can be shown that the following equality holds (Sy, B., & Gupta A. (2004). Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics. eBook ISBN: 978-1-4419-9001-3, DOI: 10.1007/978-1-4419-9001-3, Springer):

[0060] In the quantization process, combining two terms will reduce the number of terms by one, and at the same time results in an information loss amounting to the second term on the right-hand side of the above equation.

[0061] The quantization of a data set of Real disclosed in this disclosure utilizes the entropy equation just shown that incrementally combines terms until it reaches the reflection point where there is a change of direction on the rate of change of information loss. The details of the algorithm is shown below:

[0062] Let be a data set of Real. For each dimension j = l..n of X ⁿ , perform the following steps for the data of the j ^th dimension: [0063] Step 1 : Order in an ascending order. Create a bucket/bin for each term in X ^j Treat each bucket/bin as a state of a discrete variable of Y and associate a value for a bucket/bin equal to the mean of the term(s) in the bucket/bin. In other words, Y is a discrete variable of N states. If the values of are all different, the distribution of Y is then even and the probability of Y for every term is equal to 1/N.

[0064] Step 2: Initialize an iteration count C = 1. Derive the entropy and record it as

[0065] Step 3: Increment the iteration count by 1; i.e., C = C+l. Identify two adjacent buckets/bins, say, the j ^th and (j+l) ^th in the ordered list where the difference between the mean of the terms in the jth bucket/bin and that in the (j+l) ^th is the smallest. Combine the two adjacent buckets/bins into one. Update the mean of the data in the combined bucket/bin, and update the probability distribution of Y. Re-derive the entropy Record the information loss l ^C+1 due to combining the two terms; i.e.,

[0067] Step 4: Repeat step 3 until the direction on the rate of change of / ^C+j is changed. When this occurs at the kth iteration, the following result is obtained:

[0068]

[0069] be a data set.

[0070] Let F: be an one-on-one bijective mapping function that defines the discretization of the multivariate data set X. Below shows an example:

X ⁿ = {[0.76, 0.69, 0.86, 0.81], [0.87, 0.49, 0.72, 0.98], [0.11, 0.67,0.57,0.75], [0.39,0.71,0.63,0.82]} F([0.76, 0.69, 0.86, 0.81] )=[1, 1,2,1] F([0.87, 0.49, 0.72, 0.98] )=[2, 0,1,2]

F([0.11, 0.67, 0.57, 0.75] )=[0, 1,0,0] F([0.39, 0.71, 0.63, 0.82] )=[1,2,0,1]

Y ⁿ ={[1,1, 2,1], [2,0, 1,2], [0, 1,0,0], [1,2,0,!]} [0071] Algorithm for deriving data clustering on manifold

[0072] Given X ⁿ , Y ⁿ , and F, and a predefined error threshold d, the algorithm for the disclosed manifold clustering based on statistical significant association patterns is shown below;

[0073] Step 1: Based on derive the set of statistically significant association patterns; i.e., order statistically significant association pattern for j = 1 ... m };

[0074] Step 2: Define disjoint clusters such that each cluster has one and only one statistically significant association pattern. Let W be the set of disjoint clusters; i.e.,

[0075] Step 3: Partition by assigning each data point Xi to the cluster is the pattern that defines the cluster is the membership function defined previously. If is zero in all cases, Xfs assigned to a non-semantic cluster NS. In this manner, the number of clusters is determined.

[0076] Step 4: Let be the set of subspaces corresponding to the clusters defined in step 2. Repeat the following for each j where the corresponding cluster has more than one element:

[0077] Let be the data set of the cluster The subspace S _j corresponding to is then derived based on the following:

[0078] Step 4.1 : Derive the mean vector and variance matrix of

[0079] Step 4.2: Conduct eigendecomposition on M ^nj to obtain the eigenvector matrix Q ^n,j and the eigenvalue matrix A ^n,j such that

[0080] Step 4.3 : Let P be the number of non-zero eigenvalues obtained in step 4.2.

[0081] Step 4.4: Use the P ’ (leading) eigenvectors in Q ^n,j to define the local coordinate frame for the subspace S _j , and rewrite

[0082] Step 4.5: The projection error of mapping a data point to the subspace S _j defined by the local coordinate frame is then equal to Or the square-magnitude projection error of to the subspace S _j is then equal to . Calculate the total error:

[0083] Step 4.6: Repeat step 4.4 and step 4.5 with a new P that is one less; i.e., P-1. Record the total error.

[0084] Step 4.7: Compute the total reconstruction error ratio of two successive rounds in step 4.6; i.e., (total reconstruction error using P-q-1 leading eigenvector)/(total reconstruction error using P-q leading eigenvector) where q = 0 .. P-2.

[0085] Step 4.8: Finalize the local coordinate frame for the subspace S _j with a dimension P-q when the error ratio in step 4.7 is the largest for the given q.

[0086] Step 5: An error calculation is utilized to determine if the same (or an acceptably similar) degree of error can be obtained with fewer clusters. If fewer clusters can be utilized, while maintaining an acceptable error, then computer processing expenses can be reduced. Merge two or more clusters that do not involve NS. If there are clusters with only one data point, these clusters will take the priority; then repeat step 4. Retain the solution with a lower total error.

[0087] Step 6: Repeat step 5 until the total error is below the predefined error threshold d, or the algorithm reaches the maximum number of iterations allowed.

[0088] One noteworthy observation on the step 5 of the algorithm above is that the merged cluster will be characterized by not one, but multiple statistically significant association patterns, and the meaning of a data point in terms of its closeness to the semantic interpretation of some association pattern in a merged cluster, in terms of scope coverage and membership function, is still preserved.

[0089] Experimental results and analysis

[0090] Behavior readiness measure is a 1x4 vector of Real composed of behavior constructs [motivation, intention, attitude, ownership]. There are 53 such vectors, and each vector is discretized to become a vector of finite discrete values using the disclosed algorithm. The vectors of discrete values become the data set for discovering statistically significant association patterns based on equation 1. Using a support measure threshold 0.2, twenty statistically significant association patterns are found and listed in Table 1.

[0091] By way of illustration, and not limitation, pattern 1 in Table 1 shows an attitude score of 2 and an ownership score of 1 is a significant association pattern. A customized message may be sent to patients in this specific manifold cluster that is customized to enhance their compliance with self-health management. This customized message is designed to target those individuals with a high attitude score and a moderate ownership score. This same customized message may not be as effective on patients in other manifold clusters and, as such, this particular customized message is not necessarily sent to the patients in other clusters.

[0092] One would expect 20 manifold clusters when there are 20 statistically significant association patterns. But in reality there could be fewer non-empty manifold clusters. For example, pattern 20 [ownership: 1 self-monitoring: 1 daily-wisdom: 1] is a special case under the scope covered by pattern 9 [ownership: 1 daily-wisdom: 1] And there could be clusters with only one data point when the data set is sparse, which is our case. As a result, only six manifold clusters are non-empty, and four of them contain only one data point as shown in Tables 2 to 4.

[0093] As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method, or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “service,” “circuit,” “circuitry,” “module,” and/or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

[0094] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a non-transient computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0095] Program code and/or executable instructions embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0096] Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language, mobile application development such as ANDROID® programming language, front end programming language such as Angular or React Native, or similar programming languages. The program code may execute entirely on the user's computer (device), partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). [0097] Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

[0098] These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

[0099] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

[00100] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.