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    SUPERVISED AND UNSUPERVISED MACHINE LEARNING TECHNIQUES FOR COMMUNICATION SUMMARIZATION
    1.
    发明公开
    SUPERVISED AND UNSUPERVISED MACHINE LEARNING TECHNIQUES FOR COMMUNICATION SUMMARIZATION 审中-公开

    公开(公告)号：US20230351099A1

    公开(公告)日：2023-11-02

    申请号：US17734655

    申请日：2022-05-02

    申请人： Optum, Inc.

    发明人： Suman Roy ,  Vijay Varma Malladi ,  Ayan Sengupta

    IPC分类号： G06F40/166 ,  G06F40/30 ,  G06F40/40 ,  G06N3/08

    CPC分类号： G06F40/166 ,  G06F40/30 ,  G06F40/40 ,  G06N3/08

    摘要： Various embodiments provide for summarization of an interaction, conversation, encounter, and/or the like in at least an abstractive manner. In one example embodiment, a method is provided. The method includes generating, using an encoder-decoder machine learning model, a party-agnostic representation data object for each utterance data object. The method further includes generating an attention graph data object to represent semantic and party-wise relationships between a plurality of utterance data objects. The method further includes modifying, using the attention graph data object, the party-agnostic representation data object for each utterance data object to form a party-wise representation data object for each utterance data object. The method further includes selecting a subset of party-wise representation data objects for each of a plurality of parties. The method further includes decoding, using the encoder-decoder machine learning model, the subset of party-wise representation data objects for each party to form abstractive summary data object(s).

    QUERY-FOCUSED EXTRACTIVE TEXT SUMMARIZATION OF TEXTUAL DATA
    5.
    发明申请
    QUERY-FOCUSED EXTRACTIVE TEXT SUMMARIZATION OF TEXTUAL DATA 有权

    公开(公告)号：US20230054726A1

    公开(公告)日：2023-02-23

    申请号：US17405555

    申请日：2021-08-18

    申请人： Optum, Inc.

    发明人： Suman Roy ,  Vijay Varma Malladi ,  Gaurav Ranjan

    IPC分类号： G06F40/284 ,  G06F40/30 ,  G06F16/332 ,  G06F16/35 ,  G10L15/26 ,  G06N3/04 ,  G06N3/08

    摘要： Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for providing a summarization of a conversation, such as a telephonic conversation. In an embodiment, a method is provided. The method comprises receiving an input data object comprising textual data of a conversation, the textual data comprising sentence-level tokens. The method further comprises classifying some sentence-level tokens as interrogative sentence-level tokens, and identifying subtopic portions of the textual data, each interrogative sentence-level token located within one subtopic portion. The method further comprises determining whether an interrogative sentence-level token is substantially similar to one of a plurality of target queries, and for such interrogative sentence-level tokens, selecting sentence-level tokens from a subtopic portion corresponding to the such interrogative sentence-level tokens. The method then comprises generating a summarization data object comprising the selected sentence-level tokens for each interrogative sentence-level token substantially similar to a target query and performing summarization-based actions.

    CLASSIFICATION PREDICTION USING ATTENTION-BASED MACHINE LEARNING TECHNIQUES WITH TEMPORAL SEQUENCE DATA AND DYNAMIC CO-OCCURRENCE GRAPH DATA OBJECTS
    6.
    发明公开
    CLASSIFICATION PREDICTION USING ATTENTION-BASED MACHINE LEARNING TECHNIQUES WITH TEMPORAL SEQUENCE DATA AND DYNAMIC CO-OCCURRENCE GRAPH DATA OBJECTS 审中-公开

    公开(公告)号：US20240232590A1

    公开(公告)日：2024-07-11

    申请号：US18153047

    申请日：2023-01-11

    申请人： Optum, Inc.

    发明人： Ayan Sengupta ,  Amit Kumar ,  Suman Roy

    IPC分类号： G06N3/049 ,  G06N3/0442

    CPC分类号： G06N3/049 ,  G06N3/0442

    摘要： Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a representative embeddings for a plurality of temporal sequences by using a graph attention augmented temporal network based on dynamic co-occurrence graphs for preceding temporal sequences and initial embeddings, where the dynamic co-occurrence graphs are projections of a global guidance co-occurrence graph on classification features of the preceding temporal sequences, and the initial embeddings are generated by processing a latent representation of corresponding classification features that is generated by a sequential long short term memory model as well as a classification feature tree using a tree-based long short term memory model.

    GRAPH-EMBEDDING-BASED PARAGRAPH VECTOR MACHINE LEARNING MODELS
    7.
    发明申请
    GRAPH-EMBEDDING-BASED PARAGRAPH VECTOR MACHINE LEARNING MODELS 有权

    公开(公告)号：US20230079343A1

    公开(公告)日：2023-03-16

    申请号：US17466594

    申请日：2021-09-03

    申请人： Optum, Inc.

    发明人： Suman Roy ,  Amit Kumar ,  Ayan Sengupta ,  Riccardo Mattivi ,  Ahmed Selim ,  Shashi Kumar

    IPC分类号： G06F16/93 ,  G06F16/901

    摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis on document data objects that are associated with an ontology graph. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations on document data objects that are associated with an ontology graph using document embeddings that are generated by graph-embedding-based paragraph vector machine learning models.

    MACHINE LEARNING TECHNIQUES FOR CROSS-DOMAIN TEXT CLASSIFICATION
    9.
    发明申请
    MACHINE LEARNING TECHNIQUES FOR CROSS-DOMAIN TEXT CLASSIFICATION 有权

    公开(公告)号：US20230119402A1

    公开(公告)日：2023-04-20

    申请号：US18069771

    申请日：2022-12-21

    申请人： Optum, Inc.

    发明人： Amit Kumar ,  Suman Roy ,  Ayan Sengupta

    IPC分类号： G06N5/022

    摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing text classification predictions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform text classification predictions by using at least one of Word Mover's Similarity measures, Relaxed Word Mover's Similarity measures, or cross-domain classification machine learning model.

    MACHINE LEARNING TECHNIQUES FOR DENOISING INPUT SEQUENCES
    10.
    发明申请
    MACHINE LEARNING TECHNIQUES FOR DENOISING INPUT SEQUENCES 有权

    公开(公告)号：US20230082485A1

    公开(公告)日：2023-03-16

    申请号：US17471886

    申请日：2021-09-10

    申请人： Optum, Inc.

    发明人： Ayan Sengupta ,  Amit Kumar ,  Suman Roy ,  Sourabh Kumar Bhattacharjee

    IPC分类号： G06N7/08 ,  G06N20/00

    摘要： Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing data denoising. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform data denoising by utilizing at least one of encoder transformer machine learning models, decoder transformer machine learning models, contextual relevance determination non-linear machine learning models, contextual relevance decision-making machine learning models, denoising decision-making machine learning model, and denoising decision gates.