Model Serialization for Accurate Regulatory Documentation

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Solution Overview

Problem

Existing systems struggle to accurately and efficiently generate human-readable descriptions of models for regulatory review, leading to potential errors and costly penalties.

Innovation Solution

A network-based system and method for automatically serializing and deserializing models, using a serializer to convert models into human-readable documents and a deserializer to recreate the model from the document, ensuring accuracy and efficiency in generating and updating these descriptions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual generation of human-readable model descriptions is used, then flexibility and customization are improved, but time consumption and error rates increase

Engineering Contradiction:

Improveaccuracy of human-readable descriptionVSAvoidtime for generating model description

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automatic self-generation of human-readable model descriptions through the serialization component that automatically extracts model parameters, structure, and behavior information without requiring manual intervention, thereby resolving the contradiction between accuracy and time consumption

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes of model description generation with an automated computer-based serialization system that uses algorithms to convert model data into human-readable documents, eliminating human error and time constraints while maintaining descriptive accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated serialization is implemented, then time efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvespeed of generating model descriptionVSAvoidcomplexity of serialization system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The serialization system is segmented into distinct functional components including the serialization component, deserialization component, and validation component, each handling specific tasks independently. This modular segmentation reduces overall system complexity while maintaining high productivity through automated operations

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces standardized intermediate representations (such as JSON schemas or XML formats) as intermediaries between the model data and human-readable descriptions. These intermediaries simplify the conversion process and reduce system complexity by providing a clear, standardized interface for data transformation

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If manual review of model descriptions is performed, then accuracy can be verified, but cost and time for regulatory compliance increase

Engineering Contradiction:

Improvereliability of model description accuracyVSAvoidtime for regulatory review preparation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system incorporates validation mechanisms that provide feedback to ensure the generated descriptions accurately reflect the underlying model. The validation component checks for consistency, completeness, and accuracy of the serialized output, thereby maintaining reliability while reducing the time needed for regulatory review preparation through automated verification

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12481619B1Systems and methods for automatically serializing and deserializing models

Publication Date: 2025.11.25 QUANATA LLC

AI summary

A system serializing and deserializing models configured to (i) store a first model, wherein the first model includes a plurality of functionalities; (ii) generate a human-readable document based on the first model, wherein the human-readable document describes the first model; (iii) generate a second model based on the human-readable document, wherein the second model includes the plurality of functionalities; (iv) train the second model; (v) generate a new human-readable document based on the trained second model; and (vi) generate an updated second model based on the new human-readable document.