Unstructured Data Parsing for Parallel AI Document Generation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional content generation, such as legal documents, is inefficient, time-consuming, and resource-wasteful due to the lack of effective methods to transform unstructured data into structured data for processing by artificial intelligence engines, leading to inaccurate and off-target research.

Innovation Solution

The use of large language models and parsing algorithms to transform unstructured data into structured data, combined with an artificial intelligence engine that executes multiple functions in parallel to generate documents efficiently, reducing resource consumption and enhancing content generation efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional methods are used to generate content, then the process is simple to implement, but it is time-consuming and inefficient

Engineering Contradiction:

Improvecontent generation efficiencyVSAvoidtime required for research and drafting

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual research and drafting processes with an AI-based system that automatically generates legal documents. The system uses natural language processing and machine learning models to transform unstructured input data into structured outputs, eliminating the need for attorneys to perform extensive manual research and drafting work while maintaining document quality and accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service content generation by allowing users to input unstructured data through simple interfaces and automatically receiving professionally drafted documents. The AI model processes the input data autonomously, performing research, analysis, and document generation without requiring specialized legal expertise from the user, thus making efficient content generation accessible to anyone

Inventive Principle:

Principle #25Self-service

2Reliability

If manual research and drafting is performed, then accuracy can be maintained, but the process is resource-wasteful

Engineering Contradiction:

Improveaccuracy of research and draftingVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent replaces resource-intensive manual research and drafting processes with computational AI systems. The machine learning models process large volumes of legal data efficiently, performing research and analysis that would otherwise require numerous hours of attorney work, thereby reducing resource consumption while maintaining or improving accuracy through systematic data processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of data processing by transforming unstructured input data into structured data representations that AI models can process efficiently. This data transformation enables the system to handle complex legal documents and research tasks with improved accuracy and reduced resource consumption compared to manual processing

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If unstructured data is processed directly, then the system is simpler, but the AI engine cannot process it effectively

Engineering Contradiction:

Improveability to process various data formatsVSAvoiddata transformation process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a data transformation layer that acts as an intermediary between unstructured input data and the AI engine. This intermediary component converts various types of unstructured data (emails, documents, notes) into standardized structured formats that the AI model can process effectively, enabling the system to handle diverse data types while maintaining processing efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system achieves universality by implementing a unified data transformation pipeline that can handle multiple types of unstructured input data and convert them into a common structured format. This multi-functional approach allows the same AI engine to process various data types (legal documents, emails, research materials) without requiring separate processing systems for each data type

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12461932B1Method and system for transforming data using artificial intelligence to generate content

Publication Date: 2025.11.04 WWW AI LAW CORP

AI summary

In one embodiment, a computer-implemented method may include receiving unstructured input data and transforming the unstructured input data into structured data. The transforming may be performed using a large language model and a parsing algorithm configured for the unstructured input data. The method may include generating, based on the structured data, one or more outputs configured to be processed by an artificial intelligence engine. The method may include generating, using the artificial intelligence engine, a document by converting the one or more outputs from a first format to a second format. The artificial intelligence engine may be configured to generate the document by executing a plurality of functions in parallel to reduce execution time of a processing device. The method may include providing the document to a computing device for presentation on a user interface of the computing device.