公开(公告)号：US12222937B2

公开(公告)日：2025-02-11

申请号：US17668358

申请日：2022-02-09

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/242 ,  G06F16/2457 ,  G06F16/28 ,  G06F18/214 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US20250005629A1

公开(公告)日：2025-01-02

申请号：US18498967

申请日：2023-10-31

Applicant: Maplebear Inc.

Inventor： Li Tan ,  Haixun Wang ,  Jian Li

IPC: G06Q30/0251 ,  G06N20/00 ,  G06Q50/12

Abstract: A computer system finetunes a machine-learned language model to generate a personalized response to a user request. The system may generate a user representation for each of a plurality of users by applying a transformer model to a sequence of tokens representing a sequence of activities of the user. The system may train an evaluation model coupled to receive a user representation and a response to a user request and generate an estimated evaluation score indicating a level of personalization of the response to the user. The system may finetune a first machine-learned language model to generate a second machine-learned language model. The finetuned machine-learned language model is configured to provide personalized responses for customer services at an online concierge system.

公开(公告)号：US20240289861A1

公开(公告)日：2024-08-29

申请号：US18587655

申请日：2024-02-26

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Tejaswi Tenneti ,  Taesik Na ,  Yuanzheng Zhu ,  Vinesh Reddy Gudla ,  Lee Cohn

IPC: G06Q30/0601

CPC classification number: G06Q30/0631 ,  G06Q30/0627 ,  G06Q30/0635 ,  G06Q30/0643

Abstract: Responsive to an input query from a user, an online system presents a list of recommended items that are related to the input query. The input query may be formulated as a natural language query. The online system performs an inference task in conjunction with the model serving system to generate one or more additional queries that are related to the input query and/or are otherwise related to the recommended items presented in response to the input query. The additional queries may be presented to the user in conjunction with the list of recommended items.

公开(公告)号：US20240289632A1

公开(公告)日：2024-08-29

申请号：US18588622

申请日：2024-02-27

Applicant: Maplebear Inc.

Inventor： Li Tan ,  Haixun Wang ,  Jian Li

IPC: G06N3/092 ,  H04L51/02

CPC classification number: G06N3/092 ,  H04L51/02

Abstract: An online system trains a specific-purpose LLM. The online system obtains training examples and divides training examples across batches. The online system generates a specific response by applying parameters of the specific-purpose LLM to a batch of training examples. The online system generates a general response by applying parameters of a general-purpose LLM to the batch of training examples. The online system computes a human readability score representing the difference between the specific response and the general response. The online system computes an objective compliance score by applying an evaluation model to the specific response, the evaluation model trained to score the first response based on a specific objective. The online system updates the parameters of the specific-purpose LLM based on the human readability score and the objective compliance score.

公开(公告)号：US20240257221A1

公开(公告)日：2024-08-01

申请号：US18631964

申请日：2024-04-10

Applicant: Maplebear Inc.

Inventor： Negin Entezari ,  Sharath Rao Karikurve ,  Shishir Kumar Prasad ,  Haixun Wang

IPC: G06Q30/0601 ,  G06Q10/087

CPC classification number: G06Q30/0633 ,  G06Q10/087

Abstract: An online concierge shopping system identifies candidate items to a user for inclusion in an order based on prior user inclusion of items in orders and items currently included in the order. From a multi-dimensional tensor generated from cooccurrences of items in orders from various users, the online concierge system generates item embeddings and user embeddings in a common latent space by decomposing the multi-dimensional tensor. From items included in an order, the online concierge system generates an order embedding from item embeddings of the items included in the order. Scores for candidate items are determined based on similarity of item embeddings for the candidate items to the order embedding. Candidate items are selected based on their scores, with the selected candidate items identified to the user.

公开(公告)号：US20230306023A1

公开(公告)日：2023-09-28

申请号：US17668358

申请日：2022-02-09

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/2457 ,  G06F16/242 ,  G06F16/28 ,  G06N20/00 ,  G06K9/62

CPC classification number: G06F16/24534 ,  G06F16/2448 ,  G06F16/24578 ,  G06F16/283 ,  G06K9/6257 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US20250147958A1

公开(公告)日：2025-05-08

申请号：US19012864

申请日：2025-01-08

Applicant: Maplebear Inc.

Inventor： Taesik Na ,  Yuqing Xie ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2453 ,  G06F16/242 ,  G06F16/2457 ,  G06F16/28 ,  G06F18/214 ,  G06N20/00

Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.

公开(公告)号：US20240354793A1

公开(公告)日：2024-10-24

申请号：US18761756

申请日：2024-07-02

Applicant: Maplebear Inc.

Inventor： Ze He ,  Asif Haque ,  Allan Stewart ,  Haixun Wang ,  Xinyu Li

IPC: G06Q30/0204 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0601

CPC classification number: G06Q30/0205 ,  G06N3/049 ,  G06N3/084 ,  G06Q30/0282 ,  G06Q30/0635 ,  G06Q30/0639 ,  G06Q30/0641

Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.

公开(公告)号：US20240330718A1

公开(公告)日：2024-10-03

申请号：US18625042

申请日：2024-04-02

Applicant: Maplebear Inc.

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

IPC: G06N5/022 ,  G06F16/901 ,  G06F40/205 ,  G06F40/40

CPC classification number: G06N5/022 ,  G06F16/9024 ,  G06F40/205 ,  G06F40/40

Abstract: An online system generates a knowledge graph database representing relationships between entities in the online system. The online system generates the knowledge graph database by at least obtaining descriptions for an item. The online system generates one or more prompts to a machine-learned language model, where a prompt includes a request to extract a set of attributes for the item from the description of the item. The online system receives a response generated from executing the machine-learned language model on the prompts. The online system parses the response to extract the set of attributes for the item. For each extracted attribute, the online system generates connections between an item node representing the item and a set of attribute nodes for the extracted set of attributes in the database.

公开(公告)号：US20240320063A1

公开(公告)日：2024-09-26

申请号：US18608368

申请日：2024-03-18

Applicant: Maplebear Inc.

Inventor： Haixun Wang ,  Riddhima Sejpal

IPC: G06F9/54

CPC classification number: G06F9/541

Abstract: An online system receives, from a model serving system, an application programming interface (API) request from a plug-in provided by an online system. The API request includes a list of items obtained from a conversation session of a user with a machine-learned language model application of the model serving system. The online system generates a URL to a landing page for the user for creating a purchase list with the online system based on the list of items. Responsive to receiving a request to access the URL, the online system causes display of the landing page on a client device of the user that displays the purchase list including retailer items for one or more retailers corresponding to the list of items in the API request.