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公开(公告)号：US20240320523A1
公开(公告)日：2024-09-26
申请号：US18605580
申请日：2024-03-14
Applicant: Maplebear Inc.
Inventor： Ryan McColeman , Ryan Martin , Brent Scheibelhut , Shaun Navin Maharaj , Mark Oberemk
Abstract: An online system performs an inference task in conjunction with the model serving system or the interface system to continuously monitor conversations between requesting users and fulfillment users to determine whether the online system can intervene to automatically respond to a message sent by a sending party, rather than prompting the receiving party for a manual reply. Upon inferring that a message can be automatically responded to, the online system automatically provides a response to the message without the receiving party's manual involvement. The online system can further be augmented to classify and reroute certain requesting user or fulfillment user queries that impact an order's end state by intercepting the conversation on behalf of either party and performing one or more automated actions. If the message is action-oriented, the online system may perform one or more automated actions in response to the message.
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公开(公告)号：US20240311840A1
公开(公告)日：2024-09-19
申请号：US18184565
申请日：2023-03-15
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Qing Maio , Robert Border
IPC: G06Q30/016 , G06F18/23213
CPC classification number: G06Q30/016 , G06F18/23213 , G06Q10/0837
Abstract: An online concierge system determines whether a user's appeasement request is fraudulent. The online concierge system compares the user's appeasement request rate to the appeasement request rates of similar users in a user segment identified with a user segmentation model. The online concierge system computes an appeasement model that represents the appeasement request rates of the users in the user segment. The online concierge system computes an outlier score for the user based on the appeasement model. The online concierge system compares the outlier score to a threshold. If the outlier score exceeds the threshold, the online concierge system may determine that the appeasement request is not likely fraudulent and thus applies an appeasement action to the user. If the outlier score does not exceed the threshold, the online concierge system may determine that the appeasement request is likely fraudulent and thus applies a security action to the user.
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公开(公告)号：US20240303711A1
公开(公告)日：2024-09-12
申请号：US18596592
申请日：2024-03-05
Applicant: Maplebear Inc.
Inventor： Li Tan , Tejaswi Tenneti , Shishir Kumar Prasad , Huapu Pan , Allan Stewart , Taesik Na , Tyler Russell Tate , Joshua Roberts , Haixun Wang
IPC: G06Q30/0601 , G06F16/9532
CPC classification number: G06Q30/0627 , G06F16/9532 , G06Q30/0635
Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process high-level natural language queries received from users. The system receives a natural language query from a user of a client device. The system determines contextual information associated with the query. Based on this information, the system generates a prompt for the machine learning based language model. The system receives a response from the machine learning based language model. The system uses the response to generate a search query for a database. The system obtains results returned by the database in response to the search query and provides them to the user. The system allows users to specify high level natural language queries to obtain relevant search results, thereby improving the overall user experience.
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194.发明公开公开(公告)号：US20240289866A1
公开(公告)日：2024-08-29
申请号：US18657781
申请日：2024-05-08
Applicant: Maplebear Inc.
Inventor： Weian Sheng , Peng Qi , Changyao Chen
IPC: G06Q30/0601 , G06N5/04 , G06N20/00
CPC classification number: G06Q30/0631 , G06N5/04 , G06N20/00 , G06Q30/0633 , G06Q30/0641
Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a generic item description. When the online concierge system receives a generic item description from a user for inclusion in an order, the online concierge system uses the taxonomy to select a set of items associated with the generic item description. Based on probabilities of the user purchasing various items of the set, the online concierge system selects an item of the set for inclusion in the order For example, the online concierge system selects an item of the set for which the user has a maximum probability of being purchased. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item of the set.
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195.发明公开公开(公告)号：US20240289862A1
公开(公告)日：2024-08-29
申请号：US18587668
申请日：2024-02-26
Applicant: Maplebear Inc.
Inventor： Haixun Wang , Shishir Kumar Prasad , Tejaswi Tenneti , Li Tan
IPC: G06Q30/0601 , G06N5/04
CPC classification number: G06Q30/0631 , G06N5/04
Abstract: An online system performs an inference task in conjunction with the model serving system to infer one or more purposes of the order of a user that includes a list of ordered items. The model serving system may host a machine-learned language model, and in one instance, the machine-learned language model is a large language model. The online system generates recommendations to the user based on the inferred purpose of the order. The online system may generate one or more recommendations that are equivalent orders having the same or similar purpose as the existing order.
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Patent Agency Ranking
公开(公告)号：US12050960B2
公开(公告)日：2024-07-30
申请号：US17703076
申请日：2022-03-24
Applicant: Maplebear Inc.
Inventor： Shiyuan Yang , Yilin Huang , Wentao Pan , Xiao Zhou
CPC classification number: G06K7/1413 , G06T7/10 , G06F2218/12 , G06T2207/20081
Abstract: A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation.
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公开(公告)号：US20240249333A1
公开(公告)日：2024-07-25
申请号：US18100739
申请日：2023-01-24
Applicant: Maplebear Inc. (dba Instacart)
IPC: G06Q30/0601 , G06N20/00
CPC classification number: G06Q30/0631 , G06N20/00
Abstract: An online concierge system may receive, from a customer, a selection of an item that is associated with a first brand. The online concierge system may extract features associated with the customer and features associated with the item. The online concierge system may input the extracted features to a machine learning model that is trained to predict a degree of association between the customer and the first brand associated with the item. The online concierge system may identify candidate alternatives for replacing the item. The candidate alternatives may include a first alternative that is associated with the first brand and a second alternative that is associated with a second brand different from the first brand. The online concierge system may select, based on the degree of association between the customer and the first brand, one or more candidate alternatives to be presented to the customer to replace the item.
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公开(公告)号：US20240241897A1
公开(公告)日：2024-07-18
申请号：US18415551
申请日：2024-01-17
Applicant: Maplebear Inc.
Inventor： Haixun Wang , Taesik Na , Li Tan , Jian Li , Xiao Xiao
IPC: G06F16/33 , G06F16/338 , G06N20/00
CPC classification number: G06F16/3344 , G06F16/338 , G06N20/00
Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.
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公开(公告)号：US12033205B2
公开(公告)日：2024-07-09
申请号：US17524469
申请日：2021-11-11
Applicant: Maplebear Inc.
Inventor： Girija Narlikar
IPC: G06Q30/0601
CPC classification number: G06Q30/0631 , G06Q30/0641
Abstract: An online concierge system modifies generic item descriptions included in a recipe displayed to a user based on the user's preferences. The online concierge system generates a replacement graph identifying a replacement generic item description for a generic item description, one or more preferences causing replacement of the generic item description with the replacement generic item description, and a replacement quantity of the replacement generic item description. To customize a recipe for the user, the online concierge system selects replacement generic item descriptions for one or more generic item descriptions in the recipe satisfying one or more stored preferences for the user based on the replacement graph.
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公开(公告)号：US20240220859A1
公开(公告)日：2024-07-04
申请号：US18393349
申请日：2023-12-21
Applicant: Maplebear Inc.
Inventor： Jonathan Gu , Bo Xiao , Yixi Ouyang , Jennifer Wiersema , Sophia Li , Matias Cersosimo , Rustin Partow , Levi Boxell , Tilman Drerup , Oleksii Stepanian
Abstract: An online system uses an offline iterative clustering process to evaluate the performance of a set of content selection frameworks. To perform an iteration of the iterative clustering process, an online system clusters the testing example data into a set of clusters. An online system computes a set of framework scores for each of the generated clusters. An online system computes an improvement score for each cluster based on the performance scores of the clusters. To determine whether to perform another iteration, an online system computes an aggregated improvement score based on the improvement scores of the clusters. If an online system determines that the aggregated improvement score does not meet the threshold, an online system performs another iteration of the process above. When an online system finishes the iterative process, an online system outputs the improvement scores of the most-recent iteration.
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