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 .
Claims Status
Claims 1-20 are pending.
Claims 1-20 are examined.
Priority
The instant application claims priority to provisional US Application No. 63/194714, filed 05/28/2021. Therefore, the Effective Filing Date (EFD) assigned to each of the claims 1-20 is the provisional filing date of Application No. 63/194714, filed 05/28/2021.
Information Disclosure Statement
The Information Disclosure Statements filed 05/26/2022 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The drawings filed 05/26/2022 are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claims 6 and 19, the claims recite the limitation of “performing unsupervised machine learning techniques such as principal component analysis (PCA) to build a model”. The phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims are directed to a method and a machine-accessible non-transitory medium.
With respect to step (2A)(1): The claims are directed to an abstract idea of mathematical concepts.
“Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mathematic concepts recited in claims 1 and 14:
performing data fusion between the first absorption spectral output and the second emission spectral output to generate fused data
Dependent claims 2-9, 11-13, and 15-20 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claim 1, and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claims 2 and 15: “applying artificial intelligence (Al) of an Al module to the fused data to identify a coronavirus (CoV-2) in saliva from a panel of viruses of the sample”
Claims 3 and 16: “utilizing machine learning to extract absorption features from the first absorption spectral output; and utilizing machine learning to extract emission features from the second emission spectral output”
Claims 4 and 17: “performing data fusion between the first absorption spectral output, the second emission spectral output, and third spectral output to generate fused data”
Claims 5 and 18: “wherein combining UV absorption and UV fluorescence to generate fused data in combination with machine learning allows measured concentrations down to approximately 103 copies/ml (viral load) range”
Claims 6 and 19: “simulating variation in the first absorption spectral output and the second emission spectral output due to different types of multiplicative and additive artificial noise to generate spectra; and performing feature extraction from the generated spectra and performing unsupervised machine learning techniques such as principal component analysis (PCA) to build a model”
Claims 7 and 20: “wherein the extracted features are represented as numerical vectors that encode salient information about each spectrum”
Claim 8: “wherein the extracted features are jointly combined and inputted into a neural network”
Claim 9: “developing a classifier using a weighted K-nearest neighbors (KNN) algorithm to predict an accuracy for virus detection as well as a confidence score for virus detection measurements”
Claim 11: “plotting two dimensions of principal component analysis (PCA) features that were extracted from original viral samples with each plot providing a visualization of each generated spectra's features plotted in a color for a type of virus family”
Claim 12: “determining whether a spectrum from a data sample is viable; and when the data sample is deemed viable, preprocessing is performed to characterize the data sample including quantifying a number of spectral channels, determining statistics of the spectrum that can be queried for analysis, and determining a signal-to-noise ratio for the spectrum; and identifying a targeted virus from a data set of known virus spectra”
Claim 13: “determining learned features from a self-supervised autoencoder, and from trained supervised networks”
The abstract ideas in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mathematic concepts because the claims recite no more than performing statistical analysis on spectroscopy data.
With respect to step (2A)(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone or in combination to determine if the judicial exception is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claims 1 and 14 recite the following additional elements that are not abstract ideas:
generating, with a first miniature UV absorption spectrometer of a multi-spectral optical device, a first absorption spectral output based on receiving an absorbance light channel from a sample
generating, with a second miniature UV fluorescence spectrometer of the multi-spectral optical device, a second emission spectral output based on receiving an emission light channel from the sample
a machine-accessible non-transitory medium containing executable computer program instructions
The steps of generating the spectral outputs are directed to steps of data gathering as they gather the data on which the judicial exceptions are performed. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). The element of a non-transitory storage medium is directed to generic computer elements. Hence, these are interpreted as mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc. ... are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(f)).
Dependent claims 4, 10, and 17 are directed to further steps of data gathering or limitations as to how the data is gathered.
None of these dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
Lastly, the claims have been evaluated with respect to step (2B): Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. Under said analysis, Applicant is reminded that the judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d)(I), determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claims 1 and 14: The additional elements of generating with a miniature UV absorption spectrometer and a miniature UV fluorescence spectrometer, and a machine-accessible non-transitory medium do not rise to the level of significantly more than the judicial exception. With respect to the non-transitory medium, as exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). Furthermore, with respect to generating spectral output using miniature UV absorption and miniature UV fluorescence spectrometers, the prior art to Sandhu et al. (US 2014/0092238 A1, published April 2014) discloses that various commercial UV sensors are currently available currently, and that popular forms of UV exposure meters comprise sensors mounted on wearable accessories and sensor readings from multiple sensors being fused (paragraph [0003]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claims 4 and 17: The additional element of generating, with a third miniature UV reflectance spectrometer of the multi-spectral optical device, a third spectral out based on the sample does not rise to the level of significantly more than the judicial exception. The prior art to Sandhu et al. discloses that various commercial UV sensors are currently available currently, and that popular forms of UV exposure meters comprise sensors mounted on wearable accessories and sensor readings from multiple sensors being fused (paragraph [0003]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 10: The additional element of a handheld multi-spectral device does not rise to the level of significantly more than the judicial exception. The prior art to Sandhu et al. discloses that various commercial UV sensors are currently available currently, and that popular forms of UV exposure meters comprise sensors mounted on wearable accessories and sensor readings from multiple sensors being fused (paragraph [0003]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
In combination, the collection or generation of the data, acted upon by the judicial exception, fail to rise to the level of significantly more. The data gathering steps provide the data for the judicial exception. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found lacking.
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, 4, 10, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Poteet et al. (US 9013686 B2, patented April 2015, IDS reference) in view of Bi et al. (“A Handheld Miniature Ultraviolet LED Fluorescence Detection Spectrometer”, Journal of Applied Spectroscopy, published July 2019).
Regarding claims 1 and 14, Poteet et al. teaches a method comprising:
generating a first absorption spectral output based on receiving an absorbance light channel from a sample using a UV absorption spectrometer of a multi-spectral optical device; generating a second emission spectral output based on receiving an emission light channel from the sample using a UV fluorescence spectrometer of the multi-spectral optical device; and performing, with the multi-spectral optical device, data fusion between the first absorption spectral output and the second emission spectral output to generate fused data (claim 8).
Furthermore, Poteet et al. teaches a computing system included within the system that comprises a processor and a memory storing instructions to execute on the processor (column 6, line 7). Poteet et al. teaches a hand-held device (column 4, line 7).
Poteet et al. does not teach the claim elements of a miniature UV absorption spectrometer and UV fluorescence spectrometer.
However, Bi et al. teaches a handheld miniature ultraviolet LED fluorescence detection spectrometer (Abstract). Bi et al. teaches a handheld micro-fluorescence spectrometer with an integrated ultraviolet light-emitting diode with the advantages of compact structure, small size, light weight, fast detection speed, convenient use, and low cost (Abstract). Furthermore, Bi et al. teaches the handheld spectrometer being composed of a UV light-emitting diode light source, a light dispersive system, a detection system, and a data processing system (page 538, Section “Structure and Characteristics of the Handheld Spectrometer”).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the miniature spectrometers of Bi et al. to the method of Poteet et al. because both Poteet et al. and Bi et al. are directed to data analysis using spectroscopy (see Abstract of both), and thus one of ordinary skill in the art would have a reasonable expectation of success in combining the prior art elements, and would be motivated to do so in order to achieve a compact structure, faster detection speed, convenient use, and a lower cost system.
Regarding claims 4 and 17, the claims are directed to generating, with a third miniature UV reflectance spectrometer of the multi-spectral optical device, a third spectral output based on the sample; and performing data fusion between the first absorption spectral output, the second emission spectral output, and third spectral output to generate fused data. Poteet et al. teaches the method of claim 1 and the non-transitory medium of claim 14 in view of Bi et al. Poteet et al. also teaches generating an output using a reflectance spectrometer (Abstract) and generating fused data with the first, second, and third spectrometers (claim 8).
Regarding claim 10, the claim is directed to the multi-spectral optical device being a handheld multi-spectral optical device. Poteet et al. teaches the method of claim 1 in view of Bi et al. Poteet et al. also teaches the device being a handheld multi-spectral optical device (column 4, line 7).
Claims 2, 3, 5-9, 12, 13, 15, 16, are 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Poteet et al. in view of Bi et al., as applied to claims 1, 4, 10, 14, and 17 above, and further in view of Cui et al. (“Advancing Biosensors with Machine Learning”, ACS Sensors, published 2020).
Regarding claims 2 and 15, the claims are directed to applying artificial intelligence (AI) of an AI module to the fused data to identify a coronavirus (CoV-2) in saliva from a panel of viruses of the sample. Poteet et al. teaches the method of claim 1 and the non-transitory medium of claim 14 in view of Bi et al. Poteet et al. also teaches data modules including algorithms to give a high degree of certainty for matching spectral information from the generated fused spectral data to stored signature data (column 6, line 36).
Neither Poteet et al. nor Bi et al. teach the claim elements of applying AI of an AI module to the fused data to identifying a coronavirus in saliva from a panel of viruses of the sample.
However, Cui et al. advancing biosensors with machine learning. Cui et al. teaches applying AI to spectroscopy data, including spectra-based biosensors and fluorescence biosensors, and discussed multibiosensor data fusion (Abstract). Furthermore, Cui et al. teaches coupling physiological data with state-of-the-art machine learning techniques will create a valuable platform to detect COVID-19 infection (page 3353, column 2, paragraph 2), and teaches machine learning being employed in the biosensor field as a tool for data processing and analysis such as extracting features or predicting the species and concentration of the analytes (page 3348, Section “Various ML Algorithms and their Merits for Biosensors”, paragraph 1).
Regarding claims 3 and 16, the claims are directed to utilizing machine learning to extract absorption features from the first absorption spectral output; and utilizing machine learning to extract emission features from the second emission spectral output. Poteet et al. teaches the method of claim 1 and the non-transitory medium of claim 14 in view of Bi et al.
Neither Poteet et al. nor Bi et al. teach the claim elements of utilizing machine learning to extract absorption features from the first absorption spectral output; and utilizing machine learning to extract emission features from the second emission spectral output.
However, Cui et al. teaches machine learning being a system or computer program capable of acquiring knowledge by extracting features from raw data (page 3348, Section “Various ML Algorithms and their Merits for Biosensors”, paragraph 1) and teaches applications of machine learning models such as a KNN to fluorescence based biosensors to extract small features (Table 1).
Regarding claims 5 and 18, the claims are directed to combining UV absorption and UV fluorescence to generate fused data in combination with machine learning allowing measured concentrations down to approximately 103 copies/ml (viral load) range. Poteet et al. teaches the method of claim 1 and the non-transitory medium of claim 14 in view of Bi et al. Poteet et al. also teaches the method and system comparing a generated spectral signature to a stored spectral signature in order to identify a substance (Abstract)
Neither Poteet et al. nor Bi et al. teach the claim element of combining UV absorption and UV fluorescence to generate fused data in combination with machine learning allowing measured concentrations down to approximately 103 copies/ml (viral load) range.
However, Cui et al. teaches the machine learning allowing for identification and pattern recognition (page 3347, column 2, paragraph 1). Although Cui et al. does not specifically teach the element of measuring viral concentrations down to 103 copies/ml, the combined references that teach the instant claim limitations in combination would allow to measure a viral load concentration of 103 copies/ml, and thus would be prima facie obvious.
Regarding claims 6 and 19, the claims are directed to simulating variation in the first absorption spectral output and the second emission spectral output due to different types of multiplicative and additive artificial noise to generate spectra; and performing feature extraction from the generated spectra and performing unsupervised machine learning techniques such as principal component analysis (PCA) to build a model. Poteet et al. teaches the method of claim 1 and the non-transitory medium of claim 14 in view of Bi et al. Poteet et al. teaches adjusting the spectral data to account for the container of the target (claim 11).
Neither Poteet et al. nor Bi et al. teach the claim element of performing feature extraction from the generated spectra and performing unsupervised machine learning techniques such as principal component analysis (PCA) to build a model.
However, Cui et al. teaches training machine learning models to distinguish the actual signal from the noise (page 3347, column 2, paragraph 1) and teaches using PCA to build a model to extract features of data (page 3357, column 1, paragraph 3).
Regarding claims 7 and 20, the claims are directed to the extracted features being represented as numerical vectors that encode salient information about each spectrum. Poteet et al. teaches the method of claim 6 and the non-transitory medium of claim 19 in view of Bi et al. and further in view of Cui et al.
Neither Poteet et al. nor Bi et al. teach the claim element of extracted features being represented as numerical vectors that encode salient information about each spectrum.
However, Cui et al. teaches extracted features of data being represented as numerical vectors (page 3352, column 2, paragraph 1; page 3353, column 1, paragraph 2).
Regarding claim 8, the claim is directed to the extracted features being jointly combined and inputted into a neural network. Poteet et al. teaches the method of claim 7 in view of Bi et al. and further in view of Cui et al.
Neither Poteet et al. nor Bi et al. teach the claim element of extracted features being jointly combined and inputted into a neural network.
However, Cui et al. teaches using a kNN for application of fluorescent based biosensors, wherein the input is features extracted from the data (Table 1) and teaches using CNNs with features extracted from spectral data being the input, wherein the data can be preprocessed using PCA (page 3351, column 2, paragraph 1).
Regarding claim 9, the claim is directed to developing a classifier using a weighted K-nearest neighbors (KNN) algorithm to predict an accuracy for virus detection as well as a confidence score for virus detection measurements. Poteet et al. teaches the method of claim 1 in view of Bi et al.
Neither Poteet et al. nor Bi et al. teach the claim element of developing a classifier using a weighted K-nearest neighbors (KNN) algorithm to predict an accuracy for virus detection as well as a confidence score for virus detection measurements.
However, Cui et al. teaches 100% pattern recognition accuracy for kNN with the application of fluorescent spectra (Table 2),
Regarding claim 12, the claim is directed to determining whether a spectrum from a data sample is viable; and when the data sample is deemed viable, preprocessing being performed to characterize the data sample including quantifying a number of spectral channels, determining statistics of the spectrum that can be queries for analysis, and determining a signal-to-noise ratio for the spectrum; and identifying a targeted virus from a data set of known virus spectra. Poteet et al. teaches the method of claim 1 in view of Bi et al. Poteet et al. also teaches the method including comparing a generated spectral signature to a stored spectral signature in order to identify a substance (Abstract). Furthermore, Poteet et al. teaches filtering the energy data gathered (Figure 6).
Neither Poteet et al. nor Bi et al. teach the claim elements of preprocessing being performed to characterize the data sample including quantifying a number of spectral channels, determining statistics of the spectrum that can be queries for analysis, and determining a signal-to-noise ratio for the spectrum; and identifying a targeted virus from a data set of known virus spectra.
However, Cui et al. teaches the four-color spaces of the colorimetric biosensor and teaches determining the color changes within the channels of the different color spaces and teaches determining differentiability and sensitivity (page 3355, column 2, paragraph 2). Furthermore, Cui et al. teaches application of machine learning in biosensors to improve the signal-to-noise ratio for single molecule, single particle, or single cell detection (page 3357, column 2, paragraph 3).
Regarding claim 13, the claim is directed to determining learned features from a self-supervised autoencoder, and from trained supervised networks. Poteet et al. teaches the method of claim 1 in view of Bi et al.
Neither Poteet et al. nor Bi et al. teach the claim elements of determining learned features from a self-supervised autoencoder, and from trained supervised networks.
However, Cui et al. teaches applying an autoencoder to denoise and reduce dimensionality of data (page 3348, column 2, paragraph 2), and teaches trained supervised networks having achieved great progress for spectrometric biosensors (page 3348, column 1, paragraph 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the machine learning aspects of Cui et al. to the method of Poteet et al. in view of Bi et al. because both Poteet et al. and Cui et al. are directed to spectroscopy systems (see Abstract of both). Thus, one of ordinary skill would have a reasonable expectation in success of combining the prior art elements and would be motivated to do so because machine learning integrated into biosensors can create intelligent biosensors that can automatically predict species or concentrations of analytes based on a decision system (page 3346, column 1, paragraph 1) and Poteet et al. is directed to comparing a generated target signature of a sample with a stored signature to determine substances in a target (Abstract).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Poteet et al. in view of Bi et al., as applied to claims 1, 4, 10, 14, and 17 above, and further in view of Lim et al. (“Identification of Newly Emerging Influenza Viruses by Detecting the Virally Infected Cells Based on Surface Enhanced Raman Spectroscopy and Principal Component Analysis”, Analytical Chemistry, published 2019).
The claim is directed to plotting two dimensions of PCA features that were extracted from original viral samples with each plot providing visualization of each generated spectra’s features plotted in a color for a type of virus family. Poteet et al. teaches the method of claim 1 in view of Bi et al.
Neither Poteet et al. nor Bi et al. teach the claim element of plotting two dimensions of PCA features that were extracted from original viral samples with each plot providing visualization of each generated spectra’s features plotted in a color for a type of virus family.
However, Lim et al. teaches the identification of influenza viruses in cells using Raman spectroscopy and PCA features (Abstract). Lim et al. teaches two dimensionally plotting PCA features of viral samples wherein the features are color coordinated for each virus type, either the influenza virus or the newly emerging influenza virus (Figure 1). Lim et al. teaches PCA allowing for comparison of spectra as a whole between cells infected by different types of viruses and systemically extracting the key features of the spectra corresponding to each type of virus and finally successfully distinguishing cell infected by different strains (page 5679, column 1, paragraph 1)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the plotting of the PCA features of Lim et al. to the method of Poteet et al. in view of Bi et al. because Poteet et al. is directed to using spectroscopy data to compare a target signature to a known signature (Abstract) and Lim et al. is directed to identifying a virus based the comparison of the spectra (page 5679, column 1, paragraph 1). Thus, one would have a reasonable expectation of success of comparing spectral signatures by extracting the key features of the data using PCA, by combining the prior art elements, and would be motivated to do so in order to successfully identify viruses in a sample.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3, 4, 10, 14, 16, and 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 10, and 11 of U.S. Patent No. 12487178. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims limitations are obvious over the Patent’s Specification.
Instant Claims
Patent ‘178
Claim
Limitations
Claim
Limitations
1, 14
A method comprising: generating, with a first miniature UV absorption spectrometer of a multi-spectral optical device, a first absorption spectral output based on receiving an absorbance light channel from a sample; generating, with a second miniature UV fluorescence spectrometer of the multi-spectral optical device, a second emission spectral output based on receiving an emission light channel from the sample; and performing, with the multi-spectral optical device, data fusion between the first absorption spectral output and the second emission spectral output to generate fused data.
1
A method for multi-spectral analysis comprising: providing two or more separate and independent types of spectroscopies with a spectrometer system, the spectrometer system providing at least a first spectroscopy with a first miniature spectrometer and at least a second spectroscopy with a second miniature spectrometer to examine a fluid after that fluid has passed through one or more sample preparation processes to increase a purity of a target biological entity in the fluid to enhance signal to noise ratios in measurements and related data analysis operations performed by the spectrometer system; generating, with the first miniature spectrometer, a first absorption spectral output signal based on the fluid; generating, with the second miniature spectrometer, a second emission spectral output signal based on the fluid; performing a data fusion with the first absorption spectral output signal and the second emission spectral output signal to generate output data
5
wherein the first miniature spectrometer comprises a first miniature UV absorption spectrometer and the second miniature spectrometer comprises a second miniature UV fluorescence spectrometer
3, 16
utilizing machine learning to extract absorption features from the first absorption spectral output; and utilizing machine learning to extract emission features from the second emission spectral output
11
wherein the processing system is further configured to generate multiple independent spectra during a single analysis corresponding to each of multiple spectroscopies, and wherein the multiple spectra are treated by one or more of the following to develop a pre-processed data set: summing of the multiple spectra generated by each of the multiple spectroscopies separately to achieve a single multi-dimensional spectrum corresponding to each of the spectroscopies and having a lower signal-to-noise ratio than corresponding original spectra, wherein the single multi-dimensional spectrum includes extracted characteristic features from the multiple independent spectra; truncating averaged spectra to include specific frequency ranges of interest for the first and second spectra wherein a type of wavelength selection process is done before and after data collections; smoothing and reduction of data sets by using one or more curve-fitting techniques; smoothing and reduction of data sets by using signal processing techniques; normalizing data sets as needed, by one or more of the parameters from the curve-fitting, and normalizing input into a machine learning model; developing a reduced set of characteristic parameters for each data set to compare with future measurements producing data sets as inputs to signal processing algorithms and machine learning models that are developed to be used as pre-determination of target molecules; and utilizing training sets and machine learning models to match a new sample with a known set by normalizing data sets to a specific machine learning model, replacing the normalized data sets by set of parameters generated by curve-fitting, and using the fit-parameters as input into other machine learning models
4, 17
generating, with a third miniature UV reflectance spectrometer of the multi-spectral optical device, a third spectral output based on the sample; and performing data fusion between the first absorption spectral output, the second emission spectral output, and third spectral output to generate fused data
10
a third miniature spectrometer of to provide a third spectroscopy to generate a spectral reflectance output signal based on spectral reflectance properties of a particle of interest in the fluid, wherein the processing system performs data fusion with the absorption spectral output signal, the emission spectral output signal and the spectral reflectance output signal
10
wherein the multi-spectral optical device is a handheld multi-spectral optical device
1
the spectrometer system providing at least a first spectroscopy with a first miniature spectrometer and at least a second spectroscopy with a second miniature spectrometer
Although the claims of patent ‘178 are silent with regard to a non-transitory medium, the Specification of the patent discloses in column 11, line 18 that the multi-spectral detection system may comprise a data storage system including a machine-accessible non-transitory medium on which is stored one or more sets of instructions. Thus, it would be obvious to include a non-transitory storage medium comprising stored instructions to perform the method.
Furthermore, although the claims of patent ‘178 are silent with regard to the device being handheld as recited in claim 10, the claims do recite the limitation that the spectrometers are miniature, and the Specification of the patent discloses in column 3, line 66, that any of the spectrometer system can be handheld miniature spectrometer systems. Thus, it would be obvious for the miniature spectrometers to be handheld.
Claims 1-4, 10, and 14-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5-7, and 10 of U.S. Patent No. 12292376. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims limitations are obvious over the Patent’s Specification.
Instant Claims
Patent ‘376
Claim
Limitations
Claim
Limitations
1, 14
A method comprising: generating, with a first miniature UV absorption spectrometer of a multi-spectral optical device, a first absorption spectral output based on receiving an absorbance light channel from a sample; generating, with a second miniature UV fluorescence spectrometer of the multi-spectral optical device, a second emission spectral output based on receiving an emission light channel from the sample; and performing, with the multi-spectral optical device, data fusion between the first absorption spectral output and the second emission spectral output to generate fused data.
1
A miniature multi-spectral system comprising: a first Fourier transform infrared (FTIR) miniature spectrometer to generate a first spectral output based on receiving a first light channel from a sample; a second miniature spectrometer to generate a second spectral output based on receiving a second light channel from the sample; a micro-electromechanical system (MEMS) infrared (IR) light source for the first FTIR miniature spectrometer; a movable FTIR beamsplitter for a sample Fourier scan; a beamsplitter actuator to move the movable FTIR beamsplitter by a distance d1; and one or more processors coupled to the first and the second miniature spectrometers, wherein the one or more processors is configured to execute instructions to perform data fusion of the first and second spectral outputs, to generate fused data and to apply artificial intelligence (AI) of an AI module to the fused data to identify a pathogen, biomarker, or any compound from the sample.
5
wherein the first miniature spectrometer and the second miniature spectrometer comprise two of a UV Fluorescence spectrometer, a UV absorption/reflection spectrometer, a near-IR (NIR) spectrometer, a Raman spectrometer, or Fourier transform infrared (FTIR) spectrometer
2, 15
applying artificial intelligence (Al) of an Al module to the fused data to identify a coronavirus (CoV-2) in saliva from a panel of viruses of the sample
1
apply artificial intelligence (AI) of an AI module to the fused data to identify a pathogen, biomarker, or any compound from the sample
3, 16
utilizing machine learning to extract absorption features from the first absorption spectral output; and utilizing machine learning to extract emission features from the second emission spectral output
1
apply artificial intelligence (AI) of an AI module to the fused data to identify a pathogen, biomarker, or any compound from the sample
4, 17
generating, with a third miniature UV reflectance spectrometer of the multi-spectral optical device, a third spectral output based on the sample; and performing data fusion between the first absorption spectral output, the second emission spectral output, and third spectral output to generate fused data
6
further comprising: a third miniature spectrometer to generate a third spectral output based on the sample
7
wherein the third miniature spectrometer comprises a Raman spectrometer or a Fourier Transform Infrared (FTIR) spectrometer
10
wherein the multi-spectral optical device is a handheld multi-spectral optical device
10
wherein the miniature multi-spectral system is a handheld optical instrument for optical detection
Although the claims of patent ‘376 are silent with regard to a claimed non-transitory medium, the Specification of the patent discloses in column 6, line 50, that Figure 5 illustrates a diagrammatic representation of a machine in the exemplary form of a computer system or device within which a set of instructions for causing the machine to performing the method may be executed and column 7, line 5, discloses this exemplary device comprises RAM. Thus, it would be obvious to store executable code in order to perform the given method on a computer storage device.
Furthermore, although the claims of patent ‘376 are silent with regard to the identification of CoV-2, as recited in the instant claims and 15, the Specification of the patent discloses in column 8, line 34, that the methodology allows for detecting and characterizing of pathogens, in particular SARS-COV2 in bodily fluid. Thus, is would be obvious to use the methodology to identify SARS-COV2 in a sample.
Conclusion
No claims are allowed.
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/E.A.S./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686