AI Report Context Modeling for Easier Report Generation
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Solution Overview
Problem
Current report generator software requires operators to have knowledge of database structures, limiting the number of users who can efficiently generate reports and increasing the time and difficulty of the process.
Innovation Solution
A method and system that uses artificial intelligence to collect and model existing reports, determine contexts, generate an index, predict the context of new reports, and suggest reports in a graphical user interface, reducing the need for operator knowledge and simplifying the report generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional report generator software is used, then reports can be generated from database sources, but the operator must have knowledge of database structures, fields, and tables which limits the number of capable operators and increases training requirements
Solution Approach 1:
The patent introduces an intermediary layer between the operator and the database structure. This intermediary automatically translates high-level report parameters into database-specific queries, eliminating the need for operators to understand database fields, tables, and columns. The system acts as a mediator that handles the complexity of database structures while presenting a simplified interface to users.
Solution Approach 2:
The system performs self-service by automatically generating the necessary database queries and selecting appropriate fields based on the report parameters provided by the operator. The software autonomously handles the translation from user-friendly parameters to technical database requirements, reducing the operator's burden to zero technical knowledge.
2Productivity
If traditional report generator software is used, then reports can be generated, but the process takes more time and is more difficult for operators who do not generate reports frequently
Solution Approach 1:
The system performs preliminary action by pre-defining report templates and parameters that capture common reporting needs. When an operator initiates a report, the system already has prepared structures and can quickly map the requested parameters to these pre-defined templates, significantly reducing the time required to generate reports compared to building reports from scratch each time.
Solution Approach 2:
The patent utilizes parameter changes by allowing operators to define reports through simple parameter specifications rather than complex database queries. The system dynamically adjusts and translates these parameters into the appropriate database operations, enabling rapid report generation without requiring operators to write or understand complex query language.
3Adaptability or versatility
If operators must understand database structures to generate reports, then accurate reports can be created, but the number of operators who can generate reports is limited
Solution Approach 1:
The patent replaces the mechanical requirement for human expertise in database structures with an automated software system. Instead of relying on operators to manually understand and navigate database fields, tables, and relationships, the system automatically performs this function through intelligent parameter translation and query generation, thereby expanding access to report generation capabilities across all operators regardless of their technical knowledge.
Data Source
AI summary
A method, computer system, and computer program product are provided for generating reports. Existing reports are collected and modeled to determine a number of contexts. An index of the existing reports is generated according the contexts determined by the modeling, a predicted context of a new report is predicted according to the modeling. According to the index, suggested reports are identified based on the predicted context for the new report. The suggested reports are presented in a graphical user interface.