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公开(公告)号：US20230306023A1
公开(公告)日：2023-09-28
申请号：US17668358
申请日：2022-02-09
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Taesik Na , Yuqing Xie , Tejaswi Tenneti , Haixun Wang
IPC: G06F16/2453 , G06F16/2457 , G06F16/242 , G06F16/28 , G06N20/00 , G06K9/62
CPC classification number: G06F16/24534 , G06F16/2448 , G06F16/24578 , G06F16/283 , G06K9/6257 , G06N20/00
Abstract: An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.
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公开(公告)号：US20240289867A1
公开(公告)日：2024-08-29
申请号：US18113870
申请日：2023-02-24
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Xuan Zhang , Vinesh Reddy Gudla , Tejaswi Tenneti , Haixun Wang
IPC: G06Q30/0601
CPC classification number: G06Q30/0633 , G06Q30/0619 , G06Q30/0631
Abstract: An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.
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公开(公告)号：US20230056148A1
公开(公告)日：2023-02-23
申请号：US17406027
申请日：2021-08-18
Applicant: Maplebear Inc.(dba Instacart)
Inventor： Negin Entezari , Sharath Rao Karikurve , Shishir Kumar Prasad , Haixun Wang
Abstract: An online concierge shopping system identifies candidate items to a user for inclusion in an order based on prior user inclusion of items in orders and items currently included in the order. From a multi-dimensional tensor generated from cooccurrences of items in orders from various users, the online concierge system generates item embeddings and user embeddings in a common latent space by decomposing the multi-dimensional tensor. From items included in an order, the online concierge system generates an order embedding from item embeddings of the items included in the order. Scores for candidate items are determined based on similarity of item embeddings for the candidate items to the order embedding. Candidate items are selected based on their scores, with the selected candidate items identified to the user.
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4.发明公开公开(公告)号：US20230252032A1
公开(公告)日：2023-08-10
申请号：US17666531
申请日：2022-02-07
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Taesik Na , Zhihong Xu , Guanghua Shu , Tejaswi Tenneti , Haixun Wang
IPC: G06F16/2457 , G06F16/242
CPC classification number: G06F16/24578 , G06F16/2438
Abstract: An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.
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公开(公告)号：US20230222529A1
公开(公告)日：2023-07-13
申请号：US17572450
申请日：2022-01-10
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Ze He , Asif Haque , Allan Stewart , Haixun Wang , Xinyu Li
CPC classification number: G06Q30/0205 , G06Q30/0282 , G06Q30/0639 , G06Q30/0635 , G06Q30/0641 , G06N3/049 , G06N3/084
Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.
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Patent Agency Ranking
公开(公告)号：US20230058829A1
公开(公告)日：2023-02-23
申请号：US17407158
申请日：2021-08-19
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Shih-Ting Lin , Jonathan Newman , Min Xie , Haixun Wang
Abstract: An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.
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公开(公告)号：US20230055760A1
公开(公告)日：2023-02-23
申请号：US17405011
申请日：2021-08-17
Applicant: Maplebear Inc.(dba Instacart)
Inventor： Saurav Manchanda , Krishnakumar Subramanian , Haixun Wang , Min Xie
Abstract: An online concierge system trains a classification model as a domain adversarial neural network from training data labeled with source classes from a source domain that do not overlap with target classes from a target domain output by the classification model. The online concierge system maps one or more source classes to a target class. The classification model extracts features from an image, classifies whether an image is from the source domain or the target domain, and predicts a target class for an image from the extracted features. The classification model includes a gradient reversal layer between feature extraction layers and the domain classifier that is used during training, so the feature extraction layers extract domain invariant features from an image.
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公开(公告)号：US20240029132A1
公开(公告)日：2024-01-25
申请号：US17868572
申请日：2022-07-19
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Shih-Ting Lin , Amirali Darvishzadeh , Min Xie , Haixun Wang
CPC classification number: G06Q30/0627 , G06F40/20 , G06N20/00
Abstract: To improve attribute prediction for items, item categories are associated with a schema that is augmented with additional attributes and/or attribute labels. Items may be organized into categories and similar categories may be related to one another, for example in a taxonomy or other organizational structure. An attribute extraction model may be trained for each category based on an initial attribute schema for the respective category and the items of that category. The extraction model trained for one category may be used to identify additional attributes and/or attribute labels for the same or another, related category.
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公开(公告)号：US20230252049A1
公开(公告)日：2023-08-10
申请号：US17736716
申请日：2022-05-04
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Taesik Na , Tejaswi Tenneti , Haixun Wang , Xiao Xiao
IPC: G06F16/28 , G06F16/2457 , G06F16/248 , G06K9/62
CPC classification number: G06F16/285 , G06F16/24573 , G06F16/24575 , G06F16/248 , G06K9/6276
Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.
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公开(公告)号：US20230146336A1
公开(公告)日：2023-05-11
申请号：US17524491
申请日：2021-11-11
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Haixun Wang , Taesik Na , Tejaswi Tenneti , Saurav Manchanda , Min Xie , Chuan Lei
CPC classification number: G06Q30/0603 , G06N20/00
Abstract: To simplify retrieval of items from a database that at least partially satisfy a received query, an online concierge system trains a model that outputs scores for items from the database without initially retrieving items for evaluation by the model. The online concierge system pre-trains the model using natural language inputs corresponding to items from the database, with a natural language input including masked words that the model is trained to predict. Subsequently, the model is refined using multi-task training where a task is trained to predict scores for items from the received query. The online concierge system selects items for display in response to the received query based on the predicted scores.
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