Focus Area Row Structure for ERP Report Generation
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Solution Overview
Problem
Generating reports in enterprise resource programs (ERPs) is a time-consuming process as users must individually select each record to compile financial data, especially for multi-financial dimensions analysis, which is not efficient for producing reports compatible with GAAP and XBRL standards.
Innovation Solution
A method and apparatus that allow users to define a focus area with composite data elements and create row definitions by dragging and dropping data or using expressions to customize reporting structures, enabling the exclusion of specific records and reducing redundant data through exception reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users individually select each record to compile financial data, then report accuracy and compliance with GAAP/XBRL standards is improved, but report generation time and manual effort increase significantly
Solution Approach 1:
The patent segments the record selection process into focused areas (e.g., financial statements, balance sheets, income statements) with predefined row structures. Users can select entire focused areas rather than individual records, maintaining accuracy while reducing time. The system pre-organizes data into standardized rows that correspond to accounting principles, allowing bulk selection without manual record-by-record review.
Solution Approach 2:
The system performs preliminary organization of data into standardized row structures before report generation. Predefined row templates are created based on GAAP and XBRL requirements, so when a user selects a focused area, the system already has the appropriate structure ready. This eliminates the need for users to manually organize records during report generation, maintaining compliance while reducing time consumption.
2Loss of information
If users individually select each record for multi-financial dimensions analysis, then analysis completeness is improved, but productivity decreases due to time-consuming manual selection
Solution Approach 1:
The patent divides the complex multi-financial dimensions analysis into separate focused areas, each with its own row structure. Users can select entire focused areas (e.g., organizational dimensions, business line dimensions, policy dimensions) rather than individually selecting records across multiple dimensions. This maintains analysis completeness while dramatically improving productivity by allowing bulk selection across all dimensions simultaneously.
Solution Approach 2:
The row structure system provides universal applicability across different financial report types and analysis dimensions. A single row structure framework can be applied to generate various financial statements and multi-dimensional analyses without requiring users to reconfigure or manually select records for each specific analysis type. This multi-functionality maintains analysis completeness while improving efficiency across all report generation tasks.
3Adaptability or versatility
If users manually select records to customize reporting structure, then report customization capability is improved, but operational complexity and time consumption increase
Solution Approach 1:
The patent segments the customization process into focused area selections with predefined row structures. Instead of requiring users to manually select and configure individual records, users simply select the desired focused areas (e.g., specific financial statements or analysis dimensions), and the system automatically applies the appropriate pre-configured row structures. This maintains report customization capability while significantly simplifying operation and reducing time consumption.
4Loss of information
If comprehensive data selection is performed to ensure accuracy, then data completeness is improved, but redundant data processing and manual review requirements increase
Solution Approach 1:
The system performs preliminary filtering and organization of data into focused areas with predefined row structures before report generation. By pre-organizing data according to accounting principles and report requirements, the system automatically eliminates redundant data and ensures only relevant information is included. This maintains data completeness while reducing manual processing effort by eliminating the need for users to manually review and filter redundant records.
Data Source
AI summary
A method and an apparatus for generating reports and other outputs from a computer program. A user first defines a focus area, composed of chosen data types and then creates a row definition for the focus area. In defining the row definition the user defines groups and subgroups from the data represented by the focus. This definition process allows the user to generate reports from data organized in similar rows. The user has the ability to define the data to be included in the report through the use of an expression. The expression can also be used to exclude data from the report. Additional embodiments allow the user to verify the use of data and if the data has been used multiple times, and to capture on the output the missings and duplicates.