Patent Description:
A synthetic world interface to model digital environments, sensors, and motions for the evaluation and development of computer vision and speech algorithms is used. Some aspects disclosed herein are directed to determining the plausibility of a particular computer vision and speech algorithm for a consumer scenario (e.g. Virtual Reality (VR)). This may be accomplished by observing the performance of a candidate algorithm on synthetic (virtually generated) scenes that have been generated by a synthetic world interface to model digital environments, sensors, and motions. Furthermore, a computer vision algorithm may require tuning of multiple parameters for proper function in intended usage scenarios. Using a synthetic data cloud service with a library of sensor primitives, motion generators, and environments with procedural and game-like capabilities, may facilitate engineering design for a manufactural solution involving computer vision capabilities.

In some embodiments, a sensor platform simulator operates with a motion orchestrator, an environment orchestrator, an experiment generator, and an experiment runner to test various candidate hardware configurations and computer vision algorithms in a virtual environment, advantageously speeding development and reducing cost. Some aspects disclosed herein are directed to testing various candidate hardware configurations for a given computer vision algorithm. A motion orchestrator, environment orchestrator, and experiment generator may rapidly produce relatively large quantities of data to inform the optimal candidate hardware configuration, thus advantageously speeding development and reducing cost.

Some aspects disclosed herein are directed to a networked environment of servers configured for rapidly generating synthetic testing data or evaluating algorithm performance and hardware configurations. For instance, a sensor platform simulator may be used to simulate hardware configurations comprising the one or more virtual sensors (e.g., sensors and/or microphones). An environment orchestrator may simulate one or more virtual environments. A motion orchestrator may simulate motion of the one or more simulated hardware configurations within the one or more virtual environments. An experiment generator may generate synthetic experiment data for the one or more simulated hardware configurations having the simulated motion within the one or more virtual environments. And an experiment runner for iterating the experiment generator to generate the synthetic experiment data for one or more combinations of hardware configurations, virtual environment, motion and computer vision speech algorithms.

Alternatively or in addition to the other examples described herein, some examples include any combination of the following: the experiment generator further calculates computer vision and speech data for the one or more hardware configurations from the synthetic experiment data; the servers, method, or instructions further comprise an evaluator for comparing the calculated computer vision data with ground truth data; the synthetic experiment data comprises synthetic images; the synthetic experiment data comprises IMU data; the IMU data comprises accelerometer data; the IMU data comprises gyroscope data.

References made throughout this disclosure relating to specific examples and implementations are provided solely for illustrative purposes but, unless indicated to the contrary, are not meant to limit all examples.

In general, designing solutions that inform both the hardware and algorithmic requirements for a new computer vision and speech platform are extremely difficult. The efforts involve factors of environment, motion and physics, and sensors. Developing computer vision and speech algorithms typically requires exploration and analysis across a wide range of use cases that ultimately utilize a combination of sensors, processors, and algorithms. The wide range of sensor configurations, environmental conditions, and motion conditions that must often be investigated (within the range of solutions to be explored) to verify that an algorithm and device will reliably perform as intended, can introduce significant cost and scheduling issues. It may also require substantial and specific intelligence by the developers of the various hardware and algorithms being used.

Setting up an experimentation workflow for a manual build-test-repeat type process typically requires dependence on manually collecting and labelling large amounts of data that must be collected by sending testers with prototype devices into real-world environments to collect sensor data streams. As data is gathered on an ad-hoc and exploratory basis, the data is ingested into computer networks that may use some form of a pipeline for analyzing data quality and experimental results. Ensuring that the proper amount and format of data is available and properly cataloged for analysis by the engineers and researchers may be challenging. For example, computer vision (CV) experts may find that the available test data had been collected using off-the-shelf sensors that do not properly represent the planned target device. Therefore, designing solutions that can match well-performing algorithms with cost-effective hardware configurations may be slow and expensive.

Some aspects disclosed herein are directed to using a synthetic world interface to model digital environments, sensors, and motions for the evaluation and development of computer vision and speech algorithms. Systems, methods, and programmed computer memory devices are disclosed herein for a synthetic world interface to model digital environments, sensors, and motions that may, in turn, be supplied as input to computer vision and speech functions for hardware in the form of a collection of real or virtual images. Using a synthetic data cloud service with a library of sensor primitives, motion generators, and environments with procedural and game-like capabilities, may facilitate engineering design for a manufactural solution involving computer vision and speech capabilities. In some embodiments, a sensor platform simulator operates with a motion orchestrator, an environment orchestrator, an experiment generator, and an experiment runner to test various candidate hardware configurations and computer vision and/or speech algorithms in a virtual environment, advantageously speeding development while at the same time reducing cost and development complexity. Thus, examples disclosed herein may relate to virtual reality (VR) or mixed reality (MR) implementations.

A service (referenced below as the "computer vision and speech design service") generates synthetic image(s) for camera sensors, audio recordings for microphone sensors, and inertial sensor data for inertial measurement unit (IMU) sensors corresponding to a modeled environment, to permit iterative testing that can be leveraged to improve the functionality of the virtual sensor in a synthetically (or artificially) generated scene. Computer vision and speech recognition are performed in a synthetic video scene, modeling various configurations of hardware; including particular lenses; sensors; and IMUs that can include accelerometers, gyroscopes, and magnetometers. Computer vision and speech parameters that include coordinates of a virtual sensor in a synthetic environment may be algorithmically determined based on the hardware configuration, specified movement (if any), and other objects specified in a synthetic scene. For example, a user may specify that a synthetic scene has a virtual sensor with a particular type of camera lens and other sensors (e.g., microphone, IMU with a gyroscope, accelerometer, etc.). The architectures and work flows disclosed herein may then compute the computer vision and speech parameters (e.g., x-, y-, and z- direction coordinates) of the virtual sensor in the synthetic environment that had virtually captured synthetic imagery in the simulation, and may also move that sensor, if required. Users can test how well the specified architecture (hardware and algorithms) performed computer vision and speech functions of the virtual sensor (e.g., specified camera lens, microphone, and/or other sensors) by measuring the resultant computer vision and speech performance relative to ground truth (GT) input data.

The desired scenario is that, if the virtual sensor had not moved, but yet some object within a scene had mover (for example a chair), then the computer vision and speech solution should calculate the sensor's position to indicate the lack of movement. The best localization algorithm or service may be evaluated out of a multitude of localization algorithms and services using the techniques disclosed in the `<NUM> Patent Application.

In some embodiments, the computer vision and speech design service iteratively evaluates-either alone or in combination-localization services, recognition services, object tracking services, object reconstructions services, or speech recognition services using artificial intelligence in order to determine the best-functioning services to use in computer vision models. As referenced herein, "computer vision" refers to any combination of services related to localization, object recognition, object tracking, and object reconstruction; and "speech" services refer to any audio or speech recognition services. Collectively, "computer vision and speech" services refer to the combination of the two: computer vision services and speech services.

For instance, the computer vision and speech design service may evaluate and select the best localization services out of a multitude of localization algorithms and services using the techniques disclosed in the `<NUM> Patent Application. Alternatively or additionally, the computer vision and speech design service may evaluate a multitude of other services besides localization services using artificial intelligence or machine learning, including, for example but without limitation services for: recognizing objects (object recognition services), tracking objects as they move movement (object tracking services), reconstructing physical and internal properties of objects (object reconstruction services), and/or recognizing speech or other audio (collectively "speech recognition services"). For the sake of clarity, these services (object recognition services, object tracking services, object reconstruction services, or speech recognition services, or the like) are referred to herein as "computer vision and speech design services.

In operation, the computer vision and speech design service iteratively evaluates-either alone or in combination-localization services, recognition services, object tracking services, object reconstructions services, or speech recognition services using artificial intelligence in order to determine the best-functioning services to use in computer vision models. For example, the computer vision and speech design service may evaluate <NUM> different algorithms performing object tracking and determine the top object tracking algorithm therefrom using the disclosed synthetic architecture described below. Any of the other services may likewise be evaluated as well.

To further clarify, "computer vision" refers herein to computerized tasks focused on acquiring, processing, analyzing and understanding actual or synthetic digital images, with the ultimate goal being to use computers to emulate human vision, including autonomously analyzing, learning, inferring, and taking actions based on the actual or synthetic images. As referenced in more detail below, computer vision includes, without limitation, object recognition, object tracking, object reconstruction, object reconstruction, and/or speech recognition.

Speech recognition, as referenced herein, refers to the use of computers for the identification, analysis, and understanding of speech in actual and synthetic video (e.g., a sequence of digital images). The synthetic environments discussed below model sensors (e.g., microphones) for capturing speech data in actual or synthetic environments. The captured speech data may be analyzed myriad speech-recognition software using AI, machine learning, or a combination thereof, to identify optimal speech-recognition software and microphone sensors to use in given scenes. It should be noted, however, that speech recognition mentioned herein refers to audio sensors and speech identification, recognition, and analysis in the broader context of computer vision within a synthetic environment.

For additional clarity, some examples and embodiments disclosed herein specifically refer to localization services and algorithms-and the evaluation thereof-but the disclosed computer vision and speech design service may evaluate any of the aforementioned computer vision and speech design services (e.g., object recognition services, object tracking services, object reconstruction services, or speech recognition services, or the like) in the same ways disclosed herein.

A computer vision and speech design service may thus be evaluated or tuned, based upon the accuracy of its determination. The processing of the synthetic imagery are affected by factors such a lens quality, light sensor resolution, and the dynamic range in lighting levels to which the light sensor responds within acceptable parameters. Poor imagery quality may negatively computer vision and speech recognition accuracy or precision. So, an experiment includes testing a first hardware configuration, with a first lens and light sensor, generate a synthetic scene, generate synthetic imagery with a digital model of the first hardware configuration, use that synthetic imagery to solve a computer vision and speech design service problem, compare the calculated position with the known simulated position, and repeat for environmental changes. Such processing are repeated or concurrently performed for a second hardware configuration. Designers may then use the results to ascertain which hardware configuration is preferable for solving the computer vision and speech design service calculations, such as whether a particular hardware configuration is required or optional, has better resolution, or a different light sensor dynamic range. In some situations, it may be determined by the designers that an additional sensor is needed, or perhaps stereo imagery processing.

In some embodiments, a synthetic video scene, which is a virtual rendering of a scene, may not be based on imagery of an actual physical set-up, but may instead be created using three-dimensional (3D) rendering techniques. Applicable 3D rendering techniques may include a content repository (for virtual objects), a device and sensor model library, a procedural environment, scene interaction controls, a lighting model, a physics simulation, and animating motions. A synthetic video may be generated by defining one or more virtual sensor platforms that consists of various sensor(s), inertial measuring unit(s) (IMUs), lenses, processors, etc.; specifying the movement of the sensor; specifying the objects in the room; and integrating with various algorithms (physics, scene interactions, animation, etc.). Additionally, a defined synthetic scene present virtualized objects that are viewed by synthetic sensors undergoing virtual movement to create a set of imagery that may be mined for data useable not only for localization, but for definition of a derived synthetic scene. Just as a real-world camera may be moved around a room by users, capturing video of a room to generate data for a synthesized version of that room, a synthetic camera placed virtually into a synthetic scene can generate an equivalent data set. Thus, a cost-effective end-to-end computer vision and speech design service is disclosed that may be leveraged for rapidly optimizing both localization, object recognition, object tracking, object reconstruction, and/or speech recognition algorithms and hardware configurations. For example, in comparison with a manual build-and-test process, a synthetic design solution may reduce development timelines from months to days, and potentially cut some development costs by orders of magnitude.

<FIG> is a block diagram of an example computing device <NUM> for implementing aspects disclosed herein, and is designated generally as computing device <NUM>. Computing device <NUM> is but one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing device <NUM> be interpreted as having any dependency or requirement relating to any one or combination of components/modules illustrated.

The examples and embodiments disclosed herein may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program components, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program components including routines, programs, objects, components, data structures, and the like, refer to code that performs particular tasks, or implement particular abstract data types. The discloses examples may be practiced in a variety of system configurations, including personal computers, laptops, smart phones, mobile tablets, hand-held devices, consumer electronics, specialty computing devices, etc. The disclosed examples may also be practiced in distributed computing environments, such as those disclosed in <FIG> described in more detail below, where tasks are performed by remote-processing devices that are linked through a communications network.

Computing device <NUM> includes a bus <NUM> that directly or indirectly couples the following devices: computer-storage memory <NUM>, one or more processors <NUM>, one or more presentation components <NUM>, input/output (I/O) ports <NUM>, I/O components <NUM>, a power supply <NUM>, and a network component <NUM>. Computer device <NUM> should not be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. While computer device <NUM> is depicted as a seemingly single device, multiple computing devices <NUM> may work together and share the depicted device resources. For instance, computer-storage memory <NUM> may be distributed across multiple devices, processor(s) <NUM> may provide housed on different devices, and so on.

Bus <NUM> represents what may be one or more busses (such as an address bus, data bus, or a combination thereof). Although the various blocks of <FIG> are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. Such is the nature of the art, and reiterate that the diagram of <FIG> is merely illustrative of an exemplary computing device that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as "workstation," "server," "laptop," "hand-held device," etc., as all are contemplated within the scope of <FIG> and the references herein to a "computing device.

Computer-storage memory <NUM> may take the form of the computer-storage media references below and operatively provide storage of computer-readable instructions, data structures, program modules and other data for the computing device <NUM>. For example, computer-storage memory <NUM> may store an operating system, a universal application platform, or other program modules and program data. Computer-storage memory <NUM> may be used to store and access instructions configured to carry out the various operations disclosed herein.

As mentioned below, computer-storage memory <NUM> may include computer-storage media in the form of volatile and/or nonvolatile memory, removable or non-removable memory, data disks in virtual environments, or a combination thereof. And computer-storage memory <NUM> may include any quantity of memory associated with or accessible by the display device <NUM>. The memory <NUM> may be internal to the display device <NUM> (as shown in <FIG>), external to the display device <NUM> (not shown), or both (not shown). Examples of memory <NUM> in include, without limitation, random access memory (RAM); read only memory (ROM); electronically erasable programmable read only memory (EEPROM); flash memory or other memory technologies; CDROM, digital versatile disks (DVDs) or other optical or holographic media; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; memory wired into an analog computing device; or any other medium for encoding desired information and for access by the display device <NUM>. Additionally or alternatively, the computer-storage memory <NUM> may be distributed across multiple display devices <NUM>, e.g., in a virtualized environment in which instruction processing is carried out on multiple devices <NUM>. For the purposes of this disclosure, "computer storage media," "computer-storage memory," "memory," and "memory devices" are synonymous terms for the computer-storage media <NUM>, and none of these terms include carrier waves or propagating signaling.

Processor(s) <NUM> may include any quantity of processing units that read data from various entities, such as memory <NUM> or I/O components <NUM>. Specifically, processor(s) <NUM> are programmed to execute computer-executable instructions for implementing aspects of the disclosure. The instructions may be performed by the processor, by multiple processors within the computing device <NUM>, or by a processor external to the client computing device <NUM>. In some examples, the processor(s) <NUM> are programmed to execute instructions such as those illustrated in the flowcharts discussed below and depicted in the accompanying drawings. Moreover, in some examples, the processor(s) <NUM> represent an implementation of analog techniques to perform the operations described herein. For example, the operations may be performed by an analog client computing device <NUM> and/or a digital client computing device <NUM>.

Presentation component(s) <NUM> present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. One skilled in the art will understand and appreciate that computer data may be presented in a number of ways, such as visually in a graphical user interface (GUI), audibly through speakers, wirelessly between computing devices <NUM>, across a wired connection, or in other ways.

Ports <NUM> allow computing device <NUM> to be logically coupled to other devices including I/O components <NUM>, some of which may be built in. Examples I/O components <NUM> include, for example but without limitation, a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc..

The computing device <NUM> may operate in a networked environment via the network component <NUM> using logical connections to one or more remote computers, such as those shown in <FIG> and <FIG>. In some examples, the network component <NUM> includes a network interface card and/or computer-executable instructions (e.g., a driver) for operating the network interface card. Communication between the computing device <NUM> and other devices may occur using any protocol or mechanism over any wired or wireless connection. In some examples, the network component <NUM> is operable to communicate data over public, private, or hybrid (public and private) using a transfer protocol, between devices wirelessly using short range communication technologies (e.g., near-field communication (NFC), BLUETOOTH branded communications, or the like), or a combination thereof.

Turning now to <FIG>, an exemplary block diagram illustrates a cloud-computing environment for an end-to-end computer vision and speech design service. Cloud environment <NUM> illustrates an exemplary cloud-computing infrastructure, suitable for use in implementing aspects of this disclosure. Cloud environment <NUM> should not be interpreted as having any dependency or requirement related to any single component or combination of components illustrated therein. In addition, any number of nodes, virtual machines, data centers, role instances, or combinations thereof may be employed to achieve the desired functionality within the scope of embodiments of the present disclosure.

The distributed computing environment of <FIG> includes a public network <NUM>, a private network <NUM>, and a dedicated network <NUM>. Public network <NUM> may be a public cloud-based network of computing resources, for example. Private network <NUM> may be a private enterprise network or private cloud-based network of computing resources. And dedicated network <NUM> may be a third-party network or dedicated cloud-based network of computing resources. In some examples, private network <NUM> may host a customer data center <NUM>, and dedicated network <NUM> may host cloud synthetics services <NUM>, which are discussed in more detail below relative to <FIG>.

Hybrid cloud <NUM> may include any combination of public network <NUM>, private network <NUM>, and dedicated network <NUM>. For example, dedicated network <NUM> may be optional, with hybrid cloud <NUM> comprised of public network <NUM> and private network <NUM>. Along these lines, some customers may opt to only host a portion of their customer data center <NUM> in the public network <NUM> and/or dedicated network <NUM>, retaining some of the customers' data or hosting of customer services in the private network <NUM>. For example, a customer that manages healthcare data or stock brokerage accounts may elect or be required to maintain various controls over the dissemination of healthcare or account data stored in its data center or the applications processing such data (e.g., software for reading radiology scans, trading stocks, etc.). Myriad other scenarios exist whereby customers may desire or need to keep certain portions of data centers under the customers' own management. Thus, in some examples, customer data centers may use a hybrid cloud <NUM> in which some data storage and processing is performed in the public network <NUM> while other data storage and processing is performed in the dedicated network <NUM>.

Public network <NUM> may include data centers configured to host and support operations, including tasks of a distributed application, according to the fabric controller <NUM>. It will be understood and appreciated that data center <NUM> and data center <NUM> shown in <FIG> are merely examples of suitable implementations for accommodating one or more distributed applications, and are not intended to suggest any limitation as to the scope of use or functionality of examples disclosed herein. Neither should data center <NUM> and data center <NUM> be interpreted as having any dependency or requirement related to any single resource, combination of resources, combination of servers (e.g., servers <NUM> and <NUM>) combination of nodes (e.g., nodes <NUM> and <NUM>), or a set of application programming interfaces (APIs) to access the resources, servers, and/or nodes.

Data center <NUM> illustrates a data center comprising a plurality of servers, such as servers <NUM> and <NUM>. A fabric controller <NUM> is responsible for automatically managing the servers <NUM> and <NUM> and distributing tasks and other resources within the data center <NUM>. By way of example, the fabric controller <NUM> may rely on a service model (e.g., designed by a customer that owns the distributed application) to provide guidance on how, where, and when to configure server <NUM> and how, where, and when to place application <NUM> and application <NUM> thereon. One or more role instances of a distributed application, may be placed on one or more of the servers <NUM> and <NUM> of data center <NUM>, where the one or more role instances may represent the portions of software, component programs, or instances of roles that participate in the distributed application. In other examples, one or more of the role instances may represent stored data that are accessible to the distributed application.

Data center <NUM> illustrates a data center comprising a plurality of nodes, such as node <NUM> and node <NUM>. One or more virtual machines may run on nodes of data center <NUM>, such as virtual machine <NUM> of node <NUM> for example. Although <FIG> depicts a single virtual node on a single node of data center <NUM>, any number of virtual nodes may be implemented on any number of nodes of the data center in accordance with illustrative embodiments of the disclosure. Generally, virtual machine <NUM> is allocated to role instances of a distributed application, or service application, based on demands (e.g., amount of processing load) placed on the distributed application. As used herein, the phrase "virtual machine" is not meant to be limiting, and may refer to any software, application, operating system, or program that is executed by a processing unit to underlie the functionality of the role instances allocated thereto. Further, the virtual machine(s) <NUM> may include processing capacity, storage locations, and other assets within the data center <NUM> to properly support the allocated role instances.

In operation, the virtual machines are dynamically assigned resources on a first node and second node of the data center, and endpoints (e.g., the role instances) are dynamically placed on the virtual machines to satisfy the current processing load. In one instance, a fabric controller <NUM> is responsible for automatically managing the virtual machines running on the nodes of data center <NUM> and for placing the role instances and other resources (e.g., software components) within the data center <NUM>. By way of example, the fabric controller <NUM> may rely on a service model (e.g., designed by a customer that owns the service application) to provide guidance on how, where, and when to configure the virtual machines, such as virtual machine <NUM>, and how, where, and when to place the role instances thereon.

As discussed above, the virtual machines may be dynamically established and configured within one or more nodes of a data center. As illustrated herein, node <NUM> and node <NUM> may be any form of computing devices, such as, for example, a personal computer, a desktop computer, a laptop computer, a mobile device, a consumer electronic device, a server, the computing device <NUM> of <FIG>, and the like. In one instance, the nodes <NUM> and <NUM> host and support the operations of the virtual machine(s) <NUM>, while simultaneously hosting other virtual machines carved out for supporting other tenants of the data center <NUM>, such as internal services <NUM> and hosted services <NUM>. Often, the role instances may include endpoints of distinct service applications owned by different customers.

Typically, each of the nodes include, or is linked to, some form of a computing unit (e.g., central processing unit, microprocessor, etc.) to support operations of the component(s) running thereon. As utilized herein, the phrase "computing unit" generally refers to a dedicated computing device with processing power and storage memory, which supports operating software that underlies the execution of software, applications, and computer programs thereon. In one instance, the computing unit is configured with tangible hardware elements, or machines, that are integral, or operably coupled, to the nodes to enable each device to perform a variety of processes and operations. In another instance, the computing unit may encompass a processor (not shown) coupled to the computer-readable medium (e.g., computer storage media and communication media) accommodated by each of the nodes.

The role of instances that reside on the nodes may be to support operation of service applications, and thus they may be interconnected via APIs. In one instance, one or more of these interconnections may be established via a network cloud, such as public network <NUM>. The network cloud serves to interconnect resources, such as the role instances, which may be distributed across various physical hosts, such as nodes <NUM> and <NUM>. In addition, the network cloud facilitates communication over channels connecting the role instances of the service applications running in the data center <NUM>. By way of example, the network cloud may include, without limitation, one or more communication networks, such as local area networks (LANs) and/or wide area networks (WANs). Such communication networks are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet, and therefore need not be discussed at length herein.

<FIG> is a block diagram of an example computing environment <NUM> that may be implemented as a real-world device or synthesized using some of the various examples disclosed herein. That is, the computing device <NUM> may represent a real-world device that is designed using an end-to-end computer vision and speech design service, or may represent a synthetic version used as a test candidate for data generation. Computing device <NUM> represents any device executing instructions (e.g., as application programs, operating system functionality, or both) to implement the operations and functionality as described herein. Computing device <NUM> may include a mobile computing device or any other portable device. In some examples, a mobile computing device includes a mobile telephone, laptop, tablet, computing pad, netbook, gaming device, wearable device, head mounted display (HMD) and/or portable media player. Computing device <NUM> may also represent less portable devices such as desktop personal computers, kiosks, tabletop devices, industrial control devices, wireless charging stations, electric automobile charging stations, and other physical objects embedded with computing resources and/or network connectivity capabilities. Additionally, computing device <NUM> may represent a group of processing units or other computing devices.

The computing device <NUM> has at least one processor <NUM>, a memory area <NUM>, and at least one user interface. These may be the same or similar to processor(s) <NUM> and memory <NUM> of <FIG>, respectively. Processor <NUM> includes any quantity of processing units, and is programmed to execute computer-executable instructions for implementing aspects of the disclosure. The instructions may be performed by the processor or by multiple processors within the computing device, or performed by a processor external to the computing device. In some examples, processor <NUM> is programmed to execute instructions such as those that may be illustrated in the others figures.

In some examples, processor <NUM> represents an implementation of analog techniques to perform the operations described herein. For example, the operations may be performed by an analog computing device and/or a digital computing device.

Computing device <NUM> further has one or more computer readable media such as the memory area <NUM>. Memory area <NUM> includes any quantity of media associated with or accessible by the computing device. Memory area <NUM> may be internal to computing device <NUM> (as shown in <FIG>), external to the computing device (not shown), or both (not shown). In some examples, memory area <NUM> includes read-only memory and/or memory wired into an analog computing device.

Memory area <NUM> stores, among other data, one or more applications or algorithms <NUM> that include both data and executable instructions <NUM>. The applications, when executed by the processor, operate to perform functionality on the computing device. Exemplary applications include computer vision and speech applications having computer vision and speech algorithms for identifying the coordinates of computing device <NUM>. The applications may communicate with counterpart applications or services such as web services accessible via a network, such as communications network <NUM>. For example, the applications may represent downloaded client-side applications that correspond to server-side services executing in a cloud. In some examples, applications generated may be configured to communicate with data sources and other computing resources in a cloud during runtime, or may share and/or aggregate data between client-side services and cloud services. Memory area <NUM> may store data sources <NUM>, which may represent data stored locally at memory area <NUM>, data access points stored locally at memory area <NUM> and associated with data stored remote from computing device <NUM>, or any combination of local and remote data.

The user interface component <NUM>, may include instructions executed by processor <NUM> of computing device <NUM>, and cause the processor <NUM> to perform operations, including to receive user selections during user interaction with universal application platform <NUM>, for example. Portions of user interface component <NUM> may thus reside within memory area <NUM>. In some examples, user interface component <NUM> includes a graphics card for displaying data to a user <NUM> and receiving data from user <NUM>. User interface component <NUM> may also include computer-executable instructions (e.g., a driver) for operating the graphics card. Further, user interface component <NUM> may include a display (e.g., a touch screen display or natural user interface) and/or computer-executable instructions (e.g., a driver) for operating the display. In some examples the display may be a 3D display, such as may be found in an HMD. User interface component <NUM> may also include one or more of the following to provide data to the user or receive data from the user: a keyboard (physical or touchscreen display), speakers, a sound card, a camera, a microphone, a vibration motor, one or more accelerometers, a BLUETOOTH brand communication module, global positioning system (GPS) hardware, and a photoreceptive light sensor. For example, the user may input commands or manipulate data by moving the computing device in a particular way. In another example, the user may input commands or manipulate data by providing a gesture detectable by the user interface component, such as a touch or tap of a touch screen display or natural user interface. In still other examples, a user, such as user <NUM>, may interact with a separate user device <NUM>, which may control or be controlled by computing device <NUM> over communications network <NUM>, a wireless connection, or a wired connection.

As illustrated, computing device <NUM> further includes a camera <NUM> (though other types of sensors may be used), which may represent a single camera, a stereo camera set, a set of differently-facing cameras, or another configuration. Computing device <NUM> may also further include an IMU <NUM> that may incorporate one or more of an accelerometer, a gyroscope, and/or a magnetometer. The accelerometer gyroscope, and/or a magnetometer may each output measurements in 3D. The combination of 3D position and 3D rotation may be referred to as six degrees-of-freedom (6DoF), and a combination of 3D accelerometer and 3D gyroscope data may permit 6DoF measurements. In general, linear accelerometer data may be the most accurate of the data from a typical IMU, whereas magnetometer data may be the least accurate.

Also illustrated, computing device <NUM> additionally includes a generic sensor <NUM> and a transceiver <NUM>. In some embodiments, transceiver <NUM> is an antenna capable of transmitting and receiving radio frequency ("RF") or other wireless signals over the network <NUM>. One skilled in the art will appreciate and understand that various antennae and corresponding chipsets may be used to provide communicative capabilities between the display device <NUM> and other remote devices. Examples are not limited to RF signaling, however, as various other communication modalities may alternatively be used. The computing device <NUM> may communicate over a network. Examples of computer networks <NUM> include, without limitation, a wireless network, landline, cable line, fiber-optic line, local area network (LAN), wide area network (WAN), or the like, and such networks may comprise subsystems that transfer data between servers or computing devices <NUM>. For example, the network <NUM> may also include a point-to-point connection, the Internet, an Ethernet, a backplane bus, an electrical bus, a neural network, or other internal system.

Generic sensor <NUM> may include an infrared (IR) sensor, a light detection and ranging (LIDAR) sensor, an RGB-D sensor, an ultrasonic sensor, or any other sensor, including sensors associated with position-finding and range-finding. Transceiver <NUM> may include BLUETOOTH, Wi-Fi, cellular, or any other radio or wireless system. Transceiver <NUM> may act as a sensor by detecting signal strength, direction-of-arrival and location-related identification data in received signals. Together, one or more of camera <NUM>, IMU <NUM>, generic sensor <NUM>, and transceiver <NUM> may collect data (either real-world, or synthetics may virtually collect) for use in computer vision and speech algorithms.

<FIG> is a block diagram of a computer vision and speech design service <NUM> that is suitable for implementing some of the various examples disclosed herein. Computer vision and speech design service <NUM> includes a sensor platform simulator <NUM> that may implement a synthetics service for improving computer vision through simulated hardware optimization. Sensor platform simulator <NUM> may include a pipeline for simulating performance of sensor platforms (such as a synthetic version of computing device <NUM> of <FIG>). Sensor platform simulator <NUM> may be used for simulating one or more hardware configurations comprising one or more virtual sensors.

Computer vision and speech design service <NUM> additionally operates four other illustrated modules, although it should be understood that various functionality described as being included within one module may actually be spread among multiple modules. The illustrated modules include a motion orchestrator <NUM>, an environment orchestrator <NUM>, and experiment generator <NUM>, an experiment runner <NUM>, and a computer vision and speech application evaluator <NUM>.

In some embodiments, motion orchestrator module <NUM> permits users of computer vision and speech design service <NUM> to model motion that is relevant for testing computer vision and speech sensor platforms and algorithms by expressing targeted motion profiles. Motion orchestrator <NUM> may be used for simulating motion of the one or more simulated hardware configurations within one or more virtual environments. Examples may include creating instances of multiple random walks through a virtual scene or room, having various durations, speeds, and motion pathways.

Environment orchestrator <NUM> is used for simulating one or more virtual environments. In some embodiments, environment orchestrator <NUM> permits users to manipulate synthetic environments, such as light settings and the state of certain objects, such as doors. Additionally or alternatively, environment orchestrator <NUM> defines the dimensions, objects, lighting, spacing, or other attributes of a room in scene and the contents therein.

Experiment generator <NUM> converts high level parameters into multiple instances of complete experiments, which define sets of motions and environments to be used in data generation. Experiment generator <NUM> may be used for generating synthetic experiment data for the one or more simulated hardware configurations having the simulated motion within the one or more virtual environments. That is, experiment generator <NUM> generates a plurality of candidate computer vision and speech solutions having differing hardware configurations to be tested or computer vision and speech algorithm parameters that may be tuned. In some embodiments, experiment runner <NUM> provides a framework for scheduling, monitoring, managing, and reviewing results of batches of experiments used in data generation.

Experiment runner <NUM> may be used for iterating the experiment generator to generate the synthetic experiment data for one or more combinations of hardware configurations, virtual environment, and motion. This iterative processing may be done using different computer vision and speech algorithms accessible to the computer vision and speech design service <NUM> via the cloud environment <NUM>. For example, the virtual hardware configurations in the synthetic environment may be independently processed with <NUM>, <NUM>, or <NUM> different computer vision and speech algorithms to test how well those algorithms model the computer vision and speech given the hardware configuration. In this vein, experiment generator calculates computer vision and speech data for the one or more hardware configurations in the synthetic experiment-with or without the simulated motion-using the various computer vision and speech algorithms to produce computer vision and speech algorithm output data that indicates computer vision and speech parameters (e.g., coordinates, relative location information, disambiguation, object size, object direction, object acceleration, speech, audio, object assembly, dynamic and properties of object, and the like) for the simulated hardware configuration in the synthetic environment. Such computer vision and speech algorithm output data for the different computer vision and speech algorithms may comprise synthetic images and/or IMU data (which may comprise accelerometer data and/or gyroscope data and/or magnetometer data) for the virtualized hardware being tested.

In some embodiments, computer vision and speech application evaluator <NUM> computer vision and speech algorithm output data to GT input data for the virtualized hardware configuration (with or without simulated motion) to determine how effectively the various computer vision and speech algorithms are performing. Such determinations of the effectiveness of the various computer vision and speech algorithms may be determined by comparing the variance of the computer vision and speech algorithm output data to GT data for virtual hardware configuration in the synthetic environment, computer vision and speech design algorithms identified as being the closest or within a range of closeness (e.g., within X percent, Y virtual distance, or a combination thereof) to the GT for the virtual hardware configuration may be identified as more accurately computing computer vision and speech parameters and stored accordingly. Other computer vision and speech algorithms may be determined to be less accurate and/or needing additional configuring (e.g., needing more programming or analyzing more images, synthetic scenes, motion, and/or hardware configurations). For these lesser-accurate computer vision and speech applications, disparity data of a simulated virtual hardware configuration compared with its ground truth may be fed back to improve the performance of such computer vision and speech applications. For instance, a computer vision and speech application that produced computer vision and speech algorithm output data comprising computer vision and speech results that exceeded a certain variance threshold (e.g., more than X percentage away from the GT of a simulated hardware configuration) away from the underlying GT may be used by an artificial intelligence (AI) application running in the cloud environment <NUM> to optimize the deficient computer vision and speech application by running more testing against other synthetic scenes, motions, and hardware configurations until the computer vision and speech application performs within the variance threshold. Such AI processing and machine-learning may use the computer vision and speech results described herein to improve computer vision and speech algorithms without user intervention.

Operation of computer vision and speech design service <NUM> includes multiple processes that use the various modules illustrated. A user may set up devices, environments, and motion engines using sensor platform simulator <NUM>. These parameters define the target device, scenes and environments, and the types of motions that will be used for the design process. A user may activate motion orchestrator module <NUM> to design how the motion engine can be manipulated for a particular type of experiment and also activate environment orchestrator module <NUM> to design how the environment engine can be manipulated for a particular type of experiment. For example, a researcher/engineer may be interested in the re-localization portion of the environment and may thus generate thousands of <NUM>-second tests, all within the same area of a single modeled room, where the internal conditions of the room are varied systematically, such as moving by the furniture or varying lighting conditions.

A user may activate experiment generator module <NUM> to generate sets of experiments that accrue to a particular aspect of the computer vision and speech design problem. Experiments may fall into a variety of categories, such as general testing, research and development, or stress testing. General testing often aims to generate the minimal required representative dataset for base coverage of the computer vision and speech unit under test. Research and development experiments may be nuanced, attempting target minimal or maximal lighting conditions that the device supports or to discover specific product or algorithm bugs. Stress testing tends to target the broadest set of experiments that the device may encounter, in addition to simulated long running or erratic usage patterns. In general, a user may leverage experiment generator <NUM> to parameterize the underlying frameworks and generate workloads for fully automated simulation and analysis of computer vision and speech algorithms. The user may then use experiment runner <NUM> to schedule, kick-off, manage, and monitor the various jobs that were designed with experiment generator <NUM>. An optional evaluator (see <FIG>) may be used for comparing the calculated computer vision and speech data with ground truth data.

<FIG> is an illustration of an architecture <NUM> for creating synthetic imagery, according to some of the various examples disclosed herein. In architecture <NUM> several inputs, including an artist workflow <NUM>, an asset management <NUM>, and other workflows (a scripted workflow 506a, a guided workflow 506b, and a custom workflow 506c) interface via a synthetics API <NUM> to a synthetics service <NUM>. Synthetics service <NUM> has multiple components or modules, including a renderer <NUM>, a sensor modeler <NUM>, a motion module <NUM>, a scene generation module <NUM>, and a scheduler <NUM>. External functionality is illustrated as a physics service <NUM> and other external support <NUM>, which may include off-loaded rendering computations. It should be understood that different functionalities may be internal or external services, and that <FIG> is only used for illustrative purposes. Together, the various functionality is able to intake virtual objects (assets), lighting models, orchestrated motion, camera and other sensor positions, to render synthetic (virtual) scene imagery.

The generated synthetic imagery, scene data and other associated data may then be archived in a storage medium <NUM> for use in the described virtual experimentation. As illustrated, various data sets are stored, including the scene data <NUM>, device data <NUM>, motion data <NUM>, asset data <NUM>, and results <NUM>. The scene data <NUM>, device data <NUM>, motion data <NUM>, asset data <NUM>, and results <NUM> enable the evaluation of the quality of a computer vision and speech algorithm and its associated parameters when provided to the evaluator <NUM> (described in relation to <FIG>). Furthermore, the stored results can inform experiment generator <NUM> (of <FIG>) as to which experiment to run next to further analyze the quality of one or more computer vision and speech algorithms.

<FIG> is another a block diagram of a networked environment <NUM> for a computer vision and speech design service suitable to provide computer vision and speech services for simulated hardware configurations in a synthetic scene. As shown, environmental orchestrator <NUM> is read from a scripted set of pre-configured environment data <NUM>, or programmatically-generated environment data <NUM>, for instance created by an algorithm or script that assists in automating test scenario generation. Various lighting conditions may be simulated that can affect image collection, and a single scene or multiple scenes can be used. Sensor platform simulator <NUM> may be operated similarly, with a user <NUM>, read from a scripted set of pre-configured simulation data <NUM>, or programmatically-generated simulation data <NUM>. Sensor platform simulator <NUM> simulates sensor data, including images, IMU readings, wireless signals and data (Wi-Fi, GPS), and depth measurements (from RGB-D, LIDAR and time-of-flight cameras).

Motion orchestrator <NUM> simulates the trajectory and orientation of the synthetic device under test (DUT). Environmental orchestrator <NUM> may be operated similarly, with a user <NUM>, read from a scripted set of pre-configured simulation data <NUM>, or programmatically-generated simulation data <NUM>. The motion is modeled or recorded from actual real-world devices, to provide typical movement scenarios for VR devices, including HMDs. Both IMU data and data from other 6DoF device sensors may be captured, perhaps by a user walking around a real-world scene, that is later modeled, and pointing the cameras or other sensors at various points of interest.

A simulator <NUM>, perhaps similar to architecture <NUM> of <FIG> takes the input data and produces a set of images <NUM> that will be used by computer vision and speech algorithms to attempt to calculate the 3D coordinates of the synthetic DUT relative to the synthetic scene. Simulator <NUM> also outputs IMU data <NUM>, illustrated as plots of accelerometer (linear displacement) data, gyroscope (rotational) data, and magnetometer data, all with 3D components. It should be understood that multiple sets of data, representing multiple positions, orientations, and velocities of one or more synthetic DUTs passing through a synthetic scene may be generated by some embodiments.

In some embodiments, a 3D computer vision and speech engine <NUM> intakes a particular computer vision and speech algorithm for algorithm database <NUM> and calculates 3D coordinates of the synthetic DUT. In some embodiments, the output of 3D computer vision and speech engine <NUM> is one or more 3D position and rotation data sets <NUM>. 3D computer vision and speech engine <NUM> may use a single computer vision and speech algorithm or multiple different algorithms. The illustrated environment <NUM> thus has the ability to conduct tests with multiple scenarios: (<NUM>) a single hardware configuration or multiple hardware configurations, each with (<NUM>) a single scene or multiple scenes; each with (<NUM>) a single motion profile or multiple motion profiles; each with (<NUM>) a single computer vision and speech algorithm or multiple different computer vision and speech algorithms. Thus, there are at least <NUM> different classes of experiments with this configuration, identified by whether a particular variable is held constant or varied.

Once the computer vision and speech data set (3D position and rotation data set <NUM>) is calculated, the computer vision and speech data sets are evaluated by an evaluator <NUM> for accuracy and precision. Evaluator <NUM> may be used for comparing the calculated computer vision and speech data with ground truth data. Thus, the calculated values must be compared against the correct values. Because environment orchestrator <NUM> and motion orchestrator <NUM> produce the output for simulator <NUM> to use, the information about where the synthetic DUT is within the synthetic environment is available for use in the evaluation process. This known data, actual position of DUT and objects) is collectively known as the ground truth (GT) <NUM>. The 3D position and rotation data set <NUM> is compared with GT <NUM> to output an error profile or score <NUM>, which may be used by researchers or hardware designers to select a computer vision and speech solution (hardware configuration and algorithm).

<FIG> is a block diagram of a process flow <NUM> for a computer vision and speech design service suitable for implementing some of the various examples disclosed herein. Process flow <NUM> provides an alternate visualization of both block diagram <NUM> (of <FIG>) and flow chart <NUM> (of <FIG>). GT data <NUM> is fed into a simulation <NUM>, which takes as input specific configuration data <NUM>, including scene configuration, hardware configuration, and hardware location/motion. Simulation <NUM> outputs data sets <NUM>, including synthetic imagery and simulated IMU readings, to an algorithm application <NUM>. Algorithm application <NUM> tests various computer vision and speech algorithms from a library <NUM> of computer vision and speech algorithms that take the image and IMU data to calculate the virtual position of the synthetic hardware device. The calculations are computer vision and speech result sets <NUM> that are then compared by a computer vision and speech application evaluator <NUM> with GT data <NUM>, to produce evaluation results <NUM>. Evaluation results <NUM> may be used by hardware designers to select a particular hardware configuration and computer vision and speech algorithm.

Alternatively or additionally, evaluation results <NUM> may be supplied, or fed back, to the algorithm application <NUM> for improving the computer vision and speech algorithms in the library <NUM>. As previously mentioned, computer vision and speech algorithms identified as being the closest or within a range of closeness (e.g., within X percent, Y virtual distance, or a combination thereof) to the GT data <NUM> for the virtual hardware configuration may be identified as more accurately computing computer vision and speech parameters and stored accordingly. Other computer vision and speech algorithms may be determined to be less accurate and/or needing additional configuring (e.g., needing more programming or analyzing more images, synthetic scenes, motion, and/or hardware configurations). For these lesser-accurate computer vision and speech applications, disparity data of the simulated virtual hardware configuration compared with its GT data <NUM> may be fed back to the algorithm application <NUM> to improve the performance of such computer vision and speech applications. In some embodiments, the algorithm application <NUM> uses AI processing or machine-learning to improve the computer vision and speech applications in the library <NUM> based on-or triggered by-the evaluation results <NUM>. For example, computer vision and speech applications that did not produce evaluation results <NUM> within a specific variance threshold (e.g., proximity to a ground truth) may be marked for additional testing or simulations or identified as faulty.

Testing and comparing results of the various computer vision and speech algorithms using the GT data <NUM> the data sets <NUM> enables the disclosed embodiments to intelligently and more efficiently improve computer vision and speech algorithms that traditionally needed to be evaluated one at a time. This ultimately leads to a more efficient way to design and test sets of computer vision and speech algorithms because the disclosed embodiment provide a feedback mechanism where simulated data for hardware configurations in synthetic environments may be used to identify the best performing computer vision and speech algorithms while improving others.

<FIG> is a flow chart <NUM> illustrating an example work flow of a computer vision and speech design service suitable for implementing some of the various examples disclosed herein. Flow chart <NUM> starts with step <NUM> importing one or more device configuration specifications and creating digital models. As indicated, some number, K, of different device specifications may be imported. In some embodiments, step <NUM> may use sensor platform simulator <NUM> of <FIG> and <FIG>. Step <NUM> generates the environment, consisting of GT data, such as one or more scenes to be used in the simulations, along with extended operating conditions (EOCs), which are variations of a scene, including variations such as lighting and object positions. Some number, L, of different scenes and EOCs may be created for use with the simulation experiments. Generating synthetic experiment data comprises simulating, for one or more hardware configurations sensor data that can be supplied to a computer vision and speech algorithm. In some embodiments, step <NUM> may use environment orchestrator <NUM> of <FIG> and <FIG>.

In step <NUM>, some number, M, of different device positions and orientations are generated. The number M may be large. In some embodiments, step <NUM> may use motion orchestrator <NUM> of <FIG> and <FIG>. It should be understood that the processes may be implemented in various optional orders, for example motion orchestration and environment orchestration may be performed separately, as different processes, or together as a combined process.

Experimental data is generated for each combination of K, L, and M, for which an experiment is to be run, in step <NUM>. The experiment generator (See <NUM> of <FIG>) generates a plurality of candidate computer vision and speech solutions having differing hardware configurations to be tested or computer vision and speech algorithm parameters to be tuned. In step <NUM> a computer vision and speech algorithm, one of some number N, out of a set of algorithms is applied to the generated synthetic data, to obtain results <NUM> for the combination of device specification K, scene and EOC L, motion vector M, and algorithm N. In some embodiments, steps <NUM>-<NUM> may use experiment generator <NUM> of <FIG>.

In steps 814a-814d, various ones of steps <NUM>-<NUM> are iterated to generate the desired set of synthetic experiment data for one or more combinations of hardware configurations, virtual environment, motion and computer vision and speech algorithms. Step 814a iterates on various different candidate computer vision and speech algorithms; step 814b iterates on various different motion profiles; step 814c iterates on various different environments and EOCs; and step 814d iterates on various different candidate hardware configurations. As illustrated, there are <NUM> different possible iteration scenarios (<NUM>^<NUM>) for generating the synthetic experiment data. Although the iteration controls steps 814a-814d are illustrated in a given order, it should be understood that the illustration is merely exemplary of a possible embodiment, and the iteration may occur in any order or combination.

In some embodiments steps 814a-814d may use experiment runner <NUM> of <FIG>. Experimental data is evaluated in step <NUM>, for example by comparing computer vision and speech algorithm output data with GT data, and a recommendation step <NUM> may provide a recommended best performing configuration or data from which a designer may select an adequately performing configuration. In this manner hardware configurations may be selected from a set of candidate hardware configurations, computer vision and speech algorithms may be selected from a set of candidate algorithms, or computer vision and speech algorithm parameters may be fine-tuned for optimal performance.

While the aspects of the disclosure have been described in terms of various examples with their associated operations, a person skilled in the art would appreciate that a combination of operations from any number of different examples is also within scope of the claims.

By way of example and not limitation, computer readable media comprise computer storage media and communication media. Computer storage media include volatile and nonvolatile, removable and non-removable memory implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or the like. Computer storage media are tangible and mutually exclusive to communication media. Computer storage media are implemented in hardware and exclude carrier waves and propagated signals. Computer storage media for purposes of this disclosure are not signals per se. Exemplary computer storage media include hard disks, flash drives, solid-state memory, phase change random-access memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media typically embody computer readable instructions, data structures, program modules, or the like in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media.

The examples illustrated and described herein, as well as examples not specifically described herein but within the scope of the claims, constitute exemplary means for providing an end-to-end computer vision and speech design service in a cloud computing environment. For example, the elements described in <FIG>, such as when encoded to perform the operations illustrated in <FIG>, constitute exemplary means for providing an end-to-end computer vision and speech design service, one that saves developers a considerable amount of time and processing resources by simulated virtual configurations of real hardware in a synthetic environment to determine whether computer vision and speech parameters may be accurately generated.

The order of execution or performance of the operations in examples of the disclosure illustrated and described herein is not essential, and may be performed in different sequential manners in various examples.

Claim 1:
A system, using a synthetic world interface to model digital environments comprising:
memory embodied with executable instructions for simulating the two or more hardware configurations comprising one or more virtual sensors (<NUM>) from sensor data;
at least one processor programmed for:
simulating one or more virtual environments (<NUM>) from a scripted set of pre-configured environment data or a programmatically-generated environment;
simulating motion of the two or more simulated hardware configurations within the one or more virtual environments (<NUM>) based on real world device data, wherein the two or more simulated hardware configurations comprise a first hardware configuration with different hardware than a second hardware configuration and wherein the first hardware configuration and the second hardware configuration are based on one or more factors including a lens quality, a light sensor resolution, and a dynamic range in lighting levels;
generating synthetic experiment data for the two or more simulated hardware configurations having the simulated motion within the one or more virtual environments (<NUM>), wherein the two or more simulated hardware configuration are based on the one or more factors;
iterating the synthetic experiment data for the first and second hardware configurations with the simulated motion in the one or more virtual environments (<NUM>) for the motion and computer vision or speech algorithms (814a);
simulating motion of the two or more hardware configurations with the simulated motion in the one or more virtual environments; and
applying one or more of computer vision algorithms or speech algorithms to the simulated motion of the two or more hardware configurations with the simulated motion in the one or more virtual environments to determine one of the two or more hardware configurations as a computer vision solution for output;
wherein the first hardware configuration is a first camera and the second hardware configuration is a second camera that is different than the first camera and wherein at least one of the first and second hardware configurations include a speech sensor.