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    INTERACTIONS WITH VIRTUAL OBJECTS FOR MACHINE CONTROL
    11.
    发明申请
    INTERACTIONS WITH VIRTUAL OBJECTS FOR MACHINE CONTROL 有权

    公开(公告)号：US20220083880A1

    公开(公告)日：2022-03-17

    申请号：US17532976

    申请日：2021-11-22

    Applicant: Ultrahaptics IP Two Limited

    Inventor： David S. HOLZ ,  Raffi BEDIKIAN ,  Adrian GASINSKI ,  Maxwell SILLS ,  Hua YANG ,  Gabriel HARE

    IPC: G06N5/04 ,  G06F3/0481 ,  G06F3/0484 ,  G06F3/03 ,  G06F3/042 ,  G06F3/01

    Abstract: The technology disclosed relates to manipulating a virtual object. In particular, it relates to detecting a hand in a three-dimensional (3D) sensory space and generating a predictive model of the hand, and using the predictive model to track motion of the hand. The predictive model includes positions of calculation points of fingers, thumb and palm of the hand. The technology disclosed relates to dynamically selecting at least one manipulation point proximate to a virtual object based on the motion tracked by the predictive model and positions of one or more of the calculation points, and manipulating the virtual object by interaction between at least some of the calculation points of the predictive model and the dynamically selected manipulation point.

    Improving Predictive Information for Free Space Gesture Control and Communication
    12.
    发明申请
    Improving Predictive Information for Free Space Gesture Control and Communication 审中-公开

    公开(公告)号：US20200167513A1

    公开(公告)日：2020-05-28

    申请号：US16695136

    申请日：2019-11-25

    Applicant: Ultrahaptics IP Two Limited

    Inventor： David S. HOLZ ,  Kevin HOROWITZ ,  Raffi BEDIKIAN ,  Hua YANG

    IPC: G06F30/20 ,  G06F3/01 ,  G06K9/00

    Abstract: The technology disclosed relates to simplifying updating of a predictive model using clustering observed points. In particular, it relates to observing a set of points in 3D sensory space, determining surface normal directions from the points, clustering the points by their surface normal directions and adjacency, accessing a predictive model of a hand, refining positions of segments of the predictive model, matching the clusters of the points to the segments, and using the matched clusters to refine the positions of the matched segments. It also relates to distinguishing between alternative motions between two observed locations of a control object in a 3D sensory space by accessing first and second positions of a segment of a predictive model of a control object such that motion between the first position and the second position was at least partially occluded from observation in a 3D sensory space.

    PREDICTIVE INFORMATION FOR FREE SPACE GESTURE CONTROL AND COMMUNICATION
    13.
    发明申请
    PREDICTIVE INFORMATION FOR FREE SPACE GESTURE CONTROL AND COMMUNICATION 有权

    公开(公告)号：US20250165671A1

    公开(公告)日：2025-05-22

    申请号：US19031367

    申请日：2025-01-18

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： David S. HOLZ ,  Kevin HOROWITZ ,  Raffi BEDIKIAN ,  Hua YANG

    IPC: G06F30/20 ,  G06F3/01 ,  G06V20/64 ,  G06V40/20

    Abstract: The technology disclosed relates to simplifying updating of a predictive model using clustering observed points. In particular, it relates to observing a set of points in 3D sensory space, determining surface normal directions from the points, clustering the points by their surface normal directions and adjacency, accessing a predictive model of a hand, refining positions of segments of the predictive model, matching the clusters of the points to the segments, and using the matched clusters to refine the positions of the matched segments. It also relates to distinguishing between alternative motions between two observed locations of a control object in a 3D sensory space by accessing first and second positions of a segment of a predictive model of a control object such that motion between the first position and the second position was at least partially occluded from observation in a 3D sensory space.

    Hand Pose Estimation for Machine Learning Based Gesture Recognition
    15.
    发明公开
    Hand Pose Estimation for Machine Learning Based Gesture Recognition 审中-公开

    公开(公告)号：US20230214458A1

    公开(公告)日：2023-07-06

    申请号：US16508231

    申请日：2019-07-10

    Applicant: Ultrahaptics IP Two Limited

    Inventor： Jonathan MARSDEN ,  Raffi BEDIKIAN ,  David Samuel HOLZ

    IPC: G06K9/62 ,  G06K9/00 ,  G06K9/46 ,  G06T7/13

    CPC classification number: G06K9/6269 ,  G06K9/00355 ,  G06K9/4604 ,  G06T7/13 ,  G06T2207/10028

    Abstract: The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.

    Predictive Information for Free Space Gesture Control and Communication
    16.
    发明申请
    Predictive Information for Free Space Gesture Control and Communication 有权

    公开(公告)号：US20210256182A1

    公开(公告)日：2021-08-19

    申请号：US17308903

    申请日：2021-05-05

    Applicant: Ultrahaptics IP Two Limited

    Inventor： David S. HOLZ ,  Kevin HOROWITZ ,  Raffi BEDIKIAN ,  Hua YANG

    IPC: G06F30/20 ,  G06K9/00 ,  G06F3/01

    Abstract: The technology disclosed relates to simplifying updating of a predictive model using clustering observed points. In particular, it relates to observing a set of points in 3D sensory space, determining surface normal directions from the points, clustering the points by their surface normal directions and adjacency, accessing a predictive model of a hand, refining positions of segments of the predictive model, matching the clusters of the points to the segments, and using the matched clusters to refine the positions of the matched segments. It also relates to distinguishing between alternative motions between two observed locations of a control object in a 3D sensory space by accessing first and second positions of a segment of a predictive model of a control object such that motion between the first position and the second position was at least partially occluded from observation in a 3D sensory space.

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