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    Standardization Of Text Data In Content Items
    1.
    发明申请
    Standardization Of Text Data In Content Items 有权

    公开(公告)号：US20250094715A1

    公开(公告)日：2025-03-20

    申请号：US18414144

    申请日：2024-01-16

    Applicant: Oracle International Corporation

    Inventor： Karempudi V. Ramarao ,  Cody Alan Kingham ,  Rajiv Kumar

    IPC: G06F40/289 ,  G06F40/253

    Abstract: Techniques for standardizing text data are disclosed. The system may identify, within a content item, a target phrase that is to be standardized. A subset of characters of a verb in the target phrase may be selected for comparison to a list of nouns. The subset of characters may be compared to a list of nouns identified in a data corpus. A noun in the list of nouns may be added to a candidate subset of nouns to replace the verb if the noun includes a sequence of characters that matches the subset of characters. A particular noun to replace the verb may be selected from the candidate subset of nouns based on a frequency associated with the particular noun occurring within the data corpus. The system may convert the target phrase to generate a standard phrase at least by replacing the verb with the particular noun.

    Method and system for multistage candidate ranking
    2.
    发明授权
    Method and system for multistage candidate ranking 有权

    公开(公告)号：US11727327B2

    公开(公告)日：2023-08-15

    申请号：US16940769

    申请日：2020-07-28

    Applicant: Oracle International Corporation

    Inventor： Sonali Vijay Inamdar ,  Rajiv Kumar ,  Simon Chow

    IPC: G06Q10/00 ,  G06Q10/0631 ,  G06F16/2457 ,  G06N20/00

    CPC classification number: G06Q10/063112 ,  G06F16/24578 ,  G06N20/00

    Abstract: Systems and methods for candidate recommendation are provided. Candidate vectors are generated from candidate documents, and an initial ranking is performed according to a distance metric between the candidate vector and an objective vector generated based on an objective document to select a subset of the candidate documents. A feature vector is generated for each of the selected candidate documents. The feature vector includes features derived from a first vectorized representation of content from one of the candidate document and the objective document and a second vectorized representation of content from the one of the candidate document and the objective document. The feature vector is provided to a machine learning model to generate a score for each of the selected candidate documents. The selected candidate documents are ranked according the scores generated at the machine learning model to provide a ranked candidate list.

    IDENTIFYING A CLASSIFICATION HIERARCHY USING A TRAINED MACHINE LEARNING PIPELINE
    3.
    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.

    UPDATING MACHINE LEARNING TRAINING DATA USING GRAPHICAL INPUTS
    5.
    发明申请
    UPDATING MACHINE LEARNING TRAINING DATA USING GRAPHICAL INPUTS 有权

    公开(公告)号：US20220261687A1

    公开(公告)日：2022-08-18

    申请号：US17178360

    申请日：2021-02-18

    Applicant: Oracle International Corporation

    Inventor： Ketakee Kishorkumar Nimavat ,  Rajiv Kumar

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Techniques are disclosed for using a machine learning model to identify and present a ranked array of interface elements representing entities. The location of individual interface elements within the ranked array of interface elements is based on a level of match between entity attributes and a set of requirements established by a user. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within a graphical user interface displaying the ranked array. Upon receiving the user input, the trained machine learning model may update training data to include an updated match score for the particular user interface element that reflects the new location.

    Machine learning ranking system
    8.
    发明授权
    Machine learning ranking system 有权

    公开(公告)号：US12260303B2

    公开(公告)日：2025-03-25

    申请号：US17178365

    申请日：2021-02-18

    Applicant: Oracle International Corporation

    Inventor： Ketakee Kishorkumar Nimavat ,  Rajiv Kumar

    IPC: G06F3/0481 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/22 ,  G06N20/00

    Abstract: Techniques are disclosed for training a machine learning model to identify and rank entities relative to a set of requirements. The trained machine learning model may present an array of interface elements (e.g., icons) in a graphical user interface (GUI), where the interface elements represent corresponding entities. These interface elements are arranged in the GUI based on their corresponding ranks. The ranks of entities, and therefore the locations of corresponding interface elements are based, at least in part, on a degree of match between values of a subset of entity attributes and a corresponding subset of the set of requirements. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within the graphical user interface displaying the ranked user interface elements.

    MACHINE LEARNING RANKING SYSTEM
    10.
    发明申请
    MACHINE LEARNING RANKING SYSTEM 有权

    公开(公告)号：US20220261688A1

    公开(公告)日：2022-08-18

    申请号：US17178365

    申请日：2021-02-18

    Applicant: Oracle International Corporation

    Inventor： Ketakee Kishorkumar Nimavat ,  Rajiv Kumar

    IPC: G06N20/00 ,  G06K9/62 ,  G06F3/0481

    Abstract: Techniques are disclosed for training a machine learning model to identify and rank entities relative to a set of requirements. The trained machine learning model may present an array of interface elements (e.g., icons) in a graphical user interface (GUI), where the interface elements represent corresponding entities. These interface elements are arranged in the GUI based on their corresponding ranks. The ranks of entities, and therefore the locations of corresponding interface elements are based, at least in part, on a degree of match between values of a subset of entity attributes and a corresponding subset of the set of requirements. The machine learning model may be further trained by receiving a user input that changes a location of a particular user interface element within the graphical user interface displaying the ranked user interface elements.

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