Abstract: A beverage cup lid device is described herein that may include a beverage flavoring pod adhered to the beverage cup lid. The device may include the beverage flavoring pod comprising: a first beverage filter, beverage flavoring material on the first beverage filter, and a second beverage filter covering the beverage flavoring material and adhered to the first beverage filter.

Abstract: A unique implementation of a machine learning application for suggesting actions for a user to undertake is described herein. The application transforms a history of user behavior into a set of models that represent user actions given a set of parameters. These models are then used to suggest that users in a payments or banking environment take certain actions based on their history. The models are created using the DensiCube, random forest or k-means algorithms.

Abstract: A method for securing data by embedding the data in a data structure and utilizing a sensor device to detect transfer of the data structure. The data is embedded such that the data is only accessible by first executing an executable program. If the executable program determines that the device attempting to access the data (the accessing device) does not have permission to access the data, then the executable program destroys all or a portion of the data. If the data structure is transferred to another device, a sensor device positioned to detect the data structure when transferred will identify the data. If the sensor device determines that the data structure is not permitted to be transferred, then the sensor device destroys all or a portion of the data.

Abstract: A unique implementation of a machine learning application for suggesting actions for a user to undertake is described herein. The application transforms a history of user behavior for a plurality of users into a set of models that represent user actions, and the optimal actions, given a set of parameters. These models are then used to suggest that users in a payments or banking environment take certain actions based on a best in class model derived from the best performing user. The models are created using the DensiCube, random forest, K-means or other machine learning algorithms.

Abstract: A unique implementation of a machine learning application for suggesting actions for a user to undertake is described herein. The application transforms a history of user behavior into a set of models that represent user actions given a set of parameters. These models are then used to suggest that users in a payments or banking environment take certain actions based on their history. The models are created using the DensiCube, random forest or k-means algorithms.

Abstract: A unique implementation of a machine learning application for suggesting actions for a user to undertake is described herein. The application transforms a history of user behavior into a set of models that represent user actions given a set of parameters. These models are then used to suggest that users in a payments or banking environment take certain actions based on their history. The models are created using the DensiCube, random forest or k-means algorithms.

Abstract: A method for securing data by embedding the data in a data structure and utilizing a sensor device to detect transfer of the data structure. The data is embedded such that the data is only accessible by first executing an executable program. If the executable program determines that the device attempting to access the data (the accessing device) does not have permission to access the data, then the executable program destroys all or a portion of the data. If the data structure is transferred to another device, a sensor device positioned to detect the data structure when transferred will identify the data. If the sensor device determines that the data structure is not permitted to be transferred, then the sensor device destroys all or a portion of the data.

Abstract: A method for securing data by embedding the data in a data structure and utilizing a sensor to detect transfer of the data structure. The data is embedded such that the data is only accessible by first executing an executable program. If the executable program determines that the device attempting to access the data (the accessing device) does not have permission to access the data, then the executable program destroys the data. If the data structure is transferred to another device, a sensor positioned to detect the data structure when transferred will identify the data. If the sensor determines that the data structure is not permitted to be transferred, then the sensor destroys the data.

Abstract: A method for securing data by embedding the data in a data structure and utilizing a sensor to detect transfer of the data structure. The data is embedded such that the data is only accessible by first executing an executable program. If the executable program determines that the device attempting to access the data (the accessing device) does not have permission to access the data, then the executable program destroys the data. If the data structure is transferred to another device, a sensor positioned to detect the data structure when transferred will identify the data. If the sensor determines that the data structure is not permitted to be transferred, then the sensor destroys the data.

Abstract: A method and apparatus are presented for predicting the behavior or state of a negative occurrence class by scoring histories of members of the negative occurrence class against pasts of members of a positive occurrence class. The method and apparatus predicts the members of the negative occurrence class that are most likely to next transition to members of the positive occurrence class.