Future Wei Technologies, Inc.

Jianyu Zhang

Chia Chin Chong

Chia-Chin Chong

Hong Heather Yu

Dong-Qing Zhang

Bo Wang

A device, method and system provide a wellness management process and/or an exercise management process for use with a smartphone or other mobile computing device. Various data about the user is obtained and used for determining and recommending an action or exercise to the user to improve the user's wellness/physique/health. The action determined can be based on: (1) current biometric and/or motion data about the user (from the sensors), and (2) current physical condition(s), such as health/medical information or condition about the user (from the user's personal information, e.g., health library or programmed into the smartphone). Specific information about the user is taken into consideration when recommending user action or exercise, such as the user's specific physical, health or medical conditions.

Wellness management method and system by wellness mode based on context-awareness platform on smartphone

A method comprising acquiring a plurality of measurements from at least one sensor in a mobile device, determining an activity classification of a user of the mobile device based on the plurality of measurements, acquiring an audio file for the mobile device, wherein the audio file is selected based on the activity classification, and playing the audio file by the mobile device.

Motion-based music recommendation for mobile devices

A method implemented by a mobile device for music recommendation to a user of the mobile device, the method comprising acquiring one or more measurements from at least one biometric sensor to obtain biometric information of the user, determining a music based at least in part on the biometric information, and recommending the music to the user for playing.

Music recommendation based on biometric and motion sensors on mobile device

Embodiments are provided herein for providing biometrics-based music recommendations to users. The biometric-based music recommendations take into account the changing music preferences of users from time to time as their biometrics change, such as due to users wellness states, being in different moods, engagement in different activities, and/or entering different environments. The schemes herein are implemented on user devices equipped or coupled to wearable devices capable of collecting biometrics data from users, such as heart rate, perspiration, and skin temperature data. The biometrics data of a user are collected and then processed into biometrics information. The biometrics information is then classified into a current wellness state of the user. A music recommendation is then provided to the user according to the biometrics information and the current wellness state of the user.

System and method for biometrics-based music recommendation

A device, method and system processes data about the user for determining and recommending a health/wellness action to the user. The action determined can be based on: (1) current biometric and/or motion data about the user (from the sensors), and (2) current health/medical information or condition about the user (from the user's personal information, e.g., health library or programmed into the smartphone). Specific information about the user is taken into consideration when recommending user action, such as the user's specific health or medical conditions.

Embodiments are provided herein to achieve video or image sequence encoding with an improved denoising algorithm that is both efficient computationally and has acceptable overhead cost in comparison to other denoising schemes for video encoding. The embodiments include using recursive bilateral filtering as part of the denoising algorithm, which is integrated into a video encoder to overcome limitations of other encoder-integrated denoising algorithms. An embodiment method includes receiving, at a filtering and residual computation function at the encoder, a macro block comprising a plurality of pixels. The filtering and residual computation function also receives, from a motion estimation function at the encoder, a reference block. The reference block comprises a plurality of reference pixels corresponding to the macro block. The filtering and residual computation function further applies a recursive bilateral filter function to each of the pixels of the macro block using the reference pixels of the reference block.

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San Jose, CA, United States of America

Jianyu Zhang