Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

Abstract: A method for populating an inventory catalog includes receiving an image showing an item in the inventory catalog and comprising a plurality of pixels. A machine learned segmentation neural network is retrieved to determine location of pixels in an image that are associated with an image label and the property. The method determines a subset of pixels associated with the item label in the received image and identifies locations of the subset of pixels of the received image, and extracts the subset of pixels from the received image. The method retrieves a machine learned classifier to determine whether an image shows the item label. The method determines, using the machine learned classifier, that the extracted subset of pixels shows the item label. The method updates the inventory catalog for the item to indicate that the item has the property associated with the item label.

Abstract: A system is provided for utilizing a track for moving doors into position to be coated and subsequently removing the doors once coated.

Abstract: A method for populating an inventory catalog includes receiving an image showing an item in the inventory catalog and comprising a plurality of pixels. A machine learned segmentation neural network is retrieved to determine location of pixels in an image that are associated with an image label and the property. The method determines a subset of pixels associated with the item label in the received image and identifies locations of the subset of pixels of the received image, and extracts the subset of pixels from the received image. The method retrieves a machine learned classifier to determine whether an image shows the item label. The method determines, using the machine learned classifier, that the extracted subset of pixels shows the item label. The method updates the inventory catalog for the item to indicate that the item has the property associated with the item label.

Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

Abstract: In one or implementations, electronic usage information that is associated with recency, frequency and monetary spending from a plurality of computing devices associated with a user base representing a plurality of users is processed. For example, the electronic usage information is associated with activity, and a portion of the user base is segmented as a function of the associated electronic usage activity. Moreover, using the at least one processor, the associated electronic usage information and the segmented portion of the user base is processed to generate at least one predictive model of future behavior of the segmented portion. Aa respective recommendation of a good and/or service is determined for each of the users in the segmented portion of the user base in accordance with the at least one generated predictive model, and is provided.

Abstract: A backup site and a client are coupled to a network and the backup site obtains backup data for the client using a portable storage device by providing a direct coupling between the portable storage device and the backup site. The portable storage device contains full backup data for the client. The direct coupling is separate from the network. Full backup data is uploaded from the portable storage device to the backup site via the direct coupling. At least one incremental backup, based on the prior full backup, is performed to transfer data from the client to the backup site through the network. The network may be the Internet. The direct coupling may be USB, Firewire, or eSATA. Only a subset of data corresponding to a backup dataset may be provided on the portable storage device. Data on the portable storage device may be encrypted.

Abstract: Backing up data from a client includes providing a direct coupling between the client and a portable storage device, copying full backup data from the client to the portable storage device using the direct coupling, and performing at least one incremental backup from the client to the backup site through a network that is separate from the direct coupling. The at least one incremental backup is based on the prior full backup. The network may be the Internet. Following copying full backup data to the portable storage device, the portable storage device may be shipped from the client to the backup site. The direct coupling may be USB, Firewire, or eSATA. Only a subset of data corresponding to a backup dataset may be copied from the client to the portable storage device.

Abstract: A backup site and a client are coupled to a network and the backup site obtains backup data for the client using a portable storage device by providing a direct coupling between the portable storage device and the backup site. The portable storage device contains full backup data for the client. The direct coupling is separate from the network. Full backup data is uploaded from the portable storage device to the backup site via the direct coupling. At least one incremental backup, based on the prior full backup, is performed to transfer data from the client to the backup site through the network. The network may be the Internet. The direct coupling may be USB, Firewire, or eSATA. Only a subset of data corresponding to a backup dataset may be provided on the portable storage device. Data on the portable storage device may be encrypted.

Abstract: Backing up data from a client includes providing a direct coupling between the client and a portable storage device, copying full backup data from the client to the portable storage device using the direct coupling, and performing at least one incremental backup from the client to the backup site through a network that is separate from the direct coupling. The at least one incremental backup is based on the prior full backup. The network may be the Internet. Following copying full backup data to the portable storage device, the portable storage device may be shipped from the client to the backup site. The direct coupling may be USB, Firewire, or eSATA. Only a subset of data corresponding to a backup dataset may be copied from the client to the portable storage device.