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公开(公告)号：US11689878B2
公开(公告)日：2023-06-27
申请号：US17467883
申请日：2021-09-07
Applicant: QUALCOMM Incorporated
Inventor： Diyan Teng , Junsheng Han , Rashmi Kulkarni
Abstract: A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to obtain electrical activity data corresponding to electrical signals from one or more electrical sources within a user's head. The one or more processors are also configured to execute the instructions to render, based on the electrical activity data, audio data to adjust a location of a sound source in a sound field during playback of the audio data.
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公开(公告)号：US11368573B1
公开(公告)日：2022-06-21
申请号：US17317416
申请日：2021-05-11
Applicant: QUALCOMM Incorporated
Inventor： Diyan Teng , Mehul Soman , Nisarg Trivedi , Rashmi Kulkarni , Justin McGloin
IPC: H04M1/72454 , G06F3/01 , G01C21/16
Abstract: In some aspects, a user equipment (UE) determines, using an inertial measurement unit, an orientation of the UE and determines, using ambient light sensors, an ambient light condition of the UE. The UE determines, using a machine learning module and based on the orientation and the ambient light condition, a position of the UE. If the position comprises an on-body position, the UE uses the machine learning module and touch data received by a touchscreen of the UE to determine whether the position comprises an in-hand position. If the position comprises the in-hand position, the UE determines, using the machine learning module and based on the orientation and the touch data, a grip mode. If the position comprises an off-body position, the UE determines, using the machine learning module and at least one of the inertial measurement unit or the ambient light sensors, a user presence or a user absence.
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公开(公告)号：US20210138350A1
公开(公告)日：2021-05-13
申请号：US16680642
申请日：2019-11-12
Applicant: QUALCOMM Incorporated
Inventor： Diyan Teng , Mehul Soman , Shashank Narayanan
IPC: A63F13/795 , G06N20/00 , G06N5/04 , A63F13/67
Abstract: Methods, systems, and devices for game matchmaking are described. The methods, systems, and devices for game matchmaking may include determining a ranking of a first user of a set of users in a game environment, monitoring, via a sensor, a performance attribute of the first user while the first user plays a game in the game environment, modifying the ranking of the first user in the game environment based on the monitored performance attribute of the first user, and, in some examples, matching the first user with a second user of the set of users based on the modified ranking of the first user.
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公开(公告)号：US11877059B2
公开(公告)日：2024-01-16
申请号：US17196516
申请日：2021-03-09
Applicant: QUALCOMM Incorporated
Inventor： Mehul Soman , Diyan Teng
IPC: H04N23/68
CPC classification number: H04N23/6811 , H04N23/683
Abstract: In some aspects, a device may receive measured camera motion information associated with an image. The device may generate a shared image based on the image. The device may apply, based on the measured camera motion information, an image stabilization process to the image to generate a true image. The device may store the true image in a memory associated with the device. The device may provide the shared image and the measured camera motion information to another device. Numerous other aspects are described.
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公开(公告)号：US11844589B2
公开(公告)日：2023-12-19
申请号：US17302444
申请日：2021-05-03
Applicant: QUALCOMM Incorporated
Inventor： Diyan Teng , Mehul Soman , Rashmi Kulkarni
IPC: A61B5/0205 , A61B5/18
CPC classification number: A61B5/0205 , A61B5/18 , A61B2562/0219 , A61B2562/0257 , A61B2562/046
Abstract: Cardiovascular or respiratory data of a subject is measured using a multi-sensor system. The multi-sensor system includes a mm-wave FMCW radar sensor, an IMU sensor, and one or more proximity sensors. The mm-wave FMCW radar sensor may be selected and its view angle adjusted based on positioning data regarding the subject obtained from the one or more proximity sensors. Each of the mm-wave FMCW radar sensor and the IMU sensor may acquire cardiovascular or respiratory measurements of the subject, and the measurements may be fused for improved accuracy and performance.
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Patent Agency Ranking
公开(公告)号：US11564053B1
公开(公告)日：2023-01-24
申请号：US17447717
申请日：2021-09-15
Applicant: QUALCOMM Incorporated
Inventor： Mehul Soman , Diyan Teng , Junsheng Han
Abstract: A method of controlling spatial audio rendering includes comparing a first heartbeat pattern to a second heartbeat pattern to generate a comparison result. The first heartbeat pattern is based on sensor information associated with a first sensor of a first sensor type, and the second heartbeat pattern is based on sensor information associated with a second sensor of a second sensor type. The method also includes, based on the comparison result, controlling a spatial audio rendering function associated with media playback.
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公开(公告)号：US11821754B2
公开(公告)日：2023-11-21
申请号：US17675725
申请日：2022-02-18
Applicant: QUALCOMM Incorporated
Inventor： Diyan Teng , Junsheng Han , Victor Kulik , Mehul Soman , Rashmi Kulkarni
Abstract: Systems and techniques are described herein. For example, a process can include obtaining first sensor measurement data associated with a and second sensor measurement from one or more sensors. In some cases, the first measurement data can be associated with a first time and the second sensor measurement data can be associated with a second time occurring after the first time. In some aspects, the process includes determining that the first sensor measurement data and the second sensor measurement data satisfy at least one batching condition. In some examples, the process includes, based on determining that the first sensor measurement data and the second sensor measurement data satisfy the at least one batching condition, generating a sensor measurement data batch including the first sensor measurement data, the second sensor measurement data, and at least one target sensor measurement data. Ins examples the process includes outputting the sensor measurement data batch.
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公开(公告)号：US11580421B2
公开(公告)日：2023-02-14
申请号：US16655031
申请日：2019-10-16
Applicant: Qualcomm Incorporated
Inventor： Diyan Teng , Rashmi Kulkarni , Justin McGloin
IPC: G06F9/00 , G06N5/04 , G06N20/00 , G06F9/4401
Abstract: A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.
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公开(公告)号：US11314337B1
公开(公告)日：2022-04-26
申请号：US17302612
申请日：2021-05-07
Applicant: QUALCOMM Incorporated
Inventor： Diyan Teng , Mehul Soman , Rashmi Kulkarni
IPC: G06F3/023 , G06F3/04886
Abstract: Various aspects of the present disclosure generally relate to object detection. In some aspects, a device may include a housing; an input device adjoined to the housing, the input device configured to receive an input associated with a press of a key of a plurality of keys; one or more transmitters disposed in the housing, the one or more transmitters configured to transmit one or more signals toward the plurality of keys; one or more receivers disposed in the housing, the one or more receivers configured to receive one or more return signals corresponding to the one or more signals; and a processor configured to determine a location of the key based at least in part on the one or more return signals.
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公开(公告)号：US20210117818A1
公开(公告)日：2021-04-22
申请号：US16655031
申请日：2019-10-16
Applicant: Qualcomm Incorporated
Inventor： Diyan Teng , Rashmi Kulkarni , Justin McGloin
Abstract: A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.
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