DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to amendment filed on 12/12/2025, in which claims 1 – 2, 5 – 11, 13, 16 – 17, 19, 21 – 24, and 26 was amended, and claims 1 – 26 was presented for further examination.
3. Claims 1 – 26 are now pending in the application.
Response to Arguments
4. Applicant’s arguments with respect to claims 1 - 26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
5. Claims 1 – 2 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al (US 2015/0198591 A1), in view of Bisen et al (US 20070287165 A1).
As per claim 1, Patel et al (US 2015/0198591 A1) discloses,
A method comprising: receiving an image of an agglutination assay (para.[0116]; “taking images of the agglutination using an imaging device, such as a scanner, camera, detector, or sensor, which may be coupled to a microscope”).
wherein the agglutination assay (para.[0116]; “agglutination using an imaging device”).
wherein the negative control sample comprises a first fluid sample combined with a first reagent that does not induce agglutination (para.[0121]; “HA assay is performed using a sample known not to contain an agglutinating virus, to ensure that observed agglutination is a result of agglutinating virus or agglutinating particle (i.e. a negative control)”).
wherein the positive control sample comprises a second fluid sample combined with a second reagent that induces agglutination (para.[0121]; “HA assay is performed using a sample known to contain an agglutinating virus, to ensure that erythrocytes or visualization particles used in the assay are capable of undergoing agglutination (i.e. a positive control)”).
wherein the test sample comprises a third fluid sample (para.[0109]; “A subject may provide a sample, and/or the sample may be collected from a subject”).
providing the image of the agglutination assay to a machine learning algorithm trained to classify agglutination of the test sample on a linear quantitative scale ((para.[0033]; “measurement of the agglutination of the particles in the mixture is a quantitative measurement of degree of agglutination” and para.[0346]; “Features extracted from the images were provided to a support vector machine classifier. The support vector machine classifier was previously trained with information from images of assays of known agglutination status (agglutinated or non-agglutinated). Using features extracted from the images, the support vector machine classified each ROI as containing agglutinated or non-agglutinated assay material”).
and wherein the machine learning algorithm calibrates the linear quantitative scale based at least in part on the negative control sample and the positive control sample (para.[0033]; “measurement of the agglutination of the particles in the mixture is a quantitative measurement of degree of agglutination” and para.[00346]; “support vector machine classifier was previously trained with information from images of assays of known agglutination status (agglutinated or non-agglutinated). Using features extracted from the images, the support vector machine classified each ROI as containing agglutinated or non-agglutinated assay material” and para.[0239]; “cut-off distance can be determined from calibration or by an estimate of the distance over which RBCs experience attraction in an assay”).
Patel does not specifically disclose a test card comprising: a negative control sample deposited into a negative control testing region of the test card, a positive control sample deposited into a positive control testing region of the test card, and a test sample deposited into a test sample testing region of the test card.
However, Bisen et al (US 2007/0287165 A1) in an analogous art discloses,
wherein the agglutination assay comprises a test card comprising: a negative control sample deposited into a negative control testing region of the test card (para.[0010]; “applying positive control, negative control & test sample each in circular motion on the test card”).
a positive control sample deposited into a positive control testing region of the test card (para.[0010]; “applying positive control, negative control & test sample each in circular motion on the test card”).
and a test sample deposited into a test sample testing region of the test card (para.[0010]; “applying positive control, negative control & test sample each in circular motion on the test card”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate diagnostic kit of the system of Bisen into agglutination of molecules or cells forms on the basis of various useful biological assays of the system of Patel for improving the speed of sample testing and reduce the quantity of labor requirement.
As per claim 2, the rejection of claim 1 is incorporated and further Bisen et al (US 2007/0287165 A1) discloses,
wherein the image of the agglutination assay comprises an image of a single test card comprising: a negative control testing region, wherein the first fluid sample and the reagent are deposited on the single test card within the negative control testing region (para.[0010]; “applying positive control, negative control & test
sample each in circular motion on the test card coated with hydrophobic material adding said antigen suspension to each of the positive, negative & test sample”).
a positive control testing region, wherein the second fluid sample and the second reagent are deposited on the single test card within the positive control testing region (para.[0010]; “applying positive control, negative control & test sample each in circular motion on the test card coated with hydrophobic material adding said antigen suspension to each of the positive, negative & test sample”).
a test sample testing region, wherein the third fluid sample is deposited on the single test card within the test sample testing region and a unique code that is scannable by a computing device (para.[0010]; “applying positive control, negative control & test sample each in circular motion on the test card coated with hydrophobic material adding said antigen suspension to each of the positive, negative & test sample”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate diagnostic kit of the system of Bisen into agglutination of molecules or cells forms on the basis of various useful biological assays of the system of Patel for improving the speed of sample testing and reduce the quantity of labor requirement.
6. Claims 3 – 14, 16, and 21 – 26 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al (US 2015/0198591 A1), in view of Bisen et al (US 2007/0287165 A1), and further in view of Hacker et al (US 2022/0214352 A1 A1).
As per claim 3, the rejection of claim 2 is incorporated, Patel et al (US 2015/0198591 A1) and Bisen et al (US 2007/0287165 A1) does not disclose wherein the test sample testing region further comprises a recombinant protein and one or more of an antigen or a byproduct of the antigen deposited on the test card, and wherein the recombinant protein is configured to mediate binding of red blood cells.
However, Hacker et al (US 2022/0214352 A1 A1) in an analogous art discloses,
wherein the test sample testing region further comprises a recombinant protein and one or more of an antigen or a byproduct of the antigen deposited on the test card, and wherein the recombinant protein is configured to mediate binding of red blood cells (para.[0034]; “recombinant proteins containing the Glycophorin A-binding nanobody IH4vs2 bind human red blood cells” and para.[0091]; “the subject sample and/or control samples in steps (b) and/or ( c) are dispensed on a test card”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 4, the rejection of claim 3 is incorporated and further Hacker et al (US 2022/0214352 A1 A1) discloses,
wherein the antigen is a SARS-CoV-2 virus, and wherein the recombinant protein comprises a nanobody that mediates the binding of red blood cells within the third fluid sample to one or more of: a receptor-binding domain of a spike protein associated with the SARS-CoV-2 virus; or a nucleocapsid protein associated with the SARS-CoV-2 virus (para.[0034]; “recombinant proteins containing the Glycophorin A-binding nanobody IH4vs2 bind human red blood cells” and para.[0051]; “Coronaviruses' spike proteins are glycoproteins that are embedded over the viral envelope. This spike protein attaches to specific cellular receptors and initiates structural changes of spike protein, and causes penetration of cell membranes, which results in the release of the viral nucleocapsid into the cell”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 5, the rejection of claim 1 is incorporated, Patel et al (US 2015/0198591 A1) and Bisen et al (US 2007/0287165 A1) does not disclose wherein classifying the agglutination of the test sample on the linear quantitative scale comprises determining whether the third fluid sample comprises antibodies for an identified antigen.
However, Hacker et al (US 2022/0214352 A1 A1) in an analogous art discloses,
wherein classifying the agglutination of the test sample on the linear quantitative scale comprises determining whether the third fluid sample comprises antibodies for an identified antigen (para.[0035]; “nanobody-fusion proteins bind RBCs rapidly and quantitatively” and para.[0054]; “evaluation of SARSCo V-2-specific antibodies …… antibody tests with appropriate specificity and Sensitivity”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 6, the rejection of claim 1 is incorporated, Patel et al (US 2015/0198591 A1) and Bisen et al (US 2007/0287165 A1) does not disclose wherein classifying the agglutination of the test sample on the linear quantitative scale comprises quantifying a presence of antibodies within the third fluid sample, wherein the antibodies are generated in response to an identified antigen, and wherein the test sample further comprises the identified antigen or a byproduct of the identified antigen, and a recombinant protein configured to mediate binding of red blood cells.
However, Hacker et al (US 2022/0214352 A1 A1) in an analogous art discloses,
wherein classifying the agglutination of the test sample on the linear quantitative scale comprises quantifying a presence of antibodies within the third fluid sample (para.[0035]; “nanobody-fusion proteins bind RBCs rapidly and quantitatively”).
wherein the antibodies are generated in response to an identified antigen (para.[0055]; “fusion proteins permits the detection of subject antibodies against these various SARS-CoV-2 antigens”).
and wherein the test sample further comprises the identified antigen or a byproduct of the identified antigen, and a recombinant protein configured to mediate binding of red blood cells (para.[0034]; “recombinant proteins containing the Glycophorin A-binding nanobody IH4vs2 bind human red blood cells”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 7, the rejection of claim 6 is incorporated and further Hacker et al (US 2022/0214352 A1 A1) discloses,
wherein each of the first fluid sample, the second fluid sample, and the third fluid sample constitutes an agglutination sample retrieved from a patient in a single session (para.[0091]; “providing a sample of a biological fluid from a subject in need of diagnosis; (b) combining the biological fluid with a diagnostic ……..combining a negative control reagent or a positive control ….(negative control) or SEQ ID NO: 16 or 18 (positive controls), with the biological fluid”).
and wherein the machine learning algorithm is further configured to output a test result for the patient comprising one or more of: an indication that no antibodies for the identified antigen were identified in the agglutination sample (para.[0255]; “comparing texture information from an assay of interest to texture information generated from the samples of known agglutination status (e.g. agglutinated
or non-agglutinated) ………….This information may be included in or used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest”).
Patel et al (US 2015/0198591 A1) discloses,
an indication that the antibodies for the identified antigen were identified in the agglutination sample or a quantified result on the linear quantitative scale indicating a degree to which the antibodies for the identified antigen were identified in the agglutination sample (Patel: para.[0255]; “comparing texture information from an assay of interest to texture information generated from the samples of known agglutination status (e.g. agglutinated or non-agglutinated) ………….This information may be included in or used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest” and para.[0256]; “quantitative assessment of agglutination levels of a sample, training sets containing samples of varying degrees of agglutination (e.g. weakly agglutinated, moderately agglutinated, strongly agglutinated, 10% agglutinated, 20% agglutinated, etc.) may be provided”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 8, the rejection of claim 7 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the test result further comprises a qualitative result indicating a degree to which the antibodies for the identified antigen were identified in the agglutination sample (para.[0256]; “quantitatively classify sample of interest, such as by assessing the degree of agglutination of a sample”).
As per claim 9, the rejection of claim 8 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the qualitative result comprises one or more of: the indication that no antibodies for the identified antigen were identified in the agglutination sample (para.[0256]; “quantitatively classify sample of interest, such as by assessing the degree of agglutination of a sample” and para.[0332]; “Samples 1 and 3 were negative for viral antibody”).
an indication that a low quantity of antibodies for the identified antigen were identified in the agglutination sample (para.[0323]; “samples with high antibody concentrations (low dilutions, samples 1-3) show low values for the association factor, whereas samples 4, 5, and 6 (low antibody concentrations) show high values of the association factor. The transition between sample 3 and sample 4 is also quite evident. As shown in FIG. 14D, samples with high antibody concentrations (low dilutions, samples 1-3) show high values for perimeter”).
an indication that a moderate quantity of antibodies for the identified antigen were identified in the agglutination sample or an indication that a high quantity of antibodies for the identified antigen were identified in the agglutination sample (para.[0323]; “samples with high antibody concentrations (low dilutions, samples 1-3) show low values for the association factor, whereas samples 4, 5, and 6 (low antibody concentrations) show high values of the association factor. The transition between sample 3 and sample 4 is also quite evident. As shown in FIG. 14D, samples with high antibody concentrations (low dilutions, samples 1-3) show high values for perimeter”).
As per claim 10, the rejection of claim 7 is incorporated and further Hacker et al (US 2022/0214352 A1 A1) discloses,
wherein the test result further comprises an estimation of whether the patient has been exposed to the identified antigen (para.[0062]; “This assay enables simple, rapid, sensitive, specific, and inexpensive diagnosis and identification of subjects with SARS-CoV-2-specific antibodies, either as result of previous infection or successful vaccination”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 11, the rejection of claim 7 is incorporated and further Hacker et al (US 2022/0214352 A1 A1) discloses,
wherein the test result further comprises an estimation of whether the patient has been vaccinated against the identified antigen (para.[0062]; “assay method described herein was validated
using 40 COVID19-positive subjects (and 42 control subjects), with 98% sensitivity and 98% specificity. This assay enables simple, rapid, sensitive, specific, and inexpensive
diagnosis and identification of subjects with SARS-CoV-2-specific antibodies, either as result of previous infection or successful vaccination”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 12, the rejection of claim 1 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein each of the first fluid sample, the second fluid sample, and the third fluid sample is a blood sample retrieved from a patient in a single session (para.[0011]; “bodily fluid, processed or unprocessed, including but not limited to fresh or anti-coagulated whole blood, plasma and serum”).
As per claim 13, the rejection of claim 12 is incorporated and further Hacker et al (US 2022/0214352 A1 A1) discloses,
wherein the blood sample is retrieved from the patient by way of a fingerstick sampling, and wherein one or more drops of blood from the fingerstick sampling are deposited directly on to the test card for the agglutination assay for each of the negative control sample, the positive control sample, and the test sample (para.[0137]; “blood plasma is not the intended major type of material typically used for the assay (i.e., the preferred sample is finger stick capillary blood)” and para.[0091]; “the subject sample and/or control samples in steps (b) and/or (c) are dispensed on a test card, glass slide”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 14, the rejection of claim 1 is incorporated, Patel et al (US 2015/0198591 A1) and Bisen et al (US 2007/0287165 A1) does not disclose wherein the machine learning algorithm is trained on a dataset comprising images with agglutinated labels and images with non-agglutinated labels.
However, Hacker et al (US 2022/0214352 A1 A1) in an analogous art discloses,
wherein the machine learning algorithm is trained on a dataset comprising images with agglutinated labels and images with non-agglutinated labels (para.[0255]; “texture information generated from the samples of known agglutination status (e.g. agglutinated or non-agglutinated) ……… this information may be included in or used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 16, the rejection of claim 1 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the machine learning algorithm calibrates the linear scale based on an appearance of: the negative control sample comprising a reaction of the first fluid sample with the reagent that does not induce agglutination (para.[0121]; “HA assay is performed using a sample known not to contain an agglutinating virus, to ensure that observed agglutination is a result of agglutinating virus or agglutinating particle (i.e. a negative control)” and para.[0255]; “this information may be included in or used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest” ).
and the positive control sample comprising a reaction of the second fluid sample with the reagent that induces agglutination (para.[0121]; “HA assay is performed using a sample known to contain an agglutinating virus, to ensure that erythrocytes or visualization particles used in the assay are capable of undergoing agglutination (i.e. a positive control)” and para.[0255]; “this information may be included in or used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest”).
As per claim 21, the rejection of claim 1 is incorporated, Patel et al (US 2015/0198591 A1) and Bisen et al (US 2007/0287165 A1) does not disclose wherein the machine learning algorithm is trained to calibrate the linear quantitative scale based on a training dataset, and wherein the training dataset comprises a plurality of agglutination images that have been classified with a quantified agglutination level on a scale ranging from no agglutination to maximum agglutination.
However, Hacker et al (US 2022/0214352 A1 A1) in an analogous art discloses,
wherein the machine learning algorithm is trained to calibrate the linear quantitative scale based on a training dataset, and wherein the training dataset comprises a plurality of agglutination images that have been classified with a quantified agglutination level on a scale ranging from no agglutination to maximum agglutination ( and para.[0255]; “used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest.……. images of samples of known agglutination status may be used with methods and algorithms provided herein as part of "training sets" to be used to aid in the classification of images of samples of unknown agglutination status”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate dispensing of sample on test card of the system of Hacker into diagnostic kit of the system of Bisen to provide a visual presence or absence of subject antibodies against SARS-CoV-2 antigens from human or animal samples in the system of Patel.
As per claim 22, the rejection of claim 1 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the agglutination assay further comprises a midrange calibrator sample deposited into a midrange calibrator testing region of the test card, wherein the midrange calibrator sample comprises a fourth fluid sample combined with a standardized quantity of antibodies for an identified antigen (para.[0247]; “quantification of the viral particle or antibody concentration may be carried out by performing parallel assays using the biological sample to be tested and viral particle or antibody with known concentration or titer (calibration standards)”).
As per claim 23, the rejection of claim 22 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the machine learning algorithm calibrates the quantitative scale further based on the midrange calibrator sample (para.[0033]; “measurement of the agglutination of the particles in the mixture is a quantitative measurement of degree of agglutination”).
As per claim 24, the rejection of claim 1 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
further comprising classifying of the test sample on a non-linear scale, and wherein the machine learning algorithm is configured to: assess agglutination of the test sample based on a visual representation of the agglutination as shown in the image of the agglutination assay; plot the agglutination of the test sample on the non-linear quantitative scale; and output a quantitative assessment of the agglutination of the test sample (para.[0248]; “histograms of cluster sizes of different samples, mean cluster sizes of clusters of different samples, or Association Factors of different samples, may be used to determine the agglutination level of different samples” and para.[0256]; “qualitatively classify samples of interest as either agglutinated or not agglutinated. .. methods provided herein may be used to quantitatively classify sample of interest, such as by assessing the degree of agglutination of a sample”).
As per claim 25, the rejection of claim 24 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the machine learning algorithm is further configured to output a qualitative result for the agglutination assay based at least in part on the quantitative assessment (para.[0033]; “measurement of the agglutination of the particles in the mixture is a quantitative measurement of degree of agglutination” and para.[0255]; “used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest”)
As per claim 26, the rejection of claim 24 is incorporated and further Patel et al (US 2015/0198591 A1) discloses,
wherein the machine learning algorithm is configured to calibrate the non-linear quantitative scale to account for a patient’s background agglutination such that the quantitative assessment of the agglutination of the test sample is normalized across a population (para.[0139]; “includes treatments to reduce background or false positive measurements associated with a biological sample” and para.[0255]; “used with a machine-learning algorithm, in order to assist in the classification of the agglutination status of a sample of interest”).
7. Claims 15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al (US 2015/0198591 A1), in view of Bisen et al (US 2007/0287165 A1), in view of Hacker et al (US 2022/0214352 A1 A1), and further in view of Lapointe et al (US 2021/0389308 A1).
As per claim 15, the rejection of claim 1 is incorporated, Patel et al (US 2015/0198591 A1), Bisen et al (US 2007/0287165 A1), and Hacker et al (US 2022/0214352 A1 A1) does not specifically disclose wherein the machine learning algorithm is trained on a dataset comprising: a plurality of images of blood samples from patients who have not been infected with an identified antigen and who have not been vaccinated against the identified antigen; a plurality of images of blood samples from patients who have not been infected with the identified antigen and have been vaccinated against the identified antigen; a plurality of images of blood samples from patients who have been infected with the identified antigen and have not been vaccinated against the identified antigen; and a plurality of images of blood samples from patients who have been infected with the identified antigen and have been vaccinated against the identified antigen.
However, Lapointe et al (US 2021/0389308 A1) in an analogous art discloses,
wherein the machine learning algorithm is trained on a dataset comprising: a plurality of images of blood samples from patients who have not been infected with an identified antigen and who have not been vaccinated against the identified antigen (para.[0045]; “biological sample obtained from a patient that has not been exposed to SARS-CoV-2”)
a plurality of images of blood samples from patients who have not been infected with the identified antigen and have been vaccinated against the identified antigen (para.[0045]; “biological sample obtained from a patient that has not been exposed to SARS-CoV-2”).
a plurality of images of blood samples from patients who have been infected with the identified antigen and have not been vaccinated against the identified antigen (para.[0046]; “a biological sample obtained from a patient exposed to SARS-CoV-2” and para.[0106]; “a biological sample obtained from the subject exposed to a coronavirus is
assayed using the assay assembly”).
and a plurality of images of blood samples from patients who have been infected with the identified antigen and have been vaccinated against the identified antigen (para.[0017]; “detecting a neutralizing antibody against the spike (S) protein of a coronavirus (CoV) in a biological sample from a human subject that was vaccinated against the CoV, the method comprising: (a) obtaining the biological sample obtained
from the human subject that was vaccinated against the Co V”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate a biological sample obtained from a patient under different conditions of the system of Lapointe into dispensing of a sample on the test card of the system of Hacker and diagnostic kit of the system of Bisen for performing a covid test that can measure neutralizing antibodies in the system of Patel.
As per claim 18, the rejection of claim 17 is incorporated, Patel et al (US 2015/0198591 A1), Bisen et al (US 2007/0287165 A1), and Hacker et al (US 2022/0214352 A1 A1) does not specifically disclose wherein the machine learning algorithm classifies the agglutination of the test sample in view of the natural background agglutination for the patient.
However, Lapointe et al (US 2021/0389308 A1) in an analogous art discloses,
wherein the machine learning algorithm classifies the agglutination of the test sample in view of the natural background agglutination for the patient (para.[0446]; “wherein the data analytics module is further configured to normalize the result by subtracting background noise”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate a biological sample obtained from a patient under different conditions of the system of Lapointe into dispensing of a sample on the test card of the system of Hacker and diagnostic kits of the system of Bisen for performing a covid test that can measure neutralizing antibodies in the system of Patel.
As per claim 20, the rejection of claim 1 is incorporated, Patel et al (US 2015/0198591 A1), Bisen et al (US 2007/0287165 A1), and Hacker et al (US 2022/0214352 A1 A1) does not specifically disclose wherein the machine learning algorithm is configured to identify one or more objects of interest within the image of the agglutination assay and draw a bounding box around each of the one or more objects of interest and wherein the one or more objects of interest comprise one or more of: a region comprising negative control sample; a region comprising the positive control sample; or a region comprising the test sample.
However, Lapointe et al (US 2021/0389308 A1) in an analogous art discloses,
wherein the machine learning algorithm is configured to identify one or more objects of interest within the image of the agglutination assay and draw a bounding box around each of the one or more objects of interest (para.[0018]; “imaging device operatively coupled to the first device, wherein the imaging device is configured to capture an image of the test zone. …….. the systems further comprise an imaging device operatively coupled to the second device, the imaging device configured to capture an image of the surface” and para.[0143]; “the agglutination assay comprises a lateral flow assay”).
and wherein the one or more objects of interest comprise one or more of: a region comprising negative control sample; a region comprising the positive control sample; or a region comprising the test sample (para.[0110]; “the LF A assembly has one or more zones situated laterally, including a detectable zone. The detectable zone comprise at least a control region and a test region” and para.[0111]; “the LFA assembly further comprises a sample receptor (e.g., a sample pad) configured
to receive a biological sample”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate a biological sample obtained from a patient under different conditions of the system of Lapointe into dispensing of a sample on the test card of the system of Hacker and diagnostic kits of the system of Bisen for performing a covid test that can measure neutralizing antibodies in the system of Patel.
8. Claims 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al (US 2015/0198591 A1), in view of Bisen et al (US 2007/0287165 A1), in view of Hacker et al (US 2022/0214352 A1 A1), and further in view of Hu et al (US 2023/0183817 A1).
As per claim 17, the rejection of claim 16 is incorporated, Patel et al (US 2015/0198591 A1), Bisen et al (US 2007/0287165 A1), and Hacker et al (US 2022/0214352 A1 A1) does not specifically disclose wherein the machine learning algorithm calibrates the linear quantitative scale by measuring a relative difference between the negative control sample and the positive control sample to identify natural background agglutination for a patient that provided the first fluid sample, the second fluid sample, and the third fluid sample.
However, Hu et al (US 2023/0183817 A1) in an analogous art discloses,
wherein the machine learning algorithm calibrates the linear quantitative scale by measuring a relative difference between the negative control sample and the positive control sample to identify natural background agglutination for a patient that provided the first fluid sample, the second fluid sample, and the third fluid sample (para.[0132]; “positive control sample demonstrated that both approaches produced strong signal relative to the background present in their matching negative control samples (FIG. lC), and this difference was observed for both assay targets (FIG. lD), and signal detected with MERS-Co V and SARS-Co V samples did not differ from negative control signal”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate identification of a background signal that is present in the sample of the system of Hu into dispensing of a sample on the test card of the system of Hacker and diagnostic kits of the system of Bisen for performing a highly sensitive test for SARS-CoV-2 that has high specificity and quick turn-around rate (Hu: para.[0009]).
As per claim 19, the rejection of claim 17 is incorporated and further Hacker et al (US 2022/0214352 A1 A1) discloses,
wherein: the appearance of the negative control sample represents zero agglutination for the patient (para.[0146]; “presence of hemagglutination was a positive result. The absence of hemagglutination was a negative result”).
the appearance of the positive control sample represents maximum agglutination for the patient (para.[0146]; “presence of hemagglutination was a positive result. The absence of hemagglutination was a negative result”).
and the appearance of the test sample represents relative agglutination for the patient in a presence of a recombinant protein based on the linear quantitative scale from the zero agglutination to the maximum agglutination (para.[0054]; “recombinant protein(s) that trigger(s) visible hemagglutination instantly in the presence of SARS-Co V-2 antibodies”).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate identification of background signal that present in sample of the system of Hu into dispensing of sample on test card of the system of Hacker and diagnostic kits of the system of Bisen for performing highly sensitive test for SARS-CoV-2 that has high specificity and quick turn-around rate (Hu: para.[0009]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AUGUSTINE K. OBISESAN/
Primary Examiner
Art Unit 2156
1/23/2026