Abstract: Aspects of the present disclosure relate to identifying friction points in customer data. In some embodiments, identifying friction points can include receiving a set of input sequence data and predicted class labels for the set of input sequence data; selecting input sequences, from the set of input sequence data, that have class labels matching a ground truth class label; reducing the selected sequences to anchor points; and grouping the reduced selected sequences into critical data set signatures using discriminatory subsequence mining.

Abstract: Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.

Abstract: An action recommendation system uses reinforcement learning that provides a next action recommendation to a traffic controller to give to a vehicle pilot such as an aircraft pilot. The action recommendation system uses data of past human actions to create a reinforcement learning model and then uses the reinforcement learning model with current ABS-B data to provide the next action recommendation to the traffic controller. The action recommendation system may use an anisotropic reward function and may also include an expanding state space module that uses a non-uniform granularity of the state space.

Abstract: A method, computer system, and a computer program product for identifying feature importance in deep learning models is provided. Embodiments of the present invention may include building a reconstruction model. Embodiments of the present invention may include intercepting an output of a trained prediction model at a bottleneck layer. Embodiments of the present invention may include processing the output of the trained model using the reconstruction model. Embodiments of the present invention may include identifying a plurality of features based on the reconstruction model.

Abstract: A method, system and computer program product for identifying a geographic market share. Mobility data is acquired from applications running on mobile devices of users located within a geographic area. Mobility data is then used to infer shopping habits within the geographic area. Geo-demographic profiles are then created. The geographic market share is then determined using the created geo-demographic profiles.

Abstract: A method, system and computer program product for identifying a geographic market share. Mobility data is acquired from applications running on mobile devices of users located within a geographic area. Mobility data is then used to infer shopping habits within the geographic area. Geo-demographic profiles are then created. The geographic market share is then determined using the created geo-demographic profiles.