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.
Type:
Grant
Filed:
February 13, 2023
Date of Patent:
December 5, 2023
Assignee:
Cognism Limited
Inventors:
Eliot S Frazier, Johannes Julien Frederik Erett, James A Hodson
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.
Type:
Application
Filed:
August 13, 2023
Publication date:
November 30, 2023
Applicant:
Cognism Limited
Inventors:
Eliot S Frazier, James A Hodson, Johannes Julien Frederik Erett
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.
Type:
Grant
Filed:
March 20, 2021
Date of Patent:
August 15, 2023
Assignee:
Cognism Limited
Inventors:
Eliot S Frazier, James A. Hodson, Johannes Julien Frederik Erett
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.
Type:
Application
Filed:
March 20, 2021
Publication date:
March 17, 2022
Applicant:
Cognism Limited
Inventors:
Eliot S. Frazier, James A. Hodson, Johannes Julien Frederik Erett
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.
Type:
Grant
Filed:
September 15, 2020
Date of Patent:
May 4, 2021
Assignee:
Cognism Limited
Inventors:
Eliot S Frazier, James A Hodson, Johannes Julien Frederik Erett
Abstract: A system and method for inferring an organizational structure of a record based on position role transitions from a parsed plurality of record profiles using machine learning techniques described herein. A piece-wise graph of transitions between positions across a normalized user employment landscape are computed to recover properties of user hierarchical structures across a plurality of position information by analysis of transition trajectories.
Type:
Grant
Filed:
September 5, 2017
Date of Patent:
December 4, 2018
Assignee:
Cognism Limited
Inventors:
James Hodson, Johannes Erett, Sinan James Isilay