Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.

Abstract: Systems and methods for using a mathematical model based on historical natural language inputs to automatically complete form fields are disclosed. An incident report may be defined with a set of required parameter fields such as category, priority, assignment, and classification. Incident report submission forms may also have other free text input fields providing information about a problem in the natural vocabulary of the person reporting the problem. Automatic completion of these so-called parameter fields may be based on analysis of the natural language inputs and use of machine learning techniques to determine appropriate values for the parameter fields. The machine learning techniques may include parsing the natural language input to determine a mathematical representation and application of the mathematical representation to “match” historically similar input. Once matched the parameter values from the historically similar input may be used instead of generic default values.

Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.

Abstract: Systems and methods for using a mathematical model based on historical information to automatically schedule and monitor work flows are disclosed. Prediction methods that use some variables to predict unknown or future values of other variables may assist in reducing manual intervention when addressing incident reports or other task-based work items. For example, work items that are expected to conform to a supervised model built from historical customer information. Given a collection of records in a training set, each record contains a set of attributes with one of the attributes being the class. If a model can be found for the class attribute as a function of the values of the other attributes, then previously unseen records may be assigned a class as accurately as possible based on the model. A test data set is used to determine model accuracy prior to allowing general use of the model.

Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: A remote network management platform is provided that includes an end-user computational instance dedicated to a managed network, a training computational instance, and a prediction computational instance. The training instance is configured to receive a corpus of textual records from the end-user instance and to determine therefrom a machine learning (ML) model to determine the numerical similarity between input textual records and textual records in the corpus of textual records. The prediction instance is configured to receive the ML model and an additional textual record from the end-user instance, to use the ML model to determine respective numerical similarities between the additional textual record and the textual records in the corpus of textual records, and to transmit, based on the respective numerical similarities, representations of one or more of the textual records in the corpus of textual records to the end-user computational instance.

Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.

Abstract: The embodiments herein provide a framework for and specific implementations of machine learning (ML) analysis of incident, online chat, knowledgebase, skills, and perhaps other types of databases. The ML techniques described herein may include various forms of semantic analysis of textual information in these databases, such as clustering, term frequency, word embedding, paragraph embedding, and potentially other techniques. Advantageously, use of ML in the specific ways described herein can provide insights into this textual information that otherwise would be impossible to determine in an accurate or concise fashion.

Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.

Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.

Abstract: A system may include memory containing: (i) a master data set representable in columns and rows, and (ii) a query expression. The system may include a software application configured to apply a machine learning (ML) pipeline to an input data set. The system may include a computing device configured to: obtain the master data set and the query expression; apply the query expression to the master data set to generate a test data set, where applying the query expression comprises, based on content of the query expression, generating the test data set to have one or more columns or one or more rows fewer than the master data set; apply the ML pipeline to the test data set, where applying the ML pipeline results in either generation of a test ML model from the test data set or indication of an error in the test data set; and delete the test data set from the memory.

Abstract: A system is provided that includes a memory containing a target data set, a software application configured to apply a machine learning (ML) pipeline to an input data set, and a computing device. The computing device is configured to obtain, from the memory, the target data set; apply the ML pipeline to the target data set, and provide an indication of the determined inadequacy of the target data set. Applying the ML pipeline results in at least one of generation of an ML model from the target data set or determination of an inadequacy of the target data set. Determining an inadequacy of the target data set includes determining that generation of the ML model failed or that ML model generation would result in a deficient ML model, and determining that the target data set is inadequate in a manner related to the determined failure metric.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.

Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current leads, and to output a likelihood of successfully closing an associated transaction. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a likelihood of successfully closing each current opportunity. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.

Abstract: A database contains a corpus of incident reports, a machine learning (ML) model trained to calculate paragraph vectors of the incident reports, and a look-up set table that contains a list of paragraph vectors respectively associated with sets of the incident reports. A plurality of ML worker nodes each store the look-up set table and are configured to execute the ML model. An update thread is configured to: determine that the look-up set table has expired; update the look-up set table by: (i) adding a first set of incident reports received since a most recent update of the look-up set table, and (ii) removing a second set of incident reports containing timestamps that are no longer within a sliding time window; store, in the database, the look-up set table as updated; and transmit, to the ML worker nodes, respective indications that the look-up set table has been updated.