Raw Raman Spectral Vector Matching for Bioreactor Monitoring

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Solution Overview

Problem

Existing methods for monitoring chemical interactions in non-directly-viewable environments, such as bioreactors, suffer from inefficient and slow manual data matching of spectral vector data, leading to missed changes and reactive control, which is costly and inefficient.

Innovation Solution

A system and method for automatically matching and evaluating spectral vector data with time series data to proactively control chemical interactions, using AI optimization and machine learning models to process large volumes of data rapidly and accurately.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual data matching methods are used for spectral vector data, then data processing can be performed with simple equipment, but the processing speed is slow and changes are missed

Engineering Contradiction:

Improvedata processing speedVSAvoidtime delay in detecting chemical changes

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical data matching with an automated computer-based system that uses algorithms to match spectral vectors with time series data, eliminating the slow manual process and enabling real-time detection of chemical changes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically matching and evaluating spectral data without requiring manual intervention, continuously processing data streams and identifying chemical changes autonomously in real-time

Inventive Principle:

Principle #25Self-service

2Extent of automation

If manual data matching is used, then system complexity is low, but control is reactive and costly

Engineering Contradiction:

Improveautomation of data matchingVSAvoidsystem complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces simple manual operations with an automated computer-based system that uses spectral vector analysis and time series matching algorithms, increasing automation while managing complexity through software-based solutions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary computational layer that automatically matches spectral data with time series data, enabling proactive control without requiring direct manual intervention in the data analysis process

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If rapid data processing is implemented, then proactive control is enabled, but data processing complexity increases

Engineering Contradiction:

Improveaccuracy of chemical change detectionVSAvoiddata processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the complex data processing task into distinct components: spectral vector extraction, time series data preparation, matching algorithms, and evaluation steps, making the overall complex process manageable and reliable

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system uses computer-based automated algorithms to handle complex matching and evaluation tasks that would be infeasible manually, enabling reliable proactive control through sophisticated data processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250321973A1Time series matching of raw spectral vector data

Publication Date: 2025.10.16 THERMO SCIENTIFIC PORTABLE ANALYTICAL INSTRUMENTS INC

AI summary

Embodiments herein relate to a process for chemical interaction monitoring, such as employing data output from a Raman spectroscopy system relative to a composition undergoing the chemical interaction in a bioreactor. A system can comprise a memory that stores, and a processor that executes, computer executable components. The computer executable components can comprise an identifying component that identifies a raw dataset corresponding to a chemical interaction, and a matching component that generates matched data comprising a set of matches between time series data, corresponding to a range of time over which the chemical interaction was observed, and chemical interaction data comprised by the raw dataset.