公开(公告)号：US20250005644A1

公开(公告)日：2025-01-02

申请号：US18217324

申请日：2023-06-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Chuanwei Ruan ,  Yunzhi Ye ,  Han Li ,  David Vengerov ,  Allan Stewart ,  Aref Kashani Nejad

IPC: G06Q30/0601

Abstract: An online system accesses a two-tower model trained to identify candidate items for presentation to users, in which the model includes an item tower trained to compute item embeddings and a user tower trained to compute user embeddings. The user tower includes a long-term sub-tower trained to compute long-term embeddings for users and a short-term sub-tower trained to compute short-term embeddings for users. The model is trained based on item data associated with items, user data associated with users, and session data associated with user sessions. The system uses the item tower to compute an item embedding for each of multiple candidate items. The system also uses the long-term sub-tower to compute a long-term embedding for a user. The system then receives session data associated with a current session of the user and uses the short-term sub-tower to compute a short-term embedding for the user based on this session data.

公开(公告)号：US20240428309A1

公开(公告)日：2024-12-26

申请号：US18214150

申请日：2023-06-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Robert Fletcher ,  Ramasubramanian Balasubramanian ,  Tilman Drerup ,  Sharath Rao Karikurve

IPC: G06Q30/0601

Abstract: Based on logged information about prior events, an online concierge system generates a set of location metrics that quantify properties of locations such as retailers at which items may be acquired, and residences to which the items are brought. The location metrics can be used for a variety of purposes to aid customers or other users of the online concierge system, such as providing the users with more information (e.g., likely delivery delays) or alternative options (e.g., pricing options), or emphasizing options that the location metrics indicate would be of particular value to the user. To determine whether to emphasize a particular option, the online concierge system applies a machine-learned model that predicts whether emphasizing that option would effect a positive change in user behavior, relative to not emphasizing it.

公开(公告)号：US20240428125A1

公开(公告)日：2024-12-26

申请号：US18339203

申请日：2023-06-21

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Amalia Rothschild-Keita ,  Brent Scheibelhut ,  Mark Oberemk ,  Hua Xiao ,  Shaun Navin Maharaj ,  Taha Amjad

IPC: G06N20/00

Abstract: An online concierge system uses a findability machine-learning model to predict the findability of items within a physical area. The findability model is a machine-learning model that is trained to compute findability scores, which are scores that represent the ease or difficulty of finding items within a physical area. The findability model computes findability scores for items based on an item map describing the locations of items within a physical area. The findability model is trained based on data describing pickers that collect items to service orders for the online concierge system. The online concierge system aggregates this information across a set of pickers to generate training examples to train the findability model. These training examples include item data for an item, an item map data describing an item map for the physical area, and a label that indicates a findability score for that item/item map pair.

公开(公告)号：US20240427808A1

公开(公告)日：2024-12-26

申请号：US18214275

申请日：2023-06-26

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vinesh Reddy Gudla ,  Sudha Rani Kolavali ,  Taesik Na ,  Xiao Xiao ,  Nkemakonam Paulet Okoye

IPC: G06F16/332 ,  G06F16/33

Abstract: Embodiments relate to using a large language model (LLM) to generate a list of items at an online system with a user defined constraint. The online system receives a query that includes at least one constraint. The online system generates a prompt for input into the LLM, based at least in part on the query. The online system requests the LLM to generate, based on the prompt, a set of constraints for a set of item types. The online system generates a list of candidate items by searching through a set of items stored in one or more non-transitory computer-readable media using the set of constraints for the set of item types. The online system causes a device of the user to display a user interface with the list of items for inclusion into a cart, the list of items obtained from the list of candidate items.

公开(公告)号：US20240427559A1

公开(公告)日：2024-12-26

申请号：US18213773

申请日：2023-06-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Aoshi Li ,  Kevin Green ,  Zhongqiang Liang ,  Francois Campbell ,  Mengyu Zhang

IPC: G06F8/20 ,  G06F8/70

Abstract: A system validates code ownership of software components identified in a build process. The system receives a pull request identifying a set of software components. The system analyzes code ownership of each software component using machine learning. The system provides features describing the software components as input to a machine learning model. The system determines based on the output of the machine learning model, whether the code ownership of the software component can be determined accurately. If the system determines that a software component identified by the pull request cannot be determined with high accuracy, the system may block the pull request or send a message indicating that the code ownership of a software component cannot be determined accurately.

公开(公告)号：US20240420051A1

公开(公告)日：2024-12-19

申请号：US18211124

申请日：2023-06-16

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Rahul Makhijani ,  Pak Tao Lee ,  Shang Li

IPC: G06Q10/0631

Abstract: Embodiments relate to order specific expansion of an area that encompasses pickers available for accepting an order placed with an online system. The online system accesses a computer model trained to predict an attractiveness metric for the order and applies the computer model to predict a value of the attractiveness metric for a first order. The online system classifies the first order into a first set or a second set, based on the value of the attractiveness metric and a threshold. Based on the classification, the online system expands over time a size of an area that encompasses a set of pickers available for accepting the first order. The online system causes a device of each picker in the set of available pickers located within the area of the expanded size to display an availability of the first order for acceptance by each picker in the set of available pickers.

公开(公告)号：US20240386471A1

公开(公告)日：2024-11-21

申请号：US18199938

申请日：2023-05-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Peter Vu ,  Ziwei Shi ,  Joseph Cohen ,  Emily Silberstein ,  Krishna Kumar Selvam ,  Jaclyn Tandler ,  Adrian McLean ,  Nicholas Rose

IPC: G06Q30/0601

Abstract: A concierge system sends batches of orders to pickers that they can review and accept in a batch list on a client device. Each batch in the batch list is presented with a hide option that enables the picker to hide a batch that they do not intend to accept. In response to receiving a hide signal, the system extracts features associated with the batch and stores those features with a negative indication of the picker towards the batch. The hide signal provides the system with a higher quality signal indicating the picker's negative intent regarding an order, as compared to simply ignoring the order in favor of fulfilling another order. This higher quality signal is then used to train models to better predict events related to the pickers' acceptance of orders, such as for ranking orders for pickers or for predicting fulfillment times.

公开(公告)号：US20240289867A1

公开(公告)日：2024-08-29

申请号：US18113870

申请日：2023-02-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Xuan Zhang ,  Vinesh Reddy Gudla ,  Tejaswi Tenneti ,  Haixun Wang

IPC: G06Q30/0601

CPC classification number: G06Q30/0633 ,  G06Q30/0619 ,  G06Q30/0631

Abstract: An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.

公开(公告)号：US20240289731A1

公开(公告)日：2024-08-29

申请号：US18113566

申请日：2023-02-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Matthew Donghyun Kim ,  Michael Chen ,  Marina Tanasyuk ,  Caleb Grisell ,  Adrian Mclean ,  Ajay Pankaj Sampat ,  Yuan Gao

IPC: G06Q10/0834 ,  G06Q10/083

CPC classification number: G06Q10/08345 ,  G06Q10/0631 ,  G06Q10/0838 ,  G06Q10/1053

Abstract: An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

公开(公告)号：US20240249238A1

公开(公告)日：2024-07-25

申请号：US18158368

申请日：2023-01-23

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Konrad Gustav Miziolek ,  Parikshit Verma

IPC: G06Q10/087 ,  G06N5/022

CPC classification number: G06Q10/087 ,  G06N5/022

Abstract: A method or a system for using machine learning to dynamically boost order delivery time. The system receives an order associated with a delivery time and a compensation value. The system applies a machine-learning model to an order to predict an amount of lateness time that an order will be fulfilled late. The system then determines a lateness value based in part on the predicted amount of lateness time. The lateness value indicates a penalty caused by the predicted amount of lateness time. For each of a plurality of proposed boost amounts for the compensation value, the system determines an uplift, indicating a reduction of the lateness value caused by the boost amount. The system then selects a boost amount from the plurality of boost amounts based in part on the determined uplifts, causing the order to be accepted sooner to thereby boost order delivery time.