Abstract: A configuration system receives, from an endpoint node of a communications network, information about a desired bot configuration. The endpoint node and the configuration system are in the communications network. The configuration system sends a request comprising a system prompt and the received information to a generative model. The configuration system receives a response to the request, the response comprising a plurality of further system prompts for implementing the desired bot configuration. For each of the plurality of further system prompts, the configuration system triggers instantiation of a bot at a node of the communications network. The instantiated bot comprises the further system prompt. The configuration system sends configuration to a voice interface, to configure the voice interface such that a telephony call associated with the endpoint node has at least one of the instantiated bots as a participant on the telephony call.

Abstract: The present application relates to developing and deploying machine learning analysis systems using a low-code or no-code approach. A cloud service is configured to receive a first data stream from a sensor device and train a machine-learning model to recognize selected elements of the first data stream that are selected from a package of template models via a graphical user interface. The cloud service deploys the machine-learning model to an edge datacenter configured to receive a second data stream via a network connection. The edge datacenter locally interrogates the second data stream based on the machine-learning model to generate an element set including the selected elements. A logic service may receive a selection of one or more properties of the element set and one or more logical operators via a graphical user interface to generate user-configured logical rules. The logic service may the user-configured logical rules to the element set.

Abstract: The present application relates to developing and deploying machine learning analysis systems using a low-code or no-code approach. A cloud service is configured to receive a first data stream from a sensor device and train a machine-learning model to recognize selected elements of the first data stream that are selected from a package of template models via a graphical user interface. The cloud service deploys the machine-learning model to an edge datacenter configured to receive a second data stream via a network connection. The edge datacenter locally interrogates the second data stream based on the machine-learning model to generate an element set including the selected elements. A logic service may receive a selection of one or more properties of the element set and one or more logical operators via a graphical user interface to generate user-configured logical rules. The logic service may the user-configured logical rules to the element set.