Multivariate Cell Selection Using Weighted Process Criteria
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Solution Overview
Problem
Current methods for selecting target cells from candidate cells are inefficient due to the complexity of processing large amounts of data, subjective analysis, and the challenge of setting hard limits for product quality attributes, leading to incorrect cell selection and resource misallocation.
Innovation Solution
A computer-implemented method that correlates data from multiple processes using multivariate evaluation criteria, including weights, prioritization ranges, and targets, to calculate scores for candidate cells and rank them objectively, thereby selecting the most suitable target cells for cell cultivation or product production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis methods are used to evaluate candidate cells, then multiple technicians can analyze data using spreadsheet programs, but the analysis becomes inconsistent and subjective, consuming many hours or days
Solution Approach 1:
The patent replaces manual mechanical analysis by technicians using spreadsheet programs with an automated computational system. The multivariate analysis system automatically processes process data, applies evaluation criteria, and ranks candidate cells, eliminating subjective human analysis while significantly reducing processing time from many hours or days to a rapid automated computation.
Solution Approach 2:
The patent transforms the analysis approach by changing from univariate or simple multivariate analysis to a comprehensive multivariate evaluation system that simultaneously considers multiple process parameters and product quality attributes with weighted priorities. This parameter transformation enables consistent, objective cell selection while automating the process.
2Ease of operation
If hard limits are set for product quality attributes to filter candidate cells, then selection criteria become simpler, but the best performing candidate cells may be incorrectly excluded
Solution Approach 1:
Instead of applying hard limits that completely exclude candidates failing any single criterion, the patent uses a weighted evaluation system where candidates receive scores based on how well they meet multiple criteria. This partial action approach allows candidates to compensate for weaknesses in one area with strengths in others, preventing premature exclusion of potentially excellent candidates while maintaining operational simplicity through automated scoring.
Solution Approach 2:
The patent replaces static hard limits with dynamic weighted evaluation criteria that can be adjusted based on process priorities and conditions. The multivariate analysis system dynamically calculates scores based on weighted combinations of process parameters and product quality attributes, allowing flexible optimization without rigid exclusion thresholds.
3Measurement precision
If multiple process parameters and product quality attributes are evaluated simultaneously, then more comprehensive cell assessment is achieved, but data processing complexity increases significantly
Solution Approach 1:
The patent segments the complex multivariate evaluation into manageable components: (1) data collection from process systems, (2) application of weighted evaluation criteria for different parameters and attributes, (3) calculation of composite scores, and (4) ranking of candidate cells. This segmentation reduces processing complexity while maintaining comprehensive evaluation by breaking down the complex analysis into systematic, automated steps.
Solution Approach 2:
The patent introduces an intermediary computational layer that processes raw process data through multivariate analysis algorithms. This intermediary system transforms complex multi-parameter data into simplified ranked outputs, mediating between the complexity of simultaneous parameter evaluation and the need for straightforward cell selection decisions.
4Reliability
If extensive data is collected from multiple processes to improve selection accuracy, then better cell performance can be identified, but incorrect selection may still occur leading to resource misallocation
Solution Approach 1:
The patent implements a feedback mechanism where the multivariate analysis system continuously evaluates candidate cells against weighted criteria and provides ranked results that guide selection decisions. This feedback loop ensures that resources are allocated to the highest-ranked candidates based on comprehensive data analysis, minimizing the risk of incorrect selection and subsequent resource waste while maximizing the likelihood of identifying truly superior cell lines.
Data Source
AI summary
According to some aspects of the disclosure, a computer-implemented method, a computer program and a process control device for selecting at least one set of target cells from multiple sets of candidate cells are provided. The method can include receiving data collected from a plurality of processes, wherein each of the processes produces a distinct set of candidate cells. The method further comprises the received data including values of process outputs being a product quality attribute or a key performance indicator for selecting the target cells.