Natural Language Business Intelligence Query System
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Solution Overview
Problem
End users of business intelligence/reporting tools face significant delays and security risks due to extensive Human-Computer Interaction, limited user-friendly features, and the lack of real-time data, leading to repetitive and non-dynamic queries with potential single points of failure.
Innovation Solution
A system and method for automated analysis of business intelligence using a query interface with natural language understanding, comprising a parser and interpreter to determine user intent, generating intermediate queries, and executing them against databases, providing real-time analytics through an interactive Chatbot interface, minimizing Human-Computer Interaction and enhancing data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional business intelligence tools are used with extensive Human-Computer Interaction, then users can access reporting features, but users face significant delays ranging from hours to days to get ad-hoc reports
Solution Approach 1:
The system enables self-service through natural language processing where users can query data using conversational language without needing to navigate complex interfaces or wait for manual query formulation by support teams. The automated natural language processor directly translates user intent into executable database queries, eliminating the need for human intermediaries and significantly reducing report generation time from hours/days to real-time.
2Ease of operation
If users navigate through multiple tabs, buttons, and links to retrieve information, then comprehensive data access is possible, but cognitive load and time effort increase significantly
Solution Approach 1:
The patent replaces the mechanical interaction system of clicking tabs, buttons, and navigating links with a natural language processing system. Users simply type or speak their information needs in conversational language, and the system automatically processes the request through the natural language processor and query processor to retrieve and present the required data, dramatically reducing both time effort and cognitive load.
3Reliability
If production support teams manually frame and execute queries against databases, then accurate data retrieval is achieved, but security risks increase as script executioners can see sensitive data
Solution Approach 1:
The system eliminates the need for manual query formulation by production support teams by implementing an automated natural language processing pipeline. The system directly translates user queries into database queries and executes them securely, removing human intermediaries who could potentially expose sensitive data. This maintains data retrieval accuracy while significantly improving security posture.
4Reliability
If developers and production support personnel maintain query knowledge, then query accuracy is maintained, but single points of failure are created with no learning involved in the process
Solution Approach 1:
The patent replaces the human-dependent query knowledge system with an automated natural language processing system that uses machine learning and artificial intelligence. The system learns from interactions and continuously improves its ability to translate user intent into accurate database queries, eliminating single points of failure while maintaining or improving query execution reliability. The system can adapt to new query patterns and data structures without requiring human reprogramming.
5Productivity
If repetitive queries are executed manually, then specific information needs are met, but queries lack dynamic capability and reuse potential
Solution Approach 1:
The system transforms static, repetitive queries into dynamic, adaptive queries through natural language processing. The system can understand and adapt to varying user intents, modify queries based on contextual information, and automatically optimize query execution. This enables the same query framework to handle diverse information needs dynamically rather than requiring separate manual query formulations for each repetitive task.
Data Source
AI summary
A method, system, and medium for automated analysis of business intelligence each: receive natural language input from a user; evaluate, via a natural language understanding processor that includes a parser and an interpreter, the natural language input to determine an intent of the user; determine the intent of the user and generate a query based on a context manager; send an identification of the failure to a failure analysis system for human intervened analysis and refinement of a natural language model used by the natural language understand processor; assess, via a context manager processor, to determine a user interest in one or more portions of results of the query, a scrolling of the user through the results of the query; and refine, based on the user interest in the one or more portions of the results of the query, an output of the results of the query.