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.

What it 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.

What it doesn't 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.

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.

Why it matters

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.

Real-world examples

• 1.Algorithmic trading platforms
• 2.Financial risk assessment tools
• 3.Hedge fund quantitative analysis systems
• 4.Economic forecasting models
• 5.Market sentiment analysis tools