Patent Application
20250032349
The following patent applications are expressly incorporated herein by reference in their entireties:
The present invention is in the field of computer control systems, and more specifically the field of control systems for sexual stimulation devices.
In the field of sexual stimulation devices, control systems are rudimentary, and primarily limited to pre-programmed, selectable stimulation routines. Where customization is possible, it is available only through manual programming of the device. Control systems requiring manipulation of physical or touch-screen controls can be cumbersome or distracting.
What is needed is voice-based control of sexual stimulation devices.
Accordingly, the inventor has conceived, and reduced to practice, a system and method for voice-based control of sexual stimulation devices. In an embodiment, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In an embodiment, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some embodiments, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some embodiments, machine learning algorithms are used to make the associations. In some embodiments, data from other biometric sensors is included in the associations.
The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way.
The inventor has conceived, and reduced to practice, a system and method for voice-based control of sexual stimulation devices. In an embodiment, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In an embodiment, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some embodiments, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some embodiments, machine learning algorithms are used to make the associations. In some embodiments, data from other biometric sensors is included in the associations.
This automated generation of control signals from historical usage and other data, and evolution of the control signals over time, acts as a sort of “autopilot” for sexual stimulation devices such that a priori programming or manual programming of the devices is either not required at all or is minimal in nature. The device can simply be turned on and stimulation will be automatically customized to the user's preferences with little or no input on the user's part.
One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements.
Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way.
Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.
A description of an aspect with several components in communication with each other docs not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.
When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article.
The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself.
Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.
The annotations may be used directly to generate device control signals 1205, such as real-time use wherein the device control signals are generated 1205 immediately or very soon after the annotations are created, or delayed use by storing the annotations for later use 1202 and generating device control signals 1205 from the stored annotations. In this use, the annotations will typically be used to generate control signals for a particular video for which the annotations were made. A single such annotation may be used or some combination of annotations for the same video (e.g., averaging of multiple annotations).
Alternatively, the annotations may be processed through machine learning algorithms to create models of movement patterns and sequences commonly associated with certain videos, or certain sexual activities, persons, etc. In this use, annotations from a plurality of different videos will typically be used. The annotations are processed through a first set of machine learning algorithms to detect and analyze movement patterns typical of certain sexual activities 1203. This first set of machine learning algorithms may use techniques such as clustering to group together similar types of movement patterns. The movement pattern data are then processed through a second set of machine learning algorithms to determine sequencing information 1204 such as how long a pattern is typically held and the probabilities of changing to different patterns after the current pattern. The sequencing information is used to create predictive models of typical or expected sequences of movement patterns, which mimic frequently-seen depictions of sexual activity in the annotated data. The data from these models may then be used to generate device control signals 1205 representing movement patterns and sequences in common sexual activities.
In this exemplary embodiment, a clip parser 1401 parses (i.e., breaks breaks or segments) a video into smaller clips to reduce the scale of the video processing by the machine learning algorithms (i.e., reduces the video to more easily manageable smaller clips of a larger video). Depending on the size of the video, available processing power, and the machine learning algorithm to be used, the clip parser 1401 may reduce the video to any size ranging from the entire video to frame-by-frame clips of the video. Where a video is annotated with known activities (e.g., where the video or segments of the video have been annotated with an indication of the type of activity that is contained therein), the clip parser 1401 may parse the video into clips corresponding to the length of the known activity, as indicated by the annotations. In such cases, the clip parser 1401 forwards the clips of known activity directly to an action detector 1402. Where the video contains depictions of unknown activities, the clip parser will parse the video into uniform sizes (e.g., frame-by-frame, or a certain number of frames representing several seconds or minutes of video), and send the video to an action classifier 1403, which classifies the activities in the video before sending them an known activities to the action detector 1402.
The action classifier 1403 comprises one or more machine learning algorithms that have been trained to classify human actions. Classification of human action is a simpler activity than human action detection. Human action classification involves identification of human objects in the video and some classification of the activity being demonstrated by the human objects (e.g., standing, walking, running, jumping, etc.). Classification does not require a determination of when the action starts, where in the frame the action occurs, or the relative motion of the action; it simply requires that an object in the video be recognized as a person and that the activity of that person be identified.
The action detector 1402 received videos of known sexual activity (i.e., those that have already been classified either manually or using machine learning algorithms), and detects when the action starts, where in the frame the action occurs, or the relative motion of the action. Because the activity in the video is already known, machine learning algorithms may be employed which have been specially-trained for the type of activity depicted in the video. Action detection involves first segmenting the video into objects and backgrounds, identifying human objects in each frame of video, and tracking the movement of those human objects across video frames.
Both action classification and action detection rely on color-based processing of pixels in each frame of the video. Most videos currently available, whether or not depicting sexual activity, are two-dimensional (2D) videos containing color information only (e.g., the RGB color model), from which depth information must be inferred. The additional of depth sensors allows the addition of depth information to the video data (e.g., RGBD color/depth model), which improves human pose estimation but requires specialized sensors that must be used at the time of filming. Due to the processing-intensive nature of analyzing videos using machine learning algorithms, some simplification techniques may be used to reduce the computing power required and/or speed up the processing time. For example, facial recognition algorithms have become widely used, fairly accurate, and can be implemented on computing devices with modest processing power. Thus, for videos where fellatio is known to be the primary sexual activity, facial recognition algorithms may be used as the machine learning component to track the relative position and orientation of the face in the video to indicate the movement component of sexual activity. This greatly reduces the amount of computing power required relative to videos containing unknown sexual activity and/or where whole body human activity must be classified and detected. As there is a limited range of possible sexual activity, and certain sexual activities are more common than others, specially-trained machine learning algorithms can be employed for given types of sexual activity to improve action classification and action detection times and accuracy.
For both action classification and action detection, a variety of machine learning algorithms may be used. For example, as noted above, a convolutional neural network (CNN) may be applied to perform segmentation of each video frame. Other machine learning algorithms or combinations of machine learning algorithms may be employed. For example, a CNN may be employed to extract the features in the video, followed by a long short-term memory (LSTM) algorithm to evaluate the temporal relationships between features. In another example, a three-dimensional CNN (3D CNN) may be employed which can directly create hierarchical representations of spatial and temporal relationships, thus obviating the need to processing through an LSTM. In another example, a two-stream CNN may be used, wherein the first stream of input into the CNN is a set of temporal relationships that are established by a pre-determined set of features, and the second stream is frames from the video. Action classification and/or action detection can be performed by averaging the predictions of the CNN, or by using the output of the CNN for each frame of the video as input to a 3D CNN. Many other variations are possible, and while CNNs are particularly suitable for video processing, other types of machine learning algorithms may be employed.
The clip annotator 1404 associates each video clip with action detection data synchronized with the playback times (or frames) of the video clip, and the clip re-integrator 1405 combines the clips back into the original video received by the clip parser 1401. The annotated video, or just the annotations data from the video, may then be used to generate device control data or may be further processed to extract models of typical sexual activity prior to generating device control data.
Convolutional neural networks are a type of artificial neural network commonly used to analyze imagery that use a mathematical operation called convolution (also called a dot product or cross-correlation) instead of general matrix multiplication as in other types of artificial neural networks. Convolutional neural networks are fully connected, meaning that each node in one layer is connected to every node in the next layer. Each layer of the CNN convolves the input from the previous layer. Each convolutional node processes data only for its receptive field, which is typically a small sub-area of the image (e.g., a 5×5 square of pixels). There may be pooling layers in a CNN which reduce the dimensionality of the data by combining the outputs of node clusters in one layer into a single node in the next layer. Each node in a CNN computes an output value by applying a specific function to the input values coming from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias. The CNN “learns” by making iterative adjustments to these biases and weights.
In this application of CNNs, an input image 1601 is processed through a CNN in which there are two stages, a convolution stage 1602 and a de-convolution stage 1603, ultimately resulting in an output image 1604 in which objects in the image are segmented (i.e., identified as separate from) the background of the image. In the convolution stage 1602, the image is processed through multiple convolution layers to extract features from the image, and then through a pooling layer to reduce the dimensionality of the data (i.e., aggregation of pixels) for the next round of convolutions. After several rounds of convolution and pooling, the features have been extracted and the data have been reduced to a manageable size. The data are then passed to the de-convolution stage 1603, in which a prediction is made as to whether each pixel or group of pixels represents an object, and passed through several layers of de-convolution before a new prediction is made at a larger level of de-aggregation of the pixels. This process repeats until an output image 1604 is obtained of a similar size as the input image 1601, wherein each pixel of the output image 1604 is labeled with an indication as to whether it represents an object or background.
To process annotation data to develop models, patterns of movement will ideally be extracted from a larger number of videos. When a machine learning algorithm is fed the annotation data from many such videos, these patterns can be identified across the various videos, and the frequency of these patterns across all videos can be extracted, as shown in the bar chart at 1720. In this bar chart 1720, one hundred total hours of video time was processed through the machine learning algorithm, and the number of hours each pattern of movement 1711-1715 was displayed is shown. For example, Pattern 4 was displayed in a total of 40 hours out of the 100 total hours of video. Machine learning algorithms suitable for this identification of patterns across videos are clustering-type algorithms such as K-means clustering (also known as Lloyd's algorithm), in which movement patterns in the annotation data are clustered into groups containing similar movement patterns. From the clusters, certain types of movement patterns can be identified. For example, in the case of a video depicting fellatio, clusters of movement will show shallow motions around the tip of the penis (e.g., Pattern 41714), deep motions around the base of the penis (e.g., Pattern 1), movements along the full length of the penis (e.g., Pattern 3), etc. Such clusters may be visually mapped in 2D or 3D to confirm the consistency and accuracy of the clustering.
Finally, other types of machine learning algorithms may be employed to create models of sexual activity shown in the processed annotation data. In one method, reinforcement learning may be employed to identify the frequency counts of certain patterns of movement, create “states” representing these patterns, and probabilities of transferring from any given state to any other state. An example of such a state diagram is shown at 1730, wherein each state represents one of the patterns of movement 1711-1715, and the lines and percentages indicate the probability of transitioning to a different state. In the diagram at 1730, Pattern 51715 is shown as the current state, and probabilities of all possible transitions to and from the current state are shown. In practice, this state diagram 1730 would be expanded to include the probabilities to and from each state to every other state, but this diagram is simplified to show only transitions to and from the current state. From these state transition probabilities, sequences of movement patterns 1711-1715 may be constructed representing models of the “typical” activities shown in the video. If annotation data are processed for selected types of videos (e.g., videos containing certain types of sexual activity, certain actors or actresses, or videos from a certain film studio or director, etc.), the models will be representative of that selected type of video. Alternatively, a wide variety of deep learning algorithms may be used for this process including, but not limited to, dense neural networks, convolutional neural networks, generative adversarial networks, and recurrent neural networks. Each of these types of machine learning algorithms may be employed to identify sequences of the patterns of movement identified in the clustering at the previous stage.
The server may be a network-connected, cloud-based, or local server 1910, and comprises a database 1911 for storage of usage data comprising user profiles, user/device feedback, and user/device settings, and a machine learning algorithm 1912 for analysis of the data stored in the database 1911 for generation of automated control signals or instructions. The machine learning algorithm 1912 is trained on the data to identify patterns within the usage data wherein certain characteristics of user profiles are correlated with satisfaction or dissatisfaction with certain aspects of stimulation profiles such as tempo, location, intensity, pressure, and patterns. The usage data may contain user profiles comprising personal information about the user such as age, sex, height, weight, and fitness level; sexual preferences such as straight, gay, bi-sexual, etc.; stimulation preferences such as stimulation tempo/speed, stimulation intensity, location of stimulation, patterns of stimulation; and feedback information such as user ratings, heartrate data from sensors, moisture data from sensors, etc. After training, when a user profile (or one or more characteristics from a user profile) is input into the machine learning algorithm 1912, the machine learning algorithm 1912 generates one or more stimulation profiles (comprising one or more stimulation aspects such as tempo/speed, stimulation intensity, location of stimulation, patterns of stimulation) that correspond with satisfaction based on the characteristics of the user profile input and outputs control signals (or instructions for generating control signals) for stimulation profiles that correspond with satisfaction based on the characteristics of the user profile input. The machine learning algorithm 1912 may periodically or continuously be re-trained based on new data from the client application 1920 (such as, but not limited to, feedback and other changes to the user's profile) and the data from other users and devices 1940 being similarly stored and processed. It should be noted that, while a machine learning algorithm is used in embodiment, the system is not necessarily limited to use of machine learning algorithms and other processes for analysis of the data may be used, including but not limited to modeling and statistical calculations.
The system of this embodiment further comprises a client application 1920, which is a software application operating on a computing device, which may be of any type including but not limited to a desktop computer, tablet, mobile phone, or even a cloud-based server accessible via a web browser. The client application 1920 acts as an interface between the stimulation device 1930 and the machine learning algorithm 1912, relaying feedback from the device to the server 1910 and relaying control signals (or translating instructions into control signals) to the device controller 1932 of the stimulation device 1930. The client application may comprise one or more applications such as the auto-pilot application 1921 and the wizard application 1922. Depending on configuration, the client application may further act as a user interface for operation of, and/or changing settings of, the stimulation device 1930.
In this embodiment, the auto-pilot application 1921 automatically controls the stimulation device 1930 for the user with little or no input from the user. The auto-pilot application stores and retrieves user-specific data for the user of the stimulation device 1930 from a user profile entered into the client application 1920, from sensors on the device (e.g., tumescence sensors, heartrate sensors or heartrate signal receivers, pressure sensors, etc.), and from user interactions with the client application 1920 via a user interface. The data gathered about the user may include such as, but not limited to, where the user prefers to be stimulated, what tempo or speed of stimulation the user prefers, what stimulation patterns the user prefers, and general preferences such as quick stimulation to orgasm, delayed orgasms, multiple edging before orgasm, etc.
The auto-pilot application 1921 provides the user-specific data to the server 1910 and requests control signals (or instructions for control signals) for a stimulation profile that is customized to the user based on the user data. The user-specific data is processed through the trained machine learning algorithm 1912, which selects appropriate stimulation routines and provides control signals or instructions back to the client application for operation of the stimulation device 1930. In some embodiments the control signals or instructions may be sent directly from the machine learning algorithm 1912 directly to the device controller 1932 of the stimulation device 1930. The client application 1920 may be configured to periodically or continuously send updated user-specific data to the server 1910 for processing by the machine learning algorithm 1912 to generate modified or updated control signals or instructions, thus changing and evolving the automated operation of the device based on changed or updated information from the device sensors 1931, client application 1920, or updating/retraining of the machine learning algorithm 1912 based on this user's data and the data from other users and devices 1940 being similarly stored and processed.
In this embodiment, the set-up wizard application 1922 builds an initial personalized stimulation profile from a series of ratings by the user of test stimulations. Completion of the set-up wizard application 1922 process accelerates customization of a stimulation profile for the user by providing a base set of ratings of various aspects of stimulation which can then be processed through the trained machine learning algorithm 1912 to automatically control the stimulation device 1930, as further shown in
In some embodiments, the client application 1920 may exist as an application on a user's mobile phone, and may interface with the stimulation device 1930 via a local network (e.g., WiFi, Bluetooth, etc.). In other embodiments, the client application 1920 may exist as an application on the server 1920 accessible via a user account also residing on the server. In other embodiments, certain components of the server 1910 and client application 1920 may reside on tablet computer or other mobile device, or on the stimulation device 1930 itself (e.g., a copy of the trained machine learning algorithm could reside on a smartphone such that automated generation of control signals can be accomplished without access to the server). In some embodiments, the client application 1920 and/or server components will be integrated into the stimulation device 1930 (e.g., stored in a memory and operable on the device controller 1932) instead of residing on a separate computing device.
The stimulation device 1930 may be any device configured to provide sexual stimulation by any variety of means, including but not limited to, linear stroking, vibration, rotation, heat, electrical stimulation, or combinations of the above. Device sensors 1931 may be any sensor on the device capable of providing data regarding an aspect of sexual arousal, including but not limited to, heartrate sensors, moisture sensors, tumescence sensors, pressure sensors, strain gauges, and length/distance sensors. Further, the device sensors 1931 include devices capable of receiving sensor data from external sensors (e.g., wearable fitness devices that record heart rates) via WiFi, Bluetooth, or other networking technologies. The device controller 1932 is a device capable of operating the stimulation device based on control signals received. The device controller 1932 may be a simple power relay switching device that receives low-powered signals and outputs corresponding power to motors, vibrators, etc., or may be a computing device with a memory, processor, and storage. In the latter case, the device controller 1932 may be configured to receive instructions to generate control signals and generate the control signals, itself. Further, in some embodiments, aspects of the client application and/or machine learning algorithm 1912 may be incorporated into the device controller 1932.
In this embodiment, the set-up wizard application 1922 process has two stages, an analysis stage and a testing stage. At the analysis stage 2010 stimulation selections are made from a set of pre-programmed aspects such as tempo, location, and pattern, and the user's ratings for each selection are used by the machine learning algorithm 1912 to generate a stimulation routine comprising one or more tempos, locations, and patterns of stimulation. At the testing stage 2020, stimulation is performed using the generated stimulation routine, and the generated stimulation routine is refined through ratings by the user and, optionally, introduction of variations deemed likely to improve those ratings. Optionally, the generated stimulation routine may be displayed on a user interface such as that shown in
In this exemplary process, the process begins at the analysis stage 2010 with the system's selection of one or more tempos of stimulation 2011 from a set of pre-programmed (or randomly chosen) and user ratings 2012 for each selected tempo. On each attempt, the tempo is changed and a new rating is obtained. For example, if the system selects a slow tempo, and the user gives it a low rating, the system may select a faster tempo for the next selection and rating. Once a tempo, or range of tempos, is established, the system goes through the same process for location 2013 and user ratings associated with location 2014 using that tempo, and again with patterns of stimulation 2015 and user ratings 2016 based around the established tempo and established location. For a device capable of producing linear stroking motions, the patterns of stimulation may include, but are not limited to, variations in the established tempo, variations in the established location, stopping or starting of stimulation at various timings, and stimulation outside of the established tempo and established location for a period of time before returning to them. The user's ratings of the tempo, location, and patterns of stimulation are processed through the machine learning algorithm 1912 to generate one or more test stimulation routines 2017 for testing. At the testing stage 2020, a routine is selected 2021 from the one or more test stimulation routines 2017 and rated by the user 2022. This process may be repeated for several test stimulation routines 2017. In some cases (for example when only a single test stimulation routine is generated or where the test routines are all rated poorly by the user), the system may introduce variations in one or more of the test routines 2023 in an attempt to increase the user's rating 2024 of that test routine. The variations come from any number of sources, including but not limited to, a list of known variations, variations generated by the machine learning algorithm 1912, and random variation. Once the testing stage 2020 is completed, one or more preferred stimulation routines are stored, along with the analysis and testing data for future use 2025.
In this example, it is assumed that the current stimulation routine is being displayed on a mobile phone or tablet device with a touch screen, although the system is not so limited. In this screenshot, a tempo selector 2110 is shown with an arrow indicating the current tempo of stimulation on a range from minimum to maximum. The tempo arrow can be moved by the user to override the tempo setting of the current stimulation routine, and the override information will be forwarded to the client application or server 1910 for adjustment of the current stimulation routine and evolution of the user's stimulation preferences over time. A location selector 2120 is shown with an slider 2121 indicating the current location of stimulation (here on a device that provides stimulation using a reciprocal linear motion). The slider 2121 can be moved by the user to override the location setting of the current stimulation routine, and the override information will be forwarded to the client application or server 1910 for adjustment of the current stimulation routine and evolution of the user's stimulation preferences over time. At the location indicated by the slider 2121, a power selector 2130 displays the current power setting for that location and allows the user to adjust the power setting for that location, and a pattern selector 2140 displays the current pattern setting for that location and allows the user to adjust the pattern setting for that location. A different position of the slider is shown at 2150, along with the power selector 2130 and pattern selector 2140 for that different location. A rating bar 2160 is shown at the bottom of the screen, allowing the user to input a rating for the current stimulation.
The server may be a network-connected, cloud-based, or local server 2210, and comprises a database 2211 for storage of user data comprising EEG brain activity patterns and control setting associations 2211, and a machine learning algorithm 2212 for analysis of the data stored in the database 2211 for generation of thought-based control signals or instructions. The machine learning algorithm 2212 is trained on the data to identify patterns within the usage data wherein certain EEG patterns are correlated with stimulation device controls and/or biometric sensor data. The user data may further contain user profiles comprising personal information about the user such as age, sex, height, weight, and fitness level; sexual preferences such as straight, gay, bi-sexual, etc.; stimulation preferences such as stimulation tempo/speed, stimulation intensity, location of stimulation, patterns of stimulation; and feedback information such as user ratings, other biometric sensor data such as heartrate data from sensors, moisture data from sensors, etc; all of which may be incorporated by the machine learning algorithm to better correlate EEG patterns with stimulation device controls for specific users. After training, when an EEG pattern from the EEG headset is input into the machine learning algorithm 2212, the machine learning algorithm 2212 generates one or more control signals or instructions for the stimulation device 2230 based on the associations between EEG patterns and control settings learned by the machine learning algorithm during training. The machine learning algorithm 2212 may periodically or continuously be re-trained based on new data from the electroencephalograph (EEG) training and control application 2300 (such as, but not limited to, new training data acquired as a result of additional EEG training by the user) and the data from other users and EEG devices 2240 being similarly stored and processed. It should be noted that, while a machine learning algorithm is used in embodiment, the system is not necessarily limited to use of machine learning algorithms and other processes for analysis of the data may be used, including but not limited to modeling and statistical calculations. For example, in some embodiments, the machine learning aspect may be bypassed altogether, having the system rely only on EEG pattern/control signal associations from the user-specific training conducted by the EEG training & control application 2300. In other embodiments, a two-stage training algorithm may be used wherein the machine learning algorithm is first trained generically on a large number of users, then re-trained for a particular user using user-specific training data. In some embodiments, control signals for the stimulation device may be based on a combination of non-machine learning algorithm EEG pattern/control signal associations and machine learning algorithm EEG pattern/control signal associations.
The system of this embodiment further comprises a electroencephalograph (EEG) training and control application 2300, which is a software application operating on a computing device, which may be of any type including but not limited to a desktop computer, tablet, mobile phone, or even a cloud-based server accessible via a web browser. The electroencephalograph (EEG) training and control application 2300 acts as an interface between the stimulation device 2230, the machine learning algorithm 2212, and the EEG headset 2500 and other biometric sensors 2222, as well as operating to train the system to make associations between EEG patterns and control signals for a particular user or users. In its role as an interface, the EEG training and control application 2300 relays feedback from the device to the server 2210 and relays control signals (or translates instructions into control signals) to the device controller 2232 of the stimulation device 2230. Details regarding the architecture and operation of the EEG training and control application 2300 are further described below. Depending on configuration, the electroencephalograph (EEG) training and control application 2300 may further act as a user interface for operation of, and/or changing settings of, the stimulation device 2230. In its role as an EEG training application, the EEG training and control application 2300 assigns training tasks to the user, receives EEG signal data comprising measurements of electrical activity in parts of the user's brain from the EEG headset 2500, and associates patterns of EEG signal data with objectives of the training tasks (e.g., think about moving an on-screen control downward, corresponding to a reduction in the speed or intensity of operation of the stimulation device).
In this embodiment, the EEG headset 2500 is worn by a user and sends EEG signal data from electrodes of the EEG headset to the EEG training & control application 2300. The user data may further comprises biometric signals data from other biometric sensors 2222. EEG signal data is a form of biometric data, but other biometric sensors 2222 may be used to provide biometric signal data that is not associated with brain activity, such as external or third-party heartrate monitors that provide heartrate data.
The EEG training and control application 2300 provides the user-specific data comprising EEG patterns, or control associations, or both to the server 2210 and requests control signals (or instructions for control signals) for the stimulation device 2230 based on the user-specific data. During training of the machine learning algorithm, the EEG patterns and control associations are used as a form of labeled training data to train or re-train the machine learning algorithm 2212. After training, the EEG patterns may be processed through the trained machine learning algorithm 2212, which provides control signals or instructions back to the electroencephalograph (EEG) training and control application for operation of the stimulation device 2230. In some embodiments, the EEG patterns are sent to the machine learning algorithm 2212 and processed into control signals in real time or near real time. In some embodiments the control signals or instructions may be sent directly from the machine learning algorithm 2212 directly to the device controller 2232 of the stimulation device 2230. The electroencephalograph (EEG) training and control application 2300 may be configured to periodically or continuously send updated user-specific data to the server 2210 for processing by the machine learning algorithm 2212 to generate modified or updated control signals or instructions, thus changing and evolving the automated operation of the device based on changed or updated information from the device sensors 2231, electroencephalograph (EEG) training and control application 2300, or updating/retraining of the machine learning algorithm 2212 based on the user's data and the data from other users and EEG devices 2240 being similarly stored and processed.
In some embodiments, the electroencephalograph (EEG) training and control application 2300 may exist as an application on a user's mobile phone, and may interface with the stimulation device 2230 via a local network (e.g., WiFi, Bluetooth, etc.). In other embodiments, the electroencephalograph (EEG) training and control application 2300 may exist as an application on the server 2300 accessible via a user account also residing on the server. In other embodiments, certain components of the server 2210 and electroencephalograph (EEG) training and control application 2300 may reside on tablet computer or other mobile device, or on the stimulation device 2230 itself (e.g., a copy of the trained machine learning algorithm could reside on a smartphone such that automated generation of control signals can be accomplished without access to the server). In some embodiments, the electroencephalograph (EEG) training and control application 2300 and/or server components will be integrated into the stimulation device 2230 (e.g., stored in a memory and operable on the device controller 2232) instead of residing on a separate computing device.
The stimulation device 2230 may be any device configured to provide sexual stimulation by any variety of means, including but not limited to, linear stroking, vibration, rotation, heat, electrical stimulation, or combinations of the above. Device sensors 2231 may be any sensor on the device capable of providing data regarding an aspect of sexual arousal, including but not limited to, heartrate sensors, moisture sensors, tumescence sensors, pressure sensors, strain gauges, and length/distance sensors. Further, the device sensors 2231 include devices capable of receiving sensor data from external sensors (e.g., wearable fitness devices that record heart rates) via WiFi, Bluetooth, or other networking technologies. The device controller 2232 is a device capable of operating the stimulation device based on control signals received. The device controller 2232 may be a simple power relay switching device that receives low-powered signals and outputs corresponding power to motors, vibrators, etc., or may be a computing device with a memory, processor, and storage. In the latter case, the device controller 2232 may be configured to receive instructions to generate control signals and generate the control signals, itself. Further, in some embodiments, aspects of the electroencephalograph (EEG) training and control application and/or machine learning algorithm 2212 may be incorporated into the device controller 2232.
Depending on its configuration, the EEG data manager 2301 is responsible for generation of labeled training data to the machine learning algorithm for supervised learning, pass-through of EEG signal data to the machine learning algorithm for unsupervised learning, receipt of control signals from the trained machine learning algorithm based on pass-through of EEG signal data, or generating control signals by direct association of EEG patterns with objectives corresponding to device controls, or any combination of the above. In this embodiment, it is assumed that the EEG data manager is configured to generate EEG pattern/objective pairs cither to directly generate control signals itself, or to pass those EEG pattern/objective pairs to the machine learning algorithm for training. In other configurations, however, the EEG data manager may pass through EEG signal data to the machine learning algorithm for unsupervised learning in which the machine learning algorithm identifies the EEG patterns and makes associations with the objectives. In cases involving complex and/or voluminous data such as detecting patterns in EEG signal data, unsupervised learning is often useful in that it can find hidden or difficult-to-identify patterns in the data that might otherwise be missed.
The EEG data manager 2301 retrieves and implements EEG training tasks from the EEG training task library 2306. The training tasks comprise a stimulus such as auditory, visual cues, or sexual stimulation, an objective such as moving a virtual slider displayed on a screen, and instructions for the user to attempt to achieve the objective using some mental image or thought. For example, a training task may involve displaying a task on a visual display using the graphical display manager, wherein the display shows a vertical sliding controller and the instructions may instruct the user to think about moving the vertical sliding controller upward (representing increased speed or intensity of some aspect of the stimulation device) or downward (representing decreased speed or intensity of some aspect of the stimulation device). While the user is performing the task, the EEG headset 2500 detects electrical signals representing brain activity of the user underneath each electrode and forwards those electrical signals as EEG signal data to the EEG data manager 2301. The EEG data manager 2301 receives EEG signal data from the EEG headset 2500 and identifies a pattern of EEG activity from the EEG signal data. The pattern of EEG activity (aka an EEG pattern) may be a spatial pattern (i.e., differences in electrical signals among electrodes spaced across the user's head), a temporal pattern (i.e., changes in the electrical signal in each electrode over time), or both. The EEG data manager 2301 associates the EEG pattern or patterns with an objective of the task (e.g., moving of the vertical control slider downward), creating EEG pattern/objective pairs that can be used either to generate controls for the stimulation device via a control signal generator 2303 or as labeled training data via a training data labeler 2304. The EEG pattern/objective pairs may be stored in the EEG pattern storage database 2305. In some embodiments, new EEG pattern/objective pairs may be compared with stored EEG pattern/objective pairs to confirm, reject, or modify associations.
In some embodiments, the stimulus for some EEG training tasks may comprise stimulation via the stimulation device as a supplement to auditory or visual tasks, or as an alternative thereto. The EEG data manager 2301 may select one or more stimulation routines from a stimulation routine library 2307, apply the stimulation to the user via the stimulation device 2230, and have the user think about an objective related to the stimulation. For example, the EEG data manager 2301 may initiate stimulation at a low speed or intensity, and ask the user to think about increasing the stimulation speed or intensity. In some cases, the objective may simply be free association of the stimulation with certain of the user's thoughts. Similarly to the EEG training for auditory and visual tasks, the EEG data manager 2301 associates the EEG pattern or patterns with an objective of the stimulation (e.g., increasing the speed or intensity of stimulation), creating EEG pattern/objective pairs that can be used either to generate controls for the stimulation device via a control signal generator 2303 or as labeled training data via a training data labeler 2304. The EEG pattern/objective pairs may be stored in the EEG pattern storage database 2305. In some embodiments, new EEG pattern/objective pairs may be compared with stored EEG pattern/objective pairs to confirm, reject, or modify associations.
In some embodiments, the associations may further incorporate biometric signal data from other biometric sensors 2222, creating more complex associations which may be stored as tables, high dimensional vectors, graphs, or other forms of complex relationship storage. In some cases, the user may provide additional user feedback via the graphical display manager 2302 by interacting with the display. Such user feedback may be, for example, indicating a level of concentration the user was able to apply, a mood of the user, or a tiredness level of the user, which user feedback may be used as additional association information.
The more complex the association data between EEG patterns, tasks, feedback, and stimulation routines, the more useful the machine learning algorithm 2212 is in determining relationships between the input data (e.g., EEG signals, biometric signals, user feedback) and the intended outputs (i.e., control of some aspect of the stimulation device).
Stage 1 of this embodiment comprises training the machine learning algorithm generically (i.e., for a typical, unspecified user) using pre-labeled data from other users 2411 who have performed EEG training tasks using their own EEG devices. This pre-labeled training data does not necessarily have to be in the field of control of sexual stimulation devices, and may be pre-labeled training data from control of other devices or performance of other tasks (e.g., biofeedback relaxation routines, mediation, etc.), as long as there is some association in the pre-labeled data between EEG patterns and some objective that could be translated or applied to control of devices.
Stage 2 of this embodiment comprises user-specific EEG training using visual tasks 2420. A visual EEG training task is selected and displayed on a display of a computing device 2421. The training task comprises visual cues with instructions for the user to associate the visual cues with some mental image or thought. For example, the training task may involve displaying a task on a computer screen or other visual display of a computing device, wherein the display shows a vertical sliding controller and the instructions may instruct the user to think about moving the vertical sliding controller upward (representing increased speed or intensity of some aspect of the stimulation device) or downward (representing decreased speed or intensity of some aspect of the stimulation device). While the user is performing the task, an EEG headset 2500 detects electrical signals representing brain activity of the user underneath each electrode and forwards those electrical signals as EEG signal data, which is received and recorded 2422. The visual display is updated with progress of the user in accomplishing the task (for example, where the user's EEG patterns match expected EEG patterns stored in the EEG pattern storage database 2300) or simply updated with an impression of progress designed to encourage the user to continue exhibiting the same EEG patterns 2423. The EEG patterns are associated with the task objective 2424. The pattern of EEG activity (aka an EEG pattern) may be a spatial pattern (i.e., differences in electrical signals among electrodes spaced across the user's head), a temporal pattern (i.e., changes in the electrical signal in each electrode over time), or both. The EEG data manager 2301 associates the EEG pattern or patterns with an objective of the task (e.g., moving of the vertical control slider downward), creating EEG pattern/objective pairs that can be used either to generate controls for the stimulation device 2425 or as labeled training data for use in training a machine learning algorithm 2440. The EEG pattern/objective pairs may be stored in an EEG pattern storage database 2305. In some embodiments, new EEG pattern/objective pairs may be compared with stored EEG pattern/objective pairs to confirm, reject, or modify associations. The process may be repeated until a desired quantity of data is obtained.
Stage 3 of this embodiment comprises user-specific EEG training using stimulation tasks 2430, comprising stimulation via the stimulation device. A stimulation routine is selected from a stimulation routine library 2307, applied to the user via the stimulation device 2230, and the user is asked to think about an aspect of the stimulation or make some other mental association with the stimulation (e.g., an image, feeling, etc.) 2431. For example, the stimulation may be initiated at a low speed or intensity, and the user may be asked to think about increasing the stimulation speed or intensity. Similarly to the EEG training for visual tasks, the EEG pattern or patterns are associated with an objective of the stimulation (e.g., increasing the speed or intensity of stimulation), creating EEG pattern/objective pairs that can be used either to generate controls for the stimulation device 2425 or as labeled training data for use in training a machine learning algorithm 2440. The EEG pattern/objective pairs may be stored in an EEG pattern storage database 2305. In some embodiments, new EEG pattern/objective pairs may be compared with stored EEG pattern/objective pairs to confirm, reject, or modify associations. The process may be repeated until a desired quantity of data is obtained.
The more complex the association data between EEG patterns, tasks, feedback, and stimulation routines, the more useful the machine learning algorithm 2212 is in determining relationships between the input data (e.g., EEG signals, biometric signals, user feedback) and the intended outputs (i.e., control of some aspect of the stimulation device).
In this embodiment, the EEG headset 2500 comprises a frame 2510, a interface 2520, and a plurality of electrodes 2530. The frame comprises side rails 2511 configured to rest horizontally along the side of the person's head just above the ears, a rear rail 2522 configured to rest horizontally along the back of the person's head, a top rail 2513 configured to rest horizontally along the top of the person's head, and a forehead extension 2514. The electrodes 2530 in this embodiment are all circular electrodes as shown at ref. 2533, but some are shown in oblique perspective 2532 or side perspective 2531 as they progress down the sides of the person's head from the top. The electrodes are configured to be lightly pressed against the person's head while in use, ideally as close to the person's scalp as possible to maximize signal capture. Electrical signals from brain activity received by electrodes are small and will typically be in the 1 microvolt (1 μV) to 10 microvolt (10 μV) range. The electrodes are shown in this diagram in the International 10-20 placement system which is the standardized EEG electrode placement of the International Federation of Clinical Neurophysiology (IFCN). Other electrode placement patterns are possible. Many other arrangements, configurations, materials of the EEG headset are possible, including frameless and controller-less configurations, configurations in which the frame is mesh-based, net-based or strap-based, frameless configurations in which the electrodes are held in place on the head using an adhesive, so long as, when in use, at least one electrode is held on or near the scalp of the person using the EEG headset such that electrical activity in the person's brain underneath the scalp can be received by the electrode and stored or transmitted. In some configurations, the storage and transmission may occur to a computing device on or within the EEG headset, itself.
The interface 2520 is electrically connected to the electrodes, and provides a means for transmission of the electrical signals from the electrodes to other devices. The interface may have a case 2521 containing electronics or may be fully integrated into the frame 2510 of the EEG headset 2500. The interface may contain electronics that receive and convert the signals before transmission (e.g., analog to digital conversion) or may simply pass through the raw electrical signals. The interface may transmit electrical signals via a wired connection 2522 or via a wireless transmitter (not shown).
The lefthand drawing 2610 shows the orientation of the user's head with electrodes 2618a-n placed according to the International 10-20 placement system within the various functional areas 2611-2616. The righthand drawing 2620 shows the same orientation and electrode placement, but illustrates a possible spatial EEG pattern of electrical activity in the user's brain. The darker borders of the electrodes show increased levels of activity in certain areas of the brain such as areas where there is little or no electrical activity 2621, areas where there is low electrical activity 2622, areas where there is a moderate level of electrical activity 2623, and areas where there is a high level of electrical activity 2624. These spatial EEG patterns may be associated with task objectives such as increasing or decreasing the speed or intensity of a controller for a stimulation device. Temporal EEG patterns (i.e., changes in one or more electrodes over time) may also be associated with task objectives.
Server 2710 may be a network-connected, cloud-based, or local server 2710, and comprises a database 2711 for storage of user data comprising voice patterns and control setting associations 2711, and a machine learning algorithm 2712 for analysis of data stored in database 2711 for generation of voice-based control signals or instructions. Machine learning algorithm 2712 is trained on data to identify patterns within usage data wherein certain voice patterns are correlated with stimulation device controls and/or biometric sensor data. User data may further contain user profiles comprising personal information about the user such as age, sex, height, weight, and fitness level; sexual preferences such as straight, gay, bi-sexual, etc.; stimulation preferences such as stimulation tempo/speed, stimulation intensity, location of stimulation, patterns of stimulation; and feedback information such as user ratings, other biometric sensor data such as heartrate data from sensors, moisture data from sensors, etc; all of which may be incorporated by machine learning algorithm 2712 to better correlate voice patterns with stimulation device controls for specific users. After training, when a voice pattern from microphone 2721 is input into machine learning algorithm 2712, machine learning algorithm 2712 generates one or more control signals or instructions for stimulation device 2730 based on associations between voice patterns and control settings learned by machine learning algorithm 2721 during training. The machine learning algorithm 2712 may periodically or continuously be re-trained based on new data from voice training and control application 2800 (such as, but not limited to, new training data acquired as a result of additional voice training by user) and data from other users and voice devices 2740 being similarly stored and processed. It should be noted that, while a machine learning algorithm is used in embodiment, system is not necessarily limited to use of machine learning algorithms and other processes for analysis of data may be used, including but not limited to modeling and statistical calculations. For example, in some embodiments, the machine learning aspect may be bypassed altogether, having system rely only on associations of voice patterns/speech recognition with control signals and/or recognitions by voice training & control application 2800. In other embodiments, a two-stage training algorithm may be used wherein machine learning algorithm 2721 is first trained generically on a large number of users, then re-trained for a particular user using user-specific training data. In some embodiments, control signals for stimulation device 2730 may be based on a combination of non-machine learning algorithm associations of voice patterns/speech recognition with control signals and machine learning algorithm associations of voice patterns/speech recognition with control signals.
The system of this embodiment further comprises a software based voice training and control application 2800 operating on a computing device which may be of any type including but not limited to a desktop computer, tablet, mobile phone, or even a cloud-based server accessible via a web browser. The voice training and control application 2800 acts as an interface between stimulation device 2730, machine learning algorithm 2712, microphone 2721 and other biometric sensors 2727, as well as operating to train system to make associations between voice patterns and control signals for a particular user or users. In its role as an interface, voice training and control application 2800 relays feedback from device to server 2710 and relays control signals (or translates instructions into control signals) to device controller 2732 of stimulation device 2730. Details regarding the architecture and operation of voice training and control application 2800 are further described below. Depending on configuration, voice training and control application 2800 may further act as a user interface for operation of, and/or changing settings of, stimulation device 2730. In its role as a voice training application for machine learning algorithm 2712, voice training and control application 2800 assigns training tasks to user, receives voice signal data from microphone 2721, and associates patterns of voice signal data with objectives of the training tasks (e.g., reduction in the speed or intensity of operation of stimulation device).
In this embodiment, microphone 2721 sends voice signal data to voice training & control application 2800. The user data may further comprise biometric signals data from other biometric sensors 2727. Voice signal data is a form of biometric data, but other biometric sensors 2727 may be used to provide biometric signal data that is not associated with voice signal data, such as external or third-party heartrate monitors that provide heartrate data.
Voice training and control application 2800 provides user-specific data comprising voice patterns/recognized speech, or control associations, or both to server 2710 and requests control signals (or instructions for control signals) for stimulation device 2730 based on user-specific data. During training of machine learning algorithm 2712, voice patterns/recognized speech and control associations are used as a form of labeled training data to train or re-train machine learning algorithm 2712. After training, voice patterns/recognized speech may be processed through trained machine learning algorithm 2712, which provides control signals or instructions back to voice training and control application for operation of stimulation device 2730. In some embodiments, voice patterns/recognized speech are sent to machine learning algorithm 2712 and processed into control signals in real time or near real time. In some embodiments, control signals or instructions may be sent directly from machine learning algorithm 2712 directly to device controller 2732 of stimulation device 2730. Voice training and control application 2800 may be configured to periodically or continuously send updated user-specific data to server 2710 for processing by machine learning algorithm 2712 to generate modified or updated control signals or instructions, thus changing and evolving the automated operation of device based on changed or updated information from device sensors 2731, voice training and control application 2800, or updating/retraining of machine learning algorithm 2712 based on user's data and data from other users and voice devices 2740 being similarly stored and processed.
In some embodiments, voice training and control application 2800 may exist as an application on a user's mobile phone, and may interface with stimulation device 2730 via a local network (e.g., WiFi, Bluetooth, etc.). In other embodiments, voice training and control application 2800 may exist as an application on server 2800 accessible via a user account also residing on server. In other embodiments, certain components of server 2710 and voice training and control application 2800 may reside on tablet computer or other mobile device, or on stimulation device 2730 itself (e.g., a copy of trained machine learning algorithm 2712 could reside on a smartphone such that automated generation of control signals can be accomplished without access to server). In some embodiments, voice training and control application 2800 and/or server components will be integrated into stimulation device 2730 (e.g., stored in a memory and operable on device controller 2732) instead of residing on a separate computing device.
Stimulation device 2730 may be any device configured to provide sexual stimulation by any variety of means, including but not limited to, linear stroking, vibration, rotation, heat, electrical stimulation, or combinations of the above. Device sensors 2731 may be any sensor on device capable of providing data regarding an aspect of sexual arousal, including but not limited to, heartrate sensors, moisture sensors, tumescence sensors, pressure sensors, strain gauges, and length/distance sensors. Further, device sensors 2731 include devices capable of receiving sensor data from external sensors (e.g., wearable fitness devices that record heart rates) via WiFi, Bluetooth, or other networking technologies. Device controller 2732 is a device capable of operating stimulation device based on control signals received. Device controller 2732 may be a simple power relay switching device that receives low-powered signals and outputs corresponding power to motors, vibrators, etc., or may be a computing device with a memory, processor, and storage. In the latter case, device controller 2732 may be configured to receive instructions to generate control signals and generate control signals, itself. Further, in some embodiments, aspects of voice training and control application and/or machine learning algorithm 2712 may be incorporated into device controller 2732.
Depending on its configuration, voice data manager 2801 is responsible for generation of labeled training data to machine learning algorithm 2712 for supervised learning, pass-through of voice signal data to machine learning algorithm 2712 for unsupervised learning, receipt of control signals from trained machine learning algorithm 2712 based on pass-through of voice signal data, or generating control signals by direct association of voice patterns/recognized speech with objectives corresponding to device controls, or any combination of above. In this embodiment, it is assumed that voice data manager 2900 is configured to generate voice pattern (or recognized speech)/objective pairs either to directly generate control signals itself, or to pass those voice pattern (or recognized speech)/objective pairs to machine learning algorithm 2712 for training. In other configurations, however, voice data manager 2900 may pass through voice signal data to machine learning algorithm 2712 for unsupervised learning in which machine learning algorithm 2712 identifies voice patterns (or recognized speech) and makes associations with objectives. In cases involving complex and/or voluminous data such as detecting patterns in voice signal data, unsupervised learning is often useful in that it can find hidden or difficult-to-identify patterns in data that might otherwise be missed.
Voice data manager 2900 retrieves and implements voice training tasks from voice training task library 2806. Training tasks comprise a stimulus such as auditory, visual cues, or sexual stimulation, an objective such as slowing down or speeding up stimulation, and instructions for user to attempt to achieve objective using a voice command or non-speech vocalization. For example, a training task may involve displaying a task on a visual display using graphical display manager, wherein display asks user to say the word “faster” (representing increased speed or intensity of some aspect of stimulation device) or “slower” (representing decreased speed or intensity of some aspect of stimulation device). While user is performing a task, microphone 2721 detects speech and/or non-speech vocalizations of user and forwards them as voice signal data to voice data manager 2801. The voice data manager 2801 receives voice signal data from microphone 2721 and detects speech or identifies a pattern of voice activity from voice signal data. The pattern of voice activity (aka a voice pattern) may be a frequency pattern, an amplitude pattern, some combination of the two, or some derivative of either or the combination (e.g., a pattern discovered by passing the voice signal data through a filter, algorithm, or function such as a Kalman filter or a Fourier transform). The voice data manager 2801 associates voice pattern (or recognized speech) with an objective of task (e.g., reducing the speed of stimulation), creating voice pattern (or recognized speech)/objective pairs that can be used either to generate controls for stimulation device via a control signal generator 2803 or as labeled training data via a training data labeler 2804. The voice pattern (or recognized speech)/objective pairs may be stored in voice pattern storage database 2805. In some embodiments, new voice pattern (or recognized speech)/objective pairs may be compared with stored voice pattern (or recognized speech)/objective pairs to confirm, reject, or modify associations.
In some embodiments, stimulus for some voice training tasks may comprise stimulation via stimulation device as a supplement to auditory or visual tasks, or as an alternative thereto. The voice data manager 2801 may select one or more stimulation routines from a stimulation routine library 2807, apply stimulation to user via stimulation device 2730, and receive non-speech vocalizations related to stimulation from the microphone. For example, voice data manager 2801 may initiate stimulation at a low speed or intensity. Infrequent or low-amplitude non-speech vocalizations may be associated with the low speed or intensity, and higher-amplitude non-speech vocalizations may be associated with a desire to increase speed or intensity. Similarly to voice training for speech, voice data manager 2801 associates voice patterns of non-speech vocalizations with an objective of stimulation (e.g., increasing speed or intensity of stimulation), creating voice pattern/objective pairs that can be used either to generate controls for stimulation device via a control signal generator 2803 or as labeled training data via a training data labeler 2804. The voice pattern/objective pairs may be stored in voice pattern storage database 2805. In some embodiments, new voice pattern/objective pairs may be compared with stored voice pattern/objective pairs to confirm, reject, or modify associations.
In some embodiments, associations may further incorporate biometric signal data from other biometric sensors 2727, creating more complex associations which may be stored as tables, high dimensional vectors, graphs, or other forms of complex relationship storage. In some cases, user may provide additional user feedback via graphical display manager 2802 by interacting with display. Such user feedback may be, for example, indicating a level of concentration user was able to apply, a mood of user, or a tiredness level of user, which user feedback may be used as additional association information.
The more complex association data between voice patterns, tasks, feedback, and stimulation routines, more useful machine learning algorithm 2712 is in determining relationships between input data (e.g., voice signals, biometric signals, user feedback) and intended outputs (i.e., control of some aspect of stimulation device).
Speech detector 2910 comprises an automated speech recognition engine 2911 and a speech quality estimator 2912. Automated speech recognition engine 2911 receives audio (i.e., acoustic sound waves, or sounds, typically from a human voice and comprising speech) from microphone 2921, detects speech within the audio, and matches it with words or phrases associated with control commands. In some configurations, the detected speech is converted directly to control signals without conversion to text. In some configurations, automated speech recognition engine 2911 transcribes the detected speech to text for further analysis. Speech quality estimator 2912 determines the quality of the detected speech for use by the speech analyzer 2920. Non-speech vocalizations (e.g., sighs, grunts, etc.) within the audio do not contain recognizable speech, and are sent directly to the voice characteristic analyzer 2930. The speech quality estimate may determine the quality of the detected speech using audio quality metrics (e.g., total harmonic distortion, signal to noise ratio, output power, frequency response, etc.) or speech characteristics (e.g., percentage of words recognized, number of unrecognizable words, etc.). Speech quality estimates may determine, for example, whether the detected speech is of sufficient quality to be processed by language detector 2921. Automated speech recognition engine 2911 may use a machine learning algorithm to perform automated speech recognition detection and transcription.
Speech analyzer 2920 uses the text and quality estimates from speech detector 2910 to identify control commands, expressions related to control commands, and/or emotions that may be relevant to control commands. Speech analyzer comprises a language detector 2921, a keyword spotter 2922, and an emotion detector 2923. Language detector 2921 may process text to identify a language (e.g., matching words and phrases of the text to a database of words and phrases from a plurality of languages to detect which language is being used in the text) or may process audio to identify acoustic characteristics in the audio that match the acoustic characteristics of certain languages. Language detector 2921 may use a machine learning algorithm to perform the matching and detection. Once a language has been detected, keyword spotter 2922 compares the words in the text against a database of keywords for that language to identify either control commands (e.g., “turn vibration down”) or speech related to control commands (e.g., “slower”). Emotion detector 2923 analyzes words and phrases in the text (e.g., “that feels good”) to determine emotions (e.g., happiness, satisfaction, dissatisfaction, etc.) that may be expressed by the text that are not necessarily control commands, but have some relevance to a control command. Emotion detector 2923 may use a machine learning algorithm to perform emotion detection.
Voice characteristic analyzer 2930 receives audio comprising non-speech vocalizations and the audio of detected speech for purposes of analyzing the voice characteristics of the audio. Voice characteristic analyzer 2930 comprises a voice stress analyzer 2951 and a gender identifier 2952. Voice stress analyzer 2951 analyzes the audio characteristics (pitch, tone, timbre, loudness, etc.) of the vocalizations to determine whether some emotion is being experienced by the person uttering the vocalization. The tonal quality and speech patterns of the human voice change when experiencing emotional situations, whether good or bad. For example, people tend to talk in loud voices when angry and to use shrill or high-pitched voices when feeling scared or panicky. People tend to speak more rapidly when they get excited or nervous, and more slowly and contemplatively when they are calm or being contemplative. Voice stress analyzer 2951 uses these audio characteristics to detect stress (good or bad) in a person's voice. Voice stress analyzer 2951 may use comparative analyses (e.g., comparisons with a database of audio characteristics indicating stress) or may use a machine learning algorithm to perform voice stress analysis. Voice stress can be used to generate control signals. For example, voice stresses indicating excitement or happiness can be used to increase the intensity of stimulation, and voice stresses indicating pain or discomfort can be used to decrease the intensity of stimulation.
Gender identifier 2952 may be used to identify the gender of the speaker. Men's voices are typically lower in pitch than women's voices. Gender identification may help in the voice stress analysis (e.g., to determine whether the high-pitched voices are elevated male voices or normal female voices) and/or to generate control signals (e.g., in a device with multiple stimulation functions wherein certain stimulation functions are intended for male stimulation and certain functions are intended for female stimulation).
Stage 1 of this embodiment comprises training machine learning algorithm generically (i.e., for a typical, unspecified user) using pre-labeled data from other users 3011 who have performed voice training tasks. This pre-labeled training data does not necessarily have to be in field of control of sexual stimulation devices, and may be pre-labeled training data from control of other devices or performance of other tasks (e.g., biofeedback relaxation routines, mediation, etc.), as long as there is some association in pre-labeled data between voice patterns (or recognized speech) and some objective that could be translated or applied to control of devices.
Stage 2 of this embodiment comprises user-specific voice training using visual tasks 3020. A visual voice training task is selected and displayed on a display of a computing device 3021. The training task comprises visual cues with instructions for user to speak commands and/or make non-speech vocalizations associated with certain intended operation of the device (e.g., speeding up and/or slowing down stimulation). While user is performing the task, a microphone 2721 detects the speech and/or non-speech vocalizations of user and forwards them as voice signal data, which is received and recorded 3022. The visual display is updated with progress of user in accomplishing task (for example, where user's voice patterns and/or recognized speech match expected voice patterns and/or recognized speech stored in voice pattern storage database 2800 or simply updated with a notification of progress (e.g., a notification that a command was recognized) 3023. The voice patterns and/or recognized speech are associated with task objective 3024. The pattern of voice activity (aka a voice pattern) may be a frequency pattern, an amplitude pattern, some combination of the two, or some derivative of either or the combination (e.g., a pattern discovered by passing the voice signal data through a filter, algorithm, or function such as a Kalman filter or a Fourier transform). Voice data manager 2801 associates voice pattern (or recognized speech) with an objective of task (e.g., reducing the speed of stimulation), creating voice pattern (or recognized speech)/objective pairs that can be used either to generate controls for stimulation device via a control signal generator 2803 or as labeled training data via a training data labeler 2804. The voice pattern (or recognized speech)/objective pairs may be stored in voice pattern storage database 2805. In some embodiments, new voice pattern (or recognized speech)/objective pairs may be compared with stored voice pattern (or recognized speech)/objective pairs to confirm, reject, or modify associations. The process may be repeated until a desired quantity of data is obtained.
Stage 3 of this embodiment comprises user-specific voice training using stimulation tasks 3030, comprising stimulation via stimulation device. A stimulation routine is selected from a stimulation routine library 2807, applied to user via stimulation device 2730, and the user is asked to make a mental association with the stimulation (e.g., picturing an image in the mind, thinking about a feeling associated with the stimulation, etc.) 3031. Non-speech vocalizations related to stimulation may be received from microphone and recorded 3032. Additional biometric data and/or user feedback may be received and recorded 3033. Machine learning algorithm them associates patters of voice signal data with the stimulation, biometric signal data, and/or user feedback 3034. For example, voice data manager 2801 may initiate stimulation at a low speed or intensity. Infrequent or low-amplitude non-speech vocalizations may be associated with the low speed or intensity, and higher-amplitude non-speech vocalizations may be associated with a desire to increase speed or intensity. Similarly to voice training for visual tasks, voice pattern or patterns are associated with an objective of stimulation (e.g., increasing speed or intensity of stimulation), creating voice pattern/objective pairs that can be used either to generate controls for stimulation device 3025 or as labeled training data for use in training a machine learning algorithm 3040. The voice pattern (or recognized speech)/objective pairs may be stored in voice pattern storage database 2805. In some embodiments, new voice pattern (or recognized speech)/objective pairs may be compared with stored voice pattern (or recognized speech)/objective pairs to confirm, reject, or modify associations. The process may be repeated until a desired quantity of data is obtained.
The more complex association data between voice patterns, tasks, feedback, and stimulation routines, more useful machine learning algorithm 2712 is in determining relationships between input data (e.g., voice signals, biometric signals, user feedback) and the intended outputs (i.e., control of some aspect of stimulation device).
In the spectrogram of the word “up” 3110, there is a diffuse, largely uniform background pattern across all frequencies 3111 with a moderate signal in the 0 to 2.5 kHz frequencies between 0.1 s and 0.2 s 3112.
In the spectrogram of the word “go” 3120, there is a very diffuse, largely uniform background pattern across all frequencies 3121 with a strong signal in the 0 to 2.5 kHz frequencies and a moderate signal in the 2.5 kHz to 8 kHz frequencies between 0.1 s and 0.2 s 3122.
In the spectrogram of the word “yes” 3130, there is a diffuse, largely uniform background pattern across all frequencies 3131 with a strong signal in the 0 to 5 kHz frequencies and a moderate signal in the 5 kHz to 9 kHz frequencies between 0.05 s and 0.15 s 3132, and a moderate signal in the 5 kHz to 9.5 kHz frequencies between 0.15 s and 0.25 s 3133.
In the spectrogram of the word “stop” 3140, there is a very diffuse, largely uniform background pattern across all frequencies 3141 with a moderate signal in the 3 kHz to 9 kHz frequencies between 0.05 s and 0.15 s 3132, and a moderate signal in the 0.5 kHz to 6.5 kHz frequencies between 0.15 s and 0.25 s 3143.
These patterns are recognizable by humans, but it can be hard to distinguish between similar patterns reliably, and recognition is slow. Trained machine learning algorithms are applied to automatically make fine distinctions between similar patterns on a near-real-time basis in audio files and streaming audio.
The exemplary computer system described herein comprises a computing device 10 (further comprising a system bus 11, one or more processors 20, a system memory 30, one or more interfaces 40, one or more non-volatile data storage devices 50), external peripherals and accessories 60, external communication devices 70, remote computing devices 80, and cloud-based services 90.
System bus 11 couples the various system components, coordinating operation of and data transmission between, those various system components. System bus 11 represents one or more of any type or combination of types of wired or wireless bus structures including, but not limited to, memory busses or memory controllers, point-to-point connections, switching fabrics, peripheral busses, accelerated graphics ports, and local busses using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) busses, Micro Channel Architecture (MCA) busses, Enhanced ISA (EISA) busses, Video Electronics Standards Association (VESA) local busses, a Peripheral Component Interconnects (PCI) busses also known as a Mezzanine busses, or any selection of, or combination of, such busses. Depending on the specific physical implementation, one or more of the processors 20, system memory 30 and other components of the computing device 10 can be physically co-located or integrated into a single physical component, such as on a single chip. In such a case, some or all of system bus 11 can be electrical pathways within a single chip structure.
Computing device may further comprise externally-accessible data input and storage devices 12 such as compact disc read-only memory (CD-ROM) drives, digital versatile discs (DVD), or other optical disc storage for reading and/or writing optical discs 62; magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices; or any other medium which can be used to store the desired content and which can be accessed by the computing device 10. Computing device may further comprise externally-accessible data ports or connections 12 such as serial ports, parallel ports, universal serial bus (USB) ports, and infrared ports and/or transmitter/receivers. Computing device may further comprise hardware for wireless communication with external devices such as IEEE 1394 (“Firewire”) interfaces, IEEE 802.11 wireless interfaces, BLUETOOTH® wireless interfaces, and so forth. Such ports and interfaces may be used to connect any number of external peripherals and accessories 60 such as visual displays, monitors, and touch-sensitive screens 61, USB solid state memory data storage drives (commonly known as “flash drives” or “thumb drives”) 63, printers 64, pointers and manipulators such as mice 65, keyboards 66, and other devices 67 such as joysticks and gaming pads, touchpads, additional displays and monitors, and external hard drives (whether solid state or disc-based), microphones, speakers, cameras, and optical scanners.
Processors 20 are logic circuitry capable of receiving programming instructions and processing (or executing) those instructions to perform computer operations such as retrieving data, storing data, and performing mathematical calculations. Processors 20 are not limited by the materials from which they are formed or the processing mechanisms employed therein, but are typically comprised of semiconductor materials into which many transistors are formed together into logic gates on a chip (i.e., an integrated circuit or IC). The term processor includes any device capable of receiving and processing instructions including, but not limited to, processors operating on the basis of quantum computing, optical computing, mechanical computing (e.g., using nanotechnology entities to transfer data), and so forth. Depending on configuration, computing device 10 may comprise more than one processor. For example, computing device 10 may comprise one or more central processing units (CPUs) 21, each of which itself has multiple processors or multiple processing cores, each capable of independently or semi-independently processing programming instructions. Further, computing device 10 may comprise one or more specialized processors such as a graphics processing unit (GPU) 22 configured to accelerate processing of computer graphics and images via a large array of specialized processing cores arranged in parallel.
System memory 30 is processor-accessible data storage in the form of volatile and/or nonvolatile memory. System memory 30 may be either or both of two types: non-volatile memory and volatile memory. Non-volatile memory 30a is not erased when power to the memory is removed, and includes memory types such as read only memory (ROM), electronically-erasable programmable memory (EEPROM), and rewritable solid state memory (commonly known as “flash memory”). Non-volatile memory 30a is typically used for long-term storage of a basic input/output system (BIOS) 31, containing the basic instructions, typically loaded during computer startup, for transfer of information between components within computing device, or a unified extensible firmware interface (UEFI), which is a modern replacement for BIOS that supports larger hard drives, faster boot times, more security features, and provides native support for graphics and mouse cursors. Non-volatile memory 30a may also be used to store firmware comprising a complete operating system 35 and applications 36 for operating computer-controlled devices. The firmware approach is often used for purpose-specific computer-controlled devices such as appliances and Internet-of-Things (IoT) devices where processing power and data storage space is limited. Volatile memory 30b is erased when power to the memory is removed and is typically used for short-term storage of data for processing. Volatile memory 30b includes memory types such as random access memory (RAM), and is normally the primary operating memory into which the operating system 35, applications 36, program modules 37, and application data 38 are loaded for execution by processors 20. Volatile memory 30b is generally faster than non-volatile memory 30a due to its electrical characteristics and is directly accessible to processors 20 for processing of instructions and data storage and retrieval. Volatile memory 30b may comprise one or more smaller cache memories which operate at a higher clock speed and are typically placed on the same IC as the processors to improve performance.
Interfaces 40 may include, but are not limited to, storage media interfaces 41, network interfaces 42, display interfaces 43, and input/output interfaces 44. Storage media interface 41 provides the necessary hardware interface for loading data from non-volatile data storage devices 50 into system memory 30 and storage data from system memory 30 to non-volatile data storage device 50. Network interface 42 provides the necessary hardware interface for computing device 10 to communicate with remote computing devices 80 and cloud-based services 90 via one or more external communication devices 70. Display interface 43 allows for connection of displays 61, monitors, touchscreens, and other visual input/output devices. Display interface 43 may include a graphics card for processing graphics-intensive calculations and for handling demanding display requirements. Typically, a graphics card includes a graphics processing unit (GPU) and video RAM (VRAM) to accelerate display of graphics. One or more input/output (I/O) interfaces 44 provide the necessary support for communications between computing device 10 and any external peripherals and accessories 60. For wireless communications, the necessary radio-frequency hardware and firmware may be connected to I/O interface 44 or may be integrated into I/O interface 44.
Non-volatile data storage devices 50 are typically used for long-term storage of data. Data on non-volatile data storage devices 50 is not erased when power to the non-volatile data storage devices 50 is removed. Non-volatile data storage devices 50 may be implemented using any technology for non-volatile storage of content including, but not limited to, CD-ROM drives, digital versatile discs (DVD), or other optical disc storage; magnetic cassettes, magnetic tape, magnetic disc storage, or other magnetic storage devices; solid state memory technologies such as EEPROM or flash memory; or other memory technology or any other medium which can be used to store data without requiring power to retain the data after it is written. Non-volatile data storage devices 50 may be non-removable from computing device 10 as in the case of internal hard drives, removable from computing device 10 as in the case of external USB hard drives, or a combination thereof, but computing device will typically comprise one or more internal, non-removable hard drives using either magnetic disc or solid state memory technology. Non-volatile data storage devices 50 may store any type of data including, but not limited to, an operating system 51 for providing low-level and mid-level functionality of computing device 10, applications 52 for providing high-level functionality of computing device 10, program modules 53 such as containerized programs or applications, or other modular content or modular programming, application data 54, and databases 55 such as relational databases, non-relational databases, and graph databases.
Applications (also known as computer software or software applications) are sets of programming instructions designed to perform specific tasks or provide specific functionality on a computer or other computing devices. Applications are typically written in high-level programming languages such as C++, Java, and Python, which are then either interpreted at runtime or compiled into low-level, binary, processor-executable instructions operable on processors 20. Applications may be containerized so that they can be run on any computer hardware running any known operating system. Containerization of computer software is a method of packaging and deploying applications along with their operating system dependencies into self-contained, isolated units known as containers. Containers provide a lightweight and consistent runtime environment that allows applications to run reliably across different computer architectures, operating systems, and environments.
The memories and non-volatile data storage devices described herein do not include communication media. Communication media are means of transmission of information such as modulated electromagnetic waves or modulated data signals configured to transmit, not store, information. By way of example, and not limitation, communication media includes wired communications such as sound signals transmitted to a speaker via a speaker wire, and wireless communications such as acoustic waves, radio frequency (RF) transmissions, infrared emissions, and other wireless media.
External communication devices 70 are devices that facilitate communications between computing device and either remote computing devices 80, or cloud-based services 90, or both. External communication devices 70 include, but are not limited to, data modems 71 which facilitate data transmission between computing device and the Internet 75 via a common carrier such as a telephone company or internet service provider (ISP), routers 72 which facilitate data transmission between computing device and other devices, and switches 73 which provide direct data communications between devices on a network. Here, modem 71 is shown connecting computing device 10 to both remote computing devices 80 and cloud-based services 90 via the Internet 75. While modem 71, router 72, and switch 73 are shown here as being connected to network interface 42, many different network configurations using external communication devices 70 are possible. Using external communication devices 70, networks may be configured as local area networks (LANs) for a single location, building, or campus, wide area networks (WANs) comprising data networks that extend over a larger geographical area, and virtual private networks (VPNs) which can be of any size but connect computers via encrypted communications over public networks such as the Internet 75. As just one exemplary network configuration, network interface 42 may be connected to switch 73 which is connected to router 72 which is connected to modem 71 which provides access for computing device 10 to the Internet 75. Further, any combination of wired 77 or wireless 76 communications between and among computing device 10, external communication devices 70, remote computing devices 80, and cloud-based services 90 may be used. Remote computing devices 80, for example, may communicate with computing device through a variety of communication channels 74 such as through switch 73 via a wired 77 connection, through router 72 via a wireless connection 76, or through modem 71 via the Internet 75. Furthermore, while not shown here, other hardware that is specifically designed for servers may be employed. For example, secure socket layer (SSL) acceleration cards can be used to offload SSL encryption computations, and transmission control protocol/internet protocol (TCP/IP) offload hardware and/or packet classifiers on network interfaces 42 may be installed and used at server devices.
In a networked environment, certain components of computing device 10 may be fully or partially implemented on remote computing devices 80 or cloud-based services 90. Data stored in non-volatile data storage device 50 may be received from, shared with, duplicated on, or offloaded to a non-volatile data storage device on one or more remote computing devices 80 or in a cloud computing service 92. Processing by processors 20 may be received from, shared with, duplicated on, or offloaded to processors of one or more remote computing devices 80 or in a distributed computing service 93. By way of example, data may reside on a cloud computing service 92, but may be usable or otherwise accessible for use by computing device 10. Also, certain processing subtasks may be sent to a microservice 91 for processing with the result being transmitted to computing device 10 for incorporation into a larger processing task. Also, while components and processes of the exemplary computer system are illustrated herein as discrete units (e.g., OS 51 being stored on non-volatile data storage device 51 and loaded into system memory 35 for use) such processes and components may reside or be processed at various times in different components of computing device 10, remote computing devices 80, and/or cloud-based services 90.
Remote computing devices 80 are any computing devices not part of computing device 10. Remote computing devices 80 include, but are not limited to, personal computers, server computers, thin clients, thick clients, personal digital assistants (PDAs), mobile telephones, watches, tablet computers, laptop computers, multiprocessor systems, microprocessor based systems, set-top boxes, programmable consumer electronics, video game machines, game consoles, portable or handheld gaming units, network terminals, desktop personal computers (PCs), minicomputers, main frame computers, network nodes, and distributed or multi-processing computer architectures. While remote computing devices 80 are shown for clarity as being separate from cloud-based services 90, cloud-based services 90 are implemented on collections of networked remote computing devices 80.
Cloud-based services 90 are Internet-accessible services implemented on collections of networked remote computing devices 80. Cloud-based services are typically accessed via application programming interfaces (APIs) which are software interfaces which provide access to computing services within the cloud-based service via API calls, which are pre-defined protocols for requesting a computing service and receiving the results of that computing service. While cloud-based services may comprise any type of computer processing or storage, three common categories of cloud-based services 90 are microservices 91, cloud computing services 92, and distributed computing services 93.
Microservices 91 are collections of small, loosely coupled, and independently deployable computing services. Each microservice represents a specific computing functionality and runs as a separate process or container. Microservices promote the decomposition of complex applications into smaller, manageable services that can be developed, deployed, and scaled independently. These services communicate with each other through well-defined application programming interfaces (APIs), typically using lightweight protocols like HTTP or message queues. Microservices 91 can be combined to perform more complex processing tasks.
Cloud computing services 92 are delivery of computing resources and services over the Internet 75 from a remote location. Cloud computing services 92 provide additional computer hardware and storage on as-needed or subscription basis. Cloud computing services 92 can provide large amounts of scalable data storage, access to sophisticated software and powerful server-based processing, or entire computing infrastructures and platforms. For example, cloud computing services can provide virtualized computing resources such as virtual machines, storage, and networks, platforms for developing, running, and managing applications without the complexity of infrastructure management, and complete software applications over the Internet on a subscription basis.
Distributed computing services 93 provide large-scale processing using multiple interconnected computers or nodes to solve computational problems or perform tasks collectively. In distributed computing, the processing and storage capabilities of multiple machines are leveraged to work together as a unified system. Distributed computing services are designed to address problems that cannot be efficiently solved by a single computer or that require large-scale computational power. These services enable parallel processing, fault tolerance, and scalability by distributing tasks across multiple nodes.
Although described above as a physical device, computing device 10 can be a virtual computing device, in which case the functionality of the physical components herein described, such as processors 20, system memory 30, network interfaces 40, and other like components can be provided by computer-executable instructions. Such computer-executable instructions can execute on a single physical computing device, or can be distributed across multiple physical computing devices, including being distributed across multiple physical computing devices in a dynamic manner such that the specific, physical computing devices hosting such computer-executable instructions can dynamically change over time depending upon need and availability. In the situation where computing device 10 is a virtualized device, the underlying physical computing devices hosting such a virtualized computing device can, themselves, comprise physical components analogous to those described above, and operating in a like manner. Furthermore, virtual computing devices can be utilized in multiple layers with one virtual computing device executing within the construct of another virtual computing device. Thus, computing device 10 may be either a physical computing device or a virtualized computing device within which computer-executable instructions can be executed in a manner consistent with their execution by a physical computing device. Similarly, terms referring to physical components of the computing device, as utilized herein, mean either those physical components or virtualizations thereof performing the same or equivalent functions.
The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents.
| Number | Date | Country | |
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| Parent | 18453247 | Aug 2023 | US |
| Child | 18913973 | US | |
| Parent | 18185284 | Mar 2023 | US |
| Child | 18453247 | US | |
| Parent | 18092438 | Jan 2023 | US |
| Child | 18185284 | US | |
| Parent | 17853316 | Jun 2022 | US |
| Child | 18092438 | US | |
| Parent | 17737974 | May 2022 | US |
| Child | 17853316 | US | |
| Parent | 17534155 | Nov 2021 | US |
| Child | 17737974 | US | |
| Parent | 16861014 | Apr 2020 | US |
| Child | 17534155 | US | |
| Parent | 16214030 | Dec 2018 | US |
| Child | 16861014 | US | |
| Parent | 16139550 | Sep 2018 | US |
| Child | 16214030 | US |
