System for determining quantitative measure of dyadic ties
Assignee6Dos, LLC
InventorEthan Francis
Life of this patent
Estimating the term…
Abstract
Described are platforms, systems, and methods for determining quantifiable measures of dyadic ties. In one aspect, a method comprises receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.
Claims20
A computer-implemented method for determining quantifiable measures of dyadic ties, the method being executed by one or more processors and comprising:
- receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals;
- receiving validation data for the plurality of individuals from at least one data enricher;
- processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users;
- processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users;
- and
- providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
A dyadic ties measurement system, comprising:
- a user-interface;
- one or more processors;
- and
- a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals;
- receiving validation data for the plurality of individuals from at least one data enricher;
- processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users;
- processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users;
- and
- providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- receiving contextual data for a user, wherein the contextual data for the user includes contact information for a plurality of individuals;
- receiving validation data for the plurality of individuals from at least one data enricher;
- processing the contextual data for the user and the validation data through a first machine-learning model to determine a mapping between the contact information for the plurality of individuals and the validation data for the plurality of individuals, the first machine-learning model trained with previously received validation data and previously received contextual data for a plurality of other users;
- processing the contextual data for the user and the mapping through a second machine-learning model to determine quantifiable measures of dyadic ties between the user and each of the plurality of individuals, the second machine-learning model trained with the previously received contextual data for the plurality of other users;
- and
- providing, through a user-interface, access to the determined quantifiable measures of dyadic ties to a plurality of members of a group, where in the plurality of members of the group includes the user.
Dependencies are inferred from the claim text.