Multi-Tool Agent Workflows for Accurate Document Data Extraction
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Solution Overview
Problem
Existing techniques struggle to efficiently extract accurate insights from large collections of documents due to their unstructured and varied nature, leading to challenges in data retrieval and analysis.
Innovation Solution
A system is provided that processes user queries by generating a dynamically generated state list, where each state in the list is associated with a tool selected to perform a specific task, enabling a modular workflow for retrieving, loading, converting, extracting, and analyzing data from documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional techniques are used to extract information from large document collections, then the process can be simple in structure, but the accuracy and efficiency of information extraction deteriorates
Solution Approach 1:
The patent segments the document processing task into multiple distinct states (retrieve, load and convert, extract and analyze, document search, identify, validate, compile), where each state is handled by specialized sub-agents. This segmentation allows each component to focus on a specific aspect of information extraction, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent implements dynamic tool selection where the system determines which states and sub-agents to invoke based on the specific query and document characteristics. This dynamic approach allows the system to adapt its complexity to the task at hand, using only the necessary processing steps for each query rather than applying a fixed complex workflow to all scenarios.
2Productivity
If manual analysis is used to extract data from documents, then the process can be simple and flexible, but the processing time and scalability deteriorate
Solution Approach 1:
The patent implements automated sub-agents that independently perform specific tasks such as retrieving documents, converting formats, extracting data, searching, identifying information, validating results, and compiling outputs. These self-service capabilities eliminate the need for manual intervention in each processing step, dramatically improving productivity while reducing the time required for large-scale document analysis.
Solution Approach 2:
The system performs preliminary actions by pre-defining the workflow states and sub-agent configurations before actual query processing begins. This preparation allows the system to quickly execute standardized processing pipelines when queries are submitted, reducing latency and improving overall processing efficiency without sacrificing adaptability.
3Measurement precision
If standalone prompt-based language models are used to analyze documents, then the system structure can be simple, but the information retrieval accuracy deteriorates due to information overload
Solution Approach 1:
The patent extracts and separates specific processing functions from a monolithic language model approach, creating dedicated sub-agents for retrieval, conversion, extraction, search, identification, validation, and compilation. Each sub-agent handles a specific aspect of information processing, preventing information overload that plagues standalone models while maintaining system simplicity through clear functional separation.
Solution Approach 2:
The patent introduces intermediary components between the user query and the final output, including workflow management layers that coordinate multiple sub-agents and intermediate processing states. These intermediaries manage information flow and prevent overload by processing data in controlled stages, improving retrieval accuracy without requiring a dramatically complex monolithic model.
4Measurement precision
If specialized models are trained for a given domain to improve extraction accuracy, then the information extraction quality improves, but the manual effort and flexibility deteriorate
Solution Approach 1:
The patent creates a universal framework with standardized workflow states and sub-agents that can handle multiple domains and document types. The same core infrastructure (retrieve, load and convert, extract and analyze, document search, identify, validate, compile states) serves diverse information extraction needs, maintaining high extraction quality through specialized sub-agent configurations while preserving flexibility to adapt to new domains without extensive retraining or manual customization.
Data Source
AI summary
A system is provided for processing user queries by using an automated agent and a workflow. The system comprises reusable components that include states, tools, and/or data sources. Based on analysis of a query's content and goals, the system generates a workflow comprising a sequence of states, each state optimized for a subtask and dynamically bound to a selected tool(s) for that specific query. The workflow can provide a structured high-level control, while allowing for flexible selection of the tool(s) for each state of the workflow for that given query. The system produces a result using the structured workflow and selected tools, answering a user's original query.