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：US20250036604A1

公开(公告)日：2025-01-30

申请号：US18786352

申请日：2024-07-26

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Paul Baranowski ,  Ashna Sebastian

IPC: G06F16/215 ,  G06F16/23 ,  G06F40/205

Abstract: An online system maintains a product catalog including products from various retailers, from which users can purchase products. Each of the products are associated with attributes such as a size value and a size unit of measure (UOM). The online system identifies products with erroneous product attributes using taxonomy attribute value homogeneity. The online system performs an inference task in conjunction with the model serving system or the interface system to infer a correct size value and size UOM of the product. The online system evaluates the accuracy of the inferred size value and size UOM of the product. Responsive to determining that the inferred data is accurate, the online system updates the product catalog with the corrected product attribute information.

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：US20240070739A1

公开(公告)日：2024-02-29

申请号：US18503084

申请日：2023-11-06

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

IPC: G06Q30/0601 ,  G06Q30/0282

CPC classification number: G06Q30/0619 ,  G06Q30/0282 ,  G06Q30/0641

Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user's search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.

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.

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：US11847676B2

公开(公告)日：2023-12-19

申请号：US17550960

申请日：2021-12-14

Applicant: Maplebear Inc.

Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

IPC: G06Q30/00 ,  G06Q30/0601 ,  G06Q30/0282

CPC classification number: G06Q30/0619 ,  G06Q30/0282 ,  G06Q30/0641

Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user's search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.

公开(公告)号：US20230186363A1

公开(公告)日：2023-06-15

申请号：US17550950

申请日：2021-12-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

IPC: G06Q30/06 ,  G06N20/00 ,  G06F16/2455

CPC classification number: G06Q30/0627 ,  G06Q30/0631 ,  G06N20/00 ,  G06F16/2455

Abstract: An online concierge system selects content for presentation to a user by using a product scoring engine. The product scoring engine generates a user embedding for user data and a query embedding for query data. The product scoring engine generates an anchor embedding based on the user embedding and the query embedding, where the anchor embedding is an embedding in a product embedding space. The product scoring engine compares the anchor embedding to a set of product embeddings to score a set of products for presentation to a user.