Generating Synthetic Heterogenous Time-Series Data

The present disclosure introduces a generative modeling framework for creating realistic and privacy-preserving synthetic records for various types of time-series data, such as electronic health records and financial data. The framework consists of sequential encoder-decoder networks and generative adversarial networks (GANs).

Two-stage generative modeling framework
Sequential encoder-decoder networks and GANs
Designed for heterogeneous time-series data
Focus on privacy preservation and realism

Potential Applications

The technology can be applied in various fields such as healthcare, finance, and research where sensitive time-series data needs to be shared or analyzed while maintaining privacy.

Problems Solved

1. Privacy concerns when sharing sensitive time-series data 2. Generating realistic synthetic data for research and analysis purposes

Benefits

1. Privacy-preserving synthetic data generation 2. Realistic data for analysis and research 3. Versatile framework for different types of time-series data

Potential Commercial Applications

"Privacy-Preserving Synthetic Data Generation Framework for Time-Series Data" can be utilized in industries such as healthcare analytics, financial services, and research institutions for generating synthetic data for analysis and modeling purposes.

Possible Prior Art

There may be existing methods for generating synthetic data for time-series data, but the focus on privacy preservation and realism in this framework sets it apart from traditional approaches.

What are the limitations of the generative modeling framework proposed in the patent application?

The limitations of the generative modeling framework proposed in the patent application may include: 1. Scalability issues when dealing with large datasets 2. Performance degradation when generating complex time-series data

How does the two-stage model in the patent application compare to other generative modeling approaches in terms of efficiency and accuracy?

The two-stage model in the patent application, which includes sequential encoder-decoder networks and GANs, may offer a balance between efficiency and accuracy compared to other generative modeling approaches. The sequential nature of the model allows for better capturing of temporal dependencies in the data, while the GANs help in generating realistic synthetic records.

Original Abstract Submitted

the present disclosure provides a generative modeling framework for generating highly realistic and privacy preserving synthetic records for heterogenous time-series data, such as electronic health record data, financial data, etc. the generative modeling framework is based on a two-stage model that includes sequential encoder-decoder networks and generative adversarial networks (gans).

Google llc (20240185043). Generating Synthetic Heterogenous Time-Series Data simplified abstract

Contents

Generating Synthetic Heterogenous Time-Series Data

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