US 2026/0065150 A1Published application

Dynamic querying process with integrated machine learning through sentiment analysis

AssigneeOptum, Inc.

InventorsSumant RAO, Pranit KEDARISETTY, Joshua BASTIAN

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Abstract

Various embodiments of the present disclosure provide a machine learning framework integrated within a dynamic querying process that improves the functionality of a computer in various aspects. The techniques comprise receiving a model input comprising a set of entity attributes and a set of initial query responses. The techniques comprise generating, using a machine learned model, a model prediction based on the model input and determining, based on the model prediction, an influential parameter from the first subset of independent parameters for the model prediction. The techniques comprise providing a set of subsequent queries based on the influential parameter to receive a set of subsequent query responses that correspond to a second subset of independent parameters and generating, using the machine learned model, an updated model prediction based on the model input and the set of subsequent query responses.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, by one or more processors, a model input comprising a set of entity attributes and a set of initial query responses, wherein the set of initial query responses comprises one or more initial query responses corresponding to (a) a set of initial queries comprising one or more initial queries and (b) a first subset of a set of independent parameters of a machine learned model;
generating, by the one or more processors and using the machine learned model, a model prediction based on the model input, wherein the model prediction comprises a dependent parameter of the machine learned model;
determining, by the one or more processors and based on the model prediction, an influential parameter, for the model prediction, from the set of independent parameters;
providing, by the one or more processors and based on the influential parameter, a set of subsequent queries to receive a set of subsequent query responses that correspond to a second subset of independent parameters of the machine learned model;
and
generating, by the one or more processors and using the machine learned model, an updated model prediction based on the model input and the set of subsequent query responses.

3 more elements

A system comprising:

one or more processors;
and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a model input comprising a set of entity attributes and a set of initial query responses, wherein the set of initial query responses comprises one or more initial query responses corresponding to (a) a set of initial queries comprising one or more initial queries and (b) a first subset of independent parameters of a machine learned model;
generating, using the machine learned model, a model prediction based on the model input, wherein the model prediction comprises a dependent parameter of the machine learned model;
determining, based on the model prediction, an influential parameter for the model prediction;
providing, based on the influential parameter, a set of subsequent queries to receive a set of subsequent query responses that correspond to a second subset of independent parameters;
and
generating, using the machine learned model, an updated model prediction based on the model input and the set of subsequent query responses.

6 more elements

One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving a model input comprising a set of entity attributes and a set of initial query responses, wherein the set of initial query responses comprises one or more initial query responses corresponding to (a) a set of initial queries comprising one or more initial queries and (b) a first subset of independent parameters of a machine learned model;
generating, using the machine learned model, a model prediction based on the model input, wherein the model prediction comprises a dependent parameter of the machine learned model;
determining, based on the model prediction, an influential parameter for the model prediction;
providing, based on the influential parameter, a set of subsequent queries to receive a set of subsequent query responses that correspond to a second subset of independent parameters;
and
generating, using the machine learned model, an updated model prediction based on the model input and the set of subsequent query responses.

3 more elements

Dependencies are inferred from the claim text.