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公开(公告)号：US20240362678A1
公开(公告)日：2024-10-31
申请号：US18141396
申请日：2023-04-29
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Chakshu Ahuja , Girija Narlikar , Karuna Ahuja
IPC: G06Q30/0251 , G06N20/00
CPC classification number: G06Q30/0261 , G06N20/00
Abstract: For each retailer location associated with multiple retailers, an online system associated with the retailers receives video data captured within the retailer location by a camera of a client device associated with an online system user. The online system detects, based at least in part on the video data, a location associated with the user within the retailer location and/or an interaction by the user with an item included among an inventory of the retailer location. The online system generates a set of signals associated with the user based at least in part on the detection of the location and/or the interaction. Based at least in part on the set of signals, the online system determines a set of preferences associated with the user, trains a machine learning model to predict a metric associated with the user, and/or sends content for display to a client device associated with the user.
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公开(公告)号：US12243008B2
公开(公告)日：2025-03-04
申请号：US17977734
申请日：2022-10-31
Applicant: Maplebear Inc.
Inventor： Karuna Ahuja , Girija Narlikar , Sneha Chandrababu , Gowri Rajeev , Lan Wang , Chakshu Ahuja , Sonal Jain
IPC: G06Q10/00 , G06K7/10 , G06K7/14 , G06Q10/087 , G06Q30/0202 , G06Q30/0601
Abstract: An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.
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公开(公告)号：US20250069723A1
公开(公告)日：2025-02-27
申请号：US18455498
申请日：2023-08-24
Applicant: Maplebear Inc.
Inventor： Bhavya Gulati , Chakshu Ahuja , Girija Narlikar , Karuna Ahuja , Radhika Goel
Abstract: The online concierge system accesses item data for a target item and item data for a candidate item. The online concierge system generates a replacement score based on the accessed item data and generates a nutrition score based on the item data for the candidate item. The online concierge system generates a nutrition replacement score based on the replacement score and the nutrition score and stores a training example based on the item data and the nutrition replacement score. The training example may include the item data for the target item and the candidate item and a label based on the nutrition replacement score.
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公开(公告)号：US20240144172A1
公开(公告)日：2024-05-02
申请号：US17977724
申请日：2022-10-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Apurvaa Subramanian , Girija Narlikar , Chakshu Ahuja , Karuna Ahuja , Radhika Goel , Sneha Chandrababu
CPC classification number: G06Q10/087 , G06Q10/083 , G06Q30/0206 , G06Q30/0635
Abstract: An online concierge system facilitates a concierge service for ordering, procurement, and delivery of food items from physical retailers. The order fulfillment is based in part on automatically inferring one or more quality metrics, such as remaining shelf-life, associated with perishable food items. A picker shopping on behalf of a customer may capture images of available food items for the order using a picker client device. The images are processed through a machine learning model to infer the one or more quality metrics, and a price is then determined based in part on a dynamic pricing model. The online concierge system communicates with a customer client device to meet quality characteristics and pricing preferences set by the customer. The online concierge system may further facilitate a checkout process for the items obtained by the picker and may facilitate delivery of the items by the picker to the customer.
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公开(公告)号：US20240112238A1
公开(公告)日：2024-04-04
申请号：US17956217
申请日：2022-09-29
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Girija Narlikar , Karuna Ahuja , Radhika Goel , Chakshu Ahuja , Xiaoming Zhang , Devlina Das
CPC classification number: G06Q30/0631 , G06Q30/0203 , G06Q30/0603 , G06Q30/08 , G06Q50/01
Abstract: An online concierge system receives a request to purchase a gift for a user of the system and retrieves a profile associated with the user. Based on the profile and attributes of items included among inventories of one or more retailer locations, the system identifies a set of candidate items for which the user is likely to have an affinity. The system accesses a machine learning model trained to predict a giftability score for an item and applies the model to attributes of each candidate item to predict its giftability score. Based on its giftability score and the profile, the system computes a composite score for each candidate item indicating an appropriateness of gifting the candidate item to the user. The system ranks the set of candidate items based on the composite scores and selects one or more suggested items for gifting to the user based on the ranking.
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Patent Agency Ranking
公开(公告)号：US20250131482A1
公开(公告)日：2025-04-24
申请号：US18490683
申请日：2023-10-19
Applicant: Maplebear Inc.
Inventor： Chakshu Ahuja , Karuna Ahuja , Radhika Goel
IPC: G06Q30/0601 , G06F16/2457
Abstract: An online concierge system receives a free-text query describing items and constraints from a client device associated with a user. The system generates a prompt including the query and a request to identify the items and constraints. The system provides the prompt to a large language model, extracts, from an output of the model, the constraints and one or more categories associated with the items, and identifies retailers based on user data associated with the user. For each retailer, the system identifies a set of items associated with each category, determines, based on the constraints, a combination of a subset of items associated with each category, and computes a score for the combination based on the user data and item data associated with items in the combination. The system ranks the combinations based on the scores and sends information describing a ranked set of the combinations to the client device.
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公开(公告)号：US20240420210A1
公开(公告)日：2024-12-19
申请号：US18211107
申请日：2023-06-16
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Bhavya Gulati , Chakshu Ahuja , Karuna Ahuja , Girija Narlikar
IPC: G06Q30/0601
Abstract: An online concierge system receives information describing items in orders placed by a customer and a sequence of events associated with each order and identifies an impulse item included in the orders based on a set of rules, attributes of each item, and/or the sequence of events. The system applies a model to predict a measure of similarity between the impulse item and each of multiple candidate items and identifies larger-size variants of the impulse item based on this prediction and attributes of the impulse item and each candidate item. The system applies another model to predict a likelihood the customer will order each variant, computes a recommendation score for each variant based on this prediction, and determines whether to recommend each variant based on the score. Based on the determination, the system generates and sends a recommendation for a variant to a client device associated with the customer.
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公开(公告)号：US20240338746A1
公开(公告)日：2024-10-10
申请号：US18627280
申请日：2024-04-04
Applicant: Maplebear Inc.
Inventor： Karuna Ahuja , Girija Narlikar , Chakshu Ahuja , Apurvaa Subramaniam
IPC: G06Q30/0601
CPC classification number: G06Q30/0631
Abstract: An online system employs real-time and pre-generated images for recommendation. The system leverages generative machine-learning models, such as diffusion models, to generate images dynamically. The selection and creation of these images rely upon user data and session data, which are collected during a user's application session. These data are employed to generate a text prompt string, which directs the image generation process. For instances where real-time computation may be a resource constraint, the system utilizes pre-generated images linked to user-context clusters—data set groupings related to user characteristics and session context. This method enables the system to present tailored recommendations to the user, making use of both dynamic generation and pre-existing image resources, thereby optimizing the balance between customization, computational resources, and latency.
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9.发明公开公开(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.
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10.发明公开公开(公告)号：US20240144173A1
公开(公告)日：2024-05-02
申请号：US17977734
申请日：2022-10-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Karuna Ahuja , Girija Narlikar , Sneha Chandrababu , Gowri Rajeev , Lan Wang , Chakshu Ahuja , Sonal Jain
CPC classification number: G06Q10/087 , G06K7/10366 , G06K7/1417 , G06Q30/0202 , G06Q30/0623
Abstract: An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.
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