AUTOMATION ENGINE USING A HYPERPARAMETER LEARNING MODEL TO OPTIMIZE SUB-SYSTEMS OF AN ONLINE SYSTEM

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

BACKGROUND

This disclosure relates generally to an online concierge system and more specifically to a marketplace automation engine for coordinating between independent sub-systems that control various marketplace parameters associated with an online concierge system.

In an online concierge system, a computing environment facilitates a process through which shoppers fulfill and deliver orders for customers at a physical retailer. The online concierge system creates a complex marketplace with many factors affecting the efficiency of operation. For example, the marketplace is affected by the number of orders being requested, the cost to the customers, the estimated and actual arrival times, the availability of shoppers, compensation or other benefits provided to shoppers, the assignment of shoppers to orders, the availability of items at the retailers, the incentives provided to retailers for participation, and numerous other factors. While the online concierge system can configure various parameters that affect the marketplace and encourage a certain outcome, the control parameters often have complex interdependencies that result in a challenging optimization problem.

SUMMARY

An online concierge system facilitates processing of requests, via a customer application, for procurement of one or more items from one or more warehouses, assignment of requests to available shoppers via a shopper application, and generation of routing instructions for delivery of the one or more items by the shoppers to the respective customers. The online concierge system includes a marketplace automation engine for configuring various control parameters affecting marketplace operation in a coordinated manner, according to a configurable outcome objective. The marketplace automation engine obtains marketplace state data associated with the online concierge system. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies a set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to control operation of the online concierge system.

In an embodiment, the marketplace automation engine furthermore logs the marketplace state data over a period of time and re-trains the hyperparameter learning model based on the stored marketplace state data.

In an embodiment, re-training the hyperparameter learning model comprises obtaining, from the marketplace state data, a set of marketplace inputs associated with the period of time, obtaining, from the marketplace state data, a set of marketplace outcomes associated with the period of time, deriving an outcome objective value from the set of marketplace outcomes, applying an optimizer to determine a set of optimal hyperparameters that optimize the outcome objective under the set of marketplace inputs, and training the hyperparameter learning model to learn relationships between the marketplace state data for the period of time and the set of optimal hyperparameters for the marketplace state data.

In an embodiment, deriving the outcome objective comprises computing a real-time outcome objective associated with the set of marketplace outcomes.

In another embodiment, deriving the outcome objective comprises simulating operation of the marketplace to compute a delayed outcome objective associated with the set of marketplace outcomes.

In an embodiment, applying the optimizer comprises computing respective outcome objective values over varying combinations of hyperparameters, and identifying the set of optimal hyperparameters based on an optimal outcome objective value of the respective outcome objective values.

In an embodiment, training the hyperparameter learning model comprises applying an offline reinforcement learning algorithm.

In an embodiment, the different control parameters include at least one of an estimated-time-of-arrival (ETA) associated with a proposed delivery request for a customer of the online concierge system, a surge pricing level for setting a surge fee associated with a proposed delivery request using the online concierge system, a shopper promotion level for setting a shopper promotion amount to encourage additional shoppers to become available for requests in the online concierge system, and a batching decision parameter for assigning multiple requests from different customers to a single shopper in the online concierge system.

In an embodiment, applying the set of parameterized control decision models comprises applying for each of the control parameters, a machine learning model trained to learn relationships between a corresponding control parameter and a local objective associated with the corresponding control parameter under the set of hyperparameters.

DETAILED DESCRIPTION

System Architecture

FIG.1is a block diagram of a system environment100in which an online system operates, such as an online concierge system102as further described below in conjunction withFIGS.2and3. The system environment100shown byFIG.1comprises one or more client devices110, a network120, one or more third-party systems130, and the online concierge system102. In alternative configurations, different and/or additional components may be included in the system environment100.

The client devices110are one or more computing devices capable of receiving user input as well as transmitting and/or receiving data via the network120. In one embodiment, a client device110is a computer system, such as a desktop or a laptop computer. Alternatively, a client device110may be a device having computer functionality, such as a personal digital assistant (PDA), a mobile telephone, a smartphone, or another suitable device. A client device110is configured to communicate via the network120. In one embodiment, a client device110executes an application allowing a user of the client device110to interact with the online concierge system102. For example, the client device110executes a customer mobile application206or a shopper mobile application212, as further described below in conjunction withFIGS.8A and8B, respectively, to enable interaction between the client device110and the online concierge system102. As another example, a client device110executes a browser application to enable interaction between the client device110and the online concierge system102via the network120. In another embodiment, a client device110interacts with the online concierge system102through an application programming interface (API) running on a native operating system of the client device110, such as IOS® or ANDROID™.

A client device110includes one or more processors112configured to control operation of the client device110by performing functions. In various embodiments, a client device110includes a memory114comprising a non-transitory storage medium on which instructions are encoded. The memory114may have instructions encoded thereon that, when executed by the processor112, cause the processor to perform functions to execute the customer mobile application206or the shopper mobile application212to provide the functions further described above in conjunction withFIGS.8A and8B, respectively.

One or more third party systems130may be coupled to the network120for communicating with the online concierge system102or with the one or more client devices110. In one embodiment, a third party system130is an application provider communicating information describing applications for execution by a client device110or communicating data to client devices110for use by an application executing on the client device110. In other embodiments, a third party system130provides content or other information for presentation via a client device110. For example, the third party system130stores one or more web pages and transmits the web pages to a client device110or to the online concierge system102. The third party system130may also communicate information to the online concierge system102, such as advertisements, content, or information about an application provided by the third party system130.

The online concierge system102includes one or more processors142configured to control operation of the online concierge system102by performing functions. In various embodiments, the online concierge system102includes a memory144comprising a non-transitory storage medium on which instructions are encoded. The memory144may have instructions encoded thereon corresponding to the modules further described below that, when executed by the processor142, cause the processor to perform the functionality further described herein. For example, the memory144has instructions encoded thereon that, when executed by the processor142, cause the processor142to facilitate operation of the online concierge system102in accordance with a marketplace automation engine that controls various parameters affecting marketplace operation in a coordinated way. Additionally, the online concierge system102includes a communication interface configured to connect the online concierge system102to one or more networks, such as network120, or to otherwise communicate with devices (e.g., client devices110) connected to the one or more networks.

One or more of a client device110, a third party system130, or the online concierge system102may be special purpose computing devices configured to perform specific functions, as further described below in conjunction withFIGS.2-8A/B, and may include specific computing components such as processors, memories, communication interfaces, and/or the like.

System Overview

FIG.2illustrates an environment200of an online platform, such as an online concierge system102, according to one or more embodiments. The figures use like reference numerals to identify like elements. A letter after a reference numeral, such as “210a,” 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 “210,” refers to any or all of the elements in the figures bearing that reference numeral. For example, “210” in the text refers to reference numerals “210a” or “210b” in the figures.

The environment200includes an online concierge system102. The online concierge system102is configured to receive orders from one or more users204(only one user204is shown for the sake of simplicity). An order specifies a list of goods (items or products) to be delivered to the user204. 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)206to place the order; the CMA206is configured to communicate with the online concierge system102.

The online concierge system102is configured to transmit orders received from users204to one or more shoppers208. A shopper208may be a contractor, employee, other person (or entity), robot, or other autonomous device enabled to fulfill orders received by the online concierge system102. The shopper208travels between a warehouse and a delivery location (e.g., the user's home or office). A shopper208may 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. The environment200also includes three warehouses210a,210b, and210c(only three are shown for the sake of simplicity; the environment could include hundreds of warehouses). The warehouses210may 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. Each shopper208fulfills an order received from the online concierge system102at one or more warehouses210, delivers the order to the user204, or performs both fulfillment and delivery. In one embodiment, shoppers208make use of a shopper mobile application212which is configured to interact with the online concierge system102.

FIG.3is a diagram of an online concierge system102, according to one or more embodiments. In various embodiments, the online concierge system102may include different or additional modules than those described in conjunction withFIG.3. Further, in some embodiments, the online concierge system102includes fewer modules than those described in conjunction withFIG.3.

The online concierge system102includes a management engine350and a marketplace automation engine360. The management engine350controls various management functions of the online concierge system102such as managing participating users204, warehouses210, and shoppers208, tracking inventory and pricing, maintaining transaction records, and managing order fulfillment. An example embodiment of a management engine350is described in further detail below with respect toFIGS.8A-B. The marketplace automation engine360controls various operational parameters that affect that overall marketplace by incentivizing or disincentivizing participation from users204, shoppers208, and warehouses210, controlling pricing parameters, estimating arrival times, or other operational parameters that may affect high-level performance of the online concierge system102. The marketplace automation engine360may operate to optimize certain high-level business objectives such as profitability, conversion rate, late arrival rate (of deliveries), or other objectives. An example embodiment of a marketplace automation engine360is described in further detail below with respect toFIG.4.

Marketplace Automation Engine

FIG.4is an example embodiment of a marketplace automation engine360. The marketplace automation engine360includes an inference engine402, a model training engine406, and a parameter decision sub-system422including a set of parameter decision engines404(e.g., parameter decision engines404-1,404-2, . . . ,404-N) and a set of parameter decision models420(e.g., parameter decision models420-1,420-2, . . . ,420-N). Alternative embodiments of the marketplace automation engine360may comprise different or additional modules.

The set of parameter decision engines404are each associated with a different control parameter418that affects marketplace operation. For example, the control parameters418may directly affect or indirectly influence participation of customers, shoppers, and/or warehouses in the online concierge system102, may affect efficiency of order fulfillment, and/or may affect various high-level business objectives such as profitability, conversion rate, late arrival rate, etc. Examples of control parameters may include estimated times of arrival (ETAs) provided to a user204in association with a prospective or pending order, a level of surge pricing or discount pricing to be charged to users204in association with a prospective order, an availability schedule specifying fulfillment time windows for advance orders, shopper promotion levels indicating promotional benefits awarded to shoppers, batching decision parameters controlling assignment of orders from multiple users204to a shopper208, matching decision parameters controlling how orders are matched to shoppers208, item pricing parameters controlling pricing of individual items, or other control parameters that may affect marketplace operation. The marketplace automation engine360configures the various control parameters418in a coordinated way to achieve one or more marketplace objectives such as, for example, maximizing conversions, maximizing profit or revenue, minimizing late deliveries (i.e., arrivals after the ETA), or a combination thereof.

Each parameter decision engine404may independently set one of the respective control parameters418based on an independent parameter decision model420specific to the control parameter418generated by the corresponding parameter decision engine404, the current marketplace state408, and a set of hyperparameters410that are applied globally. Here, the marketplace state416represents a set of observable (or predictable) state variables collectively describing the marketplace. The marketplace state416may be characterized, for example, by measures of supply and demand (e.g., number of orders, number of active users204, number of participating shoppers208, etc.), various performance measurements (e.g., number of orders in a given time period, conversion rate indicative of a number of users that place an order within a given time period, late percentage indicative of a number of orders that arrive after the ETA, user satisfaction metrics, shopper satisfaction metrics, etc.), various financial metrics (e.g., revenue, profit, expenses, etc.), a current configuration of current control parameter settings, external factors (e.g., time of day, day of week, time of year, etc., weather, etc.), or other variables characterizing a marketplace. A marketplace state416may be localized to a specific time window (e.g., last few hours, last few days, last month) and/or to a specific geographic region.

In an embodiment, each parameter decision engine404applies an optimization function that optimizes a weighted combination of parameterized sub-functions in which the hyperparameters414specify the weights. For example, a parameter decision model420for determining a target delivery time (ETA) may set the ETA based on a function specified as:

where the sub-functions pCVR(eta) represents a predicted conversion likelihood associated with a candidate ETA, pCost(eta) represents a cost associated with achieving the ETA, and pLate(eta) represents a predicted likelihood of an order an order arriving late associated with the candidate ETA. The max operator is a function that finds the candidate ETA resulting in the maximum combination, where the sub-functions are each weighted by the hyperparameters a, b, c respectively.

The parameterized sub-functions (e.g., pCVR(eta), pCost(eta), pLate (eta)) may be computed based on respective machine learning models trained based on historical or simulated marketplace state data associated with the online concierge system102. For example, a machine learning model associated with the pCVR(eta) sub-function may be trained to predict the conversion likelihood for given marketplace state data416and a specified ETA. Machine learning models may be similarly trained to predict cost, lateness, or other outcomes represented by the various sub-functions. Various types of models may be used for implementing each sub-function such as, for example, reinforcement learning models, regression models, multi-arm bandit models, or other types of machine learning models. In further embodiments, the parameterized sub-functions may be directly computed without necessarily using machine learning based on, for example, linear or nonlinear combination functions.

While the above examples are exemplary, each parameter decision model420may be based on a different set of parameterized sub-functions. Some of the sub-functions may be shared between different parameter decision engines404while other sub-functions may be unique. Furthermore, different hyperparameters (weights) may be associated with the sub-functions in the different parameter decision models420.

The outputs of the parameter decision engines404are respective individual control parameters418that are applied by the online concierge system102and may directly or indirectly affect a future marketplace state416together with external marketplace factors (as indicated by the dashed line).

The inference engine402generates the hyperparameters414(e.g., a, b, and c in the example above) based on the marketplace state416and a hyperparameter learning model412. The inference engine402and hyperparameter learning model412operate to predict the hyperparameters414to apply for the current marketplace state416that optimizes an objective function built into the model412. The objective function may be designed to predict hyperparameters414based on a high-level business objective (or combination thereof) such as profit, conversion rate, frequency of late deliveries, or other marketplace performance metrics.

The model training engine406trains the hyperparameter learning model412according to one or more machine learning algorithms. The model training engine406may train the hyperparameter learning model412based on reinforcement learning techniques, multi-arm bandit techniques, regression techniques, or other machine learning techniques. In an embodiment, the model training engine406operates in an offline manner. Here, the model training engine406accumulates marketplace state data416over a period of time and then retrains the hyperparameter learning model412after the period is complete using the accumulated marketplace state data416. In this way, the model training engine406may include outcomes in the learning process that may be delayed relative to changes in inputs. An example embodiment of a model training engine406is described in further detail below with respect toFIG.5.

The described marketplace automation engine360may include individual parameter decision engines404that operate agnostically to the outputs of other parameter decision engines404. In other words, the operation of one parameter decision engine404to compute a control parameter418under a given marketplace state416does not directly depend on the output of another parameter decision engine404. The marketplace automation engine322is therefore highly scalable and enables parameter decision engines404for different parameter decision engines404for different control parameters418to be added, removed, or modified without affecting operation of other parameter decision engines404or other system components. Moreover, the parameter decision engines404may perform computations in parallel with each other without one engine404requiring an output from a different engine404to perform its decision.

While each parameter decision engine404is designed to operate independently, the parameter decision engines404nevertheless operate to collectively generate a set of coordinated control parameters418that operate together to achieve a particular marketplace objective. This coordination is achieved, in part, because the parameter decision engines404are each parameterized using a coordinated set of sub-functions and a common set of hyperparameters414that are trained based on the overall objective function.

In an example scenario illustrative of the coordination between parameter decision engines404, changes to different individual control parameters418could achieve similar effects on the objective function. For example, both extending ETA and decreasing shopper promotions may operate to decrease demand. If left to run independently, the respective optimization engines404may each adjust their respective control parameters418to decrease demand, but the combined effect may overcompensate or lead to other undesirable results. In contrast, under the described marketplace automation engine360, the hyperparameters414function to provide coordination between different control parameters418based on the predicted combined effect. For example, under the above example scenario, hyperparameters414may be set to extend ETA without decreasing shopper promotions so as to achieve a decrease in demand without overcompensation. In practice, coordination between parameter decision engines404may be more complex. For example, rather than operating in a binary manner to turn on or off different parameter decision engine404, the hyperparameters414may operate to fine tune each of the different control parameters418such that their contributions have the intended combined effect on the marketplace state416.

FIG.5is an example embodiment of a model training engine406associated with a marketplace automation engine360. The model training engine406includes an objective evaluator508, an optimizer505, and a marketplace state data store502. The marketplace state data store502stores historical marketplace state data accumulated over a time period (e.g., hours, days, weeks, months, or years) and/or simulated marketplace state data derived from a simulation of the marketplace. The marketplace state data502may include marketplace inputs504representing various factors that control operation of the marketplace and marketplace outcomes506representing various observable characteristics of the marketplace. Examples of marketplace inputs504may include real-time supply indicators, real-time demand indicators, throughput, trailing lates, trailing cost, and trailing conversion. Examples of marketplace outcomes506may include conversion rate, cost, and late percentage.

The objective evaluator508generates an objective value510from the set of marketplace outputs506. In an embodiment, the objective evaluator508applies a function related to evaluating high-level business metrics such as overall profits, on-time deliveries, marketplace participation, etc. In an embodiment, the function computes a weighted combination of the marketplace outputs506to generate a real-time objective value510associated with corresponding marketplace outcomes506. Alternatively, the objective evaluator508may apply a machine learning model that predicts an objective value510based on the marketplace outputs506. Here, the machine learning model applied by the objective evaluator508may be trained on historical or simulated marketplace data to predict a relationship between observable real-time state data and corresponding real-time observed objective values. In other embodiments, the objective value510may include outcomes that are delayed relative to the marketplace state502, i.e., the outcome(s) associated with a given set of inputs are not necessarily observable in real-time. In this case, the objective evaluator508may include a simulator to simulate the marketplace state502over time and predict a future objective value510associated with a current marketplace state502. The simulator may comprise, for example, a stochastic model that predicts for each of a series of time steps, actions taken by the parameter decision engines404under a current state, and an effect of those actions on a subsequent marketplace state.

The optimizer505obtains the marketplace inputs504associated with a given time period and the derived objective values510associated with the corresponding time periods and determines a set of optimized hyperparameters506predicted to optimize the objective value510under the corresponding market state502. In an embodiment, the optimizer505may operate by actively varying the hyperparameters506under a constant marketplace state502to determine which combination of hyperparameters506results in a maximized objective value510. For example, for a small hyperparameter space, the optimizer505may observe the objective value510for each different combination of hyperparameters to determine the optimal combination that maximizes the objective value510. In other embodiments, the optimizer505may apply a nonlinear optimization function to compute an optimal combination of hyperparameters. The optimal hyperparameters506may then be stored as labels associated with the corresponding marketplace state502.

A learning engine512applies a machine learning algorithm to learn relationships between a marketplace state502and its corresponding optimized hyperparameters506and represent the learned relationships in the hyperparameter learning model412. The learning engine512may train the hyperparameter learning model412based on reinforcement learning techniques, regression techniques, multi-arm bandit techniques, or other machine learning techniques.

FIG.6is a flowchart of one embodiment of a method for operating a marketplace automation engine360. A marketplace automation engine360obtains605marketplace state data associated with operation of an online concierge system102. The marketplace automation engine360predicts610a set of hyperparameters for achieving an objective based on the marketplace state data and a machine learning model (e.g., the above-described hyperparameter learning model412). The marketplace automation engine360independently determines615a plurality of control parameters based on the hyperparameters and respective independent control parameter decision models420. The marketplace automation engine360logs620the state of the online concierge system102as it changes based on the updated control parameters, observed changes in marketplace input data, and observed outcomes relevant to the objective. The marketplace automation engine360may further update625the machine learning model (e.g., the hyperparameter learning model412) based on the historical marketplace states. The updates may occur in an offline process that executes on an accumulated set of states observed over a time window.

In various embodiments, the method includes different or additional steps than those described in conjunction withFIG.6. Further, in some embodiments, the steps of the method may be performed in different orders than the order described in conjunction withFIG.6. The method described in conjunction withFIG.6may be carried out by the online concierge system102in various embodiments, while in other embodiments, the steps of the method are performed by any online system capable of performing the steps described herein.

Management Engine

As shown inFIG.7, the management engine350includes an inventory management engine302, which interacts with inventory systems associated with each warehouse210. In one embodiment, the inventory management engine702requests and receives inventory information maintained by the warehouse210. The inventory of each warehouse210is unique and may change over time. The inventory management engine702monitors changes in inventory for each participating warehouse210. The inventory management engine702is also configured to store inventory records in an inventory database704. The inventory database704may store information in separate records—one for each participating warehouse210—or may consolidate or combine inventory information into a unified record. Inventory information includes attributes of items that include both qualitative and qualitative information about items, including size, color, weight, SKU, serial number, and so on. In one embodiment, the inventory database704also stores purchasing rules associated with each item, if they exist. For example, age-restricted items such as alcohol and tobacco are flagged accordingly in the inventory database704. Additional inventory information useful for predicting the availability of items may also be stored in the inventory database704. For example, for each item-warehouse combination (a particular item at a particular warehouse), the inventory database704may 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.

For each item, the inventory database704identifies one or more attributes of the item and corresponding values for each attribute of an item. For example, the inventory database704includes an entry for each item offered by a warehouse210, with an entry for an item including an item identifier that uniquely identifies the item. The entry includes different fields, with each field corresponding to an attribute of the item. A field of an entry includes a value for the attribute corresponding to the attribute for the field, allowing the inventory database704to maintain values of different categories for various items.

In various embodiments, the inventory management engine702maintains a taxonomy of items offered for purchase by one or more warehouses210. For example, the inventory management engine702receives an item catalog from warehouse210identifying items offered for purchase by warehouse210. From the item catalog, the inventory management engine determines a taxonomy of items offered by the warehouse210in which different levels in the taxonomy provide different levels of specificity about items included in the levels. In various embodiments, the taxonomy identifies a category and associates one or more specific items with the category. For example, a category identifies “milk,” and the taxonomy associates identifiers of different milk items (e.g., milk offered by different brands, milk having one or more different attributes, etc.), with the category. Thus, the taxonomy maintains associations between a category and specific items offered by the warehouse210matching the category. In some embodiments, different levels in the taxonomy identify items with differing levels of specificity based on any suitable attribute or combination of attributes of the items. For example, different levels of the taxonomy specify different combinations of attributes for items, so items in lower levels of the hierarchical taxonomy have a greater number of attributes, corresponding to greater specificity in a category, while items in higher levels of the hierarchical taxonomy have a fewer number of attributes, corresponding to less specificity in a category. In various embodiments, higher levels in the taxonomy include less detail about items, so greater numbers of items are included in higher levels (e.g., higher levels include a greater number of items satisfying a broader category). Similarly, lower levels in the taxonomy include greater detail about items, so fewer numbers of items are included in the lower levels (e.g., higher levels include a fewer number of items satisfying a more specific category). The taxonomy may be received from a warehouse210in various embodiments. In other embodiments, the inventory management engine702applies a trained classification module to an item catalog received from a warehouse210to include different items in levels of the taxonomy, so application of the trained classification model associates specific items with categories corresponding to levels within the taxonomy.

Inventory information provided by the inventory management engine702may supplement the training datasets720. Inventory information provided by the inventory management engine702may not necessarily include information about the outcome of picking a delivery order associated with the item, whereas the data within the training datasets720is structured to include an outcome of picking a delivery order (e.g., if the item in an order was picked or not picked).

The online concierge system102also includes an order fulfillment engine706which is configured to synthesize and display an ordering interface to each user204(for example, via the customer mobile application206). The order fulfillment engine706is also configured to access the inventory database704in order to determine which products are available at which warehouse210. The order fulfillment engine706may supplement the product availability information from the inventory database234with an item availability predicted by the machine-learned item availability model716. The order fulfillment engine706determines a sale price for each item ordered by a user204. Prices set by the order fulfillment engine706may or may not be identical to in-store prices determined by retailers (which is the price that users204and shoppers208would pay at the retail warehouses). The order fulfillment engine706also facilitates transactions associated with each order. In one embodiment, the order fulfillment engine706charges a payment instrument associated with a user204when he/she places an order. The order fulfillment engine706may transmit payment information to an external payment gateway or payment processor. The order fulfillment engine706stores payment and transactional information associated with each order in a transaction records database708.

In various embodiments, the order fulfillment engine706generates and transmits a search interface to a client device of a user for display via the customer mobile application206. The order fulfillment engine706receives a query comprising one or more terms from a user and retrieves items satisfying the query, such as items having descriptive information matching at least a portion of the query. In various embodiments, the order fulfillment engine706leverages item embeddings for items to retrieve items based on a received query. For example, the order fulfillment engine706generates an embedding for a query and determines measures of similarity between the embedding for the query and item embeddings for various items included in the inventory database704.

In some embodiments, the order fulfillment engine706also shares order details with warehouses210. For example, after successful fulfillment of an order, the order fulfillment engine706may transmit a summary of the order to the appropriate warehouses210. The summary may indicate the items purchased, the total value of the items, and in some cases, an identity of the shopper208and user204associated with the transaction. In one embodiment, the order fulfillment engine706pushes 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 the order fulfillment engine706, which provides detail of all orders which have been processed since the last request.

The order fulfillment engine706may interact with a shopper management engine710, which manages communication with and utilization of shoppers208. In one embodiment, the shopper management engine710receives a new order from the order fulfillment engine706. The shopper management engine710identifies the appropriate warehouse210to fulfill the order based on one or more parameters, such as a probability of item availability determined by a machine-learned item availability model716, the contents of the order, the inventory of the warehouses, and the proximity to the delivery location. The shopper management engine710then identifies one or more appropriate shoppers208to fulfill the order based on one or more parameters, such as the shoppers' proximity to the appropriate warehouse210(and/or to the user204), his/her familiarity level with that particular warehouse210, and so on. Additionally, the shopper management engine710accesses a shopper database712which stores information describing each shopper208, such as his/her name, gender, rating, previous shopping history, and so on.

As part of fulfilling an order, the order fulfillment engine706and/or shopper management engine710may access a user database714which 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 order fulfillment engine706determines whether to delay display of a received order to shoppers for fulfillment by a time interval. In response to determining to delay the received order by a time interval, the order fulfillment engine706evaluates orders received after the received order and during the time interval for inclusion in one or more batches that also include the received order. After the time interval, the order fulfillment engine706displays the order to one or more shoppers via the shopper mobile application212; if the order fulfillment engine706generated one or more batches including the received order and one or more orders received after the received order and during the time interval, the one or more batches are also displayed to one or more shoppers via the shopper mobile application212.

Machine Learning Models for Item Availability

The online concierge system102further includes a machine-learned item availability model716, a modeling engine718, and training datasets720. The modeling engine718uses the training datasets720to generate the machine-learned item availability model716. The machine-learned item availability model716can learn from the training datasets720, rather than follow only explicitly programmed instructions. The inventory management engine702, order fulfillment engine706, and/or shopper management engine710can use the machine-learned item availability model716to determine a probability that an item is available at a warehouse210. The machine-learned item availability model716may 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-learned item availability model716is used to predict the availability of any number of items.

The machine-learned item availability model716can 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-learned item availability model716may be adapted to receive any information that the modeling engine718identifies as indicators of item availability. At minimum, the machine-learned item availability model716receives 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 the inventory database704may 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 the inventory database704. 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), the online concierge system102can extract information about the item and/or warehouse from the inventory database704and/or warehouse database and provide this extracted information as inputs to the item availability model716.

The machine-learned item availability model716contains a set of functions generated by the modeling engine718from the training datasets720that 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-learned item availability model716outputs a probability that the item is available at the warehouse. The machine-learned item availability model716constructs 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-learned item availability model716includes 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 the item availability model716may be updated and adapted following retraining with new training datasets720. The machine-learned item availability model716may be any machine learning model, such as a neural network, boosted tree, gradient boosted tree or random forest model. In some examples, the machine-learned item availability model716is generated from XGBoost algorithm.

The item probability generated by the machine-learned item availability model716may be used to determine instructions delivered to the user204and/or shopper208, as described in further detail below.

The training datasets720relate 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). The training datasets720include 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 database). Each piece of data in the training datasets720includes 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-learned item availability model716to 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-learned item availability model716may weight these factors differently, where the weights are a result of a “learning” or training process on the training datasets720. The training datasets720are very large datasets taken across a wide cross section of warehouses, shoppers, items, warehouses, delivery orders, times, and item characteristics. The training datasets720are 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, the training datasets720may be supplemented by inventory information provided by the inventory management engine702. In some examples, the training datasets720are historic delivery order information used to train the machine-learned item availability model716, whereas the inventory information stored in the inventory database704include factors input into the machine-learned item availability model716to determine an item availability for an item in a newly received delivery order. In some examples, the modeling engine718may evaluate the training datasets720to 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. The modeling engine718may query a warehouse210through the inventory management engine702for updated item information on these identified items.

Machine Learning Factors for Item Availability

The training datasets720include a time associated with previous delivery orders. In some embodiments, the training datasets720include 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, the training datasets720include 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 datasets720include a time interval since an item was previously picked in a previous 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 datasets720include 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 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 datasets720may 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 engine702, 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 the modeling engine718training a machine learning model with the training datasets720, producing the machine-learned item availability model716.

The training datasets720include 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 engine702. 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 the modeling engine718training a machine learning model with the training datasets720, producing the machine-learned item availability model716.

The training datasets720may include additional item characteristics that affect the item availability and can therefore be used to build the machine-learned item availability model716relating the delivery order for an item to its predicted availability. The training datasets720may be periodically updated with recent previous delivery orders. The training datasets720may be updated with item availability information provided directly from shoppers208. Following updating of the training datasets720, a modeling engine718may retrain a model with the updated training datasets720and produce a new machine-learned item availability model716.

Customer Mobile Application

FIG.8Ais a diagram of the customer mobile application (CMA)206, according to one or more embodiments. The CMA206includes an ordering interface802, which provides an interactive interface with which the user204can browse through and select products and place an order. The CMA206also includes a system communication interface804which, among other functions, receives inventory information from the online shopping concierge system102and transmits order information to the system102. The CMA206also includes a preferences management interface806which allows the user204to manage basic information associated with his/her account, such as his/her home address and payment instruments. The preferences management interface806may also allow the user to manage other details such as his/her favorite or preferred warehouses210, preferred delivery times, special instructions for delivery, and so on.

Shopper Mobile Application

FIG.8Bis a diagram of the shopper mobile application (SMA)212, according to one or more embodiments. The SMA212includes a barcode scanning module820which allows a shopper208to scan an item at a warehouse210(such as a can of soup on the shelf at a grocery store). The barcode scanning module820may also include an interface which allows the shopper208to 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. SMA212also includes a basket manager822which maintains a running record of items collected by the shopper208for purchase at a warehouse210. This running record of items is commonly known as a “basket.” In one embodiment, the barcode scanning module820transmits information describing each item (such as its cost, quantity, weight, etc.) to the basket manager822, which updates its basket accordingly. The SMA212also includes a system communication interface824which interacts with the online shopping concierge system102. For example, the system communication interface824receives an order from the online concierge system102and transmits the contents of a basket of items to the online concierge system102. The SMA212also includes an image encoder826which encodes the contents of a basket into an image. For example, the image encoder826may 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 warehouse210at check-out.

Additional Considerations