How AI Analyzes Financial Data Across Different Timeframes
This patent describes an AI system that processes various types of financial data, like stock prices and news, by looking at it simultaneously through quarterly, weekly, and daily lenses, and then combining these views, especially adapting to market changes.
Original patent title: “Multi-Scale Temporal Attention Processing System for Multimodal Deep Learning with Vector-Quantized Variational Autoencoder”
This patent describes an AI system that processes various types of financial data, like stock prices and news, by looking at it simultaneously through quarterly, weekly, and daily lenses, and then combining these views, especially adapting to market changes. Owned by Atombeam Technologies with 20 claims and 18 forward citations, and it is expected to expire in 2045.
Coverage
What does this patent actually cover?
The system takes in different kinds of information, called "multimodal data," such as time-series data (like stock prices), text (news articles), sentiment (public mood), and structured tables (financial reports). It then splits this data into "exactly three" separate processing streams: one for quarterly patterns, one for weekly cycles, and one for real-time daily activity, as stated in claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1. Each stream uses a "scale-specific attention mechanism" optimized for its timeframe. Crucially, these streams talk to each other through "bidirectional cross-temporal gradient flow," meaning changes in understanding at one level affect the others. The system also "dynamically weights" how much each timeframe contributes based on "real-time market volatility indicators," as described in claim 1, giving more importance to short-term data during choppy markets (claim 3). Finally, it combines these insights into a single, unified data picture. For example, it could analyze a company's quarterly earnings report, weekly stock performance, and intraday trading volume, adjusting its focus if the market suddenly becomes turbulent.
The gap
What does this patent NOT cover?
- Processing data using fewer or more than exactly three temporal streams.
- Systems that do not use a hierarchical organization for their temporal processing streams.
- AI models where attention weight adjustments at one temporal scale do not automatically influence computations at the other two scales (no bidirectional cross-temporal gradient flow).
- Systems that do not dynamically adjust the weighting of temporal levels based on real-time market volatility indicators.
- Analyzing data modalities other than time-series, textual, sentiment, and structured tabular data as the core input.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in simultaneously processing multimodal data across precisely three specific, hierarchically organized temporal scales (quarterly, weekly, intraday) with bidirectional cross-temporal gradient flow and dynamic weighting based on real-time market volatility. This allows the AI to learn from interactions between different timeframes and adapt its focus when market conditions change.
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
Algorithmic trading platforms
Financial risk assessment tools
Hedge fund quantitative analysis systems
Economic forecasting models
Market sentiment analysis tools
Why it matters
The bigger picture
This patent aims to improve how AI systems understand complex, fast-changing data, particularly in finance. By combining long-term trends with immediate market shifts and adapting to volatility, it could lead to more accurate predictions and better risk management. This approach helps financial institutions make smarter decisions in areas like algorithmic trading, portfolio management, and economic forecasting.
Filed
June 10, 2025
Market context
Who's building on this
Companies in this space
Companies like Atombeam Technologies Inc. (the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →) are developing advanced AI systems for data compression and analysis. Major financial technology firms and quantitative trading desks, such as Two Sigma, Citadel Securities, and Jane Street, are constantly researching and implementing sophisticated multi-modal, multi-temporal AI models for market prediction and risk management.
Market impact
This technology could significantly enhance the accuracy and adaptability of AI models used in financial markets. By providing a more nuanced understanding of market dynamics across different time scales, it could enable more robust algorithmic trading strategies, improve risk assessment for large investment portfolios, and offer clearer, interpretable insights into complex market behaviors, potentially reducing financial losses during volatile periods.
Claim 1 — Plain English
What this patent covers
The system takes in different kinds of information, called "multimodal data," such as time-series data (like stock prices), text (news articles), sentiment (public mood), and structured tables (financial reports). It then splits this data into "exactly three" separate processing streams: one for quarterly patterns, one for weekly cycles, and one for real-time daily activity, as stated in claim 1. Each stream uses a "scale-specific attention mechanism" optimized for its timeframe. Crucially, these streams talk to each other through "bidirectional cross-temporal gradient flow," meaning changes in understanding at one level affect the others. The system also "dynamically weights" how much each timeframe contributes based on "real-time market volatility indicators," as described in claim 1, giving more importance to short-term data during choppy markets (claim 3). Finally, it combines these insights into a single, unified data picture. For example, it could analyze a company's quarterly earnings report, weekly stock performance, and intraday trading volume, adjusting its focus if the market suddenly becomes turbulent.
The clever bit
The novelty lies in simultaneously processing multimodal data across precisely three specific, hierarchically organized temporal scales (quarterly, weekly, intraday) with bidirectional cross-temporal gradient flow and dynamic weighting based on real-time market volatility. This allows the AI to learn from interactions between different timeframes and adapt its focus when market conditions change.
What it does not cover
- Processing data using fewer or more than exactly three temporal streams.
- Systems that do not use a hierarchical organization for their temporal processing streams.
- AI models where attention weight adjustments at one temporal scale do not automatically influence computations at the other two scales (no bidirectional cross-temporal gradient flow).
- Systems that do not dynamically adjust the weighting of temporal levels based on real-time market volatility indicators.
- Analyzing data modalities other than time-series, textual, sentiment, and structured tabular data as the core input.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
26/40
Moderately cited
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 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
$225K – $719K
Midpoint $449K · 18.6 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
20 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Galvin, B. How AI Analyzes Financial Data Across Different Timeframes (U.S. Patent No. 20,250,307,927). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20250307927/multi-scale-temporal-attention-processing-system-for-multimodal-deep-learning-wi
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 How AI Analyzes Financial Data Across Different Timeframes cover?
This patent describes an AI system that processes various types of financial data, like stock prices and news, by looking at it simultaneously through quarterly, weekly, and daily lenses, and then combining these views, especially adapting to market changes.
Who owns patent US 20250307927?
This patent is owned by Atombeam Technologies.
When does this patent expire?
This patent is expected to expire on June 10, 2045, when the invention enters the public domain.
What is patent US 20250307927 cited by?
This patent has been cited by 18 later patents that build on its ideas.
What problem does this patent solve?
This patent aims to improve how AI systems understand complex, fast-changing data, particularly in finance. By combining long-term trends with immediate market shifts and adapting to volatility, it could lead to more accurate predictions and better risk management. This approach helps financial institutions make smarter decisions in areas like algorithmic trading, portfolio management, and economic forecasting.
What does this patent NOT cover?
Processing data using fewer or more than exactly three temporal streams.
Same assignee
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