PATENT DOCUMENT

Publication Number: US-11119573-B2
Application Number: US-201916568782-A
Country: US
Kind Code: B2

Title: Pupil modulation as a cognitive control signal

Abstract:
One exemplary implementation provides an improved user experience on a device by using physiological data to initiate a user interaction for the user experience based on an identified interest or intention of a user. For example, a sensor may obtain physiological data (e.g., pupil diameter) of a user during a user experience in which content is displayed on a display. The physiological data varies over time during the user experience and a pattern is detected. The detected pattern is used to identify an interest of the user in the content or an intention of the user regarding the content. The user interaction is then initiated based on the identified interest or the identified intention.

Claims:
What is claimed is: 
     
       1. A method comprising:
 at a device comprising a processor, a computer-readable storage medium, a display, and a sensor: 
 obtaining physiological data of a user during a user experience in which content is displayed on the display, the physiological data obtained using the sensor and varying over time during the user experience; 
 detecting a pattern using the physiological data; 
 based on the detecting of the pattern, identifying an interest or an intention of the user to interact with an item in the content, wherein the identifying is based on:
 identifying the item based on a gaze direction, wherein the item is a selectable item; and 
 identifying the interest or intention based on a verbal command, wherein the interest or intention is an interest or intention to select the selectable item and the verbal command is confirmed based on detecting the pattern using the physiological data, wherein the pattern is a pupil dilation pattern; and 
 
 initiating a user interaction with the item based on identifying the interest or intention. 
 
     
     
       2. The method of  claim 1 , wherein:
 the physiological data is pupil dilation data representing a time-varying pupil diameter; and 
 detecting the pattern comprises detecting the pupil dilation pattern. 
 
     
     
       3. The method of  claim 2 , wherein detecting the pupil dilation pattern comprises accounting for exogenous signals corresponding to pupil diameter changes in the pupil dilation data resulting from ambient light changes, chromatic changes, accommodation of the eye, content lighting changes, cyclical pupil dilations, a change in ambient noise, or change in motion of the device. 
     
     
       4. The method of  claim 1 , wherein detecting the pattern comprises applying a machine learning technique trained to identify patterns in physiological data corresponding to user interests or user intentions. 
     
     
       5. The method of  claim 1 , wherein the physiological data represents involuntary data. 
     
     
       6. The method of  claim 1 , further comprising obtaining voluntary data, wherein identifying the interest or intention is further based on the voluntary data. 
     
     
       7. The method of  claim 6 , wherein the voluntary data comprises:
 a gesture of a body part detected by an image sensor during the user experience; 
 a voice command of a voice detected by a sound sensor during the user experience; 
 a fixed gaze detected by an eye sensor during the user experience; 
 a sequence of gaze patterns detected by an eye sensor during the user experience; 
 a head movement; 
 a movement detected by a motion sensor during the user experience; 
 a facial expression detected by an image sensor during the user experience; or 
 an attribute included in the content. 
 
     
     
       8. The method of  claim 1 , further comprising progressively identifying related interests or intentions based on previously identified interests or previously identified intentions during the user experience. 
     
     
       9. The method of  claim 1 , further comprising determining a confidence in the identified interest or the identified intention based on previously identified interests or previously identified intentions during the user experience. 
     
     
       10. The method of  claim 1 , wherein identifying the intention comprises identifying an intent to execute a movement, make a decision, or select a target in the content at a particular instant in time or at future time. 
     
     
       11. The method of  claim 1 , wherein identifying the intention comprises:
 receiving data regarding a voluntary user movement; and 
 interpreting the voluntary user movement as an intention to interact with the content based on involuntary data in the physiological data. 
 
     
     
       12. The method of  claim 1 , wherein identifying the interest comprises identifying an interest in a particular object in the content at a particular instant in time or at a future time. 
     
     
       13. The method of  claim 1 , wherein initiating the user interaction comprises:
 providing additional content association with an object in the content corresponding to the identified interest or the identified intention; 
 removing an object in the content based on the identified interest or the identified intention; or 
 automatically capturing images of the content at times during the user experience determined based on the identified interest or the identified intention. 
 
     
     
       14. The method of  claim 1 , wherein detecting the pattern comprises:
 tracking a physiological attribute associated with the physiological data using a first sensor; and 
 activating a second sensor to obtain the physiological data based on the tracking. 
 
     
     
       15. The method of  claim 1 , wherein the physiological data comprises electroencephalography (EEG) data of functional near infrared spectroscopy signal (fNIRS). 
     
     
       16. The method of  claim 1 , further comprising receiving informed consent of the user to obtain the physiological data or voluntary data of the user. 
     
     
       17. The method of  claim 1 , wherein the user interaction performs an operation associated with the item. 
     
     
       18. The method of  claim 1 , wherein the user interaction directs input received from the user to the item. 
     
     
       19. The method of  claim 1 , wherein the user interaction is further based on identifying the item based on data separate from the physiological data. 
     
     
       20. The method of  claim 1 , wherein the user interaction with the item is based on:
 identifying the item based on a gaze direction, wherein the item is a selectable item; and 
 identifying the interest or intention based on detecting the pattern, wherein the interest or intention is an interest or intention to select the selectable item and the pattern is a pupil dilation pattern. 
 
     
     
       21. A system comprising:
 an electronic device with a display and a sensor; 
 a processor; and 
 a computer-readable storage medium comprising instructions that upon execution by the processor cause the system to perform operations, the operations comprising:
 obtaining physiological data of a user during a user experience in which content is displayed on the display, the physiological data obtained using the sensor and varying over time during the user experience; 
 detecting a pattern using the physiological data; 
 based on the detecting of the pattern, identifying an interest or an intention of the user to interact with an item in the content, wherein the identifying is based on:
 identifying the item based on a gaze direction, wherein the item is a selectable item; and 
 identifying the interest or intention based on a verbal command, wherein the interest or intention is an interest or intention to select the selectable item and the verbal command is confirmed based on detecting the pattern using the physiological data, wherein the pattern is a pupil dilation pattern; and 
 
 initiating a user interaction with the item based on identifying the interest or intention. 
 
 
     
     
       22. The system of  claim 21 , wherein:
 the physiological data is pupil dilation data representing a time-varying pupil diameter; and 
 detecting the pattern comprises detecting the pupil dilation pattern, wherein detecting the pupil dilation pattern comprises accounting for exogenous signals corresponding to pupil diameter changes in the pupil dilation data resulting from ambient light changes, chromatic changes, accommodation of the eye, content lighting changes, cyclical pupil dilations, a change in ambient noise, or change in motion of the device. 
 
     
     
       23. The system of  claim 21 , wherein the electronic device is a head-mounted-device (HMD). 
     
     
       24. A non-transitory computer-readable storage medium storing program instructions that are computer-executable to perform operations comprising:
 obtaining physiological data of a user during a user experience in which content is displayed on a display, the physiological data obtained using the sensor and varying over time during the user experience; 
 detecting a pattern using the physiological data; 
 based on the detecting of the pattern, identifying an interest or an intention of the user to interact with an item in the content, wherein the identifying is based on:
 identifying the item based on a gaze direction, wherein the item is a selectable item; 
 identifying the interest or intention based on a verbal command, wherein the interest or intention is an interest or intention to select the selectable item and the verbal command is confirmed based on detecting the pattern using the physiological data, wherein the pattern is a pupil dilation pattern; and 
 
 initiating a user interaction with the item based on identifying the interest or intention.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application Ser. No. 62/738,031 filed Sep. 28, 2018, which is incorporated herein in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to providing user experiences on electronic devices, and in particular, to systems, methods, and devices for using physiological data to improve a user experience. 
     BACKGROUND 
     Electronic devices have different capabilities with respect to viewing and interacting with electronic content. A variety of input mechanisms have been incorporated into a variety of user devices to provide functionality and user interaction (e.g., keyboards, mice, touchscreens, buttons, microphones for voice commands, optical sensors, etc.). For example, touch screens have been incorporated into mobile phones (e.g., smartphones), tablet computers, wearable devices (e.g., watches, glasses, head-mounted devices, etc.), and other computing devices, allowing software developers to create engaging software applications (“apps”) for entertainment, productivity, health, and the like. In some instances, touch screens work in conjunction with a variety of other input mechanisms for interacting with a device (e.g., optical sensors, buttons, microphones for voice commands, etc.). 
     Many devices, however, can have limited device interaction and control capabilities due to device size constraints, display size constraints, operational constraints, etc. For example, small or thin user devices can have a limited number of physical buttons for receiving user input. Similarly, small user devices can have touchscreens with limited space for providing virtual buttons or other virtual user interface elements. In addition, some devices can have buttons or other interactive elements that are unnatural, cumbersome, or uncomfortable to use in certain positions or in certain operating conditions. For example, it may be cumbersome to interact with a device using both hands (e.g., holding a device in one hand while engaging interface elements with the other). In another example, it may be difficult to press small buttons or engage touchscreen functions while a user&#39;s hands are otherwise occupied or unavailable (e.g., when wearing gloves, carrying groceries, holding a child&#39;s hand, driving, etc.). In still other examples, device interaction can be limited in a variety of other ways. 
     SUMMARY 
     Various implementations disclosed herein include devices, systems, and methods that obtain physiological data (e.g., pupil dilation, electroencephalography, etc.) of a user during a user experience, in which content is displayed on a display, with one or more physiological sensors. The physiological data varies over time during the user experience and a pattern is detected. Moreover, in some implementations, the physiological data includes involuntary user responses. Dependent upon user privacy or opt-in/out settings, the detected pattern may be used to help or assist the user by identifying an interest of the user in the content or an intention of the user regarding the content. Identifying the intention may include, for example, identifying an intent to execute a movement, make a decision, or select a target in the content at a particular instant in time or in the future. Thus, a user interaction may be initiated based on the identified interest or the identified intention. In some implementations, the detected pattern is unique to the user and is stored in a user profile associated with the user. For example, the user profile may be used to provide a personalized user experience that identifies the user&#39;s interest or intention based upon the user&#39;s unique detected patterns. 
     In some implementations, the physiological data is pupil dilation data and represents a time-varying pupil diameter. Thus, the detected pattern may be a pupil dilation pattern. In some implementations, exogenous signals are accounted for when detecting the pupil dilation pattern. For example, exogenous signals may result from ambient light changes, chromatic changes, accommodation of the eye, content lighting changes, cyclical pupil dilations, changes in ambient noise, or changes in motion of the device. In some implementations, a machine learning technique is trained to identify patterns in physiological data corresponding to user interests or user intentions. 
     In some implementations, additional data is obtained, and the interest or intention is identified based on that data. The data may include, for example, a gesture of a body part detected by an image sensor during the user experience, a voice command of a voice detected by a sound sensor during the user experience, a fixed gaze detected by an eye sensor during the user experience, a sequence of gaze patterns detected by an eye sensor during the user experience, an orienting response (e.g., head movement), a movement detected by a motion sensor during the user experience, a facial expression detected by an image sensor during the user experience, or an attribute included in the content. In some implementations, the method includes identifying related interests or intentions based on previously identified interests or previously identified intentions during the user experience. In some implementations, the method includes determining a confidence in the identified interest or the identified intention based on previously identified interests or previously identified intentions during the user experience. In some implementations, data is received regarding a voluntary user movement (e.g., an arm movement) and the voluntary user movement is interpreted as an intention to interact with the content based on an involuntary characteristic of the user that is captured in the physiological data. For example, the user can use natural arm gestures and the gestures will only be recognized as intentional commands when the involuntary changes of the user&#39;s pupil reveal that intention. Moreover, identifying the interest may include identifying an interest in a particular object in content at a particular instant in time or in the future. 
     In some implementations progressive interfaces aid the usability of interactions supported by an involuntary characteristic of the user that is captured in the physiological data. For example, system feedback to the user may explicitly call out the use of a lower-confidence multimodal signal to begin a low-commitment interaction with some initial feedback to the user (e.g., highlighting or selecting an object, or displaying one or more menu items). Further input from the user in response to the low-commitment interaction may progressively lead to a higher-commitment action (e.g., acting on or deleting an item). 
     In some implementations initiating the user interaction includes providing additional content association with an object in the content, e.g., additional content corresponding to the identified interest or the identified intention. In some implementations, initiating the user interaction includes removing an object in the content based on the identified interest or the identified intention. In some implementations, initiating the user interaction includes automatically capturing images of the content at times during the user experience determined based on the identified interest or the identified intention. 
     In some implementations, detecting the pattern includes tracking a physiological attribute associated with the physiological data using a first sensor and activating a second sensor to obtain the physiological data based on the tracking. In some implementations, the device, e.g., a head-mounted-device, handheld device, laptop, or desktop, utilizes onboard sensors to obtain the physiological data and, in other implementations, the device includes a combination of multiple and physically separate devices and sensors. 
     In some implementations, informed consent is received from the user to obtain the physiological data and/or additional data. Moreover, a user may consent, opt-in/out to feature benefits, or select certain actions or types of action that may be invoked automatically based on physiological data. In some implementations, a prompt to consent includes a graphical cue rendered on a display portion of the device. For example, a graphical user interface may include a request for permission and an input portion that allows the user to input consent. In some implementations, a prompt to consent includes an audio cue generated. For example, the audio cue may include an audio segment explanatory of a request for permission and the consent may be received when the user issues a voice response that indicates consent. 
     In accordance with some implementations, a device includes one or more processors, a non-transitory memory, and one or more programs; the one or more programs are stored in the non-transitory memory and configured to be executed by the one or more processors and the one or more programs include instructions for performing or causing performance of any of the methods described herein. In accordance with some implementations, a non-transitory computer readable storage medium has stored therein instructions, which, when executed by one or more processors of a device, cause the device to perform or cause performance of any of the methods described herein. In accordance with some implementations, a device includes: one or more processors, a non-transitory memory, an image sensor, and means for performing or causing performance of any of the methods described herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the present disclosure can be understood by those of ordinary skill in the art, a more detailed description may be had by reference to aspects of some illustrative implementations, some of which are shown in the accompanying drawings. 
         FIG. 1  illustrates a device displaying content and obtaining physiological data from a user in accordance with some implementations. 
         FIG. 2  illustrates a pupil of the user of  FIG. 1  in which the diameter of the pupil varies with time. 
         FIG. 3A  is a chart illustrating detection of a pattern of physiological data in accordance with some implementations. 
         FIG. 3B  is a chart illustrating physiological data and an exogenous signal in accordance with some implementations. 
         FIG. 4  is a block diagram illustrating device components of an exemplary device according to some implementations. 
         FIG. 5  is a block diagram of an example head-mounted device (HMD) in accordance with some implementations. 
         FIG. 6  is a flowchart representation of a method for initiating a user interaction based on an identified interest or an identified intention of a user where the interest or the intention is identified based on physiological data of the user obtained using a sensor during a user experience. 
     
    
    
     In accordance with common practice the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may not depict all of the components of a given system, method or device. Finally, like reference numerals may be used to denote like features throughout the specification and figures. 
     DESCRIPTION 
     Numerous details are described in order to provide a thorough understanding of the example implementations shown in the drawings. However, the drawings merely show some example aspects of the present disclosure and are therefore not to be considered limiting. Those of ordinary skill in the art will appreciate that other effective aspects or variants do not include all of the specific details described herein. Moreover, well-known systems, methods, components, devices and circuits have not been described in exhaustive detail so as not to obscure more pertinent aspects of the example implementations described herein. 
       FIG. 1  illustrates a device  10  displaying content  15  and obtaining physiological data  45  from a user  25 . While this example and other examples discussed herein illustrate a single device  10  in a real-world environment  5 , the techniques disclosed herein are applicable to multiple devices as well as to other real-world environments. For example, the functions of device  10  may be performed by multiple devices. Furthermore, this example and other examples discussed herein operate under the assumption that the user  25  has provided informed consent to benefit from the techniques disclosed herein. For example, the user  25  may consent to the tracking or use of physiological data  45 , opt-in/out to feature benefits, or select certain actions or types of action that may be invoked automatically by device  10  based on physiological data  45 . 
     In some implementations, as illustrated in  FIG. 1 , the device  10  is a handheld electronic device (e.g., a smartphone or a tablet). In some implementations the device  10  is a laptop computer or a desktop computer. In some implementations, the device  10  has a touchpad and, in some implementations, the device  10  has a touch-sensitive display (also known as a “touch screen” or “touch screen display”). In some implementations, the device  10  is a wearable head mounted display (“HMD”). 
     In some implementations, the device  10  includes an eye tracking system for detecting eye position and eye movements. For example, an eye tracking system of an HMD may include one or more infrared (“IR”) light-emitting diodes (“LEDs”), an eye tracking camera (e.g., near-IR (“NIR”) camera), and an illumination source (e.g., an NIR light source) that emits light (e.g., NIR light) towards the eyes of the user  25 . Moreover, the illumination source of the HMD may emit NIR light to illuminate the eyes of the user  25  and the NIR camera may capture images of the eyes of the user  25 . In some implementations, images captured by the eye tracking system may be analyzed to detect position and movements of the eyes of the user  25 , or to detect other information about the eyes such as pupil dilation. For example, the point of gaze estimated from the eye tracking images may enable gaze-based interaction with content shown on the near-eye display of the HMD. 
     In some implementations, the device  10  has a graphical user interface (“GUI”), one or more processors, memory and one or more modules, programs or sets of instructions stored in the memory for performing multiple functions. In some implementations, the user  25  interacts with the GUI through finger contacts and gestures on the touch-sensitive surface. In some implementations, the functions include image editing, drawing, presenting, word processing, web site creating, disk authoring, spreadsheet making, game playing, telephoning, video conferencing, e-mailing, instant messaging, workout support, digital photographing, digital videoing, web browsing, digital music playing, and/or digital video playing. Executable instructions for performing these functions may be included in a computer readable storage medium or other computer program product configured for execution by one or more processors. 
     In some implementations, the device  10  presents an experience in which content  15  is displayed on a display of the device  10  during a user experience. A sensor  20  detects physiological data  45  of the user  25  during the user experience. In some implementations, the device  10  employs various physiological sensor, detection, or measurement systems. Detected physiological data may include, but is not limited to, electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), functional near infrared spectroscopy signal (fNIRS), blood pressure, skin conductance, or pupillary response. Moreover, the device  10  may simultaneously detect multiple forms of physiological data  45  in order to benefit from synchronous acquisition of physiological data  45 . Moreover, in some implementations, the physiological data  45  represents involuntary data, i.e., responses that are not under conscious control. For example, a pupillary response may represent an involuntary movement. 
     In some implementations, one or both eyes  30  of the user  25 , including one or both pupils  35  of the user  25  present physiological data  45  in the form of a pupillary response. The pupillary response of the user  25  results in a varying of the size or diameter of the pupil  35 , via the optic and oculomotor cranial nerve. For example, the pupillary response may include a constriction response (miosis), i.e., a narrowing of the pupil, or a dilation response (mydriasis), i.e., a widening of the pupil. In some implementations, the device  10  may detect patterns of physiological data  45  representing a time-varying pupil diameter. 
       FIG. 2  illustrates a pupil  35  of the user  25  of  FIG. 1  in which the diameter of the pupil  35  varies with time. As shown in  FIG. 2 , a present physiological state  50  may vary in contrast to a past physiological state  55 . For example, the present physiological state may include a present pupil diameter and a past physiological state may include a past pupil diameter. 
     The physiological data  45  may vary in time and the device  10  may use the physiological data  45  to detect a pattern. In some implementations, the pattern is a change in physiological data  45  from one time to another time, and, in some other implementations, the pattern is series of changes in physiological data over a period of time. Based on detecting the pattern, the device  10  may assist user  25  by identifying an interest or intent  40  of the user  25  and may initiate a user interaction based on the identified interest or intent  40 . 
       FIG. 3A  is a chart  300  illustrating detection of a pattern of physiological data  45 . Chart  300  illustrates a time-varying pattern  305  of physiological data  45 , for example, an amount of pupil dilation (y-axis) over time (x-axis). The pattern  305  includes a peak pattern  310  that may be interpreted by device  10  as an indication of an interest or intent  40  of the user  25 . In some implementations, the device  10  utilizes a model trained to determine that the peak pattern  310  indicates that the user  20  is involuntarily signaling interest or intent  40  during the time of the peak pattern  310 , e.g., based on something the user is looking at or otherwise experiencing during that time period. In some implementations, a machine learning model (e.g., a trained neural network) is applied to identify patterns in physiological data  45 . Moreover, the machine learning model may be used to match the patterns with learned patterns corresponding to indications of interest or intent  40 . The device  10  may learn patterns specific to the particular user  25 . For example, the device  10  may learn from determining that peak pattern  310  represents an indication of interest or intent of the user  25  and use this information to adapt the model to subsequently identify the similar peak pattern  320  as another indication of interest or intent of the user  25 . Such learning can take into account the user&#39;s interactions that may confirm predictions of interest or intent, e.g., if the model predicts an intent to click a button and the user clicks the button, the model can be updated accordingly. As another example, the model may be trained or adapted based on the user imagining pressing a button, i.e., not physically pressing the button. 
       FIG. 3B  is a chart  350  illustrating physiological data  45  (e.g., based on measured pupil dilation) and an exogenous signal  360  (e.g., a measure of ambient light at the device  10 ). The exogenous signal  360  corresponds to a measure of any factor or factors that could influence the physiological data. For example, the amount of ambient light at the device  10  may result in changes to the amount of dilation of the user&#39;s eyes, e.g., a decrease in ambient light may result in more dilation of the eyes, etc. 
     In some implementations, the physiological data  45  is adjusted or otherwise interpreted based on the exogenous signal  360 . For example, the peak pattern  310  corresponding to a dilation of the user&#39;s eyes may be preliminarily interpreted as an indication of intent or interest. Since the exogenous data  360  is level during the time period of peak  310  (e.g., indicating a constant ambient light level), the determination that the dilation should be interpreted as an indication of intent or interest is accepted. 
     In contrast, the peak pattern  390  corresponding to a dilation of the user&#39;s eyes may similarly be preliminarily interpreted as an indication of intent or interest but this determination may be rejected. In this example, the exogenous signal  360  indicates an increase  370  followed by a decrease  380  during the same time period as the peak pattern  390 . Thus, the exogenous signal could correspond to an exogenous factor (rather than an interest or intent of the user  25 ) that caused the peak pattern  390 . Accordingly, the device  10  may reject the preliminarily interpretation of peak pattern  390  as an indication of intent or interest. In some implementations, a model is used to account for exogenous signals, e.g., the model is trained to interpret patterns in physiological data  45  that occur during the same time periods as patterns in exogenous signals. 
     In some implementations, exogenous signals corresponding to pupil diameter changes in the pupil dilation data result from ambient light changes, chromatic changes, accommodation of the eye, content lighting changes, cyclical pupil dilations, a change in ambient noise, or change in motion of the device. For example, an increase in ambient light may correspond to a decreased pupil diameter. Likewise, chromatic changes or changes in content lighting may correspond to increases or decreases in pupil diameter. Moreover, exogenous signals may also be related to the use of drugs or medications. For example, opiates or opioids may be associated with a narrowing of the pupil and cocaine or amphetamines may be associated with a widening of the pupil. In some implementations, based on pupillary response, the device  10  detects patterns that correspond to one or more exogenous factors, for example, based on learning such patterns using a machine learning model. The device  10  may distinguish patterns of physiological data  45  that correspond to interest or intent from patterns of physiological data  45  that correspond to exogenous factors, for example, based on learning such distinctions using a machine learning model. 
     In some implementations, the device  10  detects the location of the eyes  30  of the user  25  and the pupils  35  of the user  25 , e.g., by processing and analyzing an image comprising light (typically infrared and/or a color produced by the red-green-blue additive color model) reflecting from one or both eyes, in order to locate and measure a diameter of the pupils. The reflected light may originate from a light projecting source of the device  10 , or any other natural (e.g., sunlight) or artificial (e.g., a lamp) source. Using techniques such as detecting pupil center and corneal reflections (PCCR), the device  10  may process and analyze an image comprising light reflecting from an element of the eye  30 , including the pupil  35 , in order to determine the diameter of the pupil  35 . Additionally, the device  10  may process light (e.g., from an illumination source on the device or elsewhere) reflected off the eye  30  of the user  25  as a glint. 
     In some implementations, the location and features of the head  27  of the user  25  (e.g., an edge of the eye, a nose or a nostril) are extracted by the device  10  and used in finding coarse location coordinates of the eyes  30  of the user  25 , thus simplifying the determination of precise eye  30  features (e.g., position, gaze direction, etc.) and making the pupil diameter  50  measurement more reliable and robust. Furthermore, the device  10  may readily combine the 3D location of parts of the head  27  with gaze angle information obtained via eye part image analysis in order to identify a given on-screen object at which the user  25  is looking at any given time. In some implementations, the use of 3D mapping in conjunction with gaze tracking allows the user  25  to move their head  27  and eyes  30  freely while reducing or eliminating the need to actively track the head  27  using sensors or emitters on the head  27 . 
     By tracking the eyes  30 , some implementations reduce the need to re-calibrate the user  25  after the user  25  moves their head  27 . In some implementations, the device  10  uses depth information to track the pupil&#39;s  35  movement, thereby enabling a reliable pupil diameter  50  to be calculated based on a single calibration of user  25 . Utilizing techniques such as pupil-center-corneal reflection (PCCR), pupil tracking, and pupil shape, the device  10  may calculate the pupil diameter  50 , as well as a gaze angle of the eye  30  from a fixed point of the head  27 , and use the location information of the head  27  in order to re-calculate the gaze angle. In addition to reduced recalibrations, further benefits of tracking the head  27  may include reducing the number of light projecting sources and reducing the number of cameras used to track the eye  30 . 
     Some implementations provide the device  10  with faster, more efficient methods and interfaces for navigating through user interfaces. Such methods and interfaces may complement or replace conventional methods for navigating through user interfaces. Such methods and interfaces may reduce the cognitive burden on a user  25  and produce a more efficient human-machine interface. For battery-operated computing devices, such methods and interfaces may conserve power and increase the time between battery charges. Moreover, some implementations enhance the navigation of user interfaces based on detecting patterns associated with physiological data  45 . 
     In accordance with some implementations, a user interface having one or more selectable objects is displayed on a screen of the device  10  and the interest or intent  40  of the user  25  is associated with one of the selectable objects. Moreover, in some implementations, the interest or intent  40  of the user  25  is associated with selecting one of the one of the selectable objects. In some implementations, the device  10  receives an input that corresponds to a first gesture and confirms the input with the identified interest or intent  40  of the user  25 . In some implementations, the first gesture is received by the device  10  as voluntary data, i.e., behavior over which the user  25  has control. For example, voluntary data may be received based on the user&#39;s voice inputs, hand gestures, touch input, keystrokes, etc. In some implementations, the interest or intent  40  of the user  25  is associated with multiple types of input (i.e., multimodal) communicated with the device  10  by the user  25 . For example, more than one low-commitment voluntary interactions may, in combination, be associated with the interest or intent  40  of the user  25 . 
     In some implementations, in response to receiving an input that corresponds to the identified interest or intent  40 , the device  10  searches for a target selectable object and move or otherwise alter an object selection indicator. When a target selectable object is identified, the device  10  may move an object selection indicator towards the target selectable object. When a target selectable object is not identified, moving the object selection indicator may include moving an object selection indicator in accordance with the identified user interest or intent  40 . 
     In some implementations, the device  10  searches for the target selectable object based on the identified user interest or intent  40 . In some implementations, the device  10  moves an object selection indicator in accordance with the user interest or intent  40  by calculating a trajectory of the object selection indicator based on a first identified user interest or intent  40  and then terminate the trajectory of the object selection indicator based on a second identified interest or intent  40 . Moreover, when calculating a trajectory of the object selection indicator based on the identified user interest or intent  40 , the device  10  may search for one or more candidate selectable objects that meet predefined candidate criteria, and when one or more candidate selectable objects are found, the device  10  may identify a respective candidate selectable object as the target selectable object. For example, the candidate selectable object may be identified as the target selectable object based on proximity of the candidate selectable object to a termination point of the trajectory (e.g., selectable object is closest to or within a predefined distance of a termination point of the trajectory). 
     In some implementations, when no candidate selectable objects are found, the device  10  moves the object selection indicator in accordance with the identified user interest or intent  40  to a termination point of the trajectory. In some implementations, the trajectory is calculated based on simulated physical properties of the object selection indicator. In some implementations, the object selection indicator is not visible to the user  25 . 
     Computing devices are provided with faster, more efficient methods and interfaces for navigating through user interfaces, thereby increasing the effectiveness, efficiency, and user satisfaction with such devices. Such methods and interfaces may complement or replace conventional methods for navigating through user interfaces. 
     In some implementations, the device  10  uses a detected pattern of physiological data to control a function of the device  10 . In some implementations, the device  10  identifies a given interactive item presented on a display of the device  10  at a point of the interest  40  of the user  25  (e.g. at a position in the direction of the user&#39;s gaze) and changes a state of the given interactive item responsively based on the identified interest or intent  40  of the user  25 . 
     In some implementations, changing the state of the given interactive item includes performing an operation associated with the given interactive item. For example, interactive items may include menu choices that the user  25  can select to present specific content (e.g., a movie or a television show) on the display  15 . In some implementations, the device  10  changes the state of a given interactive item by directing input received from the user  25  to the given interactive item. In some implementations, the device  10  identifies the given interactive item based on other data and interact with the interactive item based on the identified user interest or intent  40 . For example, if the user  25  is gazing at a selectable button, the device  10  may identify the selectable button based on the user&#39;s gaze and then select the selectable button based on the identified user interest or intent  40 . 
     In some implementations, the device  10  identifies a given interactive item presented on the display of the device  10  at a position in the direction of the user&#39;s gaze. Moreover, the device  10  may change a state of the given interactive item responsively to a spoken verbal command received from the user  25  in combination with the identified interest or intent  40  of the user  25 . For example, the given interactive item may comprise an icon associated with a software application, and the user  25  may gaze at the icon and say the word “start” to execute the application. The device  10  may then use the identified interest or intent  40  of the user  25  as a confirmation of the user&#39;s verbal command. In some implementations, the device  10  is configured to identify a given interactive item responsively to the direction of the user&#39;s gaze, and to manipulate the given interactive item responsively to a gesture performed by a limb or body part (e.g., a finger or a hand). The device  10  may then confirm the gesture based on identifying user interest or intent  40 . In some implementations, the device  10  removes an interactive item or object based on the identified interest or intent  40 . In other implementations, the device  10  automatically captures images of the content at times when the interest or intent  40  of the user  25  is determined. 
     In some implementations, the device  10  is configured to provide a progressive interface. The progressive interface may aid the usability of voluntary inputs from the user  25  in combination with, or supported by, the physiological data  45  (e.g., one or more involuntary characteristics of the user  25 ). For example, the device  10  may provide progressive feedback to the user  25  regarding an identified interest or intent  40  of the user  25 . In some implementations, the device  10  begins a low-commitment interaction with the user  25  in response to detecting a pattern of physiological data or in response to receiving voluntary user input. For example, in response to one or more lower-confidence detections of user interest or intent  40 , the device  10  may perform a low-commitment interaction (e.g., highlighting or selecting an object, or displaying one or more menu items) with the user  25 . In some implementations, the low-commitment interaction may direct the user  25  to progressively perform higher commitment actions to confirm the user interest or intent  40 . For example, the device  10  may act on or delete an item in response to further input from the user  25 . 
     As a power saving feature, the device  10  may detect when the user  25  is not looking at the display and the device  10  may activate power saving techniques, e.g., disabling physiological sensors when the user  25  looks away for more than some threshold period of time. Furthermore, in some implementations, the device  10  dims or darkens the display (i.e., decrease the brightness) entirely when the user  25  is not looking at the display. When the user  25  looks back toward the display, the device  10  may deactivate the power saving techniques. In some implementations, the device  10  tracks a physiological attribute using a first sensor and then activates a second sensor to obtain the physiological data  45  based on the tracking. For example, the device  10  may use a camera to identify that the user  25  is looking in the direction of the device  10  and then activate an eye sensor when it is determined that the user  25  is looking in the direction of the device  10 . 
     In some implementations, a combination of determining user intent and an input device  10  is used to create an interactive user interface that utilizes the input device  10  to identify an on-screen interactive item and determine the user&#39;s interest or intent  40  in interacting with the on-screen interactive item. For example, a user  25  may use a mouse to select an on-screen interactive item based on the user&#39;s determined interest or intent  40 , e.g., a “mouse click” type event triggered by determining the user&#39;s interest or intent  40  rather than a mouse click. In some implementations, a combination of determining user intent and gaze tracking is used to create an interactive user interface that can detect which on-screen interactive item the user  25  is looking at (e.g., gaze tracking) and determine the user&#39;s interest or intent  40  in interacting with the on-screen interactive item, thereby obviating the need for a mouse and/or a keyboard. 
     Furthermore, the combination of determining user interest or intent  40  with other modalities, such as gaze tracking, facial gesture detection, 3D mapping/gesture detection and/or voice detection, enables the user  25  to control on-screen objects fully, without the use of a mouse or a touch screen. In this manner, the user  25  can perform a full range of pointing and selection functions, including searching through large numbers of information items and choices. The combined interface modalities may also be used to search and perform control functions within the context of a certain interactive item, such as performing find, cut, copy and paste functions within an open file. In some implementations, the device  10  identifies a first interest, or group of first interests, and then progressively identifies a second interest, or second group of interests, based on the previously identified interest(s). 
     When selecting a given interactive item, the device  10  may convey visual feedback or audio feedback to the user  25  indicating the selection (i.e., before performing an action such as presenting selecting interactive content). Examples of visual feedback include changing the size and/or appearance of the selected item or highlighting the selected item by surrounding the selected item with a border. Examples of audio feedback include non-verbal types of audio feedback (e.g., clicks, beeps, chirps, or other various types of non-verbal sound effects) as well as audio feedback with varying levels of verbosity (e.g., outputting one or several spoken words confirming a selection). In some implementations, conveying visual or audio feedback enhances the user  25  experience by signaling the user  25  that an action is being taken, or will be taken, based on the user&#39;s interest or intent  40 . 
     In some implementations, the device  10  utilizes a training or calibration sequence to adapt to the specific physiological characteristics of a particular user  25 . In some implementations, the device  10  presents the user  25  with a training scenario in which the user  25  is instructed to interact with on-screen items. By providing the user  25  with a known intent or area of interest (e.g., via instructions), the device  10  may record the user&#39;s physiological data  45  and identify a pattern associated with the user&#39;s intent or interest  40 . For example, the device  10  could direct a user to mentally select the button in the center of the screen on the count of three and record the user&#39;s physiological data  45  to identify a pattern associated with the user&#39;s intent or interest  40 . In some implementations, the pattern associated with the user&#39;s intent or interest  40  is stored in a user profile associated with the user and the user profile can be updated or recalibrated at any time in the future. For example, the user profile could automatically be modified over time during a user experience to provide a more personalized user experience. 
       FIG. 4  is a block diagram of an example of a device  10  in accordance with some implementations. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity, and so as not to obscure more pertinent aspects of the implementations disclosed herein. To that end, as a non-limiting example, in some implementations the device  10  includes one or more processing units  402  (e.g., microprocessors, ASICs, FPGAs, GPUs, CPUs, processing cores, and/or the like), one or more input/output (I/O) devices and sensors  406 , one or more communication interfaces  408  (e.g., USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x, GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, SPI, I2C, and/or the like type interface), one or more programming (e.g., I/O) interfaces  410 , one or more displays  412 , one or more interior and/or exterior facing image sensor systems  414 , a memory  420 , and one or more communication buses  404  for interconnecting these and various other components. 
     In some implementations, the one or more communication buses  404  include circuitry that interconnects and controls communications between system components. In some implementations, the one or more I/O devices and sensors  406  include at least one of an inertial measurement unit (IMU), an accelerometer, a magnetometer, a gyroscope, a thermometer, one or more physiological sensors (e.g., blood pressure monitor, heart rate monitor, blood oxygen sensor, blood glucose sensor, etc.), one or more microphones, one or more speakers, a haptics engine, one or more depth sensors (e.g., a structured light, a time-of-flight, or the like), and/or the like. 
     In some implementations, the one or more displays  412  are configured to present a user experience to the user  25 . In some implementations, the one or more displays  412  correspond to holographic, digital light processing (DLP), liquid-crystal display (LCD), liquid-crystal on silicon (LCoS), organic light-emitting field-effect transitory (OLET), organic light-emitting diode (OLED), surface-conduction electron-emitter display (SED), field-emission display (FED), quantum-dot light-emitting diode (QD-LED), micro-electro-mechanical system (MEMS), a retinal projection system, and/or the like display types. In some implementations, the one or more displays  412  correspond to diffractive, reflective, polarized, holographic, etc. waveguide displays. In one example, the device  10  includes a single display. In another example, the device  10  includes a display for each eye of the user  25 , e.g., an HMD. In some implementations, the one or more displays  412  are capable of presenting MR content, including VR or AR content. 
     In some implementations, the one or more image sensor systems  414  are configured to obtain image data that corresponds to at least a portion of the face of the user  25  that includes the eyes of the user  25 . For example, the one or more image sensor systems  414  include one or more RGB camera (e.g., with a complimentary metal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor), monochrome camera, IR camera, event-based camera, and/or the like. In various implementations, the one or more image sensor systems  414  further include illumination sources that emit light upon the portion of the face of the user  25 , such as a flash or a glint source. 
     The memory  420  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  420  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. The memory  320  optionally includes one or more storage devices remotely located from the one or more processing units  302 . The memory  420  comprises a non-transitory computer readable storage medium. In some implementations, the memory  420  or the non-transitory computer readable storage medium of the memory  420  stores the following programs, modules and data structures, or a subset thereof including an optional operating system  430  and a user experience module  440 . 
     The operating system  430  includes procedures for handling various basic system services and for performing hardware dependent tasks. In some implementations, the user experience module  440  is configured to present a user experience that utilizes physiological data  45  to identify an interest or intent  40  of the user  25  via context aware dynamic distortion correction to the user  25  via the one or more input/output (I/O) devices and sensors  406 . To that end, in various implementations, the user experience module  440  includes a physiological characteristic tracking unit  442 , an interest or intention unit  444 , and a presenting unit  446 . 
     In some implementations, the physiological characteristic tracking unit  442  is configured to obtain physiological data (e.g., pupil dilation, electroencephalography, etc.) and to use the obtained physiological data to identify patterns of physiological data. To that end, in various implementations, the physiological characteristic tracking unit  442  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the interest or intention unit  444  is configured to use the identified patterns of physiological data to identify the interest or intent of a user of the device. To that end, in various implementations, the interest or intention unit  444  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     In some implementations, the presenting unit  446  is configured to present content via the one or more displays  412  based on the identified interest or intent. To that end, in various implementations, the presenting unit  446  includes instructions and/or logic therefor, and heuristics and metadata therefor. 
     Although the physiological characteristic tracking unit  442 , interest or intention unit  444 , and presenting unit  448  are shown as residing on a single device (e.g., the device  10 ), it should be understood that in other implementations, any combination of these units may be located in separate computing devices. 
     Moreover,  FIG. 4  is intended more as functional description of the various features which are present in a particular implementation as opposed to a structural schematic of the implementations described herein. As recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some functional modules shown separately in  FIG. 4  could be implemented in a single module and the various functions of single functional blocks could be implemented by one or more functional blocks in various implementations. The actual number of modules and the division of particular functions and how features are allocated among them will vary from one implementation to another and, in some implementations, depends in part on the particular combination of hardware, software, and/or firmware chosen for a particular implementation. 
       FIG. 5  illustrates a block diagram of an exemplary head-mounted device  500  in accordance with some implementations. The head-mounted device  500  includes a housing  501  (or enclosure) that houses various components of the head-mounted device  500 . The housing  501  includes (or is coupled to) an eye pad (not shown) disposed at a proximal (to the user  25 ) end of the housing  501 . In various implementations, the eye pad is a plastic or rubber piece that comfortably and snugly keeps the head-mounted device  500  in the proper position on the face of the user  25  (e.g., surrounding the eye of the user  25 ). 
     The housing  501  houses a display  510  that displays an image, emitting light towards or onto the eye of a user  25 . In various implementations, the display  510  emits the light through an eyepiece having one or more lenses  505  that refracts the light emitted by the display  510 , making the display appear to the user  25  to be at a virtual distance farther than the actual distance from the eye to the display  510 . For the user  25  to be able to focus on the display  510 , in various implementations, the virtual distance is at least greater than a minimum focal distance of the eye (e.g., 7 cm). Further, in order to provide a better user experience, in various implementations, the virtual distance is greater than 1 meter. 
     The housing  501  also houses a tracking system including one or more light sources  522 , camera  524 , and a controller  580 . The one or more light sources  522  emit light onto the eye of the user  25  that reflects as a light pattern (e.g., a circle of glints) that can be detected by the camera  524 . Based on the light pattern, the controller  580  can determine an eye tracking characteristic of the user  25 . For example, the controller  580  can determine a gaze direction and/or a blinking state (eyes open or eyes closed) of the user  25 . As another example, the controller  580  can determine a pupil center, a pupil size, or a point of regard. Thus, in various implementations, the light is emitted by the one or more light sources  522 , reflects off the eye of the user  25 , and is detected by the camera  524 . In various implementations, the light from the eye of the user  25  is reflected off a hot mirror or passed through an eyepiece before reaching the camera  524 . 
     The display  510  emits light in a first wavelength range and the one or more light sources  522  emit light in a second wavelength range. Similarly, the camera  524  detects light in the second wavelength range. In various implementations, the first wavelength range is a visible wavelength range (e.g., a wavelength range within the visible spectrum of approximately 400-700 nm) and the second wavelength range is a near-infrared wavelength range (e.g., a wavelength range within the near-infrared spectrum of approximately 700-1400 nm). 
     In various implementations, eye tracking (or, in particular, a determined gaze direction) is used to enable user interaction (e.g., the user  25  selects an option on the display  510  by looking at it), provide foveated rendering (e.g., present a higher resolution in an area of the display  510  the user  25  is looking at and a lower resolution elsewhere on the display  510 ), or correct distortions (e.g., for images to be provided on the display  510 ). 
     In various implementations, the one or more light sources  522  emit light towards the eye of the user  25  which reflects in the form of a plurality of glints. 
     In various implementations, the camera  524  is a frame/shutter-based camera that, at a particular point in time or multiple points in time at a frame rate, generates an image of the eye of the user  25 . Each image includes a matrix of pixel values corresponding to pixels of the image which correspond to locations of a matrix of light sensors of the camera. In implementations, each image is used to measure or track pupil dilation by measuring a change of the pixel intensities associated with one or both of a user&#39;s pupils. 
     In various implementations, the camera  524  is an event camera comprising a plurality of light sensors (e.g., a matrix of light sensors) at a plurality of respective locations that, in response to a particular light sensor detecting a change in intensity of light, generates an event message indicating a particular location of the particular light sensor. 
     It will be appreciated that the implementations described above are cited by way of example, and that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope includes both combinations and sub combinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art. 
       FIG. 6 , in accordance with some implementations, is a flowchart representation of a method  600  for initiating a user interaction based on an identified interest or an identified intention of a user where the interest or the intention is identified based on physiological data of the user obtained using a sensor during a user experience. In some implementations, the method  600  is performed by one or more devices. The method  600  can be performed at a mobile device, HMD, desktop, laptop, or server device. The method  600  can be performed on an HMD that has a screen for displaying 3D images or a screen for viewing stereoscopic images. In some implementations, the method  600  is performed by processing logic, including hardware, firmware, software, or a combination thereof. In some implementations, the method  600  is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory). 
     At block  610 , the method  600  obtains physiological data at a device of a user during a user experience in which content is displayed on a display of the device. In some implementations, the physiological data includes electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), functional near-infrared spectroscopy (fNIRS), blood pressure, skin conductance, pupillary response, or any combination thereof. For example, physiological data may be pupillary response, where the diameter of the pupil is measured over a period of time. 
     In some implementations, the method  600  performs a training function by presenting the user with content and instructions directing the user&#39;s interest or intent. The method  600  may record the physiological data associated with the presentation of the content in order to identify a pattern associated with the timing of the instructed moment of intent or interest. 
     At block  620 , the method  600  detects a pattern using the physiological data. In some implementations, the method  600  compares the physiological data obtained at block  610  to a pattern associated with user interest or intent. In some implementations, the method  600  accounts for any exogenous signals that may affect detecting a pattern. For example, increased ambient light may result in an exogenous signal affecting pupillary response. 
     At block  630 , the method  600  identifies an interest of the user in the content or an intention of the user regarding the content based on detecting the pattern. In some implementations, as the user interacts with the device, the device detects the pattern associated with step  620  in order to identify a current interest or intent of the user. In some implementations, the current interest on intent is a selectable object displayed on the screen of the device. Moreover, the interest or intent may be identified based on the identified pattern in combination with another input, e.g., confirming an intention of a hand gesture by identifying a pattern associated with physiological data. 
     At block  640 , the method  600  initiates a user interaction based on the identified interest or the identified intention. In some implementations, the user interaction includes moving an object selection indicator, selecting an object, changing a state of an interactive item, making a record of the object of interest or intent, or otherwise performing an operation associated with a given interactive item. For example, confidence in a user&#39;s current interest level or intention to interact may result in a discrete user interaction (e.g., a “click”-like event) or continuous user action spanning a period of time (e.g., a lingering event). 
     The present disclosure contemplates that users will be provided with an option to benefit from any use of physiological data  45  to identify user intention or interest, improve a user interface, or otherwise improve a user experience. For example, a user may tailor device preferences to select whether or not to use physiological data to enhance a user experience. Moreover, a user may be provided with an option to select actions or types of actions that may or may not be invoked automatically based on any use of physiological data  45 . 
     The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information and/or physiological data  45  will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. 
     In the case of advertisement delivery services, the present disclosure also contemplates scenarios in which users selectively block the use of, or access to, personal information data and/or physiological data  45 . That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. 
     Therefore, although the present disclosure broadly covers use of personal information data including physiological data  45  to implement one or more various disclosed implementations, the present disclosure also contemplates that the various implementations can also be implemented without the need for accessing such personal information data. That is, the various implementations of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device  10  associated with a user  25 , other non-personal information available to the content delivery services, or publicly available information. 
     Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing the terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provides a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general-purpose computing apparatus to a specialized computing apparatus implementing one or more implementations of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device. 
     Implementations of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied for example, blocks can be re-ordered, combined, or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or value beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting. 
     It will also be understood that, although the terms “first,” “second,” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first node could be termed a second node, and, similarly, a second node could be termed a first node, which changing the meaning of the description, so long as all occurrences of the “first node” are renamed consistently and all occurrences of the “second node” are renamed consistently. The first node and the second node are both nodes, but they are not the same node. 
     The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the claims. As used in the description of the implementations and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof. 
     As used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in accordance with a determination” or “in response to detecting,” that a stated condition precedent is true, depending on the context. Similarly, the phrase “if it is determined [that a stated condition precedent is true]” or “if [a stated condition precedent is true]” or “when [a stated condition precedent is true]” may be construed to mean “upon determining” or “in response to determining” or “in accordance with a determination” or “upon detecting” or “in response to detecting” that the stated condition precedent is true, depending on the context. 
     The foregoing description and summary are to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined only from the detailed description of illustrative implementations but according to the full breadth permitted by patent laws. It is to be understood that the implementations shown and described herein are only illustrative of the principles of the present invention and that various modification may be implemented by those skilled in the art without departing from the scope and spirit of the invention.

Metadata:
Filing Date: 20190912
Publication Date: 20210914
Grant Date: 20210914
Priority Date: 20180928
Inventors: BAR-ZEEV, AVI
CHALMERS, DEVIN W.
ROTHKOPF, FLETCHER R.
MULLIKEN, GRANT H.
GERHARD, HOLLY E.
JONSSON, LILLI I.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06V40/174", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/197", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/15", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06V40/193", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/193", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/16", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04842", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/147", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/017", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/167", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/017", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/015", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/167", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/015", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/013", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/017", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/013", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/015", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/167", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F3/147", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06K9/0061", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": false, "first": false, "tree": "[]"}, {"code": "A61B3/112", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F3/013", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B3/113", "inventive": true, "first": false, "tree": "[]"}, {"code": "A61B3/14", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/015", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/147", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/193", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/011", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/197", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/174", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/167", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/012", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/15", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04842", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 69945475