Abstract: Systems and methods described herein enable effective and accurate modeling of a set of existing data profiles, perform categorization of the data profiles in an explainable way such that actions can be taken on the information to have predictable results. The systems and methods further facilitate means to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short strings, in order to facilitate a more meaningful representation of the data profiles.

Abstract: Systems and methods described herein enable effective and accurate modeling of a set of existing data profiles, perform categorization of the data profiles in an explainable way such that actions can be taken on the information to have predictable results. The systems and methods further facilitate means to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the data profiles.

Abstract: Systems and methods for generating best next communication policies, for a time step of an exchange of electronic documents, fit over historical exchanges, optimizing to maximize a probability of achieving a quantified objective leveraging weighted sampling. In a preferred embodiment an electronic document is segmented whereby each constituent segment is deconstructed as a composition of custom expression varieties, pre-defined to enable fulfilment of an objective within a theme of correspondence, associating each expression with a semantic vector. A set of expression extraction models is trained independently and then a second set with knowledge of parallel label predictions, iterating to convergence. The expression compositions and associated semantic vectors are combined into a single vector for each segment. The segment vectors are appended onto profile vectors for the exchange parties, yielding a time series of profile-content vectors.

Abstract: Systems and methods for generating best next communication policies, for a time step of an exchange of electronic documents, fit over historical exchanges, optimizing to maximize a probability of achieving a quantified objective leveraging weighted sampling. In a preferred embodiment an electronic document is segmented whereby each constituent segment is deconstructed as a composition of custom expression varieties, pre-defined to enable fulfilment of an objective within a theme of correspondence, associating each expression with a semantic vector. A set of expression extraction models is trained independently and then a second set with knowledge of parallel label predictions, iterating to convergence. The expression compositions and associated semantic vectors are combined into a single vector for each segment. The segment vectors are appended onto profile vectors for the exchange parties, yielding a time series of profile-content vectors.

Abstract: Systems and methods described herein enable effective and accurate modeling of a set of existing data profiles, perform categorization of the data profiles in an explainable way such that actions can be taken on the information to have predictable results. The systems and methods further facilitate means to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the data profiles.

Abstract: Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results.

Abstract: Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results.

Abstract: Systems and methods for generating best next communication policies, for a time step of an exchange of electronic documents, fit over historical exchanges, optimizing to maximize a probability of achieving a quantified objective leveraging weighted sampling. In a preferred embodiment an electronic document is segmented whereby each constituent segment is deconstructed as a composition of custom expression varieties, pre-defined to enable fulfilment of an objective within a theme of correspondence, associating each expression with a semantic vector. A set of expression extraction models is trained independently and then a second set with knowledge of parallel label predictions, iterating to convergence. The expression compositions and associated semantic vectors are combined into a single vector for each segment. The segment vectors are appended onto profile vectors for the exchange parties, yielding a time series of profile-content vectors.

Abstract: Systems and methods for generating best next communication policies, for a time step of an exchange of electronic documents, fit over historical exchanges, optimizing to maximize a probability of achieving a quantified objective leveraging weighted sampling. In a preferred embodiment an electronic document is segmented whereby each constituent segment is deconstructed as a composition of custom expression varieties, pre-defined to enable fulfilment of an objective within a theme of correspondence, associating each expression with a semantic vector. A set of expression extraction models is trained independently and then a second set with knowledge of parallel label predictions, iterating to convergence. The expression compositions and associated semantic vectors are combined into a single vector for each segment. The segment vectors are appended onto profile vectors for the exchange parties, yielding a time series of profile-content vectors.