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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 17 February 2026 has been entered.
DETAILED ACTION
Applicant’s amendment filed on 17 February 2026 is entered. Claims 1, 3, 7, and 17-19 are amended. Claims 1-20 are pending and under examination.
Claim Objections
Applicant is advised that should claim 1 be found allowable, claim 17 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m). Likewise, claims 3 and 18 are substantial duplicates, and claims 7 and 19 are substantial duplicates.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites “testing the T1 control well at time 1 with the flow cytometer to obtain T1 enumerative control bacterial values”. It is unclear if the “enumerative control bacterial values” are the counts of bacterial cells, a value related to bacteria which is a measure of the dye or compensation beads introduced in the culturing step immediately prior, or a value that has not been recited.
Claim 1 recites determining, using compensation particles added to the sample, whether a threshold for enumerative accuracy associated with the system is met. It is unclear what the threshold for enumerative accuracy associated with the system is, or how that threshold is to be determined. It is also unclear how the compensation particles added to the sample are used to determine whether a threshold for enumerative accuracy associated with the system is met.
Claim 1 recites "compensation particles added to the sample" and "the total bacteria count" on lines 42 and 48-50, respectively. There is insufficient antecedent basis for these limitations in the claim.
Claim 2 recites “testing the sample portion from the first well with a flow cytometer to determine AT enumerative bacterial values”. It is unclear if “the first well” is referring to “the first test well” recited claim 1, or if the limitation is reciting a new first well associated with the antibiotic test wells recited earlier in claim 2.
Claim 2 recites “distributing dilution-adjusted portions of the sample”. It is unclear if the “dilution-adjusted portions of the sample” are the same diluted samples recited in claim 1, or if the “dilution-adjusted portions of the sample” are different separate dilutions of the sample.
Claim 2 recites the phrase “AT enumerative bacterial values relating to the sample”. It is unclear if the “enumerative bacterial values relating to the sample” are simply the number of bacterial cells in the sample in the AT wells as determined by flow cytometry, or if the “enumerative bacterial values” are a collection of values determined by flow cytometry, such as light scattering values, fluorescence values, live cell counts, dead cells counts, etc.
Claim 3 recites “wherein the comparing step of claim 1 comprises comparing the T0 and T1 values to determine whether the cells of interest are present, the determination being made when analysis of the T1 control values indicates a statistically significant increase in a number of the cells of interest relative to the T0 baseline values.” In order to determine the “statistically significant increase in a number of the cells of interest”, the number of cells of interest must already be determined. So it is unclear how the “statistically significant increase in a number of the cells of interest” is a conditional requirement for determining the number of cells of interest because the number of cells of interest must have already been determined in order to determine the statistically significant increase in the number of cells of interest.
Claim 3 recites “a growth of cells of interest associated with a defined bacteria-related region of interest (ROI)”. It is unclear what the meaning of the phrase “bacteria-related region of interest” is. The specification does not provide a specific definition for the phrase, and the art does not have a standard definition for the phrase either. As such, one of ordinary skill in the art would not be able to determine the metes and bounds of the phrase.
Claim 3 recites “the T0 baseline values and the T1 control values include a growth of cells of interest within a defined bacteria-related region of interest (ROI) stored in the fluid library and used by the flow cytometer software to distinguish bacterial events from non-bacterial events”. It is not clear what is being "used by the flow cytometer software", the value of the growth of the cells of interest, or the defined bacteria-related region of interest. Claim 18 recites the same issue.
Claim 3 recites “T0 baseline and T1 control values include a growth of cells of interest”, but the claim later recites “the T0 and T1 values are compared to determine whether the cells of interest are present.” It is unclear why the T0 baseline and T1 control values, which include growth of cells of interest (thus the cells are present), are required to be compared to each other to determine whether the cells of interest are present. Claim 18 recites the same issue.
Claim 3 recites “the comparing step of claim 1 comprises comparing the T0 and T1 values to determine whether the cells of interest are present.” It is unclear whether the cells of interest are determined to be present in the T0 and T1 wells, or in the at least one additional well for testing the antibiotic, or in all wells. Claim 18 recites the same issue.
Claim 3 recites “to determine whether the cells of interest are present, the determination being made in response to the T1 control values indicates a statistically significant increase in a number of the cells of interest relative to the T0 baseline values.” There is no recited method step requiring the analysis of T1 control values in order to determine statistically significant increases, thus it is unclear how the cells of interest can be determined to be present if there is no step that generates the necessary analysis of the T1 control values required to indicate a statistically significant increase in a number of the cells of interest relative to the T0 baseline values. Claim 18 recites the same issue.
Claim 3 recites “wherein the comparing step of claim 1 comprises comparing the TO and T1 values to determine whether the cells of interest are present.” It is unclear if the comparing step of claim 1 is being replaced with the comparison procedure of claim 3, such that a growth ratio is no longer required to be calculated as recited in claim 1, if the comparison recited in claim 3 is in addition to the comparison recite in claim 1, or if the comparison recited in claim 3 is further limiting how the comparison of claim 1 is performed. Claim 18 recites the same issue.
Claim 5 recites “converting a relative growth between T0 and T1 a numerical growth value representing bacterial expansion, the numerical growth value being an integer or score”. It is unclear how the relative growth between T0 and T1 is converted into the numerical growth value. The specification states that “[a] system may be programed to convert the relative growth between T0 and T1 to an integer representing bacterial population expansion” (SPEC. [0039]), but the specification does not state what method with which this system converts the relative growth between T0 and T1 into the numerical growth value.
Claim 5’s method starts with sample bacterial growth and ends with determining the type of pathogen in the sample. It is unclear how a type of pathogen can be obtained by “comparing the growth integer from T0 baseline and T1 control to at least one known growth integer from a known library of pathogens”.
Claim 6 recites “determining the presence of a type of bacterial contaminant in the sample when the numerical growth value corresponds to a reference growth value in the growth library.” It is not clear how a type of bacterial contaminant is determined by comparing a numerical growth value of T0 and T1 control to a reference growth value.
Claim 7 recites “testing each of the n AT wells at time T1 with the flow cytometer to obtain n AT sample values corresponding to bacterial cell counts and associated flow cytometry measurements.” It is unclear what the metes and bounds of the "associated flow cytometry measurements". The specification does not provide a special definition that limits this term, nor do the claims further describe what an associated flow cytometry measurement is, or how to determine if a given measurement is an associated flow cytometry measurement. Claim 19 recites the same issue.
Claim 10 recites “information accounting for known matrix noise and providing statistical confidence information specific to urine samples.” It is unclear what the metes and bounds of "information accounting for known matrix noise", and "statistical confidence information specific to urine samples" is. The specification does not provide a special definition for the term “information”, nor do the claims further limit or define the scope of the term “information”. Thus, it is unclear to one of ordinary skill in the art whether or not a given value would be considered information in the context of the claim.
Claim 14 recites “including in the clinical sample a known concentration of test-enumerative compensator (TEC) particles.” It is unclear if the term “including” means that the TEC particles are already included in the clinical sample at a known concentration, or if the method step is requiring the addition of the TEC particles to the clinical sample.
Claim 14 recites “adjusting the bacterial enumeration values obtained for the sample by multiplying the enumerated bacterial values by the compensator factor to more accurately reflect an actual bacterial concentration.” It is unclear what bacterial enumeration values are being adjusted by the method step. Claim 1 recites T0 and T1 bacterial enumeration values, but does not recite bacterial enumeration values for the clinical sample itself. The T0 and T1 bacterial enumeration values reflect the diluted sample values, not the clinical sample values, so it is unclear if the method step is adjusting the bacterial enumeration values of the diluted sample or the clinical sample.
Claim 17 recites “testing the T1 control batch at time 1 with the flow cytometer to obtain T1 enumerative control bacterial values”. It is unclear if the “enumerative bacterial values” are the counts of bacterial cells, a value measured from the dye or compensation beads introduced in the culturing step immediately prior, or a value that has not been recited.
Claim 18 recites “the comparing step of claim 17 including comparing the growth of cells of interest at T0 and T1 and determining whether the cells of interest are present when there is a statistically significant increase in a number of the cells of interest at T1 as compared to T0.” It is unclear if the phrase “the comparing step” is reciting an additional step in the method of claim 17, or if the phrase is referring to a comparison performed in the method of claim 17.
Claim 18 recites “wherein the T0 baseline values and the T1 control values include growth of cells of interest associated with a region of interest (ROI).” It is unclear what the meaning of the phrase “a region of interest (ROI)” is. The specification does not provide a specific definition for the phrase, and the art does not have a standard definition for the phrase either. As such, one of ordinary skill in the art would not be able to determine the metes and bounds of the phrase.
Claim 19 recites “comparing T0 baseline cell events in the ROI to each of the n T1 sample cell events in the ROI to determine a susceptibility or resistance of detected bacteria to the n different antibiotics.” It is unclear what the meaning of “cell events” is in the context of the claim. The specification does not provide a specific definition for the term. In addition, there is insufficient antecedent basis for the limitation “the n T1 sample cell events”.
Claims 2-16 are dependent on claim 1, claims 4, 7-8, and 13 are dependent on claim 3, claim 10 is dependent on claim 9, claims 15-16 are dependent on claim 14, and claims 18-20 are dependent on claim 17, and so those claims are rejected for the same reasons.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of a mental process without significantly more.
Claim 1 recites a method of using a flow cytometer in an automated fluid handling system for testing a clinical sample of a body fluid for a presence of bacteria and determining sample response to at least one antibiotic comprising the following steps: (1) distributing a portion of the sample into test wells using the automated fluid handling system; (2) testing the sample portion in the wells with a flow cytometer; (3) calculating a dilution factor based on a total bacteria count determined by flow cytometer; (4) reconstituting a growth media; (5) diluting the sample to a predetermined concentration with the reconstituted growth media using the dilution factor calculated in step (3); (6) dividing the diluted sample into test wells, including T0 and T1 control wells and at least one antibiotic test wells; (7) testing the T0 sample to obtain enumerative bacterial values; (8-9) culturing and testing the T1 well to obtain enumerative bacterial values; (10) comparing the T1 and T0 values; (11) using software to assess changes in scatter and fluorescence characteristics of a bacterial population present in the antibiotic test wells due to an effect of the antibiotic; (12) determining if a threshold for enumerative accuracy is met using compensation particles added to the sample; (13) determining a scaling factor based on a ratio of measured number of compensation particles and an expected number of compensation particles; (14) applying the scaling factor to the total bacteria count to determine an updated total bacteria count; and (15) applying the updated total bacteria count in the diluting step (5) in order to determine an updated target value.
Claim 2 recites the method of claim 1 further comprises, at time 0, (1) inoculating the antibiotic test well(s); (2) distributing dilution adjusted portions of the sample into the antibiotic test wells; (3) testing the sample portions in the antibiotic test wells to determine AT enumerative bacterial values; and (4) comparing the AT enumerative bacterial values to the T1 control values to determine a response of the sample to the antibiotics.
Claim 3 recites the T0 and T1 controls include growth of cells of interest associated with a bacteria-related region of interest (ROI) stored in the fluid library and used by the flow cytometer to distinguish bacterial events from non-bacterial events, and the comparing step of claim 1 comprises comparing T0 and T1 control values to determine if cells of interest are present by determining a statistically significant increase in a number of cells of interest relative to the T0 values. Claim 4 recites that the cells of interest in claim 3 are pathogenic bacteria.
Claim 5 recites the method of claim 1 further comprises: (1) converting a relative growth between T0 and T1 controls into a numerical growth value representing bacterial population expansion; (2) comparing the numerical growth value to a reference growth value stored in a growth library comprising growth profiles of known pathogens and contaminants associated with clinical diseases; and (3) determining the presence of a pathogen type in the sample when the numerical growth value corresponds to a reference growth value in the library.
Claim 6 recites the method of claim 1 further comprises: (1) converting a relative growth between T0 and T1 into a numerical growth value representing bacterial population expansion; (2) comparing the numerical growth value to one or more reference growth values stored in a growth library comprising growth profiles of bacterial contaminants associated with clinical sampling conditions for suspected disease states; and (3) determining the presence of a type of bacterial contaminant in the sample when the numerical growth value corresponds to a reference growth value in the growth library.
Claim 7 recites the method of claim 3 further comprises: (1) testing each of the n AT wells at time T1 with the flow cytometer to obtain n AT sample values corresponding to bacterial cell counts and associated flow cytometry measurements of cells of interest within the bacteria-related ROI; and (2) comparing the T0 baseline values obtained in the ROI to each of the n AT sample values obtained in the ROI to determine a susceptibility or resistance of detected cells of interest to the n different antibiotics.
Claim 8 recites the method of claim 7 further comprises comparing the T1 control values and the n AT sample values to detect the presence of multiple sub-populations of bacteria due to the sub-populations having a differing response to any one of the n antibiotics.
Claim 9 limits the body fluid of claim 1 to be selected from the group consisting of: urine, blood, pleural fluid, synovial fluid and cerebral spinal fluid.
Claim 10 recites that the flow cytometer used in the method of claim 9 is controlled by a processor executing instructions in a memory containing body-fluid specific data set for urine comprising information accounting for known matrix noise and providing statistical confidence information specific to urine samples; pre-defined growth values or integers associated with pathogens linked to pathological bacterial infections in urine; and pre-defined growth values or integers associated with possible contaminants linked to normal urine sampling.
Claim 11 recites the sample in the method of claim 1 is divided by an automatic fluid handling system between a clinical sample, the T0 sample and the T1 sample.
Claim 12 recites the method of claim 1 further comprises adding a staining reagent for bacterial determination to the clinical sample or diluted sample using the automated fluid handling system, the automated fluid handling system being configured to aspirate, deposit, and mix the staining reagent with the sample.
Claim 13 recites all of the n AT samples of claim 8 are created from the clinical sample using automated fluid handling.
Claim 14 recites the method of claim 1 further comprises: (1) including in the clinical sample a known concentration of test-enumerative compensator (TEC) particles, the TEC particles having known flow cytometric scatter and fluorescence characteristics; (2) enumerating, with the flow cytometer, the concentration of TEC particles detected in the sample; (3) determining a compensator factor as a ratio of the enumerated TEC particle concentration to the known TEC particle concentration added to the sample; and (4) adjusting the bacterial enumeration values obtained for the sample by multiplying the enumerated bacterial values by the compensator factor to more accurately reflect an actual bacterial concentration.
Claim 15 recites that claim 14’s enumerating of the TEC particles is performed during each flow cytometer analysis of the clinical sample, including analyses of the T0 baseline well, the T1 control well, and any antibiotic test (AT) wells.
Claim 16 recites the determination step of claim 14 comprises applying a unique TEC particle ROI separate from a bacteria ROI for enumerating the TEC particles.
Claim 17 recites a method of using a flow cytometer in an automated fluid handling system for testing a clinical sample of a body fluid for a presence of bacteria and determining sample response to at least one antibiotic comprising the following steps: (1) obtaining an initial concentration of bacteria from a sample, and an initial determination of infection by using a processor to calculate a dilution factor based on a total bacteria count and an automated fluid handling system and a flow cytometer module configured to obtain fluid and flow cytometer specific parameters from a fluid library; (2) reconstituting a growth media; (3) diluting the sample to a predetermined concentration with the reconstituted growth media using the dilution factor calculated in step (1); (4) dividing the diluted sample into test wells, including T0 and T1 control wells and at least one antibiotic test wells; (5) testing the T0 sample to obtain enumerative bacterial values; (6-7) culturing and testing the T1 well to obtain enumerative bacterial values; (8) comparing the T1 and T0 values; (9) using software to assess changes in scatter and fluorescence characteristics of a bacterial population present in the antibiotic test wells due to an effect of the antibiotic; (10) determining if a threshold for enumerative accuracy is met using compensation particles added to the sample; (11) determining a scaling factor based on a ratio of measured number of compensation particles and an expected number of compensation particles; (12) applying the scaling factor to the total bacteria count to determine an updated total bacteria count; and (13) applying the updated total bacteria count in the diluting step (3) in order to determine an updated target value.
Claim 18 recites the T0 and T1 control values of claim 17 include growth of cells of interest associated with a region of interest (ROI) defined in the fluid library and used by the flow cytometer software to distinguish bacterial cell events from non-bacterial events, the comparing step of claim 17 includes comparing the growth of cells of interest at T0 and T1 and determining whether the cells of interest are present when there is a statistically significant increase in a number of the cells of interest at T1 as compared to T0.
Claim 19 recites the method of claim 17 further includes n AT samples, each one being treated by a different one of n different antibiotics, wherein n is an integer greater than zero, and the method further comprises: (1) testing each of the n AT samples at time T1 with the flow cytometer to obtain n AT sample values corresponding to bacterial cell counts and associated flow cytometry measurements of cells of interest within a region of interest (ROI); and (2) comparing T0 baseline cell events in the ROI to each of the n T1 sample cell events in the ROI to determine a susceptibility or resistance of detected bacteria to the n different antibiotics.
Claim 20 recites the statistically significant increase in the method of claim 18 is an increase of 125% to 325%.
The claims recite several method steps that are mental processes that can be performed by a human being or in the human mind, such as calculating and comparing values, assessing changes in test data relative to control data, and determining steps that take into consideration the calculated, compared, and assessed values. See MPEP §2106.04(a)(2)(III). Although these mental steps require data acquired by distributing, diluting, culturing, and testing procedures, such testing procedures amount to necessary data gathering in order for the mental determinations to be made. These testing procedures are also extra-solution activities that are well-understood, routine, and conventional activities in the art, as evidenced by Super (US20150064703A1, 05 March 2015).
Super teaches methods for rapid determination of antibiotic susceptibility of a microbe within hours after a test sample is collected (Super Abstract and claim 1). The assay is based on the direct measurement of the bacteria's ability to grow in the presence of the tested antibiotic agents, which allows the detection of microbes and their antibiotic sensitivity using short growth times and only one short culture step (Super [30] and claim 1). Super teaches the test sample can be a biological fluid (Super [35] and claim 74). Super also discloses that microbial growth or a functional response of microbes can be determined or monitored using any methods known in the art for determining cell viability, growth or functional response, including real-time methods such as flow cytometry (Super [155]). Super teaches the sample can be divided (distributed) into a plurality of subsamples before incubation steps (Super [357]). Super teaches that the samples can be preprocessed by diluting the sample prior to adding one or more reagents to the sample (Super [39]). Super also teaches that the amount of diluting agent can be adjusted to achieve certain final concentrations, and suggests several desirable final concentration amounts (Super [42]). Super teaches the sample can be divided into a plurality of subsamples before incubation steps (Super [357]), and one or more multi-well plates which comprise a detection substrate with different antibiotic profiles can be used to test the subsamples (Super [195]). Super then teaches that the sample can be observed for 1) growth in the presence of the antibiotics to determine the resistance of the bacteria to particular antibiotics, 2) cell death to determine bactericidal activity to the antibiotic, and/or 3) inhibition of growth to determine bacteriostatic activity to the antibiotic, which can all be assessed by counting the total number of microbes in the samples as compared to controls or references (Super [155]). Super teaches the control or reference can be incubated without any antibiotic agents (reads on instant T1 control), and also the number of microbes in the sample can be determined before incubation with an antibiotic agent (reads on instant T0 baseline) (Super [156] and [158]). Responses of the microbes in the subsamples to the antibiotic agent can be monitored in real-time during and/or after incubation in the presence of antibiotic agents for a period of time by imaging a color change/shift of a dye. The microbes can then be tracked during and/or after antibiotic incubation to determine their individual response (Super [195]). Super teaches the set of subsamples can be cultured for any period of time, including for at least 15 seconds or up to 72 hours or longer (Super [148]). Super teaches that cell viability can be assayed using pH sensitive dyes, Trypan Blue dye, staining, cell-impermeable dyes, fluorescent dyes, chromogenic dyes, etc. (Super [158]).
Although claims 1, 3, 5-6, 10, and 17-18 recite determinations, assessments, and/or calculations are performed by a processor or using software, these steps can be practically performed in the human mind, and the claims are merely using the processor/computer as a tool to perform the mental process. MPEP § 2106.04(a)(2)(III)(A and C) states that “[c]laims…recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions” and “[c]laims can recite a mental process even if they are claimed as being performed on a computer.”
Therefore, the instant invention is directed to the judicial exception of a mental process and does not include any additional elements that amount to significantly more than the recited judicial exception of a mental process, so the instant invention is not patent eligible subject matter under 35 USC §101 (Step 2B: No).
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Super (US20150064703A1, 05 March 2015) in view of PhysiologyWeb (PhysiologyWeb Dilution Factor Calculator – Cells per Volume, https://www.physiologyweb.com/calculators/dilution_factor_calculator_cells_per_volume.html, available 25 April 2011), Shamsheyeva (US20140278136A1, 18 September 2014), Yananton (US3951747A, published 20 April 1976) and Roederer et al. (Compensation in Flow Cytometry, Current Protocols in Cytometry (2002) 1.14.1-1.14.20).
Regarding claims 1, 2, 7, 17, and 19, Super teaches methods for rapid determination of antibiotic susceptibility of a microbe within hours after a test sample is collected (Super Abstract and claim 1). The assay is based on the direct measurement of the bacteria's ability to grow in the presence of the tested antibiotic agents, which allows the detection of microbes and their antibiotic sensitivity using short growth times and only one short culture step (Super [30] and claim 1). Super teaches the test sample can be a biological fluid (Super [35] and claim 74). Super also discloses that microbial growth or a functional response of microbes can be determined or monitored using any methods known in the art for determining cell viability, growth or functional response, including real-time methods such as flow cytometry (Super [155]). Super teaches that the assay can be adapted for use in a high-throughput platform, such as an automated system or platform (Super [195]).
Super teaches the sample can be divided (distributed) into a plurality of subsamples before incubation steps (Super [357]).
Super teaches that the samples can be analyzed to confirm the presence of a microbe in the sample, which is desirable to detect or determine the presence of initial number of microbes in the sample (Super [104]).
Super teaches that the samples can be preprocessed by diluting the sample prior to adding one or more reagents to the sample (Super [39]). Super also teaches that the amount of diluting agent can be adjusted to achieve certain final concentrations, and suggests several desirable final concentration amounts (Super [42]).
Super teaches the sample can be divided into a plurality of subsamples before incubation steps (Super [357]), and one or more multi-well plates which comprise a detection substrate with different antibiotic profiles can be used to test the subsamples (Super [195]). Super then teaches that the sample can be observed for 1) growth in the presence of the antibiotics to determine the resistance of the bacteria to particular antibiotics, 2) cell death to determine bactericidal activity to the antibiotic, and/or 3) inhibition of growth to determine bacteriostatic activity to the antibiotic, which can all be assessed by counting the total number of microbes in the samples as compared to controls or references (Super [155]). Super teaches the control or reference can be incubated without any antibiotic agents (reads on instant T1 control), and also the number of microbes in the sample can be determined before incubation with an antibiotic agent (reads on instant T0 baseline) (Super [156] and [158]). Responses of the microbes in the subsamples to the antibiotic agent can be monitored in real-time during and/or after incubation in the presence of antibiotic agents for a period of time by imaging a color change/shift of a dye. The microbes can then be tracked during and/or after antibiotic incubation to determine their individual response (Super [195]).
Super teaches the set of subsamples can be cultured for any period of time, including for at least 15 seconds or up to 72 hours or longer (Super [148]). Super teaches that cell viability can be assayed using pH sensitive dyes, Trypan Blue dye, staining, cell-impermeable dyes, fluorescent dyes, chromogenic dyes, etc. (Super [158]).
The relative microbe counts in the tested antibiotic sample are compared to a control to determine the number of microbes in the sample (Super [158]). Super also teaches the use of a ratio of cells expressing at least one microbe marker in the sample to assess microbe growth and/or cell death in the sample (Super [155]).
Super does not disclose the calculation of a dilution factor based on a total bacteria count of the sample, growth media provided in a freeze-dried form, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, or assessment of changes in scatter and fluorescence characteristics using flow cytometric software.
PhysiologyWeb teaches instructions on how to calculate a dilution factor from an equation using an initial known bacteria count, and also provides a computerized calculator that can perform the calculation for the user (PhysiologyWeb pgs. 1-3).
Super and PhysiologyWeb do not disclose growth media provided in a freeze-dried form, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, or assessment of changes in scatter and fluorescence characteristics using flow cytometric software.
Shamsheyeva teaches that determination of microbial growth rate or lack thereof may be based on probabilistic assessment that a measured change in one or more attributes is likely to correspond to growth (Shamsheyeva [079]), and that this determination can be compared against a reference growth rate to assist in identifying a tested microorganism’s susceptibility or resistance to an antimicrobial agent (Shamsheyeva [082]), and furthermore that this can be performed rapidly (Shamsheyeva [086]). Shamsheyeva teaches that an attribute of a microorganism can be any detectable or measurable feature or characteristic of a microorganism, or any value related to the presence of a microorganism that may be observed, detected, or measured using any technique (Shamsheyeva [088]), and also teaches that one detection/analytical tool to determine values associated with microorganisms includes flow cytometry (Shamsheyeva [108]). Shamsheyeva then teaches the measurement and observation of fluorescence and/or scattering signals from microorganism (Shamsheyeva [096] and [101]). Shamsheyeva teaches that sample analyzers and analysis modules are any hardware, software, or hardware-software system capable of measuring, evaluating, collecting, and analyzing data about a microorganism sample (Shamsheyeva [112]-[113]). Shamsheyeva discloses to enhance fluorescence signals, microorganisms could either be coated with gold and/or silver nanoparticles [reads on TEC particles] in a sample preparation step, and the nanoparticles may be associated with microorganisms in a centrifugation step (Shamsheyeva [105]). Shamsheyeva teaches that fluorescence spectra can be obtained using various methods described therein which may be used to perform identification of microorganisms. Reference spectra may be obtained for known microorganisms, thus allowing for correlation of measured sample data with characterization of the microorganisms of interest using various mathematical methods known to those skilled in the art (Shamsheyeva [106]).
Super, PhysiologyWeb, and Shamsheyeva do not disclose growth media provided in a freeze-dried form, or culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads.
Yananton discloses lyophilized media for bacterial identification has several distinct advantages over other forms of nutrient media, including excellent storage and shelf stability and sterility (Yananton Col. 1, lines 49-51; Col. 1, line 64-Col. 2, line 6).
Super, Shamsheyeva, and Yananton do not teach culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads.
Roederer teaches the compensation of flow cytometers, which refers to the process of mathematically correcting for fluorescent spillover by removing the signal of any given fluorochrome from all detectors except the one devoted to measuring the dye (Roederer pg. 1 para. 1 and pg. 1 and 3 bridging para.). Roederer teaches that compensation beads work well for the purpose of compensating in flow cytometry, so long as the beads bind to the actual reagents that are used in the experiment (Roederer Pg. 16 para. 1). Using compensation beads in a culture of cells has many advantages in flow cytometry compensation, including that they are highly uniform allowing for precise spillover calculation of cellular autofluorescence in, for example, fluorescein (FITC) control cell samples (Roederer pg. 13 para. 2).
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the present invention to calculate the dilution factor for the dilution of the sample in Super’s method by using PhysiologyWeb’s computerized calculator (powered by a processor) because doing so would make it much easier and quicker to determine the amount of diluting reagent necessary to achieve a given concentration based on initial known bacteria counts in the sample as determined by analyzing the samples to confirm the initial amount of microbes in the sample (Super [104]).
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the present invention to use lyophilized growth medium to dilute the sample in Super’s method because Yananton discloses lyophilized media has the advantage of shelf stability and sterility over other forms of nutrient media. It would have also been obvious to add compensation beads to the subsamples of Super’s culturing step because Roederer teaches that compensation beads advantageously and precisely remove (or compensate) for unwanted fluorescence, such as autofluorescence, which ensures that only the desired signals are used in the final analysis. It would have also been obvious to perform a software-based assessment of flow cytometric scatter and fluorescence data to observe an effect of a tested antibiotic on a bacterial population because Shamsheyeva teaches that a microorganism’s susceptibility or resistance to an antimicrobial agent can be advantageously determined by rapid probabilistic assessment of a measured change in fluorescence and/or scattering signals collected by flow cytometry and analyzed by software.
It would have been obvious to one of ordinary skill in the art to include a known concentration of compensator particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerate those particles during sample testing by the flow cytometer; determine a scaling factor based on the enumerated compensator particle value as compared to the known compensator particle concentration in the sample tested; adjust the sample total bacterial count value by said scaling factor; and then use the updated sample total bacterial count to determine an updated dilution factor. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Shamsheyeva discloses fluorescence signals can be advantageously enhanced by coating the tested microorganisms with gold and/or silver nanoparticles (reads on compensator particles) in a sample preparation step, which allows for correlation of measured sample data with characterization of the microorganisms of interest.
Regarding claims 3 and 18, Super teaches detecting the growth of cells which are resistant to antibiotics (reads on cells of interest), and comparing the growth of cells in a sample prior to incubation and after incubation (Super [155], [0158], and [195]). Super also teaches once the cell counts (e.g., microbe or pathogen counts) or functional response levels for the reference or control (i.e., the microbes or pathogens cultured in the absence of any antibiotic agent) of the antibiotic agent-treated subsamples has been determined, the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing the cell counts or functional responses of the antibiotic agent-treated subsamples to those of the reference or control counts. The samples with the cells of interest that exhibit growth at similar levels to the reference or control counts (within 0.5-20% or any statistically significant determination) can indicate that the samples are resistant to the antibiotic agent with which they were treated (Super [199]).
Regarding claim 4, Super teaches that their antibiotic susceptibility testing method is able detect pathogens responsible for microbial infections in biological samples (Super [028]-[029]).
Regarding claims 5-6, Super teaches determining the ratio of cells expressing at least one microbe marker in the subsample (reads on the “integer representing bacterial population expansion“) as compared to a control or reference (reads on “a known library of pathogens/bacterial contaminants”) (Super [155]). Super teaches that their antibiotic susceptibility testing method is able detect bacteria, including pathogens responsible for microbial infections, in biological samples (Super [028]-[029]).
Regarding claim 8, Super teaches the relative microbe counts in the tested antibiotic sample are compared to a control to determine the number of microbes in the sample (Super [158]). Super also teaches that the isolated microbes from the biological fluid can be separated into a plurality of subsamples before incubation with different concentrations of antibiotic agents to be tested. The number of subsamples depends, among other factors, on the number of antibiotic agents and control combinations to be tested or the amount of microbes isolated. (Super [114]).
Regarding claims 9, Super teaches the test sample can be a biological fluid including urine (Super [35]).
Regarding claim 10, Shamsheyeva teaches systems for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance (Shamsheyeva Abstract) comprising a computer-based system comprising a processor, a tangible, non-transitory memory, and an interface configured to determine a growth rate of a microorganism (Shamsheyeva [2]). Shamsheyeva teaches that flow cytometry can be used to determine a value associated with an attribute of a microorganism for use in determining a growth rate (Shamsheyeva [108]). Shamsheyeva teaches a process (Shamsheyeva Figure 2) of providing a sample to a system (210), determining a first value associated with a microorganism (220), subjecting the microorganism to a condition (230), determining a second value associated with the microorganism (240), determining a growth rate based on the first and second values (250), and comparing the growth rate to a known growth rate (260) to be able to make a recommendation about an element, condition, or event based on the growth rate. The recommendation may comprise a determination that an element is resistant to a condition based on a rate of change not correlating with a control rate of change (or an associated rage of a control rate of change) (Shamsheyeva [127]). Shamsheyeva teaches growth rates were determined to identify microorganisms (Shamsheyeva [184]). Shamsheyeva teaches identification of MRSA using negative growth rate (Shamsheyeva [58]), wherein MRSA is a pathogen that requires rapid identification to assure adequate therapeutic coverage (Shamsheyeva [284]).
Regarding claims 11 and 13, Super does not specifically teach the sample is divided by an automatic fluid handling system between a clinical sample, the T0 sample and the T1 sample; however, Super teaches their assay can be adapted for use in a high-throughput platform, e.g., an automated system (Super [195]). Super teaches Super teaches that one of skill in the art is well aware of methods in the art for collecting, handling, and processing biological fluids, and a microfluidic device can be used to automate the process and/or allow processing of multiple samples at the same time (Super [032]). Therefore, it would have been obvious to one of ordinary skill in the art to use a high-throughput automated fluid handling system to divide the samples because Super teaches that doing so would allow for processing multiple samples at the same time, allowing for more efficient sample processing and testing.
Regarding claim 12, Super teaches that the samples are mixed after addition of the preprocessing reagent, the processing buffer, and or the coated-substrate. The mixing can be simply accomplished by agitating the sample, shaking the sample, and/or moving the sample around in a microfluidic device (Super [043], [068], and [080]). It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the present invention to have thoroughly mixed the sample and subsamples of Super throughout the assay in order to evenly distribute each component within the sample. One of ordinary skill in the art would have had reasonable expectations of success because Super teaches mixing the sample at several points during the assay by agitating the sample, shaking the sample, and/or moving the sample around, such as in a microfluidic device (Super [043], [068], and [080]).
Regarding claims 14-16, Shamsheyeva discloses to enhance fluorescence signals, microorganisms could either be coated with gold and/or silver nanoparticles [reads on TEC particles] in a sample preparation step, and the nanoparticles may be associated with microorganisms in a centrifugation step (Shamsheyeva [105]). Shamsheyeva teaches that fluorescence spectra can be obtained using various methods described therein which may be used to perform identification of microorganisms. Reference spectra may be obtained for known microorganisms, thus allowing for correlation of measured sample data with characterization of the microorganisms of interest using various mathematical methods known to those skilled in the art (Shamsheyeva [106]).
It would have been obvious to one of ordinary skill in the art to include a known concentration of test-enumerative compensator (TEC) particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerate those the TEC particles with the sample testing by the flow cytometer; determine a compensator factor based on the enumerated TEC particle value as compared to the known TEC particle concentration in the sample tested by applying a unique TEC particle ROI separate from the bacteria ROI for enumerating the TEC particles; and adjust the sample test enumeration value by said compensator factor, wherein the TEC particles are included with each flow cytometer sample test. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Shamsheyeva discloses enhancing fluorescence signals by coating the tested microorganisms with gold and/or silver nanoparticles (reads on TEC particles) in a sample preparation step.
Regarding claim 20, Super teaches detecting the growth of cells which are resistant to antibiotics (reads on cells of interest), and comparing the growth of cells in a sample prior to incubation and after incubation (Super [155], [0158], and [195]), once cell counts (e.g., microbe or pathogen counts) or functional response level for the reference or control (i.e., the microbes or pathogens cultured in the absence of any antibiotic agent) and antibiotic agent-treated subsamples have been determined, the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing these numbers. Subsamples that display cell counts or functional response level similar (within 0.5-20% or any statistically significant determination) to the reference or control counts can indicate that they are resistant to the antibiotic agent with which they were treated (Super [199]). Therefore, Super teaches, in one embodiment, always determining the presence of the cells of interest in a sample regardless of what the percentage of increase of the cells of interest was at T1 as compared to T0.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3 and 5-20 of copending Application No. 17/958,896 in view of Super (US20150064703A1; previously cited), PhysiologyWeb (PhysiologyWeb Dilution Factor Calculator – Cells per Volume, https://www.physiologyweb.com/calculators/dilution_factor_calculator_cells_per_volume.html, available 25 April 2011), Shamsheyeva (US20140278136A1, 18 September 2014), Yananton (US3951747A; newly cited) and Roederer et al. (Compensation in Flow Cytometry, Current Protocols in Cytometry (2002) 1.14.1-1.14.20).
Instant claims 1-20 are directed to an invention not patentably distinct from claims 1-3 and 5-20 of commonly assigned Application No. 17/958,896. Specifically, the instant claims are obvious over the copending claims in view of Super, PhysiologyWeb, Shamsheyeva, Yananton, and Roederer.
Copending claim 1 recites a system for detecting a target bacteria, wherein the system comprises: a flow cytometer configured to: receive a fluid sample comprising at least a target bacteria and at least a contaminant bacteria; generate a first enumeration of a total bacteria in the fluid sample during a pre- incubation phase, wherein the total bacteria comprises an aggregate of the at least a target bacteria and the at least a contaminant bacteria; incubate the fluid sample during an incubation phase as a function of at least one incubation parameter; generate a second enumeration of the total bacteria in the fluid sample during a post-incubation phase; an incubator configured to incubate the fluid sample during an incubation phase as a function of a plurality of incubation parameters, the plurality of incubation parameters including at least an incubation temperature and an agitation parameter, wherein the plurality of incubation parameters are selected from a lookup table that relates the plurality of incubation parameters to the target bacteria; an automated cassette handling device comprising one or more robotic components controlled by a processor, wherein the automated cassette handling device is configured to automatedly transport the fluid sample between the incubator and the flow cytometer for analysis by the flow cytometer, and wherein the automated cassette handling device is configured to generate a delay time to enable the flow cytometer to complete a post-incubation phase for a first cassette before beginning a post-incubation phase on a second cassette; and a computing device, wherein the computing device is configured to: generate the plurality of incubation parameters based on a type of the at least a target bacteria and a type of the fluid sample; receive the first enumeration and the second enumeration; determine a growth ratio of the total bacteria as a function of the first enumeration and the second enumeration; and identify a presence of the at least a target bacteria as a function of the growth ratio. Copending claim 2 recites the system of copending claim 1, wherein the at least a contaminant bacteria comprises all bacteria within the fluid sample that is not the target bacteria. Copending claim 3 recites the system of copending claim 1, wherein the at least a target bacteria comprises a pathogenic bacteria. Copending claim 5 recites the system of copending claim 1, wherein the fluid sample is contained within multi- well cassettes. Copending claim 6 recites the system of copending claim 1, wherein the computing device is further configured to determine a diagnosis as a function of the growth ratio and one or more of the first enumeration and the second enumeration. Copending claim 7 recites the system of copending claim 1, wherein the pre-incubation phase additionally comprises adjusting a concentration of the fluid sample by diluting the fluid sample. Copending claim 8 recites the system of copending claim 1, wherein the flow cytometer is further configured to differentiate the target bacteria and the at least a contaminant bacteria using staining techniques. Copending claim 9 recites the system of copending claim 1, wherein the pre-incubation phase additionally comprises adjusting a concentration of the fluid sample by way of adding a growth media. Copending claim 10 recites the system of copending claim 1, wherein the fluid sample comprises urine. Copending claim 11 recites a method for detecting a target bacteria, wherein the method comprises: receiving, at a flow cytometer, a fluid sample, wherein the fluid sample comprises at least a target bacteria and at least a contaminant bacteria; generating, at the flow cytometer, a first enumeration of a total bacteria in the fluid sample during a pre-incubation phase, wherein the total bacteria comprises an aggregate of the at least a target bacteria and the at least a contaminant bacteria; incubating, at the flow cytometer, the fluid sample during an incubation phase; generating, at the flow cytometer, a second enumeration of the total bacteria in the fluid sample during a post-incubation phase; receiving, at a computing device, the first enumeration and the second enumeration; determining, at the computing device, a growth ratio of the total bacteria as a function of the first enumeration and the second enumeration; and identifying, at the computing device, the presence of the at least a target bacteria as a function of the growth ratio. Copending claim 12 recites the method of copending claim 11, wherein the at least a contaminant bacteria comprises all bacteria within the fluid sample that is not the target bacteria. Copending claim 13 recites the method of copending claim 11, wherein target bacteria comprises a pathogenic bacteria. Copending claim 14 recites the method of copending claim 11, wherein the fluid sample is incubated as a function of an incubation parameter. Copending claim 15 recites the method of copending claim 11, wherein the fluid sample is contained within multi-well cassettes. Copending claim 16 recites the method of copending claim 11, wherein the method further comprises determining, at the computing device, a diagnosis as a function of the growth ratio and one or more of the first enumeration and the second enumeration. Copending claim 17 recites the method of copending claim 11, further comprising adjusting, during the pre- incubation phase, the fluid sample concentration by diluting the fluid sample. Copending claim 18 recites the method of copending claim 11, wherein the flow cytometer is further configured to differentiate between the target bacteria and the at least a contaminant bacteria using staining techniques. Copending claim 19 recites the method of copending claim 11, further comprising adjusting, during the pre- incubation phase, the fluid sample concentration by way of adding a growth media. Copending claim 20 recites the method of copending claim 11, wherein the fluid sample comprises a fluid chosen from the group consisting of urine, blood, and cerebral spinal fluid.
Regarding instant claims 1, 2, 7, 11, 13, 17, and 19, the copending claims do not recite an automated fluid handling system and optionally determining sample response to at least one antibiotic, the calculation of a dilution factor based on a total bacteria count of the sample, diluting the sample with growth media provided in a freeze-dried form, testing the sample in the T0 baseline well comprises analyzing the T0 baseline well using the flow cytometer, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, assessment of changes in scatter and fluorescence characteristics using flow cytometric software, at least two wells including n antibiotic testing (AT) samples with each one being treated by a different antibiotic, the method comprising testing each of the n AT samples at time T1 with the flow cytometer to obtain n AT sample values and comparing the T0 baseline events in the ROI to each of the n T1 sample events in the ROI to determine the susceptibility or resistance of detected bacteria to the n different antibiotics
Super teaches methods for rapid determination of antibiotic susceptibility of a microbe within hours after a test sample is collected (Super Abstract and claim 1). The assay is based on the direct measurement of the bacteria's ability to grow in the presence of the tested antibiotic agents, which allows the detection of microbes and their antibiotic sensitivity using short growth times and only one short culture step (Super [30] and claim 1). Super teaches the test sample can be a biological fluid (Super [35] and claim 74). Super also discloses that microbial growth or a functional response of microbes can be determined or monitored using any methods known in the art for determining cell viability, growth or functional response, including real-time methods such as flow cytometry (Super [155]). Super teaches that the assay can be adapted for use in a high-throughput platform, such as an automated system or platform (Super [195]).
Super teaches the sample can be divided (distributed) into a plurality of subsamples before incubation steps (Super [357]).
Super teaches that the samples can be analyzed to confirm the presence of a microbe in the sample, which is desirable to detect or determine the presence of initial number of microbes in the sample (Super [104]).
Super teaches that the samples can be preprocessed by diluting the sample prior to adding one or more reagents to the sample (Super [39]). Super also teaches that the amount of diluting agent can be adjusted to achieve certain final concentrations, and suggests several desirable final concentration amounts (Super [42]).
Super teaches the sample can be divided into a plurality of subsamples before incubation steps (Super [357]), and one or more multi-well plates which comprise a detection substrate with different antibiotic profiles can be used to test the subsamples (Super [195]). Super then teaches that the sample can be observed for 1) growth in the presence of the antibiotics to determine the resistance of the bacteria to particular antibiotics, 2) cell death to determine bactericidal activity to the antibiotic, and/or 3) inhibition of growth to determine bacteriostatic activity to the antibiotic, which can all be assessed by counting the total number of microbes in the samples as compared to controls or references (Super [155]). Super teaches the control or reference can be incubated without any antibiotic agents (reads on instant T1 control), and also the number of microbes in the sample can be determined before incubation with an antibiotic agent (reads on instant T0 baseline) (Super [156] and [158]). Responses of the microbes in the subsamples to the antibiotic agent can be monitored in real-time during and/or after incubation in the presence of antibiotic agents for a period of time by imaging a color change/shift of a dye. The microbes can then be tracked during and/or after antibiotic incubation to determine their individual response (Super [195]).
Super teaches the set of subsamples can be cultured for any period of time, including for at least 15 seconds or up to 72 hours or longer (Super [148]). Super teaches that cell viability can be assayed using pH sensitive dyes, Trypan Blue dye, staining, cell-impermeable dyes, fluorescent dyes, chromogenic dyes, etc. (Super [158]).
The relative microbe counts in the tested antibiotic sample are compared to a control to determine the number of microbes in the sample (Super [158]). Super also teaches the use of a ratio of cells expressing at least one microbe marker in the sample to assess microbe growth and/or cell death in the sample (Super [155]).
The conflicting claims do not recite and Super does not disclose the calculation of a dilution factor based on a total bacteria count of the sample, growth media provided in a freeze-dried form, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, or assessment of changes in scatter and fluorescence characteristics using flow cytometric software.
PhysiologyWeb teaches instructions on how to calculate a dilution factor from an equation using an initial known bacteria count, and also provides a computerized calculator that can perform the calculation for the user (PhysiologyWeb pgs. 1-3).
The conflicting claims do not recite and Super and PhysiologyWeb do not disclose growth media provided in a freeze-dried form, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, or assessment of changes in scatter and fluorescence characteristics using flow cytometric software.
Shamsheyeva teaches that determination of microbial growth rate or lack thereof may be based on probabilistic assessment that a measured change in one or more attributes is likely to correspond to growth (Shamsheyeva [079]), and that this determination can be compared against a reference growth rate to assist in identifying a tested microorganism’s susceptibility or resistance to an antimicrobial agent (Shamsheyeva [082]), and furthermore that this can be performed rapidly (Shamsheyeva [086]). Shamsheyeva teaches that an attribute of a microorganism can be any detectable or measurable feature or characteristic of a microorganism, or any value related to the presence of a microorganism that may be observed, detected, or measured using any technique (Shamsheyeva [088]), and also teaches that one detection/analytical tool to determine values associated with microorganisms includes flow cytometry (Shamsheyeva [108]). Shamsheyeva then teaches the measurement and observation of fluorescence and/or scattering signals from microorganism (Shamsheyeva [096] and [101]). Shamsheyeva teaches that sample analyzers and analysis modules are any hardware, software, or hardware-software system capable of measuring, evaluating, collecting, and analyzing data about a microorganism sample (Shamsheyeva [112]-[113]).
The conflicting claims do not recite and Super and Shamsheyeva do not disclose growth media provided in a freeze-dried form, or culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads.
Yananton discloses lyophilized media for bacterial identification has several distinct advantages over other forms of nutrient media, including excellent storage and shelf stability and sterility (Yananton Col. 1, lines 49-51; Col. 1, line 64-Col. 2, line 6).
The conflicting claims do not recite and Super, Shamsheyeva, and Yananton do not teach culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads.
Roederer teaches the compensation of flow cytometers, which refers to the process of mathematically correcting for fluorescent spillover by removing the signal of any given fluorochrome from all detectors except the one devoted to measuring the dye (Roederer pg. 1 para. 1 and pg. 1 and 3 bridging para.). Roederer teaches that compensation beads work well for the purpose of compensating in flow cytometry, so long as the beads bind to the actual reagents that are used in the experiment (Roederer Pg. 16 para. 1). Using compensation beads in a culture of cells has many advantages in flow cytometry compensation, including that they are highly uniform allowing for precise spillover calculation of cellular autofluorescence in, for example, fluorescein (FITC) control cell samples (Roederer pg. 13 para. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method recited in the copending claims to divide the samples utilizing an automated fluid handling system (Super [195]) as taught by Super, because it would reduce the number of sample handling steps and automate the process (Super Pg. 10, [109]). It would have been further obvious to modify the method of the copending claims to further determine sample response to at least one antibiotic in order to detect microbes and their antibiotic sensitivity in a one-step test method (Super [30, left column, last line – right column).
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the present invention to calculate the dilution factor for the dilution of the sample in the method by using PhysiologyWeb’s computerized calculator (powered by a processor) because doing so would make it much easier and quicker to determine the amount of diluting reagent necessary to achieve a given concentration based on initial known bacteria counts in the sample as determined by in the method.
It would have been obvious to use lyophilized growth medium to dilute the sample in the method, because Yananton discloses lyophilized media has the advantage of shelf stability and sterility over other forms of nutrient media. It would have also been obvious to add compensation beads to the subsamples of Super’s culturing step because Roederer teaches that compensation beads advantageously and precisely remove (or compensate) for unwanted fluorescence, such as autofluorescence, which ensures that only the desired signals are used in the final analysis. It would have also been obvious to perform a software-based assessment of flow cytometric scatter and fluorescence data to observe an effect of a tested antibiotic on a bacterial population because Shamsheyeva teaches that a microorganism’s susceptibility or resistance to an antimicrobial agent can be advantageously determined by rapid probabilistic assessment of a measured change in fluorescence and/or scattering signals collected by flow cytometry and analyzed by software.
Regarding instant claims 3 and 18, the copending claims do not recite wherein the T0 baseline values and the T1 control values include cell events of interest in a bacteria-specific region of interest (ROI), the comparing step including comparing the cell events of interest at T0 and T1 and determining whether cells of interest are present when there is a statistically significant increase in the number of cell of interest events at T1 as compared to T0.
Super teaches detecting the growth of cells which are resistant to antibiotics (reads on cells of interest), and comparing the growth of cells in a sample prior to incubation and after incubation (Super [155], [0158], and [195]). Super also teaches once the cell counts (e.g., microbe or pathogen counts) or functional response levels for the reference or control (i.e., the microbes or pathogens cultured in the absence of any antibiotic agent) of the antibiotic agent-treated subsamples has been determined, the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing the cell counts or functional responses of the antibiotic agent-treated subsamples to those of the reference or control counts. The samples with the cells of interest that exhibit growth at similar levels to the reference or control counts (within 0.5-20% or any statistically significant determination) can indicate that the samples are resistant to the antibiotic agent with which they were treated (Super [199]).
It would have been obvious to compare the cell growth of the cells of interest at T0 and T1 and determine whether the cells of interest are present when there is a statistically significant increase in the number of cells of interest at T1 as compared to T0 in order to determine which cells of interest are resistant to antibiotics. One of ordinary skill in the art would have had reasonable expectations of success because Super teaches the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing the cell counts or functional responses of the antibiotic agent-treated subsamples to those of the reference or control counts.
Regarding instant claims 5-6, the copending claims do not recite converting a relative growth between T0 and T1 to a growth integer representing bacterial population expansion; comparing the growth integer from T0 baseline and T1 control to at least one known growth integer from a known library of bacterial contaminants or pathogens represented in a disease state being tested; and determining the type of contaminant or pathogen present in the sample based on said comparing.
Super teaches determining the ratio of cells expressing at least one microbe marker in the subsample (reads on the “integer representing bacterial population expansion“) as compared to a control or reference (reads on “a known library of pathogens/bacterial contaminants”) (Super [155]). Super teaches that their antibiotic susceptibility testing method is able detect bacteria, including pathogens responsible for microbial infections, in biological samples (Super [028]-[029]).
It would have been obvious to determine the type of bacterial contaminant or pathogen in a biological sample by comparing the ratio of cells of interest to a reference library of known contaminants and/or pathogens because Super teaches a very similar method of detecting antibiotic resistance and typing bacterial species, including pathogens responsible for microbial infections, in biological samples by comparing the ratio of cells expressing at least one microbe marker to a reference. Thus, the identification of an antibiotic-resistant bacteria obtained by the method of the copending claims can be determined.
Regarding instant claim 8, the copending claims do not recite comparing the T1 control values and n antibiotic test sample values to detect the presence of multiple sub-populations of bacteria due to the sub-populations having a differing response to any one of the n antibiotics.
Super teaches the relative microbe counts in the tested antibiotic sample are compared to a control to determine the number of microbes in the sample (Super [158]). Super also teaches that the isolated microbes from the biological fluid can be separated into a plurality of subsamples before incubation with different concentrations of antibiotic agents to be tested. The number of subsamples depends, among other factors, on the number of antibiotic agents and control combinations to be tested or the amount of microbes isolated. (Super [114]).
It would have been obvious to compare T1 control values and the antibiotic test sample values to detect the presence of multiple sub-populations of bacteria due to the sub-populations having a differing response to any one of the n antibiotics because Super teaches a very similar method of detecting antibiotic resistance by separating a biological sample into a plurality of subsamples before incubation with different concentrations of antibiotic agents to be tested, which when combined with the method of the copending claims would advantageously allow one of ordinary skill in the art to detect the presence of multiple bacterial subpopulations with different antibiotic resistances in a single biological sample.
Regarding instant claim 10, the copending claims do not recite that the flow cytometer is controlled by a processor executing instructions stored in a memory, said memory further containing separate body-fluid-specific data sets for each of the urine, blood, or cerebral spinal fluid wherein each said data set accounts for: a. known matrix noise and provides statistical confidence information specific to the body fluid type, b. pre-defined growth integers for pathogens associated with pathological bacterial infections, and c. pre-defined growth integers for possible contaminants associated with normal sampling.
Shamsheyeva teaches systems for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance (Shamsheyeva Abstract) comprising a computer-based system comprising a processor, a tangible, non-transitory memory, and an interface configured to determine a growth rate of a microorganism (Shamsheyeva [2]). Shamsheyeva teaches that flow cytometry can be used to determine a value associated with an attribute of a microorganism for use in determining a growth rate (Shamsheyeva [108]). Shamsheyeva teaches a process (Shamsheyeva Figure 2) of providing a sample to a system (210), determining a first value associated with a microorganism (220), subjecting the microorganism to a condition (230), determining a second value associated with the microorganism (240), determining a growth rate based on the first and second values (250), and comparing the growth rate to a known growth rate (260) to be able to make a recommendation about an element, condition, or event based on the growth rate. The recommendation may comprise a determination that an element is resistant to a condition based on a rate of change not correlating with a control rate of change (or an associated rage of a control rate of change) (Shamsheyeva [127]). Shamsheyeva teaches growth rates were determined to identify microorganisms (Shamsheyeva [184]). Shamsheyeva teaches identification of MRSA using negative growth rate (Shamsheyeva [58]), wherein MRSA is a pathogen that requires rapid identification to assure adequate therapeutic coverage (Shamsheyeva [284]).
It would have obvious to control the flow cytometer of the copending claims with a processor executing instructions stored in memory which contains body-fluid-specific data sets for urine, blood, or cerebral spinal fluid, wherein each data set accounts for: a. known matrix noise and provides statistical confidence information specific to the body fluid type, b. pre-defined growth integers for pathogens associated with pathological bacterial infections, and c. pre-defined growth integers for possible contaminants associated with normal sampling. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because the modification would utilize known methods of analyzing bacteria growth as recited in the copending claims and as taught by Shamsheyeva to determine the presence of a target bacteria to improve diagnosis of a sample.
Regarding instant claim 12, the copending claims do note recite that staining reagents used for bacterial determinations are added using an automated fluid handling system that aspirates, deposits, and mixes the reagents and samples.
Super teaches that the samples are mixed after addition of the preprocessing reagent, the processing buffer, and or the coated-substrate. The mixing can be simply accomplished by agitating the sample, shaking the sample, and/or moving the sample around in a microfluidic device (Super [043], [068], and [080]).
It would have been obvious to one of ordinary skill in the art to use an automated fluid handling system to add staining reagents used for bacterial determinations and aspirating, depositing, and mixing the reagents and samples. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Super teaches a very similar method of detecting antibiotic resistance where the sample is mixed at several points during the assay by agitating the sample, shaking the sample, and/or moving the sample around, such as in a microfluidic device (Super [043], [068], and [080]).
Regarding claims 14-16, the copending claims do not recite including a known concentration of test-enumerative compensator (TEC) particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerating the TEC particles with the sample testing by the flow cytometer; determining a compensator factor based on the enumerated TEC particle value as compared to the known TEC particle concentration in the sample tested by applying a unique TEC particle ROI separate from the bacteria ROI for enumerating the TEC particles; and adjusting the sample test enumeration value by said compensator factor, wherein the TEC particles are included with each flow cytometer sample test.
Shamsheyeva discloses to enhance fluorescence signals, microorganisms could either be coated with gold and/or silver nanoparticles [reads on TEC particles] in a sample preparation step, and the nanoparticles may be associated with microorganisms in a centrifugation step (Shamsheyeva [105]). Shamsheyeva teaches that fluorescence spectra can be obtained using various methods described therein which may be used to perform identification of microorganisms. Reference spectra may be obtained for known microorganisms, thus allowing for correlation of measured sample data with characterization of the microorganisms of interest using various mathematical methods known to those skilled in the art (Shamsheyeva [106]).
It would have been obvious to one of ordinary skill in the art include a known concentration of test-enumerative compensator (TEC) particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerate those the TEC particles with the sample testing by the flow cytometer; determine a compensator factor based on the enumerated TEC particle value as compared to the known TEC particle concentration in the sample tested by applying a unique TEC particle ROI separate from the bacteria ROI for enumerating the TEC particles; and adjust the sample test enumeration value by said compensator factor, wherein the TEC particles are included with each flow cytometer sample test. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Shamsheyeva discloses enhancing fluorescence signals by coating the tested microorganisms with gold and/or silver nanoparticles (reads on TEC particles) in a sample preparation step.
Regarding instant claim 20, the copending claims do not recite determine whether cells of interest are present when a statistically significant increase in the number of cells of interest at T1 as compared to T0 is an increase of 125% to 325%.
Super teaches detecting the growth of cells which are resistant to antibiotics (reads on cells of interest), and comparing the growth of cells in a sample prior to incubation and after incubation (Super [155], [0158], and [195]), once cell counts (e.g., microbe or pathogen counts) or functional response level for the reference or control (i.e., the microbes or pathogens cultured in the absence of any antibiotic agent) and antibiotic agent-treated subsamples have been determined, the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing these numbers. Subsamples that display cell counts or functional response level similar (within 0.5-20% or any statistically significant determination) to the reference or control counts can indicate that they are resistant to the antibiotic agent with which they were treated (Super [199]). Therefore, Super teaches, in one embodiment, always determining the presence of the cells of interest in a sample regardless of what the percentage of increase of the cells of interest was at T1 as compared to T0.
It would have been obvious to one of ordinary skill in the art to determine whether cells of interest are present in a sample when a statistically significant increase in the number of cells of interest at T1 as compared to T0 is an increase of 125% to 325%. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Super teaches a very similar method of detecting antibiotic resistance where the presence of cells of interest in the sample are always determined in a sample regardless of what the percentage of increase of the cells of interest was at T1 as compared to T0.
This is a provisional nonstatutory double patenting rejection.
(New necessitated by amendment) Instant claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/099,099 in view of Super (US20150064703A1; previously cited), PhysiologyWeb (PhysiologyWeb Dilution Factor Calculator – Cells per Volume, https://www.physiologyweb.com/calculators/dilution_factor_calculator_cells_per_volume.html, available 25 April 2011), Shamsheyeva (US20140278136A1, 18 September 2014), Yananton (US3951747A; newly cited) and Roederer et al. (Compensation in Flow Cytometry, Current Protocols in Cytometry (2002) 1.14.1-1.14.20).
Claims 1-20 are directed to an invention not patentably distinct from claims 1-20 of commonly assigned Application No. 18/099,099. Specifically, the instant claims are obvious over the copending claims in view of Super, PhysiologyWeb, Shamsheyeva, Yananton and Roederer.
Copending claim 1 recites a system for automated testing of a sample of a body fluid for the presence of bacteria, the system comprising: a fluid handling device comprising a fluid handling system, wherein the fluid handling system comprises an automated pipetting system configured to distribute a plurality of fluid samples within a well plate comprising a plurality of wells; an incubator configured to culture the plurality of fluid samples in the well plate; a flow cytometer configured to enumerate cell counts in the plurality of fluid samples; at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: distribute a portion of the plurality of fluid samples to at least a first well; divide the portion of the plurality of fluid samples from the at least a first well into at least two wells, wherein the at least two wells comprise: a time zero (To) well; and a time one (T1) well; obtain a To enumerative baseline bacterial value relating to fluid samples in the T0 well at time To; culture the fluid samples in the T1 well using the incubator; obtain a T1 enumerative control bacterial value relating to fluid samples in the T1 well at time T1; and determine a presence of bacteria as a function of the To enumerative baseline bacterial value and the T1 enumerative control bacterial value. Copending claim 2 recites the system of copending claim 1, wherein the well plate comprises a multi-well cassette. Copending claim 3 recites the system of copending claim 1, wherein distributing the portion of the plurality of fluid samples to the at least a first well comprises determining a total bacteria count of the portion of the plurality of fluid samples by enumerating the portion of the plurality of fluid samples using the flow cytometer. Copending claim 4 recites the system of copending claim 3, wherein distributing the portion of the plurality of fluid samples to the at least a first well comprises adjusting a dilution of the portion of the plurality of fluid samples using a growth media to a predetermined concentration as a function of the total bacteria count. Copending claim 5 recites the system of copending claim 1, wherein obtaining the To enumerative baseline bacterial value comprises enumerating the fluid samples in the T0 well at time To. Copending claim 6 recites the system of copending claim 1, wherein culturing the fluid samples in the T1 well comprises: delivering, using a plate transport device, the fluid samples in the T1 well to the incubator; and returning, using the plate transport device, the fluid samples from the incubator to the fluid handling device after culturing. Copending claim 7 recites the system of copending claim 1, wherein obtaining the T1 enumerative control bacterial value comprises enumerating the fluid samples in the T1 well at time T1. Copending claim 8 recites the system of copending claim 1, wherein determining the presence of bacteria comprises: comparing the T1 enumerative control bacterial value to the To enumerative baseline bacterial value; and determining a growth ratio of the portion of the plurality of fluid samples as a function of the comparison. Copending claim 9 recites the system of copending claim 1, wherein the memory further contains instructions configuring the at least a processor to adjust a test enumerative bacterial value as a function of a compensator factor. Copending claim 10 recites the system of copending claim 9, wherein adjusting the test enumerative bacterial value comprises: including a known concentration of a test-enumerative compensator (TEC) particles in the fluid sample to be enumerated, wherein the TEC particles comprise known flow cytometric scatter and fluorescence characteristics; enumerating the TEC particles with the sample enumeration by the flow cytometer; and determining the compensator factor as a function of a comparison of a test enumerative bacterial value of the TEC particles to the know concentration of the TEC particles. Copending claim 11 recites a method for automated testing a sample of a body fluid for the presence of bacteria, the method comprises: distributing, by at least a processor, a portion of a plurality of fluid samples within a well plate to at least a first well using a fluid handling device; dividing, by the at least a processor, the portion of the plurality of fluid samples from the at least a first well into at least two wells, wherein the at least two wells comprise a time zero (To) well and a time one (T1) well; obtaining, by the at least a processor, a To enumerative baseline bacterial value relating to fluid samples in the T0 well at time To using a flow cytometer; culturing, using an incubator, the fluid samples in the T1 well; obtaining, by the at least a processor, a T1 enumerative control bacterial value relating to fluid samples in the T1 well at time T1 using the flow cytometer; and determining, by the at least a processor, a presence of bacteria as a function of the To enumerative baseline bacterial value and the T1 enumerative control bacterial value. Copending claim 12 recites the method of copending claim 11, wherein the well plate comprises a multi-well cassette. Copending claim 13 recites the method of copending claim 11, wherein distributing the portion of the plurality of fluid samples to the at least a first well comprises determining a total bacteria count of the portion of the plurality of fluid samples by enumerating the portion of the plurality of fluid samples using the flow cytometer. Copending claim 14 recites the method of copending claim 13, wherein distributing the portion of the plurality of fluid samples to the at least a first well comprises adjust a dilution of the portion of the plurality of fluid samples with a growth media to a predetermined concentration as a function of the total bacteria count. Copending claim 15 recites the method of copending claim 11, wherein obtaining the To enumerative baseline bacterial value comprises enumerating the fluid samples in the T0 well at time To. Copending claim 16 recites the method of copending claim 11, wherein culturing the fluid samples in T1 well comprises: delivering, using a plate transport device, the fluid samples in the T1 well to the incubator; and returning, using the plate transport device, the fluid samples from the incubator to the fluid handling device after culturing. Copending claim 17 recites they method of copending claim 11, wherein obtaining the T1 enumerative control bacterial value comprises enumerating the fluid samples in the T1 well at time T1. Copending claim 18 recites the method of copending claim 11, wherein determining the presence of bacteria comprises: comparing the T1 enumerative control bacterial value to the To enumerative baseline bacterial value; and determining a growth ratio of the portion of the plurality of fluid samples as a function of the comparison. Copending claim 19 recites the method of copending claim 11, wherein the method further comprises adjusting a test enumerative bacterial value as a function of a compensator factor. Copending claim 20 recites the method of copending claim 19, wherein adjusting the test enumerative bacterial value comprises: including a known concentration of a test-enumerative compensator (TEC) particles in the fluid sample to be enumerated, wherein the TEC particles comprise known flow cytometric scatter and fluorescence characteristics; enumerating the TEC particles with the sample enumeration by the flow cytometer; and determining the compensator factor as a function of a comparison of a test enumerative bacterial value of the TEC particles to the know concentration of the TEC particles.
Regarding instant claims 1, 2, 7, 11, 13, 17, and 19, the copending claims do not recite an automated fluid handling system and optionally determining sample response to at least one antibiotic, diluting the sample with growth media provided in a freeze-dried form, testing the sample in the T0 baseline well comprises analyzing the T0 baseline well using the flow cytometer, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, assessment of changes in scatter and fluorescence characteristics using flow cytometric software, at least two wells including n antibiotic testing (AT) samples with each one being treated by a different antibiotic, the method comprising testing each of the n AT samples at time T1 with the flow cytometer to obtain n AT sample values and comparing the T0 baseline events in the ROI to each of the n T1 sample events in the ROI to determine the susceptibility or resistance of detected bacteria to the n different antibiotics
Super teaches methods for rapid determination of antibiotic susceptibility of a microbe within hours after a test sample is collected (Super Abstract and claim 1). The assay is based on the direct measurement of the bacteria's ability to grow in the presence of the tested antibiotic agents, which allows the detection of microbes and their antibiotic sensitivity using short growth times and only one short culture step (Super [30] and claim 1). Super teaches the test sample can be a biological fluid (Super [35] and claim 74). Super also discloses that microbial growth or a functional response of microbes can be determined or monitored using any methods known in the art for determining cell viability, growth or functional response, including real-time methods such as flow cytometry (Super [155]). Super teaches that the assay can be adapted for use in a high-throughput platform, such as an automated system or platform (Super [195]).
Super teaches the sample can be divided (distributed) into a plurality of subsamples before incubation steps (Super [357]).
Super teaches that the samples can be analyzed to confirm the presence of a microbe in the sample, which is desirable to detect or determine the presence of initial number of microbes in the sample (Super [104]).
Super teaches that the samples can be preprocessed by diluting the sample prior to adding one or more reagents to the sample (Super [39]). Super also teaches that the amount of diluting agent can be adjusted to achieve certain final concentrations, and suggests several desirable final concentration amounts (Super [42]).
Super teaches the sample can be divided into a plurality of subsamples before incubation steps (Super [357]), and one or more multi-well plates which comprise a detection substrate with different antibiotic profiles can be used to test the subsamples (Super [195]). Super then teaches that the sample can be observed for 1) growth in the presence of the antibiotics to determine the resistance of the bacteria to particular antibiotics, 2) cell death to determine bactericidal activity to the antibiotic, and/or 3) inhibition of growth to determine bacteriostatic activity to the antibiotic, which can all be assessed by counting the total number of microbes in the samples as compared to controls or references (Super [155]). Super teaches the control or reference can be incubated without any antibiotic agents (reads on instant T1 control), and also the number of microbes in the sample can be determined before incubation with an antibiotic agent (reads on instant T0 baseline) (Super [156] and [158]). Responses of the microbes in the subsamples to the antibiotic agent can be monitored in real-time during and/or after incubation in the presence of antibiotic agents for a period of time by imaging a color change/shift of a dye. The microbes can then be tracked during and/or after antibiotic incubation to determine their individual response (Super [195]).
Super teaches the set of subsamples can be cultured for any period of time, including for at least 15 seconds or up to 72 hours or longer (Super [148]). Super teaches that cell viability can be assayed using pH sensitive dyes, Trypan Blue dye, staining, cell-impermeable dyes, fluorescent dyes, chromogenic dyes, etc. (Super [158]).
The relative microbe counts in the tested antibiotic sample are compared to a control to determine the number of microbes in the sample (Super [158]). Super also teaches the use of a ratio of cells expressing at least one microbe marker in the sample to assess microbe growth and/or cell death in the sample (Super [155]).
The conflicting claims do not recite and Super does not disclose the calculation of a dilution factor based on a total bacteria count of the sample, growth media provided in a freeze-dried form, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, or assessment of changes in scatter and fluorescence characteristics using flow cytometric software.
PhysiologyWeb teaches instructions on how to calculate a dilution factor from an equation using an initial known bacteria count, and also provides a computerized calculator that can perform the calculation for the user (PhysiologyWeb pgs. 1-3).
The conflicting claims do not recite and Super and PhysiologyWeb do not disclose growth media provided in a freeze-dried form, culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads, or assessment of changes in scatter and fluorescence characteristics using flow cytometric software.
Shamsheyeva teaches that determination of microbial growth rate or lack thereof may be based on probabilistic assessment that a measured change in one or more attributes is likely to correspond to growth (Shamsheyeva [079]), and that this determination can be compared against a reference growth rate to assist in identifying a tested microorganism’s susceptibility or resistance to an antimicrobial agent (Shamsheyeva [082]), and furthermore that this can be performed rapidly (Shamsheyeva [086]). Shamsheyeva teaches that an attribute of a microorganism can be any detectable or measurable feature or characteristic of a microorganism, or any value related to the presence of a microorganism that may be observed, detected, or measured using any technique (Shamsheyeva [088]), and also teaches that one detection/analytical tool to determine values associated with microorganisms includes flow cytometry (Shamsheyeva [108]). Shamsheyeva then teaches the measurement and observation of fluorescence and/or scattering signals from microorganism (Shamsheyeva [096] and [101]). Shamsheyeva teaches that sample analyzers and analysis modules are any hardware, software, or hardware-software system capable of measuring, evaluating, collecting, and analyzing data about a microorganism sample (Shamsheyeva [112]-[113]).
The conflicting claims do not recite and Super and Shamsheyeva do not disclose growth media provided in a freeze-dried form, or culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads.
Yananton discloses lyophilized media for bacterial identification has several distinct advantages over other forms of nutrient media, including excellent storage and shelf stability and sterility (Yananton Col. 1, lines 49-51; Col. 1, line 64-Col. 2, line 6).
The conflicting claims do not recite and Super, Shamsheyeva, and Yananton do not teach culturing the sample comprises creating a target concentration of fluid that also comprises compensation beads.
Roederer teaches the compensation of flow cytometers, which refers to the process of mathematically correcting for fluorescent spillover by removing the signal of any given fluorochrome from all detectors except the one devoted to measuring the dye (Roederer pg. 1 para. 1 and pg. 1 and 3 bridging para.). Roederer teaches that compensation beads work well for the purpose of compensating in flow cytometry, so long as the beads bind to the actual reagents that are used in the experiment (Roederer Pg. 16 para. 1). Using compensation beads in a culture of cells has many advantages in flow cytometry compensation, including that they are highly uniform allowing for precise spillover calculation of cellular autofluorescence in, for example, fluorescein (FITC) control cell samples (Roederer pg. 13 para. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method recited in the copending claims to divide the samples utilizing an automated fluid handling system (Super [195]) as taught by Super, because it would reduce the number of sample handling steps and automate the process (Super Pg. 10, [109]). It would have been further obvious to modify the method of the copending claims to further determine sample response to at least one antibiotic in order to detect microbes and their antibiotic sensitivity in a one-step test method (Super [30, left column, last line – right column).
It would have been prima facie obvious to one of ordinary skill in the art prior to the effective filing date of the present invention to calculate the dilution factor for the dilution of the sample in the method by using PhysiologyWeb’s computerized calculator (powered by a processor) because doing so would make it much easier and quicker to determine the amount of diluting reagent necessary to achieve a given concentration based on initial known bacteria counts in the sample as determined by in the method.
It would have been obvious to use lyophilized growth medium to dilute the sample in the method, because Yananton discloses lyophilized media has the advantage of shelf stability and sterility over other forms of nutrient media. It would have also been obvious to add compensation beads to the subsamples of Super’s culturing step because Roederer teaches that compensation beads advantageously and precisely remove (or compensate) for unwanted fluorescence, such as autofluorescence, which ensures that only the desired signals are used in the final analysis. It would have also been obvious to perform a software-based assessment of flow cytometric scatter and fluorescence data to observe an effect of a tested antibiotic on a bacterial population because Shamsheyeva teaches that a microorganism’s susceptibility or resistance to an antimicrobial agent can be advantageously determined by rapid probabilistic assessment of a measured change in fluorescence and/or scattering signals collected by flow cytometry and analyzed by software.
Regarding instant claims 3 and 18, the copending claims do not recite wherein the T0 baseline values and the T1 control values include cell events of interest in a bacteria-specific region of interest (ROI), the comparing step including comparing the cell events of interest at T0 and T1 and determining whether cells of interest are present when there is a statistically significant increase in the number of cell of interest events at T1 as compared to T0.
Super teaches detecting the growth of cells which are resistant to antibiotics (reads on cells of interest), and comparing the growth of cells in a sample prior to incubation and after incubation (Super [155], [0158], and [195]). Super also teaches once the cell counts (e.g., microbe or pathogen counts) or functional response levels for the reference or control (i.e., the microbes or pathogens cultured in the absence of any antibiotic agent) of the antibiotic agent-treated subsamples has been determined, the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing the cell counts or functional responses of the antibiotic agent-treated subsamples to those of the reference or control counts. The samples with the cells of interest that exhibit growth at similar levels to the reference or control counts (within 0.5-20% or any statistically significant determination) can indicate that the samples are resistant to the antibiotic agent with which they were treated (Super [199]).
It would have been obvious to compare the cell growth of the cells of interest at T0 and T1 and determine whether the cells of interest are present when there is a statistically significant increase in the number of cells of interest at T1 as compared to T0 in order to determine which cells of interest are resistant to antibiotics. One of ordinary skill in the art would have had reasonable expectations of success because Super teaches the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing the cell counts or functional responses of the antibiotic agent-treated subsamples to those of the reference or control counts.
Regarding instant claims 5-6, the copending claims do not recite converting a relative growth between T0 and T1 to a growth integer representing bacterial population expansion; comparing the growth integer from T0 baseline and T1 control to at least one known growth integer from a known library of bacterial contaminants or pathogens represented in a disease state being tested; and determining the type of contaminant or pathogen present in the sample based on said comparing.
Super teaches determining the ratio of cells expressing at least one microbe marker in the subsample (reads on the “integer representing bacterial population expansion“) as compared to a control or reference (reads on “a known library of pathogens/bacterial contaminants”) (Super [155]). Super teaches that their antibiotic susceptibility testing method is able detect bacteria, including pathogens responsible for microbial infections, in biological samples (Super [028]-[029]).
It would have been obvious to determine the type of bacterial contaminant or pathogen in a biological sample by comparing the ratio of cells of interest to a reference library of known contaminants and/or pathogens because Super teaches a very similar method of detecting antibiotic resistance and typing bacterial species, including pathogens responsible for microbial infections, in biological samples by comparing the ratio of cells expressing at least one microbe marker to a reference. Thus, the identification of an antibiotic-resistant bacteria obtained by the method of the copending claims can be determined.
Regarding instant claim 8, the copending claims do not recite comparing the T1 control values and n antibiotic test sample values to detect the presence of multiple sub-populations of bacteria due to the sub-populations having a differing response to any one of the n antibiotics.
Super teaches the relative microbe counts in the tested antibiotic sample are compared to a control to determine the number of microbes in the sample (Super [158]). Super also teaches that the isolated microbes from the biological fluid can be separated into a plurality of subsamples before incubation with different concentrations of antibiotic agents to be tested. The number of subsamples depends, among other factors, on the number of antibiotic agents and control combinations to be tested or the amount of microbes isolated. (Super [114]).
It would have been obvious to compare T1 control values and the antibiotic test sample values to detect the presence of multiple sub-populations of bacteria due to the sub-populations having a differing response to any one of the n antibiotics because Super teaches a very similar method of detecting antibiotic resistance by separating a biological sample into a plurality of subsamples before incubation with different concentrations of antibiotic agents to be tested, which when combined with the method of the copending claims would advantageously allow one of ordinary skill in the art to detect the presence of multiple bacterial subpopulations with different antibiotic resistances in a single biological sample.
Regarding instant claim 10, the copending claims do not recite that the flow cytometer is controlled by a processor executing instructions stored in a memory, said memory further containing separate body-fluid-specific data sets for each of the urine, blood, or cerebral spinal fluid wherein each said data set accounts for: a. known matrix noise and provides statistical confidence information specific to the body fluid type, b. pre-defined growth integers for pathogens associated with pathological bacterial infections, and c. pre-defined growth integers for possible contaminants associated with normal sampling.
Shamsheyeva teaches systems for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance (Shamsheyeva Abstract) comprising a computer-based system comprising a processor, a tangible, non-transitory memory, and an interface configured to determine a growth rate of a microorganism (Shamsheyeva [2]). Shamsheyeva teaches that flow cytometry can be used to determine a value associated with an attribute of a microorganism for use in determining a growth rate (Shamsheyeva [108]). Shamsheyeva teaches a process (Shamsheyeva Figure 2) of providing a sample to a system (210), determining a first value associated with a microorganism (220), subjecting the microorganism to a condition (230), determining a second value associated with the microorganism (240), determining a growth rate based on the first and second values (250), and comparing the growth rate to a known growth rate (260) to be able to make a recommendation about an element, condition, or event based on the growth rate. The recommendation may comprise a determination that an element is resistant to a condition based on a rate of change not correlating with a control rate of change (or an associated rage of a control rate of change) (Shamsheyeva [127]). Shamsheyeva teaches growth rates were determined to identify microorganisms (Shamsheyeva [184]). Shamsheyeva teaches identification of MRSA using negative growth rate (Shamsheyeva [58]), wherein MRSA is a pathogen that requires rapid identification to assure adequate therapeutic coverage (Shamsheyeva [284]).
It would have obvious to control the flow cytometer of the copending claims with a processor executing instructions stored in memory which contains body-fluid-specific data sets for urine, blood, or cerebral spinal fluid, wherein each data set accounts for: a. known matrix noise and provides statistical confidence information specific to the body fluid type, b. pre-defined growth integers for pathogens associated with pathological bacterial infections, and c. pre-defined growth integers for possible contaminants associated with normal sampling. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because the modification would utilize known methods of analyzing bacteria growth as recited in the copending claims and as taught by Shamsheyeva to determine the presence of a target bacteria to improve diagnosis of a sample.
Regarding instant claim 12, the copending claims do note recite that staining reagents used for bacterial determinations are added using an automated fluid handling system that aspirates, deposits, and mixes the reagents and samples.
Super teaches that the samples are mixed after addition of the preprocessing reagent, the processing buffer, and or the coated-substrate. The mixing can be simply accomplished by agitating the sample, shaking the sample, and/or moving the sample around in a microfluidic device (Super [043], [068], and [080]).
It would have been obvious to one of ordinary skill in the art to use an automated fluid handling system to add staining reagents used for bacterial determinations and aspirating, depositing, and mixing the reagents and samples. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Super teaches a very similar method of detecting antibiotic resistance where the sample is mixed at several points during the assay by agitating the sample, shaking the sample, and/or moving the sample around, such as in a microfluidic device (Super [043], [068], and [080]).
Regarding claims 14-16, the copending claims do not recite including a known concentration of test-enumerative compensator (TEC) particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerating the TEC particles with the sample testing by the flow cytometer; determining a compensator factor based on the enumerated TEC particle value as compared to the known TEC particle concentration in the sample tested by applying a unique TEC particle ROI separate from the bacteria ROI for enumerating the TEC particles; and adjusting the sample test enumeration value by said compensator factor, wherein the TEC particles are included with each flow cytometer sample test.
Shamsheyeva discloses to enhance fluorescence signals, microorganisms could either be coated with gold and/or silver nanoparticles [reads on TEC particles] in a sample preparation step, and the nanoparticles may be associated with microorganisms in a centrifugation step (Shamsheyeva [105]). Shamsheyeva teaches that fluorescence spectra can be obtained using various methods described therein which may be used to perform identification of microorganisms. Reference spectra may be obtained for known microorganisms, thus allowing for correlation of measured sample data with characterization of the microorganisms of interest using various mathematical methods known to those skilled in the art (Shamsheyeva [106]).
It would have been obvious to one of ordinary skill in the art include a known concentration of test-enumerative compensator (TEC) particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerate those the TEC particles with the sample testing by the flow cytometer; determine a compensator factor based on the enumerated TEC particle value as compared to the known TEC particle concentration in the sample tested by applying a unique TEC particle ROI separate from the bacteria ROI for enumerating the TEC particles; and adjust the sample test enumeration value by said compensator factor, wherein the TEC particles are included with each flow cytometer sample test. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Shamsheyeva discloses enhancing fluorescence signals by coating the tested microorganisms with gold and/or silver nanoparticles (reads on TEC particles) in a sample preparation step.
Regarding instant claim 20, the copending claims do not recite determine whether cells of interest are present when a statistically significant increase in the number of cells of interest at T1 as compared to T0 is an increase of 125% to 325%.
Super teaches detecting the growth of cells which are resistant to antibiotics (reads on cells of interest), and comparing the growth of cells in a sample prior to incubation and after incubation (Super [155], [0158], and [195]), once cell counts (e.g., microbe or pathogen counts) or functional response level for the reference or control (i.e., the microbes or pathogens cultured in the absence of any antibiotic agent) and antibiotic agent-treated subsamples have been determined, the presence of cells that exhibit a degree of antibiotic resistance (i.e. cells of interest) can be determined by comparing these numbers. Subsamples that display cell counts or functional response level similar (within 0.5-20% or any statistically significant determination) to the reference or control counts can indicate that they are resistant to the antibiotic agent with which they were treated (Super [199]). Therefore, Super teaches, in one embodiment, always determining the presence of the cells of interest in a sample regardless of what the percentage of increase of the cells of interest was at T1 as compared to T0.
It would have been obvious to one of ordinary skill in the art to determine whether cells of interest are present in a sample when a statistically significant increase in the number of cells of interest at T1 as compared to T0 is an increase of 125% to 325%. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Super teaches a very similar method of detecting antibiotic resistance where the presence of cells of interest in the sample are always determined in a sample regardless of what the percentage of increase of the cells of interest was at T1 as compared to T0.
This is a provisional nonstatutory double patenting rejection.
Response to Arguments
Applicant's arguments filed 17 February 2026 have been fully considered but they are not persuasive.
Where Applicant merely points to sections in the specification or other areas of the claim but provides no further argument or statement clearly pointing out the deficiencies in the rejection, no clear argument is given (Remarks pg. 2 para. 8 through pg. 3 para. 5, pg. 4 para. 3 through pg. 6 para. 1, pg. 7 para. 2, pg. 8 para. 1 through pg. 9 para. 2, and pg. 10 para. 1 through para. 5). Examiner would be required to interpret an argument where a rationale was not given. Thus, Examiner is unable to consider any arguments where an argument is not given.
Regarding Applicant’s argument that it is clear that "enumerative control bacterial values" are addressed in the claim as "corresponding to bacterial cell counts or other bacterial enumeration metrics of the T1 control well sample" (Remarks pg. 2 paras. 1-2), the claim language is still not clear what the entire metes and bounds of the "enumerative bacterial values" are. The claim language indicates that it corresponds to bacterial cell counts, but the claim language does not elaborate or clarify what is meant by "other bacterial enumeration metrics", thus one of ordinary skill in the art would not be able to determine what metrics would be considered an "enumerative bacterial value".
Regarding Applicant’s argument that the "first well" recited in claim 2 refers to the first well in claim 1, pointing to specification [0037] and [0040 (Remarks pg. 2 para. 5-6), although the claims are read in light of the specification, the wording of the claim is still indefinite because the claim does not differentiate or clearly point out that claim 2's "first well" is indeed the "first test well" recited in claim 1.
Regarding Applicant’s arguments that the known library of pathogens comprises an association between a growth integer and a pathogen, pointing to specification [0039] (Remarks pg. 6 para. 3), although the claims are read in light of the specification, the wording of the claim is still indefinite because the claim does not clearly point out how a type of pathogen can be obtained by comparing the growth integer from T0 baseline and T1 control to a growth integer from a known library of pathogens.
Regarding Applicant’s arguments that references Super, PhysiologyWeb, Shamsheyeva, Yananton, and Roederer do not teach or suggest determining, using compensation particles added to the sample, whether a threshold for enumerative accuracy associated with the system is met; determining, in response to the threshold for enumerative accuracy associated with the system being met, a scaling factor based on a ratio of a measured number of the compensation particles to an expected number of the compensation particles, wherein the scaling factor is linear or non-linear; applying the scaling factor to the total bacteria count in order to determine an updated total bacteria count; and applying the updated total bacteria count in the diluting step in order to determine an updated target value required for subsequent testing, thus the 103 rejections should be withdrawn (Remarks pgs. 12-16 para. 2), as discussed in the 103 rejection above, it would have been obvious to one of ordinary skill in the art to include a known concentration of compensator particles with known flow cytometric scatter and fluorescence characteristics in the sample; enumerate those particles during sample testing by the flow cytometer; determine a scaling factor based on the enumerated compensator particle value as compared to the known compensator particle concentration in the sample tested; adjust the sample total bacterial count value by said scaling factor; and then use the updated sample total bacterial count to determine an updated dilution factor. One of ordinary skill in the art would have been motivated to do so, and have reasonable expectations of success, because Shamsheyeva discloses fluorescence signals can be advantageously enhanced by coating the tested microorganisms with gold and/or silver nanoparticles (reads on compensator particles) in a sample preparation step, which allows for correlation of measured sample data with characterization of the microorganisms of interest.
Regarding Applicant’s argument that a Terminal Disclaimer has been filed, so the non-statutory double patenting rejections of claims 1-20 are obviated (Remarks pg. 16 para. 4), the filed Terminal Disclaimer was disapproved on 14 March 2026. The non-statutory double patenting rejections of claims 1-20 is therefore maintained.
Conclusion
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/Alexander M Duryee/Examiner, Art Unit 1657
/LOUISE W HUMPHREY/Supervisory Patent Examiner, Art Unit 1657
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