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    GENERATING CONFIDENCE SCORES FOR MACHINE LEARNING MODEL PREDICTIONS
    11.
    发明申请
    GENERATING CONFIDENCE SCORES FOR MACHINE LEARNING MODEL PREDICTIONS 有权

    公开(公告)号：US20220366280A1

    公开(公告)日：2022-11-17

    申请号：US17475557

    申请日：2021-09-15

    Applicant: Oracle International Corporation

    Inventor： Matthew Charles Rowe ,  Alberto Polleri ,  Rhys David Green

    IPC: G06N5/04 ,  G06N5/02

    Abstract: Techniques for generating confidence scores for machine learning predictions are disclosed. The confidence score for a predicted label corresponding to a target data point is based at least in part on how well the machine learning model predicts labels for other data points that are similar to the target data point. The system uses k data points, closest to the target data point, with known labels to compute the confidence score of a predicted label for the target data point. The accuracy of the predictions and the distance of each of the k data points from the target data point are used to compute a confidence score for a label predicted for the target data point.

    Composing human-readable explanations for user navigational recommendations
    18.
    发明授权
    Composing human-readable explanations for user navigational recommendations 有权

    公开(公告)号：US11625446B2

    公开(公告)日：2023-04-11

    申请号：US17302429

    申请日：2021-05-03

    Applicant: Oracle International Corporation

    Inventor： Amir Hossein Rezaeian ,  Alberto Polleri

    IPC: G06F16/00 ,  G06F16/954 ,  G06N20/00 ,  G06F17/18

    Abstract: Techniques for generating human-readable explanations (also referred to herein as “reasons”) for navigational recommendations are disclosed. Composing a human-readable explanation includes individually selecting words or phrases that are then analyzed, combined, rearranged, modified, or removed to generate the human-readable explanation for a navigational recommendation. A decoder trains a machine learning model to generate the human-readable reasons for the navigational recommendations based on (1) historical recommendation vectors, and (2) historical human-readable reasons associated with the recommendation vectors. The system generates a dictionary of human-readable reasons for recommendations, with each entry of the dictionary including: (1) a recommendation identifier (ID) associated with a recommended navigational target, (2) a reason identifier (ID) associated with a particular reason for the recommendation, and (3) a human-readable reason associated with the reason ID.

    IDENTIFYING A CLASSIFICATION HIERARCHY USING A TRAINED MACHINE LEARNING PIPELINE
    19.
    发明申请
    IDENTIFYING A CLASSIFICATION HIERARCHY USING A TRAINED MACHINE LEARNING PIPELINE 有权

    公开(公告)号：US20220398445A1

    公开(公告)日：2022-12-15

    申请号：US17303918

    申请日：2021-06-10

    Applicant: Oracle International Corporation

    Inventor： Alberto Polleri ,  Rajiv Kumar ,  Marc Michiel Bron ,  Guodong Chen ,  Shekhar Agrawal ,  Richard Steven Buchheim

    IPC: G06N3/08 ,  G06K9/62

    Abstract: Techniques are disclosed for using a trained machine learning (ML) pipeline to identify categories associated with target data items even though the identified categories may not already be present in the hierarchy. The ML pipeline may include trained cluster-based and classification-based machine learning models, among others. If the results of the cluster-based and classification-based machine learning models are the same, then the target data items is assigned to a hierarchical classification consistent with the identical results of the machine learning model. An assigned hierarchical classification may be validated by the operation of subsequent trained ML models that determine whether parent and child categories in the identified classification are properly associated with one another.

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