公开(公告)号：US11474598B2

公开(公告)日：2022-10-18

申请号：US17158642

申请日：2021-01-26

申请人： Vijaya Krishna Mulpuri ,  Qiang Xu ,  Wei Li ,  Chenhe Li ,  Xiaoliang Li

发明人： Vijaya Krishna Mulpuri ,  Qiang Xu ,  Wei Li ,  Chenhe Li ,  Xiaoliang Li

IPC分类号： G06F3/01 ,  G06F3/041 ,  G06F3/0488 ,  G06N3/08 ,  G06F3/04815

摘要： Systems and method for low-power gaze prediction on touch-enabled electronic devices using touch interactions are provided. The method includes receiving an input touch interaction applied to a touch interaction area on a touchscreen display of a touch-enabled device and obtaining, by a gaze prediction module, a predicted gazing area on the touchscreen display based on the input touch interaction. A touch-enabled device has a processor, a touchscreen display and memory storing instructions to carry out the method. The gaze prediction module may utilize a model based on Bayesian inference or convolutional neural networks. Advantageously, gaze prediction is achieved without the use of image-capturing devices and with low power consumption. This can be used to develop intelligent hands-free interactions and personalized recommendation systems.

公开(公告)号：US20220236793A1

公开(公告)日：2022-07-28

申请号：US17158642

申请日：2021-01-26

申请人： Vijaya Krishna MULPURI ,  Qiang XU ,  Wei LI ,  Chenhe LI ,  Xiaoliang LI

发明人： Vijaya Krishna MULPURI ,  Qiang XU ,  Wei LI ,  Chenhe LI ,  Xiaoliang LI

IPC分类号： G06F3/01 ,  G06F3/041 ,  G06F3/0481 ,  G06F3/0488 ,  G06N3/08

摘要： Systems and method for low-power gaze prediction on touch-enabled electronic devices using touch interactions are provided. The method includes receiving an input touch interaction applied to a touch interaction area on a touchscreen display of a touch-enabled device and obtaining, by a gaze prediction module, a predicted gazing area on the touchscreen display based on the input touch interaction. A touch-enabled device has a processor, a touchscreen display and memory storing instructions to carry out the method. The gaze prediction module may utilize a model based on Bayesian inference or convolutional neural networks. Advantageously, gaze prediction is achieved without the use of image-capturing devices and with low power consumption. This can be used to develop intelligent hands-free interactions and personalized recommendation systems.