218 results about "Synthetic data generation" patented technology

In various examples, synthetic question-answer (QA) pairs may be generated using question and answer generation models comprising corresponding language models (e.g., autoregressive LLMs). A repository of textual data representing a particular knowledge base may be used to source synthetic QA pairs by partitioning textual data from the repository into units of text (e.g., paragraphs) that represent context. For each unit of text, the question generation model may be prompted to generate a synthetic question from that unit of text, and the answer generation model may be prompted to generate a synthetic answer to the synthetic question. Textual entailment and / or human evaluations may be used to filter out low quality, incorrect, and / or non-productive QA pairs that may be a result of hallucinations. As such, the synthetic QA pairs may be used as, and / or may be used to generate, training data for one or more machine learning models.

A system and a method for automatically discovering and managing actions in an application is disclosed. The system includes a data ingestion layer for receiving application data from multiple sources, a scanning and systematic traversal engine for interacting with UI elements and capturing network calls, an action mapping and generation module for correlating UI actions with API calls and categorizing actions, an AI-driven icon and description generator for creating visual representations and textual descriptions of actions, a user interface for displaying and modifying discovered actions, and a continuous monitoring component for triggering re-scanning based on coverage metrics, error detection, or version updates. The system employs synthetic data generation and AI-driven exploration to uncover hidden or undocumented APIs, enabling comprehensive mapping of an application's capabilities at the API level.

Synthetic data generation systems and methods are disclosed for augmenting synthetic scenes using neural networks that are conditioned on depth information. The synthetic data generation system may use a guided latent diffusion model to generate (or augment) synthetic images, which can subsequently be used to train other models to perform tasks such as object detection. Input of the model may be an image rendered by a graphic engine with coarsely rendered objects. When the image is rendered, segmentation masks may also be generated for objects in the image. The synthetic data generation system may generate a monocular depth image. During the image generation phase, the corresponding segmentation mask, depth map, and any guiding textual input serve as constraints for the denoising process. During the denoising step, the synthetic data generation system may also crop and adjust the resolution of individual objects during diffusion to enhance the results. The newly regenerated object is then blended back into the original image to produce a synthetic image.

A Generative Adversarial Network (GAN) is used to generate synthetic network traffic data, such as for use in training Machine Learning (ML) models. In one embodiment, a new NWDAF analytic “SyntheticData” is defined. The analytic receives as input from a requesting network function (NF) at least an amount of network traffic data requested and the type of network traffic data requested. The SyntheticData analytic uses a GAN model to generate realistic synthetic network traffic data based on actual network traffic collected in the wireless communication network. The analytic sends to the requesting NF the specified amount of synthetic network traffic data of the specified type. In one embodiment, the synthetic network traffic data generation is implemented as a new logical function of an NWDAF: the Data Generator Logical Function (DGLF).

Systems, methods, devices, and computer readable storage media described herein provide techniques for generating synthetic data utilizing generative artificial intelligence (AI) models and scalable data generation tools (SDGTs). A prompt comprising a domain is provided to an AI model. A parameter associated with the domain that specifies a boundary for synthetic values in a column of data is received from the AI model. An argument comprising the parameter is provided to an SDGT to generate scaled data based on the parameter. The scaled data comprises a column of synthetic values wherein each synthetic value is within the boundary specified by the parameter. In an aspect, an emulated workload is caused to utilize synthetic data comprising the scaled data to generate a performance benchmark. In a further aspect, a lightweight model is trained to generate synthetic sentences based on training data generated by the generative AI model based on the prompt.

A method for facilitating supervised generative optimization for synthetic data generation is disclosed. The method includes receiving, via an application programming interface, inputs that include input data and parameters; partitioning the input data to generate data sets, the data sets including training data sets, validation data sets, and test data sets; tuning hyperparameters of synthesizers by using the data sets and supervised optimization that is based on downstream performance metrics; determining a mixture distribution from among the tuned synthesizers; training machine learning models based on the mixture distribution; and generating, by using the trained machine learning models, sets of synthetic data based on the input data.

The invention provides a synthetic data generation method and device, equipment and a readable storage medium, and the method comprises the steps: introducing a multi-modal large language network and a multi-modal generation network, capturing complex environment details through the powerful space imagination capability of the multi-modal generation network, and carrying out the language planning through a large language model, thereby achieving the high-speed and high-efficiency generation of synthetic data. The logic understanding and self-adaptive capability of a scene is enhanced by utilizing strong reasoning capability, more accurate prediction is provided in a complex dynamic environment, and feature information of two multi-modal networks is effectively integrated through a combined prediction fusion module of a multi-modal large language network and a multi-modal generation network. Therefore, real and logicality-rich synthetic data is generated, and a prediction scene is ensured to follow logic and also meet physical laws.

Approaches presented herein provide for the determination of realistic lighting parameters for a scene represented in an image. Realistic lighting parameters can allow for the insertion of one or more virtual objects into a scene image, where the lighting or shading applied to the virtual object(s) can be consistent with those for other objects in the scene. A machine learning model such as a discriminator or diffusion model can be used to analyze a composed image generated by a differential renderer, for example, in which at least one virtual object has been inserted into a scene image and had lighting effects applied in accordance with a set of lighting parameters. A loss value can be determined based on the results of this machine learning model, which can be used to optimize the lighting parameters and / or adjust the weights or parameters of a model used to generate the lighting parameters. Once fine-tuned or optimized, the lighting parameters can represent an accurate light map for the scene or environment that can be used to generate composed images.

The invention provides a training method, device and equipment for a synthetic data generation network and a medium, and the method comprises the steps: carrying out the training process of gradually increasing the task difficulty in three stages from the cross-view consistency training to the space-time consistency training, and then carrying out the combined prediction fusion training in combination with a multi-modal large language network. The network is enabled to fully learn at each stage, so that the capability of the synthetic data generation network is gradually enhanced, the problem of overfitting or training possibly caused by one-step training is avoided, and the synthetic data generation network can fuse multi-modal information such as images and texts. More real and diversified synthetic data with dynamic prediction capability are generated, and the training efficiency is improved.

The invention provides an anti-fact fair data synthesis method and device based on causal reasoning, and aims to generate high-quality synthesis data meeting the fairness requirement by mining the causal relationship between observable features. The synthesis method comprises the following steps: extracting observable features, sensitive features and labels from original data, extracting potential features through a variational automatic codec, and constructing a causal relationship graph; designing a generator according to a topological sequence of the causal relationship graph, connecting a causal path, inputting the potential features and the related features into the generator in sequence, and constructing a data generation process conforming to a causal structure; introducing a discriminator to carry out adversarial training on a generation result and original data, and optimizing generator parameter distribution; finally, synthetic data meeting fairness requirements are generated. According to the method, effective regulation and control on the influence of sensitive characteristics and strict constraint on a causal structure are realized, the generated data has higher fairness and interpretability, and the method can be applied to the fields with higher fairness requirements, such as finance, medical treatment and education.

The system (1000) and method are based on the generation of at least one individual biometric identifier – IBI - from at least user representative synthetic data {Ns} (S70), said user representative synthetic data being generated from neural data {N} of said user (10) received by a pre- trained generative model. Said at least one IBI enables to authenticate said user (10) when compared to at least one user's signature stored in a memory (40). Said at least one IBI can replace both identifier and password to authenticate and login said user (10) without containing any personal biometric data, guaranteeing the anonymity of the login data that can be exchanged remotely.

A method includes generating, using at least one processing device, a ground truth optical flow map for displacement of pixels within a reference image based on a motion model. The motion model determines 3D coordinates of pixels within the reference image based on estimated depths of the pixels within the reference image. The method also includes performing, using the at least one processing device, 3D to 2D reprojection of the pixels within the reference image based on the motion model to generate a reprojected image view corresponding to a shifted camera perspective. The method further includes generating, using the at least one processing device, an occlusion mask for the reference image. The occlusion mask corresponds to occluded pixels within the reprojected image view. In addition, the method includes performing, using the at least one processing device, occlusion region inpainting of the occluded pixels to generate an inpainted reprojected image view.

Disclosed are methods and systems for implementing optimization techniques for accelerated battery charging protocols. A state estimator model configured as a synthetic data generator generates a synthetic dataset from an input data. The synthetic dataset comprises one or more states of a battery. A surrogate model generates a set of features comprising exotic parameters representing internal attributes of the battery by applying time-series analysis to the synthetic dataset. A reduced order ML model determines a minimum charging time based on an optimization control parameter and a subset of features derived from the set of features while maintaining the optimization control parameter in an acceptable range. One or more charging protocols are generated based on the minimum charging time and the optimization control parameter. In some embodiments, an optimal charging protocol is further selected from the one or more charging protocols.

A method is proposed for generating a visual language model (VLM) neural network trained to perform a multi-modal task on an input dataset comprising an input image to generate a token output which is a text response to the input dataset. The VLM is trained using a training database comprising tuples of sample input datasets and corresponding sample token outputs. The sample input dataset of some of the tuples comprises an image generated from a text description by a text-to-image model, and the corresponding sample token output comprises at least part of the text description.

An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I / O control and managing multiple computers as an intelligent process automation system.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to assessing certainty of artificial intelligence models used for detection or segmentation of pathologies. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components. The computer executable components can comprise a synthetic data generation component that generates biologically-inspired synthetic data that approximates a task-specific data manifold of a medical image from a radiomic features perspective; an artificial intelligence component that uses an artificial intelligence model to learn relevant representations of the synthetic data for an at least one image task; and a training component that utilizes the relevant representations and the artificial intelligence model to generate a task-specific model for the at least one image analysis task.

In various examples, a technique for generating synthetic data includes inputting a first prompt that includes (i) a first portion of content and (ii) a plurality of user personas into a first machine learning model and generating, via the first machine learning model and based on the first prompt, a plurality of points of interest associated with the user personas and the first portion of content. The technique also includes inputting a second prompt that includes mappings between the points of interest and a plurality of question types into a second machine learning model and generating, via the second machine learning model and based on the second prompt, a plurality of questions associated with the user personas and the first portion of content. The technique further includes retrieving a second portion of content based at least on the plurality of questions and a third prompt.

An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I / O control and managing multiple computers as an intelligent process automation system.

Proposed is a method of generating synthetic data, which includes receiving, by a client apparatus, original data including personal information, acquiring, by the client apparatus, seed data based on the original data, transmitting, by the client apparatus, the seed data to a server, receiving, by the client apparatus, first candidate synthetic data which is generated based on the seed data from the server, validating, by the client apparatus, the first candidate synthetic data based on a similarity between the first candidate synthetic data and the original data, and storing, by the client apparatus, the first candidate synthetic data as a member of candidate synthetic dataset if the first candidate synthetic data is valid synthetic data from the validation result.

In various examples, high-precision semantic image editing for machine learning systems and applications are described. For example, a generative adversarial network (GAN) may be used to jointly model images and their semantic segmentations based on a same underlying latent code. Image editing may be achieved by using segmentation mask modifications (e.g., provided by a user, or otherwise) to optimize the latent code to be consistent with the updated segmentation, thus effectively changing the original, e.g., RGB image. To improve efficiency of the system, and to not require optimizations for each edit on each image, editing vectors may be learned in latent space that realize the edits, and that can be directly applied on other images with or without additional optimizations. As a result, a GAN in combination with the optimization approaches described herein may simultaneously allow for high precision editing in real-time with straightforward compositionality of multiple edits.

In various examples, systems and methods are disclosed relating to neural networks for synthetic data generation with discrete and continuous variable features. In training, an encoder can determine a plurality of encodings from a plurality of samples of training data, and the continuous generative model can operate as a decoder that is conditioned on the plurality of encodings to generate an estimated output to update the encoder and the continuous generative model. The discrete generative model can be trained over the plurality of encodings to learn to generate discrete variables corresponding to the distribution of information represented by the training data. At runtime, the discrete generative model can be used to generate a discrete variable from an input prompt, and can provide the discrete variable to the continuous generative model for the continuous generative model to generate an output, such an image, conditioned on the discrete variable.

Systems and methods for generating and utilizing synthetic data include receiving a set of real network traffic data; generating synthetic data from the received set of real network traffic data based on patterns learned from the set of real network traffic data; and utilizing the synthetic data for any of training a machine learning model, testing a machine learning model, and configuring a customer cloud environment. The systems are adapted to generate a large amount of synthetic data from a limited set of real network traffic data. The produced synthetic data is altered in one or more ways to anonymize sensitive information present in the real data. Therefore, the systems are adapted to generate a large amount of synthetic data which accurately resembles real network traffic data while complying with data privacy practices.

Disclosed is an approach for learning probability distributions as differentiable quantum circuits (DQC) that enable efficient quantum generative modelling (QGM) and synthetic data generation. A method includes training of a differentiable quantum circuits (DCQ) based model, where data is encoded in a latent space with a phase feature map, followed by a variational quantum circuit. The trained model is then mapped to the bit basis using a fixed unitary transformation, coinciding with a quantum Fourier transform circuit in the simplest case. This allows fast sampling from parametrized distributions using a single-shot readout. Simplified latent space training provides models that are automatically differentiable. Samples from propagated stochastic differential equations (SDEs) can be accessed by solving a stationary Fokker-Planck equation and time-dependent Kolmogorov backward equation on a quantum computer. A route to multidimensional generative modelling is opened with qubit registers explicitly correlated via a (fixed) entangling layer.

A method includes obtaining a ground truth image and generating multiple image frames using the ground truth image, a modeled optical blur, and a modeled global motion. The method also includes generating multiple mosaic image frames using the image frames and a color filter array and generating multiple raw input image frames using the mosaic image frames and a noise model associated with at least one imaging sensor. The method further includes providing the raw input image frames to a multi-frame processing pipeline in order to generate synthetic training data. In addition, the method includes training a machine learning-based image processing engine using the ground truth image and the synthetic training data.

Automated techniques are for generating a large volume of diverse training data that can be used for training machine learning models to extract KV pairs from document images. Given a single input document image and associated annotation data, a large number of diverse synthetic training datapoints are automatically generated by a synthetic data generation system, each datapoint including a synthetic document image and associated annotation data. The generated synthetic training datapoints can be used to train and improve the performance of ML models for extracting KV pairs from document images. In certain implementations, multiple synthetic datapoints are generated by varying the values associated with a key for a content item within the input document image.

Techniques may generate realistic synthetic data by programmatically generating a configuration file object type and relationship data. This configuration file may be used to retrieve source data matching the object type(s) and / or specific records indicated by the configuration file. The techniques may detect and anonymize private / proprietary information and may determine statistical characteristic(s) of the source data. A batch of prompt(s) may be generated using the source data, the statistical characteristic(s), and the configuration file and may be transmitted to one or more instances of a transformer-based machine-learned model. Sets of synthetic data received from the model instance(s) may be de-duplicated, checked for similarity to the source data (e.g., via embedding the synthetic data and the source data), and may be used to generate synthetic object(s) using the relationship(s) and / or other data indicated by the configuration file. These synthetic object(s) may then be deployed in a software environment.

The invention discloses a data augmentation method based on a generative adversarial model in a differential privacy scene, and relates to the technical field of Internet of Things. The method comprises the following steps: S1, obtaining a real data set without noise addition, and generating data into a discriminator in a confrontation model; s2, the discriminator learns distribution of real data without noise addition and receives synthetic data generated by the generator; and S3, the generator receives the random noise as input and tries to generate a vivid data sample. According to the method, the differential privacy and the generative adversarial model are combined, the data set of the data collection party can be expanded while the privacy protection requirement of the data holding party is met, and the problem that the data collection party does not have enough data volume is avoided; according to the method, Renyi differential privacy is adopted in the aspect of privacy protection of data, Gaussian noise adding is used in the training process in the generative adversarial model, and the privacy budget consumed in the process is tracked.

In various examples, a deep three-dimensional (3D) conditional generative model is implemented that can synthesize high resolution 3D shapes using simple guides—such as coarse voxels, point clouds, etc.—by marrying implicit and explicit 3D representations into a hybrid 3D representation. The present approach may directly optimize for the reconstructed surface, allowing for the synthesis of finer geometric details with fewer artifacts. The systems and methods described herein may use a deformable tetrahedral grid that encodes a discretized signed distance function (SDF) and a differentiable marching tetrahedral layer that converts the implicit SDF representation to an explicit surface mesh representation. This combination allows joint optimization of the surface geometry and topology as well as generation of the hierarchy of subdivisions using reconstruction and adversarial losses defined explicitly on the surface mesh.

The invention relates to a differential privacy-based competitive intelligence analysis system and method. The differential privacy-based competitive intelligence analysis system comprises a differential privacy processing and budget management module, a differential privacy feature engineering module, a privacy protection analysis and prediction module and a differential privacy synthetic data generation module. According to the design of the invention, an end-to-end advanced analysis framework with mathematics provable privacy protection capability is constructed, so that accurate, timely and reliable insight of market dynamics and competitor strategies is realized on the premise of strictly protecting enterprise sensitive commercial confidentials.

The invention relates to the technical field of respiratory lung sound recognition, in particular to a respiratory lung sound auxiliary recognition method and system for clinical nursing. The invention provides a respiratory lung sound auxiliary identification method and system for clinical nursing, solves the technical problems of data scarcity, complex noise interference and insufficient cross-device generalization ability in lung sound signals through generative data enhancement and cross-modal migration, and combines self-supervised comparative learning and causal feature discovery to identify the respiratory lung sound. Multi-source signal feature fusion and noise robustness improvement are realized, a real-time noise environment is dynamically adapted, the limitation that a traditional method depends on annotation data and feature extraction is easily influenced by hybrid noise is broken through, a full-link closed-loop system from synthetic data generation, cross-modal alignment to causal-driven decision is constructed, and multi-source signal feature fusion and noise robustness improvement are realized. And the accuracy and clinical applicability of clinical lung sound analysis are effectively improved.