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 . The rejections from the Office Action of 10/9/2025 are hereby withdrawn. New grounds for rejection are presented below.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/9/2026 has been entered.
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.
Claim(s) 1, 3, 5-10, 18, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Willis et al. (US 20170084443 A1)[hereinafter “Willis”] and Borovcova et al., Simple area determination of strongly overlapping ion mobility peaks, Elsevier, 2017 [hereinafter “Borovcova”].
Regarding Claim 1, Willis discloses a method of analysing data generated by a time-of-flight ion analyser [Abstract – “Implementations of methods and apparatuses are disclosed for decoding multiplexed information in a chromatographic system. Implementations may include the method of pulsing ions from an ion source through an analyzer according to a predetermined multiplexing scheme, each pulse including one or more ions corresponding to a sample, detecting a plurality of ion strikes at a detector, determining a data point for each ion strike, wherein each data point includes an intensity of a detected ion strike and a time of the detected ion strike, maintaining a multiplexed spectrum of the data points, the multiplexed spectrum including the data points, and demultiplexing the time shifted spectrum using the data points of the multiplexed spectrum.”Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”], the method comprising:
digitizing a signal produced by a time-of-flight ion analyser by: generating a first segment of data from the signal [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”], wherein the first segment of data comprises data associated with a first arrival time range [Paragraph [0070] – “Referring now to FIG. 5, an example set of operations for a method 400 for operating a mass spectrometer that is configured to multiplex ion packets. For purposes of explanation, the method 400 is described with respect to the mass spectrometer 10 of FIG. 1. It is noted that the method 400 may be applied to other suitable mass spectrometers 10 without departing from the scope of the disclosure.”Paragraph [0072] – “At operation 412, the pulse generator 14 pulses the ions 24 into the analyzer 16 according to a multiplexing scheme. As previously discussed, the multiplexing scheme may be divided into intervals, and each interval may be divided into non-periodic subintervals. In some implementations, each interval is divided into the same subintervals as the other intervals. In this way, the subintervals within an interval are non-periodic, but the intervals are periodic, i.e., the first interval to of each interval is of the same duration, as is the second subinterval, the third subinterval, and so on and so forth.”Paragraph [0073] – “At operation 414, the detector 18 detects the ion strikes, and in response to the ion strikes outputs data points 26 indicating the intensity and the time of the detected ion strike. At operation 416, the data processor 20 maintains a multiplexed spectrum based on the collected data points 26. FIG. 6 illustrates an example of a multiplexed spectrum in a graph 300 format. Each time the detector 18 outputs a data point 26, the data processor 20 can include the data point 26 in the multiplexed spectrum. At this juncture, the multiplexed spectrum can be referred to as “raw data.””Paragraph [0028] – “FIG. 6 illustrates an example of data points 26 output by the detector 18 illustrated in the form of a graph 300. Each line, e.g., line 302 and line 304, indicates different concentrations of ions 24. The x-axis indicates times (e.g., in us) and the y-axis indicates an intensity of a detected ion strike. It is noted that the peaks indicate detected ion strikes. It is further noted that the graph of FIG. 6 may continue along the x-axis to illustrate subsequent ion strikes as well.”See Fig. 8, the first segment being the left-most peak cluster.]; and
generating a second segment of data from the signal [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”], wherein the second segment of data comprises data associated with a second different arrival time range [Paragraph [0070] – “Referring now to FIG. 5, an example set of operations for a method 400 for operating a mass spectrometer that is configured to multiplex ion packets. For purposes of explanation, the method 400 is described with respect to the mass spectrometer 10 of FIG. 1. It is noted that the method 400 may be applied to other suitable mass spectrometers 10 without departing from the scope of the disclosure.”Paragraph [0072] – “At operation 412, the pulse generator 14 pulses the ions 24 into the analyzer 16 according to a multiplexing scheme. As previously discussed, the multiplexing scheme may be divided into intervals, and each interval may be divided into non-periodic subintervals. In some implementations, each interval is divided into the same subintervals as the other intervals. In this way, the subintervals within an interval are non-periodic, but the intervals are periodic, i.e., the first interval to of each interval is of the same duration, as is the second subinterval, the third subinterval, and so on and so forth.”Paragraph [0073] – “At operation 414, the detector 18 detects the ion strikes, and in response to the ion strikes outputs data points 26 indicating the intensity and the time of the detected ion strike. At operation 416, the data processor 20 maintains a multiplexed spectrum based on the collected data points 26. FIG. 6 illustrates an example of a multiplexed spectrum in a graph 300 format. Each time the detector 18 outputs a data point 26, the data processor 20 can include the data point 26 in the multiplexed spectrum. At this juncture, the multiplexed spectrum can be referred to as “raw data.””Paragraph [0028] – “FIG. 6 illustrates an example of data points 26 output by the detector 18 illustrated in the form of a graph 300. Each line, e.g., line 302 and line 304, indicates different concentrations of ions 24. The x-axis indicates times (e.g., in us) and the y-axis indicates an intensity of a detected ion strike. It is noted that the peaks indicate detected ion strikes. It is further noted that the graph of FIG. 6 may continue along the x-axis to illustrate subsequent ion strikes as well.”See Fig. 8, the second segment being the right-most peak cluster.]; the method further comprising:
filtering, using a control system, the first segment of data and the second segment of data so as to produce a filtered version of the first segment of data and a filtered version of the second segment of data, wherein a width associated with the filtering is configured to vary in dependence on a width of an expected ion arrival time distribution for the time-of-flight ion analyser such that the first segment of data is filtered using a first filter width that depends upon arrival times within the first arrival time range and such that the second segment of data is filtered using a second filter width that depends upon arrival times within the second different arrival time range [Paragraphs [0074]-[0075], as applied to the signal of Fig. 8];
identifying, using the control system, one or more ion peaks in the filtered version of the first segment of data and in the filtered version of the second segment of data [See Fig. 8 and Paragraph [0080] – “At operation 426, the data processor 20 determines the mass peak curve for the sample based on the i-th most minimum curve 610 and the standard deviation values corresponding to the sampled time instances on the i-th most minimum curve 610. According to some implementations, the data processor 20 determines an upper bound curve 620 and determines the mass peak curve 630 based on the data points between the i-th minimum curve 610 and the upper bound curve 620.”];
determining, using the control system, one or more characteristics of each ion peak of the one or more identified ion peaks [Paragraph [0080] – “For each time instance, the data processor 20 can, for example, calculate a mean value of the sampled data points corresponding to the time instance or can determine a median value of the sampled data points to obtain a value of the mass peak curve 630 corresponding to the time instance.”]; and
determining, using the control system, one or more physico-chemical properties including a mass to charge ratio (m/z) of ions associated with each ion peak using the one or more determined characteristics [See Fig. 8 and Paragraph [0028] – “Each time the detector 18 is struck with one or more ions 24, the detector 18 outputs a data point 26 corresponding to the ion strike. In some implementations, the data point 26 is an ordered pair, (intensity, time), where intensity is a value indicating an intensity of the strike, e.g., mass/charge and time is the time of the strike relative to the beginning of interval I.sub.0.”].
Willis discloses the use of an intensity threshold with regards to the first and second data segments [Paragraph [0080] – “Once the data processor 20 has determined the upper bound curve 620, the data processor 20 samples the data points between the upper bound curve 620 and the i-th minimum curve 610 for each specific time instance.”], but fails to disclose generating the first and second data segments when an intensity of the signal exceeds the threshold because Willis uses the intensity threshold after the data segment filtering.
However, it would have been obvious to use the intensity threshold prior to the filtering steps in order to reduce the amount of data needed to be processed, to reduce the load on computational resources, and to reduce the impact of measurement noise.
Willis fails to explicitly disclose that the filtering has the effect of smoothing a multi-modal signal produced by ions of the same species into a unimodal signal while retaining overlapping peaks produced by ions of different species.
However, Borovcova discloses characterizing such multi-modal signals in such a manner [Page 74 – “Finally obtained Gaussian functions (G, G1, G2 etc.) were combined to build FATD” which is based on signal FWHMs.See Figs. 2(d)-(f) – “Fig. 2. Scheme of data processing: c, d) fitting of individual profiles by one (c) or a few (here three) Gaussian functions (d) building ATD functions (red and blue); e, f) the application of the ATD functions on two experimental mobilograms corresponding two binary mixtures with different ratio of components.”]. It would have been obvious to apply the teachings of Willis to a multi-modal signal in order to better represent multi-modal signal data.
Regarding Claim 3, Willis discloses that the signal is produced by the time-of-flight ion analyser in response to detecting a single packet of ions; or the first segment of data and the signal is produced by combining multiple signals produced by the time-of-flight ion analyser [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”See Fig. 8 and Paragraphs [0071]-[0072].].
Regarding Claim 5, Willis discloses that the first segment of data comprises a first set of digital samples, wherein each sample of the first set is associated with a respective arrival time, and wherein the arrival times associated with the first set are within the first arrival time range; or the second segment of data comprises a second set of digital samples, wherein each sample of the second set is associated with a respective arrival time, and wherein the arrival times associated with the second set are within the second different arrival time range [Paragraph [0077] – “FIG. 8 illustrates an example of time shifted data points plotted on a graph 600. In the graph 600, data points appearing at the same time, i.e., along the x-axis, represent data points that occurred during different time intervals. For example, data point 602 and data point 604 represent intensities measured at during different time intervals and at least one of data point 602 and data point 604 was time shifted to the time value T.”].
Regarding Claim 6, Willis discloses that the width associated with the filtering is configured to vary in dependance on the arrival time range associated with the segment [See Paragraph [0074] and Equation 2, “In this example, the smoothing kernal used is a Gaussian filter with a FWHH equal to one-half the FWHH of the mass 1000 peak.”].
Regarding Claim 7, Willis discloses that the width associated with the filter is configured to vary in dependance on a mean arrival time associated with the segment [Paragraph [0074] – “In this example, the smoothing kernal used is a Gaussian filter with a FWHH equal to one-half the FWHH of the mass 1000 peak.”].
Regarding Claim 8, Willis discloses that the expected ion arrival time distribution for the time-of-flight ion analyser is determined from a calibration for the time-of-flight ion analyser [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”Paragraph [0074] – “a simplified mass calibration of TOF(ns)=20,000*√{square root over (M)}”].
Regarding Claim 9, Willis discloses that the filter utilises a Gaussian smoothing function, an asymmetric Gaussian smoothing function, or a continuous wavelet transformation (CWT)[Paragraph [0074] – “In this example, the smoothing kernal used is a Gaussian filter with a FWHH equal to one-half the FWHH of the mass 1000 peak.”].
Regarding Claim 10, Willis discloses that identifying one or more ion peaks in the filtered version of the first segment of data or in the filtered version of the second segment of data comprises:
identifying one or more local minima, zero-crossing points and/or local maxima in the filtered version of the first segment of data or in the filtered version of the second segment of data [See Fig. 8 and Paragraph [0080] – “At operation 426, the data processor 20 determines the mass peak curve for the sample based on the i-th most minimum curve 610 and the standard deviation values corresponding to the sampled time instances on the i-th most minimum curve 610. According to some implementations, the data processor 20 determines an upper bound curve 620 and determines the mass peak curve 630 based on the data points between the i-th minimum curve 610 and the upper bound curve 620.”], and dividing the filtered version of the first segment of data or the filtered version of the second segment of data into one or more intervals at the location of one or more of the identified minima, zero-crossing points and/or maxima [Fig. 8, left-most and right-most peak intervals]; and retaining only interval(s) with a maximum sample intensity above a threshold [Fig. 8, use of i-th most minimum curve 610].
Regarding Claim 17, Willis discloses using the one or more determined characteristics of each ion peak to determine a physicochemical property of ions associated with the ion peak [Paragraph [0010]].
Regarding Claim 18, Willis discloses a method of operating an analytical instrument that comprises an ion source and a time-of-flight ion analyser [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”], the method comprising: generating ions in the ion source; analysing the ions with the time-of-flight ion analyser so as to produce a signal [Fig. 2]; and analysing the signal using the method of claim 1 [See the above discussion of Claim 1].
Regarding Claim 21, Willis discloses that the time-of-flight ion analyser comprises: an ion detector configured to produce signals indicative of ion intensity as a function of arrival time [See Fig. 8 and Paragraph [0028] – “Each time the detector 18 is struck with one or more ions 24, the detector 18 outputs a data point 26 corresponding to the ion strike. In some implementations, the data point 26 is an ordered pair, (intensity, time), where intensity is a value indicating an intensity of the strike, e.g., mass/charge and time is the time of the strike relative to the beginning of interval I.sub.0.”]; and
a digitizer configured to generate a segment of data when an intensity of a signal produced by the detector exceeds the threshold [Paragraph [0080] – “Once the data processor 20 has determined the upper bound curve 620, the data processor 20 samples the data points between the upper bound curve 620 and the i-th minimum curve 610 for each specific time instance.”].
Claim(s) 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Willis et al. (US 20170084443 A1)[hereinafter “Willis”]; Borovcova et al., Simple area determination of strongly overlapping ion mobility peaks, Elsevier, 2017 [hereinafter “Borovcova”]; and Abramovitch, Improved Peak Detection for Mass Spectrometry via Augmented Dominant Peak Removal, AACC, 2020.
Regarding Claim 11, Willis fails to disclose that the step of determining the one or more characteristics of each ion peak of the one or more identified ion peaks comprises: in response to a single interval being retained for the filtered version of the segment, determining one or more characteristics of an ion peak in the single interval by fitting a peak model to the samples of the single interval, wherein the one or more characteristics comprise a centroid, intensity and/or area of the ion peak.
However, Abramovitch discloses fitting a peak model to an interval through use of the characteristics of a centroid, intensity and/or area of a peak [See Fig. 7 and associated text.Page 5011, second column – “Gaussian shape is a convenient approximation because it can be estimated over the entire axis of a region by determining its center point and its width. Using the apex center and the dominant peak’s width, we estimate the height as the difference between the peak apex and the baseline, and the FWHM by searching the peak curve. From these, we can calculate the μ, amplitude scale, and σ of a Gaussian curve.”]. It would have been obvious to take such an approach in order to ascertain peak properties.
Regarding Claim 12, Willis discloses, in response to multiple intervals being retained for the filtered version of the segment [Paragraph [0080] – “According to some implementations, the data processor 20 determines an upper bound curve 620 and determines the mass peak curve 630 based on the data points between the i-th minimum curve 610 and the upper bound curve 620.”]:
for each remaining interval, summing the intensities of the samples within that interval [Paragraph [0080] – “For each time instance, the data processor 20 can, for example, calculate a mean value of the sampled data points corresponding to the time instance or can determine a median value of the sampled data points to obtain a value of the mass peak curve 630 corresponding to the time instance.”];
but fails to disclose:
fitting a first peak model to the samples of the interval with the highest sum; using the first peak model for the interval with the highest sum to modify the samples of the interval with the second highest sum; and fitting a second peak model to the modified samples of the interval with the second highest sum.
However, Abramovitch discloses summing sample intensities [Page 5011, determination of FWHM], fitting a first peak model to the samples of the interval with the highest sum; using the first peak model for the interval with the highest sum to modify the samples of the interval with the second highest sum [Page 5011, subtracting out the data corresponding to the modelled high peak]; and fitting a second peak model to the modified samples of the interval with the second highest sum [See Fig. 7 and associated text.Page 5011, second column – “The peak model is shown on the left side of Figure 7. Removing this peak model from the abundance curve leaves the dashed curve on the right, which can then be searched to locate a second peak. The removal of the previous dominant peak has left us in a better position to estimate center peaks width, model it with a Gaussian, and remove it. The third pass, also on the right side of Figure 7, shows that after removing the first two peaks, we have revealed the previously hidden third peak. This method works extremely well, provided that the dominant peak of any particular iteration, is well determined or separated from any interfering side peaks.”]. It would have been obvious to take such an approach in order to effectively characterize the properties of multiple peaks.
Regarding Claim 13, the combination would disclose, for each interval of any remaining interval(s) of the filtered version of the segment other than an interval with the highest sum and an interval with the second highest sum, performing the following steps (a) and (b): (a) modifying the samples of the interval [Page 5011, second column of Abramovitch – removal of identified peak data prior to ] with the next highest sum using the first peak model for the interval with the highest sum, the second peak model for the interval with the second highest sum, and any other peak model(s) that have been determined for other intervals of the segment; and (b) fitting a peak model to the modified samples of the interval [See Fig. 7 and associated text of Abramovitch.Page 5011, second column of Abramovitch – “The peak model is shown on the left side of Figure 7. Removing this peak model from the abundance curve leaves the dashed curve on the right, which can then be searched to locate a second peak. The removal of the previous dominant peak has left us in a better position to estimate center peaks width, model it with a Gaussian, and remove it. The third pass, also on the right side of Figure 7, shows that after removing the first two peaks, we have revealed the previously hidden third peak. This method works extremely well, provided that the dominant peak of any particular iteration, is well determined or separated from any interfering side peaks.”].
Regarding Claim 14, Abramovitch fails to disclose, when a set of peak models has been produced by fitting a peak model to each of the multiple remaining intervals: (c) using the peak models of the set other than the first peak model to modify the samples of the interval with the highest sum; (d) fitting a first modified peak model to the modified samples of the interval with the highest sum, and replacing the first peak model with the first modified peak model in the set of peak models; (e) using the peak models of the set other than the second peak model to modify the samples of the interval with the second highest sum; and (f) fitting a second modified peak model to the modified samples of the interval with the second highest sum, and replacing the second peak model with the second modified peak model in the set of peak models; (g) for each interval of any remaining interval(s) of the segment other than the interval with the highest sum and the interval with the second highest sum, performing the following steps (h) and (i): (h) modifying the samples of the interval with the next highest sum using the peak models of the set other than the peak model for the current interval; and (i) fitting a peak model to the modified samples of the current interval, and replacing the peak model for the current interval with the modified peak model for the current interval in the set of peak models.
However, Abramovitch discloses subtracting out data corresponding to modelled peaks in order to better model further peaks [See Fig. 7 and associated text.Page 5011, second column – “The peak model is shown on the left side of Figure 7. Removing this peak model from the abundance curve leaves the dashed curve on the right, which can then be searched to locate a second peak. The removal of the previous dominant peak has left us in a better position to estimate center peaks width, model it with a Gaussian, and remove it. The third pass, also on the right side of Figure 7, shows that after removing the first two peaks, we have revealed the previously hidden third peak. This method works extremely well, provided that the dominant peak of any particular iteration, is well determined or separated from any interfering side peaks.”]. It would have been obvious to repeat this process, effectively in reverse, once secondary and tertiary peaks have been modelled because doing so would have presented an effective manner for refining the peak models; Abramovich discloses that the initial peak modelling is challenging [Page 5011, first column – “The example diagrammed in Figure 6 shows three peaks that overlap, but for which there is a dominant peak (the one on the left). The center apex can be identified, but because its FWHM point to its left is overrun by the larger left peak, any search-based determination of the center peaks width is flawed.”Page 5011, second column – “This method works extremely well, provided that the dominant peak of any particular iteration, is well determined or separated from any interfering side peaks.”].
Regarding Claim 15, Abramovitch fails to disclose iterating steps (c) to (i) one or more times. However, it would have been obvious to repeat the iterative process until each peak has been refined.
Regarding Claim 16, the combination would disclose that the step of determining one or more characteristics of each ion peak of the one or more identified ion peaks comprises fitting a multiple-peak model to the samples of the segment after a peak model has been fitted to each of the multiple remaining intervals [Page 5013, second column of Abramovitch – “The results from Successive Multi-Peak Removal are shown in Figure 14. We see that more side peaks are found and their respective contributions to the abundance curve are more accurately accounted for. We have the best of both worlds, in which we see all the peaks from the original, simple method (and some hidden ones), but also have reasonable peak width estimates.”].
Claim(s) 20 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Willis et al. (US 20170084443 A1)[hereinafter “Willis”].
Regarding Claim 20, Willis discloses an analytical instrument comprising: an ion analyser; and a control system configured to process data generated by the time-of-flight ion analyser [See Fig. 2.Abstract – “Implementations of methods and apparatuses are disclosed for decoding multiplexed information in a chromatographic system. Implementations may include the method of pulsing ions from an ion source through an analyzer according to a predetermined multiplexing scheme, each pulse including one or more ions corresponding to a sample, detecting a plurality of ion strikes at a detector, determining a data point for each ion strike, wherein each data point includes an intensity of a detected ion strike and a time of the detected ion strike, maintaining a multiplexed spectrum of the data points, the multiplexed spectrum including the data points, and demultiplexing the time shifted spectrum using the data points of the multiplexed spectrum.”Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”];
wherein the instrument is configured to digitize a signal produced by the time-of-flight ion analyser by: generating a first segment of data from the signal [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”], wherein the first segment of data comprises data associated with a first arrival time range [Paragraph [0070] – “Referring now to FIG. 5, an example set of operations for a method 400 for operating a mass spectrometer that is configured to multiplex ion packets. For purposes of explanation, the method 400 is described with respect to the mass spectrometer 10 of FIG. 1. It is noted that the method 400 may be applied to other suitable mass spectrometers 10 without departing from the scope of the disclosure.”Paragraph [0072] – “At operation 412, the pulse generator 14 pulses the ions 24 into the analyzer 16 according to a multiplexing scheme. As previously discussed, the multiplexing scheme may be divided into intervals, and each interval may be divided into non-periodic subintervals. In some implementations, each interval is divided into the same subintervals as the other intervals. In this way, the subintervals within an interval are non-periodic, but the intervals are periodic, i.e., the first interval to of each interval is of the same duration, as is the second subinterval, the third subinterval, and so on and so forth.”Paragraph [0073] – “At operation 414, the detector 18 detects the ion strikes, and in response to the ion strikes outputs data points 26 indicating the intensity and the time of the detected ion strike. At operation 416, the data processor 20 maintains a multiplexed spectrum based on the collected data points 26. FIG. 6 illustrates an example of a multiplexed spectrum in a graph 300 format. Each time the detector 18 outputs a data point 26, the data processor 20 can include the data point 26 in the multiplexed spectrum. At this juncture, the multiplexed spectrum can be referred to as “raw data.””Paragraph [0028] – “FIG. 6 illustrates an example of data points 26 output by the detector 18 illustrated in the form of a graph 300. Each line, e.g., line 302 and line 304, indicates different concentrations of ions 24. The x-axis indicates times (e.g., in us) and the y-axis indicates an intensity of a detected ion strike. It is noted that the peaks indicate detected ion strikes. It is further noted that the graph of FIG. 6 may continue along the x-axis to illustrate subsequent ion strikes as well.”See Fig. 8, the first segment being the left-most peak cluster.];
generating a second segment of data from the signal [Paragraph [0023] – “With reference now to FIG. 2, an example mass spectrometer 10 configured to multiplex or encode ion packets and demultiplex or decode resulting ion strikes is disclosed. As illustrated, the mass spectrometer 10 is a time-of-flight mass spectrometer. It is noted, however, that the present disclosure relates to any suitable mass spectrometer.”], wherein the second segment of data comprises data associated with a second different arrival time range [Paragraph [0070] – “Referring now to FIG. 5, an example set of operations for a method 400 for operating a mass spectrometer that is configured to multiplex ion packets. For purposes of explanation, the method 400 is described with respect to the mass spectrometer 10 of FIG. 1. It is noted that the method 400 may be applied to other suitable mass spectrometers 10 without departing from the scope of the disclosure.”Paragraph [0072] – “At operation 412, the pulse generator 14 pulses the ions 24 into the analyzer 16 according to a multiplexing scheme. As previously discussed, the multiplexing scheme may be divided into intervals, and each interval may be divided into non-periodic subintervals. In some implementations, each interval is divided into the same subintervals as the other intervals. In this way, the subintervals within an interval are non-periodic, but the intervals are periodic, i.e., the first interval to of each interval is of the same duration, as is the second subinterval, the third subinterval, and so on and so forth.”Paragraph [0073] – “At operation 414, the detector 18 detects the ion strikes, and in response to the ion strikes outputs data points 26 indicating the intensity and the time of the detected ion strike. At operation 416, the data processor 20 maintains a multiplexed spectrum based on the collected data points 26. FIG. 6 illustrates an example of a multiplexed spectrum in a graph 300 format. Each time the detector 18 outputs a data point 26, the data processor 20 can include the data point 26 in the multiplexed spectrum. At this juncture, the multiplexed spectrum can be referred to as “raw data.””Paragraph [0028] – “FIG. 6 illustrates an example of data points 26 output by the detector 18 illustrated in the form of a graph 300. Each line, e.g., line 302 and line 304, indicates different concentrations of ions 24. The x-axis indicates times (e.g., in us) and the y-axis indicates an intensity of a detected ion strike. It is noted that the peaks indicate detected ion strikes. It is further noted that the graph of FIG. 6 may continue along the x-axis to illustrate subsequent ion strikes as well.”See Fig. 8, the second segment being the right-most peak cluster.];
wherein the control system is configured to: filter the first segment of data and the second segment of data so as to produce a filtered version of the first segment of data and a filtered version of the second segment of data, wherein a width associated with the filtering is configured to vary in dependence on a width of an expected ion arrival time distribution for the time-of-flight ion analyser such that the first segment of data is filtered using a first filter width that depends upon arrival times within the first arrival time range [Paragraphs [0074]-[0075], as applied to the signal of Fig. 8];
identify one or more ion peaks in the filtered version of the first segment of data and in the filtered version of the second segment of data [See Fig. 8 and Paragraph [0080] – “At operation 426, the data processor 20 determines the mass peak curve for the sample based on the i-th most minimum curve 610 and the standard deviation values corresponding to the sampled time instances on the i-th most minimum curve 610. According to some implementations, the data processor 20 determines an upper bound curve 620 and determines the mass peak curve 630 based on the data points between the i-th minimum curve 610 and the upper bound curve 620.”];
determine one or more characteristics of each ion peak of the one or more identified ion peaks [Paragraph [0080] – “For each time instance, the data processor 20 can, for example, calculate a mean value of the sampled data points corresponding to the time instance or can determine a median value of the sampled data points to obtain a value of the mass peak curve 630 corresponding to the time instance.”]; and
use the one or more determined characteristics of each ion peak to determine one or more physico-chemical properties including a mass to charge ratio (m/z) of ions associated with that ion peak [See Fig. 8 and Paragraph [0028] – “Each time the detector 18 is struck with one or more ions 24, the detector 18 outputs a data point 26 corresponding to the ion strike. In some implementations, the data point 26 is an ordered pair, (intensity, time), where intensity is a value indicating an intensity of the strike, e.g., mass/charge and time is the time of the strike relative to the beginning of interval I.sub.0.”].
Willis discloses the use of an intensity threshold with regards to the first and second data segments [Paragraph [0080] – “Once the data processor 20 has determined the upper bound curve 620, the data processor 20 samples the data points between the upper bound curve 620 and the i-th minimum curve 610 for each specific time instance.”], but fails to disclose generating the first and second data segments when an intensity of the signal exceeds the threshold because Willis uses the intensity threshold after the data segment filtering.
However, it would have been obvious to use the intensity threshold prior to the filtering steps in order to reduce the amount of data needed to be processed, to reduce the load on computational resources, and to reduce the impact of measurement noise.
Regarding Claim 22, Willis discloses that the time-of-flight ion analyser comprises: an ion detector configured to produce signals indicative of ion intensity as a function of arrival time [See Fig. 8 and Paragraph [0028] – “Each time the detector 18 is struck with one or more ions 24, the detector 18 outputs a data point 26 corresponding to the ion strike. In some implementations, the data point 26 is an ordered pair, (intensity, time), where intensity is a value indicating an intensity of the strike, e.g., mass/charge and time is the time of the strike relative to the beginning of interval I.sub.0.”]; and
a digitizer configured to generate a segment of data when an intensity of a signal produced by the detector exceeds the threshold [Paragraph [0080] – “Once the data processor 20 has determined the upper bound curve 620, the data processor 20 samples the data points between the upper bound curve 620 and the i-th minimum curve 610 for each specific time instance.”].
Response to Arguments
Applicant argues:
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306
978
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Examiner’s Response:
Applicant’s argument is found convincing. The rejections under 35 USC 101 are hereby withdrawn.
Applicant argues:
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544
976
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Examiner’s Response:
The Examiner respectfully disagrees. The instant Specification teaches the use of FWHM filtering at Page 7 and Willis discloses FWHH filtering at Paragraph [0074]. Both are filtering types configured to vary in dependance on arrival distribution width.
Applicant argues:
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306
983
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Examiner’s Response:
The Examiner respectfully disagrees. Paragraph [0026] of Willis does not teach away from an analysis of overlapping peaks, but rather is concerned with their proper separation.
Applicant argues:
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229
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662
977
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Examiner’s Response:
The Examiner agrees that, although Willis discloses the use of an intensity threshold with regards to the first and second data segments [Paragraph [0080] – “Once the data processor 20 has determined the upper bound curve 620, the data processor 20 samples the data points between the upper bound curve 620 and the i-th minimum curve 610 for each specific time instance.”], Willis fails to disclose generating the first and second data segments when an intensity of the signal exceeds the threshold because Willis uses the intensity threshold after the data segment filtering.
However, it would have been obvious to use the intensity threshold prior to the filtering steps in order to reduce the amount of data needed to be processed, to reduce the load on computational resources, and to reduce the impact of measurement noise. There is no reason the intensity threshold of Willis could not be used earlier in the analysis process.
Applicant argues:
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270
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Examiner’s Response:
The Examiner agrees that Willis does not disclose an analysis of multi-modal peaks. New grounds for rejection are presented above.
Applicant argues:
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619
980
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Examiner’s Response:
Abramovitch is not relied on as disclosing the referred-to claim limitations.
Applicant argues:
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153
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Examiner’s Response:
The Examiner respectfully disagrees. New grounds for rejection are presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20050061968 A1 – Mass Spectrometer
Xiao-xing et al., Application of Iterative Curve-Fitting Method in Separating Overlapped Peaks of SF6 Decomposed Products, IEEE, 2008
US 20110054804 A1 – Method Of Improving The Resolution Of Compounds Eluted From A Chromatography Device
US 6586728 B1 – Variable Width Digital Filter For Time-of-flight Mass Spectrometry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen Vazquez can be reached at (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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