ML Report Population for Complex Layout Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data processing systems face inefficiencies and inaccuracies in extracting structured data from unstructured sources like image files, struggle with complex layouts and poor image quality, lack dynamic adaptability to new data inputs or user preferences, and often require manual steps for integrating graphical data into reports.
Innovation Solution
A system utilizing machine-learned sequence processing models to automatically generate report templates and populate them with data, incorporating user interaction and feedback for refinement, and integrating graphical data through advanced models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual entry or basic OCR methods are used for data extraction, then the system is simple to implement, but data extraction efficiency is low and accuracy is poor
Solution Approach 1:
The patent replaces traditional mechanical OCR systems with machine learning-based sequence processing models that can intelligently extract and interpret data from complex layouts, achieving higher accuracy while managing complexity through automated learning rather than manual configuration
Solution Approach 2:
The system automatically learns and adapts to different report formats and layouts through machine learning models, eliminating the need for manual reconfiguration when encountering new data structures, thereby improving accuracy without requiring complex manual setup
2Reliability
If basic OCR is used for data extraction, then the system has low complexity, but it struggles with complex layouts and poor image quality
Solution Approach 1:
The machine learning models continuously learn from feedback during training to improve their ability to handle complex layouts and poor image quality, progressively enhancing reliability through iterative improvement rather than requiring complex real-time adjustments
Solution Approach 2:
The system changes its processing parameters and model configurations based on the characteristics of input data, automatically adapting to different layouts and image qualities through learned parameters rather than manual reconfiguration
3Adaptability or versatility
If manual reconfiguration is used to adjust to changes in data structures, then the system is easy to configure initially, but it lacks dynamic adaptability to new data inputs
Solution Approach 1:
The machine learning models automatically adapt to new data structures and formats through self-learning mechanisms, eliminating the need for manual reconfiguration and enabling the system to dynamically adjust to changing requirements without time-consuming manual intervention
Solution Approach 2:
The system transitions from static manual configuration to dynamic automated learning, where the models continuously adapt to new data patterns and structures in real-time, providing flexibility and responsiveness to changing business needs
4Productivity
If manual steps are used for integrating graphical data into reports, then the process is simple to control, but overall process efficiency is reduced
Solution Approach 1:
The patent replaces manual graphical data integration processes with automated machine learning models that can identify, extract, and integrate graphical elements into reports automatically, significantly improving efficiency while managing complexity through intelligent automation rather than manual steps
Data Source
AI summary
Provided are systems and methods for automatic ingestion and generation of reports. In particular, some example implementations can include and use one or more machine-learned sequence processing models such as large language models (LLMs) and large multimodal models (LMMs) to process a data file that depicts prior renderings of reports.