Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing an incremental ML model comprising a plurality increments which sequentially increase a complexity of a predictive function of the incremental ML model, receiving performance information from an edge system that processes incoming data of an industrial asset using a current increment of the incremental ML model, dynamically determining to modify the current increment of the incremental ML model used by the edge system with a next increment of the incremental ML model having increased complexity based on the received performance information, and transmitting the next increment of the incremental ML model to the edge system.

Abstract: The example embodiments are directed to a system and method for cold start deployment of an ML model for an edge system associated with an industrial asset. In one example, the method may include one or more of storing machine learning (ML) models and local edge information where the ML models are already deployed, receiving, via a network, meta information of an edge system associated with an industrial asset in response to a cold start of the edge system, dynamically determining an optimum ML model for the cold start of the edge system from among the already deployed ML models based on the received meta information and the local edge information, and transmitting the determined optimum ML model to the edge system.

Abstract: The example embodiments are directed to a system and method for optimizing data the is transmitted from an edge device to a central server such as the cloud platform. In one example, the method may include one or more of receiving incoming data which is associated with an industrial asset positioned at an edge of an Internet of Things (IoT) network, transforming the incoming data into a pattern of data points within a feature space based on a machine learning model configured to detect patterns within the data, selecting a subset of data points from the pattern based on a distance between data points in the pattern of data points with respect to a previous pattern of data points in a previous dataset associated with the industrial asset, and transmitting the selected subset of data points to a central platform via the IoT network.

Abstract: The example embodiments are directed to a system and methods for determining to update a machine learning model based on model degradation. In one example, the method may include one or more of receiving data acquired at an edge of an Internet of things (IoT) network from an industrial asset, executing a machine learning model with the received data as input to generate a predictive output associated with the industrial asset, determining that a performance of the machine learning model on the edge has degraded based on the generated predictive output of the machine learning model, and transmitting information about the degraded performance of the machine learning model to a central server within the IoT network.

Abstract: The example embodiments are directed to a system for triggering a model update for an edge device in an IIoT network. In one example, the method may include one or more of receiving data of an operation performed by an industrial asset, the received data comprising input for a machine learning (ML) model associated with the industrial asset, determining that the received data comprises a change in data pattern with respect to a training data set which was used to previously train the ML model, storing the received data comprising the change in data pattern in a new data set, and in response to the new data set reaching a minimum threshold size, at least one of updating the ML model based on the new data set and transmitting a request to update the ML model based on the new data set.

Abstract: The example embodiments are directed to a system and method for sharing machine learning model parameters among edge devices in a clustered group of edge devices sensing data about an industrial asset. In one example, the method may include one or more of storing unique parameters of a machine learning (ML) model associated with an industrial asset which are unique with respect to unique parameters of other edge systems in the group of edge systems, receiving common parameter information from the group of edge systems which is shared among the group of edge systems, generating updated parameter values for an ML model based on a combination of the unique parameters and the received common parameter information, and executing the updated ML model based on incoming data from the industrial asset to generate predictive information about the industrial asset.

Abstract: Provided is a system and method for providing an information retrieval service on a mobile computing device such as a wireless computer or cellular telephone. The user of a mobile computing device provides vocal, visual or textual data to the service provider. The service scans a transmitted data to convert the data into a digital form and the data is converted into a text file. The service provider performs analytical computations on the data in the text file and, using context and preference rules, produces and transmits information of value to the user. In addition, the service may request additional information from the user for the purpose of better servicing the query. A query to the claimed service may be modified based upon configuration information stored with respect to a particular user or based upon previous queries from the particular user.

Abstract: A system and method of controlling copying of documents. The method includes optically capturing a document. At least one object of the captured document is recognized. A content output is determined based on the recognized object and at least one output rule. The content output is provided based on the determination.

Abstract: A method, system, and program for enabling a user to store a messaging session entry for delivery when an intended recipient is next available are provided. A messaging agent stores a message entry by a user, wherein the message entry is intended for communication in a messaging session with an intended recipient who is unavailable to receive the message entry when the message entry is entered by the user. The messaging agent then monitors the presence of the intended recipient and responsive to detecting a change in presence from “unavailable” to “available”, the messaging agent prompts the user to select whether to send the message entry to the intended recipient in a new messaging session. In addition, a user may select additional actions for the messaging agent to perform in distributing the message entry, where the additional actions are conditioned on the presence of the intended recipient and at least one non-presence based requirement.

Abstract: A method, system, and program for distributing messaging session logs to users entering an already ongoing messaging session are provided. When, first and second user are participating in an ongoing instant messaging session and an additional user enters the instant messaging session, the additional user can only see those entries in the instant messaging session that occur after the additional user enters the messaging session. A logging controller, however, automatically records a log of all the entries in the messaging session when the logging controller detects the entrance of an additional user to an ongoing messaging session. Then, the logging controller provides a selectable option for the first or second user, the selection of which triggers the logging controller to pass the log of previous entries to the additional user.