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US 12,591,802 B2Granted
Generating estimates by combining unsupervised and supervised machine learning
AssigneeIntuit Inc.
InventorsJingxian Liao, Wei Wang, Zhicheng Xue
Life of this patent
Estimating the term…
Abstract
A method may include obtaining a cluster. The cluster may include a subset of reference entities. The method may further include calculating distances between features of a target entity and features of the subset of reference entities, selecting, based on the distances, peer entities from the subset, and generating an estimated value of a metric. The generating may include applying, to the features of the target entity, a machine learning model trained using training data including values of the features for the peer entities labeled with a value of the metric. The method may further include presenting the estimated value of the metric.
Claims20
A method comprising:
- obtaining a first cluster of a first plurality of clusters, wherein the first cluster comprises a first subset of a plurality of reference entities;
- calculating a first plurality of distances between a first plurality of features of a target entity and the first plurality of features of the first subset of the plurality of reference entities;
- selecting, from the first subset, a first plurality of peer entities that are closest, within the first subset, to the target entity according to the first plurality of distances, each peer entity in the first plurality of peer entities being labeled with a corresponding value of a metric of the peer entity;
- training, after selecting, a machine learning model specifically for the target entity, wherein the machine learning model is trained to estimate, as output, the corresponding value of the metric of the peer entity using, as input, values of the first plurality of features from only the first plurality of peer entities;
- generating, by applying the machine learning model to the first plurality of features of the target entity, and after training the machine learning model specifically for the target entity, a first estimated value of the metric for the target entity;
- and
- presenting the first estimated value of the metric.
4 more elements
A system comprising:
- a computer processor;
- a repository configured to store:
- a first plurality of clusters comprising a first cluster comprising a first subset of a plurality of reference entities;
- and
- a metric estimation manager executing on the computer processor and configured to:
- calculate a first plurality of distances between a first plurality of features of a target entity and the first plurality of features of the first subset,
- select, from the first subset, a first plurality of peer entities that are closest, within the first subset, to the target entity according to the first plurality of distances, each peer entity in the first plurality of peer entities being labeled with a corresponding value of a metric of the peer entity,
- train, after selecting, a machine learning model specifically for the target entity, wherein the machine learning model is trained to estimate, as output, the corresponding value of the metric of the peer entity using, as input, values of the first plurality of features labeled from only the first plurality of peer entities,
- generate, by applying the machine learning model to the first plurality of features of the target entity, and after training the machine learning model specifically for the target entity, a first estimated value of the metric for the target entity, and
- present the first estimated value of the metric.
7 more elements
A method comprising:
- sending a target entity request to a metric estimation manager configured to perform:
- obtaining a first cluster of a first plurality of clusters, wherein the first cluster comprises a first subset of a plurality of reference entities,
- calculating a first plurality of distances between a first plurality of features of a target entity and the first plurality of features of the first subset of the plurality of reference entities,
- selecting, from the first subset, a first plurality of peer entities that are closest, within the first subset, to the target entity according to the first plurality of distances, each peer entity in the first plurality of peer entities being labeled with a corresponding value of a metric of the peer entity,
- training, after selecting, a machine learning model specifically for the target entity, wherein the machine learning model is trained to estimate, as output, the corresponding value of the metric of the peer entity using, as input, values of the first plurality of features from only the first plurality of peer entities;
- generating, by applying the machine learning model to the first plurality of features of the target entity, and after training the machine learning model specifically for the target entity, a first estimated value of the metric for the target entity, and
- transmitting the first estimated value of the metric;
- receiving, from the metric estimation manager, the first estimated value of the metric;
- and
- presenting the first estimated value of the metric.
7 more elements
Dependencies are inferred from the claim text.