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    Personalized mobility as a service
    91.
    发明授权
    Personalized mobility as a service 有权

    公开(公告)号：US12078498B2

    公开(公告)日：2024-09-03

    申请号：US17131427

    申请日：2020-12-22

    Applicant: Intel Corporation

    Inventor： Nesreen K. Ahmed ,  Ignacio J. Alvarez ,  Ravikumar Balakrishnan ,  Hesham Mostafa ,  Giuseppe Raffa ,  Nageen Himayat

    IPC: G06N20/00 ,  G01C21/34 ,  G06N3/08

    CPC classification number: G01C21/3484 ,  G06N3/08 ,  G06N20/00

    Abstract: Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

    LONG DURATION STRUCTURED VIDEO ACTION SEGMENTATION
    93.
    发明公开
    LONG DURATION STRUCTURED VIDEO ACTION SEGMENTATION 审中-公开

    公开(公告)号：US20240104915A1

    公开(公告)日：2024-03-28

    申请号：US18459824

    申请日：2023-09-01

    Applicant: Intel Corporation

    Inventor： Anthony Daniel Rhodes ,  Byungsu Min ,  Subarna Tripathi ,  Giuseppe Raffa ,  Sovan Biswas

    IPC: G06V10/82 ,  G06V10/75 ,  G06V10/86 ,  G06V20/40

    CPC classification number: G06V10/82 ,  G06V10/751 ,  G06V10/86 ,  G06V20/46 ,  G06V20/49

    Abstract: Machine learning models can process a video and generate outputs such as action segmentation assigning portions of the video to a particular action, or action classification assigning an action class for each frame of the video. Some machine learning models can accurately make predictions for short videos but may not be particularly suited for performing action segmentation for long duration, structured videos. An effective machine learning model may include a hybrid architecture involving a temporal convolutional network and a bi-directional graph neural network. The machine learning model can process long duration structured videos by using a temporal convolutional network as a first pass action segmentation model to generate rich, frame-wise features. The frame-wise features can be converted into a graph having forward edges and backward edges. A graph neural network can process the graph to refine a final fine-grain per-frame action prediction.

    System for determining anatomical feature orientation
    97.
    发明授权
    System for determining anatomical feature orientation 有权

    公开(公告)号：US11605179B2

    公开(公告)日：2023-03-14

    申请号：US17840077

    申请日：2022-06-14

    Applicant: Intel Corporation

    Inventor： Glen J. Anderson ,  Giuseppe Raffa ,  Carl S. Marshall ,  Meng Shi

    IPC: G06K9/00 ,  G06T7/70 ,  A61B90/00 ,  G06F3/01 ,  G06K9/62 ,  G06V10/44 ,  G06V30/194 ,  G06V40/16 ,  A61B5/00

    Abstract: The systems and methods disclosed herein provide determination of an orientation of a feature towards a reference target. As a non-limiting example, a system consistent with the present disclosure may include a processor, a memory, and a single camera affixed to the ceiling of a room occupied by a person. The system may analyze images from the camera to identify any objects in the room and their locations. Once the system has identified an object and its location, the system may prompt the person to look directly at the object. The camera may then record an image of the user looking at the object. The processor may analyze the image to determine the location of the user's head and, combined with the known location of the object and the known location of the camera, determine the direction that the user is facing. This direction may be treated as a reference value, or “ground truth.” The captured image may be associated with the direction, and the combination may be used as training input into an application.

    Technologies for time-delayed augmented reality presentations
    98.
    发明授权
    Technologies for time-delayed augmented reality presentations 有权

    公开(公告)号：US11557098B2

    公开(公告)日：2023-01-17

    申请号：US17082670

    申请日：2020-10-28

    Applicant: Intel Corporation

    Inventor： Pete A. Denman ,  Glen J. Anderson ,  Giuseppe Raffa

    IPC: G06T19/00 ,  G06F9/54 ,  G06F1/12 ,  G06F3/01 ,  H04L67/52 ,  H04L67/131 ,  G06T13/00

    Abstract: Technologies for time-delayed augmented reality (AR) presentations includes determining a location of a plurality of user AR systems located within the presentation site and determining a time delay of an AR sensory stimulus event of an AR presentation to be presented in the presentation site for each user AR system based on the location of the corresponding user AR system within the presentation site. The AR sensory stimulus event is presented to each user AR system based on the determined time delay associated with the corresponding user AR system. Each user AR system generates the AR sensory stimulus event based on a timing parameter that defines the time delay for the corresponding user AR system such that the generation of the AR sensory stimulus event is time-delayed based on the location of the user AR system within the presentation site.

    LEVERAGING EPISTEMIC CONFIDENCE FOR MULTI-MODAL FEATURE PROCESSING
    100.
    发明申请
    LEVERAGING EPISTEMIC CONFIDENCE FOR MULTI-MODAL FEATURE PROCESSING 有权

    公开(公告)号：US20220382787A1

    公开(公告)日：2022-12-01

    申请号：US17816468

    申请日：2022-08-01

    Applicant: Intel Corporation

    Inventor： Anthony Rhodes ,  Sovan Biswas ,  Giuseppe Raffa

    IPC: G06F16/28

    Abstract: Systems, apparatuses, and methods include technology that extracts a plurality of features from the input data. The technology generates a confidence metric for the plurality of features. The confidence metric corresponds to a degree that at least one feature of the plurality of features is relevant for classification of the input data. The technology categorizes the input data into a category based on the plurality of features and the confidence metric

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