公开(公告)号：US20250086189A1

公开(公告)日：2025-03-13

申请号：US18367185

申请日：2023-09-12

Applicant: Maplebear Inc.

Inventor： Levi Boxell ,  Esther Vasiete Allas ,  Tejaswi Tenneti ,  Tilman Drerup ,  Yueyang Rao

IPC: G06F16/2457 ,  G06F16/248

Abstract: A computer system allowing users to search for items of interest provides a search query interface. The system receives characters of a search query in the search interface as the user enters the characters and interactively calculates, ranks, and displays a set of possible search query options from which the user can select. To rank the set of possible search query options, the system modifies rankings of candidate search queries based on factors associated with third parties. More specifically, contextual relevance scores are computed for the candidate search queries based on the context, such as a user to whom the search results are provided. These contextual relevance scores are in turn adjusted using factors associated with third parties, such as values calculated based on consideration offered by third parties. Users are shown the search query options, ranked in order of the adjusted relevance scores, as possible query selections.

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：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：US20240086984A1

公开(公告)日：2024-03-14

申请号：US18509143

申请日：2023-11-14

Applicant: Maplebear Inc.

Inventor： Tejaswi Tenneti ,  Aditya Subramanian ,  Shrikar Archak ,  Tyler Russell Tate ,  Jonathan Lennart Bender

IPC: G06Q30/0601 ,  G06F16/2457 ,  G06F16/248 ,  G06F16/901

CPC classification number: G06Q30/0617 ,  G06F16/24578 ,  G06F16/248 ,  G06F16/9024 ,  G06Q30/0625 ,  G06Q30/0629

Abstract: An online concierge system generates a graph connecting items with attributes of the items and other items. Hence, the graph includes nodes corresponding to attributes and nodes corresponding to items, with an item connected to attributes of the item in the graph. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and compares different combinations of terms to the graph to determine connections between different combinations of terms in the graph. Based on measures of connectedness between combinations of terms and connections in the graph, items are identified from one or more combinations of terms. Information about the identified items is presented to the customer.

公开(公告)号：US11869055B2

公开(公告)日：2024-01-09

申请号：US17160759

申请日：2021-01-28

Applicant: Maplebear, Inc.

Inventor： Tejaswi Tenneti ,  Aditya Subramanian ,  Shrikar Archak ,  Tyler Russell Tate ,  Jonathan Lennart Bender

IPC: G06Q30/0601 ,  G06F16/248 ,  G06F16/901 ,  G06F16/2457

CPC classification number: G06Q30/0617 ,  G06F16/248 ,  G06F16/24578 ,  G06F16/9024 ,  G06Q30/0625 ,  G06Q30/0629

Abstract: An online concierge system generates a graph connecting items with attributes of the items and other items. Hence, the graph includes nodes corresponding to attributes and nodes corresponding to items, with an item connected to attributes of the item in the graph. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and compares different combinations of terms to the graph to determine connections between different combinations of terms in the graph. Based on measures of connectedness between combinations of terms and connections in the graph, items are identified from one or more combinations of terms. Information about the identified items is presented to the customer.

公开(公告)号：US11841905B2

公开(公告)日：2023-12-12

申请号：US18185091

申请日：2023-03-16

Applicant: Maplebear Inc.

Inventor： Jonathan Lennart Bender ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Qingyuan Chen

IPC: G06F16/90 ,  G06F16/901 ,  G06F16/903 ,  G06F16/9032 ,  G06F16/9035 ,  G06Q30/0201 ,  G06Q30/0601 ,  G06Q30/02 ,  G06Q30/06

CPC classification number: G06F16/9024 ,  G06F16/9035 ,  G06F16/90328 ,  G06F16/90348 ,  G06Q30/0201 ,  G06Q30/0641 ,  G06Q30/0635

Abstract: An online concierge system generates an item graph connecting item nodes with attribute nodes of the items. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and identifies item nodes and attribute nodes related to the search query. The online concierge system identifies item nodes and attribute nodes that are likely to result in a conversion. Information about the identified nodes is presented to the customer. The customer may select an item node to purchase the item, or an attribute node to execute a new search query based on terms associated with the attribute node.

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：US20230273940A1

公开(公告)日：2023-08-31

申请号：US17682187

申请日：2022-02-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Guanghua Shu ,  Taesik Na ,  Zhihong Xu ,  Wideet Shende ,  Manmeet Singh ,  Tejaswi Tenneti ,  Reza Sadri

IPC: G06F16/28 ,  G06F16/22 ,  G06F16/2455 ,  G06F11/34

CPC classification number: G06F16/283 ,  G06F16/2228 ,  G06F16/24556 ,  G06F16/285 ,  G06F11/3409

Abstract: An online system maintains item embeddings for items. As a number of items maintained by the online system increases, maintaining a single index of the item embeddings is increasingly difficult. To increase scalability, the online system partitions item embeddings into multiple indices, with each index corresponding to a value of a specific attribute maintained by the online system for items. For example, an online system generates indices that each correspond to a different warehouse offering items. To expedite retrieval of item embeddings, the online system allocates each index to one of a number of shards. When the online system receives a query, the online system determines an embedding for the query and retrieves an index from a shard based on metadata received with the query. Based on distances between the query for the embedding and the item embeddings in the retrieved index, the online system selects one or more items.

公开(公告)号：US20250156451A1

公开(公告)日：2025-05-15

申请号：US18510565

申请日：2023-11-15

Applicant: Maplebear Inc.

Inventor： Jacob Jensen ,  Fei Jia ,  Esther Vasiete Allas ,  Manmeet Singh ,  Lee Cohn ,  Tejaswi Tenneti

IPC: G06F16/332 ,  G06F40/284 ,  G06F40/40

Abstract: A language model is used to generate autosuggestions to complete or revise a user's partial search query. An initial partial query is applied to the language model to generate query candidates for completing the search query. The language model may generate the query candidates as additional or alternate tokens for the partial search query. When the user revises the partial query, the previously-generated candidates can be re-used to reduce subsequent processing time for generating additional candidates. The previously-generated candidates are compared with the revised partial query to select which of the candidates to be re-used and expanded for generating additional tokens. Additional tokens can be generated in parallel for the previously-generated candidates or with model values from the previous generation, enabling the tokens to be generated effectively with reduced latency consistent with user expectations for search-related autosuggestions.

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.