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    GENERATING SESSION-BASED RECOMMENDATIONS USING LARGE LANGUAGE MACHINE-LEARNED MODELS
    1.
    发明公开
    GENERATING SESSION-BASED RECOMMENDATIONS USING LARGE LANGUAGE MACHINE-LEARNED MODELS 审中-公开

    公开(公告)号：US20240354556A1

    公开(公告)日：2024-10-24

    申请号：US18640231

    申请日：2024-04-19

    Applicant: Maplebear Inc.

    Inventor： Yueyang Rao ,  Brian Lin ,  Angadh Singh ,  Sharath Rao Karikurve ,  Guanghua Shu

    IPC: G06N3/0455 ,  G06Q30/0601

    CPC classification number: G06N3/0455 ,  G06Q30/0631

    Abstract: An online system generates session-based recommendations for a user accessing an application of the online system. The online system receives, from one or more client devices, a sequence of actions performed by a user during a session of an application of an online system. The online system generates a sequence of tokens from the sequence of actions by tokenizing an action to a token representing a respective item identifier. The online system applies a transformer-based machine-learned model to the sequence of tokens to generate predictions for a set of items. The online system selects a subset of items based on the generated predictions for the set of items. The online system generates one or more recommendations to the user from the selected subset of items and displays the recommendations to the user.

    GENERATING DIVERSE DATASETS USING MACHINE-LEARNED LARGE LANGUAGE MODELS (LLMS) BASED ON VECTOR DISTANCE CONSTRAINTS
    3.
    发明申请
    GENERATING DIVERSE DATASETS USING MACHINE-LEARNED LARGE LANGUAGE MODELS (LLMS) BASED ON VECTOR DISTANCE CONSTRAINTS 有权

    公开(公告)号：US20250139523A1

    公开(公告)日：2025-05-01

    申请号：US18925017

    申请日：2024-10-24

    Applicant: Maplebear Inc.

    Inventor： Jacob Jensen ,  Guanghua Shu

    IPC: G06N20/00

    Abstract: An online system augments a dataset in conjunction with a model serving system. The online system accesses a dataset for training a machine-learning model. The online system generates a prompt to generate candidate samples in the training dataset to the model serving system. The online system receives a response comprising one or more candidate samples. The online system compares the one or more candidate samples to at least one existing sample of the dataset to determine whether the one or more candidate samples are within a threshold level of similarity to an existing sample. If a candidate sample received from the machine-learning language model is not within the threshold level of similarity to an existing sample, the online system updates the dataset with the candidate sample.

    Accounting for item attributes when selecting items satisfying a query based on item embeddings and an embedding for the query
    4.
    发明授权
    Accounting for item attributes when selecting items satisfying a query based on item embeddings and an embedding for the query 有权

    公开(公告)号：US12259894B2

    公开(公告)日：2025-03-25

    申请号：US17666531

    申请日：2022-02-07

    Applicant: Maplebear Inc.

    Inventor： Taesik Na ,  Zhihong Xu ,  Guanghua Shu ,  Tejaswi Tenneti ,  Haixun Wang

    IPC: G06F16/2457 ,  G06F16/242

    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.

    GUIDED CONVERSATION CONTEXT COMPRESSION WITH ADVERSARIAL HYPOTHETICAL QUESTIONS AND EVALUATING RELEVANCE OF CONTEXTUAL INFORMATION FOR LLMS
    5.
    发明申请
    GUIDED CONVERSATION CONTEXT COMPRESSION WITH ADVERSARIAL HYPOTHETICAL QUESTIONS AND EVALUATING RELEVANCE OF CONTEXTUAL INFORMATION FOR LLMS 有权

    公开(公告)号：US20250133037A1

    公开(公告)日：2025-04-24

    申请号：US18920765

    申请日：2024-10-18

    Applicant: Maplebear Inc.

    Inventor： Jacob Jensen ,  Shubhanshu Mishra ,  Aomin Wu ,  Rajpal Paryani ,  Guanghua Shu ,  Sicheng Zhou

    IPC: H04L51/02

    Abstract: A system may smartly edit the context of a conversation to be input into a chatbot LLM by using a conversation compression algorithm to prune and compress redundant elements. The system evaluates the conversation context compression algorithm using both a chatbot LLM and an adversarial LLM. The system retrieves a logged conversation and generates a compressed conversation context from the logged conversation. The system generates a synthetic user response by applying the adversarial LLM and generates a test conversation by replacing a user response in the conversation with the synthetic user response. The system generates a compressed context of the test conversation. The system generates a test chatbot LLM response by prompting the chatbot LLM with the compressed context of the test conversation. The system evaluates the conversation context compression algorithm by comparing the test chatbot response with a benchmark chatbot response.

    Feature Recommendations for Machine Learning Models Based on Feature-Model Co-Occurrences
    6.
    发明公开
    Feature Recommendations for Machine Learning Models Based on Feature-Model Co-Occurrences 审中-公开

    公开(公告)号：US20240362523A1

    公开(公告)日：2024-10-31

    申请号：US18140203

    申请日：2023-04-27

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Guanghua Shu ,  Reza Sadri ,  Jacob Jensen ,  Sahil Khanna

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: A system maintains a data store for managing machine-learning (ML) models and features that are used by the models. The system generates a graph including nodes for each model and a node for each feature, and edges linking models and features that are used by the models. For a new model to be trained, the system receives a proposed feature corresponding to a node in the graph, and identifies one or more candidate features corresponding to nodes in the graph based in part on relevancy scores between the proposed feature with other features corresponding to nodes in the graph. The system presents in a user interface a suggestion to use one or more candidate features with the new model. Responsive to receiving a user selection of at least one candidate feature, the system causes the new model to be trained using the selected candidate feature and the proposed feature.

    Feature Recommendations for Machine Learning Models Using Trained Feature Prediction Model
    7.
    发明公开
    Feature Recommendations for Machine Learning Models Using Trained Feature Prediction Model 审中-公开

    公开(公告)号：US20240362455A1

    公开(公告)日：2024-10-31

    申请号：US18140210

    申请日：2023-04-27

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Guanghua Shu ,  Reza Sadri ,  Jacob Jensen ,  Sahil Khanna

    IPC: G06N3/045 ,  G06N3/09

    CPC classification number: G06N3/045 ,  G06N3/09

    Abstract: A feature management system (the “system”) receives information about a new machine learning (ML) model to be trained. The information includes metadata about the new model. The system applies a trained feature prediction model to the information about the new model and metadata about a plurality of features. The feature prediction model is trained to predict a probability that each of the plurality of features should be selected as an input feature for the new model. The feature management system identifies one or more candidate features based on an output probability score of the feature prediction model. The system presents in a user interface a suggestion to use the one or more candidate features with the new model. The system selects at least one candidate feature and causes the new model to be trained using a set of input features, including the selected candidate feature.

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