Adaptable Machine Learning System for Enterprise Data Processing
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Solution Overview
Problem
Existing systems struggle to effectively analyze and process large volumes of diverse enterprise data in real-time, particularly in adapting to different contexts and industries, leading to inefficiencies and limited utilization of available data.
Innovation Solution
The development of adaptable machine learning systems and methods that model enterprise data into features applicable to specific contexts, incorporating domain expert knowledge to drive classification and clustering, and utilizing natural language processing to interpret unstructured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data processing systems are used to handle enterprise data, then system simplicity is maintained, but the ability to interpret and adapt to different enterprise contexts is insufficient
Solution Approach 1:
The patent introduces domain models as intermediary layers between raw enterprise data and analysis systems. These domain models serve as mediators that translate diverse enterprise data into standardized representations, enabling the system to adapt to different contexts without requiring complete system redesign. The domain models capture industry-specific knowledge and relationships, acting as a buffer that handles context adaptation while keeping the core processing system relatively simple.
Solution Approach 2:
The system employs dynamic domain models that can be configured and adapted to different enterprise contexts. Rather than using fixed, static data processing pipelines, the domain models can be modified to reflect different industry requirements, data structures, and analytical needs. This dynamic configuration capability allows the same underlying system to serve multiple enterprise contexts with varying degrees of specialization.
2Measurement precision
If machine learning systems are implemented to analyze enterprise data, then data interpretation capability is improved, but processing speed and real-time performance may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-configuring domain models with industry-specific knowledge, data relationships, and analysis rules before actual data processing occurs. Domain models are built and validated in advance, capturing contextual understanding that would otherwise require complex real-time computation. This preprocessing of analytical frameworks enables faster execution during actual data analysis while maintaining high interpretation accuracy.
Solution Approach 2:
The patent segments the data processing system into distinct components: domain model configuration, data ingestion, feature extraction, and analysis execution. By dividing the complex machine learning pipeline into manageable segments, the system can optimize each component independently. Domain models handle contextual interpretation in a separate layer from raw data processing, allowing parallel execution and improving overall processing speed without sacrificing interpretation accuracy.
3Loss of information
If comprehensive domain modeling is performed to capture all enterprise contexts, then data utility is maximized, but the time and resources required for model development increase
Solution Approach 1:
The patent designs domain models with universal structures that can serve multiple enterprise contexts and industries. Rather than creating entirely separate models for each context, the domain model framework uses configurable templates and reusable components that can be adapted to different domains. This multi-functionality approach allows a single domain modeling infrastructure to handle diverse enterprise data types and analytical requirements, reducing overall development time while maintaining comprehensive data utility.
Solution Approach 2:
The system enables comprehensive domain modeling through parameter configuration rather than structural redesign. Domain models can be customized for different enterprise contexts by changing parameters such as data sources, feature definitions, and analysis rules, without requiring complete model reconstruction. This parameter-driven approach allows rapid adaptation to different contexts while preserving the core model architecture, significantly reducing model development time while maintaining data utility.
Data Source
AI summary
A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.