Abstract: This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.

Abstract: This application relates to apparatus and methods for automatic delivery route generation and assignment. In some examples, a computing device determines delivery locations for each of a plurality of previous routes taken by each of a plurality of drivers over a period of time. The computing device also obtains a plurality of current routes to be assigned to the plurality of drivers. Further, the computing device determines a familiarity value for each of the plurality of drivers for each of the plurality of current routes based on the delivery locations of the previous routes taken by each driver. The computing device assigns each of the plurality of drivers to a current route based on the determined familiarity values, and stores the assignments in a data repository. In some examples, the computing device transmits the assignments to another computing device, such as a computing device of each driver.

Abstract: This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.

Abstract: This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a plurality of first values for a corresponding plurality of first items based on the user's engagement with each of the first items. The computing device may then determine a subset of the plurality of first items based on the first values. The computing device may receive a search request and determine a plurality of second values for a plurality of second items based on the search request. The computing device may determine a plurality of third values for the subset of items based on the plurality of second values for the plurality of second items and the user's engagement with each of the subset of items. The computing device may determine the recommended items based on the plurality of second values and the plurality of third values.

Abstract: A customer order prediction system can include a computing device configured to obtain customer order data characterizing a customer’s interaction with an electronic marketplace and to obtain environmental data characterizing at least one event expected to effect the customer’s ordering behavior. The computing device can be further configured to generate feature data based on the customer data and the environmental data wherein the feature data organizes the customer data and the environmental data based on characteristics of an order size estimation model. The computing device is also configured to determine a predicted customer order size using the order size estimation model and to adjust a delivery availability of the electronic marketplace based on the predicted order size.

Abstract: An approach generates a speed profile for one or more delivery locations. The speed profile corresponds to location data of a delivery order for a respective delivery location, and indicates a plurality of events associated with a movement of a delivery order to the respective delivery location. The approach generates, based on the speed profile and location data corresponding to the delivery order, feature data of the respective delivery location. The approach applies a machine learning model to the generated feature data to output a door-step time prediction for the respective delivery location. The door-step time prediction is based on a time difference between timestamps of two events of the plurality of events associated with the movement of the delivery order. The approach generates a planned delivery route for the one or more delivery locations, based on the one or more delivery locations and respective the door-step time predictions.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to providing personalized samples to customers. In some embodiments, a system for providing personalized samples to customers comprises an online shopping server configured to host an online shopping website and receive item selections indicating items to add to the customer's cart, a database configured to store a list of sample types, and a purchase likelihood estimator configured to receive the items to add to the customer's cart, determine an identity of the customer, determine customer traits, determine available sample types and traits associated with the available sample types, calculate a probability score based on the customer traits and the traits associated with each of the available sample types, and add, to the customer's cart based on the probability scores for each of the available sample types, one or more samples from the one or more of the available sample types.

Abstract: This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.

Abstract: A delivery route planning system can include a computing device configured to obtain order delivery data that includes information characterizing delivery locations and delivery time windows associated with a first plurality of orders and to determine a delivery plan based on the order delivery data. The computing device can be further configured to identify recyclable time in the delivery time and to obtain supplemental order delivery data that includes information characterizing supplemental orders characterizing delivery locations and delivery time windows associated a second plurality of orders. The computing system can add one or more supplemental order deliveries from the second plurality of orders to the delivery plan based on the recyclable time, the supplemental order delivery data and at least one predetermined delivery constraint.

Abstract: This application relates to apparatus and methods for automatically providing item reviews. In some examples, a website provides a widget on a website allowing a customer to request an item review for an item. In response to the customer requesting the item review, a computing device determine whether suitable reviews for the item are available. If suitable reviews are available, the reviews are provided to the customer requesting the item review. If suitable reviews are not available, the computing device determines a set of customers to request item reviews for the item from, and provides item review requests to the determined set of customers. The computing device receives responses to the item review requests received from the set of customers, and provides an item review based on the received item review requests. In some examples, the website provides the item review to the customer requesting the item review.

Abstract: This application relates to apparatus and methods for automatic delivery route generation and assignment. In some examples, a computing device determines delivery locations for each of a plurality of previous routes taken by each of a plurality of drivers over a period of time. The computing device also obtains a plurality of current routes to be assigned to the plurality of drivers. Further, the computing device determines a familiarity value for each of the plurality of drivers for each of the plurality of current routes based on the delivery locations of the previous routes taken by each driver. The computing device assigns each of the plurality of drivers to a current route based on the determined familiarity values, and stores the assignments in a data repository. In some examples, the computing device transmits the assignments to another computing device, such as a computing device of each driver.

Abstract: This application relates to apparatus and methods for automatically providing item reviews. In some examples, a website provides a widget on a website allowing a customer to request an item review for an item. In response to the customer requesting the item review, a computing device determine whether suitable reviews for the item are available. If suitable reviews are available, the reviews are provided to the customer requesting the item review. If suitable reviews are not available, the computing device determines a set of customers to request item reviews for the item from, and provides item review requests to the determined set of customers. The computing device receives responses to the item review requests received from the set of customers, and provides an item review based on the received item review requests. In some examples, the website provides the item review to the customer requesting the item review.

Abstract: A content provider may cause a client device of a user to output a personalized puzzle in response to receiving a request from the client device to access electronic content of the content provider. The puzzle may include a theme that corresponds to a determined predilection of the user, and/or the puzzle may be a type of puzzle that corresponds to the user's predilection. The client device may also output, with the puzzle, an incentive for completing (e.g., solving) the puzzle Upon receiving data indicating that the user has completed his/her personalized puzzle, the content provider may provide the reward to the user.