Content display methods

• US 10,330,940 B1
• Filed: 03/06/2017
• Issued: 06/25/2019
• Est. Priority Date: 03/20/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Raffle, Hayes Solos, Wong, Adrian, Geiss, Ryan
• Primary Examiner(s): Zhou, Hong
• Application Number:
  US15/450,864
• Time in Patent Office: 841 Days
• CPC Class Codes: G02B 2027/014 comprising information/image processing systems
• CPC Class Codes: G02B 2027/0141 characterised by the informative content of the display
• CPC Class Codes: G02B 2027/0178 Eyeglass type eyeglass details G02C
• CPC Class Codes: G02B 2027/0187 slaved to motion of at least a part of the body of the user, e.g. head, eye
• CPC Class Codes: G02B 27/0093 with means for monitoring data relating to the user, e.g. head-tracking, eye-tracking
• CPC Class Codes: G02B 27/0176 characterised by mechanical features
• CPC Class Codes: G02B 27/0179 Display position adjusting means not related to the information to be displayed
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G09G 3/001 using specific devices not provided for in groups G09G3/02 - G09G3/36, e.g. using an intermediate record carrier such as a film slide; Projection systems; Display of non-alphanumerical information, solely or in combination with alphanumerical information, e.g. digital display on projected diapositive as background slide projectors per se G03B23/00 = 42 HP
• CPC Class Codes: G09G 5/36 characterised by the display of a graphic pattern, e.g. using an all-points-addressable [APA] memory

Abstract

Methods and systems involving a graphic display in a head mounted display (HMD) are disclosed herein. An exemplary system may be configured to: (1) at a computing system associated with a head-mountable display, receive head-movement data indicative of head movement; (2) use one or more context signals to determine a first activity associated with the head-mountable device; (3) determine a head-movement interpretation scheme corresponding to the first activity; (4) apply the determined head-movement interpretation scheme to determine input data corresponding to the received head-movement data; and (5) provide the determined input data for at least one function of the head-mountable display.

Claims

1. A computer-implemented method, comprising:

at a wearable computing system having a user interface (UI), receiving data indicative of body movement;

using one or more context signals to determine a first activity associated with the wearable computing system;

determining a body-movement interpretation scheme based on the first activity, wherein determining the body-movement interpretation scheme comprises determining a first value for at least one sensitivity parameter used to distinguish between UI-targeted body movements and non-UI-targeted body movements;

applying the selected body-movement interpretation scheme to determine input data corresponding to the received body-movement data; and

providing the determined input data for at least one function of the wearable computing system,

wherein the first value for the at least one sensitivity parameter maps an amount of the received body-movement data to the non-UI-targeted body movements, and wherein determining the body-movement interpretation scheme based on the first activity comprises;

adjusting the first value to a different value for the at least one sensitivity parameter;

based on the different value, modifying the amount of body-movement data mapped to the non-UI-targeted body movements; and

determining a second activity associated with the wearable computing system using the modified body-movement data mapped to the non-UI-targeted body movements.

2. The method of claim 1, wherein the first value for the at least one sensitivity parameter is used to adjust one or more sensors, and wherein the one or more sensors provide at least one context signal to determine the second activity associated with the wearable computing system.

3. The method of claim 2, wherein using the one or more context signals to determine the second activity associated with the wearable computing system comprises:

obtaining translational and rotational data; and

using the translational and rotational data to determine the second activity associated with the wearable computing system.

4. The method of claim 1, wherein the one or more context signals are based on the body-movement data.

5. The method of claim 1, wherein the first value for the at least one sensitivity parameter maps an additional amount of the received body-movement data to the UI-targeted body movements.

6. The method of claim 1, wherein determining the body-movement interpretation scheme that corresponds to the first activity comprises:

determining an amount of virtual movement that corresponds to a particular amount of body movement; and

providing a visual representation in the UI of the amount of virtual movement.

7. The method of claim 1, wherein the one or more context signals comprise one or more of:

(a) a current time, (b) a current date, (c) a current day of the week, (d) a current month, (e) a current season, (f) a time of a future event or future context, (g) a date of a future event or future context, (h) a day of the week of a future event or future context, (i) a month of a future event or future user-context, (j) a season of a future event or future context, (k) a time of a past event or past context, (l) a date of a past event or past context, (m) a day of the week of a past event or past context, (n) a month of a past event or past context, (o) a season of a past event or past context, (p) ambient temperature, (q) a current, future, or past weather forecast at a current location, (r) a current, future, or past weather forecast at a location of a planned event, (s) a current, future, or past weather forecast at or near a location of a previous event, (t) information on a calendar associated with a user-profile, (u) information accessible via a user'"'"'s social networking account, (v) noise level or any recognizable sounds detected by a device, (w) devices that are currently available to the wearable computing system, (x) devices in proximity to the wearable computing system, (y) devices that are able to communicate with the wearable computing system, (z) information derived from cross-referencing any two or more of;

information on the user'"'"'s calendar, information available via the user'"'"'s social networking account, and/or other context signals or sources of context information, (aa) health statistics or characterizations of the user'"'"'s current health, (bb) a user'"'"'s recent context as determined from sensors on or near the user and/or other sources of context information, (cc) a current location, (dd) a past location, and (ee) a future location.

8. The method of claim 1, wherein determining the body-movement interpretation scheme based on the first activity comprises:

mapping the first activity to a corresponding body-movement interpretation scheme using an activity-to-interpretation-scheme database.

9. A system, comprising:

a wearable computing system having a user interface (UI), wherein the system is configured for;

receiving body-movement data from the wearable computing system indicative of body movement;

using one or more context signals to determine a body-movement interpretation scheme based on the first activity, wherein determining the body-movement interpretation scheme comprises determining a first value for at least one sensitivity parameter used to distinguish between the UI-targeted body movements and the non-UI-targeted body movements; and

applying the determined body-movement interpretation scheme to determine input data corresponding to the received body-movement data; and

providing the determined input data for at least one function of the wearable computing system,

wherein the first value for the at least one sensitivity parameter maps an amount of the received body-movement data to the non-UI-targeted body movements, and wherein determining the body-movement interpretation scheme based on the first activity comprises;

adjusting the first value to a different value for the at least one sensitivity parameter;

based on the different value, modifying the amount of body-movement data mapped to the non-UI-targeted body movements; and

determining a second activity associated with the wearable computing system using the modified body-movement data mapped to the non-UI-targeted body movements.

10. The system of claim 9, wherein the first value for the at least one sensitivity parameter is used to adjust one or more sensors, and wherein the one or more sensors provide at least one context signal used to determine the second activity associated with the wearable computing system.

11. The system of claim 10, wherein using the one or more context signals to determine the second activity that is associated with the wearable computing system comprises:

obtaining translational and rotational data; and

using the translational and rotational data to determine the second activity associated with the wearable computing system.

12. The system of claim 9, wherein the one or more context signals are based on the body-movement data.

13. The system of claim 9, wherein the first value for the at least one sensitivity parameter maps an additional amount of the received body-movement data to the UI-targeted body movements.

14. The system of claim 9, wherein determining the body-movement interpretation scheme that corresponds to the first activity comprises:

determining an amount of virtual movement that corresponds to a particular amount of body movement; and

providing a visual representation of the amount of virtual movement.

15. The system of claim 9, wherein the one or more context signals comprise one or more of:

(a) a current time, (b) a current date, (c) a current day of the week, (d) a current month, (e) a current season, (f) a time of a future event or future context, (g) a date of a future event or future context, (h) a day of the week of a future event or future context, (i) a month of a future event or future user-context, (j) a season of a future event or future context, (k) a time of a past event or past context, (l) a date of a past event or past context, (m) a day of the week of a past event or past context, (n) a month of a past event or past context, (o) a season of a past event or past context, (p) ambient temperature, (q) a current, future, or past weather forecast at a current location, (r) a current, future, or past weather forecast at a location of a planned event, (s) a current, future, or past weather forecast at or near a location of a previous event, (t) information on a calendar associated with a user-profile, (u) information accessible via a user'"'"'s social networking account, (v) noise level or any recognizable sounds detected by a device, (w) devices that are currently available to the wearable computing system, (x) devices in proximity to the wearable computing system, (y) devices that are able to communicate with the wearable computing system, (z) information derived from cross-referencing any two or more of;

information on the user'"'"'s calendar, information available via the user'"'"'s social networking account, and/or other context signals or sources of context information, (aa) health statistics or characterizations of the user'"'"'s current health, (bb) a user'"'"'s recent context as determined from sensors on or near the user and/or other sources of context information, (cc) a current location, (dd) a past location, and (ee) a future location.

16. The system of claim 9, wherein determining the body-movement interpretation scheme that corresponds to the first activity comprises:

mapping the first activity based on the body-movement interpretation scheme using an activity-to-interpretation-scheme database.

17. A computer-implemented method, comprising:

at a wearable computing system having a user interface (UI), receiving data indicative of body movement;

determining that at least a portion of the body-movement data corresponds to non-UI-targeted body movements, wherein one or more sensitivity parameters are used to distinguish between UI-targeted body movements and the non-UI-targeted body movements;

using (1) one or more context signals and (2) at least the portion of the body-movement data corresponding to the non-UI-targeted body movements to determine a first activity associated with the wearable computing system;

determining a body-movement interpretation scheme corresponding to the first activity, wherein determining the body-movement interpretation scheme comprises determining a first value for at least one sensitivity parameter used to distinguish between the UI-targeted body movements and the non-UI-targeted body movements;

applying the determined body-movement interpretation scheme to determine the received body-movement data corresponds to one or more UI commands; and

providing the one or more UI commands for at least one function of the wearable computing system;

wherein the first value for the at least one sensitivity parameter maps a given amount of received body-movement data to the non-UI-targeted body movements, and wherein determining the body-movement interpretation scheme based on the first activity comprises;

adjusting the first value to a different value for the at least one sensitivity parameter corresponding to the first activity;

based on the different value, modifying the given amount of body-movement data mapped to the non-UI-targeted body movements; and

determining a second activity using the modified body-movement data mapped to the non-UI-targeted body movements.

18. The method of claim 17, wherein the first value for the at least one sensitivity parameter is used to adjust one or more sensors, and wherein the one or more sensors provide at least one context signal to determine a second activity associated with the body-mountable device.