US 2023/0401624 A1 · RECOMMENDATION ENGINE GENERATION

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a recommendation engine. One of the methods includes receiving, from a plurality of data sources, input data; generating, using each of two or more propensity models, output data by providing training data from the input data to the respective propensity model; determining, for each of the propensity models, a first accuracy of the respective propensity model using the respective output data; determining, for each of the two or more propensity models, a second accuracy of the respective propensity model using testing data from the input data; selecting, using the first accuracies and the second accuracies for the two or more propensity models, a propensity model from the two or more propensity models; and providing, to a system, the selected propensity model to enable the system to generate a recommendation using the selected propensity model.

Claims20

Dependent claims

A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

receiving, from a plurality of data sources, input data;
generating, using each of two or more propensity models, output data by providing training data from the input data to the respective propensity model;
determining, for each of the two or more propensity models, a first accuracy of the respective propensity model using the respective output data;
determining, for each of the two or more propensity models, a second accuracy of the respective propensity model using testing data from the input data;
selecting, using the first accuracies and the second accuracies for the two or more propensity models, a propensity model from the two or more propensity models;
and
providing, to a system, the selected propensity model to enable the system to generate a recommendation using the selected propensity model.

4 more elements

A computer-implemented method comprising:

receiving, from a plurality of data sources, input data that includes, for each of multiple records, a) a plurality of parameters, and b) values for at least some of the parameters;
determining, for the plurality of parameters, whether characteristics of the corresponding parameter in the multiple records satisfy one or more propensity modeling thresholds;
selecting, using the parameters that satisfy the one or more propensity modeling thresholds, a propensity model from two or more propensity models;
and
providing, to a system, the selected propensity model to enable the system to generate a recommendation using the selected propensity model.

2 more elements

A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

receiving, from a plurality of data sources, input data;
generating, using each of two or more propensity models, output data by providing training data from the input data to the respective propensity model;
determining, for each of the two or more propensity models, a first accuracy of the respective propensity model using the respective output data;
determining, for each of the two or more propensity models, a second accuracy of the respective propensity model using testing data from the input data;
selecting, using the first accuracies and the second accuracies for the two or more propensity models, a propensity model from the two or more propensity models;
and
providing, to a system, the selected propensity model to enable the system to generate a recommendation using the selected propensity model.

4 more elements

Dependencies are inferred from the claim text.

Recommendation engine generation

Life of this patent

Recommendation engine generation

Life of this patent

Abstract

Claims20