公开(公告)号：US20250086395A1

公开(公告)日：2025-03-13

申请号：US18244098

申请日：2023-09-08

Applicant: Maplebear Inc.

Inventor： Prithvishankar Srinivasan ,  Saurav Manchanda ,  Shih-Ting Lin ,  Shishir Kumar Prasad ,  Riddhima Sejpal ,  Luis Manrique ,  Min Xie

IPC: G06F40/30

Abstract: Embodiments relate to utilizing a language model to automatically generate a novel recipe with refined content, which can be offered to a user of an online system. The online system generates a first prompt for input into a large language model (LLM), the first prompt including a plurality of task requests for generating initial content of a recipe. The online system requests the LLM to generate, based on the first prompt input into the LLM, the initial content of the recipe. The online system generates a second prompt for input into the LLM, the second prompt including the initial content of the recipe and contextual information about the recipe. The online system requests the LLM to generate, based on the second prompt input into the LLM, refined content of the recipe. The online system stores the recipe with the refined content in a database of the online system.

公开(公告)号：US20250005279A1

公开(公告)日：2025-01-02

申请号：US18215505

申请日：2023-06-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Shih-Ting Lin ,  Prithvishankar Srinivasan ,  Saurav Manchanda ,  Shishir Kumar Prasad ,  Min Xie

IPC: G06F40/247 ,  G06F16/21 ,  G06F16/215 ,  G06F16/28

Abstract: A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.

公开(公告)号：US20250037323A1

公开(公告)日：2025-01-30

申请号：US18785665

申请日：2024-07-26

Applicant: Maplebear Inc.

Inventor： Prithvishankar Srinivasan ,  Shih-Ting Lin ,  Yuanzheng Zhu ,  Min Xie ,  Shishir Kumar Prasad ,  Shrikar Archak ,  Karuna Ahuja

IPC: G06T11/00 ,  G06T5/70 ,  G06V10/764

Abstract: An online system performs a task in conjunction with the model serving system or the interface system. The system generates a first prompt for input to a machine-learned language model, which specifies contextual information and a first request to generate a theme. The system provides the first prompt to a model serving system for execution by the machine-learned language model, receives a first response, and generates a second prompt. The second prompt specifies the theme and a second request to generate a third prompt for input to an image generation model that includes a third request to generate one or more images of one or more items associated with the theme. The system receives the third prompt by executing the model on the second prompt, provides the third prompt to the image generation model, and receives one or more images for presentation.

公开(公告)号：US20250124498A1

公开(公告)日：2025-04-17

申请号：US18917136

申请日：2024-10-16

Applicant: Maplebear Inc.

Inventor： Prithvishankar Srinivasan ,  Shishir Kumar Prasad ,  Min Xie ,  Shrikar Archak ,  Shih-Ting Lin ,  Haixun Wang

IPC: G06Q30/08 ,  G06Q30/0601

Abstract: An online system presents a sponsored content page to a user in conjunction with a model serving system. The online system accesses a content page for a food item and identifies one or more sponsorship opportunities at the content page. The online system identifies one or more candidate sponsors for each sponsorship opportunity. The online system selects a bidding sponsor for the sponsorship opportunity from the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item. The online system provides a content page, a description of the sponsored item, and a request to generate a sponsored content page for the sponsorship opportunity to a model serving system. The online system receives a sponsored content page generated by a machine-learning language model at the model serving system and presents the sponsored content page to a user.

公开(公告)号：US20250117442A1

公开(公告)日：2025-04-10

申请号：US18987482

申请日：2024-12-19

Applicant: Maplebear Inc.

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

IPC: G06F18/23 ,  G06F18/21 ,  G06F18/214 ,  G06F18/22 ,  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：US12204614B2

公开(公告)日：2025-01-21

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

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：US20240104632A1

公开(公告)日：2024-03-28

申请号：US17935916

申请日：2022-09-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Creagh Briercliffe ,  Chuan Lei ,  Saurav Manchanda ,  Min Xie

IPC: G06Q30/06

CPC classification number: G06Q30/0635 ,  G06Q30/0613 ,  G06Q30/0627 ,  G06Q30/0639

Abstract: An online concierge system uses a co-engagement graph to assign attribute values to items for which those attribute values are uncertain. A co-engagement graph is a graph with nodes that represent items and edges that represent co-engagement between items. The online concierge system generates a co-engagement graph for a set of items based on item engagement data and item data for the items. The set of items includes items for which the online concierge system has an attribute value for a target attribute and items for which the online concierge system does not have an attribute value for the target attribute. The online concierge system identifies a node that corresponds to an unknown item and identifies a node connected to that first node that corresponds to a known item. The online concierge system assigns the attribute value for the known item to the unknown item.

公开(公告)号：US11978104B2

公开(公告)日：2024-05-07

申请号：US17894839

申请日：2022-08-24

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Min Xie ,  Gordon McCreight ,  Jonathan Newman

IPC: G06Q30/0601 ,  G06F16/56 ,  G06F16/903 ,  G06F40/166 ,  G06F40/284 ,  G06F40/30 ,  G06Q30/0201

CPC classification number: G06Q30/0629 ,  G06F16/56 ,  G06F16/90344 ,  G06F40/166 ,  G06F40/284 ,  G06F40/30 ,  G06Q30/0201

Abstract: A server receives a plurality of product data entries from a plurality of retailer computing systems. Each product data entry includes a product identifier uniquely identifying a corresponding physical product and descriptive data of the corresponding physical product. A subset of the plurality of product data entries having a same product identifier is determined. An embedding vector representative of a product data entry in the subset is pairwise compared with each of respective embedding vectors representative of other product data entries in the subset other than the product data entry to compute respective vector similarity metrics. A pooled semantic similarity metric for the product data entry based on the computed respective vector similarity metrics. It is determined whether the product data entry is an outlier in the subset based on the pooled semantic similarity metric. A notification is transmitted to a client device of a user based on the determination.

公开(公告)号：US20240070742A1

公开(公告)日：2024-02-29

申请号：US17894839

申请日：2022-08-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Saurav Manchanda ,  Min Xie ,  Gordon McCreight ,  Jonathan Newman

IPC: G06Q30/06 ,  G06F16/56 ,  G06F16/903 ,  G06F40/166 ,  G06F40/284 ,  G06F40/30 ,  G06Q30/02

CPC classification number: G06Q30/0629 ,  G06F16/56 ,  G06F16/90344 ,  G06F40/166 ,  G06F40/284 ,  G06F40/30 ,  G06Q30/0201

Abstract: A server receives a plurality of product data entries from a plurality of retailer computing systems. Each product data entry includes a product identifier uniquely identifying a corresponding physical product and descriptive data of the corresponding physical product. A subset of the plurality of product data entries having a same product identifier is determined. An embedding vector representative of a product data entry in the subset is pairwise compared with each of respective embedding vectors representative of other product data entries in the subset other than the product data entry to compute respective vector similarity metrics. A pooled semantic similarity metric for the product data entry based on the computed respective vector similarity metrics. It is determined whether the product data entry is an outlier in the subset based on the pooled semantic similarity metric. A notification is transmitted to a client device of a user based on the determination.

公开(公告)号：US20230058829A1

公开(公告)日：2023-02-23

申请号：US17407158

申请日：2021-08-19

Applicant: Maplebear Inc. (dba Instacart)

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

IPC: G06K9/62 ,  G06Q30/06

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.