Machine Learning Models for Technical Configuration Requirements
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Solution Overview
Problem
There is a knowledge gap between domain experts who understand data requirements and those who can implement technical configurations for data models, leading to difficulties in selecting suitable product configurations, such as industrial trucks or pumps, due to the complexity of available options and the need for specialized knowledge.
Innovation Solution
The use of machine learning models trained with requirements and configuration data to recommend suitable product configurations, allowing users to input their needs and receive tailored recommendations for configurable objects, including industrial trucks and pumps, by defining solution categories, subcategories, and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain experts provide data requirements, then data needs are accurately identified, but technical implementation expertise is lacking
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between domain experts and technical implementation. The model takes requirements data from domain experts and automatically generates corresponding data models and technical configurations, eliminating the need for domain experts to possess technical implementation expertise while preserving accurate requirements identification.
2Measurement precision
If multiple machine learning models are used for different solutions, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the recommendation system into multiple specialized machine learning models, each trained on specific solution categories and their associated requirements and configuration attributes. This segmentation allows each model to achieve high accuracy for its domain while the overall system manages complexity through modular architecture where models can be independently developed, trained, and maintained.
3Measurement precision
If coordination between users and suppliers is enhanced, then product selection accuracy is improved, but time consumption increases
Solution Approach 1:
The patent implements a self-service recommendation system where users independently input their requirements and receive automated configuration recommendations without needing to coordinate with suppliers or consultants. The machine learning models automatically match user requirements with suitable product configurations, eliminating time-consuming human coordination while maintaining accurate product selection.
Data Source
AI summary
Techniques and solutions are provided for obtaining a suggested configuration for a configurable object. Typically, a particular object and object configuration are recommended based on technical characteristics of the object. However, a user or process wishing to obtain a recommendation may be more familiar with their operational requirements. Disclosed techniques can include an overall solutions category containing solutions of different solutions category subtypes. Sets of requirements attributes and configuration (technical) attributes can be defined for the solutions category. In some cases, a first machine learning model is trained using input values for the requirements attributes and the configuration attributes, and is used to recommend a particular solution in response to a set of input requirement attribute values. Different machine learning models can be trained for the various solutions, including using the configuration attributes for a particular solution, and can be used to recommend a configuration of a selected/recommend solution.