DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s arguments filed in the reply on March 27, 2026 were received and fully considered. Claims 1-9 were cancelled. Please see below for more detail.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 10-14, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanna et al (WO 2019/053414) (“Hanna”) in view of Probert et al (WO 2004/008953) (“Probert”) as noted in Applicant IDS dated 3/21/2025 and further in view of Biffi (WO 2019/053604) and further in view of Haick et al (US 2019/0271685) (“Haick”) and further in view of Koo et al (US 2017/0227429) (“Koo”) as noted in Applicant IDS dated 4/04/2022.
Regarding Claim 10, while Hanna teaches a method for treating a subject who has been diagnosed with colorectal cancer (Abstract, p9, L. 6 – 31) to identify optimal treatment (p12, L. 33 – p13, L. 9), comprising:
obtaining a breath sample from the subject (Abstract, p9, L. 6 – 31);
detecting one or more volatile organic compounds (VOC)s and their quantities to obtain a VOC profile of the sample using an analytic device (Abstract, p9, L. 6 – 31, identifies a volatile organic compound and a quantity threshold that provides diagnostic values, uses SIFT-MS for analysis) wherein the VOC profile comprises one or more of the VOCs detected and a corresponding quantity of the one or more of the VOCs detected (p30, L. 17 – p32, L. 31, a number of VOCs in the 60s were tested to finding a suitable VOC with diagnostic capability, cross-referenced against GC-MS results for repeatability)
using one or more of the VOC quantities, univariate analysis, and multivariate analysis, training the models on a dataset of colorectal cancer positive and colorectal cancer negative breath samples, and generating a criteria for identifying colorectal cancer (p36, L. 2 – p 38, L. 14, p39, L. 8-12, multiple VOC considered with their quantities to identify a distinguishing biomarker, propanal identified); and
Hanna further suggests a utility of diagnosing the subject as having or not having colorectal cancer based on the output of the criteria (p39, L. 34 – p40, L. 8 and p41, L. 1-26).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the identified VOC criteria for diagnosing a subject in Hanna as Hanna teaches the high sensitivity and specificity of such a test, identifies colorectal cancer at on par accuracy with fecal tests, and showed efficacy at on a repeated basis.
Yet Hanna fails to teach the method being applied for diagnosing a subject with a Clostridioides difficile infection (CDI),
However Probert teaches a method of diagnosing a subject with a Clostridioides difficile infection (CDI) (p3-6), comprising:
detecting one or more volatile organic compounds (VOC)s and their quantities to obtain a VOC profile of the sample using an analytic device wherein the VOC profile comprises one or more of the VOCs detected and a corresponding quantity of the one or more of the VOCs detected (p-11-13, Gas chromatograph profiles collected of normal patients and patients suffering from a CDI, p17, Table 1, VOCs with particular predictive value for listed conditions, Figs. 1a-2, figures of gas chromatograph profiles of collected samples, abundance/quantities over time of the present compounds in the sample, from patients of different conditions);
wherein the analytical device comprises a selected-ion flow-tube mass spectrometry (SIFT-MS) system or a gas chromatography-mass spectrometry (GC-MS) system (p7, “Preferably, the sample is analysed by mass spectroscopy, gas chromatography or by use of a so-called electronic nose - an electrochemical sensor, or an array of sensors, which specifically detects volatile compounds, especially pre-selected volatile compounds or patterns of volatile compounds… GC-MS analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum. Plant Pathology 50, 489-496 (2001)), that the strategy of gas chromatography/mass spectroscopy vapor analysis followed by sensor development to produce a minimal array of sensors is a successful strategy in the design of relatively inexpensive electronic nose instrumentation.” Gas chromatography -mass spectrometry recommended as a detection component of an analytical device);
performing spectral analysis to generate a VOC profile (p-11-13, Gas chromatograph profiles collected of normal patients and patients suffering from a CDI, p17, Table 1, VOCs with particular predictive value for listed conditions, Figs. 1a-2, figures of gas chromatograph profiles of collected samples, abundance/quantities over time of the present compounds in the sample, from patients of different conditions);
identifying one or more of the VOC quantities as indicative of an infection based on the use of pattern recognition software and generating an output (p7, “Preferably, the sample is analysed by mass spectroscopy, gas chromatography or by use of a so-called electronic nose - an electrochemical sensor, or an array of sensors, which specifically detects volatile compounds, especially pre-selected volatile compounds or patterns of volatile compounds… An array of such sensors with appropriate pattern recognition software would then give a rapid diagnosis.”); and
diagnosing the subject as having or not having CDI based on the output of the pattern recognition (p7, “An array of such sensors with appropriate pattern recognition software would then give a rapid diagnosis.”); and
Probert further specifies that the above methodology can be applied on an obtained breath sample from the subject (p5-6, “Preferably, the emission is a gaseous emission, for example, exhaled air, eructation or flatus. However, other emission samples such as ascites, sputum, urine, faeces, blood or tissue may be used. In this manner volatiles contained in microbiological metabolites produced by micro-organisms in the sample may be used to detect the presence of pathogens…. Collection of the gas may be done in many ways, for example by emission of the gas directly into a collection chamber and/or an associated vessel, by catheterisation of the area of interest, the gas then being analysed by typical gas analysis methods, for example, gas chromatography and/or mass spectrommetry… It is therefore another object of the present invention to provide a sample collecting device which allows easier collection and reduces the amount of sample lost.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply the generation of a VOC-based diagnostic test of a colorectal disease by VOCs found in a subject’s exhaled breath of Hanna to the suggested exhaled breath samples of Probert to create a similar VOC-based test for the exhaled breath as Hanna teaches there are advantages of exhaled breath tests over fecal test (p2, L 19-21, “An alternative approach for faecal-based tests is exhaled breath testing with the potential for high compliance because of the nature of the test and the possibility for testing more than one disease with different VOC discriminative signatures [8,9].”). And Hanna is not limited to performing this test generation for a single condition – the same steps are applied to create a similar exhalation diagnostic test for pancreatic cancer. Furthermore, it would be obvious to expand the VOC testing of colorectal cancer in Hanna to specifically include a testing of CDI as Biffi teaches that colorectal cancer increases the risk of a subject experiencing CDI, where CDI would become an additional major health impairment and can be particularly difficult to treat in certain settings (Biffi: p1-3). In sum, the idea to generate an exhaled breath test for CDI was present in the art (Probert), the expected advantage and a process to find the optimal exhaled breath VOCs was also present in the art (Hanna).
Yet their combined efforts fail to teach inputting one or more of the VOC quantities into a machine learning model trained on a dataset of CDI-positive and CDI-negative breath samples stored in a non-transitory memory and implemented by a processor and generating an output; and
diagnosing the subject as having or not having CDI based on the output of the machine learning model.
However Haick teaches a method of diagnosing a disease in a subject from pattern recognition of exhaled volatile organic compounds (Abstract) comprising
obtaining a breath sample from the subject ([0040]-[0042]);
detecting one or more volatile organic compounds (VOC)s and their quantities to obtain a VOC profile of the breath sample using an analytic device, wherein the analytical device comprises a selected-ion flow-tube mass spectrometry (SIFT-MS) system or a gas chromatography-mass spectrometry (GC-MS) system ([0040]-[0042]);
a processing unit configured to perform spectral analysis to generate a VOC profile ([0012] processing unit to perform analysis of VOCs, [0040]-[0042], [0160] results of GC-MS is a VOC profile );
inputting one or more of the VOC quantities into a machine learning model trained on a dataset of disease-positive and disease-negative breath samples ([0116]-[0117] pattern recognition may be performed by machine learning models, such as a Discriminant Function Analysis and Support Vector Machine, with training examples marked with distinct categories, [0139] with training sets constructed with patients afflicted with disease and patients not afflicted with disease) stored in a non-transitory memory and implemented by a processor ([0120]) and generating an output ([0116] “Thus, the pattern recognition analyzer receives output signals of the sensor set, wherein said output signals may include a plurality of response induced parameters extracted from said signal, compares them to the disease-specific patters derived from the database and selects a closest match between the output signals of the sensor set and the disease-specific pattern. In other words, the pattern recognition analyzer chooses the disease-specific pattern, which has the closest match with the output signals of the sensor set.”); and
diagnosing the subject as having or not having CDI based on the output of the machine learning model ([0116] disease-specific pattern with closest match to patient’s VOC profile can be considered a diagnosis).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the exhaled VOC analysis of colorectal cancer and CDI of Hanna and Probert with the processing unit, non-transitory memory, and machine learning steps of Haick as a specific example of pattern recognition software motivated in Probert (p7).
Yet their combined efforts fail to teach administering a treatment to the subject if the subject has been diagnosed with having CDI wherein the treatment comprises administration of metronidazole, vancomycin, fidaxomicin, or rifaximin, or fecal bacteriotherapy, probiotic therapy, or monoclonal antibody therapy.
However Koo teaches a VOC-based diagnosis of CDI based on fecal matter (Abstract) comprising administering a treatment to a subject if the subject has been diagnosed with having a CDI ([0009] patient sample evaluated based on VOCs, a treatment is applied if the VOCs indicate CDI, [0011] “In some embodiments, the treatment comprises administration of one or more doses of one or more antibiotic compounds, e.g., metronidazole, vancomycin, fidaxomicin, or rifaximin.” [0012] “In some embodiments, the treatment comprises non-antibiotic therapy, e.g., fecal bacteriotherapy, probiotics, or monoclonal antibodies.” potential applied treatments).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to administer one of the above listed treatments of Koo to a subject if diagnosed with CDI as taught by Hanna and Probert to begin combatting the infection as soon as possible.
Regarding Claim 11, Hanna, Probert, Biffi, Haick, and Koo teach the method of claim 10, wherein the treatment comprises administration of metronidazole, vancomycin, fidaxomicin, or rifaximin (See Claim 10 Rejection).
Regarding Claim 12, Hanna, Probert, Biffi, Haick, and Koo teach the method of claim 10, wherein the treatment comprises fecal bacteriotherapy, probiotic therapy, or monoclonal antibody therapy (See Claim 10 Rejection).
Regarding Claim 13, Hanna, Probert, Biffi,Haick, and Koo teach the method of claim 10, wherein the machine learning model is developed using a population of patients with and without CDI (See Claim 10 Rejection, Haick’s machine learning pattern recognition through machine learning applied to Hanna’s subject pool of positive diagnosis and negative diagnosis patients for Hanna and Probert’s CDI diagnosis).
Regarding Claim 14, Hanna, Probert, Biffi, Haick, and Koo teach the method of claim 10, wherein the analytic device is a selected-ion flow tube mass spectrometry (SIFT-MS) (See Claim 10 Rejection).
Regarding Claim 16, Hanna, Probert, Biffi,Haick, and Koo teach the method of claim 10, and Hanna teaches wherein the one or more VOC quantities inputted into the machine learning model are selected from 2-propanol, acetaldehyde, acetone, acetonitrile, acrylonitrile, benzene, carbon disulfide, dimethyl sulfide, ethanol, isoprene, pentane, 1-decene, 1-heptene, 1-nonene, 1-octene, 3-methylhexane, (E)- 2-nonene, ammonia, ethane, hydrogen sulfide, triethyl amine, and trimethyl amine (See Claim 1 Rejection, Table 4, propanol, acetaldehyde, acetone, isoprene, ammonia, hydrogen sulfide, p40, L. 25-34, disruption of microbiome will lead to changes in VOC presence).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include common colon disease related exhaled VOCs of Hanna as inputs when generating your machine model for CDI taught by Hanna, Probert, and Haick as these VOCs have previously shown diagnostic capability as exhaled byproducts of a disease.
Regarding Claim 18, Hanna, Probert, Biffi,Haick, and Koo teach the method of claim 10, and Haick teaches wherein the analytic device is portable ([0052]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, that the system for CDI diagnosis of Probert should be made portable as taught by Haick as this enable the system to be applied in a wider range of settings, increasing its utility.
Regarding Claim 19, while Hanna, Probert, Biffi, Haick, and Koo teach the method of claim 10, and Hanna teaches wherein a diagnosis of colorectal cancer indicates that the subject is at least 70% likely to have CDI (See Claim 10 Rejection, p38, “In distinguishing CRCa from negative controls propanal as a single breath biomarker based on a threshold of 28ppbv, had a sensitivity of 96% and specificity of 76%. Propanal at a threshold of 28ppbv was also able to distinguish CRCa from positive control patients with a sensitivity of 90% and specificity of 66%.” sensitivity of 71.4% in recurrence) and Haick teaches an accuracy goal for diagnosis is between 80-95% when choosing between diseases ([0132]-[0133]) and 58% when compared to control groups ([0243]), and further teaches that accuracy is a balance of extracted sensing features, minimal number of sensors, and differentiation efficiency ([0108]), their combined efforts fail to teach wherein a diagnosis of CDI indicates that the subject is at least 70% likely to have CDI.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, that Hanna and Probert’s diagnosis can be optimized for a 70% likelihood of CDI by considering the use of greater extracted sensing features, VOCs, and number of sensors to more likely prevent the spread of CDI in undesirable situations and is an example of an optimization of the range needed of number of sensing features and number of sensors [“[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation.” In reAller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955)].
Regarding Claim 20, while Hanna, Probert, Biffi, Haick, and Koo teach the method of claim 10, and Hanna teaches wherein a diagnosis of colorectal cancer indicates that the subject is at least 80% likely to have CDI (See Claim 10 Rejection, p38, “In distinguishing CRCa from negative controls propanal as a single breath biomarker based on a threshold of 28ppbv, had a sensitivity of 96% and specificity of 76%. Propanal at a threshold of 28ppbv was also able to distinguish CRCa from positive control patients with a sensitivity of 90% and specificity of 66%.” sensitivity of 71.4% in recurrence) and Haick teaches an accuracy goal for diagnosis is between 80-95% when choosing between diseases ([0132]-[0133]) and 58% when compared to control groups ([0243]), and further teaches that accuracy is a balance of extracted sensing features, minimal number of sensors, and differentiation efficiency ([0108])., their combined efforts fail to teach wherein a diagnosis of CDI indicates that the subject is at least 70% likely to have CDI.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, that Hanna and Probert’s diagnosis can be optimized for a 80% likelihood of CDI by considering the use of greater extracted sensing features, VOCs, and number of sensors to more likely prevent the spread of CDI in undesirable situations and is an example of an optimization of the range needed of number of sensing features and number of sensors [“[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation.” In reAller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955)].
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanna in view of Probert and further in view of Biffi and further in view of Haick and further in view of Koo and further in view of Laskowski et al (US 2017/0191910) (“Laskowski”).
Regarding Claim 17, while Hanna, Probert, Biffi, Haick, and Koo teach the method of claim 10, Hanna teaches wherein the VOC quantities inputted into the machine learning model are selected from 2-propanol, acetaldehyde, acetone, acetonitrile, acrylonitrile, benzene, carbon disulfide, dimethyl sulfide, ethanol, isoprene, pentane, 1-decene, 1-heptene, 1-nonene, 1-octene, 3-methylhexane, (E)- 2-nonene, ammonia, ethane, hydrogen sulfide, triethyl amine, and trimethyl amine (See Claim 1 Rejection, Table 4, propanol, acetaldehyde, acetone, isoprene, ammonia, hydrogen sulfide, p40, L. 25-34, disruption of microbiome will lead to changes in VOC presence).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include common colon disease related exhaled VOCs of Hanna as inputs when generating your machine model for CDI taught by Hanna, Probert, and Haick as these VOCs have previously shown diagnostic capability as exhaled byproducts of a disease.
Yet their combined efforts fail to teach wherein the VOC quantities inputted into the machine learning model comprise 2-propanol, acetaldehyde, acetone, acetonitrile, acrylonitrile, benzene, carbon disulfide, dimethyl sulfide, ethanol, isoprene, pentane, 1-decene, 1-heptene, 1-nonene, 1-octene, 3-methylhexane, (E)-2-nonene, ammonia, ethane, hydrogen sulfide, triethyl amine, and trimethyl amine.
However Laskowski teaches an exhaled breath VOC collector and analyzer (Abstract, [0039]) wherein VOCs in exhaled breath related to disease commonly include 2-propanol, acetaldehyde, acetone, acetonitrile, acrylonitrile, benzene, carbon disulfide, dimethyl sulfide, ethanol, isoprene, pentane, 1-decene, 1-heptene, 1-nonene, 1-octene, 3-methylhexane, (E)- 2- nonene, ammonia, ethane, hydrogen sulfide, triethyl amine, and trimethyl amine ([0033] “Several possible volatile organic compounds, i.e. any compound of carbon, excluding carbon monoxide, and carbon dioxide, can be targeted for analysis and/or collection, including but not limited to, nitric oxide, isoprene, beta hydroxybutyrate, 2-propanol, acetaldehyde, acetone, acetonitrile, acrylonitrile, benzene, carbon disulfide, dimethy! sulfide, ethanol, isoprene, pentane, 1-decene, 1-heptane, 1-nonene, 1-octene, 3-methylhexane, E-2-nonene, ammonia, ethane, hydrogen sulfide, triethyl amine, and trimethyl amine.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to additionally utilize common disease related exhaled VOCs of Laskowski as inputs when generating your machine model for CDI of Hanna, Probert, and Haick as these VOCs have previously shown diagnostic capability as exhaled byproducts of a disease.
Claim(s) 10 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanna in view of Probert and further in view of Thomas et al (US 2017/0242018) (“Thomas”) and further in view of Haick and further in view of Huang et al (“Improved Real-Time Puff-by-Puff GC–MS System for Whole Smoke Analysis”) (“Huang”) and further in view of Koo.
Regarding Claim 10, while Hanna teaches a method of diagnosing a subject with pancreatic cancer (Abstract, p18, L. 17 – p27, L. 13) to identify optimal treatment (p12, L. 33 – p13, L. 9), comprising:
obtaining a breath sample from the subject (Abstract, p19, L. 32 – p20, L. 27);
detecting one or more volatile organic compounds (VOC)s and their quantities to obtain a VOC profile of the sample using an analytic device (Abstract, p20, L. 29 – p22, L. 2, identifies a volatile organic compound and a quantity threshold that provides diagnostic values, uses GC-MS for analysis) wherein the VOC profile comprises one or more of the VOCs detected and a corresponding quantity of the one or more of the VOCs detected (p22, L. 4 – p25, L. 11, 44 VOCs were tested to find suitable VOCs with diagnostic capability)
a processing unit configured to perform spectral analysis to generate a high-resolution VOC profile (p21, L. 30 – p22, L. 2, p31, L. 30 – p32, L. 31, data of invention was processed by software, necessitating the use of a processing unit, and p21, L. 19 - p22, L. 32, GC-MS was utilized as the detection methodology and recognized as providing real-time results, p. 37, L 2-11, particulars of sampling methodology noted to ensure SIFT-MS and GC-MS provide a higher resolution to distinguish “propanal from the abundant acetone peak (RT of 9.11 min) although they both possesses the common fragment ion of 58 m/z.” Furthermore, Examiner notes that Applicant considers GC-MS as high-resolution analyses as the supporting paragraphs [0034]-[0035] for the claim limitation “real-time spectral analysis to generate a high-resolution VOC profile” notes it is one of various methods that “identify thousands of substances in the breath.” As the term “resolution” does not appear in these paragraphs and no threshold is given for what constitutes a “high resolution,” Examiner believes all of the stated methods must be considered examples of methods that lead to high resolution VOC profiles due to their ability to identify numerous substances in the breath);
using one or more of the VOC quantities, univariate analysis, and multivariate analysis, training the models on a dataset of pancreatic cancer positive and pancreatic cancer negative breath samples, and generating a criteria for identifying pancreatic cancer (p24, L. 8 – p 25, L. 15, p39, L. 8-12, multiple VOC considered with their quantities to identify a distinguishing biomarker); and
Hanna further suggests a utility of diagnosing the subject as having or not having pancreatic cancer based on the output of the criteria (p26, L. 1 – p27, L. 13).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the identified VOC criteria for diagnosing a subject in Hanna as Hanna teaches the high sensitivity and specificity of such a test.
Yet Hanna fails to teach the method being applied for diagnosing a subject with a Clostridioides difficile infection (CDI).
However Probert teaches a method of diagnosing a subject with a Clostridioides difficile infection (CDI) (p3-6), comprising:
detecting one or more volatile organic compounds (VOC)s and their quantities to obtain a VOC profile of the sample using an analytic device wherein the VOC profile comprises one or more of the VOCs detected and a corresponding quantity of the one or more of the VOCs detected (p-11-13, Gas chromatograph profiles collected of normal patients and patients suffering from a CDI, p17, Table 1, VOCs with particular predictive value for listed conditions, Figs. 1a-2, figures of gas chromatograph profiles of collected samples, abundance/quantities over time of the present compounds in the sample, from patients of different conditions);
wherein the analytical device comprises a selected-ion flow-tube mass spectrometry (SIFT-MS) system or a gas chromatography-mass spectrometry (GC-MS) system (p7, “Preferably, the sample is analysed by mass spectroscopy, gas chromatography or by use of a so-called electronic nose - an electrochemical sensor, or an array of sensors, which specifically detects volatile compounds, especially pre-selected volatile compounds or patterns of volatile compounds… GC-MS analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum. Plant Pathology 50, 489-496 (2001)), that the strategy of gas chromatography/mass spectroscopy vapor analysis followed by sensor development to produce a minimal array of sensors is a successful strategy in the design of relatively inexpensive electronic nose instrumentation.” Gas chromatography -mass spectrometry recommended as a detection component of an analytical device);
performing spectral analysis to generate a VOC profile (p-11-13, Gas chromatograph profiles collected of normal patients and patients suffering from a CDI, p17, Table 1, VOCs with particular predictive value for listed conditions, Figs. 1a-2, figures of gas chromatograph profiles of collected samples, abundance/quantities over time of the present compounds in the sample, from patients of different conditions);
identifying one or more of the VOC quantities as indicative of an infection based on the use of pattern recognition software and generating an output (p7, “Preferably, the sample is analysed by mass spectroscopy, gas chromatography or by use of a so-called electronic nose - an electrochemical sensor, or an array of sensors, which specifically detects volatile compounds, especially pre-selected volatile compounds or patterns of volatile compounds… An array of such sensors with appropriate pattern recognition software would then give a rapid diagnosis.”); and
diagnosing the subject as having or not having CDI based on the output of the pattern recognition (p7, “An array of such sensors with appropriate pattern recognition software would then give a rapid diagnosis.”); and
Probert further specifies that the above methodology can be applied on an obtained breath sample from the subject (p5-6, “Preferably, the emission is a gaseous emission, for example, exhaled air, eructation or flatus. However, other emission samples such as ascites, sputum, urine, faeces, blood or tissue may be used. In this manner volatiles contained in microbiological metabolites produced by micro-organisms in the sample may be used to detect the presence of pathogens…. Collection of the gas may be done in many ways, for example by emission of the gas directly into a collection chamber and/or an associated vessel, by catheterisation of the area of interest, the gas then being analysed by typical gas analysis methods, for example, gas chromatography and/or mass spectrommetry… It is therefore another object of the present invention to provide a sample collecting device which allows easier collection and reduces the amount of sample lost.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply the generation of a VOC-based diagnostic test of a pancreatic disease by VOCs found in a subject’s exhaled breath of Hanna to the suggested exhaled breath samples of Probert to create a similar VOC-based test for the exhaled breath as Hanna teaches there are advantages of exhaled breath tests over fecal test (p2, L 19-21, “An alternative approach for faecal-based tests is exhaled breath testing with the potential for high compliance because of the nature of the test and the possibility for testing more than one disease with different VOC discriminative signatures [8,9].”). And Hanna is not limited to performing this test generation for a single condition – the same steps are applied to create a similar exhalation diagnostic test for colorectal cancer. Furthermore, it would be obvious to expand the VOC testing of pancreatic cancer in Hanna to specifically include a testing of CDI as Thomas teaches that aldehyde VOCs have shown a notable increase in both pancreatic cancer and CDI.
In sum, the idea to generate an exhaled breath test for CDI was present in the art (Probert), the expected advantage and a process to find the optimal exhaled breath VOCs was also present in the art (Hanna).
Yet their combined efforts fail to teach inputting one or more of the VOC quantities into a machine learning model trained on a dataset of CDI-positive and CDI-negative breath samples stored in a non-transitory memory and implemented by a processor and generating an output; and
diagnosing the subject as having or not having CDI based on the output of the machine learning model.
However Haick teaches a method of diagnosing a disease in a subject from pattern recognition of exhaled volatile organic compounds (Abstract) comprising
obtaining a breath sample from the subject ([0040]-[0042]);
detecting one or more volatile organic compounds (VOC)s and their quantities to obtain a VOC profile of the breath sample using an analytic device, wherein the analytical device comprises a selected-ion flow-tube mass spectrometry (SIFT-MS) system or a gas chromatography-mass spectrometry (GC-MS) system ([0040]-[0042]);
a processing unit configured to perform spectral analysis to generate a VOC profile ([0012] processing unit to perform analysis of VOCs, [0040]-[0042], [0160] results of GC-MS is a VOC profile );
inputting one or more of the VOC quantities into a machine learning model trained on a dataset of disease-positive and disease-negative breath samples ([0116]-[0117] pattern recognition may be performed by machine learning models, such as a Discriminant Function Analysis and Support Vector Machine, with training examples marked with distinct categories, [0139] with training sets constructed with patients afflicted with disease and patients not afflicted with disease) stored in a non-transitory memory and implemented by a processor ([0120]) and generating an output ([0116] “Thus, the pattern recognition analyzer receives output signals of the sensor set, wherein said output signals may include a plurality of response induced parameters extracted from said signal, compares them to the disease-specific patters derived from the database and selects a closest match between the output signals of the sensor set and the disease-specific pattern. In other words, the pattern recognition analyzer chooses the disease-specific pattern, which has the closest match with the output signals of the sensor set.”); and
diagnosing the subject as having or not having CDI based on the output of the machine learning model ([0116] disease-specific pattern with closest match to patient’s VOC profile can be considered a diagnosis).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the exhaled VOC analysis of colorectal cancer and CDI of Hanna and Probert with the processing unit, non-transitory memory, and machine learning steps of Haick as a specific example of pattern recognition software motivated in Probert (p7).
Yet their combined efforts fail to explicitly teach the processing unit configured to perform real-time spectral analysis to generate a high-resolution VOC profile when using GC-MS.
However Huang teaches a gas chromatography mass spectrometer system (Abstract) and further teaches that a system can be configured to perform real-time analysis of a gas sample with the GC-MS method (Abstract).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to perform the exhaled VOC analysis of CDI of Probert and Haick with a system configured for real-time analysis as taught by Huang as real-time analysis enables faster diagnosis of a patient. Furthermore, Examiner notes that Applicant considers gas chromatography-mass spectroscopy as high-resolution analyses as the supporting paragraphs [0034]-[0035] for the amendment “spectral analysis to generate a high-resolution VOC profile” as it is one of various methods that “identify thousands of substances in the breath.” As the term “resolution” does not appear in these paragraphs and no threshold is given for what constitutes a “high resolution,” Examiner believes all of the stated methods must be considered examples of methods that lead to high resolution VOC profiles.
Yet their combined efforts fail to teach administering a treatment to the subject if the subject has been diagnosed with having CDI wherein the treatment comprises administration of metronidazole, vancomycin, fidaxomicin, or rifaximin, or fecal bacteriotherapy, probiotic therapy, or monoclonal antibody therapy.
However Koo teaches a VOC-based diagnosis of CDI based on fecal matter (Abstract) comprising administering a treatment to a subject if the subject has been diagnosed with having a CDI ([0009] patient sample evaluated based on VOCs, a treatment is applied if the VOCs indicate CDI, [0011] “In some embodiments, the treatment comprises administration of one or more doses of one or more antibiotic compounds, e.g., metronidazole, vancomycin, fidaxomicin, or rifaximin.” [0012] “In some embodiments, the treatment comprises non-antibiotic therapy, e.g., fecal bacteriotherapy, probiotics, or monoclonal antibodies.” potential applied treatments).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to administer one of the above listed treatments of Koo to a subject if diagnosed with CDI as taught by Hanna and Probert to begin combatting the infection as soon as possible.
Regarding Claim 15, Hanna, Probert, Thomas, Haick, Huang, and Koo teach the method of claim 10, wherein the analytic device is a gas chromatograph (See Claim 10 Rejection).
Response to Arguments
Applicant’s amendments and arguments filed 3/27/2026 with respect to the Specification objection been fully considered, and are persuasive. The objection is withdrawn.
Applicant’s amendments and arguments filed 3/27/2026 with respect to the 35 USC 101 rejections have been fully considered, and are persuasive. The cancellation of Claims 1-9 obviate the 35 USC 101 rejection and thus the rejection is withdrawn.
Applicant’s claim cancellation of Claims 1-9, filed 3/27/2026, obviate the 35 USC 103 rejections of Claims 1-9. Therefore, the rejection has been withdrawn.
Applicant’s arguments, filed 3/27/2026, with respect to the 35 USC 103 rejection of Claim 10 have been fully considered, but are not persuasive.
Applicant notes that a multiplicity of references are being combined to reject the claims and posit that the fact that it takes 6 pages to lay out the basic arguments for rejection of claim suggests that the claim is non-obvious. Examiner respectfully disagrees. In response to applicant's argument that the examiner has combined an excessive number of references, reliance on a large number of references in a rejection does not, without more, weigh against the obviousness of the claimed invention. See In re Gorman, 933 F.2d 982, 18 USPQ2d 1885 (Fed. Cir. 1991). And Examiner will outline below how the references combine together to make a obvious combination to one of ordinary skill in the art before the effective filing date of the claimed invention.
Applicant asks how the new rejection of Claim 10 could be obvious when the arguments against the same references was found persuasive, with the exception of the reference of Biffi. Applicant then reviews the subject matter of Biffi to emphasize that it doesn’t teach the claimed invention. Examiner will note that the claim rejection has been reorganized in terms of what reference acts as an independent claim.
To clarify, Hanna teaches a methodology for generating a diagnostic test based on exhaled VOCs, where Hanna specifically applied the methodology to an example of colorectal cancer. Hanna goes on to say that the methodology would be useful in diagnosing subjects, that the methodology can be applied to multiple diseases, where a second example disease is provided for the methodology in the form of pancreatic cancer, and teaches advantages in exhaled VOC tests over flatus/stool VOC tests. From these teachings, it would be obvious to deploy the diagnostic test for diagnosis purposes in colorectal cancer. Probert then teaches a methodology for applying flatus VOC testing for identifying Clostridioides difficile infections (CDI) and makes note that this testing could theoretically be applied as an exhalation test. With the knowledge of Hanna, one of ordinary skill in the art would recognize the advantages of exhaled tests would be applicable to Probert’s test, recognize that diagnosing an additional disease of CDI would not interfere with the functioning of Hanna, and identify that this increases the utility of Hanna. Biffi’s is then cited to support this conclusion by further stating that CDI is related to colorectal cancer and is a serious patient condition that requires immediate addressing. Hanna’s inclusion of Probert and Biffi would be obvious because you can better monitor and treat patients if you successfully identify the occurrence of CDI alongside colorectal cancer. Finally, Haick is added to identify how a non-transitory memory can support the application of the above methodology and that a specific example of pattern recognition for Hanna would be machine learning. And finally, Koo is added to teach that recognizing a serious patient condition would be cause to start treatment and Koo gives examples of what those treatments could be. Haick and Koo are thus adding complimentary teachings to facilitate the performance of the method of Hanna and Probert and further ensuring best patient outcome.
Applicant’s also notes the previous persuasive arguments against the cited references. Examiner agrees with the characterization of Probert (when viewed without the context of the other references). Examiner agrees that the post-filing publication outlines advantageous effects – Examiner contends that advantageous effects would also be recognized in view of the above combination. And Examiner agrees that Haick and Hanna, as singular respective references, do not teach that breath samples could be used diagnose CDI. However, with the added consideration above, Examiner maintains the applied 35 USC 103 rejections of claim 10.
The rejection stands.
Consequently, Claims 11-20 remain rejected due to their dependency on rejected independent claim 10.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JAIRO H. PORTILLO/
Examiner
Art Unit 3791
/PUYA AGAHI/Primary Examiner, Art Unit 3791
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