DETAILED ACTION
Preliminary Amendment filed on 04/11/2024 is acknowledged. Claims 4, 10, 12 and 19 are cancelled. Claims 1-3, 5-9, 11, 13-18 and 20-24 are pending in the application and are considered on merits.
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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-9, 11, 14-18 and 20-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nirala et al. (Talanta, 2019, IDS) (Nirala) in view of Roda et al. (Analytical Chemistry, 2014, IDS) (Roda).
Regarding claim 1, Nirala teaches a method for early detection of Bovine Mastitis using enhanced chemiluminescence (abstract), the method comprising the steps:
a) Preparing one or more bio-functionalized Hemoglobin (Hb)-modified assay plates (page 259, par 0), a chemiluminescent (CL) solution (page 259, par 1), a crosslinked nanoparticle solution (page 258, par 4);
b) Collecting milk samples (page 258, par 5) and preparing a multiplicity of sample dilutions (page 259, par 3);
c) Applying sample dilutions to wells of the assay plate(s) and waiting a first predetermined time interval (page 259, par 3);
d) Adding the CL and nanoparticle solutions to the wells and waiting a second predetermined time interval (page 259, par 3);
g) Estimating a Haptoglobin (Hp) level for each well of the assay plate(s) and
combining the estimated Hp levels to form a BM clinical diagnosis (page 259, par 3).
Nirala reports Hp concentrations for healthy and mastitic milk samples as mean values based on at least three measurements and explains that its analyzed samples represent “three levels of SCC (approx. 100,000, 300,000 and 800,000 for subclinical and clinical mastitis, respectively).” Nirala further concludes that its luminol-H₂O₂-Hb system provides “sensitive detection of Hp in bovine milk samples for clinical status evaluation of BM.” Thus, Nirala calculates Hp levels from the measured CL values, combines replicate estimated Hp values into a reported Hp concentration, and uses those concentrations to determine whether a milk sample corresponds to healthy, subclinical-mastitis, or clinical-mastitis status.
Nirala does not expressly teach preparing a camera for imaging the CL assay; and acquiring camera images of the assay plate or determining CL intensity from those images.
However, Roda teaches a camera for detecting a luminol-based chemiluminescent biological assay, stating: “An Iphone 5S (Apple, Cupertino, CA, USA) with a BI-CMOS sensor and 8-megapixel (8MP) camera was used.” (page 7301, par 1); and
e) Acquiring one or more CL images of the assay plate(s); and
f) Analyzing each CL image to determine a CL intensity measurement for each well.”
Roda teaches:
“The minicartridge is then inserted into the smartphone mini darkbox accessory and the light signal measured by the smartphone camera for 30 s using LongExpo app.” (page 7301, par 7).
Roda further discloses:
“Quantitative analysis of the CL images were performed using ImageJ software v.1.46. Regions of interest (ROIs) corresponding to the detection chamber and background were selected and light emissions quantified as raw integrated densities.” (page 7301, par 7).
Roda thus captures CL images using a camera and determines a quantitative CL intensity for a selected reaction region. When Roda’s imaging technique is applied to Nirala’s multiwell assay plate, each assay well constitutes a respective reaction region or ROI, thereby providing a CL intensity measurement for each well.
Regarding claim 2, Nirala teaches wherein the CL solution comprises luminol and/or peroxide solutions (page 259, par 3).
Regarding claim 3, Nirala teaches wherein the nanoparticle solution comprises one or more materials selected from a group consisting of gold, magnetite (Fe3O4) and zinc oxide (ZnO) (page 258, par 4).
Regarding claim 5, Nirala in view of Roda teaches “wherein the camera is sensitive to CL blue light emitted in a wavelength range that includes 425 nanometers.” Nirala expressly teaches that its claimed luminol-H₂O₂-Hb reaction produces blue chemiluminescent light having a maximum emission at approximately 425 nm (“The catalytic activity of Hb on luminol-H₂O₂ CL system in alkaline conditions is well reported, producing distinct maximal emission ~425 nm during the relaxation of the excited 3-aminophthalate ions to ground state.” (page 259). Roda teaches detecting a luminol-H₂O₂ chemiluminescent reaction using a smartphone camera (“the produced H₂O₂ is detected using the CL of the luminol-H₂O₂-HRP system” and “the light signal [is] measured by the smartphone camera for 30 s”) (page 7301, par 7). Thus, in the bovine-mastitis detection method resulting from the combination, Roda’s camera receives and detects the approximately 425-nm blue CL light produced by Nirala’s luminol-H₂O₂-Hb reaction and is therefore sensitive to a wavelength range that includes 425 nm.
Regarding claim 6, Nirala in view of Roda teaches “wherein the camera is fitted with a selective blue filter and/or a shroud.” Roda teaches a smartphone camera fitted with a darkbox accessory (“The device consists of two main parts: a phone adapter comprising the darkbox and lens holder and a cartridge for the bioassays” (page 7301, par 3), and “[t]he minicartridge is then inserted into the smartphone mini darkbox accessory and the light signal measured by the smartphone camera”) (page 7301, par 7). The darkbox attached to the smartphone camera surrounds the imaging region and excludes ambient light and therefore constitutes a shroud as broadly claimed.
Regarding claim 7, Nirala teaches wherein the sample dilutions differ in dilution ratio by at least an order of magnitude (page 259, par 3).
Regarding claim 8, Nirala teaches wherein at least one of the wells of the assay plate(s) is a control well, corresponding to no significant BM disease (page 260, par 1).
Regarding claim 9, Nirala further teaches wherein an Hp-Hb binding reaction occurs during the first pre-determined time interval (Fig. 1, page 259, par 2).
Regarding claim 11, Nirala further teaches wherein a CL emission intensity approaches a steady-state during the second pre-determined time interval. Nirala teaches that the reaction conditions were selected “to suit the optimal CL stability over time” and that “all concentrations present stable CL intensity over time (120 s).” (page 259, par 5). Nirala further states that the Hb-modified plate produced “a stable CL signal over 120 s.” (page 259, par 5). Thus, after addition of the CL reagents, the CL intensity reaches and remains substantially stable during the predetermined 120-second measurement interval.
Regarding claim 14, Nirala teaches estimating the Hp level using a predetermined calibration curve (page 258, par 3).
Regarding claim 15, Nirala further teaches “wherein the BM clinical diagnosis corresponds to a BM disease level selected from a group consisting of no significant disease, sub-clinical disease, and clinical disease.” Nirala teaches that the analyzed samples included a healthy milk sample having “insignificant SCC values and no traces of any pathogen,” as well as samples having three SCC levels “for subclinical and clinical mastitis.” Nirala additionally identifies the samples having the highest SCC values as having “clinical mastitis.” Thus, Nirala classifies the milk samples as healthy or having no significant disease, subclinical mastitis, or clinical mastitis (Nirala, page 260–261).
Regarding claim 16, Nirala teaches a system for early detection of Bovine Mastitis using enhanced chemiluminescence (page 258, par 1), the system comprising:
one or more bio-functionalized Hemoglobin (Hb)-modified assay plate(s), each plate having a multiplicity of wells containing different milk sample dilutions (page 259, par 0 & 3);
a chemiluminescence (CL) solution and a crosslinked nanoparticle solution for producing an emission of CL light by the milk sample dilution in each of the wells (page 259, par 1 & 3);
Nirala does not expressly teach a camera configured to receive the emission of CL light and to produce CL images; and a processor; wherein the processor is configured to analyze the CL images to determine a CL intensity measurement and an estimate of Haptoglobin (Hp) level, for each well of the assay plate(s).
However, Roda teaches a camera and processor for imaging and quantitatively analyzing a luminol-based CL assay (page 7301, par 1). Roda states:
“An Iphone 5S … with a BI-CMOS sensor and 8-megapixel (8MP) camera was used.” (page 7301, par 1).
Roda further teaches that images of the CL reaction were collected with the smartphone camera and that:
“The images have been elaborated with ImageJ software to quantify the signal over the sample spot area and expressed as relative light units (RLUs).” (page 7301, par 1).
Roda also describes its system as using a smartphone “to image and quantify biochemiluminescence coupled biospecific enzymatic reactions to detect analytes in biological fluids.” (page 7300, par 3).
Nirala teaches estimating the Hp level from the resulting CL intensity, stating:
“The resulting Hp concentration values were calculated from the calibration curves (with and without GNPs-PDT-2).” (page 259, par 3).
Thus, in the resulting system, Roda’s camera produces a CL image of Nirala’s multiwell assay plate, and the processor quantitatively analyzes the image region corresponding to each well to determine its CL intensity, which is converted using Nirala’s calibration curve into the estimated Hp level for that well.
It would have been obvious to one of ordinary skill in the art to modify the above bovine-mastitis chemiluminescence detection system to include Roda’s smartphone camera and image-processing arrangement to provide portable, point-of-need acquisition and quantitative analysis of the CL signals without requiring Nirala’s separate microplate reader. Roda expressly teaches that an all-in-one smartphone device “would eliminate the need for separate devices” and permit analytical tests to be performed outside clinical laboratories (page 7299, par 3).
Regarding claim 17, Nirala further teaches wherein the CL solution comprises luminol and/or peroxide solutions (page 259, par 1).
Regarding claim 18, Nirala further teaches wherein at least one of the wells of the assay plate(s) is a control well, corresponding to no significant BM disease (page 260, par 1).
Regarding claim 20, Nirala teaches wherein the processor utilizes a pre-determined regression curve to determine the estimates of Hp level (page 259, par 3).
Regarding claim 21, Nirala in view of Roda teaches wherein the processor is configured to combine the estimates of Hp level to form a BM clinical diagnosis. Nirala reports estimated Hp levels as combined measurements, stating that the data are “reported as mean ± standard deviation (n ≥ 3),” and Table 2 provides a combined Hp concentration for each healthy or mastitic milk sample. Nirala concludes that the luminol-H₂O₂-Hb CL system provides “sensitive detection of Hp in bovine milk samples for clinical status evaluation of BM.” Thus, Nirala combines multiple estimated Hp measurements into a representative Hp level and uses the resulting level to evaluate the clinical status of bovine mastitis.
Roda teaches performing this quantitative analysis using the smartphone processor, explaining that a smartphone is used “to image and quantify biochemiluminescence coupled biospecific enzymatic reactions to detect analytes in biological fluids” and that smartphones provide both “imaging, and computing power.” Accordingly, in the resulting system, the smartphone processor combines the Hp estimates obtained from the respective assay measurements to produce Nirala’s BM clinical-status determination.
Regarding claim 22, Nirala further teaches wherein the BM clinical diagnosis corresponds to a BM disease level selected from a group consisting of no significant disease, sub-clinical disease, and clinical disease. Nirala teaches that sample H is “healthy milk” having “insignificant SCC values and no traces of any pathogen” and that the other samples represent “three levels of SCC (approx. 100,000, 300,000 and 800,000 for subclinical and clinical mastitis, respectively).” Nirala further identifies samples S3 and S6 as having “clinical mastitis” and presents Figure 5 as comparing “healthy, subclinical and clinical mastitis.” Thus, Nirala’s clinical diagnosis distinguishes no significant disease, subclinical disease, and clinical disease.
Regarding claim 23, Roda teaches wherein the processor and the camera are integral components of a smartphone. Roda teaches “the use of a smartphone to image and quantify biochemiluminescence” and explains that smartphones possess both “imaging, and computing power.” Roda further states that the advantage of the disclosed system is an “‘all-in-one device’ that fully exploits the multiple smartphone capabilities” and eliminates the need for separate devices. Roda therefore teaches that the camera used to acquire the CL image and the computing processor used to quantify the image are integrated components of the smartphone.
Regarding claim 24, Roda further teaches wherein the camera incorporates a charge-coupled device (CCD) sensor, a complementary metal-oxide-semiconductor (CMOS) sensor, or a photomultiplier. Roda expressly discloses that “[a]n Iphone 5S (Apple, Cupertino, CA, USA) with a BI-CMOS sensor and 8-megapixel (8MP) camera was used.” Because the claimed alternatives are recited in the disjunctive, Roda’s smartphone camera incorporating a CMOS sensor satisfies the limitation.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nirala in view of Roda as applied to claim 1 above, and further in view of Vashist et al. (WO 2014/198836) (Vashist).
Regarding claim 13, Nirala in view of Roda teaches acquiring and quantitatively analyzing CL images, and Roda specifically teaches selecting “[r]egions of interest (ROIs) corresponding to the detection chamber and background” and quantifying the light emissions (page 7301). However, Nirala and Roda do not expressly teach that the intensity is determined by averaging the image pixels within the ROI. Vashist teaches “wherein the CL intensity measurement is determined by averaging over a region of interest in the CL image,” because Vashist’s image-processing algorithm “initially determines the center of each MTP well and subsequently calculates the mean of defined neighboring pixels.” Each group of neighboring pixels surrounding the center of a respective well constitutes a region of interest for that well. (Vashist, page 28, lines 8–18).
It would have been obvious to one of ordinary skill in the art to modify the above smartphone-based bovine-mastitis chemiluminescence assay to calculate a mean pixel value within the region corresponding to each assay well, as taught by Vashist, to provide a representative well intensity and reduce the effect of individual-pixel variations. Vashist expressly teaches that its mean-pixel image-processing technique can “accurately predict the corresponding absorbance values obtained by the MTP reader.” (Vashist, page 28, lines 18–25).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOYUN R XU, Ph. D. whose telephone number is (571)270-5560. The examiner can normally be reached M-F 8am-5pm.
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/XIAOYUN R XU, Ph.D./ Primary Examiner, Art Unit 1797
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