Patent Publication Number: US-2023133511-A1

Title: Artisan learning system

Description:
BACKGROUND 
     The present invention relates generally to the field of machine learning, and more particularly to building a base corpus for hand-craft learning systems. 
     Artisanship is a technique to create material objects partly or entirely by hand, often the inheritance of a manual skill handed down over centuries. Moreover, artisanship is an important form of cultural expression to the extent that it reflects the aesthetics, the symbolism, and worldviews of productive communities. 
     A generative adversarial network (GAN) is a class of machine learning system comprising of two neural networks. Given a training set, a GAN learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers. The generative network generates candidates while the discriminative network evaluates the generated candidates. Typically, the generative network learns to map from a latent space to a data distribution of interest, while the discriminative network distinguishes candidates produced by the generator from the true data distribution. The generative network&#39;s training objective is to increase the error rate of the discriminative network (i.e., inducing misclassifications by the discriminator network by producing novel candidates that the discriminator thinks are not synthesized (are part of the true data distribution)). 
     SUMMARY 
     Embodiments of the present invention disclose a computer-implemented method, a computer program product, and a system. The computer-implemented method includes one or more computer processers deriving a base corpus by capturing time series haptic data from an artisan performing an artisanal skill comprising a plurality of artisanal actions, wherein each artisanal action in the plurality of artisanal actions is associated with a time interval. The one or more computer processors train a multi-agent reinforcement learning (MARL) model utilizing the derived base corpus, wherein the MARL model outputs a vector representing one or more predicted artisanal actions required to complete the artisanal skill. The one or more computer processors generate a training avatar utilizing a trained generated adversarial network fed with one or more user parameters identified from a user requesting guidance for the artisanal skill and the output vector of the trained MARL model. The one or more computer processors guide the user with the generated training avatar in an execution of the artisanal skill. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Figure (i.e.,  FIG.  1    is a functional block diagram illustrating a computational environment, in accordance with an embodiment of the present invention; 
         FIG.  2    is a flowchart depicting operational steps of a program, on a server computer within the computational environment of  FIG.  1   , artisanship learning utilizing smart haptics and multi-agent reinforcement learning with generative adversarial networks, in accordance with an embodiment of the present invention; and 
         FIG.  3    is a block diagram of components of the server computer, in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     As technology and modernization advance the number of traditional artisans has tremendously diminished. The acquisition and mastering of an artisanal skill requires significant time and dedication, normally acquired through years of physical apprenticeship with an expert artisan. For example, for a carpenter to correctly plane a piece of wood (i.e., artisanal skill), appropriate palm pressure needs to be applied while supporting the piece of wood at a specific position. The reduction of capable artisans has introduced the problem of losing artisanal skills and talents common to a plurality of industries, cultures, and customs. Although many organizations and governments have recognized this issue and actively attempt to generate artisanal employment in order to preserve associated traditional skills, said attempts are limited due to resource constraints and economic obstacles. 
     Embodiments of the present invention solve artisanal skill loss by deriving a base corpus of artisanal skills through a haptic capture device, allowing the incorporation of the base corpus into educational or collaborative tools. Embodiments of the present invention utilize specifically trained GAN models to generate training avatars specific to the conditions, skill level, mannerisms, and limitations of the user, where the present invention guides the user in an artisanal skill utilizing the created training avatars to present suggests, recommendations, or instructions. Embodiments of the present invention improve existing educational tools through the incorporation of haptic capture devices and haptic response feedback. Embodiments of the present invention improve artisanal user guidance by providing dynamic instructions relative to the user, environment, and artisanal skill. Embodiments of the present invention improve robotic manufacturing systems through the mapping of artisanal skills and actions to tools available within the robotic manufacturing system. Implementation of embodiments of the invention may take a variety of forms, and exemplary implementation details are discussed subsequently with reference to the Figures. 
     The present invention will now be described in detail with reference to the Figures. 
       FIG.  1    is a functional block diagram illustrating a computational environment, generally designated  100 , in accordance with one embodiment of the present invention. The term “computational” as used in this specification describes a computer system that includes multiple, physically, distinct devices that operate together as a single computer system.  FIG.  1    provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. 
     Computational environment  100  includes haptic capture device  110  and server computer  120  connected over network  102 . Network  102  can be, for example, a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, or a combination of the three, and can include wired, wireless, or fiber optic connections. Network  102  can include one or more wired and/or wireless networks that are capable of receiving and transmitting data, voice, and/or video signals, including multimedia signals that include voice, data, and video information. In general, network  102  can be any combination of connections and protocols that will support communications between server computer  120 , and other computing devices (not shown) within computational environment  100 . In various embodiments, network  102  operates locally via wired, wireless, or optical connections and can be any combination of connections and protocols (e.g., personal area network (PAN), near field communication (NFC), laser, infrared, ultrasonic, etc.). 
     Haptic capture device  110  captures artisanal skills (e.g., handcrafts, sports, manufacturing, dancing, playing musical instruments, etc.) conducted by an artisan (e.g., expert) or user (e.g., trainee) utilizing a plurality of sensors and actuators providing haptic data  124 , such as user joint angles and 3D hand positioning, acceleration, tactile feedback, and/or user grip pressure. In an embodiment, haptic capture device  110  includes a motion tracker, such as a magnetic tracking device or inertial tracking device, capturing global position/rotation data of the artisan or user. In another embodiment, haptic capture device  110  is a full body haptic suit that monitors and captures information associated with a plurality of joints and appendages. In an embodiment, haptic capture device  110  includes visual sensors that monitor general body positions. For example, haptic capture device  110  visually monitors and collects information regarding the body position (e.g., user posture, relative position of user appendages, user head position, etc.) of a standing carpenter hand sawing. In an embodiment, haptic captures device  110  utilizes markerless motion capture techniques to capture haptic data  124  from the perspective of the artisan. 
     Server computer  120  can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data. In other embodiments, server computer  120  can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In another embodiment, server computer  120  can be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any programmable electronic device capable of communicating with other computing devices (not shown) within computational environment  100  via network  102 . In another embodiment, server computer  120  represents a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within computational environment  100 . In the depicted embodiment, server computer  120  includes base corpus  122  and program  150 . In other embodiments, server computer  120  may contain other applications, databases, programs, etc. which have not been depicted in computational environment  100 . Server computer  120  may include internal and external hardware components, as depicted and described in further detail with respect to  FIG.  3   . 
     Base corpus  122  is a repository for data used by program  150 . In the depicted embodiment, base corpus  122  resides on server computer  120 . In another embodiment, base corpus  122  may reside elsewhere within computational environment  100  provided program  150  has access to base corpus  122 . Base corpus  122  is an organized collection of data. Base corpus  122  can be implemented with any type of storage device capable of storing data and configuration files that can be accessed and utilized by program  150 , such as a database server, a hard disk drive, or a flash memory. Base corpus  122  contains haptic data  124  collected through haptic capture device  110 , wherein haptic data  124  is a structured plurality of time series information. In this embodiment, haptic data  124  describes time series information collected as an artisan performs one or more artisan skills, sequences, or actions. For example, haptic capture device  110  captures how an artisan (e.g., potter) performs a “throw off the hump” method (i.e., artisanal skill), where the artisanal skill is comprised of one or more artisanal actions comprising wedging the clay, centering, opening, etc. In this example, haptic data  124  represents each artisanal action in an artisanal skill and describes the action needed to successful accomplish the artisanal skill, such as the required force, pressure, body position, etc. Haptic data  124  describes one or more artisanal skills comprising a plurality of artisanal steps, such as moving the position of a tool with a specific velocity replicating the artisan (e.g., expert). In an embodiment, haptic data  124  includes, but is not limited to, pressure strokes and variations, directional movement, duration of applied pressure, and directional appendage pattern combinations (e.g., elbow, wrist, knee, and neck joints). 
     Program  150  is a program for artisanship learning utilizing smart haptics and multi-agent reinforcement learning with generative adversarial networks. In various embodiments, program  150  may implement the following steps: derive a base corpus by capturing time series haptic data from an artisan performing an artisanal skill comprising a plurality of artisanal actions, wherein each artisanal action in the plurality of artisanal actions is associated with a time interval; train a multi-agent reinforcement learning (MARL) model utilizing the derived base corpus, wherein the MARL model outputs a vector representing one or more predicted artisanal actions required to complete the artisanal skill; generate a training avatar utilizing a trained generated adversarial network fed with one or more user parameters identified from a user requesting guidance for the artisanal skill and the output vector of the trained MARL model; and guide the user with the generated training avatar in an execution of the artisanal skill. In the depicted embodiment, program  150  is a standalone software program. In another embodiment, the functionality of program  150 , or any combination programs thereof, may be integrated into a single software program. In some embodiments, program  150  may be located on separate computing devices (not depicted) but can still communicate over network  102 . In various embodiments, client versions of program  150  resides on any other computing device (e.g., haptic capture device  110 ) within computational environment  100 . In the depicted embodiment, program  150  includes multi-agent reinforcement learning model (MARL)  152  and generative adversarial network (GAN)  154 . Program  150  is depicted and described in further detail with respect to  FIG.  2   . 
     MARL  152  utilizes multi-agent reinforcement to address sequential decision-making problems (i.e., artisan skill learning). MARL  152  is a multi-agent learning system, trained from base corpus  122 , comprising a group of autonomous, interacting agents (i.e., artisans with varied skills, techniques, and limitations (e.g., artisan physical dimensions)) sharing a common environment (i.e., available tools and materials). Although the agents in a multi-agent system are endowed with behaviors (i.e., artisanal actions or sequences) designed in advance, often agents need to learn new behaviors online, such that the performance of the agent or of the whole multi-agent system gradually improves. Furthermore, environment complexity impedes the a priori design of successful agent behaviors. Environments that change over time cause hardwired artisanal actions to be often inappropriate and unsuccessful. The training of MARL  152  is further described in step  204 . 
     GAN  154  is a virtual skill generative adversarial network (GAN) comprising two adversarial neural networks (i.e., generator and discriminator) trained utilizing the output vector of MARL  152  and base corpus  122 , representative of a plurality of artisanal actions associated with an artisanal skill. In an embodiment, program  150  trains a discriminator utilizing collected haptic data  124  as described in base corpus  122 . In another embodiment, program  150  initializes a generator utilizing randomized haptic data  124  sampled from a predefined latent space (e.g., a multivariate normal distribution), thereafter, candidates (i.e., skill training images and avatars) synthesized by the generator are evaluated by the discriminator. In this embodiment, program  150  applies backpropagation to both networks so that the generator produces more realistic images (e.g., training avatars), while the discriminator becomes more skilled at flagging synthetic images. In an embodiment, the discriminator model assesses the delta from the original image of the user in an augmented reality (AR) view with the avatar created by the generator model. In the depicted embodiment, the generator is a deconvolutional neural network and the discriminator is a convolutional neural network. The creation, training, and utilization of GAN  154  is depicted and described in further detail with respect to  FIG.  2   . 
     The present invention may contain various accessible data sources, such as base corpus  122 , that may include personal storage devices, data, content, or information the user wishes not to be processed. Processing refers to any, automated or unautomated, operation or set of operations such as collection, recording, organization, structuring, storage, adaptation, alteration, retrieval, consultation, use, disclosure by transmission, dissemination, or otherwise making available, combination, restriction, erasure, or destruction performed on personal data. Program  150  provides informed consent, with notice of the collection of personal data, allowing the user to opt in or opt out of processing personal data. Consent can take several forms. Opt-in consent can impose on the user to take an affirmative action before the personal data is processed. Alternatively, opt-out consent can impose on the user to take an affirmative action to prevent the processing of personal data before the data is processed. Program  150  enables the authorized and secure processing of user information, such as tracking information, as well as personal data, such as personally identifying information or sensitive personal information. Program  150  provides information regarding the personal data and the nature (e.g., type, scope, purpose, duration, etc.) of the processing. Program  150  provides the user with copies of stored personal data. Program  150  allows the correction or completion of incorrect or incomplete personal data. Program  150  allows the immediate deletion of personal data. 
       FIG.  2    depicts flowchart  200  illustrating operational steps of program  150  for artisanship learning utilizing smart haptics and multi-agent reinforcement learning with generative adversarial networks, in accordance with an embodiment of the present invention. 
     Program  150  derives base corpus from artisans (step  202 ). In an embodiment, program  150  initiates responsive to a user request for artisanal skill training or guidance. In another embodiment, program  150  initiates responsive to an identification of a skill or artisan utilizing haptic capture device  110  (e.g., visually identifying artisanal skills). Here, program  150  identifies artisans (i.e., expert craftworkers (e.g., artists)) utilizing visual identifiers and related expertise stored in base corpus  122  (e.g., cloud object storage (COS)). In a further embodiment, haptic data  124  is collected from identified artisans utilizing haptic capture device  110  and stored within base corpus  122  in a dictionary format, where haptic data  124  is specific to particular artisan, craft, job, sport, or activity. In this embodiment, haptic data  124  describes a monitored artisan unique characteristics, mannerisms (e.g., right hand vs left hand), disabilities, and positioning with respect to performing one or more skills. In an embodiment, haptic data  124  is collected, aggregated, and streamed in real-time from the internet of things (IoT) sources (i.e., haptic capture device  110 ). 
     Program  150  trains a multi-agent reinforcement learning (MARL) model utilizing derived base corpus (step  204 ). In an embodiment, program  150  trains MARL  152  utilizing derived base corpus  122 . In an embodiment, program  150  trains MARL  152  utilizing deep reinforcement learning to teach agents how to perform an artisanal skill within a dynamic environment (e.g., diverse users, materials, tools, etc.). In this embodiment, program  150  utilizes the time-series information included within haptic data  124 , to teach the agent, at each time step, to perceive the state of the environment and take an appropriate artisanal action, which transitions the environment into a new state. In a further embodiment, program  150  applies a scalar reward function to evaluate the quality of each transition, allowing the agent to maximize the cumulative reward along the course of the skill. MARL  152  collects feedback on haptic data utilized to train the user in order to adjust the reward function by internally maximizes the global reward function over iterative time samples while adjusting subsequent predicted artisanal actions. For any given combination of environmental factors and user parameters, MARL  152 , responsively, outputs a vector representing one or more artisanal actions (i.e., artisanal skill) necessary to complete the artisanal skill. For example, in a game of baseball or cricket, program  150  instructs a batter to apply a specific force or pressure at a specific timing to successfully hit a pitch into play, specifically appropriate pressure needs to be applied by the bottom hand holding the bat. Here, appropriate pressure and timing are dependent on a plurality of factors such as angular velocity of the pitch, equipment materials, weather conditions, user parameters (e.g., dominant hand, physical limitations, etc.), etc. As the plurality of factors change (e.g., weather variations, etc.) and are inputted into MARL  152 , responsively, MARL  152  outputs one or more artisanal actions, ultimately resulting in the successful execution of the artisanal skill. 
     Program  150  creates a generative adversarial network (GAN) utilizing derived base corpus and output vector from trained multi-agent reinforcement model (step  206 ). In an embodiment, program  150  creates GAN  154  comprising a generator model and a discriminator model. In this embodiment, program  150  trains said models utilizing supervised training methods with images representative of the intended image style (i.e., artisanal skills) and haptic data  124 . In an embodiment, the user indicates a particular image style such an avatar, sample photo, or a style producing varying levels of privacy or obfuscation. In the depicted embodiment, the generator is a deconvolutional neural network and the discriminator is a convolutional neural network. In an embodiment, program  150  trains GAN  154  utilizing artisan images and videos to generate a realistic representation (e.g., avatar) of an artisan performing an artisan skill through identifying the body positioning (position, alignment, and applied force of all ligaments and appendages of the artisan) of the artisan for reconstruction. Program  150  utilizes GAN  154  to create an electronic representation of the user (i.e., avatar) performing an artisanal action from MARL  152  considering a broad range of positions, environments, tools, materials, etc. 
     Program  150  derives user parameters (step  208 ). Program  150  identifies and derives a plurality of user parameters that describe the current physical condition of the user and the surrounding environment of the user, where the user requested guidance or assistance in performing an artisan skill or one or more artisanal actions within the artisan skill. In an embodiment, user parameters include, but are not limited to, user preferences (e.g., preferred dominant hand), physical limitations (e.g., reduced strength in right arm), body proportions (e.g., length of arms, ligament rotation ability, etc.), grip strength, etc. For example, program  150  identifies and derives user parameters describing a user with a left arm injury within a machine tooling environment. In these embodiments, program  150  utilizes haptic capture device  110  to visually identify the user and associated user parameters while monitoring the general body and ligament position of the user. For example, haptic capture device  110  visually monitors and collects information regarding user body position (e.g., user posture, relative position of user appendages, user head position, etc.). In an embodiment, program  150  utilizes the derived user parameters to create a digital avatar of the user, as detailed in step  210 . 
     Program  150  generates a training avatar utilizing the created GAN and derived user parameters (step  210 ). In an embodiment, program  150  generates training avatars based on derived user parameters and the output vector from MARL  152 , as described in step  204 . In this embodiment, program  150  provides generated avatars ranging from high obfuscated or genericized avatars to photo-realistic representations of the user or artisan. In an embodiment, program  150  generates a video sequence (e.g., live avatar) of a user such that the user is virtually animated according to an output vector of MARL  152  corresponding to an artisanal skill or component artisanal action. In this embodiment, at each time interval, the generator model of GAN  154  creates the training avatar in an augmented or holographic form with associated haptics asserted on the user. In a further embodiment, program  150  generates user representations (i.e., virtual audience members) that consist of a set of learned key points with corresponding local affine transformations to support complex artisan skills and actions. In an embodiment, the created avatar is unique with respect to pressure points and posture based on the skill level, characteristics, habits, mannerisms and positioning of the user attempting to perform the artisanal skill. For example, a left hander carpenter is instructed through the training avatar to apply more pressure with the left hand as compared to a right handed carpenter. In another example, program  150  creates an avatar to assist with carpentry work on an elevated stationary fixture for a user with a leg limitation. In this example, program  150  adjusts the avatar and any associated artisanal action to conform with the abilities, conditions, or limitations of the user. In an embodiment, program  150  restricts (i.e., the GAN is restricted, constrained, modified, etc.) the image generator model utilizing one or more user preferences (e.g., transmission methods, associated computing devices, etc.) and user privacy parameters (e.g., allowed location information, allowed avatar details (e.g., hair color, unique facial features, identifying details, etc.). For example, the user designates that no personal information be present in the generated avatar, thus program  150  generates an image representation only containing a genericized avatar with no applied user preferences (i.e., user physical conditions and limitations). 
     Program  150  guides a user utilizing generated training avatar (step  212 ). In an embodiment, program  150  presents and/or adjusts the generated avatar dependent on the capabilities (e.g., display size, resolution, etc.) of the associated application (e.g., chat application, etc.) or associated computing devices. For example, program  150  overlays the avatar over the body of the user while the user is utilizing an AR system. In an embodiment, program  150  projects the generated avatar using screens or projector devices on human mannequins. In a further embodiment, program  150  dictates auditory instructions utilizing existing audio systems within the environment. In an embodiment, program  150  provides the user an artisan action instruction (e.g., technique), as outputted by MARL  152 , utilizing the training avatar to demonstrate the artisanal action with user specific instructions for force, body position, pressure, etc. For example, program  150  instructs the user on correct hand direction and orientation while holding a tool needed for performing the artisanal action. In this example, program  150  instructs the user regarding proper posture through the presentation of correct posture utilizing the training avatar. Additionally, program  150  provides for required change of tools through an AR indicator surrounding the correct tool in the environment of the user. In another embodiment, program  150  utilizes the training avatar with associated smart haptics (i.e., haptic glove or suit) to apply haptic response to further assist the user. For example, program  150  applies vibration motion to a set of haptic gloves indicating an unsuccessful artisanal action. For example, program  150  applies a haptic response to the correct hand as the user incorrectly performs the artisanal action. In another embodiment, program  150  adjusts the applied haptic response based on a level of deviation from the artisanal action compared to current user performance of the artisanal action. In an embodiment, responsive to program  150  identifying that the user unsuccessfully executed an artisanal action, program  150  inputs the conditions of failure into MARL  152  and presents a new set of guidance or instructions to the user. In an embodiment, responsive to program  150  identifying that the user successfully executed an artisanal action, program  150  continues to present subsequent artisanal actions utilizing subsequently created training avatars. 
     In an embodiment, program  150  leverages the predicted actions from MARL  152  to provide computational instructions to a robot or a system controlling robotic agents. In this embodiment, the robot mimics the mannerisms (i.e., haptic data  124 ) of an artisan captured from haptic feedback. In a further embodiment, program  150  utilizes input from a skilled artisan, utilizing haptic capture device  110 , to provide robotic systems with artisan actions allowing the robots to imitate or mimic a particular artisanal style. For example, program  150  utilizes a smart haptic glove, smart sensors, and cameras to capture the style and technique of a painter painting on canvas. In this example, program  150  feds the captured information into MARL  152 , leveraging the output of MARL  152  to infuse the artisanal skill onto a robotic system. In an embodiment, program  150  maps artisanal actions or skills to a robotic manufacturing equivalent. For example, artisanal actions, intended for the user, are converted into a robotic manufacturing format, allowing the robotic manufacturing of an artisanal product. 
       FIG.  3    depicts block diagram  300  illustrating components of server computer  120  in accordance with an illustrative embodiment of the present invention. It should be appreciated that  FIG.  3    provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
     Server computer  120  each include communications fabric  304 , which provides communications between cache  303 , memory  302 , persistent storage  305 , communications unit  307 , and input/output (I/O) interface(s)  306 . Communications fabric  304  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications, and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  304  can be implemented with one or more buses or a crossbar switch. 
     Memory  302  and persistent storage  305  are computer readable storage media. In this embodiment, memory  302  includes random access memory (RAM). In general, memory  302  can include any suitable volatile or non-volatile computer readable storage media. Cache  303  is a fast memory that enhances the performance of computer processor(s)  301  by holding recently accessed data, and data near accessed data, from memory  302 . 
     Program  150  may be stored in persistent storage  305  and in memory  302  for execution by one or more of the respective computer processor(s)  301  via cache  303 . In an embodiment, persistent storage  305  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  305  can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  305  may also be removable. For example, a removable hard drive may be used for persistent storage  305 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  305 . Software and data can be stored in persistent storage  305  for access and/or execution by one or more of the respective processors  301  via cache  303 . 
     Communications unit  307 , in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit  307  includes one or more network interface cards. Communications unit  307  may provide communications through the use of either or both physical and wireless communications links. Program  150  may be downloaded to persistent storage  305  through communications unit  307 . 
     I/O interface(s)  306  allows for input and output of data with other devices that may be connected to server computer  120 . For example, I/O interface(s)  306  may provide a connection to external device(s)  308 , such as a keyboard, a keypad, a touch screen, and/or some other suitable input device. External devices  308  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., program  150 , can be stored on such portable computer readable storage media and can be loaded onto persistent storage  305  via I/O interface(s)  306 . I/O interface(s)  306  also connect to a display  309 . 
     Display  309  provides a mechanism to display data to a user and may be, for example, a computer monitor. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: 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), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, conventional procedural programming languages, such as the “C” programming language or similar programming languages, and quantum programming languages such as the “Q” programming language, Q #, quantum computation language (QCL) or similar programming languages, low-level programming languages, such as the assembly language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a 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). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     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 readable program instructions. 
     These computer readable 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. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.