Abstract: Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

Abstract: Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.

Abstract: Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.

Abstract: A system for training a model from a subset of data representing decentrally stored source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the training data from a source interface, compresses the training data, and synchronizes the training data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines training variables indicative of the meta-data, generates variable probability distributions, produces training variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface. The key interface allows for searching the meta-database for training variables, variable probability distributions, and/or variable associations.

Abstract: Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be fed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

Abstract: A system for training a model from a subset of data representing decentrally stored source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the training data from a source interface, compresses the training data, and synchronizes the training data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines training variables indicative of the meta-data, generates variable probability distributions, produces training variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface. The key interface allows for searching the meta-database for training variables, variable probability distributions, and/or variable associations.

Abstract: Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to generate predicted survey data with respect to a second user by correlating a personal data set of the second user to the personal data set of at least one of the first users. The predicted survey data includes data regarding the predicted responses of the second user to a survey from which the survey data of each first user is derived, as well as one or more assessment scores calculated from the survey. The computing system is configured to take an action with respect to a user device of the second user in reaction to the generating of the predicted survey data regarding the second user.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to predict a first predicted assessment score at a first instance and a second predicted assessment score at a second instance with respect to a second user. The computing system determines whether the first predicted assessment score is different from the second predicted assessment score, and whether a first data entry of the personal data set of the second user changed between the first instance and the second instance. The computing system takes or recommends an action corresponding to a reversal in the change in the first data entry in order to alter the predicted assessment score of the second user.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to generate predicted survey data with respect to a second user by correlating a personal data set of the second user to the personal data set of at least one of the first users. The predicted survey data includes data regarding the predicted responses of the second user to a survey from which the survey data of each first user is derived, as well as one or more assessment scores calculated from the survey. The computing system is configured to take an action with respect to a user device of the second user in reaction to the generating of the predicted survey data regarding the second user.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to generate a predicted assessment score with respect to a second user by correlating a personal data set of the second user to the personal data set of at least one of the first users, with the generating of the predicted assessment score occurring automatically when a data entry of the personal data set of the second user is determined to have changed by the computing system. The computing system is configured to report the automatically generated predicted assessment score to the second user via a user device of the second user.

Abstract: An artificial intelligence (AI) system for use by a business to process multiple channels of user feedback data. User experience feedback data is provided via multiple channels—including structured feedback in the form of surveys, and unstructured and unsolicited feedback provided by people who wish to provide ad hoc feedback. The unstructured feedback may be from social media posts, calls to a service center, emails, and other sources. The feedback is aggregated as text data in a data pool. A natural language processing machine learning system is used to analyze the feedback and extract the meaning in human-understandable terms. Clustering techniques are used to identify commonalities in the feedback data even when issues are found in different data channels using different terminology. The commonalities are analyzed to identify actionable insights which address the underlying issues. Labeled sample data is used to perform supervised learning of the AI system.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to generate a predicted assessment score with respect to a second user by correlating a personal data set of the second user to the personal data set of at least one of the first users, with the generating of the predicted assessment score occurring automatically when a data entry of the personal data set of the second user is determined to have changed by the computing system. The computing system is configured to report the automatically generated predicted assessment score to the second user via a user device of the second user.

Abstract: Disclosed are systems and methods for detecting and tracking end user connection events. The connection event data can be displayed on a dashboard graphical user interface and used by a provider to test and implement system enhancements and optimizations, such as improved chat bots, training modules, or process improvements.

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: A system for maintaining a meta-database including meta-data representing decentralized data from source databases, which cause inefficient selection of modeling data and/or variables. Each of source and meta-data interfaces communicate with the respective database(s). A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the source data from the source interface, compresses the data, and synchronizes the data with the meta-data using the meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines variables indicative of the meta-data, generates variable probability distributions, produces variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface.

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to predict a predicted assessment score of a second user. A test personal data set is generated with at least one different data entry different from the personal data set utilized in predicting the predicted assessment score, the at least one different data entry corresponding to a change in relationship between the computing system and the second user. The predictive model predicts a test predicted assessment score of the second user based on the test personal data set. The computing system takes further action with respect to the second user when a difference between the predicted assessment score and the test predicted assessment score meets or exceeds a threshold value.

Abstract: A computing system is configured to generate a predictive model during training of a machine learning program using a training data set including a personal data set of a plurality of first users. The predictive model is configured to predict a first predicted assessment score at a first instance and a second predicted assessment score at a second instance with respect to a second user. The computing system determines whether the first predicted assessment score is different from the second predicted assessment score, and whether a first data entry of the personal data set of the second user changed between the first instance and the second instance. The computing system takes or recommends an action corresponding to a reversal in the change in the first data entry in order to alter the predicted assessment score of the second user.