US 2026/0073349 A1 · ARRIVAL TIME FORECASTING MIXTURE OF EXPERTS MODEL SYSTEM WITH TIME SERIES FEATURES

Abstract

Methods, systems, and machine learning models for providing accurate estimate time of arrival (ETA) predictions are disclosed, particularly in the context of item fulfillment services. Input features including continuous, numerical, categorical, and time series features can be processed using an initial set of encoders. The resulting embeddings (and other applicable data) can be applied to a set of “expert” encoders. The embeddings produced by the expert encoders can be combined and processed using a multilayer perceptron, which can return one or more estimated arrival time predictions. Such predictions can correspond to multiple tasks and can include both point estimate predictions and distribution estimate predictions, e.g., predictions describing a probability density function of estimated arrival times. Interval regression can be used to produce distribution estimates, and machine learning models according to embodiments can be trained using multitask learning to produce estimated arrival time predictions for multiple tasks.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, from an end user device, a request for a delivery;
obtaining feature information comprising one or more continuous features, one or more categorical features, and one or more time series features, wherein the feature information includes (1) retrieval information associated with a retrieval location from which an item is to be delivered by a transporter and (2) transporter information associated with a plurality of transporter devices of transporters that are currently active for the retrieval location;
generating, via an initial encoding layer of a machine learning model, an initial embedding set based on the one or more continuous features, the one or more categorical features, and the one or more time series features;
generating, via an expert encoding layer of the machine learning model, a plurality of secondary embeddings based on the initial embedding set, wherein the expert encoding layer comprises a time series encoder, a categorical encoder, and a multiple feature encoder;
generating, via an output layer, one or more estimated arrival time predictions corresponding to the delivery based on the plurality of secondary embeddings;
and
providing, to the end user device, at least one of the estimated arrival time predictions.

4 more elements

A computer-implemented method for training a machine learning model to generate estimated arrival time predictions, the method comprising performing an iterative training process until a terminating condition has been met, the iterative training process comprising:

sampling a batch of training feature information comprising a batch of continuous features, a batch of categorical features, and a batch of time series features;
generating, via an initial encoding layer of the machine learning model, an initial embedding set based on the batch of continuous features, the batch of categorical features, and the batch of time series features;
generating, via an expert encoding layer of the machine learning model, a plurality of secondary embeddings based on the initial embedding set, the expert encoding layer of the machine learning model comprising a time series encoder, a categorical encoder, and a multiple feature encoder;
generating, via an output layer, one or more estimated arrival time predictions based on the plurality of secondary embeddings;
determining one or more loss values based on the one or more estimated arrival time predictions;
updating a parameter set of the machine learning model based on the one or more loss values, thereby training the machine learning model;
and
if the terminating condition has not been met, repeating the iterative training process until the terminating condition has been met, otherwise completing the iterative training process.

5 more elements

Dependencies are inferred from the claim text.

Arrival time forecasting mixture of experts model system with time series features

Life of this patent

Arrival time forecasting mixture of experts model system with time series features

Life of this patent

Abstract

Claims20