Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.

Abstract: An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.

Abstract: An electronic device includes one or more sensors, a device housing, and a blade assembly configured to translate around the device housing between an extended position, a retracted position, and a peek position. The electronic device includes one or more processors that, in response to the one or more sensors detecting the electronic device being handed from a first person to a second person, cause the blade assembly to translate around the device housing when signals received from the one or more sensors indicate hands of the second person are differently sized compared to other hands of the first person.

Abstract: An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.

Abstract: An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: One or more trained computer models are used to determine, at different stages of an order, an estimated time range for delivery of the order at an online system. The online system retrieves a set of candidate ranges of delivery times for the order. The online system applies the one or more computer models trained to predict a value of a metric for each candidate range in the set of candidate ranges, based on one or more features associated with the order. The online system selects a range of delivery times for the order from the set of candidate ranges, based on the predicted value of the metric for each candidate range. The online system causes a device of the user to display a user interface with the selected range of delivery times for the order.

Abstract: In aspects of converting stereo audio content to mono audio content based on earphone usage, a mobile device identifies a first earphone of a pair of earphones connected to the electronic device, the pair of earphones including the first earphone and a second earphone, converts stereo audio content to mono audio content in response to a detection that the first earphone is used by a first user and that the second earphone is not used by the first user, and transmits the mono audio content to the first earphone.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: For each retailer in the geographic region, an online system predicts a number of orders placed at the retailer and a capacity to fulfill orders during a forecast time period. The capacity of the retailer is predicted based on a number of pickers expected to be available to the retailer during the forecast time period. The online system determines demand for the services of a picker at the retailer based on a comparison of the predicted number of orders and the predicted capacity to fulfill those orders. The online system displays a user interactive map of the geographic region to the picker. The map displays a pin at the location of each retailer in the geographic region, which describes the categorization determined for the retailer. The picker selects a pin, which causes the user interactive map to display a notification characterizing the demand for services at the retailer.

Abstract: A moving object control apparatus in the present disclosure that adjusts a stop position of a moving object based on an instruction of a user, acquires instruction information of the user and acquires a captured image captured in a moving object. The apparatus determines a stop position of the moving object and controls traveling of the moving object to cause the moving object to travel toward the determined stop position. The apparatus (i) determines a first stop position using position information of a communication device used by the user or position information corresponding to a destination included in first instruction information of the user, and (ii) determines a second stop position based on second instruction information of the user and a region of a predetermined target identified in the captured image.

Abstract: A method provides techniques for electronic device customization based on environmental and location data. The method includes obtaining, on an electronic device, from one or more data sources, context data associated with one or more of the user and the electronic device. A device context is determined based on the context data. The method includes loading a customization display theme for the electronic device, based on the device context, and presenting the customized display theme within a user interface displayed on the display of the electronic device.

Abstract: A method provides techniques for electronic device display screen customization based on image information. The method includes detecting, by at least one processor of an electronic device that comprises an electronic display and an image capturing device, a self-photo within received image content from the image capturing device. The method includes determining a user context from information within the received image content, and loading a customization display theme for the electronic device, based on the user context, wherein the customization display theme includes one or more theme elements.

Abstract: Embodiments relate to determining an availability of a service option for delivery of an order placed with an online system. The online system receives an order placed with the online system. The online system accesses a computer model trained to predict a value of metric for an order placed with the online system. The online system applies the computer model to predict the value of the metric for the order. The online system determines which service option of a plurality of service options of the online system is available for delivery of the order, based at least in part on the predicted value of the metric and a threshold. The online system causes the device of the user to display an availability of the determined service option for delivery of the order.

Abstract: An online concierge system generates a set of candidate estimated times of arrival (ETAs) for delivery of a set of items being purchased by a user. Each candidate ETA is scored by using a machine-learned model to estimate values for different criteria of interest, such as likelihood of acceptance of the ETA, cost of delivery of the items to the user, and the like. The values for the different criteria may be combined to generate the overall score for a candidate ETA. One or more of the highest-scoring ETAs are selected and provided to the user, who may then approve one of the ETAs for use with delivery of the user's set of items.

Abstract: An online concierge system receives an order from a user including items to obtain from a retailer for delivery to a location. A picker selects the order and obtains items from the retailer. The user selects a time interval during which items from the order are delivered to the location. To prevent the user from selecting a time interval for fulfillment the online concierge system prevents the user from selecting a time interval when a picker may be unable to obtain the items from the retailer before a closing time of the retailer. The online concierge system evaluates time intervals by subtracting a travel time for the picker travelling from the retailer to the location from a predicted fulfillment time for the order. This prevents the time for delivering items after being obtained from affecting whether a time interval may be selected.

Abstract: An electronic device includes one or more sensors, a device housing, and a blade assembly configured to translate around the device housing between an extended position, a retracted position, and a peek position. The electronic device includes one or more processors that, in response to the one or more sensors detecting the electronic device being handed from a first person to a second person, cause the blade assembly to translate around the device housing when signals received from the one or more sensors indicate hands of the second person are differently sized compared to other hands of the first person.

Abstract: An online concierge system delivers items from retailers to customers. The online concierge predicts a range of times during which an order may be fulfilled for presentation to a user. The online concierge system uses a trained maximum time prediction model to determine a maximum time for order fulfillment based on an order. A trained minimum time prediction model determines a minimum time for order fulfillment from the order and the maximum time. The minimum time may account for one or more rules (e.g., a percentage of orders fulfilled before the minimum time, a desired rate of selection of a range including the minimum time). A range bounded by the maximum time and the minimum time is transmitted to a customer to enable the customer to select a time interval for order fulfillment.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.