# 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. - **Patent:** US 20250307927 - **Original title:** Multi-Scale Temporal Attention Processing System for Multimodal Deep Learning with Vector-Quantized Variational Autoencoder - **Owner:** Atombeam Technologies - **Status:** Active - **Times cited:** 18 - **Field:** financial_services, ai_ml, software ## What it does 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 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. ## 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. ## 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 ## 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/20250307927/multi-scale-temporal-attention-processing-system-for-multimodal-deep-learning-wi **Original patent:** https://patents.google.com/patent/US20250307927 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [AI System for Analyzing Mixed Financial Information](https://patentbrief.org/patent/us/12327190/multimodal-financial-technology-deep-learning-core-with-joint-optimization-of-ve) — 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. - [How an AI System Answers Financial Questions Using Specialized Bots](https://patentbrief.org/patent/us/12307349/systems-and-methods-of-large-language-model-driven-orchestration-of-task-specifi) — This patent describes an AI system where a large language model (LLM) directs specialized machine learning agents to answer natural language questions about financial data, then refines the answers using an adversarial AI. - [How Computers Rank Financial News for Analysts](https://patentbrief.org/patent/us/11922469/automated-news-ranking-and-recommendation-system) — This patent describes a computer system that automatically collects financial news, groups similar stories, and ranks them for financial analysts using advanced text analysis and machine learning. - [How AI Systems Learn to Predict and Act Simultaneously](https://patentbrief.org/patent/us/11170293/alphago-policy-and-value-networks) — A method for training AI models that combines supervised learning for prediction with reinforcement learning for decision-making in a single, coordinated system. - [How AI Understands Answers Across Multiple Documents](https://patentbrief.org/patent/us/20240338414/inter-document-attention-mechanism) — Microsoft's 2024 patent on using AI to find answers by understanding how information in one document relates to information in another.