Abstract: Various embodiments provide processes for identification of embedded unauthorized data within document data. Such processes may include evaluating document data properties for indications of closing headers, comparing document data sizes against reference sizes, and comparing document data sizes against other document data sizes. Certain embodiments evaluating image data may evaluate image properties, such as histograms or gradient images, to determine random or otherwise unauthorized data. Upon detection of unauthorized data, the document data may be flagged for later processing, such as removal from a storage server.

Abstract: Techniques for improved image classification are provided. Face embeddings are generated for each face depicted in a collection of images, and the face embeddings are clustered based on the individual whose face is depicted. Based on these clusters, each embedding is assigned a label reflecting the cluster assignments. Some or all of the face embeddings are then used to train a classifier model to generate cluster labels for new input images. This classifier model can then be used to process new images in an efficient manner, and classify them into appropriate clusters.

Abstract: Customers may place orders for items for delivery, where merchants provide the items, and a service provider receives the orders and facilitates the delivery of the items. Upon receiving multiple orders, the orders may be prioritized based on users scores associated with users that placed the orders. An order objective for the orders may be determined and multiple fulfillment plans for each order may be generated. For a particular order, a fulfillment plan of the multiple fulfillment plans may be selected based on the order objective for that order, where the selected fulfillment plan may govern how and when the items included in the order are to be delivered to the user that placed the order. The service provider may cause the items associated with the order to be delivered to the user pursuant to the selected fulfillment plan.

Abstract: Customers may place orders for items for delivery, where merchants provide the items, and a service provider receives the orders and facilitates the delivery of the items. Upon receiving multiple orders, the orders may be prioritized based on users scores associated with users that placed the orders. An order objective for the orders may be determined and multiple fulfillment plans for each order may be generated. For a particular order, a fulfillment plan of the multiple fulfillment plans may be selected based on the order objective for that order, where the selected fulfillment plan may govern how and when the items included in the order are to be delivered to the user that placed the order. The service provider may cause the items associated with the order to be delivered to the user pursuant to the selected fulfillment plan.

Abstract: Tracking waiting lists associated with merchants and presenting deals to users based on adding the users to waiting lists is described. A service provider may enable merchants to track waiting lists associated with their own services and services of other merchants. Users may be automatically added to waiting lists of merchants based at least in part on a service provider determining that a user is at a geographic location of a merchant. Based at least in part on adding a user to a waiting list for a particular merchant, the service provider may access and select deals from one or more alternative merchants. The one or more alternative merchants may not have waiting lists or may have waiting lists with fewer groups than the waiting list for the particular merchant. The service provider may offer the deals to the user via a user device.

Abstract: A meal ordering service is configured to receive takeout and delivery orders from customers and to place the orders with selected restaurants. Past information regarding a customer is collected and analyzed to create a predictive model of the customer's meal ordering habits. On any given day, the predictive model may be used, in light of the current and scheduled status of the customer, to predict a time of day that the customer may want to receive a meal and to also determine a restaurant from which the customer may want to receive the meal. The meal ordering service may be configured to automatically order a meal for the customer from the determined restaurant so that the meal arrives at or before the predicted time of day.