How an AI System Answers Financial Questions Using Specialized Bots

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.

What it covers

This system takes a user's natural language question about financial records, like "Find similar stocks to Apple." A central large language model (LLM) then processes this request and outputs specific instructions to various machine learning (ML) agents (Claim 1). For example, a 'similarity-based data record identification machine learning model' (Claim 1) might be instructed to compare financial instruments. These agents perform their tasks, like finding similar financial instruments based on specific attributes, and return their findings. The LLM then takes these findings and generates a natural language response. Crucially, an 'adversarial machine learning agent' (Claim 1) then automatically reviews and modifies this response until it meets certain quality standards, ensuring clarity and accuracy before it's shown to the user.

What it doesn't cover

• —Does not cover systems that process natural language queries for non-financial data records, as Claim 1 specifies 'at least one attribute associated with at least one financial instrument'.
• —Does not cover systems where a large language model directly answers the query without orchestrating separate, task-specific machine learning agents.
• —Does not cover systems that identify similar data records without using a 'similarity-based data record identification machine learning model' to determine a metric between items (Claim 1).
• —Does not cover systems that generate natural language responses without an 'adversarial machine learning agent' to recursively auto-correct the output based on predetermined criteria (Claim 1).
• —Does not cover systems where the ML agents are not configured to be instantiated in parallel (Claim 2).
• —Does not cover systems that do not generate an 'explainability prompt' to get a natural language explanation from the LLM about its decisions (Claim 3).

The clever bit

The novelty lies in using a large language model not just to answer questions, but to act as an orchestrator, directing specialized machine learning agents to perform specific data tasks. Even more clever is the 'adversarial machine learning agent' that automatically reviews and refines the LLM's final natural language output, ensuring it meets quality standards before being presented to the user.

Why it matters

This patent describes a method for AI systems to handle complex, domain-specific queries, particularly in finance, by combining the broad understanding of an LLM with the precision of specialized ML agents. This approach can improve the accuracy and reliability of AI-generated responses in critical fields. The inclusion of an adversarial agent for self-correction aims to address common issues of factual errors or 'hallucinations' in LLM outputs, making AI more trustworthy for professional use.

Real-world examples

• 1.Financial analysis platforms using AI to answer investor questions
• 2.Customer service chatbots for banking and investment firms
• 3.AI-powered tools for compliance and risk management in finance
• 4.Automated financial report generation systems
• 5.Intelligent search engines for financial data records