ML Dictation for Multidimensional Object Analysis
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Solution Overview
Problem
In enterprise data warehouses, multidimensional objects created by different users lack uniformity in naming and description, making it difficult for users to analyze data across multiple models, as the objects are referenced in a non-uniform manner, complicating the identification of related models for analysis.
Innovation Solution
A machine learning-based system that utilizes natural language processing and artificial intelligence to automatically categorize and explain multidimensional objects based on context, providing clear insights in the native language of the user, and converting textual predictions into speech synthesis for dictation, enabling semantically enriched models that provide meaningful descriptions and abstractions of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multidimensional objects are created by different users with non-uniform naming and description, then user creativity and flexibility are improved, but data analysis difficulty and system complexity increase
Solution Approach 1:
The patent introduces a machine learning-based predictive engine as an intermediary between the non-uniform multidimensional objects and the user analysis process. This engine automatically generates context-based descriptions and explanations, mediating the complexity by translating diverse user-created models into unified, understandable insights without requiring users to manually standardize the data structures.
Solution Approach 2:
The system enables self-service by allowing the predictive engine to automatically analyze and describe multidimensional objects without requiring user intervention for standardization. The engine autonomously processes non-uniform data, generates contextual explanations, and delivers insights in the user's native language, making the system self-organizing despite input variability.
2Measurement precision
If manual analysis of multidimensional objects is performed, then analysis accuracy can be maintained, but time consumption and productivity decrease
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated machine learning system. The predictive engine uses trained models to automatically generate explanations and insights from multidimensional objects, substituting human analytical effort with computational processes that maintain accuracy while dramatically increasing processing speed and productivity.
Solution Approach 2:
The system performs preliminary action by pre-training the predictive engine on contextual data and patterns before actual analysis occurs. This preliminary training enables the engine to quickly generate accurate explanations during runtime without requiring manual analysis, thus maintaining precision while improving productivity through advance preparation.
3Loss of information
If contextual explanations are generated for multidimensional objects, then user understanding is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by generating contextual explanations only when needed for specific user queries rather than pre-processing all possible explanations. The predictive engine selectively analyzes multidimensional objects based on user context and language preferences, providing sufficient information clarity while avoiding the excessive computational overhead of generating all possible explanations in advance.
Data Source
AI summary
In an example embodiment, machine learning is utilized to automatically present and explain analysis of multidimensional objects categorized based on context in an enterprise data warehouse. The system is capable of handling dependencies to provide clear insights demonstrated in a native language of an end user. The system is multilingual and capable of framing explanations based on natural language processing (NLP), artificial intelligence (AI), and machine learning. It converts the textual predictions into speech synthesis in the user-understandable native format and then dictates the analysis using the speech synthesis.