Auto-completion for gesture-input in assistant systems

Patent metadata

Field	Value
Publication number	US-11249773-B2
Application number	US-202017010750-A
Country	US
Kind code	B2
Filing date	Sep 2, 2020
Priority date	Apr 20, 2018
Publication date	Feb 15, 2022
Grant date	Feb 15, 2022

Abstract

In one embodiment, a method includes detecting a user input comprising an incomplete gesture performed by one or more hands of a first user by a client system associated with the first user; selecting one or more candidate gestures from a plurality of pre-defined gestures by the client system based on a personalized gesture-recognition model, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture, and presenting one or more suggested inputs corresponding to one or more of the candidate gestures at the client system.

First claim

The invention claimed is:

1. A method comprising:
detecting, by a head-mounted client system associated with a first user, a user input comprising an incomplete three-dimensional (3D) gesture performed by one or more hands of the first user;
selecting, by the head-mounted client system based on a personalized gesture-recognition model, one or more candidate 3D gestures from a plurality of pre-defined 3D gestures, wherein each of the candidate 3D gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate 3D gesture; and
presenting, at the head-mounted client system, one or more suggested inputs corresponding to one or more of the candidate 3D gestures.

2. The method of claim 1 , further comprising:
receiving, at the head-mounted client system, a user-selected input from the first user, wherein the user-selected input comprises one of the suggested inputs; and
executing, by the head-mounted client system, one or more tasks based on the user-selected input.

3. The method of claim 1 , wherein each pre-defined 3D gesture comprises one or more of pointing, poking, tapping, waving, or swiping.

4. The method of claim 1 , further comprising:
receiving, at the head-mounted client system, a first user-selected input from the first user, wherein the first user-selected input comprises one of the suggested inputs, and wherein the first user-selected input is associated with a first intent;
generating, by the head-mounted client system based on the first user-selected input, one or more additional candidate 3D gestures, wherein each of the one or more additional candidate 3D gestures is associated with the first intent;
presenting, at the head-mounted client system, one or more additional suggested inputs corresponding to one or more of the additional candidate 3D gestures;
receiving, at the head-mounted client system, a second user-selected input from the first user, wherein the second user-selected input comprises one of the additional suggested inputs; and
executing, by the head-mounted client system, one or more tasks based on the second user-selected input.

5. The method of claim 1 , wherein the head-mounted client system comprises a virtual-reality (VR) headset or augmented-reality (AR) glasses.

6. The method of claim 1 , wherein detecting the user input comprising the incomplete 3D gesture is based on one or more of image information, video information, or motion information.

7. The method of claim 1 , further comprising:
calculating, by the head-mounted client system for each of the one or more candidate 3D gestures, a similarity level of the candidate 3D gesture with respect to the incomplete 3D gesture.

8. The method of claim 7 , wherein the similarly level of each candidate 3D gesture with respect to the incomplete 3D gesture is based on a trajectory of the incomplete 3D gesture with respect to the head-mounted client system.

9. The method of claim 7 , wherein the similarly level of each candidate 3D gesture with respect to the incomplete 3D gesture is based on an orientation of the incomplete 3D gesture with respect to the head-mounted client system.

10. The method of claim 7 , wherein the similarly level of each candidate 3D gesture with respect to the incomplete 3D gesture is based on an object associated with the incomplete 3D gesture.

11. The method of claim 7 , wherein the similarly level of each candidate 3D gesture with respect to the incomplete 3D gesture is based on contextual information associated with the incomplete 3D gesture.

12. The method of claim 7 , wherein the similarly level of each candidate 3D gesture with respect to the incomplete 3D gesture is based on a position of the incomplete 3D gesture with respect to the head-mounted client system.

13. The method of claim 1 , further comprising:
calculating, by the head-mounted client system, one or more confidence scores for one or more intents corresponding to the incomplete 3D gesture; and
determining, by the head-mounted client system, that each of the one or more confidence scores is below a threshold score.

14. The method of claim 13 , wherein the threshold score is based on a 3D wake-up gesture performed by the first user.

15. The method of claim 13 , wherein calculating the one or more confidence scores for the one or more intents corresponding to the incomplete 3D gesture is based on a velocity associated with the incomplete 3D gesture.

16. The method of claim 13 , wherein calculating the one or more confidence scores for the one or more intents corresponding to the incomplete 3D gesture is based on temporal information associated with the incomplete 3D gesture, and wherein the temporal information comprises a pause in the user input.

17. The method of claim 13 , wherein selecting the one or more candidate 3D gestures is further based on the one or more intents.

18. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
detect, by a head-mounted client system associated with a first user, a user input comprising an incomplete three-dimensional (3D) gesture performed by one or more hands of the first user;
select, by the head-mounted client system based on a personalized gesture-recognition model, one or more candidate 3D gestures from a plurality of pre-defined 3D gestures, wherein each of the candidate 3D gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate 3D gesture; and
present, at the head-mounted client system, one or more suggested inputs corresponding to one or more of the candidate 3D gestures.

19. The media of claim 18 , wherein the software is further operable when executed to:
calculate, by the head-mounted client system for each of the one or more candidate 3D gestures, a similarity level of the candidate 3D gesture with respect to the incomplete 3D gesture.

20. A system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
detect, by a head-mounted client system associated with a first user, a user input comprising an incomplete three-dimensional (3D) gesture performed by one or more hands of the first user;
select, by the head-mounted client system based on a personalized gesture-recognition model, one or more candidate 3D gestures from a plurality of pre-defined 3D gestures, wherein each of the candidate 3D gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate 3D gesture; and
present, at the head-mounted client system, one or more suggested inputs corresponding to one or more of the candidate 3D gestures.

Assignees

• Facebook Tech Llc

Inventors

• Presant William Crosby
• Penov Francislav P
• Kumar Anuj

Classifications

• G06F16/3329

  Natural language query formulation · CPC title

• G06F3/017

  Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883) · CPC title

• G06F3/011

  Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00) · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title