Maturity Model Automation via NLP Parameter Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing maturity models are overly reliant on human decisions, which can be susceptible to bias and error, and are prone to subjective determinations, making them inefficient in assessing and updating organizational maturity in dynamic business environments.
Innovation Solution
A method that processes text-based maturity model documents using natural language processing to extract parameter value datasets, compares different generations of maturity models, and generates prompting data to assist users in objectively defining and updating maturity models, reducing human intervention and subjective bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is applied to process maturity model documents, then objectivity and efficiency of maturity assessment is improved, but complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary system comprising NLP processors and comparison modules that mediate between the maturity model documents and the assessment results. This intermediary automatically extracts parameters, compares different generations of maturity models, and generates prompting data, thereby eliminating human subjectivity while managing system complexity through modular architecture.
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated NLP-based system. Instead of humans manually analyzing maturity model documents and making subjective determinations, the system uses natural language processing to extract parameters, compare models, and generate objective assessments, thereby improving measurement precision.
2Productivity
If automated NLP processing is used to extract parameter values, then productivity of maturity model updating is improved, but reliability of the process may deteriorate due to loss of human judgment
Solution Approach 1:
The patent implements a feedback mechanism where the automated NLP system extracts parameters from maturity model documents, compares them across different generations, and generates prompting data that is presented back to users. This feedback loop allows the system to maintain high productivity through automation while preserving reliability by enabling human review and validation of the extracted parameters and comparisons.
Solution Approach 2:
The patent creates structured copies of the maturity model documents by extracting parameters into standardized data formats. This copying process transforms unstructured text into machine-readable parameter value datasets that can be efficiently compared and analyzed, maintaining productivity while ensuring reliability through systematic data representation.
3Adaptability or versatility
If multiple generations of maturity models are compared using parameter value datasets, then adaptability of the maturity model is improved, but loss of information increases due to data extraction
Solution Approach 1:
The patent segments the maturity model documents into distinct parameter value datasets that can be independently extracted, stored, and compared. By dividing the complex maturity model information into structured parameters, the system achieves adaptability for comparing multiple generations while minimizing information loss through systematic categorization and preservation of key attributes.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: processing one or more text based document defining a maturity model, wherein the maturity model defines a plurality of domains, and for at least one domain of the second plurality of domains, capabilities are associated to respective different maturity levels of the at least one domain, wherein the processing the one or more text based document includes applying natural language processing to extract from the one or more text based document a parameter value dataset.