公开(公告)号：US20240362523A1

公开(公告)日：2024-10-31

申请号：US18140203

申请日：2023-04-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Guanghua Shu ,  Reza Sadri ,  Jacob Jensen ,  Sahil Khanna

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A system maintains a data store for managing machine-learning (ML) models and features that are used by the models. The system generates a graph including nodes for each model and a node for each feature, and edges linking models and features that are used by the models. For a new model to be trained, the system receives a proposed feature corresponding to a node in the graph, and identifies one or more candidate features corresponding to nodes in the graph based in part on relevancy scores between the proposed feature with other features corresponding to nodes in the graph. The system presents in a user interface a suggestion to use one or more candidate features with the new model. Responsive to receiving a user selection of at least one candidate feature, the system causes the new model to be trained using the selected candidate feature and the proposed feature.

公开(公告)号：US20240362455A1

公开(公告)日：2024-10-31

申请号：US18140210

申请日：2023-04-27

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Guanghua Shu ,  Reza Sadri ,  Jacob Jensen ,  Sahil Khanna

IPC: G06N3/045 ,  G06N3/09

CPC classification number: G06N3/045 ,  G06N3/09

Abstract: A feature management system (the “system”) receives information about a new machine learning (ML) model to be trained. The information includes metadata about the new model. The system applies a trained feature prediction model to the information about the new model and metadata about a plurality of features. The feature prediction model is trained to predict a probability that each of the plurality of features should be selected as an input feature for the new model. The feature management system identifies one or more candidate features based on an output probability score of the feature prediction model. The system presents in a user interface a suggestion to use the one or more candidate features with the new model. The system selects at least one candidate feature and causes the new model to be trained using a set of input features, including the selected candidate feature.

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：US20240331013A1

公开(公告)日：2024-10-03

申请号：US18129454

申请日：2023-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Natalia Botía Chaparro ,  Sean D'Auria ,  Rohan Salantry

IPC: G06Q30/0601 ,  G06Q30/0201

CPC classification number: G06Q30/0633 ,  G06Q30/0201

Abstract: An online concierge system receives information describing one or more interactions with a shared shopping list by at least one of multiple users associated with the shared shopping list and identifies a set of attributes associated with the shared shopping list, in which the set of attributes is based at least in part on the interaction(s). The system accesses a machine learning model trained to predict a time that a user associated with the shared shopping list will place an order including one or more items in the shared shopping list and applies the model to the set of attributes to predict the time. The system generates a notification based at least in part on the time that the user is predicted to place the order and sends the notification to one or more client devices associated with one or more users associated with the shared shopping list.

公开(公告)号：US20240289873A1

公开(公告)日：2024-08-29

申请号：US18113562

申请日：2023-02-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chakshu Ahuja ,  Ramasubramanian Balasubramanian ,  Karuna Ahuja

IPC: G06Q30/08 ,  G06N20/00 ,  G06Q30/0601

CPC classification number: G06Q30/08 ,  G06N20/00 ,  G06Q30/0613

Abstract: An online system manages campaign participation by a plurality of sub-campaigns with a reinforcement learning model. The reinforcement learning model determines a current context and determines an action that affects the participation of the individual sub-campaigns. The reinforcement learning model may thus dynamically control the participation over time as different objectives are achieved by the sub-campaigns and may account for the different contexts that change over time.

公开(公告)号：US20240289856A1

公开(公告)日：2024-08-29

申请号：US18176233

申请日：2023-02-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Robert Russel Adams

IPC: G06Q30/0601 ,  G06K7/14 ,  H04B10/116 ,  H04L15/04 ,  H04L67/141

CPC classification number: G06Q30/0613 ,  G06K7/1413 ,  G06K7/1417 ,  H04B10/116 ,  H04L15/04 ,  H04L67/141 ,  H04N23/635

Abstract: An online concierge system for establishing a communication session between devices using a light signal. A first client device captures video data depicting a light emitter of another client device. The first client device detects a light signal transmitted by the light emitter in the video data. The first client device extracts a handshake identifier from the light signal by decoding the light signal. A machine learning model may be used to translate the light signal into a numerical or an alphanumerical identifier. The first client device established a communication session with the other client device by transmitting a request to establish the communication session via an online concierge system. The request contains the extracted handshake identifier.

公开(公告)号：US20240281817A1

公开(公告)日：2024-08-22

申请号：US18172956

申请日：2023-02-22

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Michael Joseph Sanzari ,  Graham Beauregard ,  Griffin Kelly

IPC: G06Q20/40 ,  B62B3/14 ,  B62B5/00 ,  G06Q20/20 ,  G06V10/70 ,  G06V20/64

CPC classification number: G06Q20/4016 ,  B62B3/1424 ,  B62B5/0096 ,  G06Q20/208 ,  G06V10/70 ,  G06V20/64

Abstract: An automated checkout system accesses an image of an item inside a shopping cart and receives an identifier determined for the item inside the cart. The automated checkout system determines a load measurement for the item inside the cart using load sensors coupled to the cart. The automated checkout system encodes a feature vector of the item based at least on the determined weight, the accessed image, and the determined identifier. The automated checkout system inputs the feature vector to a machine-learning model to determine a confidence score describing a likelihood that the identifier determined for the item matches the item placed inside the cart. If the confidence score is less than a threshold confidence score, the automated checkout system generates a notification alerting an operator of an anomaly in the identifier.

公开(公告)号：US20240249334A1

公开(公告)日：2024-07-25

申请号：US18158219

申请日：2023-01-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Esther Vasiete Allas

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

CPC classification number: G06Q30/0631 ,  G06Q10/083 ,  G06Q10/087 ,  G06Q30/0625 ,  G06Q30/0633 ,  G06Q30/0641

Abstract: An online concierge system generates a personalized storefront user interface to recommend items for purchase and delivery to a customer. The online concierge system obtains a user identifier for the customer and generates a set of recommended search terms that it predicts will be relevant to the customer. The recommended search terms may be identified at least in part by mapping items previously purchased by the customer to search queries that resulted in purchases of that item across a population of customers of the online concierge system. The online concierge system then executes respective search queries for the each of the set of search terms to generate respective search result sets for each of the recommended search terms. The search result sets may be presented as respective search queries on a user interface screen of a customer client device.

公开(公告)号：US20240202771A1

公开(公告)日：2024-06-20

申请号：US18084938

申请日：2022-12-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ruhan Zhang ,  Jeffrey Moulton ,  Nicholas George Gordenier

IPC: G06Q30/0241 ,  G06Q30/0242

CPC classification number: G06Q30/0249 ,  G06Q30/0242 ,  G06Q30/0277

Abstract: An online concierge system may conduct experiments in presentation of prioritized items for content campaigns with offline simulations. The offline simulation may use a joint budget for the content campaign used by several experimental variations that affect prioritized content presentation. To correct for distortions that may occur from differing rates of budget use in the variations when the budget is reached before a total period for the experiment, the budget use of each variation is compared to a “fair value” to determine an adjustment to the metrics determined in the experiment. Variants that exceed the fair value may have their metrics capping to the portion allocable to a budget use that does not exceed the fair value, while variants that use less than the fair value may have the metrics extrapolated to account for the additional budget that would be available with a fair value budget.

公开(公告)号：US20240193627A1

公开(公告)日：2024-06-13

申请号：US18079836

申请日：2022-12-12

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Jason Sanchez ,  Eric Hermann ,  Abhinav Darbari ,  Haochen Luo ,  Maksym Brodin ,  Sam Crocker

IPC: G06Q30/0202 ,  G06Q10/083

CPC classification number: G06Q30/0202 ,  G06Q10/083

Abstract: An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.