Method and system for transferring learning from one machine to another - Patents.com

Abstract

A computer-implemented method for transferring a knowledge corpus from a first machine to a second machine is provided. The method compares a digital twin model of the first machine to the second machine and identifies, based on the comparison, whether a transfer of the knowledge corpus is possible. If a transfer of the knowledge corpus is possible, the method maps the knowledge corpus of the first machine with input and output systems of the second machine. If a transfer of the knowledge corpus is not possible, the method identifies how functions of the first machine and the second machine are performed and controlled. The method then generates hierarchical functional digital twin models of the first machine and the second machine based on the identified functions. The method further transfers the mapped knowledge corpus of the first machine with the input and output systems of the second machine.

Description

[Technical field]

[0001] The present disclosure relates generally to the field of cognitive computing, and more particularly, to data processing and dynamic learning among one or more machines. [Background technology]

[0002] Recently, machines (e.g., autonomous automobiles) or devices are able to collect data from their surroundings, analyze the data, and make decisions based on the analyzed data.

[0003] However, the way one machine gathers information may not be the same as another, so direct transfer of a knowledge corpus from one machine to another may not be directly applicable. Summary of the Invention

[0004] Embodiments of the present invention disclose a method, a computer program product, and a system.

[0005] A method according to an embodiment of the present invention is provided in a data processing system including a processor and a memory. The method includes comparing a digital twin model of a first machine with a second machine. The method further includes identifying whether a transfer of a knowledge corpus is possible based on the comparison, and if the transfer of the knowledge corpus is possible, mapping the knowledge corpus of the first machine with an input and output system of the second machine. The method further includes transferring the mapped knowledge corpus of the first machine with the input and output system of the second machine.

[0006] A computer program product according to an embodiment of the present invention includes a non-transitory tangible storage device having program code embodied therein. The program code is executable by a processor of a computer to perform a method. The method includes comparing a digital twin model of a first machine to a second machine. The method further includes identifying whether a transfer of the knowledge corpus is possible based on the comparison, and if the transfer of the knowledge corpus is possible, mapping the knowledge corpus of the first machine with an input and output system of the second machine. The method further includes transferring the mapped knowledge corpus of the first machine with the input and output system of the second machine.

[0007] A computer system according to an embodiment of the invention includes one or more computer devices, each having one or more processors and one or more tangible storage devices, and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors. The program instructions implement a method. The method includes comparing a digital twin model of a first machine to a second machine. The method further includes identifying whether a transfer of the knowledge corpus is possible based on the comparison, and if the transfer of the knowledge corpus is possible, mapping the knowledge corpus of the first machine with an input and output system of the second machine. The method further includes transferring the mapped knowledge corpus of the first machine with the input and output system of the second machine. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 illustrates a learning transfer computing environment in accordance with an embodiment of the present disclosure. [Diagram 2] 2 is a flow chart illustrating the operation of the learning transfer program 120 of FIG. 1 in accordance with an embodiment of the present disclosure. [Diagram 3] FIG. 2 illustrates hardware components of the learning transfer computing environment of FIG. 1 in accordance with an embodiment of the present disclosure. [Figure 4] FIG. 1 illustrates a cloud computing environment according to an embodiment of the present disclosure. [Diagram 5] FIG. 5 illustrates an abstract model layer of the exemplary cloud computing environment of FIG. 4 in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] This disclosure describes methods by which learning may be transferred from one machine (e.g., a vehicle such as an autonomous automobile) to another.

[0010] Different machines may have different capabilities, different input collection mechanisms, different control systems, etc. Similarly, the way one machine gathers information may not be the same as another, or the way one machine makes decisions may not be the same as another, so direct transfer of a knowledge corpus from one machine to another may not be directly applicable.

[0011] This disclosure details methods and systems whereby learning from one machine can be transferred to another, even if the two machines are heterogeneous.

[0012] For example, any machine or device may perform self-learning while in use. Self-learning involves collecting data from its surroundings, analyzing the collected data, and taking appropriate action. In the case of an autonomous vehicle, the autonomous vehicle generates a knowledge corpus based on its self-learning. Various data collection modules in the vehicle collect data and use the data to build the vehicle's knowledge corpus.

[0013] Since different vehicles have different capabilities, they will invariably have different input collection mechanisms, different control systems, etc. Similarly, direct transfer of a knowledge corpus from one vehicle to another may not be directly applicable.

[0014] This disclosure describes methods and systems whereby learning from one machine (e.g., a vehicle) can be transferred to another machine.

[0015] In the following, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0016] The present disclosure is not limited to the following exemplary embodiments, and may be implemented with various modifications within the scope of the present disclosure. In addition, the drawings used in this specification are for illustrative purposes and may not be drawn to actual scale.

[0017] 1 illustrates a learning transfer computing environment 100 according to an embodiment of the present disclosure. The learning transfer computing environment 100 includes a host server 110, a first vehicle 130, and a second vehicle 140, all connected via a network 102. The setup in FIG. 1 represents an example embodiment configuration of the present disclosure, and those skilled in the art should not be limited to the illustrated setup in order to derive benefit from the present disclosure.

[0018] In an exemplary embodiment, the host server 110 includes the learning transfer program 120. In various embodiments, the host server 110 may be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smartphone, a server, or any programmable electronic device capable of communicating with the first vehicle 130 and the second vehicle 140 over the network 102. The host server 110 may include internal and external hardware components, as shown and described in further detail below with respect to FIG. 3. In other embodiments, the host server 110 may be implemented in a cloud computing environment, as described herein with respect to FIGS. 4 and 5. The host server 110 may also have wireless connectivity capabilities, allowing the host server 110 to communicate with the first vehicle 130, the second vehicle 140, and other computers or servers over the network 102.

[0019] Continuing with reference to FIG. 1, first vehicle 130 includes sensor 132 and knowledge corpus 134. In an exemplary embodiment, first vehicle 130 may include, but is not limited to, a car, a minivan, a truck, a trailer truck, a train, or any road vehicle. In alternative embodiments, first vehicle 130 may be an airborne vehicle (e.g., an airplane, a spacecraft, a hot air balloon, a hovercraft, etc.), a waterborne vehicle (e.g., a motorboat, a yacht, a jet ski, a pontoon boat, a cargo ship, etc.), and any other vehicle known to one of skill in the art.

[0020] In the exemplary embodiment, first vehicle 130 includes one or more sensors 132 (identical to sensor 132a associated with second vehicle 140).

[0021] The sensors 132 may be devices, hardware components, modules, or subsystems capable of recording, capturing, and detecting events or changes in or near a user environment, and transmitting the detected data to other electronics (e.g., the host server 110), components (e.g., the knowledge corpus 134), or programs (e.g., the learning transfer program 120) in a system such as the learning transfer computing environment 100. In various embodiments, the detected data collected by the sensors 132 helps generate a knowledge corpus 134 of capabilities for each of the first vehicle 130 and the second vehicle 140.

[0022] In an exemplary embodiment, the sensors 132 are located within the first vehicle 130 and the second vehicle 140 and may be global positioning systems (GPS), software applications, proximity sensors, cameras, microphones, light sensors, infrared sensors, weight sensors, temperature sensors, tactile sensors, motion detectors, optical character recognition (OCR) sensors, occupancy sensors, heat sensors, analog sensors (e.g., potentiometers, force sensing resistors), radar, radio frequency sensors, Quick Response (QR) Codes, video cameras, digital cameras, Internet of Things (IoT) sensors, lasers, gyroscopes, accelerometers, actuators, structured light systems, user tracking sensors (e.g., a user's eye, head, hand, and body tracking position), and other devices used to measure, detect, and record the inputs and outputs of various systems associated with the first vehicle 130 and the second vehicle 140.

[0023] In an exemplary embodiment, the sensors 132 may be capable of continuously monitoring, collecting, and storing the collected data on local storage, such as the knowledge corpus 134, or transmitting the collected data to the learning transfer program 120. In alternative embodiments, the sensors 132 may be capable of discovering, communicating, pairing, or synchronizing with Internet of Things (IoT) devices, thus creating opportunities for more direct integration of the physical world into computer-based systems, resulting in improved efficiency, accuracy, and economic benefits in addition to reduced human intervention.

[0024] In various embodiments, the sensor 132 may be integrated into various systems (e.g., lighting systems, engine, steering, safety devices, etc.) associated with the first vehicle 130 and / or second vehicle 140, including computer processing unit (CPU), memory, and power resources, and may be capable of communicating with the first vehicle 130, the second vehicle 140, and the host server 110 over the network 102. In this manner, functionality of the various systems of the first vehicle 130 and the second vehicle 140 may be obtained by one or more inputs of the sensor 132 data collection.

[0025] In an exemplary embodiment, the learning and transfer program 120 can identify how different functions are performed with different sensor involvement (i.e., sensors 132) and what types of functions are present.

[0026] In an exemplary embodiment, knowledge corpus 134 may be local data storage on first vehicle 130 that includes one or more sets of training data. The training data may include data sets that include data collected from the machine's surroundings and machine functions, data analyzed from the machine's surroundings and machine functions, decision data based on the analyzed data, and self-learning data based on the decision data. For example, sensors 132 may continuously collect data from various sensor systems associated with first vehicle 130 and organize it according to function (e.g., lighting system, engine, steering, safety equipment, etc.).

[0027] Although knowledge corpus 134 is shown as being stored on first vehicle 130 and second vehicle 140, in other embodiments knowledge corpus 134 may be stored as a separate database on host server 110, learning transfer program 120, or any other device or database connected via network 102. In alternative embodiments, knowledge corpus 136 may be comprised of a cluster or multiple computing devices operating together or separately.

[0028] In an exemplary embodiment, second vehicle 140 may include the same or similar components as first vehicle 130, such as sensors 132a and knowledge corpus 134a. In an exemplary embodiment, second vehicle 140 may be capable of communicating with first vehicle 130 via vehicle-to-vehicle (V2V) communications, Wireless Fidelity (WiFi), and radio frequency identification (RFID), or by any other means known to one of skill in the art.

[0029] Continuing with reference to FIG. 1, in an exemplary embodiment, the learning transfer program 120 may be a computer application on the host server 110 that includes a set of instructions executable by a processor. The set of instructions may be described using a set of functional modules. In an exemplary embodiment, the learning transfer program 120 may receive input from the first vehicle 130 and the second vehicle 140 over the network 102. In alternative embodiments, the learning transfer program 120 may be a computer application contained within the first vehicle 130 or a standalone program on a separate electronic device.

[0030] Continuing with reference to FIG. 1, the functional modules of the learning and transfer program 120 include a comparison module 122, an identification module 124, a mapping module 126, and a transfer module 128.

[0031] FIG. 2 is a flow chart illustrating the operation of the learning transfer program 120 of FIG. 1 in accordance with an embodiment of the present disclosure.

[0032] 1 and 2, the comparison module 122 includes a set of programming instructions in a learning transfer program 120 that compares 202 a digital twin model of a first machine to a second machine. The set of programming instructions is executable by a processor.

[0033] A digital twin is a virtual model designed to accurately reflect a physical object. The object being studied (e.g., a vehicle) is fitted with various sensors (e.g., sensors 132) related to critical areas of function. These sensors 132 produce data about different aspects of the physical object's performance, such as energy output, engine efficiency, steering effectiveness, safety performance, and other aspects of the physical object's performance. This data is then relayed to a processing system and applied to the digital copy.

[0034] Informed by such data, the virtual digital twin model may be used to run simulations, investigate performance issues and generate possible improvements, all with the goal of generating valuable insights that can be applied back to the original physical object.

[0035] By having better and constantly updated data relevant to a wider range of domains, combined with the added computing power that comes with virtual environments, digital twins can investigate many more problems from many more perspectives than standard simulations can, offering greater and infinite possibilities for improving products and processes.

[0036] In an exemplary embodiment, each vehicle (e.g., first vehicle 130) is uniquely identified and each vehicle has its own digital twin model. In an exemplary embodiment, comparison module 122 compares the digital twin models of the sending vehicle (i.e., first vehicle 130) and the receiving vehicle (i.e., second vehicle 140).

[0037] With reference to a illustrative example, the comparison module 122 compares a digital twin model of a first vehicle 130 (e.g., a sending vehicle) to a second vehicle 140 (e.g., a receiving vehicle). The first vehicle 130 is a BMW® SUV. The second vehicle 140 is a BMW® sedan. In a similar illustrative example, the first vehicle 130 is a BMW® SUV and the second vehicle 140 is a trailer. In both illustrative examples, the comparison module 122 compares similarities and differences between various sensor systems (i.e., sensors 132) associated with the sending vehicle (e.g., BMW® SUV) and the receiving vehicle (e.g., BMW® sedan and trailer).

[0038] 1 and 2, the identification module 124 includes a set of programming instructions within the learning transfer program 120 for identifying whether the knowledge corpus is transferable (step 204) based on the comparison. The set of programming instructions is executable by a processor.

[0039] In an exemplary embodiment, the digital twin model identifies each sensor feed of the first vehicle 130 and generates its digital twin. For example, a function of the first vehicle 130 may be completed by one or more collections of sensor 132 inputs. Such functions may include, but are not limited to, headlight functions, air conditioning functions, etc.

[0040] The identification module 124 identifies how different functions are performed with the participation of different sensors 132, what types of functions are present, and how the different functions can be grouped in the vehicle's digital twin.

[0041] In an exemplary embodiment, the knowledge corpus 134 of the first vehicle 130 is generated based on the identified features of the sending vehicle (i.e., the first vehicle 130). The identification module 124 then identifies whether the knowledge corpus 134 is transferable based on a one-to-one comparison of features of the first vehicle 130 (e.g., the sending vehicle) and the second vehicle 140 (e.g., the receiving vehicle), as described in detail above.

[0042] Continuing with reference to Figures 1 and 2, if transfer of the knowledge corpus 134 is not possible, the identification module 124 identifies how the functions of the first machine (i.e., the first vehicle 130) and the second machine (i.e., the second vehicle 140) are performed and controlled (step 206).

[0043] Continuing with the illustrative example provided above, the identification module 124 identifies that a one-to-one transfer of the knowledge corpus 134 from the BMW® SUV to the BMW® Sedan is possible because the digital twin models of the sending vehicle (i.e., the BMW® SUV) and the receiving vehicle (i.e., the BMW® Sedan) are nearly identical. However, a one-to-one transfer of the knowledge corpus 134 from the BMW® SUV to the trailer is not possible because the various input and output sensors 132 are different between the sending and receiving vehicles and therefore a one-to-one mapping is not possible.

[0044] 1 and 2, the mapping module 126 includes a set of programming instructions within the learning transfer program 120 for mapping 208 the knowledge corpus 134 of a first machine (e.g., the first vehicle 130) with the input and output systems of a second machine (e.g., the second vehicle 140). The set of programming instructions is executable by a processor.

[0045] In an exemplary embodiment, one-to-one mapping is possible when the vehicles have identical configurations, input collection systems, etc. Thus, the knowledge corpus 134 is transferred and mapped, along with the required capabilities, from the first vehicle 130 to the second vehicle 140 via the mapping module 126.

[0046] Continuing with reference to Figures 1 and 2, if a one-to-one mapping is not possible, the learning transfer program 120 generates hierarchical functional digital twin models of the first machine (e.g., first vehicle 130) and the second machine (e.g., second vehicle 140) based on the identified functions (step 210).

[0047] Continuing with reference to Figures 1 and 2, the mapping module 126 maps the identified capabilities of the first machine and the second machine based on the functional digital twin model (step 212).

[0048] In an exemplary embodiment, the hierarchical digital twin model is generated by grouping the functions of a first machine and a second machine in a hierarchical format and identifying at what hierarchical level the knowledge corpus 134 of the first machine can be mapped to the second machine.

[0049] In a further exemplary embodiment, the learning transfer program 120 identifies how different features of the first machine's received knowledge corpus 134 map to the features of the second machine and matches the first machine's (e.g., first vehicle 130) received knowledge corpus 134 with the input and output systems of the second machine (second vehicle 140).

[0050] In an alternative embodiment, the mapping module 126 may generate mapping metrics between the first and second machines based on the identified knowledge corpus 134. If there are functional differences between the first and second machines, the learning transfer program 120 may identify collaborations with one or more other devices to add improvements to the knowledge corpus 134 with the functionality and features of the one or more other devices.

[0051] In further alternative embodiments, the learning transfer program 120 may automatically adapt the hierarchical functional twin models of the first machine (i.e., first vehicle 130) and the second machine (i.e., second vehicle 140) based on identified integrations with one or more other devices.

[0052] In an alternative embodiment, the learning and forwarding program 120 may generate a mesh network of one or more other devices and determine a best sender device from the mesh network of one or more other devices. The learning and forwarding program 120 may forward the complete mapping, including the functional differences learned from working with the one or more other devices, to a second machine (i.e., the second vehicle 140).

[0053] Referring again to the illustrative example provided above, the mapping module 126 maps the one-to-one knowledge corpus 134 of the BMW® SUV with the input and output systems of the BMW® Sedan. In relation to mapping the knowledge corpus from the BMW® SUV to the trailer, the mapping module 126 maps the identified features of the BMW® SUV with the trailer based on the generated hierarchical functional digital twin model. The mapping module 126 then identifies to what extent a transfer of the one-to-one knowledge corpus 134 is possible between the BMW® SUV and the trailer, and to what extent a functional digital twin-based knowledge corpus transfer is possible.

[0054] 1 and 2, the transfer module 128 includes a set of programming instructions within the learning transfer program 120 for transferring 214 the mapped knowledge corpus of the first machine to and from the input and output systems of the second machine. The set of programming instructions is executable by a processor.

[0055] In an exemplary embodiment, the transfer module 128 transfers the knowledge corpus 134 from the first vehicle 130, along with its mapped functions, so that the second vehicle 140 (i.e., the receiving vehicle) may receive the knowledge corpus 134.

[0056] In an alternative embodiment, while the transfer module 128 transfers the knowledge corpus 134 from the sending vehicle to the receiving vehicle, the learning transfer program 120 identifies the best possible combination of transfer of the knowledge corpus 134 (e.g., one-to-one knowledge corpus to feature-based knowledge corpus transfer).

[0057] Continuing with the illustrative example above, the transfer module 128 performs a one-to-one transfer of the knowledge corpus 134 from the BMW® SUV to the BMW® Sedan since the digital twin models of the sending and receiving vehicles are nearly identical. The transfer module 128 performs a transfer of the feature-based knowledge corpus 134 from the BMW® SUV to the trailer since various input and output sensors are different between the sending and receiving vehicles. In this way, the learning transfer program 120 is comparing different levels of functionality of the digital twin models of each vehicle.

[0058] In an exemplary embodiment, network 102 is a communication channel capable of transferring data between connected devices and may be a telecommunications network used to facilitate telephone calls between two or more parties, including a fixed line network, a wireless network, a closed network, a satellite network, or any combination thereof. In another embodiment, network 102 may be the Internet, representing a worldwide collection of networks and gateways for supporting communication between Internet-connected devices. In this other embodiment, network 102 may include, for example, wired, wireless, or fiber optic connections and may be implemented as an intranet network, a local area network (LAN), a wide area network (WAN), or any combination thereof. In further embodiments, network 102 may be a Bluetooth® network, a WiFi® network, a vehicle-to-vehicle (V2V) network, a vehicle-to-infrastructure (V2I) network, a peer-to-peer (P2P) communications network, a mesh network, or any combination thereof. In general, the network 102 may be any combination of connections and protocols that support communication between the host server 110, the first vehicle 130, and the second vehicle 140.

[0059] 3 is a block diagram illustrating components of a computing device (such as host server 110 shown in FIG. 1) in accordance with an embodiment of the invention. It should be understood that FIG. 3 is merely provided as an illustration of one implementation and is not intended to suggest any limitation with respect to the environments in which different embodiments may be implemented. Many modifications to the illustrated environment may be made.

[0060] 3 may include one or more processors 902, one or more computer-readable RAMs 904, one or more computer-readable ROMs 906, one or more computer-readable storage media 908, device drivers 912, read / write drives or interfaces 914, and network adapters or interfaces 916, all interconnected over a communications fabric 918. The communications fabric 918 may be implemented in any architecture designed to pass data and / or control information between processors (such as microprocessors, communications and network processors), system memory, peripheral devices, and any other hardware components in the system.

[0061] One or more operating systems 910 and one or more application programs 911, such as the learning transfer program 120, may be stored on one or more of the computer-readable storage media 908 via one or more of the respective RAMs 904 (typically including cache memory) for execution by one or more of the processors 902. In the illustrated embodiment, each of the computer-readable storage media 908 may be a magnetic disk storage device of an internal hard drive, a CD-ROM, a DVD, a memory stick, a magnetic tape, a magnetic disk, an optical disk, a semiconductor storage device such as a RAM, a ROM, an EPROM, a flash memory, or any other computer-readable tangible storage device capable of storing computer programs and digital information.

[0062] The computing device of Figure 3 may also include a R / W drive or interface 914 for reading from and writing to one or more portable computer readable storage media 926. Application programs 911 on the computing device of Figure 3 may be stored on the one or more portable computer readable storage media 926 and read via the respective R / W drive or interface 914 and loaded into the respective computer readable storage media 908.

[0063] The computing device of FIG. 3 may also include a network adapter or interface 916, such as a TCP / IP adapter card or a wireless communication adapter (such as a 4G wireless communication adapter using OFDMA technology). An application program 911 on the computing device of FIG. 3 may be downloaded from an external computer or external storage device to the computing device via a network (e.g., the Internet, a local area network, or other wide area or wireless network) and the network adapter or interface 916. From the network adapter or interface 916, the program may be loaded into the computer-readable storage medium 908. The network may include copper wire, optical fiber, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers, or combinations thereof.

[0064] 3 may also include a display screen 920, a keyboard or keypad 922, and a computer mouse or touchpad 924. The device driver 912 interfaces to the display screen 920 for imaging, to the keyboard or keypad 922, to the computer mouse or touchpad 924, and / or to the display screen 920 for alphanumeric character entry and pressure sensing of user selection. The device driver 912, the R / W drive or interface 914, and the network adapter or interface 916 may include hardware and software (stored in the computer readable storage medium 908 or ROM 906, or both).

[0065] The programs described herein are identified based on the application for which they are implemented in specific embodiments of the invention, however, it will be understood that the nomenclature of any particular program herein is used merely for convenience, and thus the invention should not be limited to use only in any particular application identified and / or suggested by such nomenclature.

[0066] Although this disclosure includes a detailed description of cloud computing, it should be understood that implementations of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the invention may be practiced in conjunction with any other type of computing environment now known or later developed.

[0067] Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal administrative effort or interaction with a service provider. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

[0068] The characteristics are as follows:

[0069] On-Demand Self-Service: Cloud consumers can unilaterally provision computing capacity, such as server time and network storage, automatically as needed, without the need for human interaction with the service provider.

[0070] Broad network access: Capabilities are available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin-client or thick-client platforms (e.g., cell phones, laptops, and PDAs).

[0071] Resource Sharing: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically allocated and reallocated according to demand. Location independence is implied in that consumers generally do not have control or knowledge over the exact location of the resources provided, but may be able to specify location at a higher level of abstraction (e.g., country, state, or data center).

[0072] Rapid Scalability: Capacity can be quickly and elastically provisioned, in some cases automatically, to instantly scale out, and quickly released to instantly scale in. To the consumer, the capacity available for provisioning often appears unlimited and can be purchased in any amount at any time.

[0073] Services are meterable: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the services being used.

[0074] The service model is as follows:

[0075] Software as a Service (SaaS): The ability offered to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through thin-client interfaces such as web browsers (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0076] Platform as a Service (PaaS): The capability offered to the consumer is to deploy consumer-created or consumer-acquired applications, written using programming languages ​​and tools supported by the provider, onto a cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but does have control over the deployed applications and, to the extent possible, the application hosting environment configuration.

[0077] Infrastructure as a Service (IaaS): The capability provided to the consumer is the supply of processing, storage, networking, and other basic computing resources on which the consumer can deploy and run any software, which may include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure, but does have control over the operating systems, storage, deployed applications, and possibly limited control over select networking components (e.g., host firewalls).

[0078] The deployment model is as follows:

[0079] Private Cloud: The cloud infrastructure is operated exclusively for an organization. The cloud infrastructure may be managed by the organization or a third party and may be on-premise or off-premise.

[0080] Community Cloud: The cloud infrastructure is shared by multiple organizations to support a specific community with shared concerns (e.g., mission, security requirements, policy, and compliance considerations). The cloud infrastructure may be managed by the organization or a third party and may reside on-premise or off-premise.

[0081] Public Cloud: The cloud infrastructure is made available to the general public or large industry organizations and is owned by an organization that sells cloud services.

[0082] Hybrid Cloud: The cloud infrastructure is a composite of two or more clouds (private, community, or public) that remain a unique entity but are joined by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0083] Cloud computing environments are service-oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0084] Referring now to FIG. 4, an exemplary cloud computing environment 50 is shown. As shown, the cloud computing environment 50 includes one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as a personal digital assistant (PDA) or cell phone 54A, a desktop computer 54B, a laptop computer 54C, or an automobile computer system 54N, or combinations thereof, may communicate. The nodes 10 may communicate with each other. They may be physically or virtually grouped in one or more networks, such as a private cloud, a community cloud, a public cloud, or a hybrid cloud, or combinations thereof, as described above (not shown). This allows the cloud computing environment 50 to offer infrastructure, platform, and / or software as a service without the cloud consumer having to maintain resources on the local computing device. It should be understood that the types of computing devices 54A-54N shown in FIG. 4 are intended to be exemplary only, and that the computing node 10 and cloud computing environment 50 may communicate with any type of computerized device via any type of network and / or network addressable connections (e.g., using a web browser).

[0085] Referring now to Figure 5, a set of functional abstraction layers provided by cloud computing environment 50 (Figure 4) is shown. It should be understood in advance that the components, layers, and functions shown in Figure 5 are intended to be merely illustrative, and that embodiments of the present invention are not limited thereto. As shown, the following layers and corresponding functions are provided:

[0086] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframes 61, Reduced Instruction Set Computer (RISC) architecture based servers 62, servers 63, blade servers 64, storage devices 65, and networks and networking components 66. In some embodiments, the software components include network application server software 67 and database software 68.

[0087] The virtualization layer 70 provides an abstraction layer over which the following examples of virtual entities may be provided: virtual servers 71, virtual storage 72, virtual networks including virtual private networks 73, virtual applications and operating systems 74, and virtual clients 75.

[0088] In one example, the management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing and other resources utilized to execute tasks within the cloud computing environment. Metering and pricing 82 provides cost tracking as resources are utilized within the cloud computing environment and billing or invoicing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides protection for data and other resources as well as identity verification for cloud consumers and tasks. User portal 83 provides consumers and system administrators with access to the cloud computing environment. Service level management 84 provides cloud computing resource allocation and management so that required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides pre-positioning and procurement of cloud computing resources where future requirements are anticipated according to SLAs.

[0089] The Workload Layer 90 provides examples of functionality for which a cloud computing environment may be utilized. Examples of workloads and functions that may be provided from this layer include mapping and navigation 91, software development and lifecycle management 92, virtual classroom instructional delivery 93, data analytics processing 94, transaction processing 95, and access control to data objects 96.

[0090] The invention may be a system, method, or computer program product, or combination thereof, at any possible level of technical detail of integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the invention.

[0091] A computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but 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 computer readable storage media includes portable computer diskettes, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or Flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or ridge structures in grooves that allow instructions to be recorded thereon, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as a transitory signal per se, such as electric waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through electrical wires.

[0092] The computer readable program instructions described herein may be downloaded from the computer readable storage medium to the respective computing / processing device or to an external computer or storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof. The network may include copper transmission cables, optical transmission fiber, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface in each computing / processing device receives the computer readable program instructions from the network and transfers the computer readable program instructions for storage in the computer readable storage medium in the respective computing / processing device.

[0093] The computer readable program instructions for carrying out the operations of the present invention may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or object oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer readable program instructions may execute completely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or completely on a 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 (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer readable program instructions by individualizing the electronic circuitry with state information of the computer readable program instructions to perform aspects of the invention.

[0094] 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.

[0095] 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, executed by the processor of the computer or other programmable data processing apparatus, generate means for performing the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams. These computer readable program instructions may also be stored on a computer readable storage medium such that the computer readable storage medium having the instructions stored thereon comprises an article of manufacture including instructions for performing aspects of the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams, and may direct a computer, programmable data processing apparatus, or other device, or combination thereof, to function in a particular manner.

[0096] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operational steps to create a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device perform the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0097] The flowcharts 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 flowcharts or block diagrams may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions described in the blocks may occur out of the order described in the figures. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may be executed in the reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or executes a combination of dedicated hardware and computer instructions.

[0098] Based on the foregoing, a computer system, method, and computer program product have been disclosed. However, numerous modifications and substitutions may be made without departing from the scope of the invention. Thus, the present invention has been disclosed by way of example and not by way of limitation.

Claims

1. 1. A computer-implemented method for transferring a knowledge corpus from a first machine to a second machine, comprising: Comparing a digital twin model of the first machine to the second machine; identifying whether the knowledge corpus is transferable based on the comparison; and mapping the knowledge corpus of the first machine with the input and output systems of the second machine if transfer of the knowledge corpus is possible; transferring the mapped knowledge corpus of the first machine to and from the input and output systems of the second machine; 4. A computer-implemented method comprising:

2. if transfer of said knowledge corpus is not possible, identifying how the functions of said first machine and said second machine are executed and controlled; generating hierarchical functional digital twin models of the first machine and the second machine based on the identified capabilities; Mapping the identified capabilities of the first machine and the second machine based on the functional digital twin model; and receiving, by the second machine, the knowledge corpus of the first machine; The computer-implemented method of claim 1 , further comprising:

3. The hierarchical functional digital twin model, grouping the functions of the first machine and the second machine in a hierarchical format; identifying at what hierarchical level the knowledge corpus of the first machine can be mapped to the second machine; The computer-implemented method of claim 2 , wherein the computer-implemented method is generated by:

4. identifying, by the second machine, how different features of the knowledge corpus received by the first machine map to features of the second machine; adapting, by the second machine, the received knowledge corpus of the first machine with the input and output systems of the second machine; The computer-implemented method of claim 3 , further comprising:

5. generating mapping metrics between the first machine and the second machine based on the identified knowledge corpus; if there is a difference in functionality between the first machine and the second machine, identifying, by the first machine, a linkage with the one or more other devices to enhance the knowledge corpus with the functionality and features of the one or more other devices; The computer-implemented method of claim 3 , further comprising:

6. 6. The computer-implemented method of claim 5, further comprising automatically adapting the hierarchical functional twin models of the first machine and the second machine based on the identified collaboration with the one or more other devices.

7. creating a mesh network of the one or more other devices; determining a best sender device from the mesh network of the one or more other devices; transferring a complete mapping including the differences in capabilities learned from the interaction with the one or more other devices to the second machine; The computer-implemented method of claim 5 , further comprising:

8. A computer program product including a non-transitory tangible storage device having program code embodied therein, said program code being executable by a processor of a computer to perform a method, said method comprising: Comparing a digital twin model of the first machine to the second machine; identifying whether the knowledge corpus is transferable based on the comparison; and mapping the knowledge corpus of the first machine with the input and output systems of the second machine if transfer of the knowledge corpus is possible; transferring the mapped knowledge corpus of the first machine to and from the input and output systems of the second machine; 2. A computer program product comprising:

9. if transfer of said knowledge corpus is not possible, identifying how the functions of said first machine and said second machine are executed and controlled; generating hierarchical functional digital twin models of the first machine and the second machine based on the identified capabilities; Mapping the identified capabilities of the first machine and the second machine based on the functional digital twin model; and receiving, by the second machine, the knowledge corpus of the first machine; 9. The computer program product of claim 8, further comprising:

10. The hierarchical functional digital twin model, grouping the functions of the first machine and the second machine in a hierarchical format; identifying at what hierarchical level the knowledge corpus of the first machine can be mapped to the second machine; 10. The computer program product of claim 9, generated by:

11. identifying, by the second machine, how different features of the knowledge corpus received by the first machine map to features of the second machine; adapting, by the second machine, the received knowledge corpus of the first machine with the input and output systems of the second machine; 11. The computer program product of claim 10, further comprising:

12. generating mapping metrics between the first machine and the second machine based on the identified knowledge corpus; if there is a difference in functionality between the first machine and the second machine, identifying, by the first machine, a linkage with the one or more other devices to enhance the knowledge corpus with the functionality and features of the one or more other devices; 11. The computer program product of claim 10, further comprising:

13. 13. The computer program product of claim 12, further comprising automatically adapting the hierarchical functional twin models of the first machine and the second machine based on the identified collaboration with the one or more other devices.

14. creating a mesh network of the one or more other devices; determining a best sender device from the mesh network of the one or more other devices; transferring a complete mapping including the differences in capabilities learned from the interaction with the one or more other devices to the second machine; 13. The computer program product of claim 12, further comprising:

15. 1. A computer system comprising: one or more computing devices, each having one or more processors and one or more tangible storage devices; A program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising: Comparing a digital twin model of the first machine to the second machine; identifying whether the knowledge corpus is transferable based on the comparison; and mapping the knowledge corpus of the first machine with the input and output systems of the second machine if transfer of the knowledge corpus is possible; transferring the mapped knowledge corpus of the first machine to and from the input and output systems of the second machine; said program including instructions for A computer system comprising:

16. if transfer of said knowledge corpus is not possible, identifying how the functions of said first machine and said second machine are executed and controlled; generating hierarchical functional digital twin models of the first machine and the second machine based on the identified capabilities; Mapping the identified capabilities of the first machine and the second machine based on the functional digital twin model; and receiving, by the second machine, the knowledge corpus of the first machine; 16. The computer system of claim 15, further comprising:

17. The hierarchical functional digital twin model, grouping the functions of the first machine and the second machine in a hierarchical format; identifying at what hierarchical level the knowledge corpus of the first machine can be mapped to the second machine; 20. The computer system of claim 16, generated by:

18. identifying, by the second machine, how different features of the knowledge corpus received by the first machine map to features of the second machine; adapting, by the second machine, the received knowledge corpus of the first machine with the input and output systems of the second machine; 20. The computer system of claim 17, further comprising:

19. generating mapping metrics between the first machine and the second machine based on the identified knowledge corpus; if there is a difference in functionality between the first machine and the second machine, identifying, by the first machine, a linkage with the one or more other devices to enhance the knowledge corpus with the functionality and features of the one or more other devices; 20. The computer system of claim 17, further comprising:

20. 20. The computer system of claim 19, further comprising automatically adapting the hierarchical functional twin models of the first machine and the second machine based on the identified collaboration with the one or more other devices.

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