Machine Learning Model for Flow Cytometry Observation Space Classification
Summary
The USPTO published patent application US20260099739A1 filed by Yoshimi Noguchi and Naoko Senda. The application covers a computer system that stores measurement data from flow cytometry and a machine learning model configured to receive feature values of regions in an observation space and output class probabilities. The system maps measurement data into an observation space, divides it into regions based on distribution features, calculates feature values for each region, and sets gates based on class probability outputs from the model.
Patent application publications represent prior art as of their filing dates and can inform competitive R&D strategies. Companies developing ML-based flow cytometry analysis tools should review these claims when assessing patent landscapes. The combination of machine learning with flow cytometry data classification may have implications for automated clinical diagnostics and drug discovery workflows.
What changed
The USPTO published patent application US20260099739A1 on April 9, 2026. The application discloses a computer system and method that uses machine learning to analyze flow cytometry measurement data. The system divides an observation space into regions using signal intensity parameters and employs a trained model to output class probabilities for each coordinate, enabling automated gating in flow cytometry analysis.
This publication affects parties developing or using machine learning systems for biological sample analysis, particularly flow cytometry applications. Inventors, researchers, and companies in biotechnology, pharmaceutical development, or clinical diagnostics may need to review the claims to assess potential licensing implications or freedom-to-operate considerations. The application represents prior art as of its July 30, 2025 filing date.
Archived snapshot
Apr 20, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Computer System, Information Processing Method, and Non-transitory Computer-Readable Storage Medium
Application US20260099739A1 Kind: A1 Apr 09, 2026
Inventors
Yoshimi NOGUCHI, Naoko SENDA
Abstract
A computer system stores a data set constituted with measurement data including measurement results of intensities of a plurality types of signals of particles contained in a sample measured using a flow cytometry, and a machine learning model configured to receive, as an input, a feature value of a first region generated by dividing an observation space using intensities of two or more types of signals as parameters based on a distribution feature of the measurement data in the observation space, and configured to output a probability of a class to which each coordinate of the observation space belongs. The computer system maps the measurement data into the observation space, divides the observation space into a plurality of the regions based on the distribution feature of the measurement data in the observation space, calculates a feature value of each region, inputs the feature value of each region into the machine learning model, and sets a gate based on the probability of the class to which each coordinate in the observation space belongs, which is output from the model.
CPC Classifications
G06N 7/01
Filing Date
2025-07-30
Application No.
19284823
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