Bottomline Technologies Limited

Bottomline Technologies (de) Inc.

Fred G Ramberg

David Sander

Norman DeLuca

Brian Smith McLaughlin

Trevor Ramberg

Brian McLaughlin

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.

Personalization of a user interface using machine learning

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.

Securing electronic data by automatically destroying misdirected transmissions

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.

Specialization of a user interface using machine learning

A method of exercising effective influence over future occurrences using knowledge synthesis is described. Techniques include influencing methods that yield actions, once a proposed outcome has been assumed. This is different from methods, typically referred to as 'predictive' or 'prescriptive' that use analytics to model future results based upon existing data and predict most likely outcome. One or more methods of analysis of historical data, in a hierarchical manner, determine events which led to an observed outcome. The outcome-based algorithms use, as input, a future event or state and generate attributes that are necessary precursors. By creating these attributes, the future can be affected. Where necessary, synthetic contributors of such attributes are also created and made to act in ways consistent with generating the assumed outcome. These contributors might be called upon respectively, to post favorable opinions, to report balmy weather, or to describe sales to a certain population demographic.

Outcome creation based upon synthesis of history

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.

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North Hampton, NH, United States of America

Fred G Ramberg