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    AUTOMATED COLLABORATION SKILLS ASSESSMENT
    31.
    发明申请
    AUTOMATED COLLABORATION SKILLS ASSESSMENT 有权

    公开(公告)号：US20210390492A1

    公开(公告)日：2021-12-16

    申请号：US17348588

    申请日：2021-06-15

    Applicant: SRI International

    Inventor： Swati Dhamija ,  Amir Tamrakar ,  Nonye M. Alozie ,  Elizabeth McBride ,  Ajay Divakaran ,  Anirudh Som ,  Sujeong Kim ,  Bladimir Lopez-Prado

    IPC: G06Q10/06 ,  G06Q10/10 ,  G06K9/00 ,  G06N3/04 ,  G06N20/00

    Abstract: In some examples, a computer-implemented collaboration assessment model identifies actions of each of two or more individuals depicted in video data, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, first behaviors at a first collaboration assessment level, identify, based at least on the identified actions of each of the two or more individuals depicted in the video data, second behaviors at a second collaboration assessment level different from the first collaboration assessment level, and generate and output, based at least on the first behaviors at the first collaboration assessment level and the second behaviors at the second collaboration assessment level, an indication of at least one of an assessment of a collaboration effort of the two or more individuals or respective assessments of individual contributions of the two or more individuals to the collaboration effort.

    ANALYSIS AND DESIGN OF DYNAMICAL SYSTEM CONTROLLERS USING NEURAL DIFFERENTIAL EQUATIONS
    32.
    发明申请
    ANALYSIS AND DESIGN OF DYNAMICAL SYSTEM CONTROLLERS USING NEURAL DIFFERENTIAL EQUATIONS 有权

    公开(公告)号：US20210374531A1

    公开(公告)日：2021-12-02

    申请号：US17331150

    申请日：2021-05-26

    Applicant: SRI International

    Inventor： Ajay Divakaran ,  Anirban Roy ,  Susmit Jha

    IPC: G06N3/08 ,  G06K9/62

    Abstract: In general, the disclosure describes techniques for characterizing a dynamical system and a neural ordinary differential equation (NODE)-based controller for the dynamical system. An example analysis system is configured to: obtain a set of parameters of a NODE model used to implement the NODE-based controller, the NODE model trained to control the dynamical system; determine, based on the set of parameters, a system property of a combined system comprising the dynamical system and the NODE-based controller, the system property comprising one or more of an accuracy, safety, reliability, reachability, or controllability of the combined system; and output the system property to modify one or more of the dynamical system or the NODE-based controller to meet a required specification for the combined system.

    Zero-shot object detection
    33.
    发明授权
    Zero-shot object detection 有权

    公开(公告)号：US11055555B2

    公开(公告)日：2021-07-06

    申请号：US16383447

    申请日：2019-04-12

    Applicant: SRI International

    Inventor： Karan Sikka ,  Ajay Divakaran ,  Ankan Bansal

    IPC: G06K9/46 ,  G06K9/20 ,  G06K9/62 ,  G06K9/32 ,  G06K9/72 ,  G06N20/00 ,  G06N5/04

    Abstract: A method, apparatus and system for zero shot object detection includes, in a semantic embedding space having embedded object class labels, training the space by embedding extracted features of bounding boxes and object class labels of labeled bounding boxes of known object classes into the space, determining regions in an image having unknown object classes on which to perform object detection as proposed bounding boxes, extracting features of the proposed bounding boxes, projecting the extracted features of the proposed bounding boxes into the space, computing a similarity measure between the projected features of the proposed bounding boxes and the embedded, extracted features of the bounding boxes of the known object classes in the space, and predicting an object class label for proposed bounding boxes by determining a nearest embedded object class label to the projected features of the proposed bounding boxes in the space based on the similarity measures.

    Weakly supervised learning for classifying images
    36.
    发明授权
    Weakly supervised learning for classifying images 有权

    公开(公告)号：US10824916B2

    公开(公告)日：2020-11-03

    申请号：US16126748

    申请日：2018-09-10

    Applicant: SRI International

    Inventor： Karan Sikka ,  Ajay Divakaran ,  Parneet Kaur

    IPC: G06N3/08 ,  G06K9/62

    Abstract: Systems and methods for improving the accuracy of a computer system for object identification/classification through the use of weakly supervised learning are provided herein. In some embodiments, the method includes (a) receiving at least one set of curated data, wherein the curated data includes labeled images, (b) using the curated data to train a deep network model for identifying objects within images, wherein the trained deep network model has a first accuracy level for identifying objects, receiving a first target accuracy level for object identification of the deep network model, determining, automatically via the computer system, an amount of weakly labeled data needed to train the deep network model to achieve the first target accuracy level, and augmenting the deep network model using weakly supervised learning and the weakly labeled data to achieve the first target accuracy level for object identification by the deep network model.

    EMBEDDING MULTIMODAL CONTENT IN A COMMON NON-EUCLIDEAN GEOMETRIC SPACE
    38.
    发明申请
    EMBEDDING MULTIMODAL CONTENT IN A COMMON NON-EUCLIDEAN GEOMETRIC SPACE 审中-公开

    公开(公告)号：US20190325342A1

    公开(公告)日：2019-10-24

    申请号：US16383429

    申请日：2019-04-12

    Applicant: SRI International

    Inventor： Karan Sikka ,  Ajay Divakaran ,  Julia Kruk

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Embedding multimodal content in a common geometric space includes for each of a plurality of content of the multimodal content, creating a respective, first modality feature vector representative of content of the multimodal content having a first modality using a first machine learning model; for each of a plurality of content of the multimodal content, creating a respective, second modality feature vector representative of content of the multimodal content having a second modality using a second machine learning model; and semantically embedding the respective, first modality feature vectors and the respective, second modality feature vectors in a common geometric space that provides logarithm-like warping of distance space in the geometric space to capture hierarchical relationships between seemingly disparate, embedded modality feature vectors of content in the geometric space; wherein embedded modality feature vectors that are related, across modalities, are closer together in the geometric space than unrelated modality feature vectors.

    METHOD AND APPARATUS FOR CORRELATING AND VIEWING DISPARATE DATA
    40.
    发明申请
    METHOD AND APPARATUS FOR CORRELATING AND VIEWING DISPARATE DATA 审中-公开
    用于查询和查看不同数据的方法和装置

    公开(公告)号：US20160110433A1

    公开(公告)日：2016-04-21

    申请号：US14974871

    申请日：2015-12-18

    Applicant: SRI International

    Inventor： Harpreet Singh Sawhney ,  Jayakrishnan Eledath ,  Ajay Divakaran ,  Mayank Bansal ,  Hui Cheng

    IPC: G06F17/30

    CPC classification number: G06F17/30867 ,  G06F17/2785 ,  G06F17/2827 ,  G06F17/3087 ,  G06Q50/26

    Abstract: Methods and apparatuses of the present invention generally relate to generating actionable data based on multimodal data from unsynchronized data sources. In an exemplary embodiment, the method comprises receiving multimodal data from one or more unsynchronized data sources, extracting concepts from the multimodal data, the concepts comprising at least one of objects, actions, scenes and emotions, indexing the concepts for searchability; and generating actionable data based on the concepts.

    Abstract translation: 本发明的方法和装置通常涉及基于来自不同步数据源的多模态数据生成可操作数据。 在示例性实施例中，该方法包括从一个或多个非同步数据源接收多模态数据，从多模态数据中提取概念，所述概念包括对象，动作，场景和情绪中的至少一个，为可搜索性索引概念; 并基于这些概念生成可操作的数据。

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