Generative AI Data Analytics Platform for Unified Operational Diagnostics
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Solution Overview
Problem
Existing data analytics tools are siloed, lack user-friendly interfaces, struggle with data accessibility and processing from disparate sources, and fail to provide comprehensive end-to-end solutions for diagnostics to recommendations, leading to suboptimal and inaccurate results.
Innovation Solution
A generative artificial intelligence-based system that collects data from multiple sources, performs quality assessment, processes it for pattern recognition, generates prompts for Large Language Models (LLMs), and provides domain-specific recommendations for operational improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a siloed approach to data analysis is used, then local data analysis issues can be addressed within scope of work, but the approach becomes suboptimal and does not provide accurate results due to large-scale nature of organization's operations
Solution Approach 1:
The patent merges multiple siloed data analysis systems into a unified end-to-end data analytics platform. The system integrates data collection from multiple sources, data quality assessment, pattern recognition, and generative AI-based insights into a single cohesive system, enabling accurate analysis of large-scale organizational operations while maintaining ease of operation through centralized access.
2Productivity
If existing data analysis tools are used, then data can be processed, but the interface is not user friendly and does not provide suitable conservation and chat options for catering to user queries
Solution Approach 1:
The patent implements self-service capabilities through natural language chat interfaces that allow users to query data analytics systems without requiring technical expertise. The generative AI components automatically interpret user queries, retrieve relevant data, and provide actionable insights, enabling users to access sophisticated analytics through simple conversational interactions.
3Quantity of substance
If data is collected from multiple data sources and locations, then comprehensive data can be gathered, but it becomes difficult to uniformly process the data to determine insights
Solution Approach 1:
The patent applies parameter changes through data transformation and standardization processes. The system converts data from multiple formats and sources into a unified structure by adjusting data parameters, types, and representations. This enables uniform processing of diverse data while maintaining comprehensive data collection from multiple sources and locations.
4Productivity
If existing data analysis tools are used, then data can be accessed, but accessing, ingesting, storage and processing of data of various types is a challenging task
Solution Approach 1:
The patent introduces intermediary components that simplify data ingestion and processing. The system includes data quality assessment modules, pattern recognition algorithms, and generative AI processors that act as intermediaries between raw data and user queries. These intermediaries automatically handle data transformation, validation, and insight generation, reducing the complexity of data processing while maintaining high productivity.
Data Source
AI summary
A generative artificial intelligence-based system and method for providing an improved end-to-end data analytics tool is provided. Data from input unit(s) associated with multiple data sources located at disparate locations is collected. A data quality assessment is performed based on one or more pre-determined criteria. Transformed version of the collected data is processed for analyzing one or more data parameters associated with the transformed data to determine relationships and patterns within the transformed data. Prompts are generated related to operational issues associated with the specific domain. The prompts are provided to Large Language Models (LLMs) as input for generating diagnostic data and insights related to the operational issues. An optimized value of one or more modifiable prompt parameters associated with the generated prompts is determined for customizing the LLMs. Domain specific recommendations are provided by LLM based on the generated diagnostic data and insights for resolving the operational issues.