IP Library › Granted Patent US 12,257,378
Granted Patent B2
US 12,257,378 · App. 18/599,377 · Granted Mar 25, 2025

Dynamic adjustment of algorithms for separation and collection of blood components

Inventor: Brian C. Case (Lake Villa, IL)
Assignee: Fenwal, Inc.
A61M1/3696B01D21/262B01D21/30B01D63/16
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,257,378
App. No.
18/599,377
Granted
Mar 25, 2025
Kind
B2
Abstract

Blood is conveyed from a source into a separator, which separates at least one target blood component from the blood. The target blood component is then conveyed out of the separator, with the procedure continuing until an initial target amount of blood to be processed has been conveyed from the source into the separator and the target blood component separated from the initial target amount of blood to be processed has been conveyed out of the separator as an actual yield of the target blood component. An adjusted target amount of blood to be processed is then determined based at least in part on the difference between a target yield of the target blood component and the actual yield. The initial target amount of blood to be processed is then replaced with the adjusted target amount of blood to be processed when next executing the procedure.

Claims (39)

1. A blood processing system, comprising:

a blood separation device including

a separator configured to receive a blood separation chamber,

a pump system, and

a controller configured to control the separator and the pump system to execute a blood separation procedure having a target yield of at least one target blood component using an initial target amount of blood from a blood source, with execution of the blood separation procedure resulting in an actual yield of said at least one target blood component; and

a data processing system configured to communicate with the controller, wherein

the data processing system is configured to

access or calculate the difference between a plurality of actual yields and target yields for blood separation procedures executed for a plurality of different blood sources,

select two or more of said differences,

determine an average of said two or more differences,

determine an adjusted target amount of blood to be processed when executing the blood separation procedure for a subsequent blood source or a scaling factor to be applied when determining the adjusted target amount of blood to be processed when executing the blood separation procedure for said subsequent blood source based at least in part on said average, and

transmit said adjusted target amount of blood or said scaling factor to the controller when the blood separation device is to be used to execute a blood separation procedure for said subsequent blood source and the controller is further configured to control the separator and the pump system to execute said blood separation procedure for said subsequent blood source, with the initial target amount of blood being replaced with said adjusted target amount of blood.

2. The blood processing system of claim 1 , wherein the data processing system is configured to select said two or more differences based on a similarity between at least one of the sex, height, weight, ethnicity, hematocrit, pre-count of a cellular component of interest, and protein count among the plurality of different blood sources.

3. The blood processing system of claim 1 , wherein the data processing system is configured to apply the same weight to each one of said two or more differences when determining said average.

4. The blood processing system of claim 1 , wherein the data processing system is configured to apply different weights to at least two of said two or more differences when determining said average.

5. The blood processing system of claim 4 , wherein the weight to be assigned to at least one of said differences is determined by the data processing system using exponential smoothing techniques.

6. The blood processing system of claim 4 , wherein the weight to be assigned to at least one of said differences is determined by the data processing system using double exponential smoothing techniques.

7. The blood processing system of claim 4 , wherein the weight to be assigned to at least one of said differences is determined by the data processing system using triple exponential smoothing techniques.

8. The blood processing system of claim 4 , wherein the data processing system is configured to calculate a moving average when determining said average.

9. The blood processing system of claim 8 , wherein the data processing system is configured to calculate a second moving average from the moving average when determining said average.

10. The blood processing system of claim 1 , wherein the data processing system is configured to employ machine learning techniques when selecting said two or more differences to determine the number of differences to be selected so as to produce the smallest mean squared error when determining said average.

11. A blood separation method, comprising:

(a) conveying blood from a blood source into a separator,

(b) separating at least one target blood component from the blood in the separator,

(c) conveying said at least one target blood component out of the separator,

(d) continuing to execute (a)-(c) until an initial target amount of blood to be processed has been conveyed from the blood source into the separator and said at least one target blood component separated from the initial target amount of blood to be processed has been conveyed out of the separator as an actual yield of said at least one target blood component,

(e) recording a difference between a target yield of said at least one target blood component and the actual yield,

(f) repeating (a)-(e) for a plurality of different blood sources,

(g) determining a scaling factor based at least in part on an average of at least two of said recorded differences, and

(h) executing (a)-(d) for a subsequent blood source, with the initial target amount of blood to be processed being replaced with an adjusted target amount of blood to be processed based at least in part on said scaling factor.

12. The blood separation method of claim 11 , wherein said two or more recorded differences are selected based on a similarity between at least one of the sex, height, weight, ethnicity, hematocrit, pre-count of a cellular component of interest, and protein count among the plurality of different blood sources.

13. The blood separation method of claim 11 , wherein the same weight is applied to each one of said two or more differences when determining said average.

14. The blood separation method of claim 11 , wherein different weights are applied to at least two of said two or more differences when determining said average.

15. The blood separation method of claim 14 , wherein the weight to be assigned to at least one of said recorded differences is determined using exponential smoothing techniques.

16. The blood separation method of claim 14 , wherein the weight to be assigned to at least one of said recorded differences is determined using double exponential smoothing techniques.

17. The blood separation method of claim 14 , wherein the weight to be assigned to at least one of said recorded differences is determined using triple exponential smoothing techniques.

18. The blood separation method of claim 14 , wherein a moving average is calculated when determining said average.

19. The blood separation method of claim 18 , wherein a second moving average is calculated from the moving average when determining said average.

20. The blood separation method of claim 11 , wherein machine learning techniques are employed when selecting said two or more recorded differences to determine the number of recorded differences to be selected so as to produce the smallest mean squared error when determining said average.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 8, 2024
From: CASE, BRIAN C.
To: FENWAL, INC.
Reel/Frame 066691/0789 →
Continuity (3)
Continuation 17019397 · Sep 14, 2020
Provisional Application 62900957 · Sep 16, 2019
Related Publication 20240238499A1 · Jul 18, 2024
References Cited (110)
US 5194145A · Schoendorfer · 1993 [cited by applicant]
US 5632893A · Brown et al. · 1997 [cited by applicant]
US 5637082A · Pages et al. · 1997 [cited by applicant]
US 5868696A · Giesler et al. · 1999 [cited by applicant]
US 6251284B1 · Bischof et al. · 2001 [cited by applicant]
US 6312607B1 · Brown et al. · 2001 [cited by applicant]
US 6419822B2 · Muller et al. · 2002 [cited by applicant]
US 6471855B1 · Odak et al. · 2002 [cited by applicant]
US 6579219B2 · Dolecek et al. · 2003 [cited by applicant]
US 6582386B2 · Min et al. · 2003 [cited by applicant]
US 6629919B2 · Egozy et al. · 2003 [cited by applicant]
US 6706008B2 · Vishnoi et al. · 2004 [cited by applicant]
US 6770883B2 · McNeal et al. · 2004 [cited by applicant]
US 6808503B2 · Farrell et al. · 2004 [cited by applicant]
US 6866826B2 · Moore et al. · 2005 [cited by applicant]
US 6869411B2 · Langley et al. · 2005 [cited by applicant]
US 6884228B2 · Brown et al. · 2005 [cited by applicant]
US 7011761B2 · Muller · 2006 [cited by applicant]
US 7041076B1 · Westberg et al. · 2006 [cited by applicant]
US 7049622B1 · Weiss · 2006 [cited by applicant]
US 7081082B2 · Scholz et al. · 2006 [cited by applicant]
US 7150834B2 · Mueth et al. · 2006 [cited by applicant]
US 7186230B2 · Briggs et al. · 2007 [cited by applicant]
US 7186231B2 · Takagi et al. · 2007 [cited by applicant]
US 7211037B2 · Briggs et al. · 2007 [cited by applicant]
US 7294513B2 · Wyatt · 2007 [cited by applicant]
US 7347948B2 · Dolecek et al. · 2008 [cited by applicant]
US 7354515B2 · Coull et al. · 2008 [cited by applicant]
US 7381291B2 · Tobe et al. · 2008 [cited by applicant]
US 7422693B2 · Carter et al. · 2008 [cited by applicant]
US 7485084B2 · Borgstrom et al. · 2009 [cited by applicant]
US 7563376B2 · Oishi · 2009 [cited by applicant]
US 7648639B2 · Holmes et al. · 2010 [cited by applicant]
US 7708710B2 · Min et al. · 2010 [cited by applicant]
US 7806845B2 · Arm et al. · 2010 [cited by applicant]
US 7906771B2 · Carter et al. · 2011 [cited by applicant]
US 7951059B2 · Sweat · 2011 [cited by applicant]
US 8057377B2 · Holmes et al. · 2011 [cited by applicant]
US 8075468B2 · Min et al. · 2011 [cited by applicant]
US 8163276B2 · Hedrick et al. · 2012 [cited by applicant]
US 8287742B2 · Holmes · 2012 [cited by applicant]
US 8317672B2 · Nash et al. · 2012 [cited by applicant]
US 8337379B2 · Fletcher et al. · 2012 [cited by applicant]
US 8535210B2 · Kolenbrander et al. · 2013 [cited by applicant]
US 8545427B2 · Holmer et al. · 2013 [cited by applicant]
US 8556793B2 · Foley et al. · 2013 [cited by applicant]
US 8628489B2 · Pages et al. · 2014 [cited by applicant]
US 8758211B2 · Nash et al. · 2014 [cited by applicant]
US 8870804B2 · Jonsson · 2014 [cited by applicant]
US 8974362B2 · Nash et al. · 2015 [cited by applicant]
US 9011687B2 · Swift et al. · 2015 [cited by applicant]
US 9156039B2 · Holmes et al. · 2015 [cited by applicant]
US 9302042B2 · Pages · 2016 [cited by applicant]
US 9302276B2 · Pesetsky et al. · 2016 [cited by applicant]
US 9370615B2 · Ragusa et al. · 2016 [cited by applicant]
US 9399182B2 · Pesetsky et al. · 2016 [cited by applicant]
US 9550016B2 · Gifford · 2017 [cited by applicant]
US 9610590B2 · Hamandi · 2017 [cited by applicant]
US 9789235B2 · Gifford et al. · 2017 [cited by applicant]
US 10086128B2 · Kyle et al. · 2018 [cited by applicant]
US 10166322B2 · Sweat et al. · 2019 [cited by applicant]
US 10238787B2 · Takuwa · 2019 [cited by applicant]
US 10293097B2 · Murphy et al. · 2019 [cited by applicant]
US 10399881B2 · Donais et al. · 2019 [cited by applicant]
US 10413652B2 · Foley et al. · 2019 [cited by applicant]
US 10493467B2 · Lundquist et al. · 2019 [cited by applicant]
US 10518007B2 · Kimura · 2019 [cited by applicant]
US 10561783B2 · Hamandi et al. · 2020 [cited by applicant]
US 20020046975A1 · Langley et al. · 2002 [cited by applicant]
US 20020128583A1 · Min et al. · 2002 [cited by applicant]
US 20040195190A1 · Min et al. · 2004 [cited by applicant]
US 20050049539A1 · O'Hara, Jr. et al. · 2005 [cited by applicant]
US 20090215602A1 · Min et al. · 2009 [cited by applicant]
US 20110003675A1 · Dolecek · 2011 [cited by applicant]
US 20110294641A1 · Dolecek et al. · 2011 [cited by applicant]
US 20120010062A1 · Fletcher et al. · 2012 [cited by applicant]
US 20140378292A1 · Igarashi · 2014 [cited by applicant]
US 20150068959A1 · Zheng · 2015 [cited by applicant]
US 20150104824A1 · Walker · 2015 [cited by applicant]
US 20150218517A1 · Kusters et al. · 2015 [cited by applicant]
US 20150367063A1 · Kimura · 2015 [cited by applicant]
US 20160166755A1 · Golarits et al. · 2016 [cited by applicant]
US 20160175509A1 · Planas et al. · 2016 [cited by applicant]
US 20170153431A1 · Nguyen et al. · 2017 [cited by applicant]
US 20170354770A1 · Radwanski et al. · 2017 [cited by applicant]
US 20180043374A1 · Meinig et al. · 2018 [cited by applicant]
US 20180164141A1 · Bordignon et al. · 2018 [cited by applicant]
US 20180185772A1 · Karhiniemi et al. · 2018 [cited by applicant]
US 20190003873A1 · Araujo et al. · 2019 [cited by applicant]
US 20190030545A1 · Hamada et al. · 2019 [cited by applicant]
US 20190083696A1 · Igarashi · 2019 [cited by applicant]
EP 1946784B1 · 2012 [cited by applicant]
WO WO2002069793A2 · 2002 [cited by applicant]
WO WO2012091720A1 · 2012 [cited by applicant]
WO WO2012125457A1 · 2012 [cited by applicant]
WO WO2013043433A2 · 2013 [cited by applicant]
WO WO2014039091A1 · 2014 [cited by applicant]
WO WO2018053217A1 · 2018 [cited by applicant]
WO WO2018154115A2 · 2018 [cited by applicant]
WO WO2019047498A1 · 2019 [cited by applicant]
WO WO2019165478A1 · 2019 [cited by applicant]
WO WO2020002059A1 · 2020 [cited by applicant]
WO WO2020055958A1 · 2020 [cited by applicant]
Extended European Search Report, dated Feb. 8, 2021, for application No. EP20195921.0-1113. [cited by applicant]
Extended European Search Report, dated Apr. 25, 2022, for application No. EP21214264.0-1113. [cited by applicant]
A. Shokry, et.al, Dynamic Optimization of Batch Processes under Uncertainty via Meta-MultiParametric Approach, Computer Aided Chemical Engineering, vol. 40, 2017, pp. 2215-2220, ISSN 1570-7946, ISBN 97804446399653, http… [cited by applicant]
B. Srinivasan, et. al, Dynamic optimization of batch processes: I. Characterization of the nominal solution, Computers & Chemical Engineering, vol. 27, Issue 1, 2003, pp. 1-26, ISSN 0098-1354, https://doi.org/10.1016/S0… [cited by applicant]
Kai Liu, et. al, A survey of run-to-run control for batch processes, ISA Transactions, vol. 83, pp. 107-125, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2018.09.005. (Year: 2018). [cited by applicant]
B. Srinivasan, et. al, Dynamic optimization of batch processes: II. Role of mesaurements in handling uncertainty, Computers & Chemical Engineering, vol. 27, Issue 1, 2003, pp. 27-44, ISSN 0098-1354, https://doi.org/10.1… [cited by applicant]
Y. Wang, et. al, Survey on iterative learing control, repetitive control, and run-to-run control, Journal of Process Control, vol. 19, Issue 10, 2009, pp. 1589-1600, ISSN 0959 1524, https://doi.org/10.org/10.1016/j.jpro… [cited by applicant]