公开(公告)号：US20240378637A1

公开(公告)日：2024-11-14

申请号：US18196395

申请日：2023-05-11

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vijay Sivasubramaniam ,  Hang Li ,  Yingshi Zhang ,  Senduren Sivakumar

IPC: G06Q30/0242 ,  G06Q30/0241 ,  G06Q30/0251

Abstract: An online system receives, from an entity, a content item to be presented to online system users, in which the content item includes a landing page to a third-party website. The system accesses the landing page, identifies a set of items included in it, and determines whether the landing page is configured for performing one or more types of conversions associated with each item. The system matches one or more of the items with one or more target objects based on the determination and associates the matched target object(s) with the content item. The system receives information describing one or more impression events associated with presenting the content item to a user and information describing a conversion associated with a target object associated with the content item performed by the user, applies an attribution model to determine a contribution of the impression event(s) to the conversion, and reports the contribution.

公开(公告)号：US20240362696A1

公开(公告)日：2024-10-31

申请号：US18643890

申请日：2024-04-23

Applicant: Maplebear Inc.

Inventor： Shishir Kumar Prasad ,  Ahsaas Bajaj

IPC: G06Q30/0601 ,  G06F40/40 ,  G06V30/10

CPC classification number: G06Q30/0629 ,  G06F40/40 ,  G06V30/10

Abstract: An online system uses a machine learning based language model, for example, a large language model (LLM) to identify replacement items for an item that may not be available at a store. The online system receives a request for an item and determines that the requested item is not available. The online system identifies a replacement item. If the online system determines that the replacement item has a replacement score below a threshold value indicating a low quality of replacement for the requested item, it uses a machine learning based language model, for example, a large language model to generate an explanation for why the replacement item has a replacement score below the threshold value. The online system sends the explanation to a client device.

公开(公告)号：US20240362581A1

公开(公告)日：2024-10-31

申请号：US18141397

申请日：2023-04-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Vladimir Katz ,  Ajay Pankaj Sampat ,  Fangzhou Wang ,  Wenqi Ge ,  Charles Durham ,  Kevin Shepherd

IPC: G06Q10/087 ,  G06N7/01 ,  G06N20/00

CPC classification number: G06Q10/087 ,  G06N7/01 ,  G06N20/00

Abstract: An online concierge system allows users to place orders for fulfillment by pickers. Orders have various attributes (e.g., dimensions, weight, contents, etc.), and the pickers may have corresponding characteristics affecting capability of fulfilling orders. To optimize allocation of orders to pickers for fulfillment, the online concierge system trains an order validation model that predicts a probability of a picker encountering a problem fulfilling an order based on characteristics of the picker and attributes of the order. The order validation model is trained from training examples based on previous orders and labels indicating whether a picker encountered a problem with fulfilling the order. The order validation model can then be used to predict deliverability of future orders or to specify limits on one or more attributes of orders for fulfillment.

公开(公告)号：US20240362579A1

公开(公告)日：2024-10-31

申请号：US18141393

申请日：2023-04-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Haochen Luo ,  Kenneth Jason Sanchez ,  Eric Hermann

IPC: G06Q10/087

CPC classification number: G06Q10/087

Abstract: An inventory interaction model predicts user interactions with items of a location for a physical warehouse included with other warehouses in a region. The location is described with features that include the nearby locations and the respective user interactions with the respective item assortments, so that the item interactions for the evaluated location incorporate location-location effects in model predictions. To effectively train the model in the absence of prior interaction data for a location, training examples are generated from existing locations and user interaction data of item assortments by selecting a portion of the locations for the training examples and including nearby location interaction data, labeling the training example output with item interactions for the location. The trained model is then applied for an item assortment at a location by describing nearby locations in evaluating candidate locations and item assortments.

公开(公告)号：US20240354812A1

公开(公告)日：2024-10-24

申请号：US18137389

申请日：2023-04-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Ming Li ,  Natalie Binns ,  Dmytro Solomadin ,  Zhiyi Fan

IPC: G06Q30/0241

CPC classification number: G06Q30/0276

Abstract: An online system receives information identifying items associated with a brand, a hierarchical taxonomy of the items, and information identifying a retailer associated with the brand. The system applies a machine learning model to predict availabilities of the items at (a) retailer location(s) associated with the retailer, identifies items that are likely available at the retailer location(s), and groups the identified items into categories based on the taxonomy. The system computes an item score for each item based on its popularity, attributes, and/or attributes of a user. The system assigns items in each category to positions within a display unit associated with the category and computes a category score for each category based on the item scores. The system assigns display units associated with the categories to positions within a template based on the category score and generates a page associated with the brand and retailer based on the assignments.

公开(公告)号：US20240354556A1

公开(公告)日：2024-10-24

申请号：US18640231

申请日：2024-04-19

Applicant: Maplebear Inc.

Inventor： Yueyang Rao ,  Brian Lin ,  Angadh Singh ,  Sharath Rao Karikurve ,  Guanghua Shu

IPC: G06N3/0455 ,  G06Q30/0601

CPC classification number: G06N3/0455 ,  G06Q30/0631

Abstract: An online system generates session-based recommendations for a user accessing an application of the online system. The online system receives, from one or more client devices, a sequence of actions performed by a user during a session of an application of an online system. The online system generates a sequence of tokens from the sequence of actions by tokenizing an action to a token representing a respective item identifier. The online system applies a transformer-based machine-learned model to the sequence of tokens to generate predictions for a set of items. The online system selects a subset of items based on the generated predictions for the set of items. The online system generates one or more recommendations to the user from the selected subset of items and displays the recommendations to the user.

公开(公告)号：USD1046912S1

公开(公告)日：2024-10-15

申请号：US29856064

申请日：2022-10-10

Applicant: Maplebear Inc.

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

Abstract: FIG. 1 shows a first embodiment of a display panel of a programmed computer system with a graphical user interface; and,
FIG. 2 shows a second embodiment of a display panel of a programmed computer system with a graphical user interface.
The broken line showing of a portion of a display panel is for the purpose of illustrating environmental structure and forms no part of the claimed design. The broken lines showing of portions of the graphical user interface within the display panel form no part of the claimed design.

公开(公告)号：US20240331015A1

公开(公告)日：2024-10-03

申请号：US18129464

申请日：2023-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Kenneth Jason Sanchez ,  Eric Hermann

IPC: G06Q30/0601 ,  G06Q30/02

CPC classification number: G06Q30/0635 ,  G06Q30/0281 ,  G06Q30/0639

Abstract: An online concierge system facilitates creation of shopping lists of items for ordering from a physical retail store and at least partial self-service fulfillment of orders by the customer. To support fulfillment by the customer, the online concierge system may intelligently select one or more items of the order to be picked by a third-party picker and prepopulated to a shopping cart reserved for the customer in advance of the customer arriving at the retail location. The items for prepopulating may be selected based on various factors that optimize prepopulation decisions on an item-by-item basis in accordance with various machine learning models. The online concierge system may furthermore facilitate procurement of the remaining items by the customer through a customer client device that may track item procurement and/or provide guidance for locating items.

公开(公告)号：US20240330977A1

公开(公告)日：2024-10-03

申请号：US18129447

申请日：2023-03-31

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Cheng Jia ,  Justin Miller ,  Yan Zhuang ,  Anvar Gazizov ,  Hassan Djirdeh ,  Aakarsh Madhavan ,  Brijendra Nag ,  Ji Chao Zhang

IPC: G06Q30/0242 ,  G06Q30/0273

CPC classification number: G06Q30/0243 ,  G06Q30/0275

Abstract: A keyword campaign automatically groups keywords for customized override bids for the keyword group. The keywords of a campaign may be analyzed by a computer model to predict membership in a category in addition to the likelihood that the bid of the keyword will be modified. The keyword groups may be automatically generated based on the predictions, and performance metrics are evaluated for the keyword groups at one or more modified bids. The performance metrics of the keyword groups at the modified bids may then be used to set override bids. The automatically generated keyword groups and performance metrics permit a sponsor to intelligently group and customize keyword bids with reduced interface interactions and without requiring individual keyword bid adjustments.

公开(公告)号：US12086754B2

公开(公告)日：2024-09-10

申请号：US17752772

申请日：2022-05-24

Applicant: Maplebear Inc.

Inventor： Benjamin Knight ,  Darren Johnson ,  Salmaan Ayaz ,  Saumitra Maheshwari ,  Tomasz Debicki ,  Do Quang Phuoc Dang ,  Valery Vaskabovich

IPC: G06Q10/0833 ,  G06Q10/087 ,  G06Q20/40

CPC classification number: G06Q10/0833 ,  G06Q10/087 ,  G06Q20/4015

Abstract: An online concierge system performs asynchronous automated correction handling of incorrectly sorted items using point-of-sale data. The online concierge system receives orders from customer client devices and determines a batched order based on the received orders. The online concierge system sends the batched order to a shopper client device for fulfillment. The online concierge system receives transaction data associated with the batched order from a third party system. The online concierge system determines whether a sorting error occurred based on the transaction data and the batched order. In response to determining that a sorting error occurred, the online concierge system sends an instruction to correct the sorting error to the shopper client device.