LLM-Guided Structured Data Extraction From Custom Text
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Solution Overview
Problem
Traditional systems for extracting structured data from unstructured sources, such as free text, are inefficient and prone to failure due to their reliance on rigid methods like regular expressions, which are not robust in handling variable and unpredictable text formats, leading to time-consuming and error-prone data extraction processes.
Innovation Solution
A method that leverages large language models (LLMs) to automate the extraction of structured data from unstructured sources by generating instructions to prompt the LLM to extract relevant data from custom text data, including memo, transaction, and asset data, thereby enhancing efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional methods like regular expressions are used for data extraction, then the extraction process is simple to implement, but the system becomes fragile and error-prone when handling variable text formats
Solution Approach 1:
The patent changes the fundamental parameter of the extraction system from rigid pattern matching (regular expressions) to flexible semantic understanding (large language models). This parameter change allows the system to maintain ease of implementation while dramatically improving robustness against variable text formats, as LLMs can adapt to different text structures through their training data rather than requiring explicit pattern definitions.
Solution Approach 2:
The patent replaces the mechanical pattern-matching system (regular expressions) with an intelligent system based on large language models. This substitution eliminates the fragility inherent in mechanical approaches by using AI-driven semantic understanding that can handle variability in text formats naturally, while still providing structured extraction through programmable interfaces.
2Device complexity
If traditional data extraction systems are used, then the system structure is simple, but the process becomes time-consuming and requires extensive manual intervention
Solution Approach 1:
The patent implements self-service through the use of large language models that automatically perform data extraction without requiring manual configuration or intervention. The system extracts structured data autonomously from unstructured text by leveraging the LLM's pre-trained capabilities, eliminating the need for extensive manual rule creation and tuning while maintaining relatively simple system architecture.
Solution Approach 2:
The patent introduces large language models as an intermediary layer between the simple system structure and the complex task of extracting structured data from variable text. This intermediary handles the computational complexity of understanding and parsing diverse text formats, allowing the overall system to remain simple while achieving high productivity through the AI intermediary's sophisticated processing capabilities.
3Productivity
If automated data extraction is implemented, then productivity improves, but the system becomes more complex
Solution Approach 1:
The patent extracts the complexity of handling variable text formats into a separate, dedicated component - the large language model. By isolating this complexity in the LLM layer, the rest of the system architecture can remain relatively simple. The LLM absorbs the computational and conceptual complexity of parsing diverse text structures, while the surrounding system maintains simplicity through clear interfaces for data input and output.
Data Source
AI summary
A method, system, and non-transitory computer readable medium includes retrieving, from a database, custom text data related to a client at a client device. The custom text data can include memo data, transaction data, loan data, or asset data related to the client. Then, the method can include generating instructions to prompt a large language model to generate text data. The instructions can be configured to prompt the large language model to extract data from the custom text data for the generation of the text data. Then, the method can include generating, using the large language model, the text data. In some examples, the generated text data is generated based on the custom text data and the instructions.