US 11,861,889 B2
Analysis device
Sadao Ota, Tokyo (JP); Issei Sato, Tokyo (JP); Katsuhito Fujiu, Tokyo (JP); Satoko Yamaguchi, Tokyo (JP); Kayo Waki, Tokyo (JP); Yoko Itahashi, Tokyo (JP); and Ryoichi Horisaki, Osaka (JP)
Assigned to The University of Tokyo, Tokyo (JP); and Osaka University, Tokyo (JP)
Filed by The University of Tokyo, Tokyo (JP); and OSAKA UNIVERSITY, Osaka (JP)
Filed on Nov. 29, 2022, as Appl. No. 18/059,846.
Application 18/059,846 is a continuation of application No. 17/351,117, filed on Jun. 17, 2021, granted, now 11,542,461.
Application 17/351,117 is a continuation of application No. 15/771,180, granted, now 11,098,275, issued on Aug. 24, 2021, previously published as PCT/JP2016/082089, filed on Oct. 28, 2016.
Claims priority of provisional application 62/372,321, filed on Aug. 9, 2016.
Claims priority of application No. 2015-212356 (JP), filed on Oct. 28, 2015.
Prior Publication US 2023/0237789 A1, Jul. 27, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G01N 21/01 (2006.01); G06V 10/82 (2022.01); G01N 15/14 (2006.01); G01N 21/27 (2006.01); G01N 21/64 (2006.01); G06V 20/69 (2022.01); G06F 18/28 (2023.01); G06F 18/21 (2023.01); G06V 10/772 (2022.01); G06V 10/778 (2022.01); C12M 1/34 (2006.01); G01N 21/65 (2006.01); G01N 15/10 (2006.01)
CPC G06V 10/82 (2022.01) [C12M 1/34 (2013.01); G01N 15/14 (2013.01); G01N 15/1404 (2013.01); G01N 15/147 (2013.01); G01N 15/1429 (2013.01); G01N 15/1434 (2013.01); G01N 15/1459 (2013.01); G01N 21/01 (2013.01); G01N 21/27 (2013.01); G01N 21/64 (2013.01); G01N 21/65 (2013.01); G06F 18/2178 (2023.01); G06F 18/28 (2023.01); G06V 10/772 (2022.01); G06V 10/7784 (2022.01); G06V 20/698 (2022.01); G01N 2015/1006 (2013.01); G01N 2015/145 (2013.01); G01N 2015/1413 (2013.01); G01N 2015/1415 (2013.01); G06F 2218/12 (2023.01)] 19 Claims
OG exemplary drawing
 
1. An analysis system classifying at least one target object among one or more observed objects comprising:
(a) one or more flow cytometers comprising:
(i) at least one flow path configured to permit one or more observed objects to flow therethrough; and
(ii) a light-receiving unit comprising one or more sensors, wherein the one or more sensors are configured to (i) receive at least one electromagnetic wave from the one or more observed objects and (ii) convert the at least one electromagnetic wave into one or more time-series electrical signals;
(b) one or more processors configured to perform machine learning to train a classification model based on the one or more time-series electrical signals from the one or more flow cytometers, wherein the one or more time-series electrical signals comprise one or more signals obtained from the at least one target object; and
(c) one or more logic circuits configured to analyze the one or more time-series electrical signals from the one or more flow cytometers to classify or recognize the at least one target object among the one or more observed objects using the classification model.