Patent Application
20240282294
The present disclosure relates generally to generative models. More particularly, the present disclosure relates to diffusion models trained to generate audio data based on descriptions of types of audio.
Generative models have proven to be increasingly useful in the field of machine learning. Deep generative models are used across a wide range of domains. Some types of generative models, such as diffusion models, can be trained to generate high-quality outputs in a format different than the format of the input. For example, some diffusion models can be trained to process a textual prompt to generate high quality image outputs.
Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.
One example aspect of the present disclosure is directed to a computing system. The computing system includes one or more processors. The computing system includes a machine-learned generator model trained to generate intermediate representations of textual content. The computing system includes a machine-learned diffusion model trained to generate audio data from intermediate representations of textual content, wherein the audio data is responsive to a query described by the textual content. The computing system includes one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the participant computing device to perform operations. The operations include processing textual content with the machine-learned generator model to generate an intermediate representation of the textual content, wherein the textual content is descriptive of a query that indicates a desired type of audio content. The operations include processing the intermediate representation with the machine-learned diffusion model to obtain audio data, wherein the audio data comprises audio of the desired type of audio content.
Another example aspect of the present disclosure is directed to a computer-implemented method. The method includes generating, by a computing system comprising one or more computing devices, a corpus of textual data with a machine-learned text generation model, wherein corpus of textual data comprises a plurality of sentences, and wherein each sentence is descriptive of a type of audio. The method includes, for each of a plurality of audio recordings, processing, by the computing system, the audio recording with a machine-learned audio classification model to obtain training data comprising the audio recording and one or more sentences of the plurality of sentences closest to the audio recording within a joint audio-text embedding space of the machine-learned audio classification model. The method includes processing, by the computing system, the one or more sentences with a machine-learned generation model to obtain an intermediate representation of the one or more sentences. The method includes processing, by the computing system, the intermediate representation with a machine-learned cascaded diffusion model to obtain audio data. The method includes training, by the computing system, the machine-learned cascaded diffusion model based on a difference between the audio data and the audio recording.
Another example aspect of the present disclosure is directed to one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the participant computing device to perform operations. The operations include processing textual content with a machine-learned generator model to generate an intermediate representation of the textual content, wherein the textual content is descriptive of a query that indicates a desired type of audio content, and wherein the machine-learned generator model is trained to generate intermediate representations of textual content. The operations include processing the intermediate representation with a machine-learned diffusion model to obtain audio data, wherein the audio data comprises audio of the desired type of audio content, and wherein the machine-learned diffusion model is trained to generate audio data from intermediate representations of textual content.
Other aspects of the present disclosure are directed to various systems, apparatuses, non-transitory computer-readable media, user interfaces, and electronic devices.
These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the related principles.
Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended figures, in which:
Reference numerals that are repeated across plural figures are intended to identify the same features in various implementations.
Generally, the present disclosure is directed to diffusion models trained to generate audio data based on descriptions of types of audio. More specifically, generative models with the capability to produce high quality audio from a different type of input, such as a textual prompt, are greatly desired. However, there are number of roadblocks to implementation of such models with a sufficient degree of accuracy. As one example, training a model to generate audio data from a textual prompt requires large quantities of textual descriptions of audio clips, which can be prohibitively difficult to extract (or generate). As another example, the architectures conventionally used for such models have generally failed to produce audio to a sufficient degree of accuracy.
Accordingly, implementations of the present disclosure propose diffusion models for generation of audio data based on descriptive textual prompts. More specifically, a computing system can obtain a large quantity of audio samples and an associated corpus of descriptive textual data. For example, the computing system can extract the audio samples from the audio data of videos hosted by an audiovisual data hosting entity, and the computing system can extract the corresponding corpus of descriptive textual data from the textual content provided by users to describe the respective audio samples (e.g., a music video and the comments provided for the music video by users). The computing system can use the audio samples and corpus of descriptive textual data to train a machine-learned audio classification model using a contrastive loss function (e.g., a joint audio-text embedding model, etc.).
The computing system can leverage the machine-learned audio classification model to train and optimize a model for audio generation. More specifically, the computing system can first generate a corpus of textual data with a machine-learned text generation model (e.g., a large language model (LLM), etc.). The corpus of textual data can include a large quantity of sentences that each describe a type of audio (e.g., “A light EDM drumbeat carries a bass guitar, strings, and a simple piano”). The computing system can also obtain a large quantity of audio recordings. The computing system can evaluate the audio recordings and the corpus of textual data with the machine-learned audio classification model to select the most accurate pairs of audio recordings and descriptive sentences for training data.
For each of the pairs of audio recordings and textual content (i.e., sentences), the computing system can first process the textual content with a machine-learned generation model to obtain an intermediate representation of the textual content (e.g., a latent representation, a low-fidelity audio sample, a spectrogram, etc.). The computing system can then process the intermediate representation with a machine-learned diffusion model to obtain audio data. The computing system can train the machine-learned diffusion model and/or the machine-learned generation model with a loss function that evaluates a difference between the audio data and the audio recording that corresponds to the textual content. In such fashion, implementations of the present disclosure can efficiently and effectively train a series of models for generation of high-quality audio samples conditioned on textual content.
Aspects of the present disclosure provide a number of technical effects and benefits. As one example technical effect and benefit, conventional generative models generally produce low quality audio data from textual prompts, or lack the capacity to produce audio data from textual content at all. Furthermore, the training of such models requires substantial quantities of compute resources (e.g., power, memory, energy, compute cycles, bandwidth, etc.). However, implementations of the present disclosure provide the capability to generate specifically tailored training data to more effectively and more efficiently train a novel architecture for the generation of high quality audio data, therefore reducing the quantity of compute resources required for training while providing the capability to generate high-quality audio from textual prompts.
With reference now to the Figures, example embodiments of the present disclosure will be discussed in further detail.
The user computing device 102 can be any type of computing device, such as, for example, a personal computing device (e.g., laptop or desktop), a mobile computing device (e.g., smartphone or tablet), a gaming console or controller, a wearable computing device, an embedded computing device, or any other type of computing device.
The user computing device 102 includes one or more processors 112 and a memory 114. The one or more processors 112 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 114 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 114 can store data 116 and instructions 118 which are executed by the processor 112 to cause the user computing device 102 to perform operations.
In some implementations, the user computing device 102 can store or include one or more models 120. For example, the models 120 can be or can otherwise include various machine-learned models such as neural networks (e.g., deep neural networks) or other types of machine-learned models, including non-linear models and/or linear models. Neural networks can include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, diffusion networks, or other forms of neural networks. Some example machine-learned models can leverage an attention mechanism such as self-attention. For example, some example machine-learned models can include multi-headed self-attention models (e.g., transformer models).
Specifically, in some implementations, the models 120 can be, or otherwise include, diffusion models. As described herein, diffusion models generally refer to generative machine-learned models that work to generate audio by iteratively denoising random noise. For example, the input to a diffusion model can be a conditioning signal c, a randomly sampled time step t and a sample xt obtained by corrupting the original sample x via a normal or Gaussian diffusion process with a noise schedule parameterized by the standard deviation σt of the noise at time t. The range of time t can be set to [0,1] from which uniform sampling can occur during training.
The distribution of xt can be parameterized by a single noise vector ϵ that belongs to a standard normal distribution, as xt may be written as a function of the original sample, the deterministic noise schedule, and the noise vector ϵ such that xt(x, ϵ, σ). The loss function used to train the models 120 can be defined as x,c,ϵ,t[wt∥ϵθ(xt, c, t)−ϵ∥2], where wt represents a fixed weight function that can serve, or be viewed as, a hyperparameter. Training of the models 120 and/or 140 will be discussed in greater detail with regards to model trainer 160.
In some implementations, the user computing device 102 can utilize the models 120 by taking random noise at time t=1 (e.g., via an audio sampler capable of sampling random audio, etc.) and denoising the random noise based on noise predictions provided by the model 120. For example, ancestral sampling can be utilized to control the quality of the generated audio data. For example, the degree of stochasticity of the denoising process can be controlled by adjusting a stochasticity parameter γ of the audio sampler. Other examples include denoising step size (e.g., how often denoising occurs), variance schedule, loss weight, etc.
In some implementations, the one or more models 120 can be received from the server computing system 130 over network 180, stored in the user computing device memory 114, and then used or otherwise implemented by the one or more processors 112. In some implementations, the user computing device 102 can implement multiple parallel instances of a single model 120, or single set of models 120 (e.g., to perform parallel generation of audio data from textual prompts across multiple instances of model or set of model(s)).
More particularly, in some implementations, the model(s) 120 can include a large language model (LLM). The LLM can be utilized to generate descriptive sentences of music (e.g., generic descriptive sentences of many different types of audio data). The model(s) 120 can include a machine-learned audio classification model. The model(s) 120 can include a machine-learned generation model (e.g., a diffusion model, etc.), and a machine-learned diffusion model. The machine-learned generation model and the machine-learned diffusion model can be utilized in conjunction to generate high-quality audio data based on textual content. For example, a user of the user computing device 102 can provide textual content that includes a query for a particular type of music (e.g., with the user input component 122, etc.). The user computing device can process the textual content with the machine-learned generative model of the model(s) 120 to obtain an intermediate representation of the textual content. The user computing device 102 can then process the intermediate representation with the machine-learned diffusion model of the model(s) 120 to obtain high-quality audio that corresponds to the query.
Additionally or alternatively, one or more models 140 can be included in or otherwise stored and implemented by the server computing system 130 that communicates with the user computing device 102 according to a client-server relationship. For example, the models 140 can be implemented by the server computing system 140 as a portion of a web service (e.g., a audio generation service). Thus, one or more models 120 can be stored and implemented at the user computing device 102 and/or one or more models 140 can be stored and implemented at the server computing system 130.
The user computing device 102 can also include one or more user input components 122 that receives user input. For example, the user input component 122 can be a touch-sensitive component (e.g., a touch-sensitive display screen or a touch pad) that is sensitive to the touch of a user input object (e.g., a finger or a stylus). The touch-sensitive component can serve to implement a virtual keyboard. Other example user input components include a microphone, a traditional keyboard, or other means by which a user can provide user input.
The server computing system 130 includes one or more processors 132 and a memory 134. The one or more processors 132 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 134 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 134 can store data 136 and instructions 138 which are executed by the processor 132 to cause the server computing system 130 to perform operations.
In some implementations, the server computing system 130 includes or is otherwise implemented by one or more server computing devices. In instances in which the server computing system 130 includes plural server computing devices, such server computing devices can operate according to sequential computing architectures, parallel computing architectures, or some combination thereof.
As described above, the server computing system 130 can store or otherwise include one or more models 140. For example, the models 140 can be or can otherwise include various machine-learned models. Example machine-learned models include neural networks or other multi-layer non-linear models. Example neural networks include feed forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. Some example machine-learned models can leverage an attention mechanism such as self-attention. For example, some example machine-learned models can include multi-headed self-attention models (e.g., transformer models).
The user computing device 102 and/or the server computing system 130 can train the models 120 and/or 140 via interaction with the training computing system 150 that is communicatively coupled over the network 180. The training computing system 150 can be separate from the server computing system 130 or can be a portion of the server computing system 130.
The training computing system 150 includes one or more processors 152 and a memory 154. The one or more processors 152 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 154 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The memory 154 can store data 156 and instructions 158 which are executed by the processor 152 to cause the training computing system 150 to perform operations. In some implementations, the training computing system 150 includes or is otherwise implemented by one or more server computing devices.
The training computing system 150 can include a model trainer 160 that trains the machine-learned models 120 and/or 140 stored at the user computing device 102 and/or the server computing system 130 using various training or learning techniques, such as, for example, backwards propagation of errors. For example, a loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the loss function). Various loss functions can be used such as mean squared error, likelihood loss, cross entropy loss, hinge loss, and/or various other loss functions. Gradient descent techniques can be used to iteratively update the parameters over a number of training iterations.
In some implementations, performing backwards propagation of errors can include performing truncated backpropagation through time. The model trainer 160 can perform a number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models being trained.
In particular, the model trainer 160 can train the models 120 and/or 140 based on a set of training data 162. The training data 162 can include, for example, audio samples, a corpus of textual content, audio recordings, etc. More specifically, for example, the training data can include audio samples and corresponding descriptive text from an audiovisual hosting entity (e.g., a video hosting site, etc.). For example, the audio samples can be extracted from music videos, and the descriptive text can be extracted from the textual content provided by users for the music video. The model trainer 160 can train a machine-learned audio classification model of the model(s) 120/140 with the audio samples and descriptive textual content.
The training data 162 can also include a corpus of textual data generated using an LLM of the model(s) 120/140. The training data 162 can include a large number of audio recordings. The model trainer 160 can train the machine-learned generation model and the machine-learned diffusion model of the model(s) 120/140 with the audio recordings and the corpus of textual data.
In some implementations, if the user has provided consent, the training examples can be provided by the user computing device 102. Thus, in such implementations, the model 120 provided to the user computing device 102 can be trained by the training computing system 150 on user-specific data received from the user computing device 102. In some instances, this process can be referred to as personalizing the model.
The model trainer 160 includes computer logic utilized to provide desired functionality. The model trainer 160 can be implemented in hardware, firmware, and/or software controlling a general purpose processor. For example, in some implementations, the model trainer 160 includes program files stored on a storage device, loaded into a memory and executed by one or more processors. In other implementations, the model trainer 160 includes one or more sets of computer-executable instructions that are stored in a tangible computer-readable storage medium such as RAM, hard disk, or optical or magnetic media.
The network 180 can be any type of communications network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof and can include any number of wired or wireless links. In general, communication over the network 180 can be carried via any type of wired and/or wireless connection, using a wide variety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g., VPN, secure HTTP, SSL).
The machine-learned models described in this specification may be used in a variety of tasks, applications, and/or use cases.
In some implementations, the input to the machine-learned model(s) of the present disclosure can be text or natural language data. The machine-learned model(s) can process the text or natural language data to generate an output. As an example, the machine-learned model(s) can process the natural language data to generate a language encoding output. As another example, the machine-learned model(s) can process the text or natural language data to generate a latent text embedding output. As another example, the machine-learned model(s) can process the text or natural language data to generate a translation output. As another example, the machine-learned model(s) can process the text or natural language data to generate a classification output. As another example, the machine-learned model(s) can process the text or natural language data to generate a textual segmentation output. As another example, the machine-learned model(s) can process the text or natural language data to generate a semantic intent output. As another example, the machine-learned model(s) can process the text or natural language data to generate an upscaled text or natural language output (e.g., text or natural language data that is higher quality than the input text or natural language, etc.). As another example, the machine-learned model(s) can process the text or natural language data to generate a prediction output.
In some implementations, the input to the machine-learned model(s) of the present disclosure can be latent encoding data (e.g., a latent space representation of an input, etc.). The machine-learned model(s) can process the latent encoding data to generate an output. As an example, the machine-learned model(s) can process the latent encoding data to generate a recognition output. As another example, the machine-learned model(s) can process the latent encoding data to generate a reconstruction output. As another example, the machine-learned model(s) can process the latent encoding data to generate a search output. As another example, the machine-learned model(s) can process the latent encoding data to generate a reclustering output. As another example, the machine-learned model(s) can process the latent encoding data to generate a prediction output.
The computing device 10 includes a number of applications (e.g., applications 1 through N). Each application contains its own machine learning library and machine-learned model(s). For example, each application can include a machine-learned model. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc.
As illustrated in
The computing device 50 includes a number of applications (e.g., applications 1 through N). Each application is in communication with a central intelligence layer. Example applications include a text messaging application, an email application, a dictation application, a virtual keyboard application, a browser application, etc. In some implementations, each application can communicate with the central intelligence layer (and model(s) stored therein) using an API (e.g., a common API across all applications).
The central intelligence layer includes a number of machine-learned models. For example, as illustrated in
The central intelligence layer can communicate with a central device data layer. The central device data layer can be a centralized repository of data for the computing device 50. As illustrated in
The computing system can train a machine-learned audio classification model 206 (e.g., a joint audio-text embedding model) with the audio samples 202 and the corresponding sets of textual content 204. More specifically, the computing system can process the audio samples 202 with an audio embedding portion 208 of the machine-learned audio classification model 206 to obtain audio embeddings 212. The computing system can process the sets of descriptive textual content 204 with a text embedding portion 210 of the machine-learned audio classification model 206 to obtain text embeddings 214. The computing system can evaluate the audio embeddings 212 and the text embeddings 214 within an embedding space 216 using a contrastive loss function 218. Based on the differences, the computing system can train the machine-learned audio classification model 206.
The audio recordings 306 and the corpus of textual data 304 can be processed using the machine-learned audio classification model 206 (e.g., as described with regards to
The computing system can process the sentence(s) 308B with a machine-learned generation model 310. The machine-learned generation model 310 can be any type or manner of generative model. For example, the machine-learned generation model 310 can be a diffusion model, or cascaded diffusion model. The machine-learned generation model 310 can output an intermediate representation 312 of the sentences 308B. The intermediate representation 312 can be any type of intermediate representation (e.g., a low-fidelity audio sample, a spectrogram, an embedding, a latent representation, etc.). For example, the sentence(s) 308B can be vectorized and fed to the machine-learned generation model 310 as a cross-attention sequence. As an output, the machine-learned generation model 310 can output the intermediate representation 312, which can be or otherwise include a “low-fidelity” (e.g., −3 kHz) audio signal.
The computing system can process the intermediate representation 312 with a machine-learned diffusion model 314. The machine-learned diffusion model 314 (e.g., a cascaded diffusion model) can output audio data 316. It should be noted that the audio data 316 can be any type or manner of audio data (e.g., an .mp3 file, raw audio data, 16 kHz audio, etc.). To follow the previous example, the machine-learned diffusion model can process the intermediate representation 312 (e.g., the “low-fidelity” audio) and can output audio data 316 that includes audio that is higher fidelity (e.g., ˜16 kHz) than the low-fidelity audio of the intermediate representation 312. In some implementations, the machine-learned diffusion model 314 can up-sample the intermediate representation 312 to generate the audio data 316. For example, the machine-learned diffusion model 314 can up-sample 3 kHz audio included in the intermediate representation 312 by applying Fast Fourier Transforms (FFTs) to the audio sequence and then applying inverse FFT to obtain higher fidelity audio based on the lower fidelity Fourier coefficients.
Alternatively, in some implementations, the machine-learned generation model 310 can be a spectrogram model trained to generate a spectrogram representation of audio based on the input data (e.g., vectorized representations of the sentence(s) 308B, etc.). For example, the intermediate representation 312 can be a spectrogram that includes ˜80 channels with a frequency of ˜100 features per second. In some implementations, the pixel values of the spectrogram can be normalized to lie within [−1,1]. Similarly, in some implementations, the machine-learned diffusion model 314 can be or otherwise include a vocoder model that can process the spectrogram to generate the higher-fidelity audio. In some implementations, rather than processing the intermediate representation 312 conditioned on the sentence(s) 308B, or the corpus of textual data 304, the machine-learned diffusion model 314 can exclusively process the spectrogram (e.g., the intermediate representation 312) to generate the audio data 316.
In some implementations, both the generation model 310 and the diffusion model 314 can both be, or otherwise include, diffusion model(s), diffusion layer(s), or analogous layer(s) conventionally included in diffusion models or similar.
The computing system can evaluate a difference between the audio data 316 and the audio recording 308A of the training data 308 using loss function 318. Based on the difference, the computing system can adjust the values of one or more parameters of the machine-learned diffusion model 314. Further, in some implementations, the computing system can also adjust the values of one or more parameters of the machine-learned generation model 310 in an end-to-end manner. In such fashion, the computing system can train the machine-learned generation model 310 and the machine-learned diffusion model 314 to collectively generate high-quality audio data 316 from textual content (e.g., sentence(s) 308B).
At 502, a computing system can generate a corpus of textual data with a machine-learned text generation model. The corpus of textual data can include a plurality of sentences. Each sentence can be descriptive of a type of audio.
At 504, the computing system can obtain a plurality of audio recordings. In some implementations, the audio recordings can be sampled from various music recordings. For example, the plurality of audio recordings can be 10-second, 20-second, 30-second, etc. clips of from various songs. The computing system can, for each of the plurality of audio recordings, process the audio recording with a machine-learned audio classification model to obtain training data. The training data can include the audio recording and one or more sentences of the plurality of sentences closest to the audio recording within a joint audio-text embedding space of the machine-learned audio classification model.
In some implementations, prior to processing the audio recording with a machine-learned audio classification model, the computing system can obtain a plurality of audio samples and an associated corpus of descriptive textual data from an audiovisual data hosting entity. The corpus of descriptive textual data can include, for each of the plurality of audio samples, one or more portions of textual content provided by users of the audiovisual data hosting entity to describe the audio recording. The computing system can, for each of the plurality of audio samples, process the audio sample with an audio embedding portion of the machine-learned audio classification model to obtain an audio embedding. The computing system can then process the one or more portions of textual content that describe the audio recording with a text embedding portion of the machine-learned audio classification model to obtain a text embedding, and train the machine-learned audio classification model using a contrastive loss function that evaluates a difference between the audio embedding and the text embedding.
At 506, the computing system can process the one or more sentences with a machine-learned generation model to obtain an intermediate representation of the one or more sentences.
At 508, the computing system can process the intermediate representation with a machine-learned cascaded diffusion model to obtain audio data. In some implementations, processing the intermediate representation with the machine-learned cascaded diffusion model can include applying a Gaussian diffusion process to the audio recording, and processing the audio recording and a conditioning signal with the machine-learned cascaded diffusion model to obtain the audio data.
In some implementations, the intermediate representation of the textual content can be a spectrogram, and the computing system can process the spectrogram and with the machine-learned diffusion model to obtain the audio data. Alternatively, in some implementations, the intermediate representation of the textual content comprises a low-fidelity audio signal, and the computing system can process the low-fidelity audio signal and the textual content with the machine-learned diffusion model to obtain the audio data.
At 510, the computing system can train the machine-learned cascaded diffusion model based on a difference between the audio data and the audio recording. In some implementations, the computing system can train the machine-learned generation model and the machine-learned cascaded diffusion model based on the difference between the audio data and the audio recording.
In some implementations, the computing system can further obtain, by the textual content that describes a query. The query can indicate a desired type of audio content. The computing system can process the textual content with the machine-learned generator model to generate an intermediate representation of the textual content, and process the intermediate representation with the machine-learned diffusion model to obtain audio data. The audio data can include audio of the desired type of audio content.
The technology discussed herein makes reference to servers, databases, software applications, and other computer-based systems, as well as actions taken and information sent to and from such systems. The inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. For instance, processes discussed herein can be implemented using a single device or component or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.
While the present subject matter has been described in detail with respect to various specific example embodiments thereof, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the subject disclosure does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present disclosure cover such alterations, variations, and equivalents.
The present application is a continuation of International Application No. PCT/US2024/013191 filed on Jan. 26, 2024, which is based on and claims priority to U.S. Provisional Application 63/481,746, having a filing date of Jan. 26, 2023. Each of the aforementioned applications is hereby incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63481746 | Jan 2023 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2024/013191 | Jan 2024 | WO |
| Child | 18651296 | US |
