Abstract: Techniques for maintaining state and context of conversations between a user and digital assistant using threads. In one aspect, a method includes receiving a natural language utterance from a user during a session, obtaining a topic context instance for the natural language utterance, and generating, by a GenAI model, a list comprising an executable action based on candidate actions associated with the topic context instance. The executable action is then executed to produce an output. The executing includes determining there is no thread running within the session that is associated with the topic context instance, the executable action, or both, and responsive to determining there is no thread running, creating a thread associated with the topic context instance, the executable action, or both, and executing, using the thread, the executable action to obtain the output. The output or a communication derived from the output is then sent to the user.

Abstract: Techniques for using a LLM to detect OOS and OOD utterances. In one aspect, a method includes routing an utterance to a skill bot. The skill bot is configured to execute an action for completing a task associated with the utterance, and a workflow associated with the action includes a GenAI component state configured to facilitate completion of at least part of the task. The method further includes inputting a prompt into a GenAI model for processing. The prompt includes the utterance and scope-related elements that teach the GenAI model to output an invalid input variable when the utterance is OOS or OOD. When the GenAI model determines the utterance is OOS or OOD as part of the processing, the response is generated to include the invalid input variable, and the GenAI component state is caused to transition to a different state or workflow based on the response.

Abstract: Techniques for implementing a digital assistant with copilot support to enhance application usage. In one aspect, a method includes receiving a message payload, invoking, using a thread, a flow based on the message payload, generating a context variable data structure associated with the thread, responsive to invoking the flow, determining, using a machine learning model, an intent of the user, accessing, based on the intent, a prompt and an object schema, and revising the prompt based on the message payload, data in the context variable data structure, and the object schema. A generative artificial intelligence model then generates a list comprising one or more executable actions based on the prompt. The one or more executable actions are executed based on one or more parameters to obtain an output, and the output or a communication derived from the output are then sent to a user.

Abstract: Techniques for enhanced chatbot interaction using various large language model providers are provided. In one aspect, a method may include generating a request payload having a common request body specification based on an utterance such that the common request body specification may be a standardized data input format used by a generative artificial intelligence (GenAI) interface for interacting with GenAI model providers. In various embodiments, the method may include converting the common request body specification into a custom request body specification having a data input format associated with a GenAI model provider selected from the plurality of GenAI model providers, communicating, by the GenAI interface, the request payload with the custom request body specification to the GenAI provider for processing by a GenAI model, receiving, at the GenAI interface from the GenAI model provider, a response payload associated with: (i) an error, (ii) processing the request payload, or (iii) both.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for obtaining data items for input to a chatbot. In certain embodiments, a chatbot system includes a component that can be invoked by a chatbot in the chatbot system to obtain data items needed by the chatbot. The component can be invoked based on a reference to the component in a dialog flow definition configured for the chatbot. The reference to the component can indicate a composite entity that the component will use to determine how the data items are obtained from a user. The composite entity acts as a container for the data items and may be configured separately from the dialog flow definition of the chatbot. The data items can be obtained based on rules specified in a composite entity definition configured for the composite entity.