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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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122.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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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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Patent Agency Ranking
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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127.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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公开(公告)号：US20240070583A1
公开(公告)日：2024-02-29
申请号：US17823838
申请日：2022-08-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Amod Mital , Sherin Kurian , Kevin Ryan , Shouvik Dutta , Jason He , Aneesh Mannava , Ralph Samuel , Jagannath Putrevu , Deepak Tirumalasetty , Krishna Kumar Selvam , Wei Gao , Xiangpeng Li
CPC classification number: G06Q10/06316 , G06Q10/087 , G06Q10/06311 , G06Q10/08355
Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.
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公开(公告)号：US20240070577A1
公开(公告)日：2024-02-29
申请号：US17823850
申请日：2022-08-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Krishna Kumar Selvam , Joseph Cohen , Tahmid Sharjar , Neel Sarwal , Darren Johnson , Nicholas Rose , Ajay Pankaj Sampat , Joey Dong
IPC: G06Q10/06
CPC classification number: G06Q10/063114 , G06Q10/06316
Abstract: The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.
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130.发明公开公开(公告)号：US20240070491A1
公开(公告)日：2024-02-29
申请号：US17900533
申请日：2022-08-31
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Lanchao Liu , George Ruan , Zhiqiang Wang , Xiangdong Liang , Jagannath Putrevu , Ganesh Krishnan , Ryan Dick
Abstract: An online system accesses a machine learning model trained to predict behaviors of users of the online system, in which the model is trained based on historical data received by the online system that is associated with the users and demand and supply sides associated with the online system. The online system identifies a treatment for achieving a goal of the online system and simulates application of the treatment on the demand and supply sides based on the historical data and a set of behaviors predicted for the users. Application of the treatment is simulated by replaying the historical data in association with application of the treatment and applying the model to predict the set of behaviors while replaying the data. The online system measures an effect of application of the treatment on the demand and supply sides based on the simulation, in which the effect is associated with the goal.
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