Characterization and sorting for particle analyzers
Number of patents in Portfolio can not be more than 2000
United States of America
Stats
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May 10, 2022
Grant Date -
Mar 5, 2020
app pub date -
Aug 30, 2019
filing date -
Aug 30, 2019
priority date (Note) -
In Force
status (Latency Note)
Importance
Abstract
Non-parametric transforms such as t-distributed stochastic neighbor embedding (tSNE) are used to analyze multi-parametric data such as data derived from flow cytometry or other particle analysis systems and methods. These transforms may be included for dimensionality reduction and identification of subpopulations (e.g., gating). By nature, non-parametric transforms cannot transform new observations without training a new transformation based on the entire dataset including the new observations. The features described parameterize non-parametric transforms using a neural network thereby allowing a small training dataset to be transformed using non-parametric techniques. The training dataset may then be used to generate an accurate parametric model for assessing additional events in a manner consistent with the initial events.
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International Classification(s)
- 2019 Application Filing Year
- G06N Class
- 15814 Applications Filed
- 9207 Patents Issued To-Date
- 58.23 % Issued To-Date
Cited Art Landscape
Patent Citation Ranking
- 1 Citation Count
- G06N Class
- 73.15 % this patent is cited more than
- 3 Age
Maintenance Fees
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|---|---|---|---|---|
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