AI System for Analyzing Mixed Financial Information
This patent describes an AI system that combines different types of financial data, like stock prices, news articles, and market sentiment, to make better predictions and create new financial scenarios.
Original patent title: “Multimodal financial technology deep learning core with joint optimization of vector-quantized variational autoencoder and neural upsampler”
This patent describes an AI system that combines different types of financial data, like stock prices, news articles, and market sentiment, to make better predictions and create new financial scenarios. Granted to Atombeam Technologies in 2025 with 31 claims and 14 forward citations, and it is expected to expire in 2044.
Coverage
What does this patent actually cover?
This system processes various kinds of financial information, such as stock prices (time-series data), news articles (textual data), market mood (sentiment data), and company reports (structured tabular data) (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 2). It uses specialized AI tools, called 'modality-specific codeword generators' (Claim 1), to convert each data type into a unique digital code. These codes are then blended together by a fusion module to understand their relationships (Claim 1). A 'VQ-VAE encoder' (Claim 1) compresses this combined information into a smaller, discrete code, which is then rebuilt by a 'VQ-VAE decoder' (Claim 1). A 'neural upsampler' (Claim 1) then enhances the reconstructed data, adding back details that might have been lost during compression. All these parts are 'jointly trained' (Claim 1) simultaneously using a 'joint loss function' (Claim 1) to ensure they work together perfectly. For example, a financial firm could use this to predict stock movements by combining historical prices, company news, and social media sentiment, and then generate new, realistic crisis scenarios to test investment strategies.
The gap
What does this patent NOT cover?
- Does not cover systems that process only one type of financial data, as it specifically requires 'multimodal data' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover systems that do not use a 'Vector-quantized Variational Autoencoder (VQ-VAE)' for data compression (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover systems that lack a 'neural upsampler' specifically designed to recover information lost during compression (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover AI models where the VQ-VAE and neural upsampler are trained separately, as it requires 'jointly train[ing]' them with a combined loss function (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover general multimodal AI systems that are not specifically applied to 'financial data' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 2).
- Does not cover systems that do not encode each data modality into a 'codeword-based representation' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The truly clever part is the 'joint training' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1) of the VQ-VAE and the neural upsampler, which not only compresses and reconstructs diverse financial data but also actively recovers lost information and allows for exploring the 'latent space' (Claim 13) to generate new, realistic financial scenarios. This enables a deeper understanding and proactive testing of financial models.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
AI-driven financial trading platforms
Advanced risk management software for banks
Economic modeling tools for market analysis
Synthetic data generation for financial stress testing
Algorithmic investment strategy development
Why it matters
The bigger picture
This technology matters because it allows financial institutions to process and understand complex, diverse data more effectively than traditional methods. By combining different data types and enhancing their quality, it can lead to more accurate financial forecasting, better risk assessment, and smarter decision-making. The ability to generate new, synthetic financial scenarios also provides a powerful tool for robust model testing and developing new strategies without relying solely on historical data.
Filed
September 17, 2024
Granted
June 10, 2025
Market context
Who's building on this
Companies in this space
Atombeam Technologies, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing this technology. Companies specializing in AI for finance, such as those building advanced trading algorithms, risk assessment platforms, and financial forecasting tools, are likely to be interested in or building similar capabilities. Major financial institutions and fintech startups are continuously seeking ways to leverage multimodal data for competitive advantage.
Market impact
This patent could significantly impact the financial technology market by enabling more sophisticated and reliable AI models for financial analysis. It offers a pathway to overcome challenges in integrating disparate data sources, potentially leading to more accurate predictions and improved risk management for financial institutions. The capability to generate synthetic financial scenarios could also become a standard for stress testing and developing novel investment strategies, influencing how financial products are designed and evaluated.
Claim 1 — Plain English
What this patent covers
This system processes various kinds of financial information, such as stock prices (time-series data), news articles (textual data), market mood (sentiment data), and company reports (structured tabular data) (Claim 2). It uses specialized AI tools, called 'modality-specific codeword generators' (Claim 1), to convert each data type into a unique digital code. These codes are then blended together by a fusion module to understand their relationships (Claim 1). A 'VQ-VAE encoder' (Claim 1) compresses this combined information into a smaller, discrete code, which is then rebuilt by a 'VQ-VAE decoder' (Claim 1). A 'neural upsampler' (Claim 1) then enhances the reconstructed data, adding back details that might have been lost during compression. All these parts are 'jointly trained' (Claim 1) simultaneously using a 'joint loss function' (Claim 1) to ensure they work together perfectly. For example, a financial firm could use this to predict stock movements by combining historical prices, company news, and social media sentiment, and then generate new, realistic crisis scenarios to test investment strategies.
The clever bit
The truly clever part is the 'joint training' (Claim 1) of the VQ-VAE and the neural upsampler, which not only compresses and reconstructs diverse financial data but also actively recovers lost information and allows for exploring the 'latent space' (Claim 13) to generate new, realistic financial scenarios. This enables a deeper understanding and proactive testing of financial models.
What it does not cover
- Does not cover systems that process only one type of financial data, as it specifically requires 'multimodal data' (Claim 1).
- Does not cover systems that do not use a 'Vector-quantized Variational Autoencoder (VQ-VAE)' for data compression (Claim 1).
- Does not cover systems that lack a 'neural upsampler' specifically designed to recover information lost during compression (Claim 1).
- Does not cover AI models where the VQ-VAE and neural upsampler are trained separately, as it requires 'jointly train[ing]' them with a combined loss function (Claim 1).
- Does not cover general multimodal AI systems that are not specifically applied to 'financial data' (Claim 2).
- Does not cover systems that do not encode each data modality into a 'codeword-based representation' (Claim 1).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Strong
Citation count
23/40
Moderately cited
Claim breadth
20/20
Very broad protection
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$147K – $472K
Midpoint $295K · 17.9 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
31 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Galvin, B. (2025). AI System for Analyzing Mixed Financial Information (U.S. Patent No. 12,327,190). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12327190/multimodal-financial-technology-deep-learning-core-with-joint-optimization-of-ve
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does AI System for Analyzing Mixed Financial Information cover?
This patent describes an AI system that combines different types of financial data, like stock prices, news articles, and market sentiment, to make better predictions and create new financial scenarios.
Who owns patent US 12327190?
Atombeam Technologies owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on September 17, 2044, when the invention enters the public domain.
What is patent US 12327190 cited by?
This patent has been cited by 14 later patents that build on its ideas.
What problem does this patent solve?
This technology matters because it allows financial institutions to process and understand complex, diverse data more effectively than traditional methods. By combining different data types and enhancing their quality, it can lead to more accurate financial forecasting, better risk assessment, and smarter decision-making. The ability to generate new, synthetic financial scenarios also provides a powerful tool for robust model testing and developing new strategies without relying solely on historical data.
What does this patent NOT cover?
Does not cover systems that process only one type of financial data, as it specifically requires 'multimodal data' (Claim 1).
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