DETAILED ACTION
The applicant’s response to the restriction requirement, from 27 July 2026, has been fully considered. The election without traverse is acknowledged. Amendments to the claims, from 27 July 2026, were received and entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claim(s) 2, 6-8, 10, 12-14, 16-26, 28-30, 32, and 34-36 are cancelled.
Claim(s) 1, 3-5, 9, 11, 15, 27, 31, 33, and 37-42 are currently pending and under examination herein.
Claim(s) 1, 3-5, 9, 11, 15, 27, 31, 33, and 37-42 are rejected.
Claim(s) 15 and 42 are objected to.
Priority
The instant application claims priority as a 371 of PCT/EP2021/067438 filed 24 June 2021 and foreign priority to EP20181896.0 filed 24 June 2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. In this action, claims 1, 3-5, 9, 11, 15, 27, 31, 33, and 37-42 are examined as though they had an effective filing date of 24 June 2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03 March 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed 13 December 2022 are accepted.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code.
Links were wound in the specification (filed 31 August 2023) corresponding to the following of the published specification (US20230401449A1):
Paragraph 0058: https://towardsdatascience.com/understanding-random-forest-58381e0602d2 and https://www.javatpoint.com/machine-learning-random-forest-algorithm
Paragraph 0479: https://support.illumina.com/documents/documentation/ chemistry documentation/16 s/16s-metagenomic-library-prep-guide-15044223-b.pdf
Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http://, www., or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claims 15 and 42 are objected to because they recite “Clostridiodes difficile”. This is believed to be a typographical error of “Clostridioides difficile”, which is also recited by claim 15 in a different grouping. Additionally, while “Clostridioides difficile” is found throughout the published specification, “Clostridiodes difficile” was not found within the published specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation is: data processing device comprising means for carrying out the computer-implemented method of claim 1 in claim 37.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof. The structure of the device is interpreted as a generic computer as recited by the claim.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 15 and 42 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 15 recites the broad recitation a group of microbes in set (III)(i), and the claim also recites “preferably” a subset of the same microbes, which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. Claim 42 depends on Claim 15, and thus contain the above issues due to said dependence.
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 38 and 39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 38 recites “A computer program” which is executed by a computer. The claim does not fall within at least one of the four categories of patent eligible subject matter because it recites computer program executed by a processor. Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations are not directed to any of the statutory categories (MPEP 2106.03). This rejection can be overcome by having the program code stored on non-transitory memory and executed by the processor (although then it could repeat claim 39).
Claim 39 recites “A computer-readable storage medium” executed by a computer. The claims and specification does not define the computer readable medium as non-transitory. Transitory forms of signal transmission (often referred to as "signals per se"). The interpretation for transitory computer-readable medium encompasses signals per se (see MPEP 2106.03). Therefore, the claim is not directed to a statutory category. This rejection can be overcome by specifying the CRM is non-transitory.
Claims 1, 3-5, 9, 11, 15, 27, 31, 33, and 37-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law (Step 2A, Prong 1). Claims 1, 3-5, 9, 11, 15, 27, 31, 33, and 40-42 are directed to methods and Claims 37 is directed to a system. While claims 38 and 39 are not directed to a statutory category (see above), they are included in the subject matter eligibility analysis to facilitate compact prosecution. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites the limitation - analyzing said training data set with a supervised machine learning algorithm. Based on the broadest reasonable interpretation, analyzing data with a generic machine learning model encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - wherein the cytometric parameters of an object have been determined by flow cytometry. Based on the broadest reasonable interpretation, determining data with flow cytometry encompasses equations. This draws the limitation to a mathematical concept, which classifies the limitation as an abstract idea.
Claim 4 recites the limitation - wherein the supervised machine learning algorithm comprises an artificial neural network and/or a random forest. This limitation specifies the machine learning model of the analyzing judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation.
Claim 9 recites the limitation - wherein the data of at least one cytometric parameter are pre-processed, and wherein said pre-processing comprises the steps of (a) determining a lower and an upper boundary of said cytometric parameter, (b) adding the lower and upper boundaries of said cytometric parameter as two data points to the data of said cytometric parameter, and (c) assigning to the lower boundary a minimum value and assigning to the upper boundary a maximum value, thereby scaling the data. Based on the broadest reasonable interpretation, pre-processing data, determining the boundaries, adding data, and assigning data encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation - wherein the artificial neural network is a feedforward neural network comprising one or two hidden layers and/or analyzing the training data set with the artificial neural network comprises backpropagation. This limitation specifies the machine learning model of the analyzing judicial exception of claim 1. The refined limitation indicated still represents a judicial expectation (a generic neural network is a system of equations).
Claim 27 recites the limitation - generating a classifier for at least one target microbe by performing the method of claim 1 (see judicial exceptions of claim 1 above); assigning the objects in the sample to the labels by applying said classifier to the sample data, thereby determining the microbial composition and/or diversity of the microbial composition in said sample. Based on the broadest reasonable interpretation, applying the classifier to the data encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 33 recites the limitation - determining the carbon biomass of the microbial composition, wherein quantifying the carbon biomass comprises the steps of (a) determining the average carbon masses of the labels comprised in the classifier, and (b) multiplying the number of objects which have been assigned to a certain label with the average carbon mass of said certain label. Based on the broadest reasonable interpretation, determining mass/biomass and multiplying values encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 37 recites the limitation - carrying out the computer-implemented method of claim 1 (see judicial exceptions of claim 1 above).
Claim 38 recites the limitation - cause the computer to carry out the computer-implemented method of claim 1 (see judicial exceptions of claim 1 above).
Claim 39 recite the limitation - cause the computer to carry out the computer- implemented method of claim 1 (see judicial exceptions of claim 1 above).
Claim 40 recite the limitation - determining with flow cytometry the values of the plurality of cytometric parameters. Based on the broadest reasonable interpretation, determining data with flow cytometry encompasses equations. This draws the limitation to a mathematical concept, which classifies the limitation as an abstract idea.
These limitations recite concepts of analyzing and determining information and values that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. 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 (MPEP 2106.04(a)(2)). Additionally, both product claims and process claims may recite mental processes, which can include a claim that requires a computer (MPEP 2106.04(a)(2)). Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1, 3-5, 9, 11, 15, 27, 31, 33, and 37-42 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites a computer; obtaining a training data set, wherein said training data set comprises data of a plurality of objects, wherein said plurality of objects comprises cells of said at least one target microbe, and wherein said data comprises for each of said objects (i) a label which identifies the type of the object, and (ii) an input vector which comprises a plurality of cytometric parameters of said object (i.e. input data); obtaining said classifier as output from said supervised machine learning algorithm (i.e. output data).
Claim 5 recites wherein the target microbe is a prokaryote and/or a bacterium.
Claim 15 recites wherein the target microbes comprise a list of microbe species (see art rejection for the complete list of species of claim 15).
Claim 27 recites obtaining data of a plurality of objects from said sample, wherein said data comprises for each of said objects a vector comprising a plurality of cytometric parameters.
Claim 31 recites wherein the microbial composition is analyzed in a series of samples, wherein said samples have been obtained at different time-points from a similar location, thereby quantifying the change of the microbial composition over time in said location.
Claim 37 recites data processing device comprising means.
Claim 38 recites a computer program comprising instructions executed by a computer.
Claim 39 recites computer-readable storage medium comprising instructions which executed by a computer.
Claim 40 recites wherein the objects are stained with at least one dye before flow cytometry analysis.
Claim 41 recites wherein said at least one dye comprises a fluorescent dye that is a fluorescent stain for DNA, membrane, cell wall polysaccharide, dead cells, or metabolism.
Claim 42 recite wherein the target microbes comprise at least Clostridiodes difficile and/or Clostridium scindens.
There are no limitations that indicate that the claimed analyzing and determining information and values require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, the claims are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed analyzing and determining information and values require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Lam et al. (2019, Cell Host and Microbe, Vol. 26: 22-34) teach utilizing data from bacteria, including Clostridioides difficile, and flow cytometry from multiple samples collected over time in conjunction with fluorescence are well understood, routine, and conventional (Page 28, Column 1, Paragraph 2: targeting Clostridium difficile; Page 28, Column 2, Paragraph 3: Fluorescently tagged activity-based probes can be used to label live cells; Page 29, Figure 4: The E. coli donor was engineered to carry RFP on the chromosome and GFP on a mobilizable plasmid, allowing GFP-only transconjugant bacteria to be distinguished from the donor by flow cytometry on mouse fecal samples).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, 11, 27, 31, and 37-41 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Kumar et al. (US 20180247195 A1).
Applicable claims include:
Claim 1. A computer-implemented method for generating a classifier for at least one target microbe, wherein said target microbe is a microbial species or strain or a subpopulation thereof, and wherein said method comprises the steps of (Claim 1.i)(a) obtaining a training data set, wherein said training data set comprises data of a plurality of objects, wherein said plurality of objects comprises cells of said at least one target microbe, and wherein said data comprises for each of said objects (i) a label which identifies the type of the object, and (ii) an input vector which comprises a plurality of cytometric parameters of said object, (Claim 1.ii)(b) analyzing said training data set with a supervised machine learning algorithm, and (Claim 1.iii)(c) obtaining said classifier as output from said supervised machine learning algorithm.
Claim 3. The method of claim 1, wherein the cytometric parameters of an object have been determined by flow cytometry.
Claim 4. The method of claim 1, wherein the supervised machine learning algorithm comprises an artificial neural network and/or a random forest.
Claim 5. The method of claim 1, wherein the target microbe is a prokaryote and/or a bacterium.
Claim 11. The method of claim 4, wherein the artificial neural network is a feedforward neural network comprising one or two hidden layers and/or analyzing the training data set with the artificial neural network comprises backpropagation.
Claim 27. A computer-implemented method for analyzing the microbial composition in a sample, wherein said method comprises (Claim 27.i)(a) generating a classifier for at least one target microbe by performing the method of claim 1 (Claim 27.ii)(b) obtaining data of a plurality of objects from said sample, wherein said data comprises for each of said objects a vector comprising a plurality of cytometric parameters, and (c) assigning the objects in the sample to the labels by applying said classifier to the sample data, thereby determining the microbial composition and/or diversity of the microbial composition in said sample.
Claim 31. The method of claim 27, wherein the microbial composition is analyzed in a series of samples, wherein said samples have been obtained at different time-points from a similar location, thereby quantifying the change of the microbial composition over time in said location.
Claim 37. A data processing device comprising means for carrying out the computer-implemented method of claim 1.
Claim 38. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the computer-implemented method of claim 1.
Claim 39. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the computer- implemented method of claim 1.
Claim 40. A method comprising the computer-implemented method of claim 1, wherein said method further comprises a step of determining with flow cytometry the values of the plurality of cytometric parameters, wherein the objects are stained with at least one dye before flow cytometry analysis.
Claim 41. The method of claim 40, wherein said at least one dye comprises a fluorescent dye that is a fluorescent stain for DNA, membrane, cell wall polysaccharide, dead cells, or metabolism.
Regarding Claims 1 and 37-39, Kumar et al. teach (Claim 1.i) (a) obtaining a training data set comprising data of objects comprising cells of said target microbe, and for each of said objects (i) a label which identifies the type of the object, and (ii) an input vector which comprises a plurality of cytometric parameters of said object (Paragraph 0014: training an improved artificial neural network to generate a medical diagnosis (interpreted as identifying of cell type), comprising: (a) receiving a sample from a subject; (b) obtaining flow cytometry data from the sample; (d) transmitting a subject status (interpreted as label identifying disease/cell type); (e) performing, by a computer, analysis of the flow cytometry data at a central site using an artificial neural network to determine a classification for the flow cytometry data (training of the neural network includes obtaining training data); Paragraph 0008: obtaining measurements of a plurality of event features for each of the plurality of events of interest with a flow cytometer instrument (interpreted as obtaining data on a plurality of cells). using four or more flow cytometer measurement channels to define a feature coordinate space (indicates the data includes flow cytometry data which is multidimensional (i.e. it has a plurality of parameters)); Paragraph 0010: the dimensionality reduction algorithm comprises a principal component analysis. (PCA generates vectors (e.g. eigenvectors) for representing multidimension data from the flow cytometry data). the plurality of events of interest comprises one or more cells, the plurality of event features comprises one or more cell features (interpreted as label and flow cytometry data as indicated above), and the event population of interest comprises one or more cell populations of interest (interpreted as label data as indicated above); Paragraph 0103: A hyperspace is a coordinate space having 4 or more dimensions, each dimension having an associated coordinate axis defined by the basis vectors of the hyperspace (indicates the flow cytometry data is multidimensional and represented by vectors); Paragraph 0062: allow for the detection of the presence pathological cells and monitoring for disease (presence of pathological cells related to disease is interpreted as bacteria (i.e. microbe); see tuberculosis next); Paragraph 0189: The application of the neural network can be used in the early detection of other diseases (e.g., tuberculosis) (tuberculosis is caused by a species of bacteria, therefore the methods encompass identify species of bacteria (i.e. microbe))). Kumar et al. teach (Claim 1.ii) (b) analyzing said training data set with a supervised machine learning algorithm (Paragraph 0014: performing, by a computer, analysis of the flow cytometry data using an artificial neural network to determine a classification for the flow cytometry data). An artificial neural network is interpreted as a supervised machine learning algorithm given the limitation of Claim 4. See teachings of Claim 1.i for integration of other data into training dataset of the neural network. Kumar et al. teach (Claim 1.iii) (c) obtaining said classifier as output from said supervised machine learning algorithm (Paragraph 0006: a neural network analysis that classifies samples based on the learned characteristics of the distributions of target cells in a multidimensional data space; Paragraph 0014: performing, by a computer, analysis of the flow cytometry data at a central site using an artificial neural network to determine a classification for the flow cytometry data). Claim 1 is interpreted as training a neural network to classify cell type. The outcome of training the neural network of Kumar et al. is interpreted as a cell type classifier based on cytometry data. Additionally, Kumar et al. teach the methods are carried out by a computer, which inherently contains program code, memory, including computer readable storage, and at least one processor, to execute the steps of the method (Paragraph 0008: performing, by a computer; Paragraph 0280: suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers). Claims 37-39 recite computer devices for carrying out the computer implemented method of claim 1.
Regarding Claim 3, Kumar et al. teach the cytometric parameters of an object have been determined by flow cytometry (Paragraph 0014: obtaining flow cytometry data from the sample).
Regarding Claim 4, Kumar et al. teach the supervised machine learning algorithm comprises an artificial neural network and/or a random forest (Paragraph 0014: performing, by a computer, analysis of the flow cytometry data using an artificial neural network to determine a classification for the flow cytometry data).
Regarding Claim 5, Kumar et al. teach the target microbe is a prokaryote and/or a bacterium (Paragraph 0062: allow for the detection of the presence pathological cells and monitoring for disease (presence of pathological cells related to disease is interpreted as bacteria (i.e. microbe); Paragraph 0189: The application of the neural network can be used in the early detection of other diseases (e.g., tuberculosis) (tuberculosis is caused by a species of bacteria, therefore the methods encompass classify species of bacteria (i.e. microbe))).
Regarding Claim 11, Kumar et al. teach the artificial neural network is a feedforward neural network comprising one or two hidden layers and/or analyzing the training data set with the artificial neural network comprises backpropagation (Paragraph 0187: Training functions that are suitable for training the neural network are, among others, Resilient Backpropagation, and Variable Learning Rate Backpropagation). This is interpreted as analyzing the training data set with the artificial neural network comprises backpropagation.
Regarding Claim 27, Kumar et al. teach (Claim 27.i) (a) generating a classifier for at least one target microbe by performing the method of claim 1 (see regarding claim 1 above). Kumar et al. teach (Claim 27.ii) (b) obtaining data of a plurality of objects from said sample, wherein said data comprises for each of said objects a vector comprising a plurality of cytometric parameters, and (c) assigning the objects in the sample to the labels by applying said classifier to the sample data, thereby determining the microbial composition and/or diversity of the microbial composition in said sample. (Paragraph 0095: The data set is conventionally divided into a training set, a test set, and, in some cases, a validation set). This indicates the methods of Kumar et al. include model training and testing (i.e. implementation of the trained model). This claim is interpreted as using the trained NN to classify microbes in the sample (i.e. running on the testing data as described by Kumar et al.). Also Kumar et al teaches the training and testing on separate samples (Paragraph 0183: samples can have a similar number of events for ANN training, validation, testing, and testing of naive samples). Kumar et al. teach the method is computer based (see regarding claim 1 above).
Regarding Claim 31, Kumar et al. teach the microbial composition is analyzed in a series of samples, wherein said samples have been obtained at different time-points from a similar location, thereby quantifying the change of the microbial composition over time in said location. (Paragraph 0062: Systems and methods as described herein can involve analysis of one or more samples from a subject. Samples may be obtained once or multiple times from a subject. at different times from the individual (e.g., a series of samples)). Samples coming from the same subject is interpreted as from a similar location.
Regarding Claim 40, Kumar et al. teach determining with flow cytometry the values of the plurality of cytometric parameters, wherein the objects are stained with at least one dye before flow cytometry analysis (Paragraph 0070: cells may be labeled with one or more fluorophores and then excited by one or more lasers to emit light at the fluorophore emission frequency or frequencies; Paragraph 0071: Several types of fluorophores can be used as consistent with this application. Non-limiting examples are Alexa-Fluor dyes). Additionally, Kumar et al. teach the use of florescent dyes are a standard step of flow cytometry (Paragraph 0069: In general, flow cytometry involves the passage of individual cells through the path of one or more laser beams. A scattering of a beam and excitation of any fluorescent molecule attached to, or found within, a cell is detected by photomultiplier tubes to create a readable output).
Regarding Claim 41, Kumar et al. teach a fluorescent dye that is a fluorescent stain for DNA, membrane, cell wall polysaccharide, dead cells, or metabolism (Paragraph 0068: Cells are often labeled with a fluorophore-conjugated antibody that recognizes biomarkers associated with cells. a fluorophore-conjugated antibody recognizes cell surface antigens). Recognizing cell surface antigens is interpreted as recognizing the membrane.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-5, 9, 11, 27, 31, and 37-41 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 20180247195 A1), as applied to claims 1, 3-5, 11, 27, 31, and 37-41 in the 35 USC 102 rejection above, in view of Loukas (2020, towardsdatascience.com: 1-12). Italicized text from reference art.
Applicable claims include:
Claim 1. A computer-implemented method for generating a classifier for at least one target microbe, wherein said target microbe is a microbial species or strain or a subpopulation thereof, and wherein said method comprises the steps of (Claim 1.i)(a) obtaining a training data set, wherein said training data set comprises data of a plurality of objects, wherein said plurality of objects comprises cells of said at least one target microbe, and wherein said data comprises for each of said objects (i) a label which identifies the type of the object, and (ii) an input vector which comprises a plurality of cytometric parameters of said object, (Claim 1.ii)(b) analyzing said training data set with a supervised machine learning algorithm, and (Claim 1.iii)(c) obtaining said classifier as output from said supervised machine learning algorithm.
Claim 3. The method of claim 1, wherein the cytometric parameters of an object have been determined by flow cytometry.
Claim 4. The method of claim 1, wherein the supervised machine learning algorithm comprises an artificial neural network and/or a random forest.
Claim 5. The method of claim 1, wherein the target microbe is a prokaryote and/or a bacterium.
Claim 9. The method of claim 1, wherein (Claim 9.i) the data of at least one cytometric parameter are pre-processed, and wherein said pre-processing comprises the steps of (Claim 9.ii) (a) determining a lower and an upper boundary of said cytometric parameter, (b) adding the lower and upper boundaries of said cytometric parameter as two data points to the data of said cytometric parameter, and (Claim 9.iii) (c) assigning to the lower boundary a minimum value and assigning to the upper boundary a maximum value, thereby scaling the data.
Claim 11. The method of claim 4, wherein the artificial neural network is a feedforward neural network comprising one or two hidden layers and/or analyzing the training data set with the artificial neural network comprises backpropagation.
Claim 27. A computer-implemented method for analyzing the microbial composition in a sample, wherein said method comprises (Claim 27.i)(a) generating a classifier for at least one target microbe by performing the method of claim 1 (Claim 27.ii)(b) obtaining data of a plurality of objects from said sample, wherein said data comprises for each of said objects a vector comprising a plurality of cytometric parameters, and (c) assigning the objects in the sample to the labels by applying said classifier to the sample data, thereby determining the microbial composition and/or diversity of the microbial composition in said sample.
Claim 31. The method of claim 27, wherein the microbial composition is analyzed in a series of samples, wherein said samples have been obtained at different time-points from a similar location, thereby quantifying the change of the microbial composition over time in said location.
Claim 37. A data processing device comprising means for carrying out the computer-implemented method of claim 1.
Claim 38. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the computer-implemented method of claim 1.
Claim 39. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the computer- implemented method of claim 1.
Claim 40. A method comprising the computer-implemented method of claim 1, wherein said method further comprises a step of determining with flow cytometry the values of the plurality of cytometric parameters, wherein the objects are stained with at least one dye before flow cytometry analysis.
Claim 41. The method of claim 40, wherein said at least one dye comprises a fluorescent dye that is a fluorescent stain for DNA, membrane, cell wall polysaccharide, dead cells, or metabolism.
Regarding Claims 1 and 37-39, Kumar et al. teach (Claim 1.i) (a) obtaining a training data set comprising data of objects comprising cells of said target microbe, and for each of said objects (i) a label which identifies the type of the object, and (ii) an input vector which comprises a plurality of cytometric parameters of said object (Paragraph 0014: training an improved artificial neural network to generate a medical diagnosis (interpreted as identifying of cell type), comprising: (a) receiving a sample from a subject; (b) obtaining flow cytometry data from the sample; (d) transmitting a subject status (interpreted as label identifying disease/cell type); (e) performing, by a computer, analysis of the flow cytometry data at a central site using an artificial neural network to determine a classification for the flow cytometry data (training of the neural network includes obtaining training data); Paragraph 0008: obtaining measurements of a plurality of event features for each of the plurality of events of interest with a flow cytometer instrument (interpreted as obtaining data on a plurality of cells). using four or more flow cytometer measurement channels to define a feature coordinate space (indicates the data includes flow cytometry data which is multidimensional (i.e. it has a plurality of parameters)); Paragraph 0010: the dimensionality reduction algorithm comprises a principal component analysis. (PCA generates vectors (e.g. eigenvectors) for representing multidimension data from the flow cytometry data). the plurality of events of interest comprises one or more cells, the plurality of event features comprises one or more cell features (interpreted as label and flow cytometry data as indicated above), and the event population of interest comprises one or more cell populations of interest (interpreted as label data as indicated above); Paragraph 0103: A hyperspace is a coordinate space having 4 or more dimensions, each dimension having an associated coordinate axis defined by the basis vectors of the hyperspace (indicates the flow cytometry data is multidimensional and represented by vectors); Paragraph 0062: allow for the detection of the presence pathological cells and monitoring for disease (presence of pathological cells related to disease is interpreted as bacteria (i.e. microbe); see tuberculosis next); Paragraph 0189: The application of the neural network can be used in the early detection of other diseases (e.g., tuberculosis) (tuberculosis is caused by a species of bacteria, therefore the methods encompass identify species of bacteria (i.e. microbe))). Kumar et al. teach (Claim 1.ii) (b) analyzing said training data set with a supervised machine learning algorithm (Paragraph 0014: performing, by a computer, analysis of the flow cytometry data using an artificial neural network to determine a classification for the flow cytometry data). An artificial neural network is interpreted as a supervised machine learning algorithm given the limitation of Claim 4. See teachings of Claim 1.i for integration of other data into training dataset of the neural network. Kumar et al. teach (Claim 1.iii) (c) obtaining said classifier as output from said supervised machine learning algorithm (Paragraph 0006: a neural network analysis that classifies samples based on the learned characteristics of the distributions of target cells in a multidimensional data space; Paragraph 0014: performing, by a computer, analysis of the flow cytometry data at a central site using an artificial neural network to determine a classification for the flow cytometry data). Claim 1 is interpreted as training a neural network to classify cell type. The outcome of training the neural network of Kumar et al. is interpreted as a cell type classifier based on cytometry data. Additionally, Kumar et al. teach the methods are carried out by a computer, which inherently contains program code, memory, including computer readable storage, and at least one processor, to execute the steps of the method (Paragraph 0008: performing, by a computer; Paragraph 0280: suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers). Claims 37-39 recite computer devices for carrying out the computer implemented method of claim 1.
Regarding Claim 3, Kumar et al. teach the cytometric parameters of an object have been determined by flow cytometry (Paragraph 0014: obtaining flow cytometry data from the sample).
Regarding Claim 4, Kumar et al. teach the supervised machine learning algorithm comprises an artificial neural network and/or a random forest (Paragraph 0014: performing, by a computer, analysis of the flow cytometry data using an artificial neural network to determine a classification for the flow cytometry data).
Regarding Claim 5, Kumar et al. teach the target microbe is a prokaryote and/or a bacterium (Paragraph 0062: allow for the detection of the presence pathological cells and monitoring for disease (presence of pathological cells related to disease is interpreted as bacteria (i.e. microbe); Paragraph 0189: The application of the neural network can be used in the early detection of other diseases (e.g., tuberculosis) (tuberculosis is caused by a species of bacteria, therefore the methods encompass classify species of bacteria (i.e. microbe))).
Regarding Claim 9, Kumar et al. suggest scaling the data using a transformation (Paragraph 0211: A transformation can be employed rescaling the network sigmoid output to give a condition classification result for a chosen threshold).
Regarding Claim 11, Kumar et al. teach the artificial neural network is a feedforward neural network comprising one or two hidden layers and/or analyzing the training data set with the artificial neural network comprises backpropagation (Paragraph 0187: Training functions that are suitable for training the neural network are, among others, Resilient Backpropagation, and Variable Learning Rate Backpropagation). This is interpreted as analyzing the training data set with the artificial neural network comprises backpropagation.
Regarding Claim 27, Kumar et al. teach (Claim 27.i) (a) generating a classifier for at least one target microbe by performing the method of claim 1 (see regarding claim 1 above). Kumar et al. teach (Claim 27.ii) (b) obtaining data of a plurality of objects from said sample, wherein said data comprises for each of said objects a vector comprising a plurality of cytometric parameters, and (c) assigning the objects in the sample to the labels by applying said classifier to the sample data, thereby determining the microbial composition and/or diversity of the microbial composition in said sample. (Paragraph 0095: The data set is conventionally divided into a training set, a test set, and, in some cases, a validation set). This indicates the methods of Kumar et al. include model training and testing (i.e. implementation of the trained model). This claim is interpreted as using the trained NN to classify microbes in the sample (i.e. running on the testing data as described by Kumar et al.). Also Kumar et al teaches the training and testing on separate samples (Paragraph 0183: samples can have a similar number of events for ANN training, validation, testing, and testing of naive samples). Kumar et al. teach the method is computer based (see regarding claim 1 above).
Regarding Claim 31, Kumar et al. teach the microbial composition is analyzed in a series of samples, wherein said samples have been obtained at different time-points from a similar location, thereby quantifying the change of the microbial composition over time in said location. (Paragraph 0062: Systems and methods as described herein can involve analysis of one or more samples from a subject. Samples may be obtained once or multiple times from a subject. at different times from the individual (e.g., a series of samples)). Samples coming from the same subject is interpreted as from a similar location.
Regarding Claim 40, Kumar et al. teach determining with flow cytometry the values of the plurality of cytometric parameters, wherein the objects are stained with at least one dye before flow cytometry analysis (Paragraph 0070: cells may be labeled with one or more fluorophores and then excited by one or more lasers to emit light at the fluorophore emission frequency or frequencies; Paragraph 0071: Several types of fluorophores can be used as consistent with this application. Non-limiting examples are Alexa-Fluor dyes). Additionally, Kumar et al. teach the use of florescent dyes are a standard step of flow cytometry (Paragraph 0069: In general, flow cytometry involves the passage of individual cells through the path of one or more laser beams. A scattering of a beam and excitation of any fluorescent molecule attached to, or found within, a cell is detected by photomultiplier tubes to create a readable output).
Regarding Claim 41, Kumar et al. teach a fluorescent dye that is a fluorescent stain for DNA, membrane, cell wall polysaccharide, dead cells, or metabolism (Paragraph 0068: Cells are often labeled with a fluorophore-conjugated antibody that recognizes biomarkers associated with cells. a fluorophore-conjugated antibody recognizes cell surface antigens). Recognizing cell surface antigens is interpreted as recognizing the membrane.
Kumar et al. do not explicitly teach some specifics of the data scaling procedure used (Claim 9).
Regarding Claim 9, Loukas teaches (Claim 9.i) the data are pre-processed (Page 3, Paragraph 2: Scaling is usually used prior to model fitting). Loukas teaches (Claim 9.ii)(a) determining a lower and an upper boundary of said cytometric parameter, and (b) adding the lower and upper boundaries of said cytometric parameter as two data points to the data of said cytometric parameter (Page 3: The mathematical formulation). The min(x) and max(x) functions determine the lower and upper bounds of data respectively. For the min and max values to be used to compute the scaled value (xscaled) they must be calculated and stored (i.e. added to the data set). Loukas teaches (Claim 9.iii)(c) assigning to the lower boundary a minimum value and assigning to the upper boundary a maximum value, thereby scaling the data (Page 2, Paragraph 3: all features will be transformed into the range [0,1] meaning that the minimum and maximum value of a feature/variable is going to be 0 and 1, respectively). This claim is interpreted as applying a standard min-max transformation to the data prior to analysis, which, as indicated by Loukas, is a standard step in data analysis protocols (Page 2, Paragraph 2: I will explain the second most famous normalization method i.e. Min-Max Scaling; Page 3, Paragraph 2: Variables that are measured at different scales do not contribute equally to the model fitting & model learned function and might end up creating a bias. Thus, to deal with this potential problem feature-wise normalization such as MinMax Scaling is usually used prior to model fitting).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Loukas with Kumar et al. Loukas teaches the variable scaling methods presented are useful for machine learning analyses, a major feature of Kumar et al. and the instant application, and can lead to improved results (Page 3, Paragraphs 2-3: Variables that are measured at different scales do not contribute equally to the model fitting & model learned function and might end up creating a bias. Thus, to deal with this potential problem feature-wise normalization such as MinMax Scaling is usually used prior to model fitting. This can be very useful for some ML models, where the back-propagation can be more stable and even faster when input features are min-max scaled compared to using the original unscaled data). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field - data analytics using machine learning.
Claims 1, 3-5, 11, 15, 27, 31, and 37-42 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 20180247195 A1), as applied to claims 1, 3-5, 9, 11, 27, 31, and 37-41 above, in view of Culler et al. (WO 2019178542 A1). Italicized text from reference art.
Applicable claims include:
Claims 1, 3-5, 11, 27, 31, and 37-41 presented above.
Claim 15. The method of claim 1, wherein the target microbes comprise (I) at least one, 2 or 10 microbes selected from the group consisting of: Acinetobacter johnsonii, Acinetobacter tjernbergiae, Arthrobacter chlorophenolicus, Bacillus subtilis, Caulobacter crescentus, Cryptococcus albidus, Escherichia coli, Escherichia coli MG1655, Escherichia coli DH5a, Lactococcus lactis, Pseudomonas knackmussii, Pseudomonas migulae, Pseudomonas putida, Pseudomonas veronii, Sphingomonas wittichii, Sphingomonas yanoikuyae, and any subpopulation thereof; (II) at least one or two microbes selected from the group consisting of: Stenotrophomonas rhizophila, Kocuria rhizophila, and Paenibacillus polymyxa, and any subpopulation thereof; and/or (III) at least one, 2 or 10 microbes selected from the group consisting of the following (i) and/or (ii): (i) Bacteroides cellulosilyticus, Bacteroides caccae, Parabacteroides distasonis, Ruminococcus torques, Clostridium scindens, Collinsella aerofaciens, Bacteroides thetaiotaomicron, Bacteroides vulgatus, Bacteroides ovatus, Bacteroides uniformis, Eumicrobe rectale, Clostridium spiroforme, Faecalimicrobe prausnitzii, Ruminococcus obeum, Dorea longicatena, Clostridiodes difficile, Eschericia coli, Klebsiella sp., Salmonella sp., and any subpopulation thereof, preferably at least Clostridiodes difficile, Clostridium scindens, Eschericia coli, Klebsiella sp., and/or Salmonella sp., and any subpopulation thereof; (ii) Bacteriodes fragilis, Bacteroides vulgatus, Bifidobacterium adolescentis, Clostridioides difficile, Enterococcus faecalis, Lactobacillus plantarum, Enterobacter cloacae, Escherichia coli, Helicobacter pylori, Salmonella enterica subsp. Enterica, Yersinia enterocolitica, Fusobacterium nucleatum, Bifidobacterium longum, and any subpopulation thereof.
Claim 42. The method of claim 15, wherein the target microbes comprise at least Clostridiodes difficile and/or Clostridium scindens.
Regarding Claims 1, 3-5, 11, 27, 31, and 37-41, these limitations are taught by Kumar et al. at indicated above.
Kumar et al. does not explicitly teach some of the target bacteria indicated by claims 15 and 42.
Regarding Claims 15 and 42, Culler et al. teach methods and devices for analyzing bacteria. The bacteria targeted include Clostridioides difficile and Clostridium scindens, due to their demonstrated relationship to important health concerns (Page 108, Line 8, Table 26: Whole genome sequencing was performed on fecal samples from subject with and without cancer and the reads are classified and abundance of each species or strain was estimated computationally). Table 26 includes Clostridioides difficile and Clostridium scindens which had significant results. The analyses taught by Culler for the bacteria include identification with flow cytometry (Page 42, Line 10: the amount, identity, presence, and/or ratio of gut microbiota in a subject is manipulated to facilitate treatments; Page 51, Line 9: Confirmation of the presence of only a single bacterial type can be confirmed in multiple ways such as flow cytometry) and the application of neural networks (Page 159, Line 9: Machine learning (or artificial intelligence) techniques are used to identify correlations among species abundance. Supervised learning techniques include but are not limited to artificial neural networks). The limitations of Claims 15 and 42 can each be taught by either Clostridioides difficile or Clostridium scindens as the target microbe.
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Culler et al. with Kumar et al. Culler et al. teach species of bacteria that were targeted for research due to their demonstrated significant relationship to important health concerns (Page 108, Line 8, Table 26: Whole genome sequencing was performed on fecal samples from subject with and without cancer and the reads are classified and abundance of each species or strain was estimated computationally). Additionally, Culler et al., Kumar et al., and the instant application are directed to analyzing bacteria and flow cytometry data with machine leaning. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field - analyzing flow cytometry data of bacteria with machine learning.
Claims 1, 3-5, 11, 27, 31, 33, and 37-41 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 20180247195 A1), as applied to claims 1, 3-5, 9, 11, 27, 31, and 37-41 above, in view of Foladori et al. (2010, Water Research, Vol. 44: 3807-3818). Italicized text from reference art.
Applicable claims include:
Claims 1, 3-5, 11, 27, 31, and 37-41 presented above.
Claim 33. The method of claim 31, wherein said method further comprises a step of (Claim 33.i) determining the carbon biomass of the microbial composition, wherein quantifying the carbon biomass comprises the steps of (Claim 33.ii) (a) determining the average carbon masses of the labels comprised in the classifier, and (Claim 33.iii) (b) multiplying the number of objects which have been assigned to a certain label with the average carbon mass of said certain label.
Regarding Claims 1, 3-5, 11, 27, 31, and 37-41, these limitations are taught by Kumar et al. at indicated above.
Kumar et al. does not explicitly teach some specifics of the methods for calculating biomass of bacteria as indicated by claim 33.
Regarding Claim 33, Foladori et al. teach (Claim 33.i) determining the carbon biomass of the microbial composition (Page 3808, Column 2, Paragraph 3: A specific procedure was used for the first time to convert the number of bacterial cells into an equivalent biomass, expressed as dry weight, taking into account the bacterial biovolume estimated from flow cytometry (FCM); Page 3813, Column 1, Paragraph 2: The biovolume of each bacteria or small aggregate was converted into the corresponding biomass). Foladori et al. teach (Claim 33.ii)(a) determining the average carbon masses of class (Page 3813, Column 1, Paragraph 2: the carbon content per unit of cell volume (Cs) as 310 fg C mm-3). Foladori et al. teach (Claim 33.iii)(b) multiplying the number of objects which have been assigned to a certain label with the average carbon mass of said certain label (Page 3813, Column 2, Paragraph 2: Therefore, the conversion to an equivalent mass of VSS is: M = V x Cs x 10-12/0.53). V x Cs is biovolume and Cs is carbon content per unit of cell volume. This estimate is based on volume, but it would be obvious to base the calculation on number of cells through a simple conversion as the density of cells per unit volume is known (Page 3814, Column 2, Paragraph 2: Finally, the entire viable bacterial biomass, expressed as mg COD/L (or VSS/L), is calculated by adding up the values of M for all the viable bacteria and half the number of small aggregates present in 1 L of original sample; Page 3845, Figure 9: concentration of total cells (#/L)). Additionally, Foladori et al. teaches that the calculated biomass is based on the number of cells (Page 3808, Column 2, Paragraph 3: convert the number of bacterial cells into an equivalent biomass).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Foladori et al. with Kumar et al. Foladori et al. teach novel methods for biomass estimation of bacterial groups using flow cytometry data that were rapid (Page 3817, Column 1, Paragraph 4: An automated procedure based on the outline described (in Fig. 2) was developed to rapidly assess the bacterial biomass; Page 3817, Column 1, Paragraph 6: One great advantage of flow cytometry (FCM) is to permit rapid analyses (a few minutes) of dozens of samples per day. The applied approach provides a useful tool for understanding the bacteria dynamics and may help to obtain additional information for modelling biological processes). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because both are within the same technical field - analyzing flow cytometry data of bacteria.
Double Patenting
No double patenting instances are known.
Conclusion
No claims are allowed.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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