DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/19/2026 has been entered.
Priority
The present application was filed on 03/12/2020. This application claims benefit of U.S. Provisional Patent Application 62/818,403 filed on 03/14/2019.
Claim status
Claim 1 is amended. Claims 5, and 12-13 are canceled. Claims 6-7, 11, and 14-20 are withdrawn.
Claims 1-4, 8-10 and 21 are examined herein.
Objections/Rejection status
The objection of claim 1 is withdrawn in view of the amendment of the claim.
The rejection of claims 1-4, 8-10 and 21 under 35 U.S.C. 112(b) is withdrawn in view of the amendment of the claim.
The rejection of claims 1-4, 8-10 and 21 under 35 USC 103 is updated in view of the amendment of the claims.
Claim Rejections - 35 USC § 103
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.
Claims 1-4, 8-10 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US20210405044) in view of Ortac (US20160243262), Hassan (Continuous measurement of enzymatic kinetics in droplet flow for point-of-care monitoring, Analyst, 2016, 141, 3266-3273), Achira Labs (Calibration Curve-fitting, 2018, hereafter is Achira), and Armbruster et al. (Limit of Blank, Limit of Detection and Limit of Quantitation, Clin Biochem Rev. 2008 Aug; 29(Suppl 1): S49–S52, PTO-892 03/29/2023).
Regarding claim 1, Yang discloses a method of determining a result of an assay (see Abstract, disclosing lateral flow assay devices, systems, and methods for measuring concentration of an analyte of interest in a sample), the method comprises:
placing a test sample on a test strip to form the assay (see par.4: teaching an assay test strip including a flow path configured to receive a fluid sample), wherein the test strip comprises a label pad, a test band, and a control band (see par.34, Fig.1: showing the test strip comprises label pad, a test band, and a control band),
wherein the label pad comprises a plurality of complexes configured to attach to a target analyte in the test sample and diffuse with the test sample along the test strip toward the test band, and wherein the test band comprises an immunoassay configured to bind the target analyte with at least one of the label complexes to a substrate (see par.34 and Fig.1, see par.5: the complex includes a label, an antibody or a fragment of an antibody that specifically binds the analyte of interest, and the analyte of interest).
Yang teaches that the sample signal is generated by the binding of the target analyte with at least one of the label complexes to the substrate (see par.5: an optical signal emitted from the complex bound to the immobilized capture agent in the test zone).
Yang also discloses that the test strip goes with a reader including a light source, a detector, and a data analyzer (see par.8). The data analyzer, which includes a processor (e.g., a microcontroller, a microprocessor, or ASIC) and an analog-to-digital converter, processes the signal measurements that are obtained by the reader (see par.93). The data analyzer may be implemented in any computing or processing environment, including in digital electronic circuitry or in computer hardware, firmware, or software (see par.93). The test systems precisely determine the quantity of an analyte of interest (see par.23). These teachings encompass the method of Yang comprises the detector, the processor in the computer station.
Yang teaches collecting, with a detector in an analysis device comprising the computer station, a sample signal from the test sample in the assay formed on the test strip (see par.91, teaching that the light detector may be designed to selectively capture light from the exposed areas of the capture zone),
wherein the sample signal is generated by the binding of the target analyte with at least one of the label complexes to the substrate, and wherein the sample signal correlates with a concentration [S] of the target analyte in the test sample (see par.35 and fig.1: the complex of the target analyte from the fluid sample, the labeled antibody and the capture agent at the capture zone emits s a detectable optical signal; see par(s).37-38: the assay provides qualitative or quantitative information, such as information on the absence or presence or the quantity of the analyte of interest in the sample),
generating, with the detector, a transduced signal based on the sample signal; providing the transduced signal to the computer station in the analysis device (see par.93, the data analyzer processes the signal measurements that are obtained by the reader, wherein the data analyzer may be implemented in any computing or processing environment, including in digital electronic circuitry or in computer hardware, firmware, or software).
Yang does not teach executing, via a processor, instructions stored in a memory of a computer station as recited in step (ii). Yang does not teach determining a rate value using equation 6.1 and running the assay on multiple calibration samples as recited in step (iii). Yang does not teach identifying a value less than the pre-selected threshold as a baseline value for the concentration of the target analyte as in step (iv). Yang does not teach providing a result of the assay according to the rate value and the preselected threshold as in step (v).
Ortac teaches a method of determining a result of an assay comprising:
executing, via a processor, instructions stored in a memory of a computer station, wherein executing said instructions causes the computer station to perform steps of: collecting, with a detector in an analysis device comprising the computer station, a sample signal from the test sample at a plurality of time points in the assay; generating, with the detector, a transduced signal based on the sample signal; providing the transduced signal to the computer station in the analysis device;
(See par(s). 173, 175-176 and fig.18, teaching that the sensor device 1800 to measure an analyte in a biological system comprises an optical signal acquisition unit 1820 which includes a light source, an optical detector to sense the emitted fluorescence from SHELS particle and an optical transducer to convert the detected optical signal into an electrical signal; the sensor device 1800 comprises a data processing unit 1830 which includes a processor 1831 to process data and a memory unit 1832 in communication with the processor 1831 to store data; the data processing unit 1830 can be implemented by a computer system. See par.234, teaching that a processor performs instructions and one or more memory devices store instructions and data. See par.172, teaching that the fluorescence detector can allow frequent and/or automated measurement of the glucose level externally by detecting the optical fluorescence signals generated from the gRuSHELS, thereby it encompasses detecting signals from sample at a plurality of time points.)
wherein the sample signal correlates with a concentration [S] of the target analyte in the test sample (see par.160 and fig.12, teaching that the optical fluorescent signal and the concentration of the analyte are correlated, see par.172, teaching the fluorescence detector can allow frequent and/or automated measurement of the glucose level externally by detecting the optical fluorescence signals generated from the gRuSHELS (e.g., excited Ru(phen)3 +2), that means the teaching supports the correlation of the signal and the concentration of the analyte);
and determining a rate value R of the transduced signal over a duration of the assay based on the sample signal at the plurality of time points, wherein the rate value R is determined according to a calibration curve model (see par.68 and fig.2, teaching that the Michaelis-Menten kinetics model can be used to calculate the concentration of the analyte based on the reaction rate of the analyte with the test component; see par.172, teaching that the fluorescence detector can allow frequent and/or automated measurement of the glucose level externally by detecting the optical fluorescence signals generated from the gRuSHELS, thereby it encompasses detecting signals from sample at a plurality of time points).
While Ortac does not teach that the test sample is reacted with the assay components on a test strip as recited in step (i), Ortac is generic to a detector that can detect the sample signal generated by the label molecule following the reaction of the target analyte with the test component (see par(s).158-160, 172, 177: contacting a SHELS particle include a light-emitting molecule Ru(phen)3 +2 (e.g., label) and a glucose oxidase (e.g., test component) to a biological system (e.g., test sample), where an interaction of an analyte (e.g., glucose) and glucose oxidase can emit an optical signal (from the label Ru(phen)3 +2), then the fluorescent signal can be detected by an external optical detector).
Absence of evidence to the contrary, this teaching means that the sensor device of Ortac (including a detector, a transducer, a processor, a memory in a computer system) can also collect the emitted signal generated from the assay on the test strip.
The analysis systems of Ortac and Yang are analogous in structures (e.g., light source, detector, processor) and functions (e.g., receive the signal from the reaction of analytes and assay components, generate the signal to the concentration of the analyte in the sample). It supports that the test strip taught by Yang can be analyzed with Ortac’s analysis system because the analysis system of Ortac can detect a signal from the labeled-analyte complex by a detector, send the signal to a processor of the computer system, and determine the concentration of the analyte (e.g., by determining the reaction rate value which depends on the analyte concentration).
In addition, Hassan teaches a method of continuous analyte measurement based on the reaction rates (see Abstract). Hassan teaches that in order to convert the measured reaction rates to a known concentration, the method must first be calibrated using a calibration curve model, e.g., Michaelis–Menten model which relates the initial rate of reaction (V0) to the concentration of the analyte (e.g., glucose) as shown in equation v=Vmax.[S]/(Km +[S]). See page 11, Glucose enzymatic assay section.
Hassan teaches running the assay on multiple calibration samples having selected target analyte concentrations, determining multiple rate values for each of the multiple calibration samples, fitting the multiple rate values to a model based on the selected target analyte concentrations, finding a fiduciary curve based on the model. See page 12, Fig.4a and 4b: teaching that running the assay on six different known glucose concentrations (0.5,1.0, 2.5, 5.0, 15 and 25 mM) at multiple time points, plotting the rate values over time corresponding with each concentration in fig.4a, plotting reaction rates against the analyte concentration and fitted with the curve model in fig.4b.
Achira discloses the most commonly recommended calibration curve models, e.g., 4P logistic or Michaelis-Menten. These models use a data set consisting of calibrators of known concentration (x) and their signal readings (y). The unknown sample concentrations are then calculated from the inverse of the estimated function (see page 1). The 4P model discloses the 6.1 equation in claim 1. In the 4P model, a is a minimum obtainable value, d is a maximum obtainable value, c is a point of inflection, and b is a steepness of the curve at point c (see pages 1 and 2: disclosing the 6.1 equation and parameters’ definitions).
Achira teaches running the assay on multiple calibration samples having selected target analyte concentrations, determining multiple rate values for each of the multiple calibration samples, fitting the multiple rate values to a model based on the selected target analyte concentrations, and finding a fiduciary curve based on the model. See page 1 Background: disclosing the model using a data set consisting of calibrators of known concentration (x) and their signal readings (y). See page 4 4PL graph or page 5 4PL graph: showing that multiple rate values are generated a fiduciary curve. The unknown sample concentrations are then calculated from the inverse of the estimated function (see page 1).
Armbruster provides the standard method for determining Limit of Blank (LoB), Limit of Detection (LoD), and Limit of Quantitation (LoQ) in an assay (see Summary, Introduction par.2 page S49).
LoB is estimated by measuring replicates of a blank sample (i.e., a free analyte sample). The blank sample can produce an analytical signal that might otherwise be consistent with a low concentration of analyte.
LoD is the lowest analyte concentration likely to be reliably distinguished from the LoB and at which detection is feasible. (See Limit of Detection, page S50). LoD is determined by utilizing both the measured LoB and test replicates of a sample known to contain a low concentration (see Summary). An assay is simply not capable of accurately measuring analyte concentrations down to zero. Sufficient analyte concentration must be presented to produce an analytical signal that can reliably be distinguished from “analytical noise,” the signal being produced in the absence of an analyte. See page S50 column 1 paragraph 6.
LoQ is the lowest concentration at which the analyte can not only be reliably detected but at which some predefined goals for bias and imprecision are met. The LoQ may be equivalent to the LoD or it could be at a much higher concentration. See Limit of Quantitation, page S51 column 1 paragraph 2-3.
Under BRI, LoD or LoQ is a pre-selected threshold describing the smallest concentration of a measurand that can be reliably measured by an analytical procedure (see page S49 col.1 par.1). Any concentration value of an analyte less than LoD/LoQ is not reliable and cannot be distinguished from a zero-concentration sample (see S50 col.1 par.6, page S51 col.1 par.2). This interpretation also meets the description of a preselected threshold of the fiduciary curve in par.64 of the instant specification. As such, Armbruster teaches that the selection of a preselected threshold in an assay is commonly used.
Accordingly, a value less than the pre-selected threshold is defined as an analytical noise, i.e., baseline value. See page S50 column 1 Limit of Detection. It is also obvious that the result of the assay is provided based on the value detected from the sample with an unknown analyte concentration and the preselected threshold, because they are used to fully characterize the analytical performance of clinical laboratory tests in order to understand their capability and limitations, and to ensure that they are “fit for purpose” (see page S51 col.2 par.3).
Armbruster further discloses that LoB, LoQ and LoD is the lowest concentration at which the analyte can not only be reliably detected but at which some predefined goals for bias and imprecision are met, in other words, it is fit for purpose (see page S49 col.1 par.1, page S51 col.1 par.2 and page S51 col.2). Moreover, defining the limits of an assay at low concentration is directly related to its dynamic range, or analytical measurement range (see page S49 col.1 par.1). Therefore, it is up to the user to select a preselected threshold as a zero or a particular value that fits the user’s goals.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method taught by Yang, substituting the reader of Yang by the sensor system of Ortac because they are functionally equivalent in terms of collecting a sample signal and calculating the concentration of the analyte in the sample based on that signal. One of skill in the art would have a reasonable expectation of success in combining Ortac and Yang because they are directed to the analysis system that calculates the concentration of the analyte based on the signal emitted following the reaction of the analyte with the assay components.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Yang in view of Ortac, by determining a rate value R of the transduced signal over a duration of the assay based on the sample signal at the plurality of time points as taught by Hassan, because it provides an accurate continuous quantification of unknown analyte concentration in a bioassay (see Hassan page 3 par.5).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to determine rate value R according to equation 6.1, which means the 4PL model , in the method of Yang in view of Ortac and Hassan. It is because Ortac and Hassan teach using a calibration curve model (e.g., Michaelis-Menten) to set up a standard curve, then convert the reaction rates obtained from an unknown sample to the concentration of the unknown sample (see Ortac par.68 and fig.2, and Hassan page 11 par.5 and page 15). Achira teaches Michaelis-Menten and 4PL models are the most commonly recommended method for generating a standard curve and calculating a concentration of an unknown sample. Those models are functionally equivalent in terms of running the assay on multiple calibration samples, having selected target analyte concentrations, determining multiple rate values for each of the multiple calibration samples, fitting the multiple rate values to a model based on the selected target analyte concentrations, finding a fiduciary curve based on the model (see Achira pages 1-5). Therefore, the 4PL and Michaelis-Menten models can be used interchangeable because they are functionally equivalent.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Yang in view of Ortac and Hassan, by selecting as a pre-selected threshold, identifying a value less than the pre-selected threshold as a baseline value for the concentration of the target analyte as taught by Armbruster, to provide a result of the assay according to the rate value and the pre-selected threshold. The reason is that Armbruster teaches that sufficient analyte concentration must be presented to produce an analytical signal that can reliably be distinguished from “analytical noise”, the signal produced in the absence of an analyte and depends on predefined goals for bias and imprecision. Thus, Armbruster suggest the need of determining a threshold of an assay, e.g., LoD/LoQ/LoB, to make the result of the assay more reliable and fit for purpose. The combined teaching would establish high throughput and high accuracy analysis for detecting the concentration of an analyte (see Armbruster, Limit of Detection, page S50).
One of skill in the art would have a reasonable expectation of success in combining Yang in view of Ortac, Hassan, Achira, and Armbruster because Yang is generic for determining the concentration of an analyte, while Ortac, Hassan, and Achira disclose the common methods for setting up a standard curve to relate the rate value with the known concentrations of an analyte, e.g., Michaelis-Menten or 4PL model, so that the unknown concentration of the analyte in the sample can be defined based on the rate value. Armbruster provides a standard method to define the smallest concentration of a measurand (i.e., pre-selected threshold) that can be reliably measured by an analytical procedure (see page S51 Conclusions).
Regarding claim 2, Yang, Ortac, Hassan, Achira and Armbruster teach the invention as discussed above. Yang does not teach selecting, for the plurality of time points, a first time point and a last time point and the duration of the assay falls between the first time point and the last time point.
However, this limitation is taught in Ortac (see Fig(s).5-6, 14 and 16: showing that the optical signal from the sample is detected at plurality of time points) and in Hassan (see Fig.4: showing that the optical signal from the sample is detected at plurality of time points, a first time point is at 0 second and the last time point is at 35 second).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the method of Yang and Ortac and Hassan because the method can accurately define the concentration of the analyte in the sample (see Hassan page 3 par.5).
One of skill in the art would have a reasonable expectation of success in combining Yang in view of Ortac, Hassan, Achira, and Armbruster because Yang is generic for determining the concentration of an analyte and Ortac, Hassan and Achira teach determining the concentration of an analyte using a calibration curve model, which is for setting up a standard curve to relate the rate value with the known concentrations of an analyte, so that the unknown concentration of the analyte in the sample can be defined based on the rate value. Ortac and Hassan are directed to a method of measuring a sample signal from a test sample at a plurality of time points in an assay, and Armbruster provides a standard method to define the smallest concentration of a measurand that can be reliably measured by an analytical procedure (see page S51 Conclusions).
Regarding claim 3, Yang, Ortac, Hassan, Achira and Armbruster teach the invention as discussed above. Yang in view of Ortac, Hassan, and Achira does not teach the method that further comprises adjusting the pre-selected threshold.
Armbruster teaches defining the limits of an assay, i.e., a preselected threshold, is based on the sample with free analyte and the sample known to contain a low concentration of analyte (see Summary). Moreover, defining the limits of an assay at low concentration is directly related to its dynamic range, or analytical measurement range (see page S49 col.1 par.1). See discussion of Armbruster in claim 1 above.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Yang, Ortac, Hassan, and Achira adjusting the pre-selected threshold based on a concentration of the analyte and its dynamic range as taught by Armbruster because Armbruster teaches defining the limits of an assay at low concentration is directly related to its dynamic range, or analytical measurement range to make the result of the assay more reliable and fit the purpose. The combining teaching would establish the reliable result (see Armbruster, Limit of Detection, page S50).
One of skill in the art would have a reasonable expectation of success in combining Yang in view of Ortac, Hassan, Achira, and Armbruster because Yang is generic for determining the concentration of an analyte and Ortac, Hassan and Achira teach determining the concentration of an analyte using a calibration curve model, which is for setting up a standard curve to relate the rate value with the known concentrations of an analyte, so that the unknown concentration of the analyte in the sample can be defined based on the rate value. Ortac and Hassan are directed to a method of measuring a sample signal from a test sample at a plurality of time points in an assay, and Armbruster provides a standard method to define the smallest concentration of a measurand that can be reliably measured by an analytical procedure (see page S51 Conclusions).
Regarding claims 4 and 8, Yang, Ortac, Achira and Armbruster teach the invention as discussed above. Yang does not teach the steps of claim 4.
Ortac, Hassan, and Achira teach determining the concentration of an analyte using a calibration curve model.
Hassan also teaches determining the concentration of an analyte using a calibration curve model. Hassan runs the assay on a sample free of analyte and measures a signal from the test sample free of analyte at a plurality of time points (see page 12: blank measurements were taken by using 0 concentration glucose solution and used to calculate the absorbance of the glucose-containing droplets, the signal is measured at times of 4.94, 8.58, 13.52, 18.46, 23.4, 28.34 and 33.28 seconds). Hassan does not clearly teach how to use the 0 concentration value in calculating the concentration of the analyte.
Armbruster teaches the method of defining the baseline and limit of detection (i.e., a preselected threshold) comprising measuring a blank sample (i.e., sample free of the target analyte), determining mean value and SD of signal from blank sample, calculating LoD as the mean + 2 SD. To provide a more conservative LoD, variations of this approach use the mean plus 3, 4, or even 10 SDs is provided. See page S50 column 1 paragraph 2-3. Armbruster is generic to determining a preselected threshold in an assay, thus it could be able to apply to linear or non-linear model.
Although Armbruster do not specifically teach 10% greater value of pre-selected threshold, Armbruster teaches that LoD is the lowest analyte concentration likely to be reliably distinguished from the LoB and at which detection is feasible and sufficient analyte concentration must be present to produce an analytical signal that can reliably be distinguished from “analytical noise”, the signal produced in the absence of analyte (see page S50 column 1). Armbruster also suggests a variations of defining a pre-selected value (e.g., mean blank value plus 2, 3, 4 or even 10 SD).
It has long been settled to be no more than routine experimentation for one of ordinary skill in the art to discover an optimum value for a result effective variable. “[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum of workable ranges by routine experimentation” Application of Aller, 220 F.2d 454, 456, 105 USPQ 233, 235-236 (C.C.P.A. 1955). “No invention is involved in discovering optimum ranges of a process by routine experimentation.” Id. at 458, 105 USPQ at 236-237. The “discovery of an optimum value of a result effective variable in a known process is ordinarily within the skill of the art.”
Applicant has not disclosed that the specific limitations recited in instant claim 4 are for any particular purpose or solve any stated problem, and the prior art Armbruster teaches that the pre-selected threshold may be varied because different analyte detection agents might generate different signal levels, and the threshold is set to produce an analytical signal that can reliably be distinguished from “analytical noise”, the signal produced in the absence of analyte. Absent unexpected results, it would have been obvious for one of ordinary skill to discover the optimum workable ranges of the methods disclosed by the prior art Armbruster by normal optimization procedures known in the art of deciding pre-selected threshold so that an analytical signal can reliably be distinguished from “analytical noise”, the signal produced in the absence of analyte, thereby the result of the assay is more reliable (see Armbruster page S50 column 1).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Yang, measuring a signal from the test sample free of the target analyte to determine the pre-selected threshold as taught by Hassan and Armbruster. One of ordinary skill in the art would have been motivated to use Hassan method for an analyte analysis because it provides an accurate continuous quantification of unknown analyte concentration in a bioassay (see Hassan page 3 par.5). Armbruster teaches that sufficient analyte concentration must be presented to produce an analytical signal that can reliably be distinguished from “analytical noise”, the signal produced in the absence of analyte. Depending on predefined goals for bias and imprecision, Armbruster suggest the need of determining a threshold of an assay, e.g., LoD/LoQ/LoB, to make the result of the assay more reliable and fit the purpose. The combining teaching would establish the best window for the analysis and the reliable result (see Armbruster, Limit of Detection, page S50).
One of skill in the art would have a reasonable expectation of success in combining Yang in view of Ortac, Hassan, Achira, and Armbruster because Yang is generic for determining the concentration of an analyte and Ortac, Hassan and Achira teach determining the concentration of an analyte using a calibration curve model, which is for setting up a standard curve to relate the rate value with the known concentrations of an analyte, so that the unknown concentration of the analyte in the sample can be defined based on the rate value. Ortac and Hassan are directed to a method of measuring a sample signal from a test sample at a plurality of time points in an assay, and Armbruster provides a standard method to define the smallest concentration of a measurand that can be reliably measured by an analytical procedure (see page S51 Conclusions).
Regarding claim 9, Yang, Ortac, Hassan, Achira and Armbruster teach the invention as discussed above. Yang does not teach the limitation in claim 9.
Hassan teaches measuring the sample over the time and selecting the first time point in the plurality of time points when an expected rate value of the sample signal is different from zero (Hassan, in page 7 par.1, teaches that initially droplets composed of a 1:1 ratio of sample and reagent are generated with the T-junction microfluidic chip, and it takes time for a droplet traveling from T-junction to the first detection point. Hansan, in page 12 and Fig. 4, teaches the measurement is first done at the time 4.49 second when the time 0 is at the moment when the droplet was generated at the T-junction; the absorbance increases linearly as expected and is fitted with a straight line to obtain the initial reaction rate. Hassan, in page 15 par.1, teaches that the absorbance of each droplet at different detectors was correlated with reaction time and the initial reaction rates were obtained from lines of best fit. Hassan, in fig.6a, showing the rate value at the first time point is different from zero.)
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method taught by Yang, determining a first time point in the plurality of time points when an expected rate value of the sample signal is different from zero as taught by Hassan because Hassan teaches by doing that the absorbance increases linearly as expected and is fitted with a straight line to obtain the initial reaction rate, wherein the initial reaction rate is then converted to give the analyte concentration for each sample. Accordingly, the unknown analyte concentrations can be accurately determined (see Hassan Abstract).
One of skill in the art would have a reasonable expectation of success in combining Yang in view of Ortac, Hassan, Achira, and Armbruster because Yang is generic for determining the concentration of an analyte and Ortac, Hassan and Achira teach determining the concentration of an analyte using a calibration curve model, which is for setting up a standard curve to relate the rate value with the known concentrations of an analyte, so that the unknown concentration of the analyte in the sample can be defined based on the rate value. Ortac and Hassan are directed to a method of measuring a sample signal from a test sample at a plurality of time points in an assay, and Armbruster provides a standard method to define the smallest concentration of a measurand that can be reliably measured by an analytical procedure (see page S51 Conclusions).
Regarding claim 10, Yang, Ortac, Hassan, Achira and Armbruster teach the invention as discussed above.
Yang teaches the method further comprising transmitting the result of the assay to a remote server (see par.93, the data analyzer may also include circuits for transfer of results via a wireless connection to an external source for data analysis or for reviewing the results).
Ortac also teaches that the data processing unit 1830 can transmit or provide information/data to another entity or to a user. The data processing unit can be implemented by a remote communications device, a remote computer and/or a computer system in a communication network accessible via the Internet (referred to as ‘the cloud’) that includes one or more computational processing devices (e.g., servers in the cloud). See par.176. Thus, the teaching encompasses the result can be transmitted to a remote server.
Therefore, Yang in view of Ortac teaches transmitting the result of the assay to a remote server.
Regarding claim 21, Yang, Ortac, Hassan, Achira and Armbruster teach the invention as discussed above. Yang does not teach the detector is a detector array and the transduced signal is an image of the test strip.
Ortac teaches the detector is a detector array and the transduced signal is an image of the test strip (see par.173: the optical detector can include an image sensor or fluorometer to capture an image, e.g., CMOS arrays, thereby the transduced signal can be an image of the test strip).
Since it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the method taught by Yang, substituting the reader of Yang by the sensor system of Ortac because they are functionally equivalent in terms of collecting a sample signal and calculating the concentration of the analyte in the sample based on that signal. Accordingly, the method of determining a result of an assay of Yang in view of Ortac can have a detector array that can capture an image of the test strip as taught by Ortac. The combination of Yang and Ortac can detect the concentration of an analyte in a sample using a rate value of the sample signal over a duration of the assay, which would establish the high throughput and high accuracy analysis for detecting the concentration of an analyte (see Hassan, page 15 par.1; Armbruster, Limit of Detection, page S50).
One of skill in the art would have a reasonable expectation of success in combining Ortac and Yang because they are directed to the analysis system that calculates the concentration of the analyte based on the signal emitted following the reaction of the analyte with the assay components.
Response to Arguments
Applicant’s arguments in the Remarks filed 03/19/2026 with respect to claim(s) 1-4, 8-10 and 21 have been considered but are moot because the new ground of rejection is made in view of the amendment of the claims.
Conclusion
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/CHAU N.B. TRAN/Examiner, Art Unit 1677
/BAO-THUY L NGUYEN/Supervisory Patent Examiner, Art Unit 1677 September 21, 2026
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