Abstract: Systems and methods for the creation of human-readable cyber incident reports from cyber incident logs, in which the cyber incident reports may link cyber incidents recorded in a cyber incident log to the existing knowledge sources. To do so, the systems and methods overcome the technical problems of conventional systems as well as the technical problems inherent in adapting artificial intelligence solutions to the creation of cyber incident reports.

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 described herein for efficiently labeling user utterances, which may encompass any communication received from a user within a conversational interaction, and identifying novel user intents for large amounts of data. A machine learning model may be used, which is trained on embeddings of utterance data, and which may employ methods like prototypical networks and hierarchical local binary classification for hierarchical multi-label multi-class classification.

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: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

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 described herein for efficiently labeling user utterances, which may encompass any communication received from a user within a conversational interaction, and identifying novel user intents for large amounts of data. A machine learning model may be used, which is trained on embeddings of utterance data, and which may employ methods like prototypical networks and hierarchical local binary classification for hierarchical multi-label multi-class classification.

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: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.