公开(公告)号：US20250156926A1

公开(公告)日：2025-05-15

申请号：US18943691

申请日：2024-11-11

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Shrikar Archak ,  Tejaswi Tenneti

IPC: G06Q30/0601 ,  G06F16/9535 ,  G06F16/955 ,  G06Q30/08

Abstract: An online system receives a user request from a client device through the interface, identifies one or more featured products based on the query, and generates a prompt for input to a machine-learned generative language model. The prompt specifies both the user's request and a request to suggest the featured products in association with a response to the user request. This prompt is fed into a machine-learned language model via a model serving system for execution. The online system receives a response generated by the model, generates a query response based on the response generated by the model, and transmits instructions to the client device to display the query response. The online system collects data on user interactions with the uses the collected data to fine-tune the machine-learned generative language model.

公开(公告)号：US20250131355A1

公开(公告)日：2025-04-24

申请号：US19000089

申请日：2024-12-23

Applicant: Maplebear Inc.

Inventor： Xinyu Li ,  Haixun Wang ,  Ruoming Jin

IPC: G06Q10/0631 ,  G06F16/901 ,  G06Q10/047 ,  G06Q10/087 ,  G06Q30/0601

Abstract: An online system receives an order containing a list of items from a user's client device and tracks the current locations of a client device of a shopper within a warehouse. The system applies a trained item sequence model to generate a suggested picking sequence, minimizing time required for the shopper to pick the items. The item sequence model is trained using historical order data, including durations between picking items from different aisles and pairwise distances between aisle locations. The system transmits the suggested picking sequence to the shopper's client device for display. Responsive to determining that the client device of the shopper's location deviates from the suggested sequence, the system dynamically updates the sequence by applying the model to remain items and the shopper's current location.

公开(公告)号：US12210591B2

公开(公告)日：2025-01-28

申请号：US17407158

申请日：2021-08-19

Applicant: Maplebear Inc.

Inventor： Shih-Ting Lin ,  Jonathan Newman ,  Min Xie ,  Haixun Wang

IPC: G06K9/62 ,  G06F18/21 ,  G06F18/214 ,  G06F18/22 ,  G06F18/23 ,  G06Q30/06 ,  G06Q30/0601

Abstract: An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.

公开(公告)号：US20240303711A1

公开(公告)日：2024-09-12

申请号：US18596592

申请日：2024-03-05

Applicant: Maplebear Inc.

Inventor： Li Tan ,  Tejaswi Tenneti ,  Shishir Kumar Prasad ,  Huapu Pan ,  Allan Stewart ,  Taesik Na ,  Tyler Russell Tate ,  Joshua Roberts ,  Haixun Wang

IPC: G06Q30/0601 ,  G06F16/9532

CPC classification number: G06Q30/0627 ,  G06F16/9532 ,  G06Q30/0635

Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process high-level natural language queries received from users. The system receives a natural language query from a user of a client device. The system determines contextual information associated with the query. Based on this information, the system generates a prompt for the machine learning based language model. The system receives a response from the machine learning based language model. The system uses the response to generate a search query for a database. The system obtains results returned by the database in response to the search query and provides them to the user. The system allows users to specify high level natural language queries to obtain relevant search results, thereby improving the overall user experience.

公开(公告)号：US20240289862A1

公开(公告)日：2024-08-29

申请号：US18587668

申请日：2024-02-26

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Shishir Kumar Prasad ,  Tejaswi Tenneti ,  Li Tan

IPC: G06Q30/0601 ,  G06N5/04

CPC classification number: G06Q30/0631 ,  G06N5/04

Abstract: An online system performs an inference task in conjunction with the model serving system to infer one or more purposes of the order of a user that includes a list of ordered items. The model serving system may host a machine-learned language model, and in one instance, the machine-learned language model is a large language model. The online system generates recommendations to the user based on the inferred purpose of the order. The online system may generate one or more recommendations that are equivalent orders having the same or similar purpose as the existing order.

公开(公告)号：US20240241897A1

公开(公告)日：2024-07-18

申请号：US18415551

申请日：2024-01-17

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Taesik Na ,  Li Tan ,  Jian Li ,  Xiao Xiao

IPC: G06F16/33 ,  G06F16/338 ,  G06N20/00

CPC classification number: G06F16/3344 ,  G06F16/338 ,  G06N20/00

Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

公开(公告)号：US20240176852A1

公开(公告)日：2024-05-30

申请号：US18436611

申请日：2024-02-08

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Krishnakumar Subramanian ,  Haixun Wang ,  Min Xie

IPC: G06F18/2411 ,  G06F18/214 ,  G06F18/22 ,  G06N3/084

CPC classification number: G06F18/2411 ,  G06F18/214 ,  G06F18/22 ,  G06N3/084

Abstract: An online concierge system trains a classification model as a domain adversarial neural network from training data labeled with source classes from a source domain that do not overlap with target classes from a target domain output by the classification model. The online concierge system maps one or more source classes to a target class. The classification model extracts features from an image, classifies whether an image is from the source domain or the target domain, and predicts a target class for an image from the extracted features. The classification model includes a gradient reversal layer between feature extraction layers and the domain classifier that is used during training, so the feature extraction layers extract domain invariant features from an image.

公开(公告)号：US11947632B2

公开(公告)日：2024-04-02

申请号：US17405011

申请日：2021-08-17

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Krishnakumar Subramanian ,  Haixun Wang ,  Min Xie

IPC: G06F18/2411 ,  G06F18/214 ,  G06F18/22 ,  G06N3/084

CPC classification number: G06F18/2411 ,  G06F18/214 ,  G06F18/22 ,  G06N3/084

Abstract: An online concierge system trains a classification model as a domain adversarial neural network from training data labeled with source classes from a source domain that do not overlap with target classes from a target domain output by the classification model. The online concierge system maps one or more source classes to a target class. The classification model extracts features from an image, classifies whether an image is from the source domain or the target domain, and predicts a target class for an image from the extracted features. The classification model includes a gradient reversal layer between feature extraction layers and the domain classifier that is used during training, so the feature extraction layers extract domain invariant features from an image.

公开(公告)号：US20240029132A1

公开(公告)日：2024-01-25

申请号：US17868572

申请日：2022-07-19

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shih-Ting Lin ,  Amirali Darvishzadeh ,  Min Xie ,  Haixun Wang

IPC: G06Q30/06 ,  G06F40/20

CPC classification number: G06Q30/0627 ,  G06F40/20 ,  G06N20/00

Abstract: To improve attribute prediction for items, item categories are associated with a schema that is augmented with additional attributes and/or attribute labels. Items may be organized into categories and similar categories may be related to one another, for example in a taxonomy or other organizational structure. An attribute extraction model may be trained for each category based on an initial attribute schema for the respective category and the items of that category. The extraction model trained for one category may be used to identify additional attributes and/or attribute labels for the same or another, related category.

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.