Abstract: A system includes a processor and memory. The memory stores a model database including models and a classification database including classification scores corresponding to an input. The memory stores instructions for execution by the processor. The instructions include, in response to receiving a first input from a user device of a user, determining, for the first input, classification scores for classifications by applying the models to the first input. Each model determines one of the classification scores. The instructions include storing the classification scores as associated with the first input in the classification database and identifying the first input as within a first classification in response to a first classification score corresponding to the first classification exceeding a first threshold. The instructions include transmitting, for display on an analyst device, the first input based on the first classification to a first analyst queue associated with the first classification.

Abstract: A method includes maintaining a question repository in which each question corresponds to a set of decision trees. A distance matrix encodes a distance between each pair of questions. In response to a request for a new question, the method converts the new question into a set of tokens. For each question of the existing questions, the method determines a minimum distance between each token of the new question and the tokens of the question and sums the minimum distances to calculate a distance between the question and the new question. The method includes performing cluster analysis on the distance matrix. Performing cluster analysis includes normalizing the distance matrix and applying a hierarchical clustering process to the normalized distance matrix. Based on the cluster analysis, the method transmits an alternative question proposal or adds the new question to the question repository.

Abstract: A system includes a processor and memory. The memory stores a model database including models and a classification database including classification scores corresponding to an input. The memory stores instructions for execution by the processor. The instructions include, in response to receiving a first input from a user device of a user, determining, for the first input, classification scores for classifications by applying the models to the first input. Each model determines one of the classification scores. The instructions include storing the classification scores as associated with the first input in the classification database and identifying the first input as within a first classification in response to a first classification score corresponding to the first classification exceeding a first threshold. The instructions include transmitting, for display on an analyst device, the first input based on the first classification to a first analyst queue associated with the first classification.

Abstract: A method includes defining model attributes of a machine model that organizes feedback data into topic groups based on similarities in concepts in the feedback data. The model attributes include a topic model number that defines how many topic groups are to be created, a hyperparameter optimization alpha value, and/or a hyperparameter optimization beta value. The method also includes generating the machine model using the model attributes that are defined and the feedback data, and applying the machine model to the feedback data to divide different portions of the feedback data into the different topic groups based on contents of the feedback data, the topic model number, the hyperparameter optimization alpha value, and/or the hyperparameter optimization beta value.

Abstract: A computer system for transforming a user interface according to data store mining includes a data store configured to store a parameter related to a user and index event data of a set of events. A data processing circuit is configured to identify a first set of identifiers and train a machine learning model based on event data by the data store. An interface circuit is configured to receive an indication of a selected identifier of the plurality of identifiers, determine a first intake metric of the selected identifier using the machine learning model, and a second intake metric of the selected identifier and the parameter using the machine learning model. The interface circuit is configured to transform the user interface according to the first intake metric and the second intake metric.

Abstract: A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

Abstract: A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

Abstract: A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

Abstract: A computer system includes an input configured to receive a first image of medication located in a receptacle, memory, and a processor configured to execute instructions including creating a second image based on the first image, dividing pixels of the second image into first and second subsets, and scanning the second image along a first axis to count, for each point along the first axis, a number of pixels in the first subset along a line perpendicular to the first axis that intersects the first axis at the point. The instructions also include estimating positions of first and second edges of the receptacle along the first axis based on the counts of the pixels, defining an opening of the receptacle based on the estimated positions of the first and second edges, and outputting a processed image that indicates areas of the image that are outside of the defined opening.

Abstract: A method includes capturing a first image of medication held by a receptacle. The method includes creating a second image based on the first image. The method includes determining a first subset of pixels of the second image that are more likely to correspond to the receptacle. The method includes processing the second image along a first axis by, for each point: defining a line perpendicularly intersecting the first axis at the point and counting how many of the pixels located along the line are in the first subset. The method includes determining first and second local maxima of the counts. The method includes estimating positions of first and second edges of the receptacle based on positions of the local maxima. The method includes defining an ellipse based on the first and second edges and excluding areas of the first image outside the defined ellipse from further processing.

Abstract: A method includes capturing a first image of medication held by a receptacle. The method includes creating a second image based on the first image. The method includes determining a first subset of pixels of the second image that are more likely to correspond to the receptacle. The method includes processing the second image along a first axis by, for each point: defining a line perpendicularly intersecting the first axis at the point and counting how many of the pixels located along the line are in the first subset. The method includes determining first and second local maxima of the counts. The method includes estimating positions of first and second edges of the receptacle based on positions of the local maxima. The method includes defining an ellipse based on the first and second edges and excluding areas of the first image outside the defined ellipse from further processing.