公开(公告)号：US20230325856A1

公开(公告)日：2023-10-12

申请号：US18186141

申请日：2023-03-17

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Trace Levinson ,  Nicholas Sturm

IPC: G06Q30/0202

CPC classification number: G06Q30/0202

Abstract: An online system computes an incremental cost prediction for each of a set of user-treatment pairs to select a set of treatments to apply to users to satisfy a predicted interaction gap. The online system generates a set of candidate user-treatment pairs that each include user data for a user of the online system and treatment data for a treatment of a set of treatments. The online system computes an incremental interaction prediction and a treatment cost prediction for each of the candidate user-treatment pairs by applying an incremental interaction model to the user data and the treatment data in each user-treatment pair. The online system computes incremental cost predictions for each of the user-treatment pairs based on the computed incremental interaction predictions and treatment cost predictions and selects which users to apply treatments to and which treatments to apply to those users based on the incremental cost predictions.

公开(公告)号：US20230316375A1

公开(公告)日：2023-10-05

申请号：US17709998

申请日：2022-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Girija Narlikar ,  Omar Alonso

IPC: G06Q30/06 ,  G06N3/08

CPC classification number: G06Q30/0631 ,  G06N3/08

Abstract: An online concierge system generates recipe embeddings for recipes including multiple items and user embeddings for users, with the recipe embeddings and user embeddings in a common latent space. To generate the user embeddings and the recipe embeddings, a model includes separate layers for a user model outputting user embeddings and for a recipe model outputting recipe embeddings. When training the model, a weight matrix generates a predicted dietary preference type for a user embedding and for a recipe embedding and adjusts the user model or the recipe model based on differences between the predicted dietary preference type and a dietary preference type applied to the user embedding and to the recipe embedding. Additionally cross-modal layers generate a predicted user embedding from a recipe embedding and generate a predicted recipe embedding from a user embedding that are used to further refine the user model and the recipe model.

公开(公告)号：US20230260007A1

公开(公告)日：2023-08-17

申请号：US18139289

申请日：2023-04-25

Applicant: Maplebear Inc. (dba Instacart)

Inventor： William Silverthorne Faurot, III ,  Tyler Russell Tate

IPC: G06Q30/0601 ,  G06F16/2457 ,  G06N20/00

CPC classification number: G06Q30/0631 ,  G06Q30/0641 ,  G06F16/24578 ,  G06N20/00

Abstract: An online system receives a recipe from a customer mobile device. The online system performs natural language processing on the recipe to determine parsed ingredients. For each of one or more of the determined parsed ingredients, the online system maps the parsed ingredient to a generic item. The online system queries a product database with the mapped generic item to obtain one or more products associated with the mapped generic item. The online system applies a machine-learned conversion model to each of the one or more products to determine a conversion likelihood for the product. The conversion model may be trained based on historical data describing previous conversions made by customers presented with an opportunity to add products to an order. The online system selects a product from the one or more products based on the determined conversion likelihoods and adds the selected product to an order.

公开(公告)号：US20230252049A1

公开(公告)日：2023-08-10

申请号：US17736716

申请日：2022-05-04

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Tejaswi Tenneti ,  Haixun Wang ,  Xiao Xiao

IPC: G06F16/28 ,  G06F16/2457 ,  G06F16/248 ,  G06K9/62

CPC classification number: G06F16/285 ,  G06F16/24573 ,  G06F16/24575 ,  G06F16/248 ,  G06K9/6276

Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.

公开(公告)号：US20230245213A1

公开(公告)日：2023-08-03

申请号：US17591584

申请日：2022-02-02

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Reza Faturechi ,  Site Wang ,  Jagannath Putrevu

IPC: G06Q30/06 ,  G06N3/08 ,  G06Q10/06

CPC classification number: G06Q30/0635 ,  G06N3/084 ,  G06Q10/0633

Abstract: An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.

公开(公告)号：US20230214774A1

公开(公告)日：2023-07-06

申请号：US17570038

申请日：2022-01-06

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Joey Loi ,  Viswa Mani Kiran Peddinti ,  Eugene Agronin ,  John Salaveria

IPC: G06Q10/08 ,  G06Q30/06 ,  G06N20/00

CPC classification number: G06Q10/0875 ,  G06Q30/0635 ,  G06N20/00

Abstract: An online concierge system displays an ordering interface to users that displays items offered by various warehouses. The online concierge system includes machine learning availability model that estimates an item's availability and visually distinguishes items offered by a warehouse having less than a threshold availability from other items. Because information from a warehouse that an item that was out of stock is now in stock is often delayed, the online concierge system transmits a request to a shopper fulfilling an order to check for an item's availability at a warehouse. For example, the online concierge system allows users to include a request for an indication of an item's availability when placing an order. When the online concierge system receives a threshold number of requests for the item, the online concierge system prompts a shopper fulfilling an order including items near the item for the item's availability.

公开(公告)号：US20230162038A1

公开(公告)日：2023-05-25

申请号：US17534184

申请日：2021-11-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Peng Qi ,  Zhenbang Chen

IPC: G06N3/08 ,  G06N3/04 ,  G06Q30/02

CPC classification number: G06N3/084 ,  G06N3/04 ,  G06Q30/0202

Abstract: An online system uses a trained model predicting likelihoods of a user performing a specific interaction with items to order or to rank items for display to the user. The online system trains the model using interactions by users with items displayed by the online system. However, selection, popularity, and position from display of the items affects the model during training. To improve the model, the online system further trains the model using additional training data obtained from displaying items to users in different orders. The further training is done on a limited portion of the model, such as a limited number of layers of the model, to improve the model performance while reducing an amount of additional data to acquire to further train the model.

公开(公告)号：US20230146336A1

公开(公告)日：2023-05-11

申请号：US17524491

申请日：2021-11-11

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Haixun Wang ,  Taesik Na ,  Tejaswi Tenneti ,  Saurav Manchanda ,  Min Xie ,  Chuan Lei

IPC: G06Q30/06 ,  G06N20/00

CPC classification number: G06Q30/0603 ,  G06N20/00

Abstract: To simplify retrieval of items from a database that at least partially satisfy a received query, an online concierge system trains a model that outputs scores for items from the database without initially retrieving items for evaluation by the model. The online concierge system pre-trains the model using natural language inputs corresponding to items from the database, with a natural language input including masked words that the model is trained to predict. Subsequently, the model is refined using multi-task training where a task is trained to predict scores for items from the received query. The online concierge system selects items for display in response to the received query based on the predicted scores.

公开(公告)号：US20230113122A1

公开(公告)日：2023-04-13

申请号：US18080118

申请日：2022-12-13

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Sharath Rao ,  Shishir Prasad ,  Jeremy Stanley

IPC: G06Q10/087 ,  G06Q10/0631 ,  G06Q10/067

Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

公开(公告)号：US20230062937A1

公开(公告)日：2023-03-02

申请号：US17458127

申请日：2021-08-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Xinyu Li ,  Haixun Wang ,  Ruoming Jin

IPC: G06Q10/06 ,  G06Q30/06 ,  G06Q10/04 ,  G06Q10/08 ,  G06F16/901

Abstract: An online concierge system generates a suggested picking sequence to reduce the amount of time for a shopper to fulfill an online order of items from a warehouse. The online concierge system determines an average amount of time to sequentially pick items between different aisle pairs for a warehouse based on timestamps from item fulfillment in historical orders. The system generates a distance graph including aisle nodes connected by edges representing the pairwise distance between aisles. The system solves a traveling salesperson problem to generate a ranked order of aisle nodes for each of the historical orders. The system generates a ranked global sequence of aisle nodes based on the plurality of ranked orders of aisle nodes. The system applies the ranked global sequence to new delivery orders to generate the suggested picking sequence for a shopper.