{
  "patent_number": "US 12307349",
  "country": "US",
  "title": "How an AI System Answers Financial Questions Using Specialized Bots",
  "original_title": "Systems and methods of large language model driven orchestration of task-specific machine learning software agents",
  "summary": "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_does": "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_does_not_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)."
  ],
  "filed": "2024-08-01",
  "granted": "2025-05-20",
  "expires": "2044-08-01",
  "status": "active",
  "holder": "Broadridge Financial Solutions",
  "holder_url": "https://patentbrief.org/company/broadridge-financial-solutions",
  "inventors": [
    {
      "name": "Joseph Lo",
      "url": "https://patentbrief.org/inventor/joseph-lo"
    },
    {
      "name": "Fitim Kryeziu",
      "url": "https://patentbrief.org/inventor/fitim-kryeziu"
    },
    {
      "name": "James Kwiatkowski",
      "url": "https://patentbrief.org/inventor/james-kwiatkowski"
    }
  ],
  "times_cited": 0,
  "tags": [
    "finance",
    "ai_ml",
    "software",
    "telecommunications"
  ],
  "abstract": "Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.",
  "url": "https://patentbrief.org/patent/us/12307349/systems-and-methods-of-large-language-model-driven-orchestration-of-task-specifi",
  "markdown_url": "https://patentbrief.org/patent/us/12307349/systems-and-methods-of-large-language-model-driven-orchestration-of-task-specifi/md",
  "google_patents_url": "https://patents.google.com/patent/US12307349",
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