Patent Application
20240135081
The accompanying drawings illustrate a number of exemplary embodiments and are a part of the specification. Together with the following description, these drawings demonstrate and explain various principles of the present disclosure.
Throughout the drawings, identical reference characters and descriptions indicate similar, but not necessarily identical, elements. While the exemplary embodiments described herein are susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, the exemplary embodiments described herein are not intended to be limited to the particular forms disclosed. Rather, the present disclosure covers all modifications, equivalents, and alternatives falling within the scope of the appended claims.
The present disclosure is generally directed to a system configured to design, simulate, and optimize antennas using artificial intelligence (AI). In some cases, certain design parameters or specifications may be provided when performing these AI operations. In other systems, antenna engineers have attempted to design antennas for different devices. These engineers may have designed the antennas to exhibit specific gain, efficiency, or other operational characteristics, including a specified frequency band within which the antenna was to operate. This type of antenna design work was highly time intensive and was often limited by the knowledge of the engineer or was at least subject to the design proclivities of that engineer.
The embodiments described herein may implement artificial intelligence to design antennas for a given application or device, and within certain specifications or parameters. The systems described herein may be configured to access a set of usable antenna elements of different shapes and sizes. In some cases, these antenna elements may be elements that were previously used in other antennas. These systems may then arrange the placement of the antenna elements within a specified area and form those elements into different antenna shapes and antenna types, using AI techniques to guide the placement of each element.
The systems herein may also use AI to improve the performance of the newly designed antennas by optimizing their geometric (or other) parameters. Some embodiments may implement a surrogate model to quickly predict the frequency response of each candidate antenna without having to run through a full simulation (that may be costly and time consuming). Each new antenna design may be assigned a score, and those with a high enough score may be fully simulated and/or subjected to further rounds of optimization. This process or using AI techniques to design and optimize antennas will be described further below with reference to
Features from any of the embodiments described herein may be used in combination with one another in accordance with the general principles described herein. These and other embodiments, features, and advantages will be more fully understood upon reading the following detailed description in conjunction with the accompanying drawings and claims.
At least in some cases, the AI-assisted antenna design module 101 may be configured to access prior industrial antenna structures 102. These structures may come from known antenna designs. Since electronic devices have begun implementing wireless antennas, many thousands of different antenna designs have been created and implemented. In some cases, at least some of these prior antenna designs may be accessed by the AI-assisted antenna design module 101. The AI-assisted antenna design module 101 may use these prior antenna structures as background knowledge when performing AI operations including deep learning or performing EM computations.
Still further, the AI-assisted antenna design module 101 may access design specifications 103 when performing AI-assisted antenna design. These “design specifications” 103 may indicate, for example, a minimum or maximum size for an antenna, a type of antenna (e.g., monopole, dipole, loop, slot, inverted-F, inverted-L, Yadi Uda, etc.), a frequency band in which the antenna is to operate, a minimum or maximum amount of gain, a minimum amount of antenna efficiency, minimum or maximum S-parameters, minimum or maximum power usage, effective isotropic radiated power (EIRP), minimum or maximum directionality, or other design characteristics or features. The AI-assisted antenna design module 101 may access and implement some or all of these design specifications when designing antennas. Using the prior industrial structures 102 and/or the design specifications 103, the AI-assisted antenna design module 101 may perform the AI-assisted design using deep learning models 104, EM computation models 105, or other similar AI-related models to create an antenna design 106. The resulting antenna design 106 may then be tested and/or optimized, as will be explained further below with regard to method 200 of
As illustrated in method 200 of
At step 220 of method 200, the systems herein may identify one or more parameters for an antenna that is to be formed through artificial intelligence using the accessed antenna elements. These parameters may include design specifications 103 or other indications of how an antenna is to be designed. In one example, the design parameters may specify a length of the antenna, or a total area for a printed antenna. The design parameters may also specify the type of antenna (e.g., slot, monopole, microstrip, etc.). Other parameters including minimum gain, or minimum directionality may also be provided for the antenna that is to be designed using AI techniques.
At step 230 of method 200, an artificial reality instance may assemble the antenna elements into an assembled antenna that at least partially complies with the identified parameters. As the term is used herein, an “artificial reality instance” may refer to a hardware processor (e.g., an AI or ML chip) that has been initiated, or may refer to a software model that has been initiated (e.g., deep learning model 104), or may refer to a processor and software model or algorithm that have been initialized to perform some or all of the AI antenna design process. In some embodiments, a single AI instance may perform all of the antenna design, while in other cases, different AI instances may cooperate to perform different parts of the design process (e.g., accessing antenna elements from prior antenna designs, designing a new antenna, testing a new antenna, or optimizing an antenna, etc.). Each AI instance may be subject to controls or parameters provided by a user or by a controlling software application.
In the embodiments herein, “complying with parameters” may include adhering to, meeting, working within, or otherwise acknowledging and striving to fit the newly designed antenna to those parameters or specifications. In at least some antenna designs, the AI-assisted antenna design module 101 may make tradeoffs to improve some parameters while potentially negatively affecting other parameters. The AI-assisted antenna design module 101 may thus note the minimum or maximum allowable values for a given antenna design, and may design the resulting antenna (e.g., 106) to be within the acceptable ranges for each of the parameters given in the design specifications 103. This may lead to a wide variety of different antenna designs that may potentially meet (all or some portion of) the given parameters.
For instance, in one embodiment, the AI-assisted antenna design module 101 may access a specific set of antenna elements. This set of elements may have been taken from another antenna, or may have been generated by the AI-assisted antenna design module 101. Based on a set of design parameters or design specifications 103, a single set of antenna elements may be arranged into many different antenna types or different antenna optimizations within a given antenna type.
For instance, as shown in
In
In some cases, as shown in
At least in some cases, AI techniques may be implemented to improve the performance of a given antenna by optimizing its geometric parameters. The optimizer module 601 may determine, based on domain knowledge that itself may include techniques for improving S parameters, gain, efficiency, EIRP, or other parameters, how a given antenna is to be improved. The optimizer may make changes to the subject antenna's various element, including changing the elements' positions, sizes, shapes, connection points, grounding points, feed insertion points, parasitic elements, or other characteristics. The updated antenna 603 may be provided as an output 602 to a surrogate model 604. A “surrogate model,” as the term is used herein, may refer to a software and/or hardware module configured to perform rapid performance tests on candidate antenna designs. The surrogate model 604 may perform rough or approximated simulations based on existing domain knowledge indicating how other similar antenna designs have operated. These rough simulations may be much less time- and CPU-intensive than full simulations, and may provide a general sense for how the updated antenna 603 will perform without incurring the time and cost of a full simulation.
The surrogate model 604 may, for example, predict frequency response 605 for the updated antenna 603. This frequency response 605 may indicate which frequency bands the antenna may be suitable to operate in, or may indicate antenna efficiencies at different frequencies. Each antenna design evaluated by the surrogate model 604 may be assigned a score (606) and that score may be provided to the optimizer module 601 as feedback 607. The optimizer may then take that score and either indicate that the performance metrics have been met, or may indicate that further optimization may be beneficial, or may indicate that the design has failed to meet design specifications (or is unlikely to meet design specifications) and that the antenna may be discarded or tagged for use in a different application or device.
As shown in
The classifier module 904 may be configured to determine whether the new antenna designs 903 received from the optimizer 901 are viable. As the term is used herein, a “viable” antenna may refer to an antenna that may function for its intended purpose and within (at least some or all of) its design specifications. Thus, for example, if an antenna is to function within a 2.4 GHz-2.5 GHz range, and the antenna only operates at 3.5 GHz, that antenna is not viable for those design specifications. Or, if the antenna has no resonances at all, or consumes too much power, or is too highly direction, or includes some other disqualifying characteristics taking the antenna beyond its design specification maximums, that antenna may be deemed non-viable (910) and may be provided as feedback to the optimizer. The optimizer 901 may then use that feedback as background knowledge when performing subsequent antenna design optimizations.
If, however, the classifier module 904 determines that the newly designed antenna 903 is viable (at 911), the classifier module may pass the antenna design to a surrogate model 905. If a low-cost, time-efficient simulation is to be performed, that simulation will be performed by the surrogate model 905 and the resulting frequency response 907 (or other analysis) will be provided to the scoring module 908 which will apply a score to the antenna indicating how well the antenna conformed to its design specifications, and that score may be provided to the optimizer module 901 as feedback 909 for use in subsequent design optimizations. Additionally or alternatively, if the antenna design 903 is viable (at 911), the systems herein may opt to perform a full simulation. In such cases, the computer simulation technology (CST) simulator 906 may perform a full operational analysis on the antenna to determine its operating characteristics (including any of the operating characteristics mentioned above). This full simulation may take longer, but may be more precise and may provide more accurate performance data regarding the antenna design. This full simulation may also be given a score and provided as feedback to the optimizer 901.
In some embodiments, the systems herein may perform antenna design with or without prior knowledge of other antenna architectures. Similarly, the systems herein may perform antenna design optimization with or without prior knowledge of other antenna architectures. As noted above, many existing mobile electronic devices have implemented wireless antennas for communication over many different frequency bands and using different types of radios. For instance, mobile devices have implemented cellular antennas (e.g., long-term evolution LTE, 3G, 4G, 5G, etc.), Bluetooth antennas, WiFi antennas, global positioning system (GPS) antennas, ultrawideband antennas, near-field communication (NFC) antennas, AM/FM radio antennas, and other types of antennas. In some cases, the AI design and/or optimization systems described herein may consult databases that store large numbers of such previous antenna designs when designing or optimizing new antennas. In such cases, the previous antenna designs (and potentially their corresponding frequency response (or other) outputs) may be used as background knowledge when determining how to design antennas within certain design specifications, when assembling antenna elements into an assembled antenna, or when determining how to optimize certain performance characteristics of a newly designed antenna.
In other case, the AI systems or AI instances described herein may assemble antenna elements into assembled antennas or may optimize the performance of resulting antennas without knowledge of other antenna architectures. Thus, in such cases, an AI instance may access and assemble antenna elements in various designs until a design has been found that meets some or all of the designated performance characteristics or design specifications. Performing antenna design and/or antenna optimization without consulting prior designs may result in novel architectures that have not been previously considered, and may lead to antenna performance increases that have yet been unachieved in older designs.
In cases where AI instances use prior antennas as background knowledge for antenna design and optimization as well as in cases where AI instances do not use prior antennas as background knowledge, the AI instances may use antenna elements taken from prior antennas. These antenna elements may be stored in a database of antenna elements. These may be antenna elements from all types, sizes, and shapes of antennas, including single antennas and antenna arrays. The antenna elements may be the result of decomposing different antenna architectures into various antenna elements. In some cases, the AI instances may be used to decompose prior antennas, dividing those antennas into different shapes and configurations. In other cases, the AI instances may alter these decomposed antenna elements by changing their size, shape, or other characteristics. In still other cases, the AI instances may be used to create new antenna elements and new antenna designs from those new elements.
In one example, a single antenna architecture may be decomposed into multiple different antenna elements (e.g., as shown in
In some cases, users or software applications may provide design parameters or specifications within which the AI antenna design instances are to design a new antenna. In some embodiments, those design parameters may specify a layout size constraint for the newly assembled antenna. The layout size constraint may indicate an area or volume in which the antenna is to be designed. The area or volume may be a minimum amount or a maximum amount. With such a constraint, the AI antenna design instance may know how large the antenna it is attempting to design may be. Other design parameters may specify various minimum performance characteristics for the assembled antenna, including gain, directionality, S parameter values, EIRP, frequency response, or other antenna performance characteristics. Other performance characteristics may be added, removed, or changed by a user to guide the AI antenna design instance in its design process.
Design parameters may also indicate or limit the locations in which the antenna elements may be placed within an electronic device. For instance, some antenna elements may not be placed too close to certain components such as batteries, printed circuit boards, other antennas, electromagnetic shields, or other components. In such cases, the design parameters may specify which components the antenna elements can or cannot be placed near. Allowable distances between antenna elements and other device components may also be specified. Still further, the design parameters may limit the width and/or height for specific antenna elements and/or for the assembled antenna as a whole. In some cases, certain antenna elements may need to have a minimum width, or a minimum height, or may need to be placed in a certain position within the mobile device. In such cases, the design parameters may indicate the length, width, height, placement, or other characteristics for each antenna element and/or for the assembled antenna as a whole.
In some embodiments, neural networks, machine learning, or other techniques may additionally or alternatively be implemented. Such implementations are generally described further below. For instance, at least some of the embodiments described herein may train and/or implement AI techniques, neural networks, or machine learning models to access prior antenna structures (1101), disassemble those structures (1102), design an antenna, determine whether that design meets one or more target parameters (1109), if yes, submit the new antenna design 1110, and if no, then perform optimizations using domain knowledge 1105 (e.g., electrical current flow 1106, determined antenna efficiencies 1107, determined resonances 1108 for those antennas, etc.) and/or local optimizations 1104 using PSOs, TRFs, LaMCTs, or other learning techniques.
The systems herein may implement and/or incorporate a machine learning module that includes various ML-related components. These components may include a machine learning (ML) processor, an inferential model, a feedback implementation module, a prediction module, and/or a neural network. Each of these components may be configured to perform different functions with respect to training and/or implementing a machine learning model. The ML processor, for example, may be a dedicated, special-purpose processor with logic and circuitry designed to perform machine learning. The ML processor may work in tandem with the feedback implementation module to access data and use feedback to train an ML model. For instance, the ML processor may access one or more different training data sets. The ML processor and/or the feedback implementation module may use these training data sets to iterate through positive and negative samples and improve the ML model over time.
In some cases, the machine learning module may include an inferential model. As used herein, the term “inferential model” may refer to purely statistical models, purely machine learning models, or any combination of statistical and machine learning models. Such inferential models may include neural networks such as recurrent neural networks. In some embodiments, the recurrent neural network may be a long short-term memory (LSTM) neural network. Such recurrent neural networks are not limited to LSTM neural networks and may have any other suitable architecture. For example, in some embodiments, the neural network may be a fully recurrent neural network, a gated recurrent neural network, a recursive neural network, a Hopfield neural network, an associative memory neural network, an Elman neural network, a Jordan neural network, an echo state neural network, a second order recurrent neural network, and/or any other suitable type of recurrent neural network. In other embodiments, neural networks that are not recurrent neural networks may be used. For example, deep neural networks, convolutional neural networks, and/or feedforward neural networks, may be used. In some implementations, the inferential model may be an unsupervised machine learning model, e.g., where previous data (on which the inferential model was previously trained) is not required.
At least some of the embodiments described herein may include training a neural network to identify data dependencies, identify which information from various data sources is to be altered to lead to a desired outcome, or how to alter the information to lead to a desired outcome. In some embodiments, the systems described herein may include a neural network that is trained to identify how information is to be altered using different types of data and associated data dependencies. For example, the embodiments herein may use a feed-forward neural network. In some embodiments, some or all of the neural network training may happen offline. Additionally or alternatively, some of the training may happen online. In some examples, offline development may include feature and model development, training, and/or test and evaluation.
In one embodiment, a repository that includes data about past data accessed and past data alterations may supply the training and/or testing data. In one example, when the underlying system had accessed different types of data from different data sources, the system may determine which alterations to identify based on data from a feature repository and/or an online recommendation model that may be informed by the results of offline development. In one embodiment, the output of the machine learning model may include a collection of vectors of floats, where each vector represents a data source and each float within the vector represents the probability that a specified data alteration will be identified. In some embodiments, the recent history of a data source may be weighted higher than older history data. For example, if a data source had repeatedly provided relevant data that resulted in relevant operational steps, the ML model may determine that the probability of that data source providing relevant data in the future is higher than for other data sources.
Once the machine learning model has been trained, the ML model may be used to identify which data is to be altered (e.g., antenna element characteristics and placement) and how that data is to be altered based on multiple different data sets. In some embodiments, the machine learning model that makes these determinations may be hosted on different cloud-based distributed processors (e.g., ML processors) configured to perform the identification in real time or substantially in real time. Such cloud-based distributed processors may be dynamically added, in real time, to the process of identifying data alterations. These cloud-based distributed processors may work in tandem with the prediction module to generate outcome predictions, according to the various data inputs. These predictions may identify potential outcomes that would result from the identified data alterations. The predictions output by the prediction module may include associated probabilities of occurrence for each prediction. The prediction module may be part of a trained machine learning model that may be implemented using the ML processor. In some embodiments, various components of the machine learning module may test the accuracy of the trained machine learning model using, for example, proportion estimation. This proportion estimation may result in feedback that, in turn, may be used by the feedback implementation module in a feedback loop to improve the ML model and train the model with greater accuracy.
Accordingly, the AI techniques 1103 of
In some cases, a surrogate model may be implemented to simulate or test the newly designed and/or optimized antennas. As noted above, a surrogate model may be implemented to perform a quick, low-CPU-cost simulation of a candidate antenna design that is much faster than a full antenna performance simulation. The surrogate model may analyze the optimized antenna to identify various operational characteristics of the antenna. One example of this is shown in
In some cases, these antenna designs 1301-1306 may be subject to simulation and optimization. In cases where a quick simulation is to be performed, a surrogate model may be used. The surrogate model may take the new antenna design as input and may determine one or more characteristics of the antenna. For instance, as shown in
Indeed, in some cases, the surrogate model may be implemented to perform quick analyses on AI-generated antenna designs. If those designs lack any resonances, or have poor gain values or poor directionality, or have other suboptimal characteristics, the surrogate model may filter those antenna designs out such that they are no longer part of the creation/optimization process. In some embodiments, the surrogate model may implement a knowledge database of prior antennas and their associated characteristics when determining which assembled antennas to filter out. This database knowledge may help the surrogate model to function even more quickly, easily identifying antenna designs that are nonfunctional or impractical, and quickly discarding them to focus on more promising designs. In some embodiments, this database knowledge may include full antenna simulations, which may be more reliable indicators of potential performance. Filtering out unusable candidates may avoid abbreviated simulations in the surrogate model and may avoid full simulations in a simulation model. This filtering may provide large time and cost savings.
In some embodiments, as shown in
In addition to the methods for designing and optimizing antennas described herein, corresponding systems and computer-readable media may also be provided. In one embodiment, a system may be provided that includes at least one physical processor and physical memory comprising computer-executable instructions that, when executed by the physical processor, cause the physical processor to: access one or more antenna elements, identify one or more parameters for an antenna that is to be formed using the accessed antenna elements, and assemble the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters.
Similarly, a non-transitory computer-readable medium may be provided having one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to: access one or more antenna elements, identify one or more parameters for an antenna that is to be formed using the accessed antenna elements, and assemble the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters.
Any of these systems, methods, and computer-readable media may be implemented to design and optimize different types of antennas. These antennas may then be used in all types of mobile or stationary electronic devices. AI techniques may be implemented to design these antennas, leading to more efficient antennas and more specialized antennas that are potentially more suited to each individual application or device. Still further, continued optimizations may further refine the newly created antennas, and may continue to improve and optimize the design of each antenna.
Example 1: A computer-implemented method may include accessing one or more antenna elements, identifying one or more parameters for an antenna that is to be formed using the accessed antenna elements, and assembling the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters.
Example 2: The computer-implemented method of Example 1, wherein the AI instance assembles the antenna elements into the assembled antenna without knowledge of other antenna architectures.
Example 3: The computer-implemented method of Example 1 or Example 2, further comprising accessing one or more antenna architectures and, wherein the AI instance uses the accessed antenna architectures as background knowledge when assembling the antenna elements into the assembled antenna.
Example 4: The computer-implemented method of any of Examples 1-3, further comprising decomposing one or more antenna architectures to identify the one or more antenna elements.
Example 5: The computer-implemented method of any of Examples 1-4, wherein a same antenna architecture is decomposed into a plurality of different antenna elements.
Example 6: The computer-implemented method of any of Examples 1-5, wherein a same set of antenna elements are assembled into a plurality of different assembled antennas.
Example 7: The computer-implemented method of any of Examples 1-6, wherein the plurality of different assembled antennas are of one or more different antenna types.
Example 8: The computer-implemented method of any of Examples 1-7, wherein the parameters specify a layout size constraint for the assembled antenna.
Example 9: The computer-implemented method of any of Examples 1-8, wherein the parameters specify one or more minimum performance characteristics for the assembled antenna.
Example 10: The computer-implemented method of any of Examples 1-9, further comprising optimizing the assembled antenna to improve one or more operational characteristics.
Example 11: The computer-implemented method of any of Examples 1-10, wherein the AI instance accesses an indication of which operational characteristics are most significant and optimizes the assembled antenna for at least one of the indicated operational characteristics.
Example 12: A system may include at least one physical processor and physical memory comprising computer-executable instructions that, when executed by the physical processor, cause the physical processor to: access one or more antenna elements, identify one or more parameters for an antenna that is to be formed using the accessed antenna elements, and assemble the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters.
Example 13: The system of Example 12, wherein the identified parameters limit which locations one or more of the antenna elements is placeable within an electronic device.
Example 14: The system of Example 12 or Example 13, wherein the identified parameters limit a width and/or a height for at least one of the antenna elements and/or for the assembled antenna.
Example 15: The system of any of Examples 12-14, wherein the assembled antenna is analyzed by a surrogate model to identify one or more operational characteristics of the assembled antenna.
Example 16: The system of any of Examples 12-15, wherein the surrogate model filters one or more assembled antennas whose performance characteristics are below a minimum performance threshold value.
Example 17: The system of any of Examples 12-16, wherein the surrogate model implements a knowledge database when determining which assembled antennas to filter out.
Example 18: The system of any of Examples 12-17, wherein the assembled antenna is provided to a simulation model for a performance simulation.
Example 19: The system of any of Examples 12-18, wherein performance results from the simulation are provided to a surrogate model to inform future analyses by the surrogate model.
Example 20: A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to: access one or more antenna elements, identify one or more parameters for an antenna that is to be formed using the accessed antenna elements, and assemble the antenna elements, using an artificial intelligence (AI) instance, into an assembled antenna that at least partially complies with the identified parameters.
Embodiments of the present disclosure may include or be implemented in conjunction with various types of artificial-reality systems. Artificial reality is a form of reality that has been adjusted in some manner before presentation to a user, which may include, for example, a virtual reality, an augmented reality, a mixed reality, a hybrid reality, or some combination and/or derivative thereof. Artificial-reality content may include completely computer-generated content or computer-generated content combined with captured (e.g., real-world) content. The artificial-reality content may include video, audio, haptic feedback, or some combination thereof, any of which may be presented in a single channel or in multiple channels (such as stereo video that produces a three-dimensional (3D) effect to the viewer). Additionally, in some embodiments, artificial reality may also be associated with applications, products, accessories, services, or some combination thereof, that are used to, for example, create content in an artificial reality and/or are otherwise used in (e.g., to perform activities in) an artificial reality.
Artificial-reality systems may be implemented in a variety of different form factors and configurations. Some artificial-reality systems may be designed to work without near-eye displays (NEDs). Other artificial-reality systems may include an NED that also provides visibility into the real world (such as, e.g., augmented-reality system 1600 in
Turning to
In some embodiments, augmented-reality system 1600 may include one or more sensors, such as sensor 1640. Sensor 1640 may generate measurement signals in response to motion of augmented-reality system 1600 and may be located on substantially any portion of frame 1610. Sensor 1640 may represent one or more of a variety of different sensing mechanisms, such as a position sensor, an inertial measurement unit (IMU), a depth camera assembly, a structured light emitter and/or detector, or any combination thereof. In some embodiments, augmented-reality system 1600 may or may not include sensor 1640 or may include more than one sensor. In embodiments in which sensor 1640 includes an IMU, the IMU may generate calibration data based on measurement signals from sensor 1640. Examples of sensor 1640 may include, without limitation, accelerometers, gyroscopes, magnetometers, other suitable types of sensors that detect motion, sensors used for error correction of the IMU, or some combination thereof.
In some examples, augmented-reality system 1600 may also include a microphone array with a plurality of acoustic transducers 1620(A)-1620(J), referred to collectively as acoustic transducers 1620. Acoustic transducers 1620 may represent transducers that detect air pressure variations induced by sound waves. Each acoustic transducer 1620 may be configured to detect sound and convert the detected sound into an electronic format (e.g., an analog or digital format). The microphone array in
In some embodiments, one or more of acoustic transducers 1620(A)-(J) may be used as output transducers (e.g., speakers). For example, acoustic transducers 1620(A) and/or 1620(B) may be earbuds or any other suitable type of headphone or speaker.
The configuration of acoustic transducers 1620 of the microphone array may vary. While augmented-reality system 1600 is shown in
Acoustic transducers 1620(A) and 1620(B) may be positioned on different parts of the user's ear, such as behind the pinna, behind the tragus, and/or within the auricle or fossa. Or, there may be additional acoustic transducers 1620 on or surrounding the ear in addition to acoustic transducers 1620 inside the ear canal. Having an acoustic transducer 1620 positioned next to an ear canal of a user may enable the microphone array to collect information on how sounds arrive at the ear canal. By positioning at least two of acoustic transducers 1620 on either side of a user's head (e.g., as binaural microphones), augmented-reality system 1600 may simulate binaural hearing and capture a 3D stereo sound field around about a user's head. In some embodiments, acoustic transducers 1620(A) and 1620(B) may be connected to augmented-reality system 1600 via a wired connection 1630, and in other embodiments acoustic transducers 1620(A) and 1620(B) may be connected to augmented-reality system 1600 via a wireless connection (e.g., a BLUETOOTH connection). In still other embodiments, acoustic transducers 1620(A) and 1620(B) may not be used at all in conjunction with augmented-reality system 1600.
Acoustic transducers 1620 on frame 1610 may be positioned in a variety of different ways, including along the length of the temples, across the bridge, above or below display devices 1615(A) and 1615(B), or some combination thereof. Acoustic transducers 1620 may also be oriented such that the microphone array is able to detect sounds in a wide range of directions surrounding the user wearing the augmented-reality system 1600. In some embodiments, an optimization process may be performed during manufacturing of augmented-reality system 1600 to determine relative positioning of each acoustic transducer 1620 in the microphone array.
In some examples, augmented-reality system 1600 may include or be connected to an external device (e.g., a paired device), such as neckband 1605. Neckband 1605 generally represents any type or form of paired device. Thus, the following discussion of neckband 1605 may also apply to various other paired devices, such as charging cases, smart watches, smart phones, wrist bands, other wearable devices, hand-held controllers, tablet computers, laptop computers, other external computer devices, etc.
As shown, neckband 1605 may be coupled to eyewear device 1602 via one or more connectors. The connectors may be wired or wireless and may include electrical and/or non-electrical (e.g., structural) components. In some cases, eyewear device 1602 and neckband 1605 may operate independently without any wired or wireless connection between them. While
Pairing external devices, such as neckband 1605, with augmented-reality eyewear devices may enable the eyewear devices to achieve the form factor of a pair of glasses while still providing sufficient battery and computation power for expanded capabilities. Some or all of the battery power, computational resources, and/or additional features of augmented-reality system 1600 may be provided by a paired device or shared between a paired device and an eyewear device, thus reducing the weight, heat profile, and form factor of the eyewear device overall while still retaining desired functionality. For example, neckband 1605 may allow components that would otherwise be included on an eyewear device to be included in neckband 1605 since users may tolerate a heavier weight load on their shoulders than they would tolerate on their heads. Neckband 1605 may also have a larger surface area over which to diffuse and disperse heat to the ambient environment. Thus, neckband 1605 may allow for greater battery and computation capacity than might otherwise have been possible on a stand-alone eyewear device. Since weight carried in neckband 1605 may be less invasive to a user than weight carried in eyewear device 1602, a user may tolerate wearing a lighter eyewear device and carrying or wearing the paired device for greater lengths of time than a user would tolerate wearing a heavy standalone eyewear device, thereby enabling users to more fully incorporate artificial-reality environments into their day-to-day activities.
Neckband 1605 may be communicatively coupled with eyewear device 1602 and/or to other devices. These other devices may provide certain functions (e.g., tracking, localizing, depth mapping, processing, storage, etc.) to augmented-reality system 1600. In the embodiment of
Acoustic transducers 1620(I) and 1620(J) of neckband 1605 may be configured to detect sound and convert the detected sound into an electronic format (analog or digital). In the embodiment of
Controller 1625 of neckband 1605 may process information generated by the sensors on neckband 1605 and/or augmented-reality system 1600. For example, controller 1625 may process information from the microphone array that describes sounds detected by the microphone array. For each detected sound, controller 1625 may perform a direction-of-arrival (DOA) estimation to estimate a direction from which the detected sound arrived at the microphone array. As the microphone array detects sounds, controller 1625 may populate an audio data set with the information. In embodiments in which augmented-reality system 1600 includes an inertial measurement unit, controller 1625 may compute all inertial and spatial calculations from the IMU located on eyewear device 1602. A connector may convey information between augmented-reality system 1600 and neckband 1605 and between augmented-reality system 1600 and controller 1625. The information may be in the form of optical data, electrical data, wireless data, or any other transmittable data form. Moving the processing of information generated by augmented-reality system 1600 to neckband 1605 may reduce weight and heat in eyewear device 1602, making it more comfortable for the user.
Power source 1635 in neckband 1605 may provide power to eyewear device 1602 and/or to neckband 1605. Power source 1635 may include, without limitation, lithium-ion batteries, lithium-polymer batteries, primary lithium batteries, alkaline batteries, or any other form of power storage. In some cases, power source 1635 may be a wired power source. Including power source 1635 on neckband 1605 instead of on eyewear device 1602 may help better distribute the weight and heat generated by power source 1635.
As noted, some artificial-reality systems may, instead of blending an artificial reality with actual reality, substantially replace one or more of a user's sensory perceptions of the real world with a virtual experience. One example of this type of system is a head-worn display system, such as virtual-reality system 1700 in
Artificial-reality systems may include a variety of types of visual feedback mechanisms. For example, display devices in augmented-reality system 1600 and/or virtual-reality system 1700 may include one or more liquid crystal displays (LCDs), light emitting diode (LED) displays, microLED displays, organic LED (OLED) displays, digital light projector (DLP) micro-displays, liquid crystal on silicon (LCoS) micro-displays, and/or any other suitable type of display screen. These artificial-reality systems may include a single display screen for both eyes or may provide a display screen for each eye, which may allow for additional flexibility for varifocal adjustments or for correcting a user's refractive error. Some of these artificial-reality systems may also include optical subsystems having one or more lenses (e.g., concave or convex lenses, Fresnel lenses, adjustable liquid lenses, etc.) through which a user may view a display screen. These optical subsystems may serve a variety of purposes, including to collimate (e.g., make an object appear at a greater distance than its physical distance), to magnify (e.g., make an object appear larger than its actual size), and/or to relay (to, e.g., the viewer's eyes) light. These optical subsystems may be used in a non-pupil-forming architecture (such as a single lens configuration that directly collimates light but results in so-called pincushion distortion) and/or a pupil-forming architecture (such as a multi-lens configuration that produces so-called barrel distortion to nullify pincushion distortion).
In addition to or instead of using display screens, some of the artificial-reality systems described herein may include one or more projection systems. For example, display devices in augmented-reality system 1600 and/or virtual-reality system 1700 may include micro-LED projectors that project light (using, e.g., a waveguide) into display devices, such as clear combiner lenses that allow ambient light to pass through. The display devices may refract the projected light toward a user's pupil and may enable a user to simultaneously view both artificial-reality content and the real world. The display devices may accomplish this using any of a variety of different optical components, including waveguide components (e.g., holographic, planar, diffractive, polarized, and/or reflective waveguide elements), light-manipulation surfaces and elements (such as diffractive, reflective, and refractive elements and gratings), coupling elements, etc. Artificial-reality systems may also be configured with any other suitable type or form of image projection system, such as retinal projectors used in virtual retina displays.
The artificial-reality systems described herein may also include various types of computer vision components and subsystems. For example, augmented-reality system 1600 and/or virtual-reality system 1700 may include one or more optical sensors, such as two-dimensional (2D) or 3D cameras, structured light transmitters and detectors, time-of-flight depth sensors, single-beam or sweeping laser rangefinders, 3D LiDAR sensors, and/or any other suitable type or form of optical sensor. An artificial-reality system may process data from one or more of these sensors to identify a location of a user, to map the real world, to provide a user with context about real-world surroundings, and/or to perform a variety of other functions.
The artificial-reality systems described herein may also include one or more input and/or output audio transducers. Output audio transducers may include voice coil speakers, ribbon speakers, electrostatic speakers, piezoelectric speakers, bone conduction transducers, cartilage conduction transducers, tragus-vibration transducers, and/or any other suitable type or form of audio transducer. Similarly, input audio transducers may include condenser microphones, dynamic microphones, ribbon microphones, and/or any other type or form of input transducer. In some embodiments, a single transducer may be used for both audio input and audio output.
In some embodiments, the artificial-reality systems described herein may also include tactile (i.e., haptic) feedback systems, which may be incorporated into headwear, gloves, bodysuits, handheld controllers, environmental devices (e.g., chairs, floor mats, etc.), and/or any other type of device or system. Haptic feedback systems may provide various types of cutaneous feedback, including vibration, force, traction, texture, and/or temperature. Haptic feedback systems may also provide various types of kinesthetic feedback, such as motion and compliance. Haptic feedback may be implemented using motors, piezoelectric actuators, fluidic systems, and/or a variety of other types of feedback mechanisms. Haptic feedback systems may be implemented independent of other artificial-reality devices, within other artificial-reality devices, and/or in conjunction with other artificial-reality devices.
By providing haptic sensations, audible content, and/or visual content, artificial-reality systems may create an entire virtual experience or enhance a user's real-world experience in a variety of contexts and environments. For instance, artificial-reality systems may assist or extend a user's perception, memory, or cognition within a particular environment. Some systems may enhance a user's interactions with other people in the real world or may enable more immersive interactions with other people in a virtual world. Artificial-reality systems may also be used for educational purposes (e.g., for teaching or training in schools, hospitals, government organizations, military organizations, business enterprises, etc.), entertainment purposes (e.g., for playing video games, listening to music, watching video content, etc.), and/or for accessibility purposes (e.g., as hearing aids, visual aids, etc.). The embodiments disclosed herein may enable or enhance a user's artificial-reality experience in one or more of these contexts and environments and/or in other contexts and environments.
As detailed above, the computing devices and systems described and/or illustrated herein broadly represent any type or form of computing device or system capable of executing computer-readable instructions, such as those contained within the modules described herein. In their most basic configuration, these computing device(s) may each include at least one memory device and at least one physical processor.
In some examples, the term “memory device” generally refers to any type or form of volatile or non-volatile storage device or medium capable of storing data and/or computer-readable instructions. In one example, a memory device may store, load, and/or maintain one or more of the modules described herein. Examples of memory devices include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State Drives (SSDs), optical disk drives, caches, variations or combinations of one or more of the same, or any other suitable storage memory.
In some examples, the term “physical processor” generally refers to any type or form of hardware-implemented processing unit capable of interpreting and/or executing computer-readable instructions. In one example, a physical processor may access and/or modify one or more modules stored in the above-described memory device. Examples of physical processors include, without limitation, microprocessors, microcontrollers, Central Processing Units (CPUs), Field-Programmable Gate Arrays (FPGAs) that implement softcore processors, Application-Specific Integrated Circuits (ASICs), portions of one or more of the same, variations or combinations of one or more of the same, or any other suitable physical processor.
Although illustrated as separate elements, the modules described and/or illustrated herein may represent portions of a single module or application. In addition, in certain embodiments one or more of these modules may represent one or more software applications or programs that, when executed by a computing device, may cause the computing device to perform one or more tasks. For example, one or more of the modules described and/or illustrated herein may represent modules stored and configured to run on one or more of the computing devices or systems described and/or illustrated herein. One or more of these modules may also represent all or portions of one or more special-purpose computers configured to perform one or more tasks.
In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.
In some embodiments, the term “computer-readable medium” generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions. Examples of computer-readable media include, without limitation, transmission-type media, such as carrier waves, and non-transitory-type media, such as magnetic-storage media (e.g., hard disk drives, tape drives, and floppy disks), optical-storage media (e.g., Compact Discs (CDs), Digital Video Discs (DVDs), and BLU-RAY discs), electronic-storage media (e.g., solid-state drives and flash media), and other distribution systems.
The process parameters and sequence of the steps described and/or illustrated herein are given by way of example only and can be varied as desired. For example, while the steps illustrated and/or described herein may be shown or discussed in a particular order, these steps do not necessarily need to be performed in the order illustrated or discussed. The various exemplary methods described and/or illustrated herein may also omit one or more of the steps described or illustrated herein or include additional steps in addition to those disclosed.
The preceding description has been provided to enable others skilled in the art to best utilize various aspects of the exemplary embodiments disclosed herein. This exemplary description is not intended to be exhaustive or to be limited to any precise form disclosed. Many modifications and variations are possible without departing from the spirit and scope of the present disclosure. The embodiments disclosed herein should be considered in all respects illustrative and not restrictive. Reference should be made to the appended claims and their equivalents in determining the scope of the present disclosure.
Unless otherwise noted, the terms “connected to” and “coupled to” (and their derivatives), as used in the specification and claims, are to be construed as permitting both direct and indirect (i.e., via other elements or components) connection. In addition, the terms “a” or “an,” as used in the specification and claims, are to be construed as meaning “at least one of.” Finally, for ease of use, the terms “including” and “having” (and their derivatives), as used in the specification and claims, are interchangeable with and have the same meaning as the word “comprising.”
