Machine-Generated Insights from Validated Multi-Source Data
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Solution Overview
Problem
Data silos within organizations lead to inefficiencies, lack of synergy, and difficulties in data management and analysis, hindering comprehensive decision-making and collaboration, particularly in business analytics, and existing machine learning systems face challenges with data quality, integration, complexity, and explainability.
Innovation Solution
A system for generating machine learning-based insights that integrates data from multiple sources, applies machine learning models to generate and prioritize insights, and presents them in a human-readable format, using a multi-agent AI framework and dynamic prioritization engine to ensure data quality and explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is integrated from multiple sources to overcome data silos, then data completeness and availability are improved, but data quality and consistency deteriorate due to varying data formats and standards
Solution Approach 1:
The patent introduces a data validation and quality assurance layer as an intermediary between data ingestion and machine learning model training. This layer includes validation rules, data cleaning mechanisms, and quality metrics that filter and transform raw data from multiple sources into standardized, reliable datasets suitable for ML models, thereby resolving the contradiction between data availability and data quality
Solution Approach 2:
The system dynamically adjusts data processing parameters based on source characteristics and quality metrics. Different validation rules and transformation parameters are applied to different data sources, and quality thresholds are dynamically tuned to balance data completeness with reliability, enabling the system to handle varied data formats while maintaining high data quality standards
2Productivity
If machine learning models are applied to generate business insights, then analytical capability is improved, but model complexity and explainability deteriorate
Solution Approach 1:
The patent segments the machine learning pipeline into distinct modules: data validation module, model training module, insight generation module, and explanation module. Each module handles specific aspects of the analysis process, making the overall system more manageable and explainable while maintaining high analytical capability through specialized algorithms in each segment
Solution Approach 2:
The system incorporates feedback mechanisms where model outputs are automatically validated against business rules and data quality metrics. This feedback loop enables the system to self-correct and provide explainable insights by tracking which data sources and model components contributed to specific conclusions, thereby reducing perceived complexity while maintaining advanced analytical capabilities
3Reliability
If comprehensive data analysis is performed across multiple business aspects, then decision-making quality is improved, but time required for analysis increases
Solution Approach 1:
The patent implements preliminary data validation and quality assurance activities during the data ingestion phase, before machine learning models are applied. By validating data formats, checking for missing values, and establishing quality metrics upfront, the system prepares clean, ready-to-analyze datasets that reduce processing time during the actual analysis phase while maintaining comprehensive coverage of multiple business aspects
Solution Approach 2:
The system maintains continuous data validation and quality monitoring throughout the entire analytics pipeline, from data ingestion through model training to insight generation. This continuous quality assurance enables comprehensive analysis of multiple business aspects without significant time delays, as validation activities proceed parallel to analysis activities rather than as sequential bottlenecks
Data Source
AI summary
A system configured to extract, classify, and order entity insights may include a computing system, a computer readable memory, at least one processor having instruction configured for ingesting data into the system from at least one external application programming interface configured to retrieve data over a network, integrating the data to form a cohesive data set using the at least one processor, storing the cohesive data set into the computer readable memory, processing the cohesive data set by the at least one processor using at least one machine learning model to generate a plurality of entity insights, each of the insights having at least one classification associated therewith, ordering the plurality of the entity insights based on a machine generated prioritization, and generating a presentation comprising the plurality of entity insights in human-readable form.