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公开(公告)号：US20240232976A9
公开(公告)日：2024-07-11
申请号：US18047990
申请日：2022-10-19
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Shitao Wang , Apurvaa Subramaniam , Nicholas George Gordenier
IPC: G06Q30/06
CPC classification number: G06Q30/0631
Abstract: An online concierge system generates an aggregated lift score for a test feature for the online concierge system. The online concierge presents prioritized items from a set of item groups to two sets of users: a test set and a control set. The online concierge system uses the test feature to present prioritized items to users in the test set, and the online concierge system uses existing functionality to present prioritized items to users in the control set. For each test group, the online concierge system creates holdout subsets out of the test set and the control set. The online concierge system tracks user interactions with items in an item group and computes a group lift score for the item group. The online concierge system generates an aggregated lift score for the test feature based on the group lift scores and presents items to users based on the aggregated lift score.
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102.发明公开公开(公告)号：US20240193663A1
公开(公告)日：2024-06-13
申请号：US18064129
申请日：2022-12-09
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Shaun Navin Maharaj , Brent Scheibelhut , Mark Oberemk
IPC: G06Q30/0601
CPC classification number: G06Q30/0631 , G06F40/279
Abstract: A system or a method for using machine learning to automatically route user inquiries to a retailer are presented. The system receives an inquiry from a client device associated with a user. The inquiry includes text content and an image. The system uses a natural language model to analyze the received text to identify a first category of items. The system applies the received image to an image recognition model to identify a second category of items contained in the received image. The system then identifies a retailer that carries items in at least one of the first or second category of items, and suggests the retailer to the user via the client device associated with the user. A retail associate at the retailer can then respond to the inquiry via a client device associated with the retailer.
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103.发明公开公开(公告)号：US20240193540A1
公开(公告)日：2024-06-13
申请号：US18079317
申请日：2022-12-12
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Krishna Kumar Selvam , Ali Soltani Sobh , Kevin Charles Ryan , Bing Hong Leonard How , Rahul Makhijani , Bita Tadayon
IPC: G06Q10/087 , G06N20/00
CPC classification number: G06Q10/087 , G06N20/00
Abstract: An online concierge system accesses and applies a model to predict likelihoods of acceptance of a service request for an order by pickers. The system accesses timespan distributions for accepted service requests and identifies sets of pickers based on the order. Based on the likelihoods and distributions, the system generates simulated responses of the sets of pickers to the service request and trains an additional model based on attributes of the order, the simulated responses, and information associated with corresponding sets of pickers. The system receives a new order, identifies additional sets of pickers based on the new order, and applies the additional model to predict responses of the additional sets of pickers to an additional service request for the new order. Based on the predicted responses and a delivery time associated with the new order, a minimum number of pickers to send the additional service request is determined.
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公开(公告)号：US20240177212A1
公开(公告)日：2024-05-30
申请号：US18072353
申请日：2022-11-30
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Aditya Subramanian , Prakash Putta , Tejaswi Tenneti , Jonathan Lennart Bender , Xiao Xiao , Taesik Na
IPC: G06Q30/0601
CPC classification number: G06Q30/0631
Abstract: To determine search results for an online shopping concierge platform, the platform may receive, from a computing device associated with a customer of an online shopping concierge platform, data describing one or more search parameters input by the customer; identify, based at least in part on the data describing the search parameter(s), products offered by the online shopping concierge platform that are at least in part responsive to the search parameter(s); and determine, for each product and based at least in part on one or more machine learning (ML) models, a relevance of the product to one or more taxonomy levels of a product catalog associated with the online shopping concierge platform, a likelihood that the customer would be offended by inclusion of the product amongst displayed responsive search results, and/or the like.
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105.发明公开公开(公告)号：US20240177108A1
公开(公告)日：2024-05-30
申请号：US18072311
申请日：2022-11-30
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Youdan Xu , Krishna Kumar Selvam , Michael Chen , Radhika Anand , Rebecca Riso , Ajay Sampat
IPC: G06Q10/087 , G06Q30/0202
CPC classification number: G06Q10/087 , G06Q30/0202
Abstract: An online concierge system receives location information associated with pickers and actual orders associated with a geographical zone. A model trained to predict a likelihood an actual order associated with the zone will be available for servicing within a timeframe is accessed and applied to forecasted orders. Each picker is matched to an order for servicing by minimizing a value of a function that is based on a difference between a location associated with each picker matched to an actual order and an associated retailer location, a difference between the location associated with each picker matched to a forecasted order and an associated retailer location, and the predicted likelihood. Recommendations for accepting an actual order, moving to a retailer location associated with a forecasted order, or checking back later with the system are generated based on the matches and sent for display to a client device associated with each picker.
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Patent Agency Ranking
公开(公告)号：US20240104494A1
公开(公告)日：2024-03-28
申请号：US17955415
申请日：2022-09-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Brent Scheibelhut , Shaun Maharaj
IPC: G06Q10/08 , G06V10/774 , G06V10/776 , G06V10/82 , G06V20/68
CPC classification number: G06Q10/087 , G06V10/774 , G06V10/776 , G06V10/82 , G06V20/68
Abstract: An online concierge system may receive multi-angle images of a plurality of instances of a grocery item carried at a physical store. Each instance of the grocery item is associated with one or more multi-angle images that are captured through a checkout process of the instance of the grocery item. The online concierge system may apply a machine learning model to the multi-angle images to identify expiration information of the plurality of instances of the grocery item. The online concierge system may use the identified expiration information to predict that a batch of the grocery item remaining in inventory of the physical store is close to expiration. The online concierge system may generate one or more item-specific suggestions associated with the expiration information with respect to the grocery item offered in the physical store.
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公开(公告)号：US20240104449A1
公开(公告)日：2024-03-28
申请号：US17955395
申请日：2022-09-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Krishna Kumar Selvam , Mouna Cheikhna , Michael Chen , Dylan Wang , Joseph Cohen , Tahmid Shahriar , Graham Adeson , Ajay Pankaj Sampat
CPC classification number: G06Q10/06311 , G06Q10/06398 , G06Q30/0635
Abstract: An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.
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公开(公告)号：US20240104271A1
公开(公告)日：2024-03-28
申请号：US17955468
申请日：2022-09-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Konrad Gustav Miziolek , Jacob Jensen
Abstract: A variation testing system environment for simulating adaptive experiments of objects is disclosed. An experiment system conducts one or more simulations of an adaptive experiment that includes a plurality of variants of an object. Simulation results based on the one or more simulations are generated that are indicative of at least an estimated amount of time to conduct a real-world adaptive experiment based on the one or more simulations.
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公开(公告)号：US20240070745A1
公开(公告)日：2024-02-29
申请号：US17899190
申请日：2022-08-30
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Sharath Rao Karikurve
CPC classification number: G06Q30/0631 , G06Q30/0201 , G06Q30/0202 , G06Q30/0625
Abstract: An online concierge system recommends a larger size variant for replacement. The system receives one or more items for an order from a user. The one or more items include a first item. The system identifies a set of candidate replacement items for the first item, and the candidate replacement items comprise one or more larger size variants. The system estimates a benefit value for each of the candidate larger size variants to replace the first item and applies a machine learned acceptance model to each candidate larger size variant to predict a likelihood that the user would accept a suggestion to replace the respective candidate larger size variant for the first item. Based on the estimated benefit value and the predicted likelihood, the system determines a larger size variant as a replacement item and sends the replacement item for display in a user interface on a user device.
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110.发明公开公开(公告)号：US20240070715A1
公开(公告)日：2024-02-29
申请号：US17897049
申请日：2022-08-26
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Konrad Gustav Miziolek
CPC classification number: G06Q30/0243 , G06N3/126
Abstract: An online system generates a set of genomic representations, each including multiple genes, in which each gene represents users assigned to a control or test group for performing a test. A metric is identified based on a treatment associated with the test group and a score for each representation is computed based on a difference between two values, in which each value is based on the metric associated with users assigned to the test or control group. A propagation process is executed by identifying representations having at least a threshold score, propagating genes included in the representations to an additional set of representations through recombination and/or mutation, and computing the score for each additional representation. The propagation process is repeated for each additional set of representations until stopping criteria are met and a representation is selected based on scores associated with one or more representations.
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