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：US20240354825A1

公开(公告)日：2024-10-24

申请号：US18138657

申请日：2023-04-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vinesh Reddy Gudla ,  Prakash Putta ,  Tejaswi Tenneti ,  Prathyusha Bhaskar Karnam

IPC: G06Q30/0601 ,  G06Q10/083 ,  G06Q10/087

CPC classification number: G06Q30/0625 ,  G06Q10/083 ,  G06Q10/087 ,  G06Q30/0635

Abstract: A search module for an online concierge system executes searches in response to a search query with respect to item databases of retailers. The search module dynamically configures a recall set size that controls a number of search results returned for a search query based in part on a query entropy representing an estimated breadth of the search term. The query entropy may be determined relative to a diversity of items in a retailer's database. The recall set size may be configured relative to the query entropy in a manner that manages a tradeoff between latency of search execution and search result quality.

公开(公告)号：US20230252032A1

公开(公告)日：2023-08-10

申请号：US17666531

申请日：2022-02-07

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Zhihong Xu ,  Guanghua Shu ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06F16/2457 ,  G06F16/242

CPC classification number: G06F16/24578 ,  G06F16/2438

Abstract: An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.

公开(公告)号：US20230080205A1

公开(公告)日：2023-03-16

申请号：US17476475

申请日：2021-09-16

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Manmeet Singh ,  Tejaswi Tenneti ,  Will Faurot, III ,  Tyler Tate ,  Toni Chau ,  Sathya Hariesh Prakash

IPC: G06Q10/08 ,  G06Q10/04 ,  G06K9/62 ,  G06F16/9535

Abstract: An online concierge system suggests recipes to users to encourage them to include items from the recipes in orders. When suggesting a recipe to a user, the online concierge system maps generic item descriptions in a recipe to specific items for sale at a warehouse identified by the user. The online concierge system maintains a recipe store including mappings of generic item descriptions in recipes to specific items. To determine mappings between generic item descriptions and specific items, the online concierge system determines a category from a taxonomy for a generic item description in a recipe and excludes specific items in different categories than the determined category. As recipes are displayed to users, the online concierge system may receive inputs from users that are used to modify mappings between generic item descriptions and specific items

公开(公告)号：US20220414746A1

公开(公告)日：2022-12-29

申请号：US17929797

申请日：2022-09-06

Applicant: Maplebear, Inc. (dba Instacart)

Inventor： Tyler Russell Tate ,  Jason Scott ,  Logan William Murdock ,  Tejaswi Tenneti

IPC: G06Q30/06 ,  G06F16/9538 ,  G06Q30/02 ,  G06Q10/08

Abstract: An online system provides options for selection by a user. The online system receives a query entered on a client device. The online system queries an item database to retrieve a set of items related to the query and assigns each item to a product category in a predefined taxonomy that maps items to product categories. The online system inputs each item into a prediction model trained to predict a probability that an item is available at a warehouse location. The online system determines that a first product category has low availability based on predicted probabilities for items in the first product category. Responsive to determining that a first product category has low availability, the online system generates a generic item for the first product category and sends a list of items including the generic item to the client device for display responsive to the query.

公开(公告)号：US20220277373A1

公开(公告)日：2022-09-01

申请号：US17188214

申请日：2021-03-01

Applicant: Maplebear, Inc. (dba Instacart)

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

IPC: G06Q30/06 ,  G06F17/18 ,  G06F16/901

Abstract: An online concierge system generates an item graph connecting item nodes with attribute nodes of the items. 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 may determine that no item nodes meet presentation criteria. The online concierge system may determine that a reformulated search query has a higher conversion probability than the search query received from the customer. The online concierge system reformulates the search query. The online concierge system selects item nodes as search results. The online concierge system transmits the search results to the customer.

公开(公告)号：US20250005654A1

公开(公告)日：2025-01-02

申请号：US18217329

申请日：2023-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Raochuan Fan ,  Prakash Putta ,  Vinesh Reddy Gudla ,  Nkemakonam Paulet Okoye ,  Taesik Na ,  Tejaswi Tenneti

IPC: G06Q30/0601

Abstract: An online concierge system allows a customer to search items offered by a retailer by providing a set of items to the customer based on a search query. To account for varying availability of items at the retailer, the online concierge system modifies rankings in the set of items having less than a threshold predicted availability at the retailer. This reduces a likelihood selection of an item likely to be unavailable at the retailer. To maintain customer confidence in the items selected based on the search results by maintaining visibility of items relevant to the search query, the online concierge system determines how much an item is modified within the set based on search query attributes, item attributes, or customer characteristics. This allows different items to be adjusted different amounts in a set based on the item, as well as the search query for which the item was selected.

公开(公告)号：US20240104622A1

公开(公告)日：2024-03-28

申请号：US17955250

申请日：2022-09-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vinesh Reddy Gudla ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Akshay Nair

IPC: G06Q30/06 ,  G06Q30/02

CPC classification number: G06Q30/0629 ,  G06Q30/0201 ,  G06Q30/0204

Abstract: An online system receives a search query from a client device associated with a user and queries a database including item data for a set of items matching the query, in which the set of items is at a retailer location associated with a retailer type and each item is associated with an item category. For each item of the set, a machine learning model is applied to predict a probability of conversion for the user and item and a score is computed based on an expected value, in which the expected value is based on a value associated with the item and the probability. The score for each item is boosted based on the item category, retailer type, or a user segment that is based on the user's historical order data. The items are ranked based on the boosted scores and the ranking is sent to the client device.

公开(公告)号：US20230252049A1

公开(公告)日：2023-08-10

申请号：US17736716

申请日：2022-05-04

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Taesik Na ,  Tejaswi Tenneti ,  Haixun Wang ,  Xiao Xiao

IPC: G06F16/28 ,  G06F16/2457 ,  G06F16/248 ,  G06K9/62

CPC classification number: G06F16/285 ,  G06F16/24573 ,  G06F16/24575 ,  G06F16/248 ,  G06K9/6276

Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.