Maplebear Inc.

Intuit, Inc.

Houtao Deng

Zi Wang

Ji Chen

Yijia Chen

Ganesh Krishnan

Soren Zeliger

George Roumeliotis

Aman Jain

Jagannath Putrevu

Deepak Tirumalasetty

Wa Yuan

Jian Wang

Mingzhe Zhuang

Site Wang

Xiangyu Wang

Yile Liu

Christopher Shey-Tau Sun

Michael Scheibe

Shuai Wang

Liang Chen

Jaspreet Singh

Sean Arnold Pudney

S Satishkumar

John A Bocharov

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.

Using computer model to determine availability of service option for delivery of order placed with online system

An online concierge system maintains a data store with discrete time intervals for fulfilling orders and information on shoppers categorized in a tree structure based on their characteristics. When an order is received from a user's client device, the system employs a machine learning model trained on historical data to estimate the number of available shoppers and their corresponding levels in the tree structure. The order is tagged based on the items it contains, and these tags are matched to the levels of capable shoppers. Shoppers are grouped and scored based on their levels, and a group with the minimum score and capability to fulfill the order is selected. A shopper from this group is then dispatched for order fulfillment, leading to a decrement in the estimated number of shoppers in the selected group. This approach optimizes the allocation of shoppers to orders, ensuring efficient and timely fulfillment.

Allocating shoppers for order fulfillment by an online concierge system accounting for variable numbers of shoppers across different time windows and varying capabilities for fulfilling orders

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.

Selectively providing machine learning model-based services

An online concierge system receives two types of orders, one of which requires fulfillment in a specific time interval, while the other can be fulfilled anytime up to a specific time interval. A machine learning model, trained on historical data about available shoppers in discrete time intervals, is used to predict how many shoppers will be available to fulfill orders in each time interval. For each time interval, the system retrieves the relevant orders of both types and creates candidate groups including orders of both types. For each group, the system determines a fulfillment cost based on items in the orders. The candidate group with the lowest cost is selected, and the orders in the selected group are sent to devices of available shoppers in that interval, prompting the shoppers to view and fulfill the orders.

Allocating shoppers and orders for fulfillment by an online concierge system to account for variable numbers of shoppers across different time windows

An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.

Adjusting demand for order fulfillment during various time intervals for order fulfillment by an online concierge system

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.

Generating a range of estimated fulfillment times for an order based on characteristics of an order

An online concierge system allows users to order items within discrete time intervals later than a time when an order was received. Each order may require a different set of characteristics for fulfilment by shoppers. Because different shoppers may have different capabilities, it is most efficient to reserve shoppers with specialized characteristics for orders that require them. The online concierge system maintains a set of hierarchical structures for different characteristics of shoppers, with each level in a hierarchical structure having a value. To select a shopper to fulfill an order, the online concierge system scores identifies groups of shoppers having characteristics capable of fulfilling the order based on levels in the hierarchical structure for each characteristic of a group. A shopper from a group having a minimum score is selected to fulfill the order.

An online concierge system allows users to order items within discrete time intervals later than a time when an order was received. The online concierge system allocates a specified percentage of an estimated number of shoppers during a discrete time interval to fulfilling orders received before the discrete time interval. An order may include a flag authorizing flexible fulfillment of the order along with a discrete time interval, which allows the order to be fulfilled earlier than the identified discrete time interval. The online concierge system generates groups of multiple orders authorizing flexible fulfillment and determines a cost for fulfilling different groups of orders. The online concierge system identifies a group of orders authorizing flexible fulfillment having a minimum cost for fulfillment by a shopper, allowing for more allocation of shoppers by enabling grouping of orders identifying different discrete time intervals.

An online concierge system displays an interface to a user identifying an estimated time of arrival for an order. To generate the estimated time of arrival for the order, the online concierge system trains a prediction engine to predict delivery time based on a predicted selection time for a shopper to select the order for fulfillment and predicted travel time for the shopper to deliver items of the order to a location identified by the order. The online concierge system generates a policy optimization model that computes an adjustment for the predicted delivery time. The adjustment is determined by solving a stochastic optimization problem with a constraint on a probability of the order being delivered after the estimated time of arrival. The predicted delivery time combined with the adjustment determines the estimated time of delivery displayed to the user to balance between minimizing late deliveries and wait times.

Determining estimated delivery time of items obtained from a warehouse for users of an online concierge system to reduce probabilities of delivery after the estimated delivery time

A method, system, and computer readable medium for automatically selecting target consumers and delivering business-initiated messages to the selected target consumers, as well as automatically selecting target businesses and delivering consumer-initiated messages to the selected target businesses. In particular, financial management applications used by the businesses and consumers are employed in targeting and delivering the business-initiated messages and consumer-initiated messages.

Real-time financial system ads sharing system

Computer-implemented methods, articles of manufacture and systems for identifying an on-line bank account that is a business account or utilized for business purposes. Keywords are determined and comparisons are performed with different types of financial account data and data from different resources. Comparison results are embodied within a data set that is generated and provided as an input to a rule generator such as a rule-based classifier. The rule-based classifier outputs a rule utilized to determine whether an account is a business account or utilized for business purposes, in contrast to a personal account utilize for non-business, personal or residential matters.

Systems methods and computer program products for identifying financial accounts utilized for business purposes

A method for selecting advertisements to embed in an online application, including detecting an access to the online application by a customer, determining, based on an impression tracker associated with the customer, and using an ad type selection model, an advertisement to embed in the online application. The advertisement is displayed to the user and the impression tracker associated with the customer is incremented. The method further includes receiving, from the user, a selection of the advertisement, storing a type of the advertisement and the value of the impression tracker as ad selection data, determining that the ad type selection model needs to be updated, and updating the ad type selection model based on the ad selection data to obtain an updated ad type selection model.

Method and system for selecting advertisements to minimize ad fatigue

A list of known names of entities is obtained along with entity name search data entered by searching parties in an attempt to identify one or more entities. The historical entity name search data entered by each individual searching party in a defined search time window is aggregated and analyzed to identify pairs of potentially related entity name searches that represent two attempts by the searching party to identify the same entity. The data representing the potentially related entity name searches is analyzed to identify a matched entity name in the list of known names that matches one of the entity names of the pair of potentially related entity name searches. Both of the entity names of the pair of potentially related entity name searches are then added to an alias list associated with the matched entity name.

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Sunnyvale, CA, United States of America

Houtao Deng