MICROLOCATIONS USING TAGGED DATA
    2.
    发明公开

    公开(公告)号:US20230179671A1

    公开(公告)日:2023-06-08

    申请号:US18102680

    申请日:2023-01-27

    Applicant: Apple Inc.

    CPC classification number: H04L67/52 G06N20/00 H04L67/535 H04W4/33 H04W4/38

    Abstract: A semi-supervised machine learning model can provide for classifying an input data point as associated with a particular target location or a particular action. Each data point comprises one or more sensor values from one or more signals emitted by one or more signal sources located within a physical area. A tagged sample set and an untagged sample set are combined to train the machine learning model. Each tagged sample includes a respective data point and a label representing a respective location/action. Each untagged sample includes a data point but is unlabeled. Once trained, given a current data point, the machine learning model can classify the current data point as associated with a particular location/action, after which a target object (e.g., other device or application to be used) can be predicted.

    Spectral aggregation to generate a wideband channel estimation

    公开(公告)号:US10243710B2

    公开(公告)日:2019-03-26

    申请号:US15257866

    申请日:2016-09-06

    Applicant: Apple Inc.

    Abstract: A device implementing spectral aggregation to generate a wideband channel estimation may include a processor configured to generate a combined channel estimation for one or more channels. For each channel, the processor may be configured to: transmit a first signal to another device; after an amount of time elapses, open a receive window for facilitating detection of a second signal from the other device; receive the second signal from the other device; generate a first channel estimation based on the second signal; and generate the combined channel estimation based on the first channel estimation and a second channel estimation received from the other device. The processor may be configured to aggregate the combined channel estimation generated for each channel into an aggregated channel estimation. The processor may be configured to estimate a time of arrival for a third signal based on the aggregated channel estimation.

    Microlocations using tagged data
    4.
    发明授权

    公开(公告)号:US11870563B2

    公开(公告)日:2024-01-09

    申请号:US18102680

    申请日:2023-01-27

    Applicant: Apple Inc.

    CPC classification number: H04L67/52 G06N20/00 H04L67/535 H04W4/33 H04W4/38

    Abstract: A semi-supervised machine learning model can provide for classifying an input data point as associated with a particular target location or a particular action. Each data point comprises one or more sensor values from one or more signals emitted by one or more signal sources located within a physical area. A tagged sample set and an untagged sample set are combined to train the machine learning model. Each tagged sample includes a respective data point and a label representing a respective location/action. Each untagged sample includes a data point but is unlabeled. Once trained, given a current data point, the machine learning model can classify the current data point as associated with a particular location/action, after which a target object (e.g., other device or application to be used) can be predicted.

    Microlocations using tagged data
    5.
    发明授权

    公开(公告)号:US11601514B2

    公开(公告)日:2023-03-07

    申请号:US17496479

    申请日:2021-10-07

    Applicant: Apple Inc.

    Abstract: A semi-supervised machine learning model can provide for classifying an input data point as associated with a particular target location or a particular action. Each data point comprises one or more sensor values from one or more signals emitted by one or more signal sources located within a physical area. A tagged sample set and an untagged sample set are combined to train the machine learning model. Each tagged sample includes a respective data point and a label representing a respective location/action. Each untagged sample includes a data point, but is unlabeled. Once trained, given a current data point, the machine learning model can classify the current data point as associated with a particular location/action, after which a target object (e.g., other device or application to be used) can be predicted.

    Power Saving for Non-trigger-based Ranging
    6.
    发明申请

    公开(公告)号:US20200077334A1

    公开(公告)日:2020-03-05

    申请号:US16550508

    申请日:2019-08-26

    Applicant: Apple Inc.

    Abstract: Methods for performing a ranging procedure according to the non-trigger-based protocol may include negotiating timing parameters associated with the ranging procedure, performing a ranging measurement, and transmitting/receiving, after completion of the ranging measurement, a message announcing initiation of another ranging measurement. The timing parameters may indicate a time window in which an initiating device can initiate a subsequent ranging measurement and the message announcing initiation of the second ranging measurement may be received during the time range specified. Timing parameters may indicate a responding device's required minimum and maximum time between ranging measurements. Additional parameters may indicate an initiating device's required minimum and maximum time between ranging measurements. A power savings mode may be entered after the first ranging measurement and during at least a portion of a time period specified by the parameters.

    LOCATION MEASUREMENT TECHNIQUES
    8.
    发明申请

    公开(公告)号:US20240402211A1

    公开(公告)日:2024-12-05

    申请号:US18677583

    申请日:2024-05-29

    Applicant: Apple Inc.

    Abstract: In some implementations, responsive to a trigger signal at an associated first time, a mobile device generating a first location value using a first ranging session with one or more other devices. The technique may include storing the first location value in a memory. The technique may include tracking, using a motion sensor of the mobile device, motion of the mobile device to determine a present location relative to the first location value. Further, the technique may include determining that a present location for the mobile device has changed by a predetermined threshold amount from the first location value since the associated first time. Responsive to the present location for the mobile device having changed by more than the predetermined threshold amount since the associated first time, the technique may include, generating a second location value using a second ranging session with the one or more other devices.

    SYSTEM AND TECHNIQUES FOR IMPROVING IN-ROOM PERSON DETECTION

    公开(公告)号:US20230400574A1

    公开(公告)日:2023-12-14

    申请号:US18140517

    申请日:2023-04-27

    Applicant: APPLE INC.

    CPC classification number: G01S17/04 G01S17/87 G01S17/48 G01S7/52004 G01S15/04

    Abstract: A technique for determining a presence of a person in a room may include an electronic device transmitting an electromagnetic wireless signal of a first sensor. The technique may include receiving an electromagnetic return signal from the electromagnetic wireless signal. The technique may include detecting a potential target in the room based on the electromagnetic return signal. The technique may include determining that the potential target is in the room using a second sensor. Responsive to determining the potential target is in the room, the technique may include saving a training signature of the electromagnetic return signal for training a machine learning model. This technique can be repeated to obtain a set of training signatures corresponding to potential targets. The technique may include training, using the set of training signatures, the machine learning model to detect when a target is in the room using the first sensor.

Patent Agency Ranking