Automated Report Data Hierarchy Detection
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Solution Overview
Problem
Automating the analysis and processing of data reports is challenging due to varying document structures and mixed raw data with presentation elements, requiring manual adaptation and leading to slow workflows and errors, especially when the underlying data structure is not apparent.
Innovation Solution
A computer-implemented method builds a hierarchy of elements in reports by parsing data to simulate relationships between parent and child elements, adjusting data for consistency, and creating a tree structure, which allows for automated data processing and consistency checks, including identifying duplicates and performing rounding adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adaptation is used to map named ranges and build hierarchies, then data processing accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing report documents, extracting data elements, determining their hierarchical relationships, and building structured representations without requiring manual intervention. The automated analysis engine independently identifies parent-child relationships, data types, and structural patterns through machine learning and natural language processing techniques.
Solution Approach 2:
The patent replaces the mechanical manual process of mapping named ranges and building hierarchies with an automated computational system. The manual adaptation process is substituted by an automated analysis engine that uses algorithms to detect data structures, identify relationships between elements, and construct hierarchical representations automatically.
2Reliability
If manual review is performed for data consistency, then data quality is improved, but workflow efficiency deteriorates
Solution Approach 1:
The system performs self-service by automatically checking data consistency through multiple validation mechanisms. The automated analysis engine verifies data types, detects anomalies, validates hierarchical relationships, and ensures consistency across extracted data elements without requiring manual review processes.
Solution Approach 2:
The system implements feedback mechanisms where the automated analysis engine continuously validates extracted data against expected patterns and constraints. Consistency checks provide feedback on data quality, and the system adjusts its processing accordingly to maintain high data reliability while operating automatically.
3Productivity
If automated analysis is implemented, then processing speed is improved, but system complexity deteriorates
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: document parsing component, data extraction engine, hierarchical relationship detector, data validation module, and hierarchy builder. Each module performs a specific function, making the overall complex system manageable through modular design and independent development of components.
Solution Approach 2:
The patent introduces intermediary components such as data structures that represent hierarchical relationships, intermediate processing layers that transform raw extracted data into structured formats, and mediator algorithms that bridge the gap between unstructured document content and organized data hierarchies.
4Measurement precision
If data structure is not apparent from raw numbers, then data processing accuracy is improved through manual mapping, but time consumption deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing report documents to automatically identify and structure data elements before actual processing occurs. The automated analysis engine预先 detects data patterns, establishes hierarchical relationships, and prepares structured representations that make subsequent processing accurate and efficient without requiring time-consuming manual mapping.
Solution Approach 2:
The patent replaces the manual mechanical process of interpreting and mapping data structures with an automated computational system. The system uses algorithms to automatically detect hierarchical relationships and data structures that are not apparent from raw numbers, substituting human interpretation with machine-based pattern recognition and structure detection.
Data Source
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AI summary
A computer-implemented method for building a hierarchy of elements depending on data disclosed in reports includes identifying one or more areas containing one or more elements in each of multiple reports which contain data, where the elements comprise parent elements and child elements. The method includes parsing the data of each of the elements to simulate relationship operations between the parent elements and the child elements until consistent aggregations are found, identifying data with a same unit and adjusting the data to have a same scale or a same precision, recursively searching the aggregated data by applying the found relationship operations to build a tree structure having at least a portion of the elements of each report built in the tree structure arranged in a hierarchy of elements and storing the tree structure, including a level of each of the elements and the relationship operation.