US 2024/0177068 A1Published application

Predicting user attributes using uncertainty estimate modeling

AssigneeFmr LLC

InventorsChristopher Fusting, Lei Zhang, Wenlu Yan

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Abstract

Methods and apparatuses are described for predicting user attributes using uncertainty estimate modeling. A server trains a plurality of machine learning (ML) models to predict a distribution of values for a plurality of user attributes. The server determines an uncertainty measure of the ML models for each user attribute based upon the predicted distribution of values. The server receives a request for prediction of user attributes from a client device and generates for each user attribute a first predicted distribution using one or more of the trained models. The server classifies, for each user attribute, an accuracy of the first predicted distribution based upon the uncertainty measure and provides the first predicted distribution and the accuracy for each user attribute to the client device for presentation. The server updates the first predicted value for the user attributes based upon input received from the client computing device.

Claims24

Dependent claims

A system for predicting user attributes using uncertainty estimate modeling, the system comprising a server computing device with a memory for storing computer-executable instructions and a processor that executes the computer-executable instructions to:

train each of a plurality of gradient boosting machine learning models using historical user profile data to predict a distribution of values for each of a plurality of user attributes;
determine an uncertainty measure of the plurality of gradient boosting machine learning models for each user attribute based upon the predicted distribution of values generated by each machine learning model for the user attribute;
receive a request for prediction of one or more user attributes from a client computing device, the request including a user identifier of a user at the client computing device;
generate, for each of the one or more user attributes, a first predicted distribution for the user attribute using one or more of the trained gradient boosting machine learning models;
classify, for each of the one or more user attributes, an accuracy of the first predicted distribution for the user attribute based upon the uncertainty measure of the plurality of gradient boosting machine learning models for the user attribute;
provide the first predicted distribution and the accuracy for each of the one or more user attributes to the client computing device, wherein a first predicted value of the user attribute from the first predicted distribution is displayed in a user interface on the client computing device and one or more sensory features of the displayed first value correspond to the accuracy;
update, for each of the one or more user attributes, the first predicted value for the user attribute based upon input received from the client computing device.

4 more elements

A computerized method of predicting user attributes using uncertainty estimate modeling, the method comprising:

training, by a server computing device, each of a plurality of machine learning models using historical user profile data to predict a distribution of values for each of a plurality of user attributes;
determining, by the server computing device, an uncertainty measure of the plurality of machine learning models for each user attribute based upon the predicted distribution of values generated by each machine learning model for the user attribute;
receiving, by the server computing device, a request for prediction of one or more user attributes from a client computing device, the request including a user identifier of a user at the client computing device;
generating, by the server computing device for each of the one or more user attributes, a first predicted distribution for the user attribute using one or more of the trained gradient boosting machine learning models;
classifying, by the server computing device for each of the one or more user attributes, an accuracy of the first predicted distribution for the user attribute based upon the uncertainty measure of the plurality of machine learning models for the user attribute;
providing, by the server computing device, the first predicted distribution and the accuracy for each of the one or more user attributes to the client computing device, wherein a first predicted value of the user attribute from the first predicted distribution is displayed in a user interface on the client computing device and one or more sensory features of the displayed first value correspond to the accuracy;
updating, by the server computing device for each of the one or more user attributes, the first predicted value for the user attribute based upon input received from the client computing device.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/070,603Filed 29 Nov 2022
Published: 30 May 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06N 20/20
Machine learning: ensemble learning
G06N 7/01
Computing arrangements based on specific mathematical models: probabilistic graphical models, e.g. probabilistic networks
G06N 20/00
Machine learning
G06Q 40/06
Finance; Insurance; Tax strategies; Processing of corporate or income taxes: asset management; Financial planning or analysis

Family

US 12,626,195 B2Related patent
US 2024/0177068 A1This patent

From the US filing history. Foreign family members aren’t listed.

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