VIRTUAL TILE ROUTING FOR NAVIGATING COMPLEX TRANSIT HUBS

    公开(公告)号:US20210018321A1

    公开(公告)日:2021-01-21

    申请号:US16932301

    申请日:2020-07-17

    Abstract: A computer implemented method or system for creating a route for navigating a transit hub or plaza using an application executing on a user's mobile device. The application accesses a tessellated map comprising first tiles each including a different area of interest on the map; and second tiles including a walkable area connecting the different areas of interest. The application highlights one of the first tiles including one of the different areas of interest selected using input from the user; highlights one of the second tiles including a location of the mobile device on the map; and highlights a series of the second tiles linking the location to the one of the areas of interest. A method of creating the tessellated map is also disclosed.

    Virtual tile routing for navigating complex transit hubs

    公开(公告)号:US11959755B2

    公开(公告)日:2024-04-16

    申请号:US16932301

    申请日:2020-07-17

    CPC classification number: G01C21/3423 G01C21/28 G01C21/3629 G01C21/3664

    Abstract: A computer implemented method or system for creating a route for navigating a transit hub or plaza using an application executing on a user's mobile device. The application accesses a tessellated map comprising first tiles each including a different area of interest on the map; and second tiles including a walkable area connecting the different areas of interest. The application highlights one of the first tiles including one of the different areas of interest selected using input from the user; highlights one of the second tiles including a location of the mobile device on the map; and highlights a series of the second tiles linking the location to the one of the areas of interest. A method of creating the tessellated map is also disclosed.

    MACHINE LEARNING ENABLED PATIENT STRATIFICATION

    公开(公告)号:US20250095857A1

    公开(公告)日:2025-03-20

    申请号:US18292291

    申请日:2022-07-29

    Abstract: A method for patient stratification may include applying a first machine learning model to determine, based on a clinical data of a patient, a risk score for the patient. Where the risk score for the patient exceeds a threshold, a second machine learning model may be applied to determine a first probability of the risk score being a false positive. Where the risk score for the patient fails to exceed the threshold, a third machine learning model may be to determine a second probability of the risk score being a false negative. Clinical recommendations for the patient may be determined based on the risk score, the first probability of the risk score being the false positive, and the second probability of the risk score being the false negative. Related systems and computer program products are also provided.

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