Abstract: Systems and methods for uses and/or improvements to artificial intelligence applications, particularly in the area of generating conversational dynamic responses. As one example, systems and methods are for generating conversational dynamic responses using a multi-tiered guardrail architecture. As one example, systems and methods are for generating conversational dynamic responses using a multi-tiered guardrail architecture in data sparse environments.

Abstract: Systems and methods for customizing a machine learning model for responding to a user query. The system receives a user query from a user of a conversational program. The system determines a set of model requirements for a first machine learning model. Based on the set of model requirements, the system determines first supplemental data corresponding to the required input data and a formatting requirement corresponding to an activation pattern. The system performs pre-processing to obtain the first supplemental data from a first supporting software program and generates the first response using the first machine learning model.

Abstract: The present disclosure describes complex modeling of dialogues that allows querying of the modeled dialogues. Embeddings may be generated for each multi-party dialogue of a plurality of multi-party dialogues. Embeddings may include speaker-aware embeddings, key-utterance embeddings, and/or discourse-aware embeddings. In addition to the embeddings, a directed acyclic graph (DAG) to show a relationship between the one or more utterances of the multi-party dialogue. The embeddings and the DAG may be stored in a datastore. In response to receiving a request to identify dialogues associated with a topic, the datastore may be queried to retrieve dialogues associated with the received topic. The dialogues may be provided to the requesting party, which may use the information retrieved from the datastore to respond to a requesting party.

Abstract: Systems and methods for a dual-pathway model that includes a deterministic word graph that operates in parallel with a semantic autocomplete model. The system generates outputs first using the deterministic word graph, and the system then uses the output of the deterministic word graph to determine whether to invoke the functionality of the semantic autocomplete model. By doing so, the semantic autocomplete model is used only sparingly, thus reducing aggregate latency in the system, but still allowing for increased scalability and customizability in the overall system as the deterministic word graph and the semantic autocomplete model may be updated and trained in parallel in the dual-pathway model.

Abstract: Methods and systems are presented for generating real-time dynamic conversational responses during conversational interactions using machine learning models based on historic intents for a plurality of users and user-specific interactions. The machine learning models comprise a neural network trained to select a first intent from a plurality of intents based on historic data accumulated prior to the conversational interaction and a neural network trained to select a first interaction-specific intent from a plurality of interaction-specific intents based on interaction-specific data for a user.

Abstract: Systems and methods for a dual-pathway model that includes a deterministic word graph that operates in parallel with a semantic autocomplete model. The system generates outputs first using the deterministic word graph, and the system then uses the output of the deterministic word graph to determine whether to invoke the functionality of the semantic autocomplete model. By doing so, the semantic autocomplete model is used only sparingly, thus reducing aggregate latency in the system, but still allowing for increased scalability and customizability in the overall system as the deterministic word graph and the semantic autocomplete model may be updated and trained in parallel in the dual-pathway model.

Abstract: Systems and methods for a dual-pathway model that includes a deterministic word graph that operates in parallel with a semantic autocomplete model. The system generates outputs first using the deterministic word graph, and the system then uses the output of the deterministic word graph to determine whether to invoke the functionality of the semantic autocomplete model. By doing so, the semantic autocomplete model is used only sparingly, thus reducing aggregate latency in the system, but still allowing for increased scalability and customizability in the overall system as the deterministic word graph and the semantic autocomplete model may be updated and trained in parallel in the dual-pathway model.

Abstract: Systems and methods for a dual-pathway model that includes a deterministic word graph that operates in parallel with a semantic autocomplete model. The system generates outputs first using the deterministic word graph, and the system then uses the output of the deterministic word graph to determine whether to invoke the functionality of the semantic autocomplete model. By doing so, the semantic autocomplete model is used only sparingly, thus reducing aggregate latency in the system, but still allowing for increased scalability and customizability in the overall system as the deterministic word graph and the semantic autocomplete model may be updated and trained in parallel in the dual-pathway model.

Abstract: Methods and systems are presented for generating real-time dynamic conversational responses during conversational interactions using machine learning models based on historic intents for a plurality of users and user-specific interactions. The machine learning models comprise a neural network trained to select a first intent from a plurality of intents based on historic data accumulated prior to the conversational interaction and a neural network trained to select a first interaction-specific intent from a plurality of interaction-specific intents based on interaction-specific data for a user.