Algorithmic adjustment of icon hitboxes based on prior gaze and click information.

Abstract

1. A method comprising: determining historical user data relating to events occurring on a wearable device; determining a probability of interacting with an object on a display of the wearable device based on the historical user data; scaling a hit box associated with the object to form a scaled hit box; detecting user input based on eye tracking within the scaled hit box; and in response to detecting the user input, initiating an action corresponding to the object.

Claims

1. A method, To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, The system includes, in response to the detection of the user input, initiating an action corresponding to the object, The aforementioned historical user data includes user interactions with objects on the display of the wearable device over multiple frames. The aforementioned historical user data includes a time history. A method for determining the probability of interacting with the object, based on a prior distribution of object interactions in a frame and the time history.

2. A method, To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, The system includes, in response to the detection of the user input, initiating an action corresponding to the object, The aforementioned historical user data includes user interactions with objects on the display of the wearable device over multiple frames. The aforementioned historical user data includes a time history. The method for determining the probability of interacting with the object is based on a combined prior distribution of object interactions in a frame and the time history.

3. A method, To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, The system includes, in response to the detection of the user input, initiating an action corresponding to the object, The aforementioned historical user data includes focus trends. The scaling of the detection element related to the object is based on the focus tendency, A method wherein the detection element is non-uniformly scaled based on the focus tendency.

4. The aforementioned historical user data includes a single frame of user interaction with an object on the display of the wearable device, The method according to any one of claims 1 to 3, wherein determining the probability of interacting with the object is based on a histogram of the historical user data.

5. Determining the probability of interacting with the object on the display of the wearable device includes determining a first probability of selecting a first object. The second probability of interacting with a second object on the display of the wearable device is determined based on the historical user data and the selection of the first object. Scaling the second detection element associated with the second object, Based on the eye movement data within the scaled second detection element, a second user input is detected. The method according to any one of claims 1 to 3, further comprising initiating a second action corresponding to the second object in response to the detection of the user input.

6. The eye movement data includes determining the Cartesian coordinates on the display of the wearable device, The method according to any one of claims 1 to 3, wherein the Cartesian coordinates are filtered in time.

7. The method according to any one of claims 1 to 3, further comprising storing historical user data related to a selected object on the display of the wearable device.

8. A wearable device, At least one processor, A device comprising at least one memory containing computer program code, The at least one memory and the computer program code, together with the at least one processor, are provided to the wearable device. To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, It is configured to initiate an action corresponding to the object in response to the detection of the user input, The aforementioned historical user data includes user interactions with objects on the display of the wearable device over multiple frames. The aforementioned historical user data includes a time history. The determination of the probability of interacting with the object is based on a prior distribution of object interactions in a frame and the time history of the wearable device.

9. A wearable device, At least one processor, A device comprising at least one memory containing computer program code, The at least one memory and the computer program code, together with the at least one processor, are provided to the wearable device. To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, It is configured to initiate an action corresponding to the object in response to the detection of the user input, The aforementioned historical user data includes user interactions with objects on the display of the wearable device over multiple frames. The aforementioned historical user data includes a time history. The determination of the probability of interacting with the object is based on a combined prior distribution of object interactions in a frame and the time history of the wearable device.

10. A wearable device, At least one processor, A device comprising at least one memory containing computer program code, The at least one memory and the computer program code, together with the at least one processor, are provided to the wearable device. To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, It is configured to initiate an action corresponding to the object in response to the detection of the user input, The aforementioned historical user data includes focus trends. The scaling of the detection element related to the object is based on the focus tendency, A wearable device in which the detection elements are non-uniformly scaled based on the focus tendency.

11. The aforementioned historical user data includes a single frame of user interaction with an object on the display of the wearable device, The wearable device according to any one of claims 8 to 10, wherein the determination of the probability of interacting with the object is based on a histogram of the historical user data.

12. Determining the probability of interacting with the object on the display of the wearable device includes determining a first probability of selecting a first object, and the computer program code: The second probability of interacting with a second object on the display of the wearable device is determined based on the historical user data and the selection of the first object. Scaling the second detection element associated with the second object, Based on the eye movement data within the scaled second detection element, a second user input is detected. A wearable device according to any one of claims 8 to 10, further configured to initiate a second action corresponding to the second object in response to the detection of the user input.

13. The eye movement data includes determining the Cartesian coordinates on the display of the wearable device. The wearable device according to any one of claims 8 to 10, wherein the Cartesian coordinates are filtered temporally.

14. The aforementioned computer program code further, The wearable device according to any one of claims 8 to 10, configured to store historical user data related to a selected object on the display of the wearable device.

15. A computer program comprising instructions, wherein, when executed by at least one processor, the device: To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, It is configured to initiate an action corresponding to the object in response to the detection of the user input, The aforementioned historical user data includes user interactions with objects on the display of the wearable device over multiple frames. The aforementioned historical user data includes a time history. A computer program determines the probability of interacting with the object based on a prior distribution of object interactions in a frame and the time history.

16. A computer program comprising instructions, wherein, when executed by at least one processor, the device: To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, It is configured to initiate an action corresponding to the object in response to the detection of the user input, The aforementioned historical user data includes user interactions with objects on the display of the wearable device over multiple frames. The aforementioned historical user data includes a time history. The computer program described above determines the probability of interacting with the object based on a combined prior distribution of object interactions in a frame and the time history.

17. A computer program comprising instructions, wherein, when executed by at least one processor, the device: To determine historical user data related to events occurring on wearable devices, Based on the aforementioned historical user data, the probability of interacting with an object on the display of the wearable device is determined. To form scaled detection elements, the detection elements associated with the object are scaled, Detecting user input based on eye movement data within the scaled detection element, It is configured to initiate an action corresponding to the object in response to the detection of the user input, The aforementioned historical user data includes focus trends. The scaling of the detection element related to the object is based on the focus tendency, A computer program in which the detection elements are non-uniformly scaled based on the focus tendency.

18. The historical user data includes a single frame of user interaction with an object on the display of the wearable device. The computer program according to any one of claims 15 to 17, wherein determining the probability of interacting with the object is based on a histogram of the historical user data.

19. Determining the probability of interacting with the object on the display of the wearable device includes determining a first probability of selecting a first object, and the instruction causes the device to The second probability of interacting with a second object on the display of the wearable device is determined based on the historical user data and the selection of the first object. Scaling the second detection element associated with the second object, Based on the eye movement data within the scaled second detection element, a second user input is detected. A computer program according to any one of claims 15 to 17, further configured to initiate a second action corresponding to the second object in response to the detection of user input.

20. A computer program including instructions, wherein, when executed by at least one processor, the instructions are configured to cause a computing system to perform the method according to any one of claims 1 to 3.

21. An apparatus comprising means for performing the method described in any one of claims 1 to 3.

22. It is a device, At least one processor, A device comprising at least one memory containing computer program code, The apparatus is configured such that the at least one memory and the computer program code, together with the at least one processor, cause the apparatus to perform at least one of the methods described in any one of claims 1 to 3.

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