Abstract: Systems and methods for improved data processing of communications across computer networks using trifurcated prompts during communication exchanges are described. For example, the system may receive a first inbound communication, wherein the first inbound communication system may determine a first context for the first inbound communication based on the first text string. The system may process the first context in a perturbation model to determining a first perturbed context, wherein the perturbation model determines the first perturbed context by determining a first alternative token for a first token in the first context. The system may determine a first prompt for a first large language model based on the first perturbed context.

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 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: 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: The described embodiments relate to systems, methods, and apparatus for providing a multimodal deep memory network (200) capable of generating patient diagnoses (222). The multimodal deep memory network can employ different neural networks, such as a recurrent neural network and a convolution neural network, for creating embeddings (204, 214, 216) from medical images (212) and electronic health records (206). Connections between the input embeddings (204) and diagnoses embeddings (222) can be based on an amount of attention that was given to the images and electronic health records when creating a particular diagnosis. For instance, the amount of attention can be characterized by data (110) that is generated based on sensors that monitor eye movements of clinicians observing the medical images and electronic health records.

Abstract: Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model (108) may be trained for sentence classification, and may combine what is referred to herein as a “residual stacked de-noising autoencoder” (“RSDA”) (220), which may be unsupervised, with a supervised classifier (218) such as a classification neural network (e.g., a multilayer perceptron, or “MLP”). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA “remember” forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.

Abstract: The described embodiments relate to systems, methods, and apparatus for providing a multimodal deep memory network (200) capable of generating patient diagnoses (222). The multimodal deep memory network can employ different neural networks, such as a recurrent neural network and a convolution neural network, for creating embeddings (204, 214, 216) from medical images (212) and electronic health records (206). Connections between the input embeddings (204) and diagnoses embeddings (222) can be based on an amount of attention that was given to the images and electronic health records when creating a particular diagnosis. For instance, the amount of attention can be characterized by data (110) that is generated based on sensors that monitor eye movements of clinicians observing the medical images and electronic health records.

Abstract: Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model (108) may be trained for sentence classification and may combine what is referred to herein as a “residual stacked de-noising autoencoder” (“RSDA”) (220), which may be unsupervised, with a supervised classifier (218) such as a classification neural network (e.g., a multilayer perceptron, or “MLP”). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA “remember” forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.