DETAILED ACTION
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 . The objections and rejections from the Office Action of 11/20/2025 are hereby withdrawn. New grounds for rejection are presented below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/20/2026 has been entered.
Claim Objections
Claim 9 is objected to because of the following informalities:
Claim 9 – Please change “parameter parameter curves” to “pressure parameter curves.”
Appropriate correction is required.
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.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quint et al. (US 20210063361 A1)[hereinafter “Quint”], Shkolnik et al. (US 20230211349 A1)[hereinafter “Shkolnik”], Quinones (US 20170068257 A1), and Uchiyama (US 20170326771 A1).
Regarding Claim 1, Quint discloses a method for monitoring the quality of fluid handling in automated analyser systems [Paragraph [0045] – “The automated method of monitoring a state of an analyzer includes monitoring 101 an electrospray ionization current of the ESI source and identifying 107 a condition of multiple conditions of the analyzer based on the monitored ionization current of the ESI source. One of the conditions is a presence of a dead volume in a liquid chromatography stream of the analyzer downstream of an LC column of the LC stream.”], including monitoring for pipette clogging through fluid pressure measurements [Paragraph [0034] – “The analyzer can comprise functional units such as liquid handling units for pipetting”Paragraph [0066] – “In some examples, the monitoring techniques of the present disclosure further comprise monitoring one or more additional parameters including a pressure in a liquid chromatography (LC) stream of the analyzer: The identification of a condition of the analyzer is further based on the monitored pressure in the LC stream to distinguish the multiple conditions.”Paragraph [0129] – “A clogging of the fluid path can be identified if the ESI current decreases and the monitored pressure increases.”].
Shkolnik discloses a pipette cleaning method/apparatus that includes monitoring pipette control and working parameters (including cleaning fluid pressure) in order to clear the pipette of slugs and contaminants [Paragraph [0011] – “As another aspect of the invention, a pipette washing system is provided. The pipette washing system comprises a pipette washing device as described herein, and a pump or vacuum fluidically connected to the exhaust tube. In some embodiments, the pipette washing system further comprises an apparatus such as a pipettor configured for pushing a fluid through an interior of a pipette tip, and the pipette wash device is configured for washing an exterior of a pipette tip. In some embodiments, the pipettor or other apparatus comprises valves and passages for introducing liquid and pressurized air to the interior of the pipette tip.”Paragraph [0013] – “In some embodiments, the method further comprises purging the interior of the pipette tip with pressurized air or inert gas to push substantially all of the liquid slugs and contaminant, if any, out of the pipette tip.”Paragraph [0069] – “A controller such as a data processing unit, a conventional PC or workstation, can be connected to one or more of the present devices in order to receive information and/or control operation. For example, the controller might control operation of the pipettor and receive there from information regarding the actual working conditions (such as fluid pressure). The controller might also control operation of the washing liquid and/or gas supply (for instance setting control parameters such as pressure or vacuum level) and might receive therefrom information regarding the actual working conditions (such as flow rate, vacuum level, etc.). The controller might further control operation of the pipette wash device (for instance controlling washing fluid and/or gas provided to the nozzles).”] by engaging a rinsing device to prevent clogs [Paragraph [0013] – “In some embodiments, the method further comprises purging the interior of the pipette tip with pressurized air or inert gas to push substantially all of the liquid slugs and contaminant, if any, out of the pipette tip.”] by performing one or more of a plurality of tailored rinsing processes by setting specific positioning of a rinsing piston, a specific movement pattern of the rinsing piston during rinsing and a specific rinsing duration [Paragraph [0036] – “FIG. 3 illustrates an exemplary embodiment of a pipettor and shows some of the features for pipette tip washing. The pipettor 100 comprises an aspiration block 102, and a piston 104 which controls movement of fluids in and out of a pipette tip connected to the aspiration block 102. The piston 104 can be actuated by any suitable mechanism. In FIG. 3, a motor 106 spins a lead screw 108, which moves a stage 112, thereby causing the piston 104 to move up and down. The piston 104 is moved up to aspirate a fluid into the tip and down to dispense the fluid out of the tip. The pipettor 100 also comprises a valve pack 110 for controlling which fluids are provided to the tip. In some embodiments, the pipettor is configured to deliver at least two types of liquids to the pipette tip for washing its inside surfaces. For example the pipettor can be configured to deliver one or more washing fluids, such as a wash buffer and deionized (DI) water. The wash buffer can act as a detergent, to dislodge and wash away any residual of a previously used reagent. Suitable wash buffers include Dako Wash Buffer. The DI water is then used to wash away any remains of the wash buffer. Pressurized air or other gas can be used to move the washing liquid through the inside of the tip and/or to clear and dry the same inside surfaces. The pressurized gas can be provided at two different flow rates (low & high) to perform different steps or functions.”Paragraph [0037] – “After the liquid slugs have moved through and exited the pipette tip, the pipettor/aspiration block delivers high flow, pressurized gas through the inside of the tip for a time sufficient to remove all liquid remaining in the pipette tip and to dry it.”].
It would have been obvious to incorporate the method/apparatus of Shkolnik into the context of the analyzer monitoring system of Quint in order to assess and prevent clogging.
Quint and Shkolnik fail to disclose the remaining claim limitations. However, Quinones discloses a method for performing fluid dispensing control [See Fig. 5 and associated text] including the steps of:
preparing a first set of data, comprising the steps of: recording a first set of parameters by measuring with a pressure sensor data and preparing pressure parameter curves from the determined pressure data; assigning to each pressure parameter curve from the first set of pressure parameter curves a value indicating whether each respective pressure parameter curve is judged to be correct or erroneous; storing the pressure parameter curve(s) and assigned values in a data base [Paragraph [0048] – “In an embodiment, the dispensing controller 210 can be configured to process a mathematical equation to represent a master dispensing recipe, which characterizes the complete dispensing process including, tooling, material and process parameters, and boundary limits as defined by a user.”Paragraphs [0076]-[0079] – “The collected sample set can be analyzed with well-known business intelligence/big data and/or data analysis tools; c) Validate response data 506, wherein: i. sample data is validated against a predetermined set of error conditions, which for example can indicate hardware issues or fluid quality issues, and will make a determination to halt the process and alert the user; for example if no-flow is detected even though fluid flow is expected to occur, the system will alert user. Alternatively, if fluid flow is greater than a predetermined maximum fluid flow or lower than a predetermined minimum fluid flow, an error or warning can be issued, and the process can be halted. ii. If all the expected trends and system response are checked and consistent with the values predicted by the algorithm and with previous runs, then the dispense system parameterization starts.” See the “yes” and “no” paths of step 506.Paragraph [0107] – “FIG. 8 shows the fluid flow response to process parameter input, i.e., fluid pressure and the master parameterization curve. The target response and the band are also shown and two points lying away from the expected value (F(P.sub.T),P.sub.T), the master curve dictates what pressure differential to apply to come to targeted mean. The point below (F(P.sub.L),P.sub.L) and the point above (F(P.sub.U),P.sub.U) require different delta-P adjustment, known from the parameterized system[.]”See Fig. 8, the curves connecting 1. P.sub.L to P.sub.T and 2. P.sub.T to P.sub.U. See also the repeating process of the “yes” and “no” paths of step 506 of Fig. 5.];
establishing a determination basis, comprising the steps of: defining multiple features for characterizing a parameter curve; applying the defined multiple features on the stored pressure parameter curve(s) from the first set of data; selecting features from the multiple features reflecting the manually assigned value for each pressure parameter curve from the first set of data [Fig. 8, the definition, application, and selection of points (F(P.sub.T),P.sub.T), (F(P.sub.L),P.sub.L), and (F(P.sub.U),P.sub.U) of the master parameterization curve.];
monitoring pressure parameter curves in running processes, comprising the steps of: recording a second set of pressure parameter curves for the same parameters of the first set of parameters during operation of the automated analyser system; transferring the data relating to the recorded second set of pressure parameter curves to the data base; calculating the quality of the second set of pressure parameter curves by applying the selected features from the multiple features on the second set of pressure parameter curves; and labelling the second set of pressure parameter curves to be correct or erroneous based on the calculation [See Figs. 7 and 8, the comparison of input parameter features to the features of the master parameterization curve in the determination of any needed pressure adjustments.Paragraph [0107] – “FIG. 8 shows the fluid flow response to process parameter input, i.e., fluid pressure and the master parameterization curve. The target response and the band are also shown and two points lying away from the expected value (F(P.sub.T),P.sub.T), the master curve dictates what pressure differential to apply to come to targeted mean. The point below (F(P.sub.L),P.sub.L) and the point above (F(P.sub.U),P.sub.U) require different delta-P adjustment, known from the parameterized system[.]”].
It would have been obvious to use such an approach to determine appropriate fluid control during dispensing and/or rinsing in an appropriate manner.
Quinones fails to disclose, during the “preparing” step, assigning manually to each parameter curve a value indicating whether the respective parameter curve is judged to be correct or erroneous.
However, Uchiyama discloses the use of supervised learning by which a machine learning device is fed input data along with corresponding appropriate output data as teacher data [See Fig. 1A and Paragraphs [0026] and [0032]]. It would have been obvious to have a person manually perform the validation step of Quinones (Step 506) as a way of providing teacher data as a training data set because doing so would have been an effective manner of implementing supervised machine learning through which the system could effectively learn to classify the parameter curves itself. Quinones teaches the use of machine learning [Paragraphs [0087]-[0089]].
Regarding Claim 2, Quinones discloses that during establishing a determination basis an assessed sum is calculated from a group of features selected from the multiple features so that the assessed sum is in accordance with the manually assigned value [Paragraph [0084] – “Although the intelligence built-in in the algorithm may censor out data, it may also keep such data for future upgrading should that become a norm or permanent shift seen from the data itself. For example, if the fluid material becomes such that its physical properties are different from original batch used for the master parameterization the intelligence of the algorithm will adopt the new data representing the actual norm of the material. This is done for instance, by accounting for the frequency of occurrence of a system response and/or a permanent shift in the mean of the distribution to a fixed process parameter subset input;”].
Regarding Claim 3, Quinones fails to disclose that the measured data from the running process are distinguished between four different applications so that for each of the four different applications a determination basis is established. However, Quint discloses that the automated analyser system can include four different applications [See Fig. 5a]. It would have been obvious to establish different determination bases for each different application in order to perform fluid control during dispensing and/or rinsing in an appropriate manner.
Regarding Claim 4, Quinones that x pressure parameter curves with n calculated features are determined for preparing the first set of data, wherein x and n can be any positive integer [Fig. 5, “502 Perform n Parameter SWEEPS”Paragraphs [0080]-[0083] – “In a related embodiment, FIG. 7 depicts an example of the parameterization curve of the fluid dispensing system response to the dispense parameter pressure sweep: F(P)=20 ln(P)−25K; this is for time-pressure dispense type; d) Calculating dispensing system response 508, wherein: The parameterization manager 214 of the fluid dispensing control device 102 is configured with a parameterized system response function, which is calculated to fit parameter sweep data, such that i. In general, the response 318 of the system to a sweep of n dispensing parameters can be a surface in n-space, if parameters are non-independent the sweeps would include those sweeps of correlated sweeps. In general, the system response function, can be defined as a parameterized function in the form: F(j-Input Dispense Parameters)|Configuration (such that F is specific to the particular system 100 configuration)”].
Regarding Claim 5, Quinones discloses that the selected features from the multiple features for calculating the assessed sum are changed during monitoring pressure parameter curves in running processes for optimizing the determination basis [Paragraph [0084] – “Although the intelligence built-in in the algorithm may censor out data, it may also keep such data for future upgrading should that become a norm or permanent shift seen from the data itself. For example, if the fluid material becomes such that its physical properties are different from original batch used for the master parameterization the intelligence of the algorithm will adopt the new data representing the actual norm of the material. This is done for instance, by accounting for the frequency of occurrence of a system response and/or a permanent shift in the mean of the distribution to a fixed process parameter subset input;”].
Regarding Claim 6, Quinones discloses that a separate basic machine learning model is applied based on the selected features for each pressure parameter curve [Paragraphs [0087]-[0089] – “In a related embodiment, the parameterized system response function can be calculated well-known machine learning algorithms, including for example neural network back-propagation, support vector machines, etc. e) Calculating dispensing parameters 510 to attain target dispensing system response, wherein: i. the dispensing controller 210 of the fluid dispensing control device 102 can be configured to perform an inverse calculation with the parameterized system response function, in communication with the parameterization manager 214, to compute an input dispensing parameter subset 420 to obtain a desired/target response 424;”].
Regarding Claim 7, Quinones discloses that, based on the output of the applied machine learning models, the parameter curves are labelled as erroneous or correct [Paragraphs [0092]-[0093] – “f) Testing system conformance 511, wherein conformity is tested of the current system in relation to the original system used for sampling; such that: i. If the present system is well represented or consistent with the original master system, deviation should not be very significant or have a large mean shift away from the master system, such that for example if a measured response deviates more than a predetermined maximum percentage deviation the current system will be declared non-conformant.”].
Regarding Claim 8, the combination would disclose that the parameter curves are verified [Step 506 of Quinones] wherein the manually assigned value for a correct pressure parameter curve is 1 and for an erroneous curve is 0 [See Fig. 1A of Uchiyama, objective variable values y are 0 and 1 which would correspond to the “yes” and “no” outputs for step 506 of Quinones during the validation].
Regarding Claim 9, Quinones discloses that besides monitoring pressure parameter curves in running processes, the results are controlled by checking whether a different selection of features from the n features provide a more accurate labelling of the pressure curves [Paragraph [0084] – “Although the intelligence built-in in the algorithm may censor out data, it may also keep such data for future upgrading should that become a norm or permanent shift seen from the data itself. For example, if the fluid material becomes such that its physical properties are different from original batch used for the master parameterization the intelligence of the algorithm will adopt the new data representing the actual norm of the material. This is done for instance, by accounting for the frequency of occurrence of a system response and/or a permanent shift in the mean of the distribution to a fixed process parameter subset input;”].
Regarding Claim 10, Quinones discloses that the parameters are selected from the group comprising pressure, flow rate, flow volume or viscosity of the fluid [See Figs. 6-8 – pressure, flow rate, and flow responseParagraph [0106] – “The stochastic behavior and intrinsic random variation of the volume/mass of the fluid being dispensed, is considered as part of the response function”Paragraph [0144] – “the dispensing controller 210 is configured to integrate measurements of the actual fluid flow to calculate a dispensing volume, and calculate a density of the fluid, as a ratio between the dispensing weight and the dispensing volume.”].
Response to Arguments
Applicant argues:
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Examiner’s Response:
The Examiner respectfully disagrees. Quint is not relied on for disclosing pressure measurements. Quint does not teach away from using only pressure measurements, nor do the instant claims recite doing so.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20020059945 A1 – Sample Wash Station Assembly
US 20040265185 A1 – Method Of Washing Liquid Pipetting Apparatus And Dispensing Head
US 20150314341 A1 – PIPETTE TIP WASHING DEVICE
US 20230060352 A1 – IMPROVED FLUID DISPENSING PROCESS CONTROL USING MACHINE LEARNING AND SYSTEM IMPLEMENTING THE SAME
US 5610069 A – Apparatus And Method For Washing Clinical Apparatus
US 3992947 A – Pipetting Device
US 5976470 A – Sample Wash Station Assembly
US 6003531 A – Pipette-washing Device For Automatic Biochemical Analyzer
US 5593893 A – Dispensing Device With Syringe Driving Compensator For Flexible Tube
Kolb et al, Cleaning patch-clamp pipettes for immediate reuse, Scientific Reports, 2016
Roder et al., A Multifunctional Frontloading Approach for Repeated Recycling of a Pressure-Controlled AFM Micropipette, PLOS ONE, 2015
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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