Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Abstract: A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

Abstract: A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Abstract: An alert detection system that detects anomalies in business activities, and a method of operating the same. The method includes receiving alert configuration data associated with an alert via an alert configuration interface. The method further includes accessing a data value stored at a data source using a data path of the received alert configuration data and analyzing the accessed data value using at least one of an anomaly detection rule or a trained anomaly detection model associated with the alert. The method further includes determining that the alert is triggered based on analyzing the accessed data value and sending an alert communication of the triggered alert using at least one alert communication channel defined in the received alert configuration data.

Abstract: A device controls a power flow in an AC network and has a series converter with a DC side to connect to a DC link and an AC side to connect to the AC network via a series transformer. The device further has a bridging arrangement between the series transformer and the series converter configured to bridge the series converter. The bridging arrangement contains at least one bridging branch having a switching unit with antiparallel thyristors and a resistance in series with the switching unit. Furthermore, a method of operation operates the device.

Abstract: Various methods, apparatuses/systems, and media for data migration readiness of a target data source are disclosed. A processor invokes, in response to triggering a process, an application programming interface (API) to call corresponding source application or microservice hosted on a source database; updates, by the source application or microservice, the source database to reflect the changes made to one or more fields data of user profile; generates a mapping identifier (ID) in response to updating the source database; stores the mapping ID onto a mapping database in a predefined format; invokes another API call to obtain source table field details of the updated source database and the mapping ID that maps source column of the source table to a corresponding target column of a target table of a target database; and automatically updates the target database to match the updated data of the source database based on the mapping ID.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive information associated with a set of value chain network entities. A VCN process may provide the information to a first set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the first set of AI-based learning models is trained to generate a prediction of future demand for an item. A VCN process may provide the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity. A VCN process may determine a potential risk in the value chain network associated with the at least one value chain network entity based upon, at least in part, an output of the AI-based learning models.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a potential risk in the value chain based upon, at least in part, an output of the AI-based learning classification. A VCN process may execute an action to mitigate the potential risk in the value chain network.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.

Abstract: A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Abstract: Disclosed are an electronic device capable of efficiently performing speech recognition and natural language understanding and a method for controlling thereof. The electronic device includes: a microphone; a non-volatile memory configured to store virtual assistant model data comprising data that is classified according to a plurality of domains and data that is commonly used for the plurality of domains; a volatile memory; and a processor configured to: based on receiving, through the microphone, a trigger input to perform speech recognition for a user speech, initiate loading the virtual assistant model data from the non-volatile memory into the volatile memory, load, into the volatile memory, first data from among the data classified according to the plurality of domains and, while loading the first data into the volatile memory, load at least a part of the data commonly used for the plurality of domains into the volatile memory.

Abstract: Various methods, apparatuses/systems, and media for data migration readiness of a target data source are disclosed. A processor invokes, in response to triggering a process, an application programming interface (API) to call corresponding source application or microservice hosted on a source database; updates, by the source application or microservice, the source database to reflect the changes made to one or more fields data of user profile; generates a mapping identifier (ID) in response to updating the source database; stores the mapping ID onto a mapping database in a predefined format; invokes another API call to obtain source table field details of the updated source database and the mapping ID that maps source column of the source table to a corresponding target column of a target table of a target database; and automatically updates the target database to match the updated data of the source database based on the mapping ID.

Abstract: Systems and methods for integrating a gaming engine and a smart contract system in a platform are provided. The gaming engine is programmed with a software development environment and an architecture that provides a set of gaming engine services with predefined tools for digital content developers to create a set of game engine generated environments. The smart contract system programmed with smart contract services associated with transactions that are based on electronically verifiable conditions. The integration platform is programmed with an execution framework that is common to the gaming engine and to the smart contract system to integrate the smart contract services with at least one of the gaming engine and the set of game engine generated environments.

Abstract: A dynamic vision system for a robotic system includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. A robotic system is configured to simulate human or animal species capabilities having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to the robotic system. A processing system is configured to train a machine learning model to recognize an object relating to the robotic system from training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic system.

Abstract: A device controls a power flow in an AC network and has a series converter with a DC side to connect to a DC link and an AC side to connect to the AC network via a series transformer. The device further has a bridging arrangement between the series transformer and the series converter configured to bridge the series converter. The bridging arrangement contains at least one bridging branch having a switching unit with antiparallel thyristors and a resistance in series with the switching unit. Furthermore, a method of operation operates the device.

Abstract: An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

Abstract: An electronic device and a method for controlling thereof are provided. The electronic device includes a communicator comprising circuitry, a microphone, at least one memory configured to store at least one instruction and dialogue history information, and a processor configured to execute the at least one instruction, and the processor, by executing the at least one instruction, is further configured to determine whether to transmit, to a server storing a first dialogue system, a user speech that is input through the microphone, based on determining that the user speech is transmitted to the server, control the communicator to transmit the user speech and at least a part of the stored dialogue history information to the server, receive, from the server, dialogue history information associated with the user speech, through the communicator, and control the received dialogue history information to be stored in the memory.