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    Ranking Search Queries Using Contextual Relevance and Third-Party Factors
    1.
    发明申请
    Ranking Search Queries Using Contextual Relevance and Third-Party Factors 有权

    公开(公告)号：US20250086189A1

    公开(公告)日：2025-03-13

    申请号：US18367185

    申请日：2023-09-12

    Applicant: Maplebear Inc.

    Inventor： Levi Boxell ,  Esther Vasiete Allas ,  Tejaswi Tenneti ,  Tilman Drerup ,  Yueyang Rao

    IPC: G06F16/2457 ,  G06F16/248

    Abstract: A computer system allowing users to search for items of interest provides a search query interface. The system receives characters of a search query in the search interface as the user enters the characters and interactively calculates, ranks, and displays a set of possible search query options from which the user can select. To rank the set of possible search query options, the system modifies rankings of candidate search queries based on factors associated with third parties. More specifically, contextual relevance scores are computed for the candidate search queries based on the context, such as a user to whom the search results are provided. These contextual relevance scores are in turn adjusted using factors associated with third parties, such as values calculated based on consideration offered by third parties. Users are shown the search query options, ranked in order of the adjusted relevance scores, as possible query selections.

    Counterfactual Policy Evaluation of Model Performance
    2.
    发明公开
    Counterfactual Policy Evaluation of Model Performance 审中-公开

    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

    IPC: G06N20/00 ,  G06Q30/01

    CPC classification number: G06N20/00 ,  G06Q30/01

    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.

    Predicting Replacement Items using a Machine-Learning Replacement Model
    4.
    发明申请
    Predicting Replacement Items using a Machine-Learning Replacement Model 有权

    公开(公告)号：US20240403938A1

    公开(公告)日：2024-12-05

    申请号：US18326900

    申请日：2023-05-31

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Tilman Drerup ,  Shishir Kumar Prasad ,  Zoheb Hajiyani ,  Luis Manrique

    IPC: G06Q30/0601 ,  G06N20/00

    Abstract: An online system predicts replacement items for presentation to a user using a machine-learning model. The online system receives interaction data describing a user's interaction with the online system. In particular, the interaction data describes an initial item that the user added to their item list. The online system identifies a set of candidate items that could be presented to the user as potential replacements for the initially-added item. The online system applies a replacement prediction model to each of these candidate items to generate a replacement score for the candidate items. The online system selects a proposed replacement item and transmits that item to the user's client device for display to the user. If the user selects the proposed replacement item, the online concierge system replaces the initial item with the proposed replacement item in the user's item list.

    TREATMENT LIFT SCORE AGGREGATION FOR NEW TREATMENT TYPES
    6.
    发明公开
    TREATMENT LIFT SCORE AGGREGATION FOR NEW TREATMENT TYPES 审中-公开

    公开(公告)号：US20230368236A1

    公开(公告)日：2023-11-16

    申请号：US17744526

    申请日：2022-05-13

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Tilman Drerup ,  Anne Moxie ,  Sophia Li ,  Vibin Kundukulam ,  Jonathan Gu ,  Ashley Denney

    IPC: G06Q30/02 ,  G06Q30/06

    CPC classification number: G06Q30/0211 ,  G06Q30/0239 ,  G06Q30/0617

    Abstract: An online concierge system uses a new treatment engine to score users for applying treatments of a new treatment type. The new treatment engine uses treatment models to generate treatment lift scores for the user. The new treatment engine applies an aggregation function model to the treatment lift scores to generate an aggregated lift score for the user. If the aggregated lift score exceeds a threshold, the new treatment engine applies a treatment of the new treatment type to the user. The new treatment engine trains the aggregation function model based on training examples used to train the treatment models. For a training example associated with a particular treatment type, the new treatment engine uses a target lift score generated by the treatment model for the treatment type to evaluate the performance of the aggregation function model, and to update the aggregation function model accordingly.

    REINFORCEMENT LEARNING BASED OPTIMIZATION OF TEXTUAL ARTIFACTS USING GENERATIVE ARTIFICIAL INTELLIGENCE
    7.
    发明申请
    REINFORCEMENT LEARNING BASED OPTIMIZATION OF TEXTUAL ARTIFACTS USING GENERATIVE ARTIFICIAL INTELLIGENCE 有权

    公开(公告)号：US20250077976A1

    公开(公告)日：2025-03-06

    申请号：US18820097

    申请日：2024-08-29

    Applicant: Maplebear Inc.

    Inventor： Tilman Drerup ,  Jiuyun Zhang

    IPC: G06N20/00

    Abstract: A system generates text artifacts using a machine learned language model. The text artifacts may be provided to a search engine for providing to users along with search results. The system iteratively improves the set of text artifacts by performing the following steps. The system updates the prompt used to generate the text artifacts based on the performance of the text artifacts to obtain a new prompt. The system executes the machine learned language model using the new prompt to generate a new set of text artifacts. The system evaluates the new set of text artifacts to determine performance of each of the new set of text artifacts. These steps are repeatedly performed to improve the set of text artifacts.

    RANKING SEARCH RESULTS BASED ON APPEASEMENT SIGNALS AND QUERY SPECIFICITY
    8.
    发明申请
    RANKING SEARCH RESULTS BASED ON APPEASEMENT SIGNALS AND QUERY SPECIFICITY 有权

    公开(公告)号：US20250077529A1

    公开(公告)日：2025-03-06

    申请号：US18241093

    申请日：2023-08-31

    Applicant: Maplebear Inc.

    Inventor： Levi Boxell ,  Vinesh Reddy Gudla ,  Michael Kurish ,  Raochuan Fan ,  Tilman Drerup ,  Tejaswi Tenneti

    IPC: G06F16/2457 ,  G06F16/248 ,  G06N20/00

    Abstract: An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.

    Attributing Loss of Engagement with an Online System Using Temporal Partitioning of Training Data for a Churn Prediction Model
    9.
    发明申请
    Attributing Loss of Engagement with an Online System Using Temporal Partitioning of Training Data for a Churn Prediction Model 有权

    公开(公告)号：US20250061350A1

    公开(公告)日：2025-02-20

    申请号：US18233828

    申请日：2023-08-14

    Applicant: Maplebear Inc.

    Inventor： Ganesh Krishnan ,  Sharath Rao Karikurve ,  Angadh Singh ,  Changyao Chen ,  Tilman Drerup

    IPC: G06N5/022 ,  G06Q10/087

    Abstract: An online system trains a churn prediction model to attribute a churn event to one or more causal events. The churn prediction model receives customer features and online system features as inputs. Various causal events that occur affect one or more online system features. To avoid biasing the churn prediction model using input features that are related to possible causal events, the online system determines customer features and online system features based on customer interactions occurring in different time intervals. The customer features are determined from interactions in a time interval that is earlier than a time interval from which interactions are used to determine online system features. Such time segmenting decorrelates the features input to the model from the events, reducing potential bias from the causal events on the churn prediction model.

    MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES
    10.
    发明申请
    MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES 有权

    公开(公告)号：US20240428309A1

    公开(公告)日：2024-12-26

    申请号：US18214150

    申请日：2023-06-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Robert Fletcher ,  Ramasubramanian Balasubramanian ,  Tilman Drerup ,  Sharath Rao Karikurve

    IPC: G06Q30/0601

    Abstract: Based on logged information about prior events, an online concierge system generates a set of location metrics that quantify properties of locations such as retailers at which items may be acquired, and residences to which the items are brought. The location metrics can be used for a variety of purposes to aid customers or other users of the online concierge system, such as providing the users with more information (e.g., likely delivery delays) or alternative options (e.g., pricing options), or emphasizing options that the location metrics indicate would be of particular value to the user. To determine whether to emphasize a particular option, the online concierge system applies a machine-learned model that predicts whether emphasizing that option would effect a positive change in user behavior, relative to not emphasizing it.

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