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：US20240185324A1

公开(公告)日：2024-06-06

申请号：US18442466

申请日：2024-02-15

Applicant: Maplebear Inc.

Inventor： Ramasubramanian Balasubramanian ,  Girija Narlikar ,  Omar Alonso

IPC: G06Q30/0601 ,  G06N3/08

CPC classification number: G06Q30/0631 ,  G06N3/08

Abstract: An online concierge system generates recipe embeddings for recipes including multiple items and user embeddings for users, with the recipe embeddings and user embeddings in a common latent space. To generate the user embeddings and the recipe embeddings, a model includes separate layers for a user model outputting user embeddings and for a recipe model outputting recipe embeddings. When training the model, a weight matrix generates a predicted dietary preference type for a user embedding and for a recipe embedding and adjusts the user model or the recipe model based on differences between the predicted dietary preference type and a dietary preference type applied to the user embedding and to the recipe embedding. Additionally cross-modal layers generate a predicted user embedding from a recipe embedding and generate a predicted recipe embedding from a user embedding that are used to further refine the user model and the recipe model.

公开(公告)号：US20240104631A1

公开(公告)日：2024-03-28

申请号：US18528744

申请日：2023-12-04

Applicant: Maplebear Inc.

Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

IPC: G06Q30/0601 ,  G06Q30/0202 ,  G06Q30/0241

CPC classification number: G06Q30/0631 ,  G06Q30/0202 ,  G06Q30/0241

Abstract: An online concierge system uses a machine learning click through rate model to select promoted items based on user embeddings, item embeddings, and search query embeddings. Embeddings obtained by an embedding model may be used as inputs to the click through rate model. The embedding model may be trained using different actions to score the strength of a customer interaction with an item. For example, a customer purchasing an item may be a stronger signal than a customer placing an item in a shopping cart, which in turn may be a stronger signal than a customer clicking on an item. The online concierge system generates a ranking of candidate promoted items based on the search query and using the click through rate model. Based on the ranking, the online concierge system displays promoted items along with the organic search results to the customer.

公开(公告)号：US20240005096A1

公开(公告)日：2024-01-04

申请号：US17855799

申请日：2022-07-01

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

IPC: G06F40/284 ,  G06F40/186 ,  G06N5/02

CPC classification number: G06F40/284 ,  G06F40/186 ,  G06N5/022

Abstract: A masked language model is used to predict an attribute of an object, such as a physical item or product based on the predicted value of a masked token. The masked language model may be trained on a general corpus of text for the language, such that the masked language model learns context and text token relationships. Information about the object may then be added to a query template that structures the item information in an attribute query that may be interpretable by the masked language model to provide a resulting token related to the provided information or to confirm or reject an attribute specified in the query template.

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：US12051081B2

公开(公告)日：2024-07-30

申请号：US17514177

申请日：2021-10-29

Applicant: Maplebear Inc.

Inventor： Chuanwei Ruan ,  Ramasubramanian Balasubramanian ,  Peng Qi

IPC: G06Q30/02 ,  G06Q10/087 ,  G06Q30/0202

CPC classification number: G06Q30/0202 ,  G06Q10/087

Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

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：US20240070210A1

公开(公告)日：2024-02-29

申请号：US17899441

申请日：2022-08-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ramasubramanian Balasubramanian ,  Taesik Na ,  Karuna Ahuja

IPC: G06F16/9532 ,  G06Q30/06

CPC classification number: G06F16/9532 ,  G06Q30/0631

Abstract: A computer-implemented method for suggesting keywords as a search term of a content item includes receiving, from a content provider, information about the content item in a database of content items. The method further includes generating a set of seed keywords related to the content item, and expanding the set of seed keywords to a plurality of candidate keywords. The plurality of candidate keywords are then scored based, at least in part, on an engagement metric measuring a user engagement with the content item in response to being presented with results from a search query comprising the candidate keyword. A candidate keyword is then selected from the plurality of candidate keywords based on the scoring, and stored relationally to the content item to define an audience for a recommendation about the content item, providing a suggestion to the content provider.

公开(公告)号：US20230385886A1

公开(公告)日：2023-11-30

申请号：US17752800

申请日：2022-05-24

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Daniel Summerhays ,  Ramasubramanian Balasubramanian

IPC: G06Q30/06 ,  G06N5/02

CPC classification number: G06Q30/0601 ,  G06N5/022

Abstract: An online concierge system uses a cumulative incrementality score to evaluate the performance of incrementality models used by the online concierge system to identify users for treatment. The online concierge system applies an incrementality model to a set of examples to generate predicted incrementality scores for the examples. The online concierge system ranks the examples based on the predicted incrementality scores for the examples and groups the examples based on their rankings. The online concierge system iteratively computes cumulative incrementality scores for each grouping based on the examples of each grouping, and computes a final cumulative incrementality score for the incrementality model based on each of the cumulative incrementality scores.

公开(公告)号：US11972464B2

公开(公告)日：2024-04-30

申请号：US17752800

申请日：2022-05-24

Applicant: Maplebear Inc.

Inventor： Daniel Summerhays ,  Ramasubramanian Balasubramanian

IPC: G06Q30/0601 ,  G06N5/022

CPC classification number: G06Q30/0601 ,  G06N5/022

Abstract: An online concierge system uses a cumulative incrementality score to evaluate the performance of incrementality models used by the online concierge system to identify users for treatment. The online concierge system applies an incrementality model to a set of examples to generate predicted incrementality scores for the examples. The online concierge system ranks the examples based on the predicted incrementality scores for the examples and groups the examples based on their rankings. The online concierge system iteratively computes cumulative incrementality scores for each grouping based on the examples of each grouping, and computes a final cumulative incrementality score for the incrementality model based on each of the cumulative incrementality scores.