Formulated Product Design System Using User Conversation and Prediction Models
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Solution Overview
Problem
Conventional methods for designing formulated products, such as personal care and detergents, are labor-intensive and time-consuming, and struggle to effectively capture and analyze user requirements to produce customized or personalized formulations.
Innovation Solution
A system and method that involves continuous conversation with users to extract functional requirements, using prediction models to determine optimal ingredient compositions and process parameters based on user intent, facilitated by a processor-implemented system with conversation, prediction, and optimization modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lab-based trial and error methodologies are used for designing formulations, then functional and sensorial attributes can be achieved, but the process becomes labor intensive and time consuming
Solution Approach 1:
The system performs preliminary analysis of user requirements, ingredient properties, and formulation interactions before actual formulation creation. Prediction models pre-assess potential formulation outcomes, allowing the system to narrow down the solution space before laboratory validation, thereby reducing the number of trial-and-error iterations needed.
Solution Approach 2:
The patent replaces manual laboratory trial-and-error processes with computer-based prediction models and automated optimization algorithms. The system uses computational methods to predict formulation properties and optimize ingredient combinations, substituting the mechanical trial-and-error approach with intelligent computational design.
2Loss of information
If conventional techniques are used to capture and analyze user requirements, then some user needs can be understood, but the techniques are limited and challenging to map user requirements to functional requirements accurately
Solution Approach 1:
The system introduces an intermediary layer of natural language processing and semantic analysis between user requirements and technical specifications. This intermediary layer translates ambiguous user language into structured functional requirements, bridging the gap between user intent and formulation specifications with higher accuracy.
Solution Approach 2:
The system dynamically adjusts the granularity and detail level of requirement analysis based on the complexity of user input. It transforms qualitative user descriptions into quantitative formulation parameters, adapting the analysis depth to match the specificity of user requirements while maintaining mapping accuracy.
3Adaptability or versatility
If customized or personalized formulations are created based on user requirements, then market innovation and user satisfaction increase, but the complexity of capturing and analyzing individual user requirements increases
Solution Approach 1:
The system employs a universal requirement analysis framework that handles diverse user inputs through a single integrated platform. The same prediction models and optimization algorithms serve multiple formulation types and user scenarios, achieving high customization capability without proportionally increasing system complexity.
Solution Approach 2:
The system uses template-based formulation structures and reusable ingredient profiles that can be adapted to individual user requirements. By maintaining master templates of successful formulations and ingredient combinations, the system can quickly generate personalized formulations without analyzing every parameter from scratch, thus managing complexity while enabling customization.
Data Source
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AI summary
This disclosure relates generally to method and system for designing the formulated products. Conventional techniques for designing the formulated products, meeting final functional properties, are limited. Further, understanding user requirements and active incorporation of the user requirements during design phase is quite challenging. The present disclosure herein provides method and system that solve the technical problem of extracting the functional requirement by establishing continuous conversation with the user. An optimal prediction function for each functional requirement is determined by using a plurality of prediction models. An optimization technique along with an objective function is employed to determine optimized solutions comprising list of ingredients, possible concentration level of each ingredient, the process parameters, and the operating parameters for obtaining the desired formulation based on the user requirement.