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 disambiguating attributes associated with one or more entity profiles using timeslicing is described. An entity disambiguation computer receives information associated with one or more entities. The received information includes attributes related to one or more entities. Attributes are extracted and disambiguated from the information to generate timeslice objects representing the attributes. The timeslice objects are associated with one or more indices based on the respective durations of the timeslice objects. The timeslice objects and respective one or more indices are arranged based on timelines associated with the timeslice objects. On the generation of a new timeslice object, the entity disambiguation computer determines the position of the new timeslice object with respect to positions of existing timeslice objects and updates the arrangement of the timeslice objects based on the position of the new timeslice object.

Abstract: A system and method for disambiguating entities for managing customer relationships are described. An entity disambiguation computer receives information associated with candidate entities in an entity database. The received information comprises multiple versions of attributes related to one or more entities. Attributes are disambiguated and extracted from the information. A set of timeslice objects representing the multiple versions of each attribute is created. A subset of timeslice objects is selected for comparison based on an overlap between durations in respective timeslice objects. The system and method use a similarity model comprising weight and biases assigned to sets of previously used overlapping durations to predict if the subset of timeslice objects corresponds to the same entity. The subset of timeslice objects is merged if predicted to correspond to the same entity. This merging of timeslice objects disambiguates the information present in the entity database.

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 disambiguating company profiles are disclosed. The system builds a database of candidate companies with timed attributes. The system further ingests timed company metadata using a PostgreSQL database. The system disambiguates location and geocoding by matching text patterns and cross-referencing one or more identified location components against one or more geocode databases and classifies and disambiguates company name component from a company name associated with the company using a conditional random field (CRF) model. Further, the system disambiguates employee attributes using a Latent Dirichlet Allocation (LDA) topic model algorithm and train a tree model for pre-selection of candidate companies for the company. The system trains a similarity model for comparison of the candidate companies and in response to a determination that two given candidate companies are same merge the company profiles associated with the given candidate companies.

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.