Expert-Enhanced Quantitative Formulation Network Model

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Solution Overview

Problem

Modern product formulation is complex and often lacks a centralized source of institutional knowledge, leading to formulation data gaps, anomalies, and conflicts that reduce efficiency and accuracy in product formulations.

Innovation Solution

A computer-implemented method for creating a virtual product formulation using an expert-enhanced quantitative formulation network, which sources qualitative expert data to create a qualitative network, expands it with additional data, and transforms it into a quantitative network to design a virtual product formulation that satisfies specific objectives.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If formulation knowledge is distributed across multiple sources without centralization, then expert knowledge can be preserved in its original form, but formulation data gaps, anomalies, and conflicts occur that reduce efficiency and accuracy

Engineering Contradiction:

Improveformulation data accuracyVSAvoidformulation system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent combines multiple distributed formulation data sources into a single centralized formulation network model that integrates expert knowledge, historical data, and product information. This merging eliminates data gaps and conflicts while maintaining the reliability of original sources through structured integration.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The formulation network model serves multiple functions simultaneously: it stores expert knowledge, manages formulation data, identifies relationships between variables, and generates new formulation insights. This multi-functionality resolves the contradiction by providing a universal platform that handles diverse formulation tasks without requiring separate systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Loss of information

If a centralized formulation network model is created to integrate all formulation data, then formulation data gaps and conflicts are reduced, but the complexity of creating and maintaining the model increases

Engineering Contradiction:

Improveformulation data completenessVSAvoidformulation model complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The formulation network model is segmented into distinct components including formulation variables, parameters, constraints, and relationships. This segmentation allows the complex model to be built and maintained through modular units, reducing the overall complexity while ensuring complete information integration.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces computational algorithms and data processing intermediaries that automatically manage the integration of formulation data into the network model. These intermediaries handle the complexity of data consolidation, relationship mapping, and conflict resolution, reducing the burden of model maintenance while ensuring data completeness.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If expert formulation knowledge is captured in qualitative form, then expert insights are preserved accurately, but the knowledge cannot be easily processed or transferred into new product formulations

Engineering Contradiction:

Improveexpert knowledge accuracyVSAvoidformulation development efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent transforms qualitative expert knowledge parameters into quantitative parameters that can be processed computationally. This parameter transformation maintains the accuracy of expert insights while enabling automated processing, relationship analysis, and application to new formulation scenarios, thereby improving productivity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces manual qualitative knowledge processing with computational algorithms and automated systems. This substitution maintains the integrity of expert knowledge while enabling efficient processing, storage, and application to multiple formulation scenarios, significantly improving formulation development efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11748529B2Systems and methods for generating an extensible digital formulation network model and implementing an intelligent formulation using the formulation network model

Publication Date: 2023.09.05 TURING LABS INC

AI summary

A method and system for an accelerated design of a virtual product formulation based on an expert-enhanced quantitative formulation network includes sourcing qualitative expert formulation; creating a qualitative formulation network; extracting qualitative network-expansion data based on a category associated with a target product associated with the qualitative formulation network, creating a second set of network components including formulation variable nodes and formulation edge connections; integrating the second set of network components into the qualitative formulation network; transforming the qualitative formulation network integrated with the second set of network components to a quantitative formulation network; designing at least part of a virtual product formulation based on the quantitative formulation network; and generating a target formulation proposal that likely satisfies the target formulation objective based on executing the virtual product formulation as initialized.