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：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：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：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：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.

公开(公告)号：US20230055760A1

公开(公告)日：2023-02-23

申请号：US17405011

申请日：2021-08-17

Applicant: Maplebear Inc.(dba Instacart)

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

IPC: G06K9/62 ,  G06N3/08

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.