US20230419390A1 - Leveraging information about prior orders from various users associated with an account when receiving an order from a user associated with the account - Google Patents

Abstract

Multiple users of a household can each have a different profile associated with a common account for an online concierge system. This association of different profiles with the common accounts allows the concierge system to show a user of the household what other users of the household purchased and rank items for suggestion to the user based one purchases of other users, facilitating order building. The online concierge system also enables a user profile to designate a user profile associated with the common account for an order pickup or as a contact for a home delivery. Additionally, association of different user profiles with the common account may be used for account recovery of one of the user profiles. Further, different user profiles may have different permissions for creating an order with the online concierge system.

Description

CROSS REFERENCE TO RELATED APPLICATIONS
• This application is a continuation of co-pending U.S. application Ser. No. 16/911,381, filed Jun. 24, 2020, which is incorporated by reference in its entirety.
BACKGROUND
• This disclosure relates generally to users placing orders via an online concierge system, and more particularly to an online concierge system leveraging information for multiple user profiles associated with a common account with a user is placing an order via the account.
• In current online concierge systems, shoppers (or “pickers”) fulfill orders at a physical warehouse, such as a retailer, on behalf of users as part of an online shopping concierge service. A user adds goods to an online shopping cart from a list of goods offered by one or more retail locations via the online concierge system. These goods create the user's order, which is retrieved by a shopper at the retail location who “shops” for the user.
• Conventional online concierge systems receive orders from individual users and the online concierge systems fill orders for individual users. However, many orders placed through an online concierge system are for a household or other location including multiple users. Because conventional online concierge systems are configured for individual users, conventional online concierge systems force different users at a location including multiple users to place different orders for fulfillment to the location by the online concierge system. Additionally, the configuration for individual order fulfillment results limits information the online concierge system may access to recommend goods to different users at a location including multiple users. For example, conventional online concierge systems are unable to leverage information about orders placed by other users at the location including multiple users of the online concierge system to recommend goods for inclusion in an order to a user at the location including multiple users. This may increase an amount of time for a user to place an order through the online concierge system, or increase an amount of resources allocated by the online concierge system to fulfill multiple orders from different individual users at a common location.
SUMMARY
• An online concierge system generates an account associated with a user profile corresponding to a user of the online concierge system. In various embodiments, the online concierge system receives a request to generate the account from the user and generates the account in response to receiving the request from the user. Alternatively, the online concierge system prompts the user to generate the account in response to the online concierge system determining one or more conditions are satisfied. For example, in response to determining that a user profile of the user includes a physical location, such as an address, that is also included in a threshold number of other user profiles, the online concierge system transmits a prompt to the user to generate the account and generates the account in response to receiving a response to the prompt.
• The online concierge system obtains information identifying a plurality of user profiles to associate with the account and stores associations between each of the plurality of user profiles and the account. In various embodiments, the online concierge system stores associations between each of a plurality of user profiles corresponding to different users of the online concierge system and the account. The user requesting the online concierge system generate the account identifies other users to associate with the account in various embodiments, allowing the user initiating generation of the account to specify other users who are associated with the account by the online concierge system.
• When the online concierge system receives a request to create an order from a user corresponding to a user profile associated with the account, the online concierge system retrieves stored information identifying prior orders received from the user from whom the request was received. In various embodiments, the online concierge system stores information identifying prior orders in association with a user profile corresponding to the user from whom the request was received. The retrieved information identifies one or more items that were included in prior orders the online concierge system received from the user. In some embodiments, the online concierge system retrieves stored information identifying prior orders received form the user from whom the request was received and that satisfy one or more criteria. For example, the online concierge system retrieves information identifying prior orders the online concierge system received from the user from whom the request was received within a specific time interval (e.g., orders previously received from the user from whom the request was received within a specific time interval of a time when the request was received, orders previously received from the user from whom the request was received and fulfilled within a specific time interval of a time when the request was received).
• Additionally, the online concierge system retrieves stored information identifying items previously included in one or more previous orders received from each of a set of the other users corresponding to user profiles also associated with the account. Hence, the online concierge system retrieves stored information identifying previous orders received from different users having user profiles associated with the account. From the previous orders received from the other users with corresponding user profiles associated with the account, the online concierge system retrieves stored information identifying one or more items included in one or more previous orders received from the other users having corresponding user profiles associated with the account. In some embodiments, the online concierge system retrieves stored information identifying items included in at least a threshold amount (e.g., a threshold percentage, a threshold number) of orders previously received from one or more other users having corresponding user profiles associated with the account. As another example, the online concierge system retrieves stored information identifying items included in orders received from other users having user profiles associated with the account received within a threshold time interval (e.g., previously received within a specific time interval of a time when the online concierge system received the request to create the order). The online concierge system may account for items included in the request to create the order when retrieving stored information identifying items included in one or more previous orders received from other users having user profile associated with the account. For example, the online concierge system identifies an item included in the received request, identifies one or more previous orders from other users with user profiles associated with the account that include the item, and retrieves information identifying one or more additional items included in the identified previous orders from other users with user profiles associated with the account that include the item. This allows the online concierge system to identify additional items that different users with user profiles associated with the account included in previous orders that included the item included in the request to create the order.
• The online concierge system transmits an interface to a client device of the user from whom the request was received that displays information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account. This allows the user from whom the request was received to readily view additional items that other users with user profiles associated with the account previously included with orders, enabling the user to more readily include the additional items in the order. Leveraging information about prior orders from other users with user profiles associated with the account allows the online concierge system to simplify order creation by the user from whom the request was received by more readily identifying possibly relevant items from other users having user profiles associated with the account.
• In various embodiments, the interface transmitted by the online concierge system displays information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account in conjunction with information identifying one or more items included in prior orders received form the user from whom the request was received. For example, the interface displays information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account in a region of the interface and displays information identifying one or more items included in prior orders received form the user from whom the request was received in a different region of the interface. This allows the user from whom the request was received to easily identify items that the user previously included in orders, while also accessing items that other users with user profiles associated with the account previously included in orders.
• In addition to leveraging stored information describing orders previously received from other users with user profiles associated with the account, the online concierge system may allow the other users to modify the order requested by the user. For example, the online concierge system receives an additional request from another user, different than the user from whom the request was received, to include one or more additional items in the order and includes the one or more additional items in the order. The online concierge system may transmit notifications to client devices other users with user profiles associated with the account when the request to create the order was received to allow other users with user profiles associated with the account to include items in the order. The user from whom the request to create the order was received may include a close date for the order in the request or may otherwise specify a close date for the order, and the online concierge system adds additional items included in additional requests from other users with user profiles associated with the account that are received by the online concierge system before the close date and does not include additional items included in additional requests received by the online concierge system after the close date in the order.
• In various embodiments, the online concierge system maintains different permissions for association with user profiles associated with the account, where different permissions affect how a user corresponding to a user profile may modify an order requested by another user with a user profile associated with the account. The user from whom the request for an order may associate permissions with other user profiles associated with the account to regulate how other users may modify the order. For example, a permission allows a user associated with the account and other than the user from whom the request for the order was received to add items to the order, another permission allows a user associated with the account and other than the user from whom the request for the order was received to add or to remove items to the order, and another permission allows a user associated with the account and other than the user from whom the request for the order was received to complete the order, while another permission allows a user associated with the account and other than the user from whom the request for the order was received to modify logistical information for the order (e.g., a delivery address for the order, a delivery time for the order, etc.). In some embodiments, the online concierge system may maintain one or more rules that are applied to user profiles associated with the account to associate permissions with different user profiles associated with the account; the permissions associated with the user profiles by application of the rules may be default settings that different users with user profiles associated with the account modify.
• Additionally, the online concierge system allows the user from whom the request to create the order was received to identify an alternative user with a user profile associated with the account to receive subsequent communications about the order or to take delivery of the items in the order when the order is fulfilled by a shopper. Further, the online concierge system may use associations between different user profiles and the account to allow a user having a user profile associated with the account to access the account. For example, if a user with a user profile associated with the account is unable to provide authentication information for accessing the account to the online concierge system, the online concierge system transmits a request to a different user with a user profile associated with the account identifying the user and indicating the user is attempting to access the account. In response to the different user transmitting an authorization for the user to access the account to the online concierge system, the online concierge system authorizes the user to access the account.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG.1 illustrates an environment of an online shopping concierge service, according to one embodiment.
• FIG.2 is a diagram of an online shopping concierge system, according to one embodiment.
• FIG.3A is a diagram of a customer mobile application (CMA), according to one embodiment.
• FIG.3B is a diagram of a shopper mobile application (SMA), according to one embodiment.
• FIG.4 is a flowchart of a process for predicting inventory availability, according to one embodiment.
• FIG.5 is a flowchart of a process for updating training datasets for a machine-learned model, according to one embodiment.
• FIG.6 is a flowchart of a method for maintaining multiple user profiles associated with a common account at an online concierge system, according to one embodiment.
• FIG.7 is an example interface displayed by a customer mobile application including information about items from prior orders received from other users associated with an account, according to one embodiment.
• The figures depict embodiments of the present disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles, or benefits touted, of the disclosure described herein.
DETAILED DESCRIPTIONSystem Overview
• FIG.1 illustrates anenvironment100 of an online platform, according to one embodiment. The figures use like reference numerals to identify like elements. A letter after a reference numeral, such as “110a,” indicates that the text refers specifically to the element having that particular reference numeral. A reference numeral in the text without a following letter, such as “110,” refers to any or all of the elements in the figures bearing that reference numeral. For example, “110” in the text refers to reference numerals “110a” and/or “110b” in the figures.
• Theenvironment100 includes anonline concierge system102. Thesystem102 is configured to receive orders from one or more users104 (only one is shown for the sake of simplicity). An order specifies a list of goods (items or products) to be delivered to theuser104. The order also specifies the location to which the goods are to be delivered, and a time window during which the goods should be delivered. In some embodiments, the order specifies one or more retailers from which the selected items should be purchased. The user may use a customer mobile application (CMA)106 to place the order; theCMA106 is configured to communicate with theonline concierge system102.
• Theonline concierge system102 is configured to transmit orders received fromusers104 to one ormore shoppers108. Ashopper108 may be a contractor, employee, or other person (or entity) who is enabled to fulfill orders received by theonline concierge system102. Theshopper108 travels between a warehouse and a delivery location (e.g., the user's home or office). Ashopper108 may travel by car, truck, bicycle, scooter, foot, or other mode of transportation. In some embodiments, the delivery may be partially or fully automated, e.g., using a self-driving car. Theenvironment100 also includes three warehouses110a,110b, and110c(only three are shown for the sake of simplicity; the environment could include hundreds of warehouses). The warehouses110 may be physical retailers, such as grocery stores, discount stores, department stores, etc., or non-public warehouses storing items that can be collected and delivered to users. Eachshopper108 fulfills an order received from theonline concierge system102 at one or more warehouses110, delivers the order to theuser104, or performs both fulfillment and delivery. In one embodiment,shoppers108 make use of a shoppermobile application112 which is configured to interact with theonline concierge system102.
• FIG.2 is a diagram of anonline concierge system102, according to one embodiment. Theonline concierge system102 includes an inventory management engine202, which interacts with inventory systems associated with each warehouse110. In one embodiment, the inventory management engine202 requests and receives inventory information maintained by the warehouse110. The inventory of each warehouse110 is unique and may change over time. The inventory management engine202 monitors changes in inventory for each participating warehouse110. The inventory management engine202 is also configured to store inventory records in aninventory database204. Theinventory database204 may store information in separate records—one for each participating warehouse110—or may consolidate or combine inventory information into a unified record. Inventory information includes both qualitative and qualitative information about items, including size, color, weight, SKU, serial number, and so on. In one embodiment, theinventory database204 also stores purchasing rules associated with each item, if they exist. For example, age-restricted items such as alcohol and tobacco are flagged accordingly in theinventory database204. Additional inventory information useful for predicting the availability of items may also be stored in theinventory database204. For example, for each item-warehouse combination (a particular item at a particular warehouse), theinventory database204 may store a time that the item was last found, a time that the item was last not found (a shopper looked for the item but could not find it), the rate at which the item is found, and the popularity of the item.
• Inventory information provided by the inventory management engine202 may supplement thetraining datasets220. Inventory information provided by the inventory management engine202 may not necessarily include information about the outcome of picking a delivery order associated with the item, whereas the data within thetraining datasets220 is structured to include an outcome of picking a delivery order (e.g., if the item in an order was picked or not picked).
• Theonline concierge system102 also includes anorder fulfillment engine206 which is configured to synthesize and display an ordering interface to each user104 (for example, via the customer mobile application106). Theorder fulfillment engine206 is also configured to access theinventory database204 in order to determine which products are available at which warehouse110. Theorder fulfillment engine206 may supplement the product availability information from theinventory database204 with an item availability predicted by the machine-learneditem availability model216. Theorder fulfillment engine206 determines a sale price for each item ordered by auser104. Prices set by theorder fulfillment engine206 may or may not be identical to in-store prices determined by retailers (which is the price thatusers104 andshoppers108 would pay at the retail warehouses). Theorder fulfillment engine206 also facilitates transactions associated with each order. In one embodiment, theorder fulfillment engine206 charges a payment instrument associated with auser104 when he/she places an order. Theorder fulfillment engine206 may transmit payment information to an external payment gateway or payment processor. Theorder fulfillment engine206 stores payment and transactional information associated with each order in atransaction records database208.
• In some embodiments, theorder fulfillment engine206 also shares order details with warehouses110. For example, after successful fulfillment of an order, theorder fulfillment engine206 may transmit a summary of the order to the appropriate warehouses110. The summary may indicate the items purchased, the total value of the items, and in some cases, an identity of theshopper108 anduser104 associated with the transaction. In one embodiment, theorder fulfillment engine206 pushes transaction and/or order details asynchronously to retailer systems. This may be accomplished via use of webhooks, which enable programmatic or system-driven transmission of information between web applications. In another embodiment, retailer systems may be configured to periodically poll theorder fulfillment engine206, which provides detail of all orders which have been processed since the last request.
• Theorder fulfillment engine206 may interact with ashopper management engine210, which manages communication with and utilization ofshoppers108. In one embodiment, theshopper management engine210 receives a new order from theorder fulfillment engine206. Theshopper management engine210 identifies the appropriate warehouse to fulfill the order based on one or more parameters, such as a probability of item availability determined by a machine-learneditem availability model216, the contents of the order, the inventory of the warehouses, and the proximity to the delivery location. Theshopper management engine210 then identifies one or moreappropriate shoppers108 to fulfill the order based on one or more parameters, such as the shoppers' proximity to the appropriate warehouse110 (and/or to the user104), his/her familiarity level with that particular warehouse110, and so on. Additionally, theshopper management engine210 accesses ashopper database212 which stores information describing eachshopper108, such as his/her name, gender, rating, previous shopping history, and so on.
• As part of fulfilling an order, theorder fulfillment engine206 and/orshopper management engine210 may access a user database214 which stores information describing each user. This information could include each user's name, address, gender, shopping preferences, favorite items, stored payment instruments, and so on. In various embodiments, the user database214 includes a user profile for each user, with a user profile corresponding to a user including the user's name, address, gender, items included in one or more prior orders, payment instructions, or other information. A user profile corresponding to a user also includes information uniquely identifying the user.
• In various embodiments, the user database214 also includes one or more accounts and associations between user profiles and accounts. For example, an account corresponds to a physical location (e.g., an address) and is associated with one or more user profiles. As further described below in conjunction withFIG.6, a user may identify an account to theonline concierge system102 as well as other user profiles to associate with the account, and theonline concierge system102 stores associations between the account and other user profiles identified by the user. In various embodiments, the account includes a physical location (e.g., an address), delivery instructions for orders identifying the physical location, and other information for the physical location. As further described below in conjunction withFIG.6, theorder fulfillment engine206 may leverage associations between different user profiles and the account to retrieve information about previously received orders from multiple users with user profiles associated with the account. This allows theorder fulfillment engine206 to provide a user with a user profile associated with the account with items that other users with other user profiles associated with the account included in previously received orders, providing a user with a user profile associated with the account with additional recommendations for items to include in an order created by the user, as further described below in conjunction withFIG.6.
Machine Learning Model
• Theonline concierge system102 further includes a machine-learneditem availability model216, amodeling engine218, andtraining datasets220. Themodeling engine218 uses thetraining datasets220 to generate the machine-learneditem availability model216. The machine-learneditem availability model216 can learn from thetraining datasets220, rather than follow only explicitly programmed instructions. The inventory management engine202,order fulfillment engine206, and/orshopper management engine210 can use the machine-learneditem availability model216 to determine a probability that an item is available at a warehouse110. The machine-learneditem availability model216 may be used to predict item availability for items being displayed to or selected by a user, or included in received delivery orders. A single machine-learneditem availability model216 is used to predict the availability of any number of items.
• The machine-learneditem availability model216 can be configured to receive as inputs information about an item, the warehouse for picking the item, and the time for picking the item. The machine-learneditem availability model216 may be adapted to receive any information that themodeling engine218 identifies as indicators of item availability. At minimum, the machine-learneditem availability model216 receives information about an item-warehouse pair, such as an item in a delivery order and a warehouse at which the order could be fulfilled. Items stored in theinventory database204 may be identified by item identifiers. As described above, various characteristics, some of which are specific to the warehouse (e.g., a time that the item was last found in the warehouse, a time that the item was last not found in the warehouse, the rate at which the item is found, the popularity of the item) may be stored for each item in theinventory database204. Similarly, each warehouse may be identified by a warehouse identifier and stored in a warehouse database along with information about the warehouse. A particular item at a particular warehouse may be identified using an item identifier and a warehouse identifier. In other embodiments, the item identifier refers to a particular item at a particular warehouse, so that the same item at two different warehouses is associated with two different identifiers. For convenience, both of these options to identify an item at a warehouse are referred to herein as an “item-warehouse pair.” Based on the identifier(s), theonline concierge system102 can extract information about the item and/or warehouse from theinventory database204 and/or warehouse database, and provide this extracted information as inputs to theitem availability model216.
• The machine-learneditem availability model216 contains a set of functions generated by themodeling engine218 from thetraining datasets220 that relate the item, warehouse, and timing information, and/or any other relevant inputs, to the probability that the item is available at a warehouse. Thus, for a given item-warehouse pair, the machine-learneditem availability model216 outputs a probability that the item is available at the warehouse. The machine-learneditem availability model216 constructs the relationship between the input item-warehouse pair, timing, and/or any other inputs and the availability probability (also referred to as “availability”) that is generic enough to apply to any number of different item-warehouse pairs. In some embodiments, the probability output by the machine-learneditem availability model216 includes a confidence score. The confidence score may be the error or uncertainty score of the output availability probability, and may be calculated using any standard statistical error measurement. In some examples, the confidence score is based in part on whether the item-warehouse pair availability prediction was accurate for previous delivery orders (e.g., if the item was predicted to be available at the warehouse and not found by the shopper, or predicted to be unavailable but found by the shopper). In some examples, the confidence score is based in part on the age of the data for the item, e.g., if availability information has been received within the past hour, or the past day. The set of functions of theitem availability model216 may be updated and adapted following retraining withnew training datasets220. The machine-learneditem availability model216 may be any machine learning model, such as a neural network, boosted tree, gradient boosted tree or random forest model. In some examples, the machine-learneditem availability model216 is generated from XGBoost algorithm.
• The item probability generated by the machine-learneditem availability model216 may be used to determine instructions delivered to theuser104 and/orshopper108, as described in further detail below.
• Thetraining datasets220 relate a variety of different factors to known item availabilities from the outcomes of previous delivery orders (e.g. if an item was previously found or previously unavailable). Thetraining datasets220 include the items included in previous delivery orders, whether the items in the previous delivery orders were picked, warehouses associated with the previous delivery orders, and a variety of characteristics associated with each of the items (which may be obtained from the inventory database204). Each piece of data in thetraining datasets220 includes the outcome of a previous delivery order (e.g., if the item was picked or not). The item characteristics may be determined by the machine-learneditem availability model216 to be statistically significant factors predictive of the item's availability. For different items, the item characteristics that are predictors of availability may be different. For example, an item type factor might be the best predictor of availability for dairy items, whereas a time of day may be the best predictive factor of availability for vegetables. For each item, the machine-learneditem availability model216 may weight these factors differently, where the weights are a result of a “learning” or training process on thetraining datasets220. Thetraining datasets220 are very large datasets taken across a wide cross section of warehouses, shoppers, items, warehouses, delivery orders, times and item characteristics. Thetraining datasets220 are large enough to provide a mapping from an item in an order to a probability that the item is available at a warehouse. In addition to previous delivery orders, thetraining datasets220 may be supplemented by inventory information provided by the inventory management engine202. In some examples, thetraining datasets220 are historic delivery order information used to train the machine-learneditem availability model216, whereas the inventory information stored in theinventory database204 include factors input into the machine-learneditem availability model216 to determine an item availability for an item in a newly received delivery order. In some examples, themodeling engine218 may evaluate thetraining datasets220 to compare a single item's availability across multiple warehouses to determine if an item is chronically unavailable. This may indicate that an item is no longer manufactured. Themodeling engine218 may query a warehouse110 through the inventory management engine202 for updated item information on these identified items.
Machine Learning Factors
• Thetraining datasets220 include a time associated with previous delivery orders. In some embodiments, thetraining datasets220 include a time of day at which each previous delivery order was placed. Time of day may impact item availability, since during high-volume shopping times, items may become unavailable that are otherwise regularly stocked by warehouses. In addition, availability may be affected by restocking schedules, e.g., if a warehouse mainly restocks at night, item availability at the warehouse will tend to decrease over the course of the day. Additionally, or alternatively, thetraining datasets220 include a day of the week previous delivery orders were placed. The day of the week may impact item availability, since popular shopping days may have reduced inventory of items, or restocking shipments may be received on particular days. In some embodiments,training datasets220 include a time interval since an item was previously picked in a previously delivery order. If an item has recently been picked at a warehouse, this may increase the probability that it is still available. If there has been a long time interval since an item has been picked, this may indicate that the probability that it is available for subsequent orders is low or uncertain. In some embodiments,training datasets220 include a time interval since an item was not found in a previous delivery order. If there has been a short time interval since an item was not found, this may indicate that there is a low probability that the item is available in subsequent delivery orders. And conversely, if there is has been a long time interval since an item was not found, this may indicate that the item may have been restocked, and is available for subsequent delivery orders. In some examples,training datasets220 may also include a rate at which an item is typically found by a shopper at a warehouse, a number of days since inventory information about the item was last received from the inventory management engine202, a number of times an item was not found in a previous week, or any number of additional rate or time information. The relationships between this time information and item availability are determined by themodeling engine218 training a machine learning model with thetraining datasets220, producing the machine-learneditem availability model216.
• Thetraining datasets220 include item characteristics. In some examples, the item characteristics include a department associated with the item. For example, if the item is yogurt, it is associated with the dairy department. The department may be the bakery, beverage, nonfood and pharmacy, produce and floral, deli, prepared foods, meat, seafood, dairy, the meat department, or dairy department, or any other categorization of items used by the warehouse. The department associated with an item may affect item availability, since different departments have different item turnover rates and inventory levels. In some examples, the item characteristics include an aisle of the warehouse associated with the item. The aisle of the warehouse may affect item availability, since different aisles of a warehouse may be more frequently re-stocked than others. Additionally, or alternatively, the item characteristics include an item popularity score. The item popularity score for an item may be proportional to the number of delivery orders received that include the item. An alternative or additional item popularity score may be provided by a retailer through the inventory management engine202. In some examples, the item characteristics include a product type associated with the item. For example, if the item is a particular brand of a product, then the product type will be a generic description of the product type, such as “milk” or “eggs.” The product type may affect the item availability, since certain product types may have a higher turnover and re-stocking rate than others, or may have larger inventories in the warehouses. In some examples, the item characteristics may include a number of times a shopper was instructed to keep looking for the item after he or she was initially unable to find the item, a total number of delivery orders received for the item, whether or not the product is organic, vegan, gluten free, or any other characteristics associated with an item. The relationships between item characteristics and item availability are determined by themodeling engine218 training a machine learning model with thetraining datasets220, producing the machine-learneditem availability model216.
• Thetraining datasets220 may include additional item characteristics that affect the item availability, and can therefore be used to build the machine-learneditem availability model216 relating the delivery order for an item to its predicted availability. Thetraining datasets220 may be periodically updated with recent previous delivery orders. Thetraining datasets220 may be updated with item availability information provided directly fromshoppers108, as described in further detail with reference toFIG.5. Following updating of thetraining datasets220, amodeling engine218 may retrain a model with the updatedtraining datasets220, and produce a new machine-learneditem availability model216.
Customer Mobile Application
• FIG.3A is a diagram of the customer mobile application (CMA)106, according to one embodiment. TheCMA106 includes an orderinginterface302, which provides an interactive interface with which theuser104 can browse through and select products and place an order. TheCMA106 also includes asystem communication interface304 which, among other functions, receives inventory information from the onlineshopping concierge system102 and transmits order information to thesystem102. TheCMA106 also includes apreferences management interface306 which allows theuser104 to manage basic information associated with his/her account, such as his/her home address and payment instruments. Thepreferences management interface306 may also allow the user to manage other details such as his/her favorite or preferred warehouses110, preferred delivery times, special instructions for delivery, and so on.
Shopper Mobile Application
• FIG.3B is a diagram of the shopper mobile application (SMA)112, according to one embodiment. TheSMA112 includes abarcode scanning module320 which allows ashopper108 to scan an item at a warehouse110 (such as a can of soup on the shelf at a grocery store). Thebarcode scanning module320 may also include an interface which allows theshopper108 to manually enter information describing an item (such as its serial number, SKU, quantity and/or weight) if a barcode is not available to be scanned.SMA112 also includes abasket manager322 which maintains a running record of items collected by theshopper108 for purchase at a warehouse110. This running record of items is commonly known as a “basket”. In one embodiment, thebarcode scanning module320 transmits information describing each item (such as its cost, quantity, weight, etc.) to thebasket manager322, which updates its basket accordingly. TheSMA112 also includes asystem communication interface324 which interacts with the onlineshopping concierge system102. For example, thesystem communication interface324 receives an order from thesystem102 and transmits the contents of a basket of items to thesystem102. TheSMA112 also includes animage encoder326 which encodes the contents of a basket into an image. For example, theimage encoder326 may encode a basket of goods (with an identification of each item) into a QR code which can then be scanned by an employee of the warehouse110 at check-out.
Predicting Inventory Availability
• As described with reference toFIG.2, the machine-learneditem availability model216 of theonline concierge system102 can determine an availability of an item requested by theuser104.FIG.4 is a flowchart illustrating aprocess400 for predicting inventory availability, according to one embodiment. Theonline concierge system102 receives402 a delivery order that includes a set of items and a delivery location. The delivery location may be any location associated with a user, such as a user's home or office. The delivery location may be stored with the user location in the user database214. Based on the delivery order, theonline concierge system102 identifies awarehouse404 for picking the set of items in the delivery order based on the set of items and the delivery location. In some cases, the user specifies a particular warehouse or set of warehouses (e.g., a particular grocery store or chain of grocery stores) in the order. In other cases, theonline concierge system102 selects the warehouse based on the items and the delivery location. In some examples, there are a number of different possible warehouses that the set of items may be picked from. The warehouses may be identified by theorder fulfillment engine206 based on warehouses stored by the inventory management engine202, and warehouses are identified with a suitable inventory and within a threshold distance of the delivery address. In some embodiments, a single delivery order can be split into multiple orders and picked at multiple warehouses, e.g., if the items cannot be fulfilled at a single warehouse. In this example, each possible warehouse is input into the machine-learneditem availability model216.
• After the warehouses are identified, theonline concierge system102 retrieves406 the machine-learneditem availability model216 that predicts a probability that an item is available at the warehouse. The items in the delivery order and the identified warehouses are input into the machine-learneditem availability model216. For example, theonline concierge system102 may input the item, warehouse, and timing characteristics for each item-warehouse pair into the machine-learneditem availability model216 to assess the availability of each item in the delivery order at each potential warehouse at a particular day and/or time. The machine-learneditem availability model216 predicts408 the probability that one of the set of items in the delivery order is available at the warehouse. If a number of different warehouses are identified404, then the machine-learneditem availability model216 predicts the item availability for each one. In some examples, the probability that an item is available includes a probability confidence score generated by the machine-learneditem availability model216.
• Theorder fulfillment engine206 uses the probability to generate410 an instruction to a shopper. Theorder fulfillment engine206 transmits the instruction to the shopper through theSMA112 via theshopper management engine210. The instruction is based on the predicted probability. In some examples, theshopper management engine210 instructs the shopper to pick an item in the delivery order at a warehouse with the highest item availability score. For example, if a warehouse is more likely to have more items in the delivery order available than another warehouse, then theshopper management engine210 instructs the shopper to pick the item at the warehouse with better availability. Other examples of theshopper management engine210 instruction to the shopper are described in further detail with reference toFIGS.5 and6. In some other examples, theorder fulfillment engine206 sends a message and/or instruction to a user based on the probability predicted by the machine-learneditem availability model216.
Updating the Training Datasets
• FIG.5 is a flowchart illustrating aprocess500 for updating training datasets for a machine-learned model, according to one embodiment. The training datasets may be thetraining datasets220 as shown inFIG.2. While thetraining datasets220 include large datasets of information collected from previous delivery orders (e.g., information identifying items and whether the items were available at a warehouse), certain items or warehouses might have less information associated with them in thetraining datasets220 than other items or warehouses. For example, if an item is not frequently ordered, or has not been ordered for a long period of time, then it may be more difficult to build an accurate availability prediction in the machine-learneditem availability model216. One way to improve the ability of the machine-learneditem availability model216 to accurately predict item availability is to increase the information about the item in thetraining datasets220, and add new information. With larger and/or more recent datasets on the item, themodeling engine218 can build more statistically meaningful connections between the machine-learning factors described with reference toFIG.2 and the predicted item availability.
• Process500 thus improves the machine-learneditem availability model216 by increasing the datasets for particular items in thetraining datasets220 with low confidence scores.Process500 may be carried out by theonline concierge system102, e.g., by the inventory management engine202 in conjunction with theshopper management engine210, theitem availability model216, and themodeling engine218. In some examples,process500 is carried out by theonline concierge system102 following retrieving406 a machine-learned model that predicts a probability that an item is available at a warehouse, as described inFIG.4.
• The online concierge system102 (e.g., the inventory management engine202 using the item availability model216) identifies502 an item-warehouse pair. For example, the item and warehouse in the item-warehouse pair may be an item in a received order and warehouse or potential warehouse for picking the items from the received order, e.g., to evaluate the suitability of the warehouse or likelihood of successfully picking the order before the order is picked.
• As another example, the item-warehouse pair may be identified from items for which the availability predicted by the machine-learneditem availability model216 was incorrect (e.g., the item was predicted to be available and was determined by the shopper to be out of stock, or the item was predicted to be unavailable and the shopper was able to find it in the warehouse). For items for which the availability prediction was incorrect, theonline concierge system102 may determine if the items have sufficient associated information within thetraining datasets220. If theonline concierge system102 determines that the incorrect probability was a result of insufficient or stale information in thetraining datasets220, it may identify item-warehouse pairs and carry outprocess500 to update thetraining datasets220.
• Additionally, or alternatively, item-warehouse pairs are identified from new items offered by theonline concierge system102. For new items, there may not be previous delivery order information relating the item availability to item characteristics, delivery order information, or time information in thetraining datasets220. The lack of previous delivery orders may lead to a low confidence score for new items. The inventory management engine202 may initiate theprocess500 for new items until sufficient information about the items are collected in thetraining datasets220 to improve the item availability confidence score associated with the items.
• The online concierge system102 (e.g., the inventory management engine202 using the machine-learned item availability model216) inputs the item, warehouse, and timing characteristics for the identified item-warehouse pair into the machine-learneditem availability model216 and determines504 a confidence score associated with a probability that an item is available at the warehouse. Theonline concierge system102 may determine probabilities and/or confidence scores for all or selected items in an inventory, e.g., items that are expected to be picked based on already-received orders, sales, promotions, holidays, weather, historical trends, or other factors. The confidence score is generated along with the item availability probability (also referred to as “availability”) by the machine-learneditem availability model216. The confidence score may be an error associated with the availability probability. The confidence score indicates items that may not have enough training data in thetraining datasets220 to generate a statistically significant link between the item's availability and information from the delivery order and/or item characteristics. In some alternate embodiments, theonline concierge system102 may identify, using theitem availability model216, item-warehouse pairs with a low confidence score, e.g., all item-warehouse pairs with a confidence score below a particular threshold. This list of item-warehouse pairs may be filtered, e.g., based on item popularity, predicted items to be ordered, warehouse, or one or more other factors.
• In response to the determined confidence level of an item-warehouse pair being below a threshold, the online concierge system102 (e.g., the shopper management engine210) instructs506 the shopper to collect new information about items with a confidence score below a threshold. A confidence score threshold may be an item availability probability between 0 and 1. A threshold confidence score may be 0.3, such that in response to a confidence score below 0.3, the shopper is instructed to collect new information about an item. In some embodiments, theonline concierge system102 also considers the availability probability for the item-warehouse pair. For example, if an item-warehouse pair has a confidence level slightly below the threshold, but a very low or very high availability probability, theonline concierge system102 may determine not to collect new information about the item-warehouse pair. In some embodiments, the threshold used for the confidence score may depend on the availability probability, or vice versa.
• In response to the instruction, theshopper108 determines whether the item is available at the warehouse. The shopper may be instructed to try to find the item at the warehouse, and indicate, through theSMA112, whether the item is available. This information is transmitted to theonline concierge system102 via theshopper management engine210, and used to update508 thetraining datasets220. In some embodiments, a shopper may be given a list of items with low confidence scores to seek within the warehouse. Theonline concierge system102updates508 thetraining dataset220 with new information about the item, which includes whether or not the item is available in the warehouse, and any additional item characteristics, warehouse information, or time information as described with respect toFIG.2. Theonline concierge system102 also updates theinventory database204 based on the received information; e.g., if theinventory database204 stores the time at which the item was most recently found or not found, this time will be updated based on the input from theshopper108. In response to the new information collected by the shopper, themodeling engine218 may update or retrain the machine learningitem availability model216 with the updatedtraining datasets220.Process500 may be carried out by theonline concierge system102 until a confidence score associated with a probability that an item is available is above a threshold.
Use Case Examples
• An example ofprocess500 used in conjunction withprocess400 is described below. Theonline concierge system102 receives402 a delivery order from auser104 through theCMA106. Theuser104 schedules a delivery at their home of three items to be delivered the following day. As an example, theuser104 may order grated mozzarella, pizza dough, and tomato sauce, each of which is included in the delivery order. Theonline concierge system102 sends the delivery order to theorder fulfillment engine206. Theorder fulfillment engine206 uses the inventory management engine202 and user database214 to identify404 a warehouse for picking the requested items based on the items and the delivery location (i.e., the user's home). A number of possible warehouses may be identified. For each possible warehouse, theorder fulfillment engine206 identifies502 an item-warehouse pair with one of the items in the delivery order. Thus, a set of item-warehouse pairs is identified for each of the grated mozzarella, pizza dough and tomato sauce. Theonline concierge system102 retrieves406 the machine-learneditem availability model216 that predicts a probability that an item is available at the warehouse. Theonline concierge system102 inputs the item, warehouse, and timing characteristics for each of the identified item-warehouse pairs into the machine-learneditem availability model216. The machine-learneditem availability model216 predicts408 the probability that each of the grated mozzarella, pizza dough and tomato sauce are available at the identified warehouses. For each of the availability probabilities, theonline concierge system102 also determines504 a confidence score associated with the probability from the machine-learneditem availability model216.
• It is possible that the confidence score for pizza dough confidence score at one or more of the warehouses is below a threshold, given that people frequently make their own pizza dough and it may not be frequently ordered. Thus, pizza dough may have a relatively small and/or old associated dataset in thetraining dataset220, leading to a low confidence score on the pizza dough availability probability within the machine-learneditem availability model216. Theonline concierge system102, using theshopper management engine210, instructs506 a shopper to collect new information about pizza dough at one or more of the warehouses. Theshopper management engine210 may identify an off-duty shopper, or a shopper already at one of the warehouses identified502 in an item-warehouse pair to collect information about whether or not pizza dough is available at the warehouse. Theshopper management engine210 transmits this instruction through theSMA112. Theshopper108 may find that pizza dough is in fact available, and transmit the availability to theonline concierge system102 through theSMA112. This new information is used to update508 thetraining dataset220 and theinventory database204. Theshopper management engine210 may transmit the same instruction tomultiple shoppers108 at different warehouses, or at different times, such that there is a larger set of data about pizza dough availability added to thetraining dataset220, and more recent data in theinventory database204.
• In this example, themodeling engine218 uses the updatedtraining datasets220 to retrain the machine-learneditem availability model216. Theonline concierge system102 then re-inputs the pizza dough-warehouse pairs into the updated machine-learneditem availability model216 and determines504 a confidence score associated with the probability that pizza dough is available at a number of possible warehouses. It is possible that the confidence scores are now above a threshold, because the increased data about pizza dough added to thetraining datasets220 has improved the machine-learneditem availability model216, and/or the newer data in theinventory database204 has improved the confidence score. Theonline concierge system102 then generates410 an instruction to ashopper108 based on the availability probabilities for pizza dough. The instruction may be to pick the pizza dough at the warehouse with the highest availability probability. In other examples, the instruction may be to pick the pizza dough, grated mozzarella and tomato sauce at a warehouse with the highest availability probability for all of these items in the user's delivery order. Theonline concierge system102 transmits the instruction to a mobile device of theshopper108.
• Additionally, or alternatively, theonline concierge system102 may use the machine-learneditem availability model216 to predict an anticipated demand for an item at a warehouse. Theonline concierge system102 may compare the number of times an item is included in a set of delivery orders to the item availability predictions generated by the machine-learneditem availability model216, and identify items that are frequently ordered but have low corresponding availability probabilities. For example, around the holidays, there may be an increase in delivery orders including Brussels sprouts, whereas Brussels sprouts may have a low availability prediction since they are not typically stocked in large quantities. The online concierge system may identify the discrepancy between a large volume of item orders and the low availability probability and convey this information to a warehouse110. Additionally, or alternatively, theonline concierge system102 may transmit information about items that have availability predictions below a threshold.
• Leveraging Multiple User Profiles Associated with a Common Account for Order Configuration
• FIG.6 is a flowchart of one embodiment of a method for maintaining multiple user profiles associated with a common account at anonline concierge system102. The method may be carried out by the online concierge system102 (e.g., the order fulfillment engine206) communicating with a user via theCMA106. In various embodiments, the process performs the steps described in conjunction withFIG.6 in a different order than the order shown inFIG.6. Further, in some embodiments, the process includes different or additional steps than those described in conjunction withFIG.6.
• Theonline concierge system102 generates605 an account associated with a user profile corresponding to a user of theonline concierge system102. In various embodiments, theonline concierge system102 receives a request to generate the account from the user and generates605 the account in response to receiving the request from the user. Alternatively, theonline concierge system102 prompts the user to generate605 the account in response to theonline concierge system102 determining one or more conditions are satisfied. For example, in response to determining that a user profile of the user includes a physical location, such as an address, that is also included in a threshold number of other user profiles, theonline concierge system102 transmits a prompt to the user to generate605 the account and generates605 the account in response to receiving a response to the prompt.
• Theonline concierge system102 obtains information identifying a plurality of user profiles to associate with the account and stores610 associations between each of the plurality of user profiles and the account. In various embodiments, theonline concierge system102 stores610 associations between each of a plurality of user profiles corresponding to different users of theonline concierge system102 and the account. The user requesting theonline concierge system102 generate605 the account identifies other users to associate with the account in various embodiments, allowing the user initiating generation of the account to specify other users who are associated with the account by theonline concierge system102.
• When theonline concierge system102 receives615 a request to create an order from a user corresponding to a user profile associated with the account, theonline concierge system102 retrieves stored information identifying prior orders received from the user from whom the request was received615. In various embodiments, theonline concierge system102 stores information identifying prior orders in association with a user profile corresponding to the user from whom the request was received615. The retrieved information identifies one or more items that were included in prior orders theonline concierge system102 received from the user. In some embodiments, theonline concierge system102 retrieves stored information identifying prior orders received form the user from whom the request was received and that satisfy one or more criteria. For example, the online concierge system120 retrieves information identifying prior orders theonline concierge system102 received from the user from whom the request was received615 within a specific time interval (e.g., orders previously received from the user from whom the request was received615 within a specific time interval of a time when the request was received615, orders previously received from the user from whom the request was received615 and fulfilled within a specific time interval of a time when the request was received615).
• Additionally, theonline concierge system102 retrieves620 stored information identifying items previously included in one or more previous orders received from each of a set of the other users corresponding to user profiles also associated with the account. Hence, the online concierge system120 retrieves stored information identifying previous orders received from different users having user profiles associated with the account. From the previous orders received from the other users with corresponding user profiles associated with the account, theonline concierge system102 retrieves620 stored information identifying one or more items included in one or more previous orders received from the other users having corresponding user profiles associated with the account. In some embodiments, theonline concierge system102 retrieves620 stored information identifying items included in at least a threshold amount (e.g., a threshold percentage, a threshold number) of orders previously received from one or more other users having corresponding user profiles associated with the account. As another example, theonline concierge system102 retrieves620 stored information identifying items included in orders received from other users having user profiles associated with the account received within a threshold time interval (e.g., previously received within a specific time interval of a time when theonline concierge system102 received615 the request to create the order). Theonline concierge system102 may account for items included in the request to create the order when retrieving620 stored information identifying items included in one or more previous orders received from other users having user profile associated with the account. For example, theonline concierge system102 identifies an item included in the received request, identifies one or more previous orders from other users with user profiles associated with the account that include the item, and retrieves620 information identifying one or more additional items included in the identified previous orders from other users with user profiles associated with the account that include the item. This allows theonline concierge system102 to identify additional items that different users with user profiles associated with the account included in previous orders that included the item included in the request to create the order.
• Theonline concierge system102 transmits an interface to a client device of the user from whom the request was received615 that displays625 information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account. This allows the user from whom the request was received615 to readily view additional items that other users with user profiles associated with the account previously included with orders, enabling the user to more readily include the additional items in the order. Leveraging information about prior orders from other users with user profiles associated with the account allows theonline concierge system102 to simplify order creation by the user from whom the request was received615 by more readily identifying possibly relevant items from other users having user profiles associated with the account.
• In various embodiments, the interface transmitted by theonline concierge system102 displays625 information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account in conjunction with information identifying one or more items included in prior orders received form the user from whom the request was received615. For example, the interface displays625 information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account in a region of the interface and displays information identifying one or more items included in prior orders received form the user from whom the request was received615 in a different region of the interface. This allows the user from whom the request was received615 to easily identify items that the user previously included in orders, while also accessing items that other users with user profiles associated with the account previously included in orders. Alternatively, the interface has a common region displaying625 information identifying one or more of the identified items included in previous orders received from other users having user profiles associated with the account in a region of the interface information identifying one or more items included in prior orders received form the user from whom the request was received615 in a different region of the interface; however, the interface also displays a visual indication in conjunction with information identifying items that were included in a previous order received from another user having a user profile associated with the account (or displays a visual indication in conjunction with information identifying items that were included in a prior order from the user from whom the request was received615).
• FIG.7example interface700 displayed by a customermobile application106 including information about items from prior orders received from other users associated with an account. In the example ofFIG.7, theinterface700 includesinformation identifying items705,710,715 included in the order by a user who has a user profile associated with an account by theonline concierge system102. Additionally, theinterface700 includes aregion720 displayinginformation identifying items725,727 that the user included in prior orders received by theonline concierge system102 from the user. Theregion720 allows the user to more easily selectitems725,727 that the user has previously included in other orders in the current order.
• As further described above in conjunction withFIG.6, other user profiles corresponding to different users are also associated with the account by theonline concierge system102. Theonline concierge system102 retrieves stored information describing orders previously received from other users with user profiles associated with the account and retrieves information identifying items included in one or more of the orders previously received from the other users with user profiles associated with the account. Theinterface700 includes aregion730 displayinginformation identifying items735,737 that were included in orders previously received from the other users having user profiles associated with the account; as shown inFIG.7, theregion730 is different than theregion720 displayinginformation identifying items725,727 that the user creating the order included in prior orders. In some embodiments, theregion730 displays a specific number ofitems735,737 included in orders previously received from other users with user profiles associated with the account, while in other embodiments, theregion730 displays any suitable number ofitems735,737 that were included in orders previously received from other users with user profiles associated with the account.
• Referring back toFIG.6, in addition to leveraging stored information describing orders previously received from other users with user profiles associated with the account, theonline concierge system102 may allow the other users to modify the order requested by the user. For example, theonline concierge system102 receives an additional request from another user, different than the user from whom the request was received615, to include one or more additional items in the order and includes the one or more additional items in the order. Theonline concierge system102 may transmit notifications to client devices other users with user profiles associated with the account when the request to create the order was received615 to allow other users with user profiles associated with the account to include items in the order. The user from whom the request to create the order was received615 may include a close date for the order in the request or may otherwise specify a close date for the order, and theonline concierge system102 adds additional items included in additional requests from other users with user profiles associated with the account that are received by theonline concierge system102 before the close date. However, when a close date is specified for the order, theonline concierge system102 does not include additional items included in additional requests received by theonline concierge system102 after the close date in the order. This allows the user from whom the request to create the order was received615 to limit a length of time when other users with user profiles associated with the account may include additional items in the order.
• In various embodiments, theonline concierge system102 maintains different permissions for association with user profiles associated with the account, where different permissions affect how a user corresponding to a user profile may modify an order requested by another user with a user profile associated with the account. The user from whom the request for anorder615 may associate permissions with other user profiles associated with the account to regulate how other users may modify the order. For example, a permission allows a user associated with the account and other than the user from whom the request for the order was received615 to add items to the order, another permission allows a user associated with the account and other than the user from whom the request for the order was received615 to add or to remove items to the order, and another permission allows a user associated with the account and other than the user from whom the request for the order was received615 to complete the order, while another permission allows a user associated with the account and other than the user from whom the request for the order was received615 to modify logistical information for the order (e.g., a delivery address for the order, a delivery time for the order, etc.). In some embodiments, theonline concierge system102 may maintain one or more rules that are applied to user profiles associated with the account to associate permissions with different user profiles associated with the account; the permissions associated with the user profiles by application of the rules may be default settings that different users with user profiles associated with the account modify.
• Additionally, theonline concierge system102 allows the user from whom the request to create the order was received615 to identify an alternative user with a user profile associated with the account to receive subsequent communications about the order or to take delivery of the items in the order when the order is fulfilled by a shopper. For example, the user form whom the order was received615 provides a selection of an alternative user with a corresponding user profile associated with the account to theonline concierge system102, which stores an indication to contact the alternative user about the order in association with the order. This allows the user from whom the request to create the order was receive615 to identify an alternative user with a user profile associated with the account to receive the items in the order when a shopper fulfills the order.
• Further, theonline concierge system102 may use associations between different user profiles and the account to allow a user having a user profile associated with the account to access the account. For example, if a user with a user profile associated with the account is unable to provide authentication information for accessing the account to theonline concierge system102, theonline concierge system102 transmits a request to a different user with a user profile associated with the account identifying the user and indicating the user is attempting to access the account. In response to the different user transmitting an authorization for the user to access the account to theonline concierge system102, theonline concierge system102 authorizes the user to access the account. However, in response to the different user transmitting a rejection to access the account to theonline concierge system102, theonline concierge system102 prevents the user from accessing the account.
Additional Considerations
• The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.
• Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.
• Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
• Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible computer readable storage medium, which include any type of tangible media suitable for storing electronic instructions, and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
• Embodiments of the invention may also relate to a computer data signal embodied in a carrier wave, where the computer data signal includes any embodiment of a computer program product or other data combination described herein. The computer data signal is a product that is presented in a tangible medium or carrier wave and modulated or otherwise encoded in the carrier wave, which is tangible, and transmitted according to any suitable transmission method.
• Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

Claims (20)

What is claimed is:

1. A method comprising:

generating a group account at an online concierge system, the group account associated with a plurality of user profiles, each user profile corresponding to a different user of the online concierge system;

receiving, from one or more users associated with the group account, a request to create a group order including one or more items;

receiving a request to modify an item in the group order from a user having a user profile associated with the group account, the user is different from the one or more users that sent the request to create the group order;

accessing a set of user permissions for the group account;

determining, based on the set of user permissions, whether the user is permitted to modify the order; and

modifying the group order in response to the user being permitted to modify the group order.

2. The method ofclaim 1, further comprising:

receiving a selection of an alternative user having a corresponding user profile associated with the group account; and

storing an indication to contact the alternative user regarding fulfillment of the group order at the online concierge system.

3. The method ofclaim 1, wherein the online concierge system maintains different user permissions for different user profiles associated with the group account, and the set of user permissions include permissions regulating modifications to the group order by users having corresponding user profiles associated with the group account.

4. The method ofclaim 1, further comprising:

receiving a notification from a first user having a first user profile associated with the group account, the notification indicating that the first user does not have authentication information for accessing the group account; and

transmitting, to a second user, a request for the authentication information, the second user having a second user profile associated with the group account that is different from the first user profile of the first user.

5. The method ofclaim 4, further comprising:

responsive to the second user transmitting the authentication information, authorizing the first user to access the group account.

6. The method ofclaim 1, wherein the item in the group order was added by a different user from the user from whom the request to modify the item in the group order was received.

7. The method ofclaim 1, further comprising:

displaying an interface to the one or more users from whom the request to create a group order user was received.

8. The method ofclaim 7, wherein the interface comprises one or more user interface elements allowing the one or more users to modify the one or more items in the group order.

9. The method ofclaim 1, further comprising:

identifying items included in at least a threshold amount of previous orders received from the plurality of users associated with the group account; and

selecting one or more of the identified items for display.

10. The method ofclaim 1, further comprising:

identifying items included in previous orders received from the plurality of users associated with the group account within a threshold time interval from when the request to create the group order was received; and

selecting one or more of the identified items for display.

11. A method comprising:

generating a group account at an online concierge system, the group account associated with a plurality of user profiles, each user profile corresponding to a different user of the online concierge system;

receiving a request to create an order including one or more items from a user with a user profile associated with the group account;

retrieving stored information identifying items previously included in one or more previous orders received from each of the plurality of users associated with the group account;

selecting one or more of the identified items previously included in one or more previous orders received from each of the plurality of users for display;

displaying an interface to the user from whom the request was received;

transmitting a notification of the order from the online concierge system to client devices associated with each of the plurality of users associated with the group account;

receiving an additional request to include one or more additional items in the order from another user having a corresponding user profile associated with the group account, the another user is different from the user from whom the request to create the order was received; and

including the one or more additional items in the order in response to determining the online concierge system received the additional request before a close date for the order specified by the user from whom the request to create the order was received.

12. The method ofclaim 11, further comprising:

receiving a selection of an alternative user having a corresponding user profile associated with the group account; and

storing an indication to contact the alternative user regarding fulfillment of the order at the online concierge system.

13. The method ofclaim 11, wherein the online concierge system maintains different permissions for different user profiles associated with the group account, one or more of the permissions regulating modifications to the order by another user who is different from the user from whom the request to create the order was received.

14. The method ofclaim 11, further comprising:

receiving a notification from a first user having a first user profile associated with the group account, the notification indicating that the first user does not have authentication information for accessing the order; and

transmitting, to a second user, a request for the authentication information, the second user having a second user profile associated with the group account, wherein the second user profile is different from the first user profile.

15. The method ofclaim 14, further comprising:

responsive to the second user transmitting the authentication information, authorizing the first user to access the order.

16. The method ofclaim 11, wherein displaying an interface to the user from whom the request was received comprises:

displaying information identifying one or more of the identified items previously included in one or more previous orders received from other users in a different region of the interface than a region of the interface displaying the information identifying one or more items previously included in one or more previous orders received form the user from whom the request to create the order was received.

17. The method ofclaim 11, wherein displaying an interface to the user from whom the request was received comprises:

displaying an indication in conjunction with each of the identified items that an identified item was included in a previous order received from another user having a user profile different from the user from whom the request was received.

18. The method ofclaim 11, wherein retrieving stored information identifying items previously included in one or more previous orders received from each of the plurality of users associated with the group account comprises:

retrieving the stored information identifying items included in at least a threshold amount of orders previously received from one or more of the plurality of users.

19. The method ofclaim 11, wherein retrieving stored information identifying items previously included in one or more previous orders received from each of the plurality of users associated with the group account comprises:

retrieving the stored information identifying items included in orders previously received from one or more of the plurality of users within a threshold time interval from when the request was received.

20. A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause a processor to:

generate a group account at an online concierge system, the group account associated with a plurality of user profiles, each user profile corresponding to a different user of the online concierge system;

receive, from one or more users associated with the group account, a request to create a group order including one or more items;

receive a request to modify an item in the group order from a user having a user profile associated with the group account, the user is different from the one or more users that sent the request to create the group order;

access a set of user permissions for the group account;

determine, based on the set of user permissions, whether the user is permitted to modify the order; and

modify the group order in response to the user being permitted to modify the group order.

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