Patent ID: 12205229

DETAILED DESCRIPTION

Example methods and systems are directed to context-based augmented reality (AR) content and audio notification in a head-mounted device (HMD). Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details.

An HMD displays AR information in a display of the HMD to provide workers with relevant information in their work environment. The present disclosure describes an HMD that includes a directional AR content application designed to selectively capture audio, for example, speech from another user and display a translation of the speech to another preselected language as AR content in a transparent display of the HMD. The AR content appears as a text bubble hovering above and following the other user. In other words, the user of the HMD perceives the text bubble as hovering above the other user and following the other user relative to a field of view of the HMD.

In one example, the user of the HMD may approach another person who may speak a different language. The HMD determines the position of the other person relative to the HMD (e.g., the person is on the left side within a field of view of a camera in the HMD). The HMD also determines how far the other person is located relative to the HMD. Those of ordinary skill in the art will recognize that different types of sensors can be used to determine the position and distance of the other person relative to the HMD. For example, audio sensors use beamforming techniques of an array of microphones disposed in and around the HMD to identify the position and distance of another person who is speaking. Visual sensors such as infrared sensors and time-of-flight sensors can be used to determine the presence, location, and distance of the other person relative to the HMD. When a device is located on the other person, other wireless sensors (e.g., Wi-Fi, Bluetooth, zigbee) can be used to locate and identify the position of the other person relative to the HMD. Furthermore, if the device on the other person includes a microphone, the device can record audio from the other person and send the audio data to the HMD for processing. In another example, the device on the other person transcribes the audio from the other person using speech recognition techniques and sends the text to the HMD for processing. The HMD uses the position and location information to display the AR content as appearing coupled to the person who is speaking.

In one example embodiment, an HMD includes a transparent display. The HMD determines location information of a second user relative to a first user of the HMD. The second user is located within a predefined distance of the HMD. The location information identifies a distance and a direction of the second user relative to the HMD. The HMD receives audio content from the second user, generates AR content based on the audio content, and displays the AR content in the transparent display based on the location information of the second user. The AR content appears coupled to the second user.

In another example embodiment, the HMD translates the audio content from a first language associated with the audio content to a second language selected by the first user. The HMD then transcribes the translated audio content and forms the AR content with text from the transcribed and translated audio content.

In another example embodiment, the size of the text decreases in response to the increasing distance between the second user and the HMD.

In another example embodiment, the HMD determines location information of a third user relative to the first user of the HMD. The third user is located within the predefined distance of the HMD. The location information of the third user identifies a distance and a direction of the third user relative to the HMD. The HMD receives audio content from the third user. The HMD generates other AR content based on the audio content from the third user. The other AR content is displayed in the transparent display based on the location information of the third user. The other AR content appears coupled to the third user.

In another example embodiment, the HMD includes an array of microphones. The array of microphones is used to receive first audio data from the second user and second audio data from the third user. Adaptive beamforming is used on the first audio data to determine the distance and direction of the second user relative to the HMD. Adaptive beamforming is used on the second audio data to determine the distance and direction of the third user relative to the HMD.

In another example embodiment, a distance sensor is configured to detect a presence of the second user within the predefined distance, and to measure the distance and the direction of the second user relative to the HMD.

In another example embodiment, the HMD receives an electronic communication from a second HMD of the second user, the electronic communication including the location information of the second user and the audio content from the second user.

In another example embodiment, the HMD extracts audio data from the audio content in the electronic communication and generates the AR content based on the audio data.

In another example embodiment, the HMD processes the audio data with speech recognition to generate text data corresponding to the audio data. The text data is translated from a first language associated with the audio data to a second language selected by the first user. The AR content is formed with the translated text data.

In another example embodiment, the transparent display displays a speech bubble that appears coupled to the second user. The speech bubble includes the translated text data.

In another example embodiment, the HMD includes an AR application that identifies an object in an image captured with a camera, retrieves a three-dimensional model of a virtual object from an AR content based on the identified object, and renders the three-dimensional model of the virtual object in the transparent display. The virtual object is perceived as an overlay on the real-world object.

The display of the HMD may be retracted inside the helmet and extended outside the helmet to allow a user to view the display. The position of the display may be adjusted based on an eye level of the user. The display includes a display lens capable of displaying AR content. The helmet may include a computing device such as a hardware processor with an AR application that allows the user wearing the helmet to experience information, such as in the form of a virtual object such as a three-dimensional (3D) virtual object, overlaid on an image or a view of a physical object (e.g., a gauge) captured with a camera in the helmet. The helmet may include optical sensors. The physical object may include a visual reference (e.g., a recognized image, pattern, or object, or unknown objects) that the AR application can identify using predefined objects or machine vision. A visualization of the additional information (also referred to as AR content), such as the 3D virtual object overlaid on or engaged with a view or an image of the physical object, is generated in the display lens of the helmet. The display lens may be transparent to allow the user see through the display lens. The display lens may be part of a visor or face shield of the helmet or may operate independently from the visor of the helmet. The 3D virtual object may be selected based on the recognized visual reference or captured image of the physical object. A rendering of the visualization of the 3D virtual object may be based on a position of the display relative to the visual reference. Other AR applications allow the user to experience visualization of the additional information overlaid on top of a view or an image of any object in the real physical world. The virtual object may include a 3D virtual object and/or a two-dimensional (2D) virtual object. For example, the 3D virtual object may include a 3D view of an engine part or an animation. The 2D virtual object may include a 2D view of a dialog box, menu, or written information such as statistics information for properties or physical characteristics of the corresponding physical object (e.g., temperature, mass, velocity, tension, stress). The AR content (e.g., image of the virtual object, virtual menu) may be rendered at the helmet or at a server in communication with the helmet. In one example embodiment, the user of the helmet may navigate the AR content using audio and visual inputs captured at the helmet or other inputs from other devices, such as a wearable device. For example, the display lenses may extend or retract based on a voice command of the user, a gesture of the user, or a position of a watch in communication with the helmet.

In another example embodiment, a non-transitory machine-readable storage device may store a set of instructions that, when executed by at least one processor, causes the at least one processor to perform the method operations discussed within the present disclosure.

FIG.1is a network diagram illustrating a network environment100suitable for operating an AR application of an HMD with display lenses, according to some example embodiments. The network environment100includes an HMD101and a server110, communicatively coupled to each other via a network108. The HMD101and the server110may each be implemented in a computer system, in whole or in part, as described below with respect toFIG.10.

The server110may be part of a network-based system. For example, the network-based system may be or include a cloud-based server system that provides AR content (e.g., augmented information including 3D models of virtual objects related to physical objects in images captured by the HMD101) to the HMD101.

The HMD101may include a helmet or glasses that a user102may wear to view the AR content related to captured images of several physical objects (e.g., object A116, object B118) in a real-world physical environment114. The objects A116and B118are located within a field of view120of the HMD101. In one example embodiment, the HMD101includes a computing device with a camera and a display (e.g., smart glasses, smart helmet, smart visor, smart face shield, or smart contact lenses). The computing device may be removably mounted to the head of the user102. In one example, the display may be a screen that displays what is captured with a camera of the HMD101. In another example, the display of the HMD101may be a transparent display, such as in the visor or face shield of a helmet, or a display lens distinct from the visor or face shield of the helmet.

The user102may be a user of an AR application in the HMD101and at the server110. The user102may be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the HMD101), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user102is not part of the network environment100, but is associated with the HMD101.

In one example embodiment, the AR application determines the AR content to be rendered and displayed in the display lenses of the HMD101based on sensor data related to the user102and sensor data related to the HMD101. The sensor data related to the user102may include measurements of a heart rate, a blood pressure, brain activity, and biometric data related to the user102. The sensor data related to the HMD101may include a geographic location of the HMD101, an orientation and position of the HMD101, an ambient pressure, an ambient humidity level, an ambient light level, and an ambient noise level detected by sensors in the HMD101. The sensor data related to the user102may also be referred to as “user-based sensor data.” The sensor data related to the HMD101may be also referred to as “ambient-based sensor data.” For example, the HMD101may display first AR content when the user102wearing the HMD101is on the first floor of a building. The HMD101may display second AR content, different from the first AR content, when the user102is on the second floor of the building. In another example, the HMD101may display AR content when the user102is alert and located in front of machine M1. The HMD101may display different AR content when the user102is nervous or sleepy and is located in front of the same machine M1. In another example, the HMD101provides a first AR application (e.g., showing schematic diagrams of a building) when the user102is identified as a firefighter and is located on the first floor of a building. The HMD101may provide a second AR application (e.g., showing locations of non-functioning sprinklers) when the user102is identified as a firefighter and sensors in the building indicate a temperature exceeding a threshold (e.g., because of a fire in the building). Therefore, different AR content and different AR applications may be provided to the HMD101based on a combination of the user-based sensor data and the ambient-based sensor data.

In another example embodiment, the AR application may provide the user102with an AR experience triggered by identified objects in the physical environment114. The physical environment114may include identifiable objects such as a 2D physical object (e.g., a picture), a 3D physical object (e.g., a factory machine), a location (e.g., at the bottom floor of a factory), or any references (e.g., perceived corners of walls or furniture) in the real-world physical environment114. The AR application may include computer vision recognition to determine corners, objects, lines, and letters. The user102may point a camera of the HMD101to capture an image of the objects A116and B118in the physical environment114.

In one example embodiment, the objects A116, B118in the image are tracked and recognized locally in the HMD101using a local context recognition dataset or any other previously stored dataset of the AR application of the HMD101. The local context recognition dataset module may include a library of virtual objects associated with the real-world physical objects A116, B118or references. In one example, the HMD101identifies feature points in an image of the objects A116, B118to determine different planes (e.g., edges, corners, surface, dial, letters). The HMD101may also identify tracking data related to the objects A116, B118(e.g., GPS location of the HMD101, orientation, or distances to the objects A116, B118). If the captured image is not recognized locally at the HMD101, the HMD101can download additional information (e.g., a 3D model or other augmented data) corresponding to the captured image, from a database of the server110over the network108.

In another embodiment, the objects A116, B118in the image are tracked and recognized remotely at the server110using a remote context recognition dataset or any other previously stored dataset of an AR application in the server110. The remote context recognition dataset module may include a library of virtual objects or augmented information associated with the real-world physical objects A116, B118or references.

Sensors112may be associated with, coupled to, or related to the objects A116and B118in the physical environment114to measure a location, information, or captured readings from the objects A116and B118. Examples of captured readings may include, but are not limited to, weight, pressure, temperature, velocity, direction, position, intrinsic and extrinsic properties, acceleration, and dimensions. For example, the sensors112may be disposed throughout a factory floor to measure movement, pressure, orientation, and temperature. The server110can compute readings from data generated by the sensors112. The server110can generate virtual indicators such as vectors or colors based on data from the sensors112. The virtual indicators are then overlaid on top of a live image of the objects A116and B118to show data related to the objects A116and B118. For example, the virtual indicators may include arrows with shapes and colors that change based on real-time data. The visualization may be provided to the HMD101so that the HMD101can render the virtual indicators in a display of the HMD101. In another embodiment, the virtual indicators are rendered at the server110and streamed to the HMD101. The HMD101displays the virtual indicators or a visualization corresponding to a display of the physical environment114(e.g., data is visually perceived as displayed adjacent to the objects A116and B118).

The sensors112may include other sensors used to track the location, movement, and orientation of the HMD101externally without having to rely on internal sensors in the HMD101. The sensors112may include optical sensors (e.g., a depth-enabled 3D camera), wireless sensors (e.g., Bluetooth, Wi-Fi), GPS sensors, and audio sensors to determine the location of the user102having the HMD101, a distance of the user102to the sensors112in the physical environment114(e.g., sensors112placed in corners of a venue or a room), and the orientation of the HMD101to track what the user102is looking at (e.g., a direction in which the HMD101is pointed, such as towards a player on a tennis court or at a person in a room).

In another embodiment, data from the sensors112and internal sensors in the HMD101may be used for analytics data processing at the server110(or another server) for analysis of usage and how the user102is interacting with the physical environment114. Live data from other servers may also be used in the analytics data processing. For example, the analytics data may track at what locations (e.g., points or features) on a physical or virtual object the user102has looked, how long the user102has looked at each location on the physical or virtual object, how the user102moved with the HMD101when looking at the physical or virtual object, which features of the virtual object the user102interacted with (e.g., whether the user102tapped on a link in the virtual object), and any suitable combination thereof. The HMD101receives a visualization content dataset related to the analytics data. The HMD101then generates a virtual object with additional or visualization features, or a new experience, based on the visualization content dataset.

In one example embodiment, the HMD101uses an array of audio sensors to detect and record audio content from another person interacting with the user102. The HMD101transcribes and translates the audio content. The transcribed and translated audio content is displayed as AR content at the HMD101so that the user102perceives the AR content as coupled to the other person.

Any of the machines, databases, or devices shown inFIG.1may be implemented in a general-purpose computer modified (e.g., configured or programmed) by software to be a special-purpose computer to perform one or more of the functions described herein for that machine, database, or device. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect toFIG.10. As used herein, a “database” is a data storage resource and may store data structured as a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. Moreover, any two or more of the machines, databases, or devices illustrated inFIG.1may be combined into a single machine, database, or device, and the functions described herein for any single machine, database, or device may be subdivided among multiple machines, databases, or devices.

The network108may be any network that enables communication between or among machines (e.g., the server110), databases, and devices (e.g., the HMD101). Accordingly, the network108may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network108may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof.

FIG.2is a block diagram illustrating modules (e.g., components) of the HMD101, according to some example embodiments. The HMD101may be a helmet that includes sensors202, a display204, a storage device208, an audio output (e.g., speakers)205, and a processor212. The HMD101may not be limited to a helmet and may include any type of device that can be worn on the head of a user, e.g., the user102, such as glasses, a hat, or a visor.

The sensors202may be used to generate internal tracking data of the HMD101to determine a position and an orientation of the HMD101. The position and the orientation of the HMD101may be used to identify real-world objects in a field of view of the HMD101. For example, a virtual object may be rendered and displayed in the display204when the sensors202indicate that the HMD101is oriented towards a real-world object (e.g., when the user102looks at object A116that is within a field of view120of the sensors202) or in a particular direction (e.g., when the user102tilts his head to watch his wrist). The HMD101may also display a virtual object based on a geographic location of the HMD101. For example, a set of virtual objects may be accessible when the user102of the HMD101is located in a particular building. In another example, virtual objects including sensitive material may be accessible when the user102of the HMD101is located within a predefined area associated with the sensitive material and the user is authenticated. Different levels of content of the virtual objects may be accessible based on a credential level of the user. For example, a user who is an executive of a company may have access to more information or content in the virtual objects than a manager at the same company. The sensors202may be used to authenticate the user prior to providing the user with access to the sensitive material (e.g., information displayed as a virtual object such as a virtual dialog box in a see-through display). Authentication may be achieved via a variety of methods such as providing a password or an authentication token, or using the sensors202to determine biometric data unique to the user.

FIG.3is a block diagram illustrating examples of the sensors202in the HMD101. For example, the sensors202may include a camera302, an audio sensor304, an Inertial Measurement Unit (IMU) sensor306, a location sensor308, a barometer310, a humidity sensor312, an ambient light sensor314, and a biometric sensor316. It is to be noted that the sensors202described herein are for illustration purposes. The sensors202are thus not limited to the ones described.

The camera302includes an optical sensor(s) (e.g., camera) that may encompass different spectrums. The camera302may include one or more external cameras aimed outside the HMD101. For example, the external camera may include an infrared camera or a full-spectrum camera. The external camera may include a rear-facing camera and a front-facing camera disposed in the HMD101. The front-facing camera may be used to capture a front field of view of the HMD101, while the rear-facing camera may be used to capture a rear field of view of the HMD101. The pictures captured with the front- and rear-facing cameras may be combined to recreate a 360-degree view of the physical world around the HMD101. In another example, the camera302includes a time-of-flight camera used to determine distance between the HMD101and a subject (e.g., another user) in an image captured with the camera.

The camera302may include one or more internal cameras aimed at the user102. The internal camera may include an infrared (IR) camera configured to capture an image of a retina of the user102. The IR camera may be used to perform a retinal scan to map unique patterns of the retina of the user102.

Blood vessels within the retina absorb light more readily than the surrounding tissue in the retina and therefore can be identified with IR lighting. The IR camera may cast a beam of IR light into the user102's eye as the user102looks through the display204(e.g., lenses) towards virtual objects rendered in the display204. The beam of IR light traces a path on the retina of the user102. Because retinal blood vessels absorb more of the IR light than the rest of the eye, the amount of reflection varies during the retinal scan. The pattern of variations may be used as biometric data unique to the user102.

In another example embodiment, the internal camera may include an ocular camera configured to capture an image of an iris of the eye of the user102. In response to the amount of light entering the eye, muscles attached to the iris expand or contract the aperture at the center of the iris, known as the pupil. The expansion and contraction of the pupil depends on the amount of ambient light. The ocular camera may use iris recognition as a method for biometric identification. The complex pattern on the iris of the eye of the user102is unique and can be used to identify the user102. The ocular camera may cast IR light to acquire images of detailed structures of the iris of the eye of the user102. Biometric algorithms may be applied to the images of the detailed structures of the iris to identify the user102.

In another example embodiment, the ocular camera includes an IR pupil dimension sensor that is pointed at an eye of the user102to measure the size of the pupil of the user102. The IR pupil dimension sensor may sample the size of the pupil (e.g., using an IR camera) on a periodic basis or based on predefined trigger events (e.g., the user102walks into a different room, there are sudden changes in the ambient light, or the like).

The audio sensor304may include an array of microphones disposed around the HMD101. For example, the array of microphones may be used to record audio content from the ambient environment or audio content from the user102. In other examples, the array of microphones may be used to measure an ambient noise level to determine an intensity of background noise ambient to the HMD101. In another example, the array of microphones may be used to capture speech from another person within a preset distance of the HMD101. Analytics may be applied to the captured speech to identify a language, transcribe the speech, translate the transcribed speech to another language, and provide the transcribed translated speech to an AR application216.

The IMU sensor306may include a gyroscope and an inertial motion sensor to determine an orientation and movement of the HMD101. For example, the IMU sensor306may measure the velocity, orientation, and gravitational forces on the HMD101. The IMU sensor306may also detect a rate of acceleration using an accelerometer, and changes in angular rotation using a gyroscope.

The location sensor308may determine a geolocation of the HMD101using a variety of techniques such as near-field communication, GPS, Bluetooth, and Wi-Fi. For example, the location sensor308may generate geographic coordinates of the HMD101.

The barometer310may measure an atmospheric pressure differential to determine an altitude of the HMD101. For example, the barometer310may be used to determine whether the HMD101is located on a first floor or a second floor of a building.

The humidity sensor312may determine a relative humidity level ambient to the HMD101. For example, the humidity sensor312determines the humidity level of a room in which the HMD101is located.

The ambient light sensor314may determine an ambient light intensity around the HMD101. For example, the ambient light sensor314measures the ambient light in a room in which the HMD101is located.

The biometric sensor316include sensors configured to measure biometric data unique to the user102of the HMD101. In one example embodiment, the biometric sensor316includes an ocular camera, an EEG (electroencephalogram) sensor, and an ECG (electrocardiogram) sensor. It is to be noted that the descriptions of the biometric sensors316disclosed herein are for illustration purposes. The biometric sensor316is thus not limited to any of the ones described.

The EEG sensor includes, for example, electrodes that, when in contact with the skin of the head of the user102, measure electrical activity of the brain of the user102. The EEG sensor may also measure the electrical activity and wave patterns through different bands of frequency (e.g., Delta, Theta, Alpha, Beta, Gamma, Mu). EEG signals may be used to authenticate a user based on fluctuation patterns unique to the user.

The ECG sensor includes, for example, electrodes that measure a heart rate of the user102. In particular, the ECG sensor may monitor and measure the cardiac rhythm of the user102. A biometric algorithm is applied to the user102to identify and authenticate the user102. In one example embodiment, the EEG sensor and the ECG sensor may be combined into a same set of electrodes to measure both brain electrical activity and heart rate. The set of electrodes may be disposed around the helmet so that the set of electrodes comes into contact with the skin of the user102when the user102wears the HMD101.

Referring back toFIG.2, the display204may include a display surface or lens capable of displaying AR content (e.g., images, video) generated by the processor212. The display204may be transparent so that the user102can see through the display204(e.g., such as in a head-up display).

The storage device208stores a library of AR content, a speech recognition application, a language dictionary, a contextual content dataset, and reference objects. The AR content may include two- or three-dimensional models of virtual objects with corresponding audio. In other examples, the AR content may include an AR application that includes interactive features such as displaying additional data (e.g., locations of sprinklers) in response to user input (e.g., a user says, “Show me the locations of the sprinklers” while looking at an AR overlay showing locations of the exit doors). AR applications may have their own different functionalities and operations. Therefore, each AR application may operate distinctly from other AR applications.

The storage device208may also store a database of identifiers of wearable devices capable of communicating with the HMD101. In another embodiment, the database may also identify reference objects (visual references or images of objects) and corresponding experiences (e.g., 3D virtual objects, interactive features of the 3D virtual objects). The database may include a primary content dataset, a contextual content dataset. The primary content dataset includes, for example, a first set of images and corresponding experiences (e.g., interaction with 3D virtual object models). For example, an image may be associated with one or more virtual object models. The primary content dataset may include a core set of images or the most popular images determined by the server110. The core set of images may include a limited number of images identified by the server110. For example, the core set of images may include images depicting covers of the ten most viewed devices and their corresponding experiences (e.g., virtual objects that represent the ten most viewed sensing devices on a factory floor). In another example, the server110may generate the first set of images based on the most popular or often scanned images received at the server110. Thus, the primary content dataset does not depend on the objects A116, B118or images scanned by the HMD101.

The contextual content dataset includes, for example, a second set of images and corresponding experiences (e.g., 3D virtual object models) retrieved from the server110. For example, images captured with the HMD101that are not recognized (e.g., by the HMD101) in the primary content dataset are submitted to the server110for recognition. If the captured image is recognized by the server110, a corresponding experience may be downloaded at the HMD101and stored in the contextual content dataset. Thus, the contextual content dataset relies on the contexts in which the HMD101has been used. As such, the contextual content dataset depends on objects or images scanned by the AR application216of the HMD101.

In one example embodiment, the HMD101may communicate over the network108with the server110to access a language dictionary and AR content at the server110. The HMD101may also communicate with the server110to authenticate the user102. In another example embodiment, the HMD101retrieves a portion of a database of visual references, corresponding 3D virtual objects, and corresponding interactive features of the 3D virtual objects.

The processor212may include a directional content application214and the AR application216. The directional content application214detects audio content and a location of the source of the audio content. For example, the directional content application214detects audio content originating from another user located within a preset distance or radius of the HMD101(e.g., a person standing in front of the HMD101and speaking in a first language (e.g., Spanish) to the user102of the HMD101). The HMD101includes an array of microphones used to estimate the direction and distance of the person speaking to the user102. Triangulation of the position or location of the person relative to multiple HMDs can be used to further refine the location of the person. The directional content application214transcribes a translation of the audio content and provides the text and location data to the AR application216.

In another example embodiment, the directional content application214receives an electronic communication from a second HMD worn by the person speaking to the user102. The second HMD records a speech from the person and wirelessly provides the audio content via an electronic file (e.g., audio file) sent to the HMD101. In another example, the second HMD sends the electronic file to the server110for translation and transcription. In yet another example, the second HMD transcribes and translates the speech from the person wearing the second HMD. The second HMD includes a translated transcript of the speech in the electronic file sent to the HMD101.

The AR application216generates AR content that includes the text of translated audio content from the person speaking to the user102. The AR application216can further adjust the size or content of the AR content based on a location and orientation of the person relative to the HMD101, and the distance between the HMD101and the person.

In another example embodiment, the AR application216generates a display of information related to the objects A116, B118. In one example embodiment, the AR application216generates a visualization of information related to the objects A116, B118when the HMD101captures an image of the objects A116, B118and recognizes the objects A116, B118, or when the HMD101is in proximity to the objects A116, B118. For example, the AR application216generates a display of a holographic or virtual menu visually perceived as a layer on the objects A116, B118.

FIG.4is a block diagram illustrating an example embodiment of the directional content application214. The directional content application214is shown, by way of example, to include a direction module402, a data extraction module404, a data computation module406, and an AR content module408.

The direction module402uses the array of microphones to periodically (e.g., every 30 seconds) sample and analyze the user's environment. For example, the direction module402detects audio content (e.g., speech from another person) and determines a location of the person relative to the HMD101using a beamforming technique. In another example, the direction module402uses other types of sensors (e.g., a time-of-flight sensor) to detect the presence of a person within a preset radius of the HMD101, a relative location of the person with respect to the HMD101, and a distance between the person and the HMD101. Other computer-vision based techniques (e.g., facial recognition) can be used to determine whether the person is speaking, facing the user102, and addressing the user102.

Those of ordinary skill in the art will recognize that beamforming techniques can be used to identify the location and distance of a sound origin relative to the HMD101. In another example embodiment, the direction module402uses other types of sensors outside the HMD101(e.g., external sensors112) to determine and identify the location and distance of the person (e.g., using motion sensors or sensors placed on the person). For example, the HMD101receives an identification of the location and distance of the person relative to a location and orientation of the HMD101.

The data extraction module404records audio content from the speech of the person facing the user102. In one example, the array of microphones in the HMD101uses a beamforming technique to isolate the audio content from the person facing the user102. In another example, the data extraction module404receives audio data from another HMD worn by the person speaking to the user102. The data extraction module404receives an electronic communication from the other HMD. The electronic communication includes the audio content (e.g., an audio file or transcribed audio).

The data computation module406uses voice recognition techniques or text translation algorithms to translate the audio content of the person speaking to the user102. For example, the data computation module406uses voice recognition techniques to first transcribe the audio content to text and translation techniques to translate the text to another language. In another example embodiment, the HMD101identifies the person speaking to the user102and retrieves or determines a language associated with the identification of the person. The HMD101then uses voice recognition techniques to first transcribe the audio content from the identified language and translate the transcribed audio content to a preset language associated with the user102of the HMD101. In another example embodiment, the HMD101receives an electronic file that includes audio content from the HMD worn by the person speaking to the user102and an identification of the language associated with the person wearing the HMD.

The AR content module408generates AR content based on the translated audio content. The AR content can be, for example, a text of the translated speech of the person speaking to the user102. The text may be displayed in a speech bubble above the head of the person. In another example, the format or font of the text may change based on an identification of the person speaking to the user102. The text may include bold font for an executive-level person. In another example, the text appears to be coupled to the person so that the text bubble appears to follow the person. The size of the text and text bubble may increase or decrease based on the distance between the person and the user102of the HMD101. For example, the size of the text appears smaller with an increasing distance between the person and the HMD101.

FIG.5is a flowchart illustrating a method500for extracting audio data and displaying AR content, according to an example embodiment. At operation502, the HMD101determines a location of a second user relative to the HMD101. The second user includes a person speaking to the user102of the HMD101. The HMD101detects that the second user is located within a preset radius of the HMD101. Operation502can be implemented with the direction module402.

At operation504, the HMD101receives audio content from the second user. This can be accomplished in several ways. In one example, the HMD101includes an array of microphones that records audio from the second user. Operation504can be implemented with the data extraction module404.

At operation506, the HMD101generates AR content based on the audio content received from the second user. For example, the AR content includes a text translation of a speech from the second user. Operation506can be implemented with the AR content module408.

At operation508, the HMD101displays the AR content in the display204based on the location of the second user relative to the HMD101such that the AR content appears coupled to the second user as perceived by the user102. Operation508can be implemented with the AR content module408.

FIG.6is a flowchart illustrating a method600for receiving audio data and displaying AR content, according to an example embodiment. At operation602, the HMD101determines location information of a second HMD relative to the first HMD (e.g., the HMD101). A second user wears the second HMD. There can be several ways to determine the location information of the second HMD. Location sensors from the second HMD can be used to identify a location of the second HMD. In another example, the location information can be determined based on sensors external to both the first HMD and the second HMD. Location sensors can include GPS devices, Bluetooth devices, Wi-Fi devices, infrared sensors, and time-of-flight cameras. Operation602can be implemented with the direction module402.

At operation604, the HMD101receives an electronic message from the second HMD. The electronic message includes the audio content from the second user. In other words, the HMD worn by the second user includes a microphone that records speech from the second user. Operation604can be implemented with the data extraction module404.

At operation606, the HMD101generates AR content based on the electronic message. For example, the AR content includes a text translation of the audio content from the electronic message from the second user. Operation606can be implemented with the data computation module406.

At operation608, the HMD101displays the AR content in the display204based on the location of the second HMD relative to the first HMD (e.g., the HMD101) such that the AR content appears coupled to the second HMD as perceived by the user102. Operation608can be implemented with the AR content module408.

FIG.7is a flowchart illustrating a method700for detecting audio data and displaying AR content, according to an example embodiment.

At operation702, the HMD101receives audio content from a second user. The second user includes a person speaking to the user102of the HMD101. This can be accomplished in several ways. In one example, the HMD101includes an array of microphones that records audio from the second user. Operation702can be implemented with the data extraction module404.

At operation704, the HMD101determines a location of the second user using a beamforming technique on the audio content from the array of microphones in the HMD101. The HMD101detects that the second user is located within a preset radius of the HMD101. Operation704can be implemented with the direction module402.

At operation706, the HMD101determines a distance between the second user and the HMD101using the array of microphones in combination with a distance sensor (e.g., a time-of-flight camera). Operation706can be implemented with the direction module402.

At operation708, the HMD101generates and displays AR content based on the audio content received from the second user. For example, the AR content includes a text translation of a speech from the second user. The HMD101displays the AR content in the display204based on the location of the second user relative to the HMD101such that the AR content appears coupled to the second user as perceived by the user102. Operation708can be implemented with the AR content module408.

FIG.8Ais a block diagram illustrating an example of the HMD101detecting audio from users812,814. The HMD101includes an array of sensors804,806,808, and810disposed around the HMD101. The array of sensors detects audio from the user812and the user814. Beamforming techniques enable the HMD101to filter out audio content of the user812from the audio content of the user814. The HMD101detects that both users812,814are located within an area815of a preset radius.

FIG.8Bis a block diagram illustrating an example of AR content being displayed in a transparent display1300in the HMD illustrating the detected audio ofFIG.8A. The transparent display1300displays AR content (e.g., speech bubbles816,818) as an overlay on top of the users812and814. The speech bubble816appears to be coupled to the user812. The speech bubble818appears to be coupled to the user814. The speech bubble816includes a text of a translation of speech content from the user812. The speech bubble818includes a text of a translation of speech content from the user814. The size of the speech bubble816and the text therein is relatively larger than the size of the speech bubble818and the text therein since the user812is relatively closer to the HMD101than the user814.

FIG.9Ais a block diagram illustrating a front view of a head-mounted device (HMD)900, according to some example embodiments.FIG.9Bis a block diagram illustrating a side view of the HMD900ofFIG.9A. The HMD900may be an example of the HMD101ofFIG.1. The HMD900includes a helmet902with an attached visor904. The helmet902may include sensors (e.g., optical and audio sensors908and910provided at the front, back, and a top section906of the helmet902). Display lenses912are mounted on a lens frame914. The display lenses912include the display204ofFIG.2. The helmet902further includes ocular cameras911. Each ocular camera911is directed to an eye of the user102to capture an image of the iris or retina. Each ocular camera911may be positioned on the helmet902above and facing one eye. The helmet902also includes EEG/ECG sensors916to measure brain activity and a heart rate pattern of the user102.

In another example embodiment, the helmet902also includes lighting elements in the form of LED lights913on each side of the helmet902. An intensity or brightness of the LED lights913is adjusted based on the dimensions of the pupils of the user102. The AR application216may control lighting elements to adjust a size of the iris of the user102. Therefore, the AR application216may capture images of the iris at different sizes for different virtual objects.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A hardware module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client, or server computer system) or one or more hardware modules of a computer system (e.g., a processor212or a group of processors212) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor212or other programmable processor212) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor212configured using software, the general-purpose processor212may be configured as respective different hardware modules at different times. Software may accordingly configure a processor212, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.

Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses that connect the hardware modules). In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices and can operate on a resource (e.g., a collection of information).

The various operations of example methods described herein may be performed, at least partially, by one or more processors212that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors212may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors212or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors212, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors212may be located in a single location (e.g., within a home environment, an office environment, or a server farm), while in other embodiments the processors212may be distributed across a number of locations.

The one or more processors212may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors212), these operations being accessible via a network108and via one or more appropriate interfaces (e.g., application programming interfaces (APIs)).

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry, in computer hardware, firmware, or software, or in combinations of them. Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor212, a computer, or multiple computers.

A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a standalone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network108.

In example embodiments, operations may be performed by one or more programmable processors212executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special-purpose logic circuitry (e.g., an FPGA or an ASIC).

A computing system can include clients and servers110. A client and server110are generally remote from each other and typically interact through a communication network108. The relationship of client and server110arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures merit consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor212), or in a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments.

Example Machine Architecture

FIG.10is a block diagram of a machine in the example form of a computer system1000within which instructions1024for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server110or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing instructions1024(sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions1024to perform any one or more of the methodologies discussed herein.

The example computer system1000includes a processor1002(e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory1004, and a static memory1006, which communicate with each other via a bus1008. The computer system1000may further include a video display unit1010(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system1000also includes an alphanumeric input device1012(e.g., a keyboard), a user interface (UI) navigation (or cursor control) device1014(e.g., a mouse), a disk drive unit1016, a signal generation device1018(e.g., a speaker), and a network interface device1020.

Machine-Readable Medium

The disk drive unit1016includes a machine-readable medium1022on which is stored one or more sets of data structures and instructions1024(e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions1024may also reside, completely or at least partially, within the main memory1004and/or within the processor1002during execution thereof by the computer system1000, the main memory1004and the processor1002also constituting machine-readable media1022. The instructions1024may also reside, completely or at least partially, within the static memory1006.

While the machine-readable medium1022is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers110) that store the one or more instructions1024or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding, or carrying instructions1024for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present embodiments, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such instructions1024. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media1022include non-volatile memory, including by way of example semiconductor memory devices (e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices); magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and compact disc-read-only memory (CD-ROM) and digital versatile disc (or digital video disc) read-only memory (DVD-ROM) disks.

Transmission Medium

The instructions1024may further be transmitted or received over a communication network1026using a transmission medium. The instructions1024may be transmitted using the network interface device1020and any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Examples of communication networks1026include a local-area network (LAN), a wide-area network (WAN), the Internet, mobile telephone networks, plain old telephone service (POTS) networks, and wireless data networks (e.g., Wi-Fi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium capable of storing, encoding, or carrying instructions1024for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.

The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.