DETAILED ACTION Notice of 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. 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 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. Status of the Claims Claims 1- 20 are pending. Claims 16 and 18 are objected to. Claims 1- 20 are rejected. Priority This US Application 18/055,793 (11/15/2022) claims priority from US Application 63/279,484 (11/15/2021), as reflected in the filing receipt mailed on 08/24/2023. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 1-20 is 11/15/2021 . Information Disclosure Statement The information disclosure statements (IDS) submitted on 02/05/2024 was considered. Specification The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Drawings The drawings filed 11/15/2022 are objected to because Fig. 1A, Fig. 1B, Fig s . 2A-C, Fig s . 3 - 4 , Fig. 6 , Figs. 7 A-C, Figs. 8-12 , Figs. 14 A-C, Figs. 15-20 , Figs. 21 A-C, Fig. 22 , Fig. 24 , Fig. 25 A-E, and Figs. 26 A-D are executed in color . Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification . The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2). The replacement drawings filled 08/18/2023 are not executed in color but are objected to for presenting illegible labels in Figs. 21A-C, Fig. 22, Fig. 25E, Fig. 26A, and Fig. 26 C-D . Corrected drawing sheets in compliance with 37 CPR 1.12 1 (d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 3 7 CPR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Claim interpretation Claim terminology Claim 1 recites " asynchronous fingerprinting " which , in light of the instant specification ( pg. 24 line 18 ), is being interpreted as a type of fingerprinting method for detecting analytes in which all of the sensors are not applied simultaneously in any given volume. Claim objections Claims 1, 3, 8, 10- 16, and 18 - 20 are objected to because of the following informalities related to grammar/punctuation. Appropriate correction is required. In claim 1 , the recited " the target analytes" (step (a)) should read " the multiple target analytes" for proper antecedent basis. Claim s 10 -11, 13-15, and 19 repeat the issue above. Claim s 1 , 3, 8, 11-12, 18, and 20 repeat the issue above for " the partitions". Claim 16 is missing a period at the end of the claim . Claim 18 recites " PCR ", which should be spelled out completely . In claim 20 , the recited steps "(d) to (f)" should read "(a) to (c) . " Claim Rejections - 35 USC § 112(b) 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. Claims 3 -11 , 16-17, and 20 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite: The following recitations require but lack antecedent basis , rendering their claims indefinite because there is no previous recitations of the followings terms as written: Claim 3 , " the measurements in each partition" (line 2) , " the target analyte quantities" (step (a)), " the observed measurements from partitions" (step (c)) , and " the solution" (step (c)) ; Claim 4 , " the quantity of each analyte" (line 1) ; Claim 5 , " the partition measurements" (step (b)) ; " the gradient" (step (b)), and " the gradients" (step (b)); Claim 6 , “nonspecific sensors " , because it depends on claim 1 . C laim 2 recites the nonspecific sensors . This rejection may be overcome by amending claim 6 to depend on claim 2. Claim 7 , " the microbial content" ; Claim 8 , " the microfluidic partitions" ; Claim 9 , " the signal " and " the analyte quantities" ; Claim 10 , " the number of target analytes" ; Claim 11 , " the small volume partitions" and " the true Poisson rates" ; and Claim 16 ," nonspecific sensors", because it depends on claim 1 . C laim 2 recites the nonspecific sensors . This rejection may be overcome by amending claim 16 to depend on claim 2. Claim 17 , " the nonspecific hydrolysis probes ", because it depends on claim 1 . C laim 16 recites " nonspecific hydrolysis probes ". This rejection may be overcome by amending claim 17 to depend on claim 16. Claims 16-17, “ the ribosomal RNA genes ." To overcome this rejection, the claim may be amended to depend on claim 15. In claim 4 , the relationship is unclear between the first and the second instance of "maximum likelihood estimation." To overcome this rejection, the second instance of "maximum likelihood estimation" may be amended to " the maximum likelihood estimation" for proper antecedent basis. In claim 5 , the definition of the λ should be defined in the claims . For compact prosecution , λ is being interpreted as a parameter that defines the average content . Claim 5 recite s " updating based on the gradient " step (c) , but also recites " computing the gradient " and " the gradients " in step ( b). It is unclear which recitation (s) the "updating based on the gradient" step (c), intends to refer to . In claim 20 , the recited "conditional on the analyte quantities" (step (d)) should read "conditional on analyte quantities" for proper instantiation of the term in this independent claim . Claim 20 repeats the issue above for "solution for the target analyte quantities" (line 1) , " the set of target analytes" (line 3) , " the measurements in each partition" (step (d)) , " the expected distribution of measurements" (step ( e)) , and " the measurements in each partition" (step (d)) . In claim 20 , the relationship between the elements in unclear because the claim is missing a conjunction (i.e. and/or ) before step ( f ). In claim 20 , it is unclear what elements are part of the preamble and what elements are related to intend use. I t is not clear whether " comprising " is intended to limi t the "evaluating", the "reporting" or the "method". It is unclear if "evaluating" and "reporting" are recited as intended use or method steps. For compact prosecution, "comprising" is interpreted as limiting the method and "evaluating" and "reporting" are interpreted as intended use. In claim 20 , the recited step (d) is indefinite for not reciting a method step – action to be performed by the method. MPEP 2173.05(p)(II) pertains. 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 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). 101 background MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials. Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)? Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e ., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))? Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))? Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)? Analysis of instant claims Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter ( MPEP 2106.03)? The instant claims are directed to a method ( claims 1- 20) ; which falls within one of the categories of statutory subject matter. [Step 1: claims 1-20: Yes ] Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))? Background With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as: • mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations) (MPEP 2106.04(a)(2)(I)); • certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or • mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)). Analysis of instant claims With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) as well as a law of nature or a natural phenomenon are as follows: • " (e) detecting the multiple target analytes through statistical estimation using a reference database of analyte fingerprints " ( independent claim 1 ); • " (a) initializing a value of λ" ( claim 5 ); • " (b) computing the gradient based on the modeled probability distribution over a subset of the partition measurements or over the entire set of available measurements, and optionally approximating the gradients with Monte Carlo approximations " ; ( claim 5 ); • " (c) updating k based on the gradient " ; ( claim 5 ); • " (d) repeating steps (b)-(c) until convergence of λ. "; ( claim 5 ); • "reporting whether the sample contains an exogenous analyte beyond the set of target analytes" ( independent claim 20); • "(d) a modeled probability distribution for the measurements in each partition that are conditional on the analyte quantities the sample and the subset of sensors applied in the partition" ( independent claim 20); • "(e) evaluating the expected distribution of measurements based on the modeled probability distribution and the candidate solution" ( independent claim 20); and • "(f) comparing the expected distribution of measurements against the distribution of observed measurements obtained from the partitions, wherein a sufficient difference in the expected and observed distributions identifies the candidate solution as containing an exogenous analyte" ( independent claim 20). Dependent claims 3-5 , 7, 11, 13-15, and 19 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claim s 3 -5 further limit the statical estimation to comprising a modeled probability distribution for the measurements in each partition and following a Poisson distribution and applying Sparse Poisson Recovery (SPoRe) algorithm ; claim 7 further limits step (e) in claim 1 to comprising quantifying the microbial content of the sample; and claim s 11 , 13-15 , and 19 further limit the target analytes . The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in " detecting the multiple target analytes through statistical estimation using a reference database of analyte fingerprints and a modeled probability distribution, calculating the gradient based on the modeled probability distribution over a subset of the partition measurements or over the entire set of available measurements ", under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at a statistical estimation using a reference database . Further support for the mathematical techniques used in the claims is provided in the specification at pgs. 11-12 and 14-15 , which describes mathematical algorithms for the use in statistical estimations of analyte quantities . Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains. The human mind is also sufficiently capable of evaluating and comparing data regarding the measurements . [Step 2A Prong One: claims 1 -20: Yes ] Step 2A, 1st prong, 1st Mayo/Alice question: natural product -- MPEP 2106.I and 2106.04 The instant claims recite a natural correlation by correlating the measurement of target analyte to the presence of a pathogen . (see MPEP 2106.04(b).I). [Step 2A, 1st prong, law of nature: claims 1, 7, 14, and 19 : Yes] Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))? Background MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application: An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2); Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c ); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e). Analysis of instant claims Instant claim 1 recite s additional elements that are not abstract ideas: • " (a) assigning fingerprints to the target analytes with sensors " ( independent claim 1 ); • " (b) splitting the sample into multiple subsamples " ( independent claim 1 ); • " (c) splitting each subsample into multiple partitions " ( independent claim 1 ); • "(d) performing asynchronous fingerprinting by contacting the partitions in each subsample with a subset of the sensors" ( independent claim 1 ) ; and • "digital PCR" (claim 18). C laim 2 further limit s the sensors to being nonspecific ; claim s 6 and 16-17 further limit s the nonspecific sensors ; claim 8 further limits the microfluidic partitions to comprising droplets, chambers or nanowells ; claim 9 further limit s the sensor signal describing the analyte quantities in the sample to being sparse; claim 10 further limit s the method to comprising fewer total sensors than the number of target analytes ; claim 12 further limits the subsamples to being split into a total of 50 to 10 7 partitions; and claim 18 further limits the step of splitting a sample into microfluidic partitions . Considerations under Step 2A, Prong Two The recited limitations in claim 1 are interpreted as requiring the use of a computer. The use of a computer is broadly interpreted as it has been described in the claims (claim 18) and in the specificatio n (pg. 7 line 1 ) . Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. The recited steps (a) to (d) in claim 1 read on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means. Dependent claims limitations constitute mere data gathering activity because the signal data from sensors is utilized to gather information that is used as input for the subsequent mathematical calculations . The specification discloses an unmet need to extract more information from simple sensors for a rapid, efficient method for broad-range sensing (pg. 14 line 5) but does not provide a clear explanation for how the additional elements provide these improvements . Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)). None of the dependent claims recite any additional non-abstract elements; they are all directed to further aspects of the information being analyzed, the manner in which that analysis is performed, or the mathematical operations performed on the information. In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements . Additional elements are further discussed in Step 2B below. Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application. [Step 2A Prong Two: claims 1-20: No] Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)? According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination 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). As explained above, the instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)). With respect to the instant claims, the prior art review to Quan et al. ("dPCR: a technology review." Sensors 18(4):271 (2018) ; newly cited) discloses that using digital PCR for the steps of partitioning samples to detect the concentration of the target in the sample using statistical approximations is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, pg. 4 Fig. 3 . When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h). [Step 2B: claims 1-20: No] Conclusion: Instant claims are directed to non-statutory subject matter For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more. Claim Rejections - 35 USC § 103 The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: (a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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. A. Claims 1-2, 6, and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Konvalina ("Sensors for breath testing: from nanomaterials to comprehensive disease detection." Accounts of chemical research 47(1):66-76 (2014)) as evidenced by Lavine ( "Clustering and classification of analytical data." Encyclopedia of analytical chemistry 11 : 9689-9710 (2000)) in view of Hayward (" Ultraspecific and amplification-free quantification of mutant DNA by single molecule kinetic fingerprinting, J. Am. Chem. Soc. 140 : 11755e11762 (2018) ), as cited on the attached Form PTO-892. Konvalina teaches a method for the detection of target volatile organic compounds (VOCs) through the analysis of breath samples (pg. 69 col. 1 para. 1) . Bullet points indicate the teachings of the instant features over the prior art. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims. Claim 1 recites: (a) assigning fingerprints to the target analytes with sensors; (b) splitting the sample into multiple subsamples; (c) splitting each subsample into multiple partitions; (d) performing asynchronous fingerprinting by contacting the partitions in each subsample with a subset of the sensors; and (e) detecting the multiple target analytes through statistical estimation using a reference database of analyte fingerprints • Konvalina teaches a method for the detection of target volatile organic compounds (VOCs) through the analysis of breath samples (i.e. detecting multiple target analytes in a sample ) (pg. 69 col. 1 para. 1); wherein the reduction of the initial volume of the breath sample (i.e. reading on splitting the sample into subsamples and partitions ) allows the reduction of the complexity of the sample (pg. 73 col. 1 para. 1); wherein a semiselective approach is applied using arrays of cross - reactive nanomaterial based VOC/gas sensors to obtain breath prints (i.e. reading of fingerprinting operation ) that involves a complex and uncertain combination of VOCs (pg. 74 col. 1 para. 1); wherein low volume sample delivery optimize the sensing capabilities of the sensors (i.e. by contacting the partitions in each subsample with a subset of the sensors ) (pg. 73 col.1 para. 1); wherein the identified target VOCs for each studied clinical state must be uploaded to a global database for enabling cross validation of the data (i.e. using a reference database to validate data ) (pg. 71 col. 2 para. 2); wherein the combined responses of arrays of semiselective sensors are used to establish VOC-specific responses by applying pattern recognition and classification algorithms (pg. 68 col.1 para. 1); wherein said classification algorithms comprise the Bayes classifier which requires statistic al knowledge of the data set including the underlying probability distribution function for each class (i.e. detecting the multiple target analytes through statistical estimation ) as evidenced by Lavine (pg. 12 col.1 para. 3 Lavine ). • Konvalina does not teach "asynchronous fingerprinting". However, Hayward teaches a kinetic fingerprinting method that directly observe s single molecule s by essentially integrating over multiple, temporally resolved probing events (i.e. analyte detection by multiple fluorescent probes not simultaneously applied characterize the asynchronous aspect of the fingerprinting process – see claim terminology above) (pg. 11761 col. 1 para. 3) . Claim 2 recites: wherein the sensors are nonspecific sensors • Konvalina teaches a semi selective approach (i.e. nonspecific ) applied using arrays of cross-reactive (i.e. nonspecific ) nanomaterial based VOC/gas sensors to obtain breath prints that involves a complex and uncertain combination of VOCs (pg. 74 col. 1 para. 1). Claim 6 recites: wherein the nonspecific sensors are nucleic acids, primers, or probes • Konvalina teaches a semiselective approach applied using arrays of cross - reactive nanomaterial based VOC/gas sensors (i.e. reading on nonspecific s ensors ) to obtain breath prints (pg. 74 col. 1 para. 1); wherein the nanomaterial recognition element is directly probed (i.e. reading on nonspecific s ensors being probes ) for the modulation of an optical property resulting from direct interactions with the VOC (pg. 68 col. 2 para. 1). Claim 20 recites: (d) a modeled probability distribution for the measurements in each partition that are conditional on the analyte quantities the sample and the subset of sensors applied in the partition; (e) evaluating the expected distribution of measurements based on the modeled probability distribution and the candidate solution; (f) comparing the expected distribution of measurements against the distribution of observed measurements obtained from the partitions, wherein a sufficient difference in the expected and observed distributions identifies the candidate solution as containing an exogenous analyte • Konvalina teaches that the combined responses of arrays of semiselective sensors are used to establish VOC-specific responses (i.e. target analyte detection ) by applying pattern recognition and classification algorithms (pg. 68 col.1 para. 1); wherein said classification algorithms comprise the Bayes classifier which requires statistic al knowledge of the data set including the underlying probability distribution function for each class (i.e. modeled probability distribution for the measurements in each partition that are conditional on the target analyte quantities the sample and the subset of sensors applied in the partition ) as evidenced by Lavine (pg. 12 col.1 para. 3 Lavine); wherein the described estimations of VOC breath levels are based on the partitions content (i.e . (e) evaluating the expected distribution of measurements based on the modeled probability distribution and the candidate solution ) (pg. 71 col. 1 para. 2); wherein 87% of the disease/VOC-concentration indications, the identified VOC marker levels were elevated in the breath of the diseased states compared to the levels in the controls or treated states (i.e. comparing the expected distribution of measurements against the distribution of observed measurements obtained from the partitions, wherein a sufficient difference in the expected and observed distributions identifies the candidate solution as containing an exogenous analyte ) (pg. 70 col. 2 para. 1) Rationale for combining (MPEP §2142-2143) Regarding claims 1- 2, 6, and 20 , 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Konvalina in view of Hayward because all references disclose methods for detecting multiple target analytes in a sample . The motivation would have been to incorporate highly specific, rare sequence detection and quantification in the detection of analytes other than DNA and RNA with specificity surpassing the thermodynamic limit (pg. 11761 col. 2 para. 2 Hayward ) . Therefore it would have been obvious to one of ordinary skill in the art to substitute the analyte detection method of Konvalina to the methods by Hayward because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for detecting multiple target analytes in a sample . B . Claims 3-5, 9, and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Konvalina , as evidenced by Lavine, and Hayward as applied to claim 1 above further in view of Rohban (" Minimax optimal sparse signal recovery with Poisson statistics." IEEE Transactions on Signal Processing 64 (13): 3495-3508 (2016)), as cited on the attached Form PTO-892. Claim 3 recites: wherein statistical estimation comprises a modeled probability distribution for the measurements in each partition that are conditional on the target analyte quantities the sample and the subset of sensors applied in the partition, further comprising:(a) a single parametrization of the target analyte quantities or concentrations that is shared across all subsamples; (b) a joint probability distribution for the measurements from partitions across all subsamples; and (c) an objective function, based on the modeled probability distribution and the observed measurements from partitions, wherein the solution or optimization results in an estimate of the target analyte quantities or concentrations • Konvalina teaches that the combined responses of arrays of semiselective sensors are used to establish VOC-specific responses (i.e. target analyte detection ) by applying pattern recognition and classification algorithms (pg. 68 col.1 para. 1); wherein said classification algorithms comprise the Bayes classifier which requires statistic al knowledge of the data set including the underlying probability distribution function for each class (i.e. modeled probability distribution for the measurements in each partition that are conditional on the target analyte quantities the sample and the subset of sensors applied in the partition ) as evidenced by Lavine (pg. 12 col.1 para. 3 Lavine); wherein estimations of VOC breath levels are based on the partitions content (i.e . (b) a joint probability distribution for the measurements from partitions across all subsamples ) (pg. 71 col. 1 para. 2); wherein the reduction of the initial volume of the sample (i.e. reading on splitting the sample into subsamples and partitions ) allows the reduction of the complexity of the sample (pg. 73 col. 1 para. 1). • Neither Konvalina or Hayward teach the recited steps (a) and (c) . However, Rohban teaches a high dimensional sparse signal estimation problem governed by Poisson distributed observations to model the rate of the Poisson process as a positive mixture of known signatures (i.e. statistical estimation comprises a modeled probability distribution ) (pg. 1 col. 1 para. 3) extracting a sparse subset of parameters from a larger number of parameters (i.e. reading on (a) single parametrization shared across subsets ) ( pg. 1 col. 1 para. 2); wherein an optimal maximum likelihood decoder is obtained by solving a convex optimization problem involving a non-linear objective function (i.e. (c) an objective function, based on the modeled probability distribution ) (pg. 1 col. 2 para . 1) . Claim 4 recites: wherein the quantity of each analyte in each partition is an integer and follows a Poisson distribution with mean given by its true concentration in the sample, wherein the objective function comprises maximum likelihood estimation under the modeled probability distribution and maximum likelihood estimation comprises applying a gradient ascent or descent algorithm • Neither Konvalina or Hayward teach the recitation above. However, Rohban teaches a high dimensional sparse signal estimation problem governed by Poisson distributed observations (pg. 1 col. 1 para. 3) ; wherein Poisson is a probability mass function on integers (i.e. each partition is an integer and follows a Poisson distribution ) (pg. 9 col. 2 para. 1); wherein an optimal maximum likelihood decoder is obtained by solving a convex optimization problem involving a non-linear objective function (i.e. the objective function comprises maximum likelihood estimation under the modeled probability distribution and maximum likelihood estimation (pg. 1 col. 2 para. 1) ; wherein the sparse linear regression and optimization of the Poisson likelihood function follow the effect of constraining the error patterns in a descent direction (i.e. descent algorithm ) (pg. 1 col. 2 para. 2) . Claim 5 recites: wherein the gradient ascent or descent algorithm is Sparse Poisson Recovery (SPoRe) algorithm comprising:(a) initializing a value of λ;(b) computing the gradient based on the modeled probability distribution over a subset of the partition measurements or over the entire set of available measurements, and optionally approximating the gradients with Monte Carlo approximations; (c) updating k based on the gradient; and (d) repeating steps (b)-(c) until convergence of λ • Neither Konvalina or Hayward teach the recitation above. However, Rohban teaches that the sparse linear regression and optimization of the Poisson likelihood function follow the effect of constraining the error patterns in a descent direction (i.e. descent algorithm ) (pg. 1 col. 2 para. 2); wherein the initial value is 100 (i.e. (a) initializing a value of λ ) and convergence of the p robability of successful recovery as a function of the number of observations (i.e. (b) computing the gradient based on the modeled probability distribution over the entire set of available measurements ) is achieve twice faster for Regularized ML in comparison to Rescaled LASSO (i.e. (c) updating k based on the gradient; and (d) repeating steps (b)-(c) until convergence of λ ) (pf. 7 Fig. 6). Claim 9 recites: wherein the signal describing the analyte quantities in the sample is sparse • Neither Konvalina or Hayward teach the recitation above. However, Rohban teaches a high dimensional sparse signal estimation problem governed by Poisson distributed observations to model the rate of the Poisson process as a positive mixture of known signatures (pg. 1 col. 1 para. 3) . Claim 11 recites: wherein the target analytes are captured in the small volume partitions according to a Poisson distribution with the true Poisson rates of all target analytes totaling to less than 20 • Neither Konvalina or Hayward teach the recitation above. However, Rohban teaches generating function for Poisson distribution with rate λ (pg. 13 col. 1 para. 1); wherein in one regime the dimensionality p = 400, sparsity level k = 5, sample size 100 ≤ n ≤ 300, signal strength 1 ≤ s ≤ 2 x 10 4 , and the rate λ = 4 (i.e. Poisson rates of all target analytes totaling to less than 20 ) (pg. 5 col. 2 para. 4) Rationale for combining (MPEP §2142-2143) Regarding claims 3-5, 9, and 11 , 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Konvalina and Hayward in view of Rohban because all references disclose methods for the investigation of the relationship of the signal from sensors to measurement quantities . The motivation would have been to apply the taught method to practical applications where the observations are the counts of an event (i.e. reading on target analyte quantities ) (pg. 1 col. 1 para. 2 Rohban ); Therefore it would have been obvious to one of ordinary skill in the art to substitute the analyte detection method of Konvalina and Hayward to the methods by Rohban because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating of the relationship of the signal from sensors to measurement quantities. C . Claims 7-8, 10, 12-14, and 18-19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Konvalina , as evidenced by Lavine, and Hayward as applied to claim 1 above further in view of Quan ("dPCR: a technology review." Sensors 18(4):271 (2018)) , as cited on the attached Form PTO-892. Claim 7 recites: wherein detecting comprises quantifying the microbial content of the sample • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches digital assays as a method to estimate the concentration of microorganisms of public health concern (i.e. reading on microbes ) by sampling a specimen at different dilutions (pg. 6 para. 3). Claim 8 recites: wherein the microfluidic partitions comprise droplets, chambers or nanowells • Neither Konvalina or Hayward teach the recitation above. However, Qu a n teaches microfluidic droplet-based platforms for dPCR applications for single molecule detection (pg. 15 para. 2) by using droplets as true partitions (pg. 14 para. 3). Claim 10 recites: wherein the method comprises fewer total sensors than the number of target analytes • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches microfluidic droplet-based platforms for dPCR applications for single molecule detection (pg. 15 para. 2); wherein droplet signal can be interrogated by a fluorescence probe using a wide field detection that allows up to 1 million of droplets to be analyzed simultaneously (i.e. total number of sensors less than the number of analytes) (pg. 15 Fig. 9C); wherein each droplet partition contains target analytes (pg. 4 Fig. 3). Claim 12 recites: wherein the subsamples are split into a total of 50 to 10 7 partitions • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches that the droplet signal can be interrogated by a fluorescence probe using a wide field detection that allows up to 1 million of droplets to be analyzed simultaneously (i.e. the subsamples are split into a total of 50 to 10 7 partitions ) (pg. 15 Fig. 9C). • Quan teaches up to 1 million of droplets range which makes obvious the instantly claimed range of 50 to 10 7 . It would have been prima facie obvious to one of ordinary skill in the art to select any portions of the disclosed ranges including the instantly claimed ranges from the ranges disclosed in the prior art references, particularly in view of the fact that: "The normal desire of scientists or artisans to improve upon what is already generally known provides the motivation to determine where in a disclosed set percentage ranges is the optimum combination of percentages" In re Peterson 65 USPQ2d 1379 (CAFC 2003). See also In re Malagari, 182 USPQ 549,533 (CCPA 1974) and MPEP 2144.05 Claim 13 recites: wherein the target analytes comprise whole cells, genomes, genes, DNA, RNA, or taxonomic groups • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches microfluidic droplet-based platforms for dPCR applications for single molecule detection (pg. 15 para. 2); wherein the target analyte can be cDNA, gDNA and RNA (pg. 4 Fig. 3). Claim 14 recites: wherein target analytes comprise microbes, microbial genes, or mutations of interest • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches digital assays as a method to estimate the concentration of microorganisms of public health concern (i.e. reading on microbes ) by sampling a specimen at different dilutions (pg. 6 para. 3). Claim 18 recites: wherein the method comprises splitting a sample into microfluidic partitions in a digital PCR (dPCR) system and reading signatures from each partition at each PCR cycle or after performing PCR to detect target nucleic acids • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches microfluidic droplet-based platforms for digital PCR applications for single molecule detection (pg. 15 para. 2); wherein partitioning and detection of analytes are performed in each cycle (pg. 4 Fig. 4) Claim 19 recites: wherein the target analytes comprise microbes associated with urinary tract infection, bacterial biofilms in chronic wounds, sepsis, meningitis, or a human microbiome • Neither Konvalina or Hayward teach the recitation above. However, Quan teaches digital assays as a method to estimate the concentration of microorganisms of public health concern (i.e. reading on human microbiome ) (pg. 6 para. 3). Rationale for combining (MPEP §2142-2143) Regarding claims 7-8, 10, 12-14, and 18-19 , 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Konvalina and Hayward in view of Quan because all references disclose methods for detecting multiple target analytes in a sample . The motivation would have been to incorporate an efficient partitioning method when determining the concentration of the target nucleic acid (pg. 1 para. 1 Quan ). Therefore it would have been obvious to one of ordinary skill in the art to substitute the analyte detection method of Konvalina and Hayward to the methods by Quan because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for detecting multiple target analytes in a sample . D . Claims 15-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Konvalina , as evidenced by Lavine, and Hayward as applied to claim 1 above further in view of Silverman ("Quenched probes for highly specific detection of cellular RNAs." TRENDS in Biotechnology 23(5):225-230 (2005)) , as cited on the attached Form PTO-892. Claim 15 recites: wherein the target analytes comprise ribosomal RNA genes or gene regions selected from 16S, 18S, 23S, or 28S ribosomal RNA genes or other marker regions such as internal transcribed spacer (ITS) and interspace (IS) regions • Neither Konvalina or Hayward teach the recitation above. However, Silverman teaches quenched fluorescently labeled oligonucleotide probes as sensors for RNA in bacterial and human cells (pg. 225 col. 1 para. 1); wherein the accessibility of the entire 16S and 23S r ibosomal RNAs in E. coli to hybridization probes has been mapped in databases (pg. 226 col. 1 para. 1). Claim 1 6 recites: wherein the nonspecific sensors are nonspecific hydrolysis probes which react with the ribosomal RNA genes • Konvalina teaches a semi selective approach (i.e. nonspecific ) applied using arrays of cross-reactive (i.e. nonspecific ) nanomaterial based VOC/gas sensors to obtain breath prints that involves a complex and uncertain combination of VOCs (pg. 74 col. 1 para. 1). • Neither Konvalina or Hayward does not teach " hydrolysis probes which react with the ribosomal RNA genes " . However, Silverman teaches quenched fluorescently labeled oligonucleotide probes as sensors (i.e. by definition fluorescent probes are nonspecific ) for RNA in bacterial and human cells (pg. 225 col. 1 para. 1); wherein autohydrolysis of the quencher occurs in the process (pg. 229 col. 2 para. 3). Rationale for combining (MPEP §2142-2143) Regarding claims 15-16, 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 combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Konvalina and Hayward in view of Silverman because all references disclose methods for detecting multiple target analytes in a sample . The motivation would have been to develop highly specific methods to probe RNA sequences (pg. 225 col. 2 para. 2 Silverman ). Therefore it would have been obvious to one of ordinary skill in the art to substitute the analyte detection method of Konvalina and Hayward to the methods by Silverman because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for detecting multiple target analytes in a sample . E . Claim 17 is rejected under 35 U.S.C. 103(a) as being unpatentable over Konvalina , as evidenced by Lavine, and Hayward as applied to claim 1 above further in view of Silverman ("Quenched probes for highly specific detection of cellular RNAs." TRENDS in Biotechnology 23(5):225-230 (2005)) in view of Navarro ("Real-time PCR detection chemistry.") Clinica chimica acta 439:231-250 (2015)) , as cited on the attached Form PTO-892. Claim 1 7 recites: wherein the nonspecific hydrolysis probes comprise 8-11 nucleotides with some bases substituted with locked nucleic acids and bind to the ribosomal RNA genes • Neither Konvalina or Hayward teach the recitation above. However, Silverman teaches quenched fluorescently labe