Abstract: Bacteriophage compositions and therapeutic uses thereof. In particular, compositions of lytic bacteriophages that are capable of lysing Propionibacterium acnes (P. acnes) bacterial strains associated with acne and biofilms, thereby treating or preventing acne and biofilms.

Abstract: A system and method for a multi-stage approach for creating topic models is presented. The method includes applying a first stage topic model to textual data, wherein the first stage topic model is trained to discover a first plurality of topics and distributions of words in each topic of the first plurality of topics from the textual data; generating at least one seeded word for a subset of topics of the first plurality of topics, wherein the at least one seeded word is determined based on a plurality of selection rules and the distributions of words in the subset of topics discovered in the first stage topic model; and creating a second stage topic model by feeding the generated at least one seeded word to direct identification of the subset of topics of the first plurality of topics.

Abstract: A system and method for generating a rephrasing model for rephrased actionable data extracted from conversations is presented. The method includes receiving a training dataset including a plurality of training samples, wherein each training sample includes a textual data extracted from recorded conversations and at least one action item, wherein the at least one action item is a portion of the textual data; associating a control signal to each training sample of the training dataset, wherein the control signal is added to the associated training sample; and training a rephrasing model using the training dataset, wherein the rephrasing model is trained to paraphrase the at least one action item to output at least one actionable data, wherein each training sample of the training dataset is iteratively fed into the machine learning algorithm of the rephrasing model.

Abstract: Embodiments may determine user intent in conversations with dialogue systems so as to improve the quality of such conversations and to reduce the number of failed conversations. For example, a method may comprise receiving, at a dialogue system, a first text utterance from a user, generating a plurality of second text utterances at the dialogue system in response to the received text utterance, generating a third text utterance based on each generated second text utterance using a trained deep neural network model, generating a score indicating a quality of each conversation, wherein each conversation includes the first text utterance, one of the second text utterances, and the third text utterance based on the one of the second text utterances, and outputting to the user the second text utterance included in the conversation having the highest quality score.

Abstract: Embodiments may determine user intent in conversations with dialogue systems so as to improve the quality of such conversations and to reduce the number of failed conversations. For example, a method may comprise receiving, at a dialogue system, a first text utterance from a user, generating a plurality of second text utterances at the dialogue system in response to the received text utterance, generating a third text utterance based on each generated second text utterance using a trained deep neural network model, generating a score indicating a quality of each conversation, wherein each conversation includes the first text utterance, one of the second text utterances, and the third text utterance based on the one of the second text utterances, and outputting to the user the second text utterance included in the conversation having the highest quality score.