Financial Data Rendering System with XBRL Taxonomy Mapping
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Solution Overview
Problem
Current methods for extracting financial data from documents are cumbersome, prone to errors, and lack bi-directional compatibility with spreadsheet programs, hindering efficient analysis and comparison of financial statements.
Innovation Solution
A system and method for rendering financial data from reporting sources into spreadsheet-compliant form using XBRL (Extensible Business Reporting Language) and SOAP (Simple Object Access Protocol), enabling automated entry of XML and XBRL compliant data into non-XML programs, and providing bi-directional compatibility with taxonomies for accurate data representation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual copy and paste method is used to transfer financial data from documents to spreadsheets, then data can be entered into spreadsheet for analysis, but the process is cumbersome, time consuming and prone to errors
Solution Approach 1:
The patent introduces an intermediary system comprising a parser, data normalization module, and mapping engine that automatically converts financial document data into spreadsheet-compatible formats. This intermediary layer eliminates manual copy-paste operations by establishing an automated data pipeline between document sources and spreadsheet destinations, thereby improving productivity and reducing time loss.
Solution Approach 2:
The patent replaces the mechanical manual process of copying and pasting data with an automated computer-based system. The system uses software agents to perform data extraction, transformation, and loading operations, substituting human manual operations with automated computational processes that are faster, more accurate, and scalable.
2Reliability
If manual copy and paste method is used to transfer financial data, then data can be entered into spreadsheet, but data errors are introduced during the process
Solution Approach 1:
The intermediary system includes validation modules and mapping engines that automatically verify data integrity during the conversion process. These intermediaries ensure that data types, formats, and relationships are preserved accurately when transforming from document format to spreadsheet format, eliminating manual errors while maintaining operational simplicity through automated workflows.
Solution Approach 2:
The system incorporates feedback mechanisms where the parser and normalization module continuously validate extracted data against expected schemas and patterns. Error detection and correction feedback loops ensure that data accuracy is maintained throughout the automated transfer process, with the system able to identify and rectify inconsistencies before final spreadsheet generation.
3Productivity
If automated data extraction system is implemented, then data entry efficiency is improved, but system complexity increases
Solution Approach 1:
The automated system is divided into distinct modular components: a parser module for extracting data from documents, a normalization module for standardizing data formats, a mapping engine for transforming data to spreadsheet structures, and a validation module for ensuring data quality. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system employs universal data structures and standardized transformation rules that can handle multiple types of financial documents and spreadsheet formats. By creating a multi-functional platform that works across different data sources and destinations, the system achieves high productivity without proportionally increasing complexity, as the same core engine serves multiple purposes.
4Adaptability or versatility
If bi-directional compatibility with spreadsheet programs is implemented, then data analysis capability is enhanced, but system complexity increases
Solution Approach 1:
The system uses an intermediary layer with standardized data representations that enable bi-directional communication between document sources and spreadsheet programs. This mediator translates between different data formats and protocols, allowing the system to work with various spreadsheet applications without requiring complex point-to-point integrations with each specific program, thereby enhancing adaptability while controlling complexity.
Data Source
AI summary
A method of populating a spreadsheet with financial data, includes, in response to a user's request for financial data, sending a request to a web service for the financial data, receiving a response to the request from the web service, processing the response, retrieving a taxonomy associated with the response and populating the spreadsheet in accordance with the response and the retrieved taxonomy.