DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-19 are pending.
Priority
This application is a 371 of PCT/US2021/050453, filed 09/15/2020, which claims benefit of application no. 63/167,088, filed 03/28/2021 and application no. 63/078,691, filed 09/15/2020. The instant application has the effective filing date of 15 September 2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings, submitted on 03/15/2023, are accepted by the examiner.
Specification: Abstract
The abstract of the disclosure is objected to because it appears only the first page of a WIPO publication was provided. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Draz et al. (Nature Com; Vol. 9: 4282; 2018) in view of Potluri et al. (Lab Chip; Vol. 19: 1; 2018) and Norman et al. (US 2003/0190602).
Claims 1 and 11 are directed to systems and methods that load a sample from a subject into a microchip with at least one channel; process the sample using nanoparticles and a catalyzer configured to generate gas bubbles in the presence of a target virus; and acquire an image of the microchip using a mobile device.
Draz et al. describes DNA engineered micromotors powered by metal nanoparticles for motion-based cellphone diagnostics.
Draz et al. teaches using a microchip with a single microchannel (page 6, column 1); mixing 10 µl of the formed assemblies with H2O2 solution and loading them on the microchip (page 11, column 2), in which the presence of H2O2 causes the motors to autonomously move in a self-propelled fashion due to the consumption of H2O2 and generate gas bubbles (page 3, column 2); and testing the developed CALM system using HIV-infected patient serum samples and fresh whole blood from HIV-negative subjects (page 12, column 1).
Draz et al. teaches the cellphone application was designed using Android Studio to record a video of the sample for 30 s at 30 frames/s (page 11, column 2); and representative digital images show the motion trajectories of motors in the absence of HIV-1 particle (control) and the presence of HIV-1 at concentrations above and below 1000 virus particles/ml (page 8, fig. 5f).
Claims 2 and 12 is directed to the nanoparticles being metal nanoparticles.
Claim 3 is directed to the metal nanoparticles being one of the following: platinum (Pt) nanoparticles, gold (Au) nanoparticles, copper (Cu) nanoparticles, iron (Fe) nanoparticles, palladium (Pd) nanoparticles, zinc (Zn) nanoparticles, cadmium (Cd) nanoparticles, and silver (Ag) nanoparticles.
Regarding claims 2-3 and 12, Draz et al. teaches uniquely adopting large stem-looped amplicons formed through loop-mediated isothermal amplification (LAMP) amplification to change the motion of specifically DNA-engineered micromotors, powered by metal NPs (i.e., platinum nanoparticles (PtNPs) and gold nanoparticles (AuNPs)) indicating the presence of the HIV-1 with the cellphone system (page 2, column 1).
Claims 4 and 13 is directed to the nanoprobes including the nanoparticle and a probe material.
Draz et al. teaches modifying AuNPs with thiolated DNA probes of 30-mer oligonucleotides that specifically target HIV-1 gag (page 2, column 2).
Claims 5 and 14 are directed to the nanoparticle being configured to label particles of the target virus.
Draz et al. teaches modifying AuNPs with thiolated DNA probes of 30-mer oligonucleotides that specifically target HIV-1 gag (page 2, column 2); and engineering the surface of Pt-motors with 30-mer DNA capture probe that specifically recognize the target amplicon to limit the binding of any non-specific amplicons and improve detection specificity and reliability (page 10, column 2).
Claims 6 and 15 are directed to the catalyzer being hydrogen peroxide (H2O2).
Draz et al. teaches the catalytic nature of the prepared motors is due to the decomposition of H2O2 by PtNPs (page 5, column 1).
Claims 7 and 16 is directed to the mobile device being a smartphone.
Draz et al. teaches designing the cellphone attachment to record the videos using the cellphone rear camera of a Moto X smartphone, Motorola XT1575 (page 11, column 2).
Draz et al. does not teach providing the acquired images to a neural network; generating, using the neural network, a probability regarding the presence of the target virus in the sample from the subject, based on the acquired image; or using a display coupled with the neural network to display the probability (claims 1 and 11).
Potluri et al. describes an inexpensive smartphone-based device for point-of-care ovulation testing.
Potluri et al. teaches using an android application developed for image data acquisition, with a smartphone attachment for analyzing fern patterns in air-dried saliva samples and predicting ovulation status (page 4, column 1) via a MobileNet neural network (page 4, column 2); and calculating positive and negative predictive values for the given test set (page 6, column 2).
Potluri et al. teaches a user interface of the smartphone application designed to be simple and convenient to use, with results stored in a calendar event (page 4, column 2); and displayed to the user (page 3, fig. 1d).
Claims 8 and 17 is directed to the neural network being a convolutional neural network.
Potluri et al. teaches MobileNet was specifically designed for efficient use of limited resources available on embedded devices such as smartphones, with a network that makes use of depth-wise separable convolutions to build light-weight deep neural networks (page 5, column 1).
Claims 9 and 18 is directed to the probability value indicating whether the sample is positive or negative for the target virus.
Draz et al. teaches classifying samples with virus concentrations above and below 1000 particles/ml as positive or negative, respectively (page 9, fig. 6); evaluating the efficiency and reliability of the developed CALM system in HIV-1 detection; finding 100% accordance between the CALM system and iSCA assay (page 6, column 2); and 90–100% accuracy as compared to RT-PCR (page 11, column 1).
Potluri et al. teaches calculating the positive predictive value (PPV) and negative predictive value (NPV) for the given test set (page 6, column 2).
Claim 10 is directed to modifying the microchip with a probe material on the surface of the microchip.
Draz et al. teaches the micromotors used in this study are PtNP-coated spherical polystyrene (PS) beads (page 2, column 2); and motor preparation reaction includes the direct coupling of AuNPs and PtNPs to the surface of amine-functionalized PS beads (page 2, column 2).
Draz et al. in view of Potluri et al. do not explicitly teach displaying the probability value.
Norman et al. describes a method for detecting and differentiating disease states with high sensitivity and specificity.
Norman et al. teaches performing several discrimination panel analyses on a specimen from a patient and reporting the results as a probability that a specific disease state is present [0132]; in which a single, comprehensive report with integrated results may be generated and delivered to the physician in hardcopy, or electronically [0120].
Norman et al. teaches exemplary infectious diseases which may be detected are cell-based diseases in which the infectious organism is a virus, bacteria, protozoan, parasite, or fungus, such as HIV, hepatitis, influenza, meningitis, mononucleosis, tuberculosis and sexually transmitted diseases [0069].
Norman et al. further teaches improved methods for specimen collection, such as point-of-care devices, in conjunction with a successful implementation of molecular diagnostics cell-based panel assay will lead to (a) characterization of the molecular profile of tumors and other disease states, (b) improved methods for disease state detection and differentiation, and (c) opportunities for improved clinical diagnoses, prognoses, customized patient treatments, and therapeutic monitoring [0036].
Norman et al. further teaches reporting the probabilities gives the doctor the ability to conclude that the test verifies that the patient has the specific disease state with high probability or recommend more extensive panel testing to ensure that the correct disease state is identified [0133].
Therefore Draz et al. teaches detecting HIV by analyzing smartphone images of a microchip that has the subject’s sample and a hydrogen peroxide catalyst. Potluri et al. teaches detecting the negative or positive probability that a subject is ovulating by analyzing smartphone images of a microchip with the subject’s sample via artificial intelligence, in the form of a convolutional neural network.
Potluri et al. further teaches smartphone cameras have been used in point-of-care tests for both qualitative and quantitative detection of clinically relevant biomarkers for disease detection or treatment monitoring such as HIV/AIDS and syphilis, herpes, sickle cell, male infertility, and Zika (page 3, column 1); artificial intelligence (AI) is gaining acceptance in medicine and one of its major applications in healthcare is its ability to accurately interpret complex medical data with super-human accuracy and consistency (page 2, column 2); and the convolutional, neural network architecture of MobileNet was specifically designed for efficient use of limited resources available on embedded devices such as smartphones (page 5, column 1).
As such it would be obvious to one of ordinary skill in the art to apply a neural network, taught by Potluri et al., to an applicable smartphone image microchip technique, such as that of Draz et al., with a reasonable expectation of success in virus detection.
Norman et al. provides further motivation for one of ordinary skill to report the probability results of a disease state, such as a virus, being present due to the increased information delivered to the physician and routine optimization. As such, it would also be obvious for one of ordinary skill in the art to display the probability of the target virus being present in the sample within the device with a reasonable expectation of success and improvement to the system.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Draz et al. (Nature Com; Vol. 9: 4282; 2018) in view of Potluri et al. (Lab Chip; Vol. 19: 1; 2018) and Norman et al. (US 2003/0190602), as applied to claim 11 above, and in further view of Kost et al (Journal of Clinical Microbiology; Vol. 45: 4; 2007).
Claim 19 is directed to loading the nanoparticles and the catalyzer onto the microchip using a sample processing cartridge.
Draz et al. in view of Potluri et al. and Norman et al. teach systems and methods of virus detection based on analyzing images of a microchip with a sample and catalyst via convolutional neural network.
Draz et al. further teaches the presented system is a platform technology that will need additional modifications to allow sample processing (page 11, column 1).
Draz et al. does not teach using a sample processing cartridge to load the nanoparticles and the catalyzer onto the microchip.
Kost et al. describes a multicenter beta trial of the GeneXpert Enterovirus assay (GXEA).
Kost et al. teaches adding binding, wash, elution, and lysis buffers to the appropriate ports on the microfluidic cartridge; adding 140 μl of CSF to sample chamber; and then loading the cartridge into a GeneXpert Dx module to complete sample preparation, target amplification, and product detection (page 3, column 1).
Kost et al. teaches the GeneXpert Dx system performs hands-off sample processing and real-time, multiplex PCR for detection of viral DNA or RNA, in which the processes are all fully automated and integrated; and the system consists of an instrument, a personal computer, and disposable fluidic cartridges that have been designed to complete sample preparation quickly (page 1, column 2).
Kost et al. further teaches the system automates all of the steps of a NAT in a disposable, microfluidic cartridge (page 5, column 1); is simple enough to be performed reliably by individuals without a background in nucleic acid diagnostics (page 5, column 1); and is uniquely suited for clinical applications of molecular diagnostics when on-demand testing and rapid-result capability are needed (page 5, column 2).
Therefore Draz et al. motivates one of ordinary skill in the art to apply sample processing modifications to the microchip virus detection system and Kost et al. teaches an applicable method, well-suited for a point-of-care device. As such, it would be obvious to one of ordinary skill in the art to combine the use of a sample processing cartridge, as taught by Kost et al., within the system of Draz et al. with a reasonable expectation of success and improvement to the system.
Conclusion
No claims are currently allowed.
In regards to subject matter eligibility under U.S.C 101, the claims are drawn to statutory categories such as systems and methods (eligibility step 1: YES); and the only element that might appear to recite a judicial exception, in the form of abstract ideas, is “generating a probability regarding the presence of target virus in the sample from the subject based on the acquired image“ presented in independent claims 1 and 11.
However, since the probability is based on an image instead of numerical data or other mathematical relationships or calculations and generated via a neural network, it merely is based on math, and not found to read on judicial exceptions such as mathematical concepts per MPEP 2106.04 (a)(2) (eligibility step 2a: NO).
As such, the claimed invention is eligible under 35 U.S.C 101, via pathway B (MPEP 2106.04).
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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