Machine Learning Configuration from User Requirements
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Solution Overview
Problem
There are knowledge gaps between domain experts who understand data needs but lack technical expertise to implement suitable data models or product configurations, and users who know their operational requirements but lack knowledge of suitable products or configurations, leading to inefficient coordination and time-consuming recommendations.
Innovation Solution
A guided product recommendation process using machine learning models that receive user needs information, recommend suitable products, and configure them based on training data from sales records, allowing users to select and configure products through user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain experts understand data needs but lack technical expertise to implement data models, then domain knowledge accuracy is improved, but technical implementation capability deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediaries between domain experts and technical implementation. The models translate domain knowledge about data needs into technical data model specifications automatically, eliminating the need for domain experts to possess technical implementation skills while preserving the accuracy of domain requirements.
2Loss of information
If users know operational requirements but lack knowledge of suitable products, then requirements clarity is improved, but product selection capability deteriorates
Solution Approach 1:
The patent enables users to perform product configuration themselves through the machine learning model. The system automatically analyzes operational requirements and generates suitable product configurations without needing expert intervention, allowing users to obtain tailored product recommendations based on their own input.
Solution Approach 2:
The patent replaces the mechanical system of expert consultation with an automated machine learning system. Instead of relying on human experts to interpret requirements and recommend products, the system uses algorithms to automatically translate operational requirements into product configurations, scaling the capability beyond what any single expert could provide.
3Measurement precision
If expert consultation is used for product recommendations, then recommendation accuracy is improved, but coordination time deteriorates
Solution Approach 1:
The patent employs machine learning models that have been pre-trained on extensive product and requirements data. This preliminary training allows the system to instantly generate accurate recommendations without requiring real-time expert consultation, significantly reducing coordination time while maintaining recommendation quality.
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.