中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

267 records (took 0.033 seconds)

    Using Language Model To Automatically Generate List Of Items At An Online System Based on a Constraint
    111.
    发明申请
    Using Language Model To Automatically Generate List Of Items At An Online System Based on a Constraint 有权

    公开(公告)号：US20240427808A1

    公开(公告)日：2024-12-26

    申请号：US18214275

    申请日：2023-06-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Vinesh Reddy Gudla ,  Sudha Rani Kolavali ,  Taesik Na ,  Xiao Xiao ,  Nkemakonam Paulet Okoye

    IPC: G06F16/332 ,  G06F16/33

    Abstract: Embodiments relate to using a large language model (LLM) to generate a list of items at an online system with a user defined constraint. The online system receives a query that includes at least one constraint. The online system generates a prompt for input into the LLM, based at least in part on the query. The online system requests the LLM to generate, based on the prompt, a set of constraints for a set of item types. The online system generates a list of candidate items by searching through a set of items stored in one or more non-transitory computer-readable media using the set of constraints for the set of item types. The online system causes a device of the user to display a user interface with the list of items for inclusion into a cart, the list of items obtained from the list of candidate items.

    VALIDATING CODE OWNERSHIP OF SOFTWARE COMPONENTS IN A SOFTWARE DEVELOPMENT SYSTEM
    112.
    发明申请
    VALIDATING CODE OWNERSHIP OF SOFTWARE COMPONENTS IN A SOFTWARE DEVELOPMENT SYSTEM 有权

    公开(公告)号：US20240427559A1

    公开(公告)日：2024-12-26

    申请号：US18213773

    申请日：2023-06-23

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Aoshi Li ,  Kevin Green ,  Zhongqiang Liang ,  Francois Campbell ,  Mengyu Zhang

    IPC: G06F8/20 ,  G06F8/70

    Abstract: A system validates code ownership of software components identified in a build process. The system receives a pull request identifying a set of software components. The system analyzes code ownership of each software component using machine learning. The system provides features describing the software components as input to a machine learning model. The system determines based on the output of the machine learning model, whether the code ownership of the software component can be determined accurately. If the system determines that a software component identified by the pull request cannot be determined with high accuracy, the system may block the pull request or send a message indicating that the code ownership of a software component cannot be determined accurately.

    ORDER-SPECIFIC EXPANSION OF AN AREA ENCOMPASSING PICKERS AVAILABLE FOR ACCEPTING ORDERS PLACED WITH AN ONLINE SYSTEM
    113.
    发明申请
    ORDER-SPECIFIC EXPANSION OF AN AREA ENCOMPASSING PICKERS AVAILABLE FOR ACCEPTING ORDERS PLACED WITH AN ONLINE SYSTEM 有权

    公开(公告)号：US20240420051A1

    公开(公告)日：2024-12-19

    申请号：US18211124

    申请日：2023-06-16

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Rahul Makhijani ,  Pak Tao Lee ,  Shang Li

    IPC: G06Q10/0631

    Abstract: Embodiments relate to order specific expansion of an area that encompasses pickers available for accepting an order placed with an online system. The online system accesses a computer model trained to predict an attractiveness metric for the order and applies the computer model to predict a value of the attractiveness metric for a first order. The online system classifies the first order into a first set or a second set, based on the value of the attractiveness metric and a threshold. Based on the classification, the online system expands over time a size of an area that encompasses a set of pickers available for accepting the first order. The online system causes a device of each picker in the set of available pickers located within the area of the expanded size to display an availability of the first order for acceptance by each picker in the set of available pickers.

    User Interface for Obtaining Picker Intent Signals for Training Machine Learning Models
    114.
    发明申请
    User Interface for Obtaining Picker Intent Signals for Training Machine Learning Models 有权

    公开(公告)号：US20240386471A1

    公开(公告)日：2024-11-21

    申请号：US18199938

    申请日：2023-05-20

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Peter Vu ,  Ziwei Shi ,  Joseph Cohen ,  Emily Silberstein ,  Krishna Kumar Selvam ,  Jaclyn Tandler ,  Adrian McLean ,  Nicholas Rose

    IPC: G06Q30/0601

    Abstract: A concierge system sends batches of orders to pickers that they can review and accept in a batch list on a client device. Each batch in the batch list is presented with a hide option that enables the picker to hide a batch that they do not intend to accept. In response to receiving a hide signal, the system extracts features associated with the batch and stores those features with a negative indication of the picker towards the batch. The hide signal provides the system with a higher quality signal indicating the picker's negative intent regarding an order, as compared to simply ignoring the order in favor of fulfilling another order. This higher quality signal is then used to train models to better predict events related to the pickers' acceptance of orders, such as for ranking orders for pickers or for predicting fulfillment times.

    MACHINE LEARNING MODEL FOR DYNAMICALLY BOOSTING ORDER DELIVERY TIME
    117.
    发明公开
    MACHINE LEARNING MODEL FOR DYNAMICALLY BOOSTING ORDER DELIVERY TIME 审中-公开

    公开(公告)号：US20240249238A1

    公开(公告)日：2024-07-25

    申请号：US18158368

    申请日：2023-01-23

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Konrad Gustav Miziolek ,  Parikshit Verma

    IPC: G06Q10/087 ,  G06N5/022

    CPC classification number: G06Q10/087 ,  G06N5/022

    Abstract: A method or a system for using machine learning to dynamically boost order delivery time. The system receives an order associated with a delivery time and a compensation value. The system applies a machine-learning model to an order to predict an amount of lateness time that an order will be fulfilled late. The system then determines a lateness value based in part on the predicted amount of lateness time. The lateness value indicates a penalty caused by the predicted amount of lateness time. For each of a plurality of proposed boost amounts for the compensation value, the system determines an uplift, indicating a reduction of the lateness value caused by the boost amount. The system then selects a boost amount from the plurality of boost amounts based in part on the determined uplifts, causing the order to be accepted sooner to thereby boost order delivery time.

    SCORING IMPROVEMENTS BY TEST FEATURES TO USER INTERACTIONS WITH ITEM GROUPS
    118.
    发明公开
    SCORING IMPROVEMENTS BY TEST FEATURES TO USER INTERACTIONS WITH ITEM GROUPS 审中-公开

    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.

    AUTOMATIC ROUTING OF USER INQUIRIES USING NATURAL LANGUAGE AND IMAGE RECOGNITION MODELS
    119.
    发明公开
    AUTOMATIC ROUTING OF USER INQUIRIES USING NATURAL LANGUAGE AND IMAGE RECOGNITION MODELS 审中-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.

Patent Agency Ranking