Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 7, 2026 has been entered.
Response to Amendment
Claims 1-6, 8, 11-20 are pending. Claims 9-10 and 21 have been canceled. Claim 1 has been amended. Claim 21 is new. Claims 16-20 have been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, and 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koswara (US 2019/0176122) in view of Dawe (US 4,32,761).
Regarding claim 1, Koswara discloses a flow reactor system ([0007], Figs. 1A-D) comprising: a plurality of liquid pumps for communicating a plurality of liquid reagents based on a set of flow conditions (syringe pumps, [0061], Fig. 1B); a fluid pump for communicating a carrier fluid that is immiscible with the liquid reagents (mass flow controller communicates N2, [0135], Fig. 1B);
a fluidic mixer for mixing the liquid reagents into a liquid mixture (microreactor to channel before liquid cooler, [0113], Fig. 1B), the fluidic mixer comprising: a plurality of liquid inlets for receiving the liquid reagents from the liquid pumps (check valves before microreactor, Fig. 1B); a fluid inlet for receiving the carrier fluid from the fluid pump (inlet after mass flow controller, Fig. 1B); and an outlet for discharging the liquid mixture and the carrier fluid (outlet from liquid cooler, Fig. 1B), wherein the liquid mixture is discharged from the outlet as a series of liquid plugs separated by the carrier fluid (slug flow crystallizer, Fig. 1B); a fluidic tubing connected to the outlet (tubing through slug flow crystallizer, Fig. 1B), such that the liquid plugs are discharged from the outlet into the fluidic tubing and dispensable from the fluidic tubing (can be dispensed to filtration or other structure, Fig. 1B); a measurement device connected to the fluidic tubing for measuring properties of the liquid plugs flowing through the fluidic tubing before dispensing therefrom ([0119], Fig. 1B video microscope; phototransistor, [0136]), the measured properties comprising a flow rate of the liquid plugs ([0030]); the measurement device configured for measuring the flow rate of the liquid plugs flowing through the fluid tubing before dispensing therefrom ([0030]); and a control module configured for controlling the liquid pumps and adjusting the flow conditions based on the measured properties of the liquid plugs, wherein the liquid plugs are representative of different flow conditions (feedback control regarding the pressure for the system, inclusive of the pumps, [0032] [0061-63] [0136]), wherein the flow rate of the liquid plugs is variable based on the different flow conditions to thereby facilitate dispensation from the fluidic tubing (feedback control regarding the pressure for the system, inclusive of the pumps, [0032] [0061-63] [0136]); wherein the control module is configured for training a machine learning model using training data derived from the measured properties (use of control theory with feedback control constitutes machine learning, [0025] [0032] [0097]); and wherein the machine learning model is trained to iteratively generate new sets of flow conditions to optimize one or more pre-selected properties of the liquid plugs (feedback control iteratively generates new flow conditions, adjustment in response to sensor input changes flow conditions, and this continues as feedback continues to lead to adjustments, seeking to optimize for a setpoint in feedback control, [0025] [0032] [0097]).
Koswara teaches a system substantially as claimed. Koswara does not disclose the measurement device comprising a first photodetector and a second photodetector disposed at a fixed distance from each other, the photodetectors configured for measuring the flow rate of the liquid plugs flowing through the fluid tubing before dispensing therefrom.
However, in the same field of endeavor of using photodetectors to analyze the flow of liquid plugs/drops through a tube (abstract), Dawe teaches the measurement device comprising a first photodetector and a second photodetector disposed at a fixed distance from each other (col. 1 ll. 60 to col. 2 ll. 21; col. 3 ll. 34-61), the photodetectors configured for measuring the flow rate of the liquid plugs flowing through the fluid tubing before dispensing therefrom (col. 1 ll. 60 to col. 2 ll. 21; col. 3 ll. 34-61).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Koswara to use two photodetectors as in Dawe because the abstract of Dawe teaches that doing so is an accurate way to determine volume of flow irrespective of velocity changes in the system.
Regarding claim 2, Koswara as modified teaches wherein the flow conditions comprise volume and flow rate of the liquid reagents (process decision variables include flow-rate which are being achieved in part by the syringe pumps, [0032] [0061]; also volume to precision, [0023] [0077]).
Regarding claim 3, Koswara as modified teaches a mass flow controller for controlling the fluid pump (flow controller of the syringe pumps, [0030]).
Regarding claim 4, Koswara as modified teaches wherein the mass flow controller is configured to control the fluid pump to communicate the carrier fluid at up to 5 secm (configured to control the flow rate nL to mL is within the range of up to 5 sccm, [0030]).
Regarding claim 5, Koswara as modified teaches wherein the mass flow controller is configured to automatically compensate for fluid pressure changes (feedback control regarding the pressure for the system, inclusive of the pumps, [0030] [0032] [0061-63] [0136]).
Regarding claim 6, Koswara as modified teaches wherein the measured properties further comprise volume (Koswara [0023] [0030]; as modified, photodetectors of Dawes measure volume (time between detection of front and back of plug, divided by speed, times area of tube), col. 1 ll. 60 to col. 2 ll. 21; col. 3 ll. 34-61) and intensity (photodiode detection of light excited by a light source, [0080-81]) of the liquid plugs ([0032] [0136]) that are measured by the photodetectors (as modified, photodetectors of Dawes measure volume (time between detection of front and back of plug, divided by speed, times area of tube), col. 1 ll. 60 to col. 2 ll. 21; col. 3 ll. 34-61; intensity by photodiode detection in Koswara [0080-81]).
Regarding claim 11, Koswara as modified teaches wherein a liquid plug is dispensable from the fluidic tubing by drop casting as a thin film on a substrate (droplet generator on a moving arm that has a tubular channel that the liquid passes through, [0089-95]).
Regarding claim 12, Koswara as modified teaches a tube holder coupled to the fluidic tubing for facilitating dispensation of the liquid plugs (moving arm is attached to and holds the droplet generator, this constitutes a tube holder because the dispenser has a tubular channel that the liquid passes through/is the fluid chamber, [0089-95].
Regarding claim 13, Koswara as modified teaches an actuation assembly for dispensing the liquid plugs over an area (droplet generator actuates, such as a piezoelectric droplet-on-demand generator, [0089]; the droplet generator moves over the area of a stationary substrate, [0095]).
Regarding claim 14, Koswara as modified teaches wherein the tube holder is coupled to the actuation assembly such that the tube holder is moveable horizontally (droplet generator moves along stationary substrate, as explained relative to the alternative of moving the substrate, movement is in x and y directions, [0095]).
Regarding claim 15, Koswara as modified teaches wherein the tube holder and actuation assembly are integrated in a 3D printing machine (ink-jet printing in x, y, and z directions over a stationary substrate to dispense droplets comprise the components for 3D printing, [0092] [0095]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Koswara (US 2019/0176122) in view of Dawe (US 4,32,761) as applied to claim 1 above, and further in view of Hirata (US 2017/0304535).
Regarding claim 8, Koswara in view of Dawe teaches a system substantially as claimed. Dawe does not teach the relevant wavelength of the photodetector.
However, in the same field of endeavor of using photodetectors to analyze passing liquid (abstract, [0163]), Hirata teaches wherein the photodetectors comprise infrared photointerrupters ([0203]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the system of Koswara to use infrared photointerrupters because Dawe is silent as to the wavelength and [0203] of Hirata teaches infrared photointerrupters performing the same task in the same technical context to detect the passage of liquid and therefore the modification would have predictably performed the function.
Response to Arguments
Applicant's arguments filed July 7, 2026 with respect to the rejections under 35 USC 103 have been fully considered but they are not persuasive. Applicant argues that Koswara’s “conventional” feedback control is materially distinct from the machine learning-driven optimization loop of Applicant’s claims. This argument is not persuasive because, for a given set of process conditions that do not reach the setpoint being optimized for, the feedback control, following conventional control theory, changes process conditions, gradually optimizing for the setpoint with newly set process conditions. Applicant does not persuasively argue that the recited machine learning is different. A specific machine learning algorithm has not been cited that would differentiate from the methods used by Koswara.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang (CN 111896616) teaches the use of a neural network for analyzing a liquid plug flow system and performing optimization training with the neural network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS J CHIDIAC whose telephone number is (571)272-6131. The examiner can normally be reached 8:30 AM - 6:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam Xiao Zhao can be reached at 571-270-5343. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICHOLAS J CHIDIAC/ Examiner, Art Unit 1744
/XIAO S ZHAO/ Supervisory Patent Examiner, Art Unit 1744
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