Google Inc.

Beijing Jingdong Shangke Information Technology Co., Ltd.

Jd.com American Technologies Corporation

Beijing Wodong Tianjun Information Technology Co., Ltd.

Samsung Electronics Co., Ltd.

Di Wu

Michael Jeffrey Procopio

Reuben Kan

Ushasree Kode

John Herndon

Ramesh Nagarajan

Yi Zhang

Gregory Lewis Dudek

Christopher Tate

Christiaan Prins

Chi Zhang

Xi Chen

Yuhui Shi

Xiaotao Feng

Fanlong Meng

Yixiong Chen

Chaofeng Li

Chenyou Fan

Yuze Zhang

Rong Yuan

Shengwei Tang

Chenyi Zhou

Hang Li

Xue Liu

John Herdon

A method and a system for predicting and using customer lifetime value (CLV). The method include: providing a classifier trained using customer feature data during a first period of time as input and whether there is spending during a second period of time as classifier label; providing a regressor trained using the customer feature data during the first period of time as input and amount of spending during a second period of time as regressor label; performing the classifier using customer feature data during a third period of time to obtain customers having positive predicted classifier labels; and performing the regressor using the customer feature data during the third period of time for the customers having positive predicted classifier labels, to obtain CLVs of the customers.

System and method for predicting customer lifetime value using two-stage machine learning

A system and a method for time series forecasting. The method includes: providing input feature vectors corresponding to a plurality of future time steps; performing bi-directional long-short term memory network (BiLSTM) on the input feature vectors to obtain hidden outputs corresponding to the plurality of future time steps; for each future time step: performing temporal convolution on the hidden outputs using a plurality of temporal scales to obtain context features at the plurality of temporal scales, and summating the context features at the plurality of temporal scales using a plurality of weights to obtain multi-scale context features; and converting the multi-scale context features to obtain the time series forecasting corresponding to the future time steps.

System and method for multi-horizon time series forecasting with dynamic temporal context learning

A method for providing restoration of third-party application data to a client device is described herein. The method includes determining whether to provide, to the client device, data associated with a third-party application to be restored at the client device. The data associated with the third-party application includes binary data stored in a first cloud storage location having a storage address, and metadata stored in a second cloud storage location. The method also includes using the metadata to determine the storage address of the first cloud storage location. The method further includes providing the binary data from the first cloud storage location to the client device for restoring the third-party application data.

System and method for automatic cloud-based full-data restore to mobile devices

A method for storing mobile device data to cloud storage includes detecting a predetermined condition to backup data associated with a first mobile device to the cloud storage. Uploading, to a cloud storage server, data associated with the first mobile device including data associated with a plurality of third-party applications stored on the first mobile device and one or more of contacts information stored on the first mobile device, text messages stored on the first mobile device, or contents of a memory card associated with the first mobile device. The data associated with the plurality of third-party applications include binary data and metadata. The method further includes storing the binary data of the plurality of third-party applications at a first cloud storage location and storing the metadata of the plurality of third-party applications to a second cloud storage location that is different from the first cloud storage location.

System and method for automatic cloud-based full-data backup on mobile devices

A system for selecting items to be stored in a warehouse from a list of candidate items. The system includes a computing device having a processor and a storage device storing computer executable code. The executed code is configured to: assign scores to the respective candidate items based on an item selection model; and select items from the candidate items to be stored in the warehouse according to the assigned scores under a capacity limit of the warehouse. The item selection model is configured with an Artificial Neural Network (ANN), having item data for an item as input and a score for that item as output. The score represents a probability that the item is to be stored in the warehouse. The ANN is trained by historical orders with an objective of maximizing a probability that the orders have their respective item(s) all stored in the same warehouse.

System and method of determining item storage strategy

A method for storing third-party application data to cloud storage is described herein. The method includes determining to upload data associated with a third-party application stored on a client device to a cloud storage server. The data associated with the third-party application includes both binary data and metadata. The method further includes storing the binary data of the third-party application at a first cloud storage location. The first cloud storage location has a storage address and the metadata is updated to include the storage address. The method includes storing the updated metadata to a second cloud storage location different from the first cloud storage location.

System and method for automatic cloud-based full-data backup and restore on mobile devices

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Mountain View, CA, United States of America

Di Wu