Abstract: A system for testing-configuring one or more vehicles includes a task communication module obtaining a task data packet having a selected software-based task to be performed on a selected vehicle, where a task command identifier identifying the task, a command matching module selecting a predefined task command from among a set of stored task commands based on the task command identifier where the predefined task command executes either a selected standardized protocol (i.e., a set of locally stored instructions) or a selected customized protocol to perform the task (i.e., a set of bypass instructions to retrieve a selected script file having a set of customizable instructions), and a command execution module executing the selected standard protocol in response to the predefined task command being the selected standard protocol, and executing the selected customized protocol employing the selected script file based on the predefined task command being the selected customized protocol.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A system for condensing user communications relating to a topic is provided. The system may include a processor and a non-transitory memory. The processor may: designate a topic of user interest; retrieve legacy communications; and remove duplicative communications. The processor may form a topic-centric training set for a neural network. The topic-centric training set may be based on the legacy communications, legacy intelligence, and the plurality of outcomes and may be delimited by an analysis of the database. The processor may synthesize the neural network using the topic-centric training set in order to assign individual weights to each of a plurality of nodes in the neural network. In response to a selection of the topic of user interest, the processor may generate a plurality of user options based on the neural network. The system may include a display in order to prompt the user to select one of the options.

Abstract: A task managing system (TMS) test-configures one or more vehicles based on a task order for a selected vehicle from a vehicle management system (VMS). The TMS includes a plurality of task execution controllers configured to communicate with the vehicle. The TMS further includes a processor configured to execute instructions stored in a nontransitory computer-readable medium to operate as a task delegation module and a task status module. The task delegation module is configured to assign a selected vehicle from among the one or more vehicles to a selected task execution controller, where the selected task execution controller is configured to execute the task order for the selected vehicle. The task status module is configured to monitor a status of the task order being executed by the selected task execution controller based on an update message from the selected task execution controller.

Abstract: A task managing system for testing and configuring one or more vehicles includes a plurality of task execution controllers. Each of the plurality of the task execution controllers defines a set of communication nodes configured to wirelessly communicate with a set of vehicles of a plurality of vehicles. The task execution controller includes a processor configured to execute instructions stored in a nontransitory computer-readable medium to operate as a task application module configured to execute a task order on a selected vehicle from the set of vehicles by way of a selected communication node from the set of communication nodes.

Abstract: A method for filtering a plurality of agent-customer interactions to determine whether one or more of a plurality of agent-customer interactions should be stored in a library of Artificial Intelligence (AI) files related to an interactive voice response system (IVR) is provided. The method may include receiving an identification of a plurality of IVR flashpoints, monitoring and/or reviewing the plurality of agent-customer interactions, and determining whether one of the plurality of agent-customer interactions meets a threshold number of the IVR flashpoints. For each of the plurality of agent-customer interactions that meets a threshold number of the IVR flashpoints, the method may further direct the IVR to convert the interaction into an IVR workflow and store the IVR workflow in the library of AI related to IVR.

Abstract: Systems and methods are disclosed for scheduling a service appointment. A method can include analyzing attributes of a customer service request with respect to a problem area. A multi-factor scheduling analysis is performed by analyzing skill level and resource data associated with the problem area attributes for servicing the customer service request. The analysis is used for scheduling a service appointment to handle the customer service request.