Abstract: A computerized method is disclosed including operations of receiving a plurality of request texts, and for each request text of the plurality of request texts: performing a pre-processing operation, performing a first text similarity procedure or a second text similarity procedure that each result in a determination of a most similar request text in a knowledge base, wherein the second text similarity procedure includes performance of word embedding operations, determining a degree of similarity between the current request text and the most similar request text, when the degree of similarity satisfies a similarity threshold comparison, associating an answer associated with the most request text with the current text request, and when the degree of similar does not satisfy the similarity threshold comparison, flagging the current request text or associating a placeholder answer with the current request text. Performing pre-processing may include removing stop words and punctuation and creating tokenized text.

Abstract: A computer-implemented method is disclosed that includes operations of receiving document to be classified, performing pre-processing operations on the document resulting in generation of a tokenized document, performing word embedding operations on the tokenized document resulting in generation of a vectorized document, performing text similarity operations on the vectorized document and each of one or more vectorized topics resulting in a set of one or more similarity scores, wherein a first similarity score indicates a level of similarity between the vectorized document and a first vectorized topic, and wherein each vectorized topic represents one of a predetermined set of topics and classifying the document into one of the predetermined set of topics based on the set of one or more similarity scores. Performing the word embedding operations includes mapping each token of the remaining subset to a multi-dimensional vector, with each multi-dimensional vector representing a semantic meaning of a token.

Abstract: A computerized method is disclosed including operations of receiving one or more request texts, including at least a first request text, automatically performing processing on the first request text to determine a most similar request text in a knowledge base, determining a degree of similarity between the first request text and the most similar request text, and in response to a comparison between the degree of similarity and a similarity threshold, retrieving, from the knowledge base, an answer corresponding to the most similar request text. Performing processing may include (i) removing stop words and punctuation and creating tokenized text, (ii) converting the tokenized text into a vector using a trained neural network, and (iii) performing an analysis of the vector with the entries of the knowledge base using one or more of a Word Mover's Distance (WMD) algorithm, or a Soft Cosine Measure (SCM) algorithm.

Abstract: The disclosed computer-implemented method for labeling automatically generated reports may include (i) identifying incident reports that describe incidents that each involve at least one computing system and that comprise automatically collected information about the incidents and a manually analyzed subset of incident reports that comprise manually generated information, (ii) assigning at least one label to at least one incident report in the manually analyzed subset based on applying a machine learning model to the manually generated information, (iii) deriving, from the automatically collected information, a set of features that describe incident reports, (iv) propagating at least one label from a labeled incident report to an incident report that is not in the manually analyzed subset and that comprises similar features with the labeled incident report, and (v) performing an action related to the label on the incident report. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A nucleic acid isothermal amplification method is based on a polymerase spiral reaction using only one pair of primers. The method employs a self-spiraling amplification method, and has a high amplification efficiency.

Abstract: The disclosed computer-implemented method for labeling automatically generated reports may include (i) identifying incident reports that describe incidents that each involve at least one computing system and that comprise automatically collected information about the incidents and a manually analyzed subset of incident reports that comprise manually generated information, (ii) assigning at least one label to at least one incident report in the manually analyzed subset based on applying a machine learning model to the manually generated information, (iii) deriving, from the automatically collected information, a set of features that describe incident reports, (iv) propagating at least one label from a labeled incident report to an incident report that is not in the manually analyzed subset and that comprises similar features with the labeled incident report, and (v) performing an action related to the label on the incident report. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A nucleic acid isothermal amplification method is based on a polymerase spiral reaction using only one pair of primers. The method employs a self-spiraling amplification method, and has a high amplification efficiency.