DETAILED ACTION
Applicant's response, filed 31 July 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Information Disclosure Statements
The Information Disclosure Statements filed 7 May 2026; 2 June 2026; and 27 July 2026 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Signed copies of the IDS documents are included with this Office Action.
Terminal Disclaimer
The terminal disclaimer filed on 31 July 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Applications 19/562,026; 19/562,004; 19/388,299; 19/374,632; 19/374,579; 19/347,104 (allowed); 19/335,786 and US Patent 12,347,842 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Claim Objections
First, Applicant is kindly reminded that any claim amendment presented must be indicated in the filing by underline, strike through, and/or brackets as appropriate. The amendments filed were not underlined.
Claims 4, 5, 13, 16, and 17 are objected to because of the following informalities:
Claims 4, 5, 13, 16 include recitation of “plasma” and appear to be directed to the “non-thermal plasma” herein. Applicant is advised to please amend the claims in accordance with the amendments in the independent claims to avoid indefiniteness.
Claim 17 is amended to include a final “wherein” clause which should be followed by a “; and”. The “and” recitation after the step directed to “wherein one ore more cloud servers receive…a testing spectrometer; and” should be deleted.
Appropriate correction is required.
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 1-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1: Are claims Directed to process, machine, manufacture/composition of matter
With respect to step (1): yes, the claims are directed to systems and a method for identifying biological molecules.
Step 2A, Prong 1: Do Claims Recite Abstract Idea(s)
With respect to step (2A)(1), the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and/or mathematical concepts (in particular mathematical relationships and formulas).
The claim steps to abstract ideas are as follows:
Claims 1, 9, and 17: develop characteristic profiles for a plurality training samples… wherein the Al module is operable to utilize the plurality of measured spectral graphs and the experimental data to determine the plurality of unknown elements, unknown molecules and/or unknown mixtures in the sample.…wherein “profiles” of characteristics may be generated mentally based on sets of elements and molecules and wherein using AI as a tool to make a “determination” is equivalent to assessing data to make a determination in a mental capacity. There are no steps by which the AI operates to do so and therefore it is a mere tool by which to perform an otherwise mental operation.
Claims 2 and 14: Al module automatically generates a report with indications and concentrations of present identified elements, molecules, and/or mixtures based on a comparison of the experimental data to the characteristic profiles, wherein reports are also equivalent to mental activities of writing up a report of observations as are steps of “comparisons”, wherein each of said tasks are given their plain meaning.
Claims 3 and 15: wherein the Al module is operable to solve a linear programming model to determine the plurality of unknown elements, unknown molecules, and/or unknown mixtures in the sample, wherein the AI module is the tool by which to solve an equation (a linear, mathematical equation) and wherein said operation may be done mentally, save for the use of the AI tool to do so.
Claims 5 and 16: operable to adjust at least one parameter of the plasma based on at least one measurement of the plasma, wherein given observed sata of such a human can mentally adjust parameters of the plasma and therefore said operation is akin to mental activity. There are no additional steps by which said adjustment is performed.
Claim 6: Al module is operable to dynamically switch methods of determining composition and/or concentration of the plurality of unknown elements, unknown molecules, and/or unknown mixtures in the sample, wherein switching methods for making a determination are a mental operation. There are no steps by which the AI operates to do so and therefore it is a mere tool by which to perform an otherwise mental operation.
Claim 8: Al module is operable to adjust integration time, spectral resolution, and/or entrance slit width of the one or more spectrometers or chemical analysis devices based on noise and/or saturation in the experimental data and/or to differentiate between two or more potential elements, potential molecules, and/or potential mixtures in the sample, wherein a human could readily make said adjustments to a spectrometer based on mental observations given data to adjust parameters of such.
Claim 12: calibrating the measured spectral graphs against known reference lines and/or normalizing the measured spectral graphs by total emission intensity or an internal reference line via an Al module, wherein calibration and normalization are techniques that can be mentally performed given data and the AI module herein is merely a tool by which to perform said mental activities. There are no steps by which the AI operates to do so and therefore said step is mental.
Claim 18: Al module automatically generates updated characteristic profiles based on a combination of stored training data and new training data when the new training data is received from the one or more spectrometers or chemical analysis devices, wherein the AI module herein is merely a tool by which to perform said mental activities of updating a profile given appropriate data. There are no steps by which the AI operates to do so and therefore said step is mental.
Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined herein to each cover performance either in the mind and/or performance by mathematical operation. For example, as pointed to above, there are no specifics as to the methodology involved in “develop characteristic profiles” or in “generating a report…based on comparison” and thus, under the BRI, one could simply, for example, make a list of data subset(s) and generate a report therefrom. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis (Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations (Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in (Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper and can include mathematical concepts.
Further, see MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind" (see Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Further, instant claims recite “mathematical concepts” by computerized algorithmic application of machine learning mathematical functions (Artificial intelligence; Als, DLs, NNs, SVMs) (see MPEP § 2106.04(a)(2), subsection I: including a mathematical relationship between enhanced directional radio activity and antenna conductor arrangement (i.e., the length of the conductors with respect to the operating wave length and the angle between the conductors), Mackay Radio & Tel. Co. v. Radio Corp. of America, 306 U.S. 86, 91, 40 USPQ 199, 201 (1939): while the litigated claims 15 and 16 of U.S. Patent No. 1,974,387 expressed this mathematical relationship using a formula that described the angle between the conductors, other claims in the patent (e.g., claim 1) expressed the mathematical relationship in words); as well as organizing information and manipulating information through mathematical correlations (in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721). Therefore, steps of “develop[ing] profiles” with AI components is organizing data based on mathematical relationships, and thus, constitute abstract ideas.
Step 2A, Prong 2: Integration to a Practical Application Assessment
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea 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 abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the following additional elements:
Claims 1, 9, and 17: “receive training data from spectrometers or chemical analysis devices”; “reactor”; system; servers; AI modules.
Dependent claims 4, 7, 10-11, 13, and 19-20 recite steps that further limit the recited additional elements in the claims wherein said limitations pertain to the types of “data” or types of spectrometers or operations of said spectrometers.
Further with respect to the additional elements in the instant claims, steps directed to data gathering, that include “receiving training data” perform functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on 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).
Further steps herein directed to additional non-abstract elements of “system, modules, spectrometer, server computer; storage medium etc…” do not describe any specific computational steps by which the “computer parts” perform or carry out the abstract idea, nor do they provide any details of how specific structures of the computer are used to implement these functions. The same is apparent for the recitation of “spectrometer” wherein the apparatus is ancillary to the steps and operates to gather data. The claims state nothing more than a generic computer and spectrometer which performs the functions that constitute the abstract idea. Hence, these are 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)).
None of the recited dependent claims recite additional elements which would integrate a judicial exception into a practical application.
Step 2B: Do Claims Provide an Inventive Concept Assessment
The claims are lastly evaluated using the (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements 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 of data gathering described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rest 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 the instant claims data gathering elements for getting spectroscopy data is routine, well-understood and conventional in the art, as per the Specification at [009] disclosing various profiling of biological material from spectra, including from OES spectrometry. As such, activities such as data gathering 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 effect a transformation of matter; nor do they provide a non-conventional or unconventional step. Rather, the data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)). Further, those steps that include non-thermal plasma source as used to provide experimental data are disclosed in the art to Thiyagarajan et al. (J. Appl. Phys. (2013) Vol. 113:9 pages) disclose use of OES for non-thermal plasma diagnostics for biomedical applications (abstract; whole reference). Prior art to Yousfi et al. (Chapter 5: Non Thermal Plasma Sources of Production of Active Species for Biomedical Uses: Analyses, Optimization and Prospect in Biomedical Engineering-Frontiers and Challenges (2011), ed. Fazel-Rezai:99-124) disclose non-thermal plasma sources for biomedical use (introduction, p. 1). As such, using such a device for non-thermal plasma for diagnostic purposes was well-known in the art.
Further, the steps would be considered routine laboratory elements for data gathering as per courts recognizing the following laboratory techniques as well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)II.): determining the level of a biomarker in blood by any means (Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)); detecting DNA or enzymes in a sample (Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017)).
With respect to claims 1-18 and 20, the computer-related elements or the general-purpose computer do not rise to the level of significantly more than the judicial exception. The additional elements are set forth at such a high level of generality that they can be met by a general-purpose computer. Therefore, the computer components constitute 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 an abstract idea (see MPEP 2106.05(b)I-III).
Dependent claims have been analyzed with respect to step 2B and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception.
For these reasons, the claims, when the limitations are considered individually and as a whole and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Applicant’s Arguments
Applicant states that, “is directed to an improved method and system of spectrometry and not an abstract idea as alleged”. Further Applicant includes that the claims are akin to those in XY, LLC v. Trans Ova Genetics, 968 F.3d 1323 (Fed. Cir. 2020) [reciting] claims to a method of operating a flow cytometry apparatus to classify and sort particles into at least two populations in real time to more accurately classify similar particles [that] was not directed to "the abstract idea of using a 'mathematical equation that permits rotating multi-dimensional data' "even though they may have involved mathematical concepts”.
It is respectfully submitted that this is not persuasive. The claim may be tied to the technology of spectrometry. That is not in dispute. However, what is an issue herein is that the instant recitations are recited as data gathering elements and not as a particular machine. There are no steps that integrate the judicial exception by altering, for example the functioning of the spectrometer or the server that houses the AI module (software to run analysis). Rather, in the claim, the spectrometer (or chemical analysis device) performs the function that is a routine and conventional function performed by all spectrometers, which is "get spectral data". This is wholly different from the cited cases such as XY, LLC v. Trans Ova Genetics, 968 F.3d 1323 (Fed. Cir. 2020), wherein the computer operated to get a signal from a first detector based on the fluid flow stream and a second detector operated to get a second signal and conversion in the processor occurred for the first and second signal into an n-dimensional parameter for the particles and further after altering the n-dimensional parameters and classification using the classification to sort the particles in the flow cytometer. Said operations directly pertained to the operation of the cytometer itself rather than receiving spectral data and using the data to somehow determine unknown elements, unknown molecules and/or unknown mixtures without any seeming change to the operation of the spectrometer itself. As such, said operations are akin to using a tool to get data (routine laboratory operations).
Applicant states that, “the claimed invention provides details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Paragraph [0060] of the application as filed explains, "Most commonly, spectroscopic tests are performed for samples with one or only a few unknown components, allowing for simpler, albeit not always very simple, investigation of the composition of a small number of chemicals. However, difficulty arises in these techniques where there are many components having different functional groups, especially where those different components are larger and have more functional components.” Applicant provides citation from [0061] of the Specification directed to tasks that are difficult for a human operator and the method that simplifies analysis of medical fluids to utilize a single device… Further, Applicant points to using non-thermal plasma sources as providing improvement in the context of medical fluid samples for organic compound identification. Applicant includes that this single-device using AI is able to correlate spectrographic data more quickly and accurately.
It is respectfully submitted that this is not persuasive as the inclusion of a non-thermal plasma source for a sample is not directly tied to the AI module operation itself and therefore the source simply provides the data necessary for AI analysis. The AI module is not directed to any particular operation or functioning whereby the “use” of the AI module is claimed in functional terms, i.e., how does the AI module operate to utilize the spectral graphs and experimental data to make a determination of elements.
Applicant directs arguments to CardioNet, LLC wherein the instant claims are said to be akin to those therein because the instant claimed invention cannot operate without one or more spectrometers or chemical analysis devices, the AI module and the testing spectrometer. Applicant states that those elements play a significant part in permitting the claimed invention to be performed.
However, it is respectfully submitted that this is not persuasive. The claims in CardioNet, LLC were specifically directed to a device with a beat detector, a ventricular beat detector, and logic to determine variability in beat-to-beat timing of a collection of beats. Further the event generator generated a specific event that detected variability in the beat-to-beat timing as identified by the ventricular beat detector. The court found that those claims, ““focus on a specific means or method that improves” cardiac monitoring technology; they are not “directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery.” McRO, 837 F.3d at 1314 (citations omitted). The claims therein included specific operation wherein variability in beats were detected and implemented into the operation of the device. The same is not apparent herein wherein the claims include an AI module to make a determination by merely using the data without any indication of how that occurs such that an improvement is apparent.
Applicant states that the components create a meaningful limit on the scope of the claim. Applicant further states that “plasma is able to be generated and sustained at different energy levels, not requiring to evaporate all the fluid, allowing detection of more sensitive and complex biomolecules…and that the non-thermal ionization process is necessary for the proper identification and classification of biological compounds which would otherwise decompose or not ionize due to their high ionization energy.
It is respectfully submitted that this is not persuasive. As discussed in the above rejection, using a non-thermal plasma source was well-known in the art and the instant claims fail to integrate that use as operational in the AI module other than to get the data necessary to perform the “determination”. Further, it is not apparent herein that the operation of the reactor is any other than one that is well-known in the art, such as OES.
It is suggested that the AI module as recited be tied to the actual operation of the recited reactor in a meaningful operation. The instant Specification includes that the AI engine is provided on a processor that can detect pattern matching on features of emission spectra for example and can further do so on less notable features to identify the unknown analyte which provides improvement over traditional spectroscopy, for example.
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.
1. Claims 1-6, 8-11, and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0020276 to Da Costa Martins (IDS reference) in view of Yousfi et al. (Chapter 5: Non Thermal Plasma Sources of Production of Active Species for Biomedical Uses: Analyses, Optimization and Prospect in Biomedical Engineering-Frontiers and Challenges (2011), ed. Fazel-Rezai:99-124). This rejection is newly recited as based on claim amendment herein.
Claim 1 is directed to the system as follows:
An artificial intelligence (AI)-based system for automatically identifying elements, molecules, and/or mixtures in a sample, comprising (The prior art to Da Costa Martins discloses an AI model for classification of metabolites pertaining health conditions from spectral information; the technology is applicable to all areas of spectroscopic analysis and extends to fields such as pharmaceuticals, food, healthcare etc. [0013]; [0016]);
one or more servers configured to receive training data from one or more spectrometers or chemical analysis devices (Da Costa Martins discloses receiving multidimensional spectral data from spectroscopy of complex mixtures [0084]);;
at least one reactor (The prior art to Da Costa Martins disclose a variety of spectroscopy techniques that include all regions of the electro-magnetic spectra used in spectroscopy analysis (x-ray, uv, vis, nir, ir, far-ir and microwaves), or with any other type of spectroscopy (absorbance, reflectance, fluorescence, phosphorescence, Raman scattering) where complex multi-scale interference and biological variability is present. It further extends to fields of non-destructive, non-invasive spectroscopy applications in fields such as healthcare, veterinary, biotechnology, pharmaceutical, food and agriculture [0016], wherein in the art of Spectroscopy it is inherent that a sample would be held in such that light separates the sample into wavelengths and interaction of the light with the sample is measured in a reactor chamber of multiple types, such as a sample cell or a cuvette. Under this interpretation, De Costa Martins teaches a “reactor” as claimed);
an Al module on the one or more servers configured to automatically develop characteristic profiles for a plurality training samples (Da Costa Martins discloses specifically using AI for the problem that is directed to solving issues with multidimensional data. For example, Da Costa Martins details the specific AI methodology for generating data from such data [0085]-[0087], at least);
wherein the training data comprises a plurality of measured spectral graphs corresponding with the training samples of known elements, known molecules, and/or known mixtures (The prior art to Da Costa Martins discloses measured spectral graphs at [0083]; [0085]; [0088]);
wherein the at least one reactor is operable to excite a sample via non-thermal plasma, source, creating an excited sample (see prior art to Yousfi et al. below);
wherein the sample comprises a plurality of unknown elements, unknown molecules, and/or unknown mixtures (Da Costa Martins discloses samples that are unknown at [0133] that includes AI that learns via new feature spaces for prediction of unknowns)
wherein the one or more devices receives experimental data for an excited sample from a testing spectrometer (Da Costa Martins discloses that data are from medical samples, such as from sample biological components in blood via spectroscopy [0075]); and
wherein the Al module is operable to utilize the plurality of measured spectral graphs and the experimental data to determine the plurality of unknown elements, unknown molecules and/or unknown mixtures in the sample (Da Costa Martins discloses samples that are unknown at [0133] that includes AI that learns via new feature spaces for prediction of unknowns).
Da Costa Martins does not specifically disclose “wherein the at least one reactor is operable to excite a sample via non-thermal plasma source, creating an excited sample” as in claim 1, or the limitations as in 4 and 5. However, the prior art to Yousfi et al. disclose using non-thermal plasma sources for biomedical use (Introduction, p. 1). With respect to claim 4, the prior art to Yousfi et al. disclose DBD at Figure 8. With respect to claim 5, Yousfi et al. further include that a parameter may be adjusted based on plasma measurement (p. 117-Section 5.1).
As such, 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 utilized a non-thermal plasma source as the parameter for data in the instant invention, as Yousfi et al. disclose doing so for biomedical fluids (see Introduction-Yousfi et al.). Further, Da Costa Martins motivates that one would want more complex model structures to capture all the non-linearity of data and provide better predictions [0005]. Analysis of non-thermal plasma allows for a very efficient source of active species (Introduction-Yousfi et al.) and various experimental tools can be utilized for low temperature and non thermal plasma sources such as OES, LIF, mass spectrometry, gas chromatography, etc (Yousfi at p. 108-109). One would have a reasonable expectation of success in using on-thermal sources as in Yousfi et al. because Da Costa Martis includes a variety of spectroscopy techniques that include all regions of the electro-magnetic spectra used in spectroscopy analysis (x-ray, uv, vis, nir, ir, far-ir and microwaves), or with any other type of spectroscopy (absorbance, reflectance, fluorescence, phosphorescence, Raman scattering) where complex multi-scale interference and biological variability is present. It further extends to fields of non-destructive, non-invasive spectroscopy applications in fields such as healthcare, veterinary, biotechnology, pharmaceutical, food and agriculture [0016].
With respect to claim 2, the prior art discloses wherein the Al module automatically generates a report with indications of present identified elements, molecules, and/or mixtures based on comparison of the experimental data to the characteristic profiles (Da Costa Martins disclose that AI does not require user intervention in this context [0132]-[0133]; the AI system quantifies and classifies data by comparisons [0132] and [0144], as example; the system can include modules [0159]).
With respect to claims 3, the Al module of Da Costa Martins is operable to solve a linear programming model to determine the plurality of unknown elements, unknown molecules, and/or unknown mixtures in the sample as disclosed at least at [0051].
With respect to claim 6, Da Costa Martins discloses the ability to determine the prediction model that has the maximum predictability of quantification of the constituent at least at [0064].
With respect to claim 8, Da Costa Martins discloses that the model can differentiate between two or more potential molecules in a sample [0013].
Independent Claim 9 is directed to the method as follows:
An artificial intelligence (AI)-based method for automatically identifying elements, molecules, and/or mixtures in a sample, comprising (The prior art to Da Costa Martins discloses an AI model for classification of metabolites pertaining health conditions from spectral information; the technology is applicable to all areas of spectroscopic analysis and extends to fields such as pharmaceuticals, food, healthcare etc. [0013]; [0016])
receiving training data from one or more spectrometers or chemical analysis devices (Da Costa Martins discloses receiving multidimensional spectral data from spectroscopy of complex mixtures [0084]);
automatically developing characteristic profiles for a plurality training samples (Da Costa Martins discloses specifically using AI for the problem that is directed to solving issues with multidimensional data. For example, Da Costa Martins details the specific AI methodology for generating data from such data [0085]-[0087], at least);
wherein the training data comprises a plurality of measured spectral graphs corresponding with the training samples of known elements, known molecules, and/or known mixtures (The prior art to Da Costa Martins discloses measured spectral graphs at [0083]; [0085]; [0088]);
exciting a sample via a plasma, creating an excited sample (see prior art to Yousfi et al.);
wherein the sample comprises a plurality of unknown elements, unknown molecules, and/or unknown mixtures (Da Costa Martins discloses samples that are unknown at [0133] that includes AI that learns via new feature spaces for prediction of unknowns)
receiving experimental data for an excited sample from a testing spectrometer (Da Costa Martins discloses that data are from medical samples, such as from sample biological components in blood via spectroscopy [0075]); and
determining the plurality of unknown elements, unknown molecules and/or unknown mixtures in the sample (Da Costa Martins discloses samples that are unknown at [0133] that includes AI that learns via new feature spaces for prediction of unknowns).
Da Costa Martins does not specifically disclose “the reactor ionizes a fluid sample via a non-thermal plasma source as in claim 9 or that the device is an OES device as in claim 11. Nor does Da Costa Martins disclose that plasma is a specific type such as RF, DBD, inductive coupled, laser-induced breakdown, microwave-induces or glow discharge as in claims 13 or that the system adjusts plasma based on measurement as in claims 16.
However, the prior art to Yousfi et al. disclose using non-thermal plasma sources for biomedical use (Introduction, p. 1). The prior art to Yousfi et al. disclose OES at p. 108, DBD at Figure 8 and further include that a parameter may be adjusted based on plasma measurement (p. 117-Section 5.1).
As such, 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 utilized a non-thermal plasma source as the parameter for data in the instant invention, as Yousfi et al. disclose doing so for biomedical fluids (see Introduction-Yousfi et al.). Further, Da Costa Martins motivates that one would want to more complex model structures to capture all the non-linearity of data and provide better predictions [0005]. Analysis of non-thermal plasma allows for a very efficient source of active species (Introduction-Yousfi et al.) and various experimental tools can be utilized for low temperature and non thermal plasma sources such as OES, LIF, mass spectrometry, gas chromatography, etc (Yousfi at p. 108-109). One would have a reasonable expectation of success in using on-thermal sources as in Yousfi et al. because Da Costa Martis includes a variety of spectroscopy techniques that include all regions of the electro-magnetic spectra used in spectroscopy analysis (x-ray, uv, vis, nir, ir, far-ir and microwaves), or with any other type of spectroscopy (absorbance, reflectance, fluorescence, phosphorescence, Raman scattering) where complex multi-scale interference and biological variability is present. It further extends to fields of non-destructive, non-invasive spectroscopy applications in fields such as healthcare, veterinary, biotechnology, pharmaceutical, food and agriculture [0016].
With respect to claim 10 Da Costa Martins disclose employing servers and using training data from multiple datasets [0116]; [0126].
With respect to claim 14, the prior art discloses wherein the Al module automatically generates a report with indications of present identified elements, molecules, and/or mixtures based on comparison of the experimental data to the characteristic profiles (Da Costa Martins disclose that AI does not require user intervention in this context [0132]-[0133]; the AI system quantifies and classifies data by comparisons [0132] and [0144], as example; the system can include modules [0159]).
With respect to claim 15, the Al module of Da Costa Martins is operable to solve a linear programming model to determine the plurality of unknown elements, unknown molecules, and/or unknown mixtures in the sample as disclosed at least at [0051].
Independent Claim 17 is directed to the methods a follows:
An artificial intelligence (AI)-based system for automatically identifying elements, molecules, and/or mixtures in a sample, comprising (The prior art to Da Costa Martins discloses an AI model for classification of metabolites pertaining health conditions from spectral information; the technology is applicable to all areas of spectroscopic analysis and extends to fields such as pharmaceuticals, food, healthcare etc. [0013]; [0016]);
one or more cloud servers configured to receive training data from one or more spectrometers or chemical analysis devices (Da Costa Martins discloses receiving multidimensional spectral data from spectroscopy of complex mixtures [0084]; Da Costa Martins discloses servers at [0159] and a knowledgebase that includes data [Figure 11]);
an Al module on the one or more cloud servers configured to automatically develop characteristic profiles for a plurality training samples (Da Costa Martins discloses specifically using AI for the problem that is directed to solving issues with multidimensional data. For example, Da Costa Martins details the specific AI methodology for generating data from such data [0085]-[0087], at least);
wherein the training data comprises a plurality of measured spectral graphs corresponding with the training samples of known elements, known molecules, and/or known mixtures (The prior art to Da Costa Martins discloses measured spectral graphs at [0083]; [0085]; [0088]);
wherein the one or more cloud servers receives experimental data for an excited sample from a testing spectrometer (Da Costa Martins discloses that data are from medical samples, such as from sample biological components in blood via spectroscopy [0075]);
wherein the Al module is operable to utilize the plurality of measured spectral graphs and the experimental data to determine the plurality of unknown elements, unknown molecules and/or unknown mixtures in the sample (Da Costa Martins discloses samples that are unknown at [0133] that includes AI that learns via new feature spaces for prediction of unknowns);
wherein the at least one reactor is operable to ionize the fluid sample via a non-thermal plasma source.
With respect to claim 18, Da Costa Martins discloses generation of updated characteristics based on combinations of stored training and new training data at least at [0135].
The prior art to Da Costa Martins does not specifically disclose that the reactor ionizes a fluid sample via a non-thermal plasma source. However, the prior art to Yousfi et al. disclose using non-thermal plasma sources for biomedical use (introduction, p. 1).
As such, 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 utilized a non-thermal plasma source as the parameter for data in the instant invention, as Yousfi et al. disclose doing so for biomedical fluids (see Introduction-Yousfi et al.). Further, Da Costa Martins motivates that one would want to more complex model structures to capture all the non-linearity of data and provide better predictions [0005]. Analysis of non-thermal plasma allows for a very efficient source of active species (Introduction-Yousfi et al.) and various experimental tools can be utilized for low temperature and non thermal plasma sources such as OES, LIF, mass spectrometry, gas chromatography, etc (Yousfi at p. 108-109). One would have a reasonable expectation of success in using on-thermal sources as in Yousfi et al. because Da Costa Martis includes a variety of spectroscopy techniques that include all regions of the electro-magnetic spectra used in spectroscopy analysis (x-ray, uv, vis, nir, ir, far-ir and microwaves), or with any other type of spectroscopy (absorbance, reflectance, fluorescence, phosphorescence, Raman scattering) where complex multi-scale interference and biological variability is present. It further extends to fields of non-destructive, non-invasive spectroscopy applications in fields such as healthcare, veterinary, biotechnology, pharmaceutical, food and agriculture [0016].
2. Claims 7, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0020276 to Da Costa Martins (IDS reference) in view of Yousfi et al. (Chapter 5: Non Thermal Plasma Sources of Production of Active Species for Biomedical Uses: Analyses, Optimization and Prospect in Biomedical Engineering-Frontiers and Challenges (2011), ed. Fazel-Rezai:99-124), as applied to claims 1, 9, and 17 and in further view of Brunnbauer et al. (Scientific reports (2020) Vol. 10:10 pages-cited previously). This rejection is newly recited as based on claim amendment herein.
With respect to claims 1, 9, and 17 Da Costa Martins in view of Yousfi et al. disclose the limitations as above. Neither Da Costa Martins, nor Yousfi et al. disclose the limitations as in claims 7, 12, and 20 directed to wherein the unknown elements, molecules and/or mixtures include polymers.
However, the prior art to Brunnbauer et al. disclose using LA-ICP-MS/LIBS as a robust tool for the characterization of polymers, such as those that end up in the environment (microplastics) and pose threats to health (page 2), wherein LIBS employs plasma excitation.
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 included plasma spectroscopy, such as LIBS, as disclosed by Brunnbauer et al. for generation of the data for analysis by the techniques of Da Costa Martins teachings that the invention is applicable to all regions of the electro-magnetic spectra used in spectroscopy analysis (x-ray, uv, vis, nir, ir, far-ir and microwaves), or with any other type of spectroscopy (absorbance, reflectance, fluorescence, phosphorescence, Raman scattering) where complex multi-scale interference and biological variability is present in view of Yousfi et al. disclosing non-thermal plasma analysis that employs such. Thus, one would have expected a reasonable success in so doing and the combination of Da Costa Martins, Yousfi et al. and Brunnbauer et al. would have been expected to operate within the context of machine learning, as machine learning tasks have the distinct ability to identify patterns and relationships within intricate datasets, such as those from multiple types of spectroscopy data. Further using said techniques of Yousfi et al. and the AI tool as in Da Costa Martins would have been an obvious implementation to analyze polymers as disclosed by the art to Brunnbauer et al. as said references are in the same field of endeavor and concerned with assessment of molecules, elements or mixtures using spectroscopy and analytical tools.
Response to Applicant’s Arguments
Applicants’ arguments have been considered but are moot in view of the new grounds of rejection set forth above including the art to Yousfi et al.
Conclusion
No claims are allowed.
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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/Lori A. Clow/Primary Examiner, Art Unit 1687
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