Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/05/2026 was filed after the mailing date of the non-final Office action on 12/19/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The examiner notes that the prior art found in the 05/05/2026 IDS, with the exception of Harant et al. US PG-Pub 20170052158 (as cited in the non-final office action as US PAT 10267776 B2), otherwise does not correlate with the relevant art in this application. The prior art found within the IDS appears to be related to US application No. 17/927347, directed towards a capillary processing module.
Response to Arguments
Applicant's arguments filed 02/27/2026 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. § 103 rejection of claim 1, the applicant states that providing a suggested remedy based on the state of the gas chromatography based on if it’s in a healthy or unhealthy state (as taught by Sutan, US PG-PUB 20120016597 A1, as cited in the prior action and IDS), is not equivalent to the claimed automated troubleshooting procedure (see remarks, page 10).
However, the applicant does not further elaborate what makes the automated troubleshooting procedure of the application functionally distinct and sophisticated from the system of Sutan diagnosing a fault and generating a report of the fault and a suggestion for remedying the issue (i.e., automated troubleshooting and providing a maintenance task to the user to resolve the fault). Additionally, the method of Sutan’s invention can automatically adjust operating parameters of the gas chromatography apparatus in response to signals indicative of the diagnosed fault (see Sutan, [0063]-[0068], [0078]). When applying broadest reasonable interpretation, the method provided by Sutan is functionally indistinguishable from the claimed subject matter regarding automated troubleshooting procedures and therefore defines over that particular claim element.
The applicant also states for claim 1 that Harant et al. (US PAT 10267776 B2, as cited in the prior action) does not teach modelling physical characteristics of the gas chromatography system and analyte-column specific thermodynamic properties to simulate chromatographic separation and calculate the expected parameter value (as cited in remarks, page 11).
However, claim 1 doesn’t explicitly teach modelling “physical” characteristics, nor analyte-column specific thermodynamic properties to simulate chromatographic separation, even in the amendment given. Harant et al. teaches modelling the transit of particles within a gas chromatography column using a direct model of the column, by simulating, sequentially and randomly, the individual transit of particles using probability laws describing the behavior of the particle in the column (i.e., modelling separation). The simulation of many particles x of a given species X then allows a statistical distribution relating to their transit time in the column, which time is also called the retention time (see Harant et al., col. 3/lines 32-42). Retention time is a parameter defined to be a chromatographic parameter (as claimed in claim 2). Given the broadest reasonable interpretation of the claim, Harant et al. modelling of a gas chromatography column would define over the given claim regarding simulating chromatographic simulation, as it is functionally indistinguishable.
Given that both Sutan and Harant et al. define over the art, Gugaliya et al. (WO 2020044165 A1, as cited in the prior action) and Van de Cotte et al. (US PG-Pub 20180283392 A1, as recited in the prior action) will have no problem being in combination with Sutan and Harant under obviousness. Additionally, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Drawings
The corrected drawings were received on 03/12/2026. These drawings for Figs. 2, 5M-5R, 10B-10G and 16A-16C are acceptable.
Claim Objections
Claim 21 is objected to because of the following informalities: It says it’s an (Original) claim, but claim 21 contains amendments. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 13-16, 18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sutan (US PG-Pub US 20120016597 A1, as cited in the IDS), in view of Harant et al (US PAT 10267776 B2).
Regarding claim 1, Sutan teaches methods of analyzing gas chromatography data, and using it to calibrate and configure the gas chromatograph (see Sutan, Abstract). The method comprises comparing the response factor data with an historical response factor data set (referred to as "footprint data") which may be, for example, acquired from the gas chromatography apparatus when it is known to be in a healthy condition. Such known conditions include after a multilevel calibration (see Sutan, [0024]). This data can be used in the diagnosis of a fault in the gas chromatography apparatus, where one may retrieve retention time data for each of a plurality of compounds contained in one or more calibration gas samples, and compare the retention time data found with the historical retention time data previously recorded (see Sutan, [0038]). These comparisons are made see whether the results suggest that the gas chromatograph is moving from a healthy state to an unhealthy state when data is falling out of the boundaries of the established footprint data. Appropriate intervention can thus be determined and the maintenance needed on the gas chromatograph system before it enters an unhealthy state. A report created by the computer is additionally generated which is visually presented and/or stored for later review. If the system is characterized as unhealthy, and data falls outside the specified footprint data range, the computer may perform additional analysis on the calculated correlations to diagnose the fault, and generate a report of the fault and a suggestion for remedying the issue, or send a signal indicative of the diagnosed fault which further relays configurations and adjustments to the gas chromatograph apparatus (see Sutan, [0063]-[0068], [0078]).
Sutan fails to teach generating a simulated chromatographic separation using a chromatographic model based on a configuration of the GC system.
However, in the analogous art of methods for estimated a retention time in chromatography columns, Harant et al teaches a method for estimating a retention time of a particle in a chromatography column. The retention time is estimated on a probabilistic basis by sequentially modelling and simulating the transit of the particles in the column, allowing for statistical distribution of the retention time in the column to be determined (see Harant et al, Abstract, col. 10, lines 44-65). This is done by modelling the transit of particles within a gas chromatography column using a direct model of the column, by simulating, sequentially and randomly, the individual transit of particles using probability laws describing the behavior of the particle in the column. The simulation of many particles x of a given species X then allows a statistical distribution relating to their transit time in the column, which time is also called the retention time (see Harant et al., col. 3/lines 32-42). 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 methods of analyzing gas chromatography data of Sutan to incorporate simulating chromatography separation via modelling column and particle transit (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
Regarding claim 2, the combination of Sutan and Harant et al teaches the exact limitations of claim 2. Specifically, Sutan teaches the method of claim 1, wherein the at least one chromatographic parameter comprises one or more of a retention time, a relative retention time, a retention index, an adjusted retention time, a peak height, a peak area, a peak width, a peak symmetry, a peak resolution, a peak capacity, a skew, a kurtosis, a Trennzahl, a capacity factor, a selectivity, an efficiency, an apparent efficiency, a tailing factor, a concentration, and a mole quantity of an analyte analyzed by the GC system (see Sutan, [0007], [0026], [0038], disclosing components of a samples composition generates peak areas in the output of the detectors during calibration. In diagnosing faults of a gas chromatography apparatus, may also involve retrieving and comparing retention times or molecular weight to historical data.).
Regarding claim 3, the combination of Sutan and Harant et al teaches the exact limitations of claim 3. Specifically, Sutan teaches the method of claim 1, wherein the automated troubleshooting procedure also uses instrument data from the sample chromatographic separation to determine the expected maintenance task, and wherein transmitting the maintenance notification comprises determining the expected maintenance task from a plurality of different maintenance tasks and alerting a user of the gas chromatograph system to the expected maintenance task (see Sutan, [0065], [0067]-[0068], disclosing that if the calibration data of the gas chromatograph is out of range of the footprint data, the gas chromatograph is then characterized as unhealthy, and a report of the fault is generated and visually displayed to the user, with a suggested remedy for the fault. Further see [0078], disclosing the automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus.).
Regarding claim 4, the combination of Sutan and Harant et al teaches the exact limitations of claim 4. Specifically, Sutan teaches the method of claim 3, wherein the instrument data comprises one or more of a temperature value, a pressure sensor value, a valve state, a motor step, a sample injection count, a motor duty cycle, a heater current value, a heater duty cycle, a motor current value, a flow sensor value, a detector signal value, a detector current value, a detector frequency value, a calibration table, an auto-zero value, a sensor zero value, a time on value, and a valve duty cycle value of the GC system (see Sutan, [0065], disclosing methods use footprint information generated when the gas chromatograph (GC) is known to be functioning correctly, including data such as oven temperature, carrier gas pressure, carrier gas flow rate, response factor, etc. are recorded.).
Regarding claim 5, the combination of Sutan and Harant et al teaches the exact limitations of claim 5. Specifically, Sutan teaches the method of claim 1, wherein the automated troubleshooting procedure performs one or more diagnostic tests to determine the expected maintenance task (see Sutan, [0015], [0026], [0031], [0034], [0036], [0065] and [0067]-[0068], disclosing that if the calibration data of the gas chromatograph is out of range of the footprint data, the gas chromatograph is then characterized as unhealthy, and a report of the fault is generated and visually displayed to the user, with a suggested remedy for the fault. There are several aspects to diagnosing a fault of a chromatography apparatus, including determining a correlation between response factor data acquired in the gas chromatograph and molecular weight data for each of a plurality of compounds in one or more calibration gas samples. The response factor data may also include comparing the retention time data with historical retention time data acquired from the gas chromatograph apparatus. Further see [0078], disclosing the automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus.).
Regarding claim 13, Sutan teaches methods that use footprint information generated when the gas chromatograph (GC) is known to be functioning correctly, including data such as oven temperature, carrier gas pressure, carrier gas flow rate, response factor, etc. are recorded. Data from these variables can be used to diagnose and adjust valve leaks or back pressure of a vent of a gas chromatograph (see Sutan, [0064], [0079]). Sutan additionally teaches calculating R2 values for the respective groups of compounds of the current calibration data to the footprint data (e.g. retention time) to determine the healthy or unhealthy condition of the GC system. The GC is considered healthy when at or exceeding the R2 value, but when beneath the given threshold, it is instead considered unhealthy and a report of the fault is generated and visually displayed to the user, with a suggested maintenance action for the fault (see Sutan, [0065], [0067]-[0068]). The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus (see Sutan, [0078]).
Sutan fails to teach the method of claim 1, wherein the automated troubleshooting procedure further comprises performing the expected maintenance task on one or more of a sample introduction system, a sample inlet, a column, a column heater, and a detector of the GC system to correct the chromatographic parameter being outside of the performance control limit and/or expected to be outside of the performance control limit.
However, Harant et al teaches chromatography as a commonly used technique for analyzing chemical species in gaseous of liquid media. It comprises of chromatography columns, where the carrier fluid transits a channel between an entrance and an exit. A chromatography column also includes a detector, placed at the exit of the channel, for detecting the presence of the particle when the latter exits the channel. (see Harant et al, col. 1, lines 13-27). Harant et al additionally teaches a temperature modulator in the column (see Harant et al, col. 5, lines 30-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify determining maintenance action of Sutan to incorporate the components of the gas chromatography system (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
Regarding claim 14, the combination of Sutan and Harant et al teaches the exact limitations of claim 14. Specifically, Sutan teaches The method of claim 1, further comprising performing a verification chromatographic separation after performing the expected maintenance task, wherein the verification chromatographic separation is compared to the simulated chromatographic separation or a previous reference chromatogram to verify that the expected maintenance task corrects the at least one chromatographic parameter from being outside of the performance control limit and/or expected to be outside of the performance control limit (see Sutan, [0081], discloses verifying the nature of a fault by analyzing response factor (RF) data for each components of a gas sample. For each component, response factor is compared to the response factor for the footprint data, or where available, a response factor trend is analyzed using several historical data sets. Where the measured response factor is greater for the majority of components in comparison to the footprint data, or where the trend is for the response factor to increase, this indicates a valve in the GC is leaking. Appropriate maintenance actions for the valve leak can then be assessed and performed.).
Regarding claim 15, The combination of Sutan and Harant et al teaches the exact limitations of claim 15. Specifically, Sutan teaches the method of claim 14, wherein if the verification chromatographic separation verifies that the at least one chromatographic parameter is within the performance control limit, the verification chromatographic separation replaces the reference chromatographic separation (see Sutan, [0081], discloses verifying the nature of a fault by analyzing response factor (RF) data for each component of a gas sample. For each component, response factor is compared to the response factor for the footprint data, or where available, a response factor trend is analyzed using several historical data sets. Where the measured response factor is greater for the majority of components in comparison to the footprint data, or where the trend is for the response factor to increase, this comparison can be used with the response factor over the footprint data to determine that the valve on the GC is leaking.).
Regarding claim 16, The combination of Sutan and Harant et al teaches the exact limitations of claim 16. Specifically, Sutan teaches the method of claim 1, wherein the chromatographic performance monitoring comprises plotting a control chart including the at least one chromatographic parameter of the sample and a sample injection count, wherein the control chart is utilized to extrapolate data of the at least one chromatographic parameter to predict if and/or when the at least one chromatographic parameter will be outside of the performance control limit, and wherein the control chart is utilized to generate the maintenance notification of an expected GC system failure prior to the at least one chromatographic parameter of the sample being outside of the performance control limit and/or expected to be outside of the performance control limit (see Sutan, [0052]-[0055], Fig. 3, discloses calculating and plotting logarithms of the response factor (RF) data and the molecular weight data to generate a graph of the logarithms of the response factor data versus the logarithms of the molecular weight data. The method outputs the square of Pearson's correlation coefficient R from a trend line, where it can be used in a pre-programmed threshold value of 0.99 R2 in determining the degree of linearity of the response factor data. If it is found to be below 0.99, it can be indicative of an operating fault in the GC apparatus and may be characterized as unhealthy. See also [0065], [0067]-[0068], [0078], disclosing that reports of the fault may be generated and visually displayed to the user if the system is deemed unhealthy based on the trends, with a suggested maintenance action for the fault. The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault.).
Regarding claim 18, Sutan teaches comparing the response factor data with an historical response factor data set (referred to as "footprint data") which may be, for example, acquired from the gas chromatography apparatus when it is known to be in a healthy condition, or nominal state (see Sutan, [0024]).
Sutan fails to teach utilizing the chromatographic model during the troubleshooting procedure comprises a comparison between two or more of a nominal simulated chromatogram, and a real-time simulated chromatogram.
However, Harant et al teaches a method for estimating a retention time of a particle in a chromatography column. The retention time is estimated on a probabilistic basis by sequentially modelling and simulating the transit of the particles in the column, allowing for statistical distribution of the retention time in the column to be determined (see Harant et al, Abstract, col. 10, lines 44-65). 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 methods of analyzing gas chromatography data of Sutan to incorporate the models and simulations of an gas chromatograph for estimating retention times (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
Regarding claim 21, Sutan teaches methods of analyzing gas chromatography data, and using it to calibrate and configure the gas chromatograph (see Sutan, Abstract). The method comprises comparing the response factor data with an historical response factor data set (referred to as "footprint data") which may be, for example, acquired from the gas chromatography apparatus when it is known to be in a healthy condition. Such known conditions include after a multilevel calibration (see Sutan, [0024]). This data can be used in the diagnosis of a fault in the gas chromatography apparatus, where one may retrieve retention time data for each of a plurality of compounds contained in one or more calibration gas samples, and compare the retention time data found with the historical retention time data previously recorded (see Sutan, [0038]). These comparisons are made see whether the results suggest that the gas chromatograph is moving from a healthy state to an unhealthy state when data is falling out of the boundaries of the established footprint data. Appropriate intervention can thus be determined and the maintenance needed on the gas chromatograph system before it enters an unhealthy state. A report created by the computer is additionally generated which is visually presented and/or stored for later review. If the system is characterized as unhealthy, and data falls outside the specified footprint data range, the computer may perform additional analysis on the calculated correlations to diagnose the fault, and generate a report of the fault and a suggestion for remedying the issue, or send a signal indicative of the diagnosed fault which further relays configurations and adjustments to the gas chromatograph apparatus (see Sutan, [0063]-[0068], [0078]). The teachings above may be implemented into a computer system, and appreciated that it is implemented in software, hardware, firmware, or a combination thereof (see Sutan, [0051]).
Sutan fails to teach a gas chromatography (GC) system for analyzing a sample, the GC system comprising: a GC column comprising an entrance and an exit, wherein the GC column is configured for chromatographic separation of a sample comprising one or more analytes; a GC detector fluidically connected to the exit of the GC column; and a controller communicably connected to at least the GC detector, the controller configured to: generate a simulated chromatographic separation using a chromatographic model based on a configuration of the GC system.
However, Harant et al teaches chromatography as a commonly used technique for analyzing chemical species in gaseous of liquid media. It comprises of chromatography columns, the operating principle of which is well known: a particle carried by a fluid, called the carrier fluid, also designated by the expression mobile phase, transits a channel between an entrance and an exit. The wall of the channel includes a coating, called the stationary phase, with which the particle has an affinity such that the particle is capable of being momentarily adsorbed then desorbed. Depending on the affinity with the stationary phase, the transit of the particle through the channel may take more or less time. A chromatography column also includes a detector, placed at the exit of the channel, for detecting the presence of the particle when the latter exits the channel (see Harant et al, col. 1, lines 13-27).
Harant et al additionally teaches methods for estimating a retention time of a particle in a chromatography column. The retention time is estimated on a probabilistic basis by sequentially modelling and simulating the transit of the particles in the column, allowing for statistical distribution of the retention time in the column to be determined (see Harant et al, Abstract, col. 10, lines 44-65). 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 comparison of response data sets of Sutan to incorporate the models and simulations of an gas chromatograph for estimating retention times and its components (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
Claims 6-11, 17, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Sutan and Harant et al as applied to claim 1 above, and further in view of Gugaliya et al (WO 2020044165 A1).
Regarding claim 6, Sutan fails to teach the method of claim 1, wherein the chromatographic model utilizes actual instrument values of the GC system collected in real-time during the sample chromatographic separation performed by the GC system.
Additionally, Harant et al teaches a method for estimating a retention time of a particle in a chromatography column. The retention time is estimated on a probabilistic basis by sequentially modelling and simulating the transit of the particles in the column, allowing for statistical distribution of the retention time in the column to be determined (see Harant et al, Abstract, col. 10, lines 44-65). 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 methods of analyzing gas chromatography data of Sutan to incorporate the models and simulations of an gas chromatograph for estimating retention times (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
Furthermore, the combination of Sutan and Harant et al fails to teach that the actual instrument values are collected in real-time during the sample chromatographic separation performed by the GC system.
However, in the analogous art of control systems for detecting faults associated with gas chromatograph devices in process plant, Gugaliya et al teaches obtaining a real-time gas chromatogram by performing a real-time gas chromatography for the gaseous mixture in the GC device. Gugaliya et al further teaches machine learning model trained by providing the plurality of faults and the historic gas chromatogram received from the at least one database (see Gugaliya et al, [0006], [0014]). 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 modelling and simulations of the combination of Sutan and Harant et al by providing real-time gas chromatography (as taught by Gugaliya et al) into the models of the combination of Sutan and Harant et al, for the benefit of avoiding propagation of error caused due to faults in the GC device by performing detection of faults in real-time (see Gugaliya et al, [0014]).
Regarding claim 7, Sutan teaches see that if the calibration data of the gas chromatograph is out of range of the footprint data, the gas chromatograph is then characterized as unhealthy, and a report of the fault is generated and visually displayed to the user, with a suggested maintenance action for the fault (see Sutan, [0065], [0067]-[0068]). The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus (see Sutan, [0078]).
The combination of Sutan and Harant et al fails to teach that the automated troubleshooting procedure utilizes a decision tree to determine the expected maintenance task.
However, Gugaliya et al teaches a machine learning model used in training for fault signature data and historic gas chromatography data, where it may be a neural network, a decision tree, a market-basket analysis, and so on (see Gugaliya et al, [0030]). 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 reports, automatic configurations and adjustments done on the system when detecting a fault of the combination of Sutan and Harant et al to incorporate the machine learning model of decision trees (as taught by Gugaliya et al), for the benefit of providing a systemic evaluation of correctness and confidence in results of the GC device by using the machine learning model, allowing for early corrective actions to further aid in accurate, consistent, and reliable GC measurements (see Gugaliya et al, [0039], Fig. 5).
Regarding claim 8, Sutan teaches that the adjustment of the operating parameter may be affected by an operator intervention, or may be affected automatically in response to the output signal (see Sutan, [0033]).
The combination of Sutan and Harant et al fails to teach that a user inputs information into the decision tree.
However, Gugaliya et al teaches a machine learning model used in training for fault signature data and historic gas chromatography data, where it may be a neural network, a decision tree, a market-basket analysis, and so on (see Gugaliya et al, [0030]). 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 operator intervention of operating parameters of the combination of Sutan and Harant et al to further include the machine learning model of decision trees (as taught by Gugaliya et al), for the benefit of providing a systemic evaluation of correctness and confidence in results of the GC device by using the machine learning model, allowing for early corrective actions to further aid in accurate, consistent, and reliable GC measurements (see Gugaliya et al, [0039], Fig. 5).
Regarding claim 9, Sutan teaches calculating R2 values for the respective groups of compounds of the current calibration data to the footprint data (e.g. retention time) to determine the healthy or unhealthy condition of the GC system. The GC is considered healthy when at or exceeding the R2 value, but when beneath the given threshold, it is instead considered unhealthy and a report of the fault is generated and visually displayed to the user, with a suggested maintenance action for the fault (see Sutan, [0065], [0067]-[0068]). The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus (see Sutan, [0078]).
Sutan fails to teach the decision tree further determines performance of the expected maintenance task on one or more of a sample introduction system, a sample inlet, a column, a column heater, and a detector of the GC system to correct the at least one chromatographic parameter being outside of the performance control limit and/or expected to be outside of the performance control limit.
Additionally, Harant et al teaches chromatography as a commonly used technique for analyzing chemical species in gaseous of liquid media. It comprises of chromatography columns, where the carrier fluid transits a channel between an entrance and an exit. A chromatography column also includes a detector, placed at the exit of the channel, for detecting the presence of the particle when the latter exits the channel. (see Harant et al, col. 1, lines 13-27). Harant et al additionally teaches a temperature modulator in the column (see Harant et al, col. 5, lines 30-37). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify determining maintenance action of Sutan to incorporate the components of the gas chromatography system (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
However, the combination of Sutan and Harant et al fails to teach a decision tree.
However, Gugaliya et al teaches a machine learning model used in training for fault signature data and historic gas chromatography data, where it may be a neural network, a decision tree, a market-basket analysis, and so on (see Gugaliya et al, [0030]). 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 maintenance action and chromatography components from the combination of Sutan and Harant et al to further include the machine learning model of decision trees (as taught by Gugaliya et al), for the benefit of providing a systemic evaluation of correctness and confidence in results of the GC device by using the machine learning model, allowing for early corrective actions to further aid in accurate, consistent, and reliable GC measurements (see Gugaliya et al, [0039], Fig. 5).
Regarding claim 10, Sutan teaches calculating R2 values for the respective groups of compounds of the current calibration data to the footprint data (e.g. retention time) to determine the healthy or unhealthy condition of the GC system. The GC is considered healthy when at or exceeding the R2 value, but when beneath the given threshold, it is instead considered unhealthy and a report of the fault is generated and visually displayed to the user, with a suggested maintenance action for the fault (see Sutan, [0065], [0067]-[0068]). The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus (see Sutan, [0078]).
The combination of Sutan and Harant et al fails to teach wherein the automated troubleshooting procedure further utilizes a neural network.
However, Gugaliya et al teaches a machine learning model used in training for fault signature data and historic gas chromatography data, where it may be a neural network, a decision tree, a market-basket analysis, and so on (see Gugaliya et al, [0030]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify determining maintenance action of Sutan to incorporate the machine learning model of an neural network (as taught by Gugaliya et al), for the benefit of providing a systemic evaluation of correctness and confidence in results of the GC device by using the machine learning model, allowing for early corrective actions to further aid in accurate, consistent, and reliable GC measurements (see Gugaliya et al, [0039], Fig. 5).
Regarding claim 11, Sutan teaches calculating R2 values for the respective groups of compounds of the current calibration data to the footprint data (e.g. retention time) to determine the healthy or unhealthy condition of the GC system. The GC is considered healthy when at or exceeding the R2 value, but when beneath the given threshold, it is instead considered unhealthy and a report of the fault is generated and visually displayed to the user, with a suggested maintenance action for the fault (see Sutan, [0065], [0067]-[0068]). The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus (see Sutan, [0078]).
The combination of Sutan and Harant et al fails to teach wherein the automated troubleshooting procedure further utilizes a machine learning process.
However, Gugalya et al teaches that a sever is configured to determined one or more faults from a plurality of faults associated with the GC device. he one or more faults are determined using the at least one real-time symptom and a fault signature data. The fault signature data is generated using a machine learning model trained by providing the plurality of faults and the historic gas chromatogram received from the at least one database. A confidence score is determined for each of the one or more determined faults (see Gugalya et al, [0006]-[0007]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify determining maintainence action of Sutan to incorporate the machine learning model (as taught by Gugaliya et al), for the benefit of providing a systemic evaluation of correctness and confidence in results of the GC device by using the machine learning model, allowing for early corrective actions to further aid in accurate, consistent, and reliable GC measurements (see Gugaliya et al, [0039], Fig. 5).
Regarding claim 17, Sutan teaches obtaining response factor data during calibration of the GC using a calibration gas of a known composition, as the peak area over the gas mole percent. Sutan further teaches comparing the response factor data with an historical response factor data set (referred to as "footprint data") which may be, for example, acquired from the gas chromatography apparatus when it is known to be in a healthy condition, or nominal state (see Sutan, [0007], [0024]).
Sutan fails to teach generating the simulated chromatographic separation comprises generating a nominal simulated chromatogram and a real-time simulated chromatogram, and wherein utilizing the chromatographic model comprises comparing the real-time simulated chromatogram to the nominal simulated chromatogram.
Additionally, Harant et al teaches a method for estimating a retention time of a particle in a chromatography column. The retention time is estimated on a probabilistic basis by sequentially modelling and simulating the transit of the particles in the column, allowing for statistical distribution of the retention time in the column to be determined (see Harant et al, Abstract, col. 10, lines 44-65). 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 obtaining and comparison of response data sets of Sutan to incorporate the models and simulations of an gas chromatograph for estimating retention times (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
The combination of Sutan and Harant et al fails to teach that the chromatogram is a real-time simulated chromatogram.
However, Gugaliya et al teaches obtaining a real-time gas chromatogram by performing a real-time gas chromatography for the gaseous mixture in the GC device (see Gugaliya et al, [0014]). 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 response factor data sets and the simulations of the combination of Sutan and Harant et al by providing real-time gas chromatography (as taught by Gugaliya et al) into the models of the combination of Sutan and Harant et al, for the benefit of avoiding propagation of error caused due to faults in the GC device by performing detection of faults in real-time (see Gugaliya et al, [0014]).
Regarding claim 38, Sutan teaches that the gas chromatography methods and systems may be implemented into a computer system, and appreciated that it is implemented in software, hardware, firmware, or a combination thereof, which can communicate through signals sent by the computer (see Sutan [0051]-[0052]).
Sutan fails to teach a gas chromatography (GC) system for analyzing a sample, the GC system comprising: a GC column comprising an entrance and an exit, wherein the GC column is configured for chromatographic separation of a sample comprising one or more analytes; a GC detector fluidically connected to the exit of the GC column; at least one sensor configured to collect instrument data of the GC system; configured to: execute a chromatographic separation of the sample loaded into the GC system; and generate a simulated chromatographic separation of the sample utilizing the instrument data collected by the at least one sensor; wherein the controller is configured to generate the simulated chromatographic separation in real-time during the chromatographic separation of the sample.
Additionally, Harant et al teaches chromatography as a commonly used technique for analyzing chemical species in gaseous of liquid media. It comprises of chromatography columns, the operating principle of which is well known: a particle carried by a fluid, called the carrier fluid, also designated by the expression mobile phase, transits a channel between an entrance and an exit. The wall of the channel includes a coating, called the stationary phase, with which the particle has an affinity such that the particle is capable of being momentarily adsorbed then desorbed during separation. Depending on the affinity with the stationary phase, the transit of the particle through the channel may take more or less time. A chromatography column also includes a detector, placed at the exit of the channel, for detecting the presence of the particle when the latter exits the channel (see Harant et al, col. 1, lines 13-27).
Harant et al additionally teaches methods for estimating a retention time of a particle in a chromatography column. The retention time is estimated on a probabilistic basis by sequentially modelling and simulating the transit of the particles in the column, allowing for statistical distribution of the retention time and separation in the column to be determined (see Harant et al, Abstract, col. 10, lines 44-65). 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 computer system of Sutan to incorporate the simulations of an gas chromatograph for estimating retention times and its components (as taught by Harant et al), for the benefit of making it possible to determine statistical indicators, such as the average, variance, or other times, through describing the behavior of a set of molecules of a given species (See Harant et al, col.2, lines 13-25).
However, the combination of Sutan and Harant et al fails to teach wherein the controller is configured to generate the simulated chromatographic separation in real-time during the chromatographic separation of the sample.
However, Gugaliya et al teaches a GC device 100 used in performing separation of a gaseous mixture, performing elution in separation column 106 to achieve separation of the plurality of compounds in the gaseous mixture. The GC device 100 may output a real-time gas chromatograph from performing gas chromatography at real-time (see Gugaliya et al, [0014]-[0016], Fig. 1). 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 computer system, the simulations and components of the combination of Sutan and Harant et al by providing real-time gas chromatography (as taught by Gugaliya et al) into the models of the combination of Sutan and Harant et al, for the benefit of avoiding propagation of error caused due to faults in the GC device by performing detection of faults in real-time (see Gugaliya et al, [0014]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sutan and Harant et al as applied to claim 1 above, and further in view of Gugaliya et al, and Van de Cotte et al (US PG-Pub 20180283392 A1).
Regarding claim 12, Sutan teaches calculating R2 values for the respective groups of compounds of the current calibration data to the footprint data (e.g. retention time) to determine the healthy or unhealthy condition of the GC system. The GC is considered healthy when at or exceeding the R2 value, but when beneath the given threshold, it is instead considered unhealthy and a report of the fault is generated and visually displayed to the user, with a suggested maintenance action for the fault (see Sutan, [0065], [0067]-[0068]). The system may additionally send signals for automatic adjustment of operating parameters of the gas chromatograph apparatus in response to a signal indicative of the diagnosed fault. Thus, a configuration or adjustment signal may be generated by the computer system and received by the gas chromatograph apparatus (see Sutan, [0078]).
Sutan fails to teach the method of claim 1, wherein the automated troubleshooting procedure utilizes a neural network to associate one or more expected maintenance tasks with correction of the chromatographic parameter being outside of the performance control limit and/or expected to be outside of the performance control limit, and wherein if the chromatographic parameter being outside of the performance control limit and/or expected to be outside of the performance control limit is a recurring GC system issue the neural network determines an alternative maintenance task to correct the recurring GC system issue.
However, Gugaliya et al teaches a machine learning model used in training for fault signature data and historic gas chromatography data, where it may be a neural network, a decision tree, a market-basket analysis, and so on (see Gugaliya et al, [0030]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify determining maintenance action of Sutan to incorporate the machine learning model of an neural network (as taught by Gugaliya et al), for the benefit of providing a systemic evaluation of correctness and confidence in results of the GC device by using the machine learning model, allowing for early corrective actions to further aid in accurate, consistent, and reliable GC measurements (see Gugaliya et al, [0039], Fig. 5).
Furthermore, the combination of Sutan, Harant et al, and Gugaliya et al fails to teach wherein if the chromatographic parameter being outside of the performance control limit and/or expected to be outside of the performance control limit is a recurring GC system issue the neural network determines an alternative maintenance task to correct the recurring GC system issue. However, in the analogous art of determining quality of gas for rotating equipment in a petrochemical plant, Van de Cotte et al teaches based on collected data, and whether equipment starts to show wear or failure, corrective action may be taken, such as the replacement of faulty parts. Alternatively, or additionally, one or more inputs or controls relating to a process may further need to be adjusted as part of the corrective action, based on data collected from sensors (see Van de Cotte et al, [0064]-[0065]). 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 maintenance actions and machine learning from the combination of Sutan, Harant et al, and Gugaliya to incorporate additional or alternative adjustments or inputs to the equipment (as taught by Van de Cotte et al) based on given data, for the benefit of being able to adjust operating conditions to prolong equipment life or avoid equipment failure (see Van de Cotte et al, Abstract).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TRACY CHING-TIAN COLENA/ Examiner, Art Unit 1797
/JENNIFER WECKER/ Primary Examiner, Art Unit 1797
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