Digital twin asset search

The exemplary embodiments disclose a method, a computer program product, and a computer system for obtaining a digital twin asset. The exemplary embodiments may include collecting input data, extracting one or more features from the input data, searching for a digital twin asset based on the extracted one or more features and one or more models, and facilitating an exchange of the digital twin asset.

BACKGROUND

The exemplary embodiments relate generally to digital twin assets, and more particularly to searching for and obtaining digital twin assets.

Many physical assets are associated with one or more digital resources which may be helpful to users of the physical assets. These digital resources are often not easily accessible and reviewable by prospective purchasers of physical assets. For example, a purchaser of a car engine may wish to quickly and efficiently review digital twin schematics to assess compatibility with their car prior to purchasing the car engine.

SUMMARY

The exemplary embodiments disclose a method, a computer program product, and a computer system for obtaining a digital twin asset. The exemplary embodiments may include collecting input data, extracting one or more features from the input data, searching for a digital twin asset based on the extracted one or more features and one or more models, and facilitating an exchange of the digital twin asset.

The drawings are not necessarily to scale. The drawings are merely schematic representations, not intended to portray specific parameters of the exemplary embodiments. The drawings are intended to depict only typical exemplary embodiments. In the drawings, like numbering represents like elements.

DETAILED DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. The exemplary embodiments are only illustrative and may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope to be covered by the exemplary embodiments to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

In the interest of not obscuring the presentation of the exemplary embodiments, in the following detailed description, some processing steps or operations that are known in the art may have been combined together for presentation and for illustration purposes and in some instances may have not been described in detail. In other instances, some processing steps or operations that are known in the art may not be described at all. It should be understood that the following description is focused on the distinctive features or elements according to the various exemplary embodiments.

Many physical assets are associated with one or more digital resources which may be helpful to users of the physical assets. These digital resources are often not easily accessible and reviewable by prospective purchasers of physical assets. For example, a purchaser of a car engine may wish to quickly and efficiently review digital twin schematics to assess compatibility with their car prior to purchasing the car engine.

Exemplary embodiments are directed to a method, computer program product, and computer system for digital twin asset searching. In embodiments, machine learning may be used to create models capable of searching for digital twin assets, while feedback loops may improve upon such models. Moreover, data from user uploads, databases, or the sensors124may be used to search for digital twin assets. A user may wish to search for one or more digital twin assets for a number of purposes. For example, a purchaser of a car engine may wish to quickly and efficiently review digital twin schematics to assess compatibility with their car prior to purchasing the car engine. In another example, a purchaser of an air purifier may wish to quickly and efficiently review research performed on the efficacy of the air purifier in preventing asthmatic symptoms. In general, it will be appreciated that embodiments described herein may relate to searching for any type of digital asset (i.e., digital twin schematics, digital research files, digital source codes, digital user manuals, etc.) within any environment and for any motivation.

FIG.1depicts the digital twin search system100, in accordance with the exemplary embodiments. According to the exemplary embodiments, the digital twin search system100may include a smart device120and a digital twin search server130, which may be interconnected via a network108. While programming and data of the exemplary embodiments may be stored and accessed remotely across several servers via the network108, programming and data of the exemplary embodiments may alternatively or additionally be stored locally on as few as one physical computing device or amongst other computing devices than those depicted.

In the exemplary embodiments, the network108may be a communication channel capable of transferring data between connected devices. Accordingly, the components of the digital twin search system100may represent network components or network devices interconnected via the network108. In the exemplary embodiments, the network108may be the Internet, representing a worldwide collection of networks and gateways to support communications between devices connected to the Internet. Moreover, the network108may utilize various types of connections such as wired, wireless, fiber optic, etc. which may be implemented as an intranet network, a local area network (LAN), a wide area network (WAN), or a combination thereof. In further embodiments, the network108may be a Bluetooth network, a Wi-Fi network, or a combination thereof. In yet further embodiments, the network108may be a telecommunications network used to facilitate telephone calls between two or more parties comprising a landline network, a wireless network, a closed network, a satellite network, or a combination thereof. In general, the network108may represent any combination of connections and protocols that will support communications between connected devices.

In the example embodiment, the smart device120includes a digital twin search client122and one or more sensors124, and may be an enterprise server, a laptop computer, a notebook, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a server, a personal digital assistant (PDA), a rotary phone, a touchtone phone, a smart phone, a mobile phone, a virtual device, a thin client, an IoT device, or any other electronic device or computing system capable of receiving and sending data to and from other computing devices. While the smart device120is shown as a single device, in other embodiments, the smart device120may be comprised of a cluster or plurality of computing devices, in a modular manner, etc., working together or working independently. The smart device120is described in greater detail as a hardware implementation with reference toFIG.5, as part of a cloud implementation with reference toFIG.6, and/or as utilizing functional abstraction layers for processing with reference toFIG.7.

The digital twin search client122may act as a client in a client-server relationship with a server, for example a digital twin search server130. The digital twin search client122may also be a software and/or hardware application capable of communicating with and providing a user interface for a user to interact with a server via the network108. Moreover, in the example embodiment, the digital twin search client122may be capable of transferring data from the sensors124between the smart device120and other devices via the network108. In embodiments, the digital twin search client122utilizes various wired and wireless connection protocols for data transmission and exchange, including Bluetooth, 2.4 gHz and 5 gHz internet, near-field communication, Z-Wave, Zigbee, etc. The digital twin search client122is described in greater detail with respect toFIG.2.

In example embodiments, the sensors124may comprise a camera, microphone, light sensor, infrared sensor, movement detection sensor, or other sensory hardware equipment. Moreover, the smart device120may incorporate an array of the one or more sensors124such that information can be obtained by the sensors124in multiple directions, at different times/intervals, in different mediums/frequencies, and the like. For example, the smart device120may include three forward-facing cameras that each record an adjacent sixty-degree viewing angle spanning a total of one-hundred and eighty degrees in the direction of a user. Moreover, data processing techniques may be implemented such that directional information of visual and audio data can be obtained based on signals received by each of the three sensors124, such as trilateration and triangulation.

While the sensors124are depicted as integrated with the smart device120, in embodiments, the sensors124may be incorporated within an environment in which the digital twin search system100is implemented. For example, the sensors124may be one or more microphones built into an auditorium, a camera built into a facility, etc. Moreover, data processing techniques may be implemented such that directional information of visual and audio data can be obtained based on signals received by each of the sensors124, such as trilateration and triangulation. In other embodiments, the sensors124may be integrated with other smart devices, e.g., smart phones and laptops, within an environment implementing the digital twin search system100. In such embodiments, the sensors124may communicate directly with other networks and devices, such as the network108. In embodiments, the digital twin searcher134may utilize data collected from the sensors124to record video. The sensors124are described in greater detail as a hardware implementation with reference toFIG.5, as part of a cloud implementation with reference toFIG.6, and/or as utilizing functional abstraction layers for processing with reference toFIG.7.

In the exemplary embodiments, the digital twin search server130may include one or more digital twin search models132and a digital twin searcher134, and may act as a server in a client-server relationship with the digital twin search client122. The digital twin search server130may be an enterprise server, a laptop computer, a notebook, a tablet computer, a netbook computer, a PC, a desktop computer, a server, a PDA, a rotary phone, a touchtone phone, a smart phone, a mobile phone, a virtual device, a thin client, an IoT device, or any other electronic device or computing system capable of receiving and sending data to and from other computing devices. While the digital twin search server130is shown as a single device, in other embodiments, the digital twin search server130may be comprised of a cluster or plurality of computing devices, working together or working independently. The digital twin search server130is described in greater detail as a hardware implementation with reference toFIG.5, as part of a cloud implementation with reference toFIG.6, and/or as utilizing functional abstraction layers for processing with reference toFIG.7.

The digital twin search models132may be one or more algorithms modelling a correlation between one or more features detected by the sensors124and an appropriate search for digital twin assets. In the example embodiment, the digital twin search models132may be generated using machine learning methods, such as neural networks, deep learning, hierarchical learning, Gaussian Mixture modelling, Hidden Markov modelling, and K-Means, K-Medoids, or Fuzzy C-Means learning, etc., and may model a likelihood of one or more features being indicative of an appropriate or successful search for digital twin assets. In embodiments, such features may include features such as names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availability, location, etc. The digital twin search models132may weight the features based on an effect that the features have on appropriately searching for digital twin assets.

In the exemplary embodiments, the digital twin searcher134may be a software and/or hardware program capable of collecting training data, extracting features from the training data, and training one or more models based on the extracted features. The digital twin searcher134may additionally be capable of configuring a session and collecting input data, extracting features from the collected data, and applying one or more models to the extracted features to search for digital twin assets. Moreover, the digital twin searcher134may be further configured for notifying the user of one or more appropriate digital twin assets and optionally facilitating a purchase or exchange of one or more digital twin assets and/or physical assets. The digital twin searcher134is additionally capable of evaluating whether the digital twin search was appropriate and adjusting the one or more models. The digital twin searcher134is described in greater detail with reference toFIG.2.

FIG.2depicts an exemplary flowchart illustrating the operations of a digital twin searcher134of the digital twin search system100in searching for digital twin assets, in accordance with the exemplary embodiments. In exemplary embodiments, the digital twin searcher134first implements a training phase in which it trains the digital twin search models132using training data including various input data (i.e., digital twin search criteria) and associated digital twin search results. The digital twin searcher134then moves on to an operational phase in which it applies the trained digital twin search models132to current input data in order to appropriately search for digital twin assets.

The digital twin searcher134may collect and/or receive training data (step204). In embodiments, training data may include input data such as digital twin search criteria and associated digital twin search results. For example, the digital twin searcher134may collect training data as digital twin search criteria for a car engine from a particular manufacturer with serial number 937620, and an associated search result of three car engines meeting (or close to meeting) all of the digital twin search criteria. The digital twin searcher134may retrieve training data via user upload, databases, or the sensors124. In embodiments, the digital twin searcher134may collect training data via the sensors124as one or more microphones built into an auditorium, a camera built into a facility, etc. For example, microphone sensors124may collect audio of a user explaining criteria for their digital twin search. In embodiments, collected training data may also be associated to one or more users. For example, training data of John's search criteria may be labelled with, “user: John.” The digital twin searcher134may collect training data associated with specific users to later train different digital twin search models132for different users based on the users' preferences, characteristics, and/or tendencies (i.e., John may prioritize low cost in digital twin searches, while Rachel may prioritize high performance specifications in digital twin searches). In embodiments, collected training data may include global positioning services (GPS) data of one or more users, for example to determine a user's location and/or whether a physical asset can be shipped to the user's location (or shipped to the user's location in a timely manner).

To further illustrate the operations of the digital twin searcher134, reference is now made to an illustrative example where the digital twin searcher134collects training data consisting of various digital twin search criteria associated with digital twin search results.

The digital twin searcher134may extract one or more features from the collected and/or received training data (step206). The extracted features may be extracted from the collected training data, which may be collected via user upload, databases, or the sensors124, and may include features such as names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availabilities, keywords, technical specifications, locations, etc. In embodiments, the digital twin searcher134may use techniques such as feature extraction, natural language processing, sentiment analysis, optical character recognition, image processing, audio processing, pattern/template matching, data comparison, etc. to identify features. For example, the digital twin searcher134may use feature extraction, optical character recognition, and data comparison to extract names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availabilities, keywords, technical specifications, locations, etc. directly from user uploaded text or text from databases. The digital twin searcher134may additionally utilize image processing, audio processing, video processing, and natural language processing to extract features from collected audio and/or video. For example, the digital twin searcher134may utilize audio and video processing to extract names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availabilities, keywords, technical specifications, locations, etc. from collected audio and video. The digital twin searcher134may later associate extracted features with associated digital twin asset search results when training one or more models.

With reference to the previously introduced example where the digital twin searcher134collects training data consisting of digital twin search criteria associated with digital twin search results, the digital twin searcher134extracts features names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availabilities, keywords, technical specifications, locations, etc. from the collected training data.

The digital twin searcher134may train one or more digital twin search models132based on the extracted features (step208). The digital twin searcher134may train one or more digital twin search models132based on an association of the one or more extracted features with one or more digital twin asset searches. As previously mentioned, such extracted features may include names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availabilities, keywords, technical specifications, locations, etc. and the one or more digital twin search models132may be generated through machine learning techniques such as convolutional neural networks. Moreover, the digital twin searcher134may train the one or more digital twin search models132to weight the features such that features shown to have a greater correlation with an appropriate digital twin asset search are weighted greater than those features that are not. Moreover, the digital twin searcher134may train different digital twin search models132for different users based on users' preferences, characteristics, and/or tendencies. Based on the digital twin search models132's extracted features and weights associated with such extracted features, the digital twin searcher134may later determine an appropriate search result of a digital twin asset search.

With reference to the previously introduced example where the digital twin searcher134extracts features names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availability, location, etc. from the collected training data, the digital twin searcher134trains a model based on an association of the extracted features with appropriate digital twin asset search results.

The digital twin searcher134may receive a configuration (step210). The digital twin searcher134may receive a configuration by receiving a user registration and user preferences. The user registration may be uploaded by a user, i.e., a person searching for one or more digital twins or digital material, and the configuration may be received by the digital twin searcher134via the digital twin search client122and the network108. Receiving the user registration may involve referencing a user profile via user login credentials, internet protocol (IP) address, media access control (MAC) address, etc., or receiving user input information such as a name, date of birth, gender, address/geographic information, phone number, email address, company name, device serial number, smart device120type, types of the sensors124, and the like. Lastly, the digital twin searcher134may receive a configuration of the one or more sensors124, whether they be fixed to one or more devices (e.g., the smart device120) or fixed within an environment in which the digital twin search system100is implemented.

During configuration, the digital twin searcher134may further receive user preferences (step210continued). User preferences may include preferences for the manner in which the digital twin searcher134should notify one or more users of a digital twin search. For example, a user may upload user preferences specifying that they are to be notified of available digital twins sorted by costs, availabilities, performance specifications, etc. In embodiments, user preferences may additionally specify that upon notification of a user and that user's confirmation, the digital twin searcher134is to facilitate an exchange of one or more digital twin assets and/or physical assets. For example, if the digital twin searcher134notifies the user of a car engine digital twin search result and the user confirms that they would like the car engine digital twin as well as the physical car engine, the digital twin searcher134may obtain the car engine digital twin and the physical car engine (i.e. via purchase, exchange of credits, etc.) on behalf of the user. In embodiments, user preferences may be configured alternatively.

With reference to the previously introduced example where the digital twin searcher134trains a model based on an association of the extracted features with appropriate digital twin asset search results, the user uploads a user registration including the user's name, user's computer as smart device120, and user's video cameras as sensors124. The user also uploads user preferences specifying that notification of digital twin search results are to be communicated to the user sorted by costs, and confirmation from the user must be received prior to facilitating an exchange of one or more digital twin assets.

The digital twin searcher134may collect input data (step212). The input data may include digital twin search criteria, and the digital twin searcher134may retrieve input data via user upload, databases, integration of the digital twin search client122with the operating system of smart device120, or the one or more sensors124. In embodiments, digital twin search criteria may include names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availabilities, keywords, technical specifications, locations, etc. of assets and/or their digital twins. In embodiments, the digital twin searcher134may collect input data via the sensors124as one or more microphones built into an auditorium, a camera built into a facility, etc. For example, video camera sensors124and microphone sensors124may collect footage of a user specifying digital twin search criteria. The digital twin searcher134may collect input data to later extract features of the input data and apply one or more digital twin search models132to appropriately search for one or more digital twin assets.

With reference to the previously introduced example where the user uploads a user registration and user preferences, and additionally with reference toFIG.3, the digital twin searcher134collects input data from the user typing their search criteria for a centrifugal pump into a user interface on their computer (smart device120).

The digital twin searcher134may extract one or more features from the collected input data (step214). The digital twin searcher134may extract one or more features from the collected input data in the same manner as described above with respect to extracting features from the training data. However, the digital twin searcher134extracts one or more features from the current collected input data instead of from the previously collected training data.

With reference to the previously introduced example where the digital twin searcher134collects input data of the user's search criteria, and additionally with reference toFIG.3, the digital twin searcher134utilizes a convolutional neural network and optical character recognition to extract the features listed in Table 1 below.

The digital twin searcher134may apply one or more models to the extracted features to determine an appropriate digital twin asset search (step216). As previously mentioned, such extracted features may include names, types, vendors, manufacturers, models, serial numbers, subassembly information, performance specifications, costs, quantities, availability, locations, etc. and the one or more digital twin search models132may be generated through machine learning techniques such as neural networks. In embodiments, the one or more digital twin search models132may be trained at initialization and/or through the use of a feedback loop to weight the features such that features shown to have a greater correlation with appropriately searching for digital twin assets are weighted greater than those features that are not. Based on the extracted features and weights associated with such extracted features, the digital twin searcher134may determine one or more digital twin asset search results.

With reference to the previously introduced example where the digital twin searcher134extracts features of the input data, and additionally with reference toFIG.3, the digital twin searcher134determines that the Centrifugal Pump-BOM Pack is the best digital twin asset for the user.

Upon the digital twin searcher134searching for digital twin assets, the digital twin searcher134may notify the user of the one or more appropriate digital twin assets (step218). In embodiments, the digital twin searcher134may notify the user of appropriate digital twin assets with corresponding available physical assets, and additionally of appropriate digital twin assets without corresponding available physical assets (i.e. if a user already has a physical asset, but seeks only the digital twin for their physical asset for modelling purposes). The digital twin searcher134may convey the one or more appropriate digital twin assets to the user in the form of audio, video, text, or any other manner via the smart device120or any other device. The notification of more than one appropriate digital twin assets may be conveyed visually in the form of a queue or ranking via text and/or audially via one or more integrated speakers. In embodiments where no digital twin assets are appropriate, and additionally with reference toFIG.4, the digital twin searcher134may display a notification to the user stating that no appropriate digital twin assets are available and/or may suggest alternative digital twin assets. As previously discussed, the digital twin searcher134may notify a user according to the user preferences of configuration.

With reference to the previously introduced example where the digital twin searcher134determines that the Centrifugal Pump-BOM Pack is the best digital twin asset for the user, and additionally with reference toFIG.3, the digital twin searcher134visually notifies the user of the Centrifugal Pump-BOM Pack as the most appropriate search result according to the user's preferences.

Upon the digital twin searcher134notifying the user of appropriate digital twin assets, the digital twin searcher134may facilitate an exchange of one or more digital twin assets and/or corresponding physical assets. In embodiments, the digital twin searcher134may facilitate an exchange of one or more digital twin assets and/or corresponding physical assets by executing one or more sale or exchange of currency or credits for one or more digital twin assets or physical assets. For example, the digital twin searcher134may facilitate an exchange of one or more digital twin assets by subtracting the cost of a digital twin asset from the user's credit balance and providing access to the digital twin asset to the user. In another example, the digital searcher134may facilitate an exchange of a digital twin asset and purchase of corresponding physical asset by charging the costs to a user's credit card. In embodiments, the digital twin searcher134may require confirmation from the user before facilitating an exchange of one or more digital twin assets. For example, the digital twin searcher134may notify a user of the most appropriate digital twin asset as discussed in step218, and subsequently require the user to click a button “confirm purchase” to facilitate the exchange of the digital twin asset. In embodiments, the digital twin searcher134may be configured to facilitate an exchange of a digital twin asset in an alternate manner.

With reference to the previously introduced example where the digital twin searcher134visually notifies the user of the Centrifugal Pump-BOM Pack as the most appropriate search result according to the user's preferences, and additionally with reference toFIG.3, the digital twin searcher134receives confirmation from the user via the clicking of button “confirm purchase” and facilitates the exchange of the Centrifugal Pump-BOM Pack digital twin asset and corresponding physical asset for $100.

The digital twin searcher134may evaluate and modify the digital twin search models132(step222). In the example embodiment, the digital twin searcher134may verify whether the digital twin asset search results were appropriate in order to provide a feedback loop for modifying the digital twin search models132. In embodiments, the feedback loop may simply provide a means for a user to indicate whether the search results were appropriate, accurate, helpful, etc. The feedback loop indication may be triggered via a toggle switch, button, slider, etc. that may be selected by the user manually by hand using a button/touchscreen/etc., by voice, by eye movement, and the like. Based on the digital twin searcher134appropriately or inappropriately determining search results, the digital twin searcher134may modify the digital twin search models132relating to determination of appropriate digital twin search results. In other embodiments, the digital twin searcher134may infer or deduce whether the digital twin search results were appropriate. For example, the digital twin searcher134may interpret user dialogue via natural language processing to determine whether the search results were reasonable. For example, if the user says, “That isn't right,” “This isn't helpful” or other expressions indicative of confusion or dissatisfaction, the digital twin searcher134may infer that the search results were inappropriate and modify the digital twin search models132accordingly. Based on feedback received in the above or any other manners, the digital twin searcher134may then modify the digital twin search models132to more accurately search for digital twin assets.

With reference to the previously introduced example where the digital twin searcher134receives confirmation from the user via the clicking of button “confirm purchase” and facilitates the exchange of the Centrifugal Pump-BOM Pack digital twin asset and corresponding physical asset for $100, the user says, “Great, all set!” and the digital twin searcher134modifies the digital twin search models132accordingly.

FIG.3depicts an exemplary user interface of the digital twin search system100illustrating a successful search for a digital twin asset, in accordance with the exemplary embodiments.

FIG.4depicts an exemplary user interface of the digital twin search system100illustrating an unsuccessful search for a digital twin asset, in accordance with the exemplary embodiments.

FIG.5depicts a block diagram of devices within the digital twin search system100ofFIG.1, in accordance with the exemplary embodiments. It should be appreciated thatFIG.5provides 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.

Devices used herein may include one or more processors02, one or more computer-readable RAMs04, one or more computer-readable ROMs06, one or more computer readable storage media08, device drivers12, read/write drive or interface14, network adapter or interface16, all interconnected over a communications fabric18. Communications fabric18may 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.

One or more operating systems10, and one or more application programs11are stored on one or more of the computer readable storage media08for execution by one or more of the processors02via one or more of the respective RAMs04(which typically include cache memory). In the illustrated embodiment, each of the computer readable storage media08may be a magnetic disk storage device of an internal hard drive, CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk, a semiconductor storage device such as RAM, ROM, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

Devices used herein may also include a RAY drive or interface14to read from and write to one or more portable computer readable storage media26. Application programs11on said devices may be stored on one or more of the portable computer readable storage media26, read via the respective R/W drive or interface14and loaded into the respective computer readable storage media08.

Devices used herein may also include a display screen20, a keyboard or keypad22, and a computer mouse or touchpad24. Device drivers12interface to display screen20for imaging, to keyboard or keypad22, to computer mouse or touchpad24, and/or to display screen20for pressure sensing of alphanumeric character entry and user selections. The device drivers12, RAY drive or interface14and network adapter or interface16may comprise hardware and software (stored on computer readable storage media08and/or ROM06).

The programs described herein are identified based upon the application for which they are implemented in a specific one of the exemplary embodiments. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the exemplary embodiments should not be limited to use solely in any specific application identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method, and computer program product have been disclosed. However, numerous modifications and substitutions can be made without deviating from the scope of the exemplary embodiments. Therefore, the exemplary embodiments have been disclosed by way of example and not limitation.

It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, the exemplary embodiments are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Referring now toFIG.7, a set of functional abstraction layers provided by cloud computing environment50(FIG.6) is shown. It should be understood in advance that the components, layers, and functions shown inFIG.7are intended to be illustrative only and the exemplary embodiments are not limited thereto. As depicted, the following layers and corresponding functions are provided: