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 .
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tudor (US 2004/0007059 – cited by Applicant on IDS1) in view of Heidari et al. (US 2021/0087925).
With respect to independent claim 1, Tudor discloses a method for determining a proppant concentration in a fracturing fluid (abstract; [0002]-[0003]), comprising stages of:
(a) installing hydrophones 50 or pressure sensors in a tubular body 64 ([0033]);
(b) flowing the fracturing fluid 60 through the tubular body 64 and measuring hydrodynamic acoustic noise spectra ([0033]); and
(c) using software to analyze the hydrodynamic acoustic noise spectra and determine the proppant concentration in the fracturing fluid, wherein the software determines the proppant concentration in the fracturing fluid using the hydrodynamic acoustic noise spectra (from 50) and a fluid flow rate (from 30) of the fracturing fluid ([0030]; [0037]-[0040]; [0056]; [0058]).
Tudor discloses the method as set forth above, wherein software within a computer 80 is used to calculate the proppant concentration in the fracturing fluid ([0037]; [0058]). The reference, however, fails to disclose using machine learning for such analysis and determinations.
Heidari et al. teaches methods that use historical data associated with hydraulic fracturing along with time-series data including pressure data and digital acoustic sensing ([0026]; [0028]) to generate a machine learning model for the purpose of determining an optimized job design for the hydraulic fracturing operation (abstract); data associated with parameters or variables for a well, including flow rate and proppant concentration, is received by a computer/processor ([0017]) and such data is used to generate the machine learning model that can be used to predict and recommend real-time changes to an original job design so as to optimize the hydraulic fracturing job and improve key performance indicators such as production or financial costs ([0014]; [0062]). The data is collected in real-time ([0026]; [0028]) and is implemented using computer software ([0052]-[0053]).
Since Tudor discloses the above method implemented using software within a computer, wherein calculations are used to determine the proppant concentration and Heidari et al. teaches implementation of a machine learning model using the same types of data collected by computer software for the purpose of optimizing the hydraulic fracturing job and improving key performance indicators such as production or financial costs, it would have been obvious to one having ordinary skill in the art to try a machine learning model to analyze the data obtained in the method of Tudor and determine the proppant concentration in the fracturing fluid therewith in order to optimize the hydraulic fracturing job and improve key performance indicators such as production and/or financial costs in real-time.
With respect to dependent claim 2, Tudor discloses wherein the tubular body comprises one as claimed ([0032]-[0033], wherein surface pipes 64 are disclosed).
With respect to dependent claim 3, Tudor discloses wherein the software determines the proppant concentration in the fracturing fluid using the hydrodynamic acoustic noise spectra (from 50) and a fluid flow rate (from 30) of the fracturing fluid ([0030]; [0037]-[0040]; [0056]; [0058]) based on correlations/calculations between the hydrodynamic acoustic noise spectra, a fluid flow rate of the fracturing fluid, and the proppant concentration for a plurality of proppant concentrations ([0033]; [0037]). Heidari et al. suggests machine learning implemented with software for determining parameters with respect to a fracturing operation, as set forth above in the rejection of claim 1; since Tudor discloses the above method implemented using software within a computer, wherein calculations are used to determine the proppant concentration and Heidari et al. teaches implementation of a machine learning model using the same types of data collected by computer software for the purpose of optimizing the hydraulic fracturing job and improving key performance indicators such as production or financial costs, it would have been obvious to one having ordinary skill in the art to try a machine learning model to analyze the data obtained in the method of Tudor and determine the proppant concentration in the fracturing fluid based on one or more correlations as claimed in order to optimize the hydraulic fracturing job, such as by adjusting the proppant concentration during the treatment should such be deemed necessary, so as to improve key performance indicators, such as production and/or financial costs.
With respect to dependent claim 4, Tudor discloses wherein the hydrophones or pressure sensors are installed prior to a fracturing treatment ([0018], wherein the fluid pumping system is established for injecting, i.e., prior to injecting fluid therethrough; [0032]), and stage (c) is performed during the hydraulic fracturing treatment ([0033], in operation particulates create noise, i.e., during the fracturing treatment).
With respect to dependent claims 5 and 6, Heidari et al. further teaches modeling software, and, further, simulation software ([0049]; see motivation to combine as set forth above in the rejection of claim 1).
With respect to dependent claim 7, Heidari et al. teaches wherein the machine learning or deep learning model comprises one as claimed ([0024]; [0035]; [0048]; [0069]; see motivation to combine as set forth above in the rejection of claim 1).
With respect to dependent claim 8, Tudor discloses wherein the particulates hit the inside wall of the pipe and create noise within a certain range of frequencies that will be detected by the acoustic sensor ([0033]). The reference further suggests such particulates to include sands ([0003]), wherein the amplitude of the noise reflects the energy of the particles as they hit in the frequency range that sand and similar particulates release when they hit the metallic inner wall of pipe 64 ([0033]). Although silent to the acoustic noise spectra as covering a frequency range as claimed, it would have been obvious to one having ordinary skill in the art to cover a frequency range between 1 kHz and 100 kHz in order to detect the noise generated by the particulates hitting the inside wall of the pipe so that such can be used in correlating with the flow rate for determination of the proppant concentration in the fracturing fluid; one having ordinary skill in the art would recognize the optimal frequency range covered in order to ensure the noise is detected since it has been held "[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). For more recent cases applying this principle, see Merck & Co. Inc. v. Biocraft Lab. Inc., 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989); In re Kulling, 897 F.2d 1147, 14 USPQ2d 1056 (Fed. Cir. 1990); and In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997); Smith v. Nichols, 88 U.S. 112, 118-19 (1874) (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art."). Additionally, the Examiner notes, obviousness can be shown in a predictable art when a difference between the claimed ranges is virtually negligible absent any showing of unexpected results or criticality. In re Brandt, 886 F. 3d 1171, 1177, 126 USPQ2d 1079, 1082 (Fed. Cir. 2018). The instant specification fails to explicitly establish the instantly claimed frequency range as critical and it is unclear if any unexpected results are achieved by covering such a range. Since the particulates of Tudor hit the inside wall of the pipe and create noise within a certain range of frequencies that will be detected by the acoustic sensor so as to enable the determination of the concentration thereof in the fluid, as is instantly claimed and disclosed by Applicant, it does not appear that such would be considered an unexpected result of providing for the frequency range instantly claimed, and, as such, the determination of optimal frequency range to cover would be achievable through routine experimentation in the art.
With respect to independent claim 9, Tudor discloses a method for determining a proppant concentration in a fracturing fluid (abstract), comprising stages of:
(a) installing hydrophones 50 or pressure sensors in a tubular body 64 ([0033]);
(b) flowing the fracturing fluid 60 through the tubular body 64 and measuring hydrodynamic acoustic noise spectra ([0033]); and
(c) using software to analyze the hydrodynamic acoustic noise spectra and determine the proppant concentration in the fracturing fluid, wherein the software determines the proppant concentration in the fracturing fluid using the hydrodynamic acoustic noise spectra (from 50) and a fluid flow rate (from 30) of the fracturing fluid ([0030]; [0037]-[0040]; [0056]; [0058]) based on correlations between the hydrodynamic acoustic noise spectra, a fluid flow rate of the fracturing fluid, and the proppant concentration for a plurality of proppant concentrations ([0033]; [0037]).
Tudor discloses the method as set forth above, wherein software within a computer 80 is used to calculate the proppant concentration in the fracturing fluid ([0037]; [0058]). The reference, however, fails to disclose using machine learning for such analysis and determinations, and, further, adjusting the proppant concentration based on the determined proppant concentration from such an analysis.
Heidari et al. teaches methods that use historical data associated with hydraulic fracturing along with time-series data including pressure data and digital acoustic sensing ([0026]; [0028]) to generate a machine learning model for the purpose of determining an optimized job design for the hydraulic fracturing operation (abstract); data associated with parameters or variables for a well, including flow rate and proppant concentration, is received by a computer/processor ([0017]), including variations of such parameters ([0040]) and such data is used to generate the machine learning model that can be used to predict and recommend real-time changes to an original job design so as to optimize the hydraulic fracturing job and improve key performance indicators such as production or financial costs ([0014]; [0062]). The data is collected in real-time ([0026]; [0028]) and is implemented using computer software ([0052]-[0053]). In one instance, such a model is taught as used to recommend a change in proppant concentration during treatment ([0040]).
Since Tudor discloses the above method implemented using software within a computer, wherein calculations are used to determine the proppant concentration and Heidari et al. teaches implementation of a machine learning model using the same types of data collected by computer software for the purpose of optimizing the hydraulic fracturing job and improving key performance indicators such as production or financial costs, it would have been obvious to one having ordinary skill in the art to try a machine learning model to analyze the data obtained in the method of Tudor and determine the proppant concentration in the fracturing fluid therewith in order to optimize the hydraulic fracturing job, such as by adjusting the proppant concentration during the treatment should such be deemed necessary, so as to improve key performance indicators, such as production and/or financial costs in real time.
With respect to dependent claim 10, Tudor discloses wherein the tubular body comprises one as claimed ([0032]-[0033], wherein surface pipes 64 are disclosed).
With respect to dependent claim 11, Heidari et al. teaches wherein the machine learning or deep learning model is trained prior to flowing of a fracturing fluid to a second treatment location ([0040]), and, thus, suggests training such prior to flowing a fracturing fluid, i.e., additional fracturing fluid, through the tubular body (see motivation to combine within the rejection of claim 9, above).
With respect to dependent claim 12, Tudor discloses wherein the hydrophones or pressure sensors are installed prior to a fracturing treatment ([0018], wherein the fluid pumping system is established for injecting, i.e., prior to injecting fluid therethrough; [0032]), and stage (c) is performed during the hydraulic fracturing treatment ([0033], in operation particulates create noise, i.e., during the fracturing treatment).
With respect to dependent claims 13 and 14, Heidari et al. further teaches modeling software, and, further, simulation software ([0049]; see motivation to combine as set forth above in the rejection of claim 1).
With respect to dependent claim 15, Heidari et al. teaches wherein the machine learning or deep learning model comprises one as claimed ([0024]; [0035]; [0048]; [0069]; see motivation to combine as set forth above in the rejection of claim 1).
With respect to dependent claim 16, Tudor discloses wherein the particulates hit the inside wall of the pipe and create noise within a certain range of frequencies that will be detected by the acoustic sensor ([0033]). The reference further suggests such particulates to include sands ([0003]), wherein the amplitude of the noise reflects the energy of the particles as they hit in the frequency range that sand and similar particulates release when they hit the metallic inner wall of pipe 64 ([0033]). Although silent to the acoustic noise spectra as covering a frequency range as claimed, it would have been obvious to one having ordinary skill in the art to cover a frequency range between 1 kHz and 100 kHz in order to detect the noise generated by the particulates hitting the inside wall of the pipe so that such can be used in correlating with the flow rate for determination of the proppant concentration in the fracturing fluid; one having ordinary skill in the art would recognize the optimal frequency range covered in order to ensure the noise is detected since it has been held "[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation." In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). For more recent cases applying this principle, see Merck & Co. Inc. v. Biocraft Lab. Inc., 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989); In re Kulling, 897 F.2d 1147, 14 USPQ2d 1056 (Fed. Cir. 1990); and In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997); Smith v. Nichols, 88 U.S. 112, 118-19 (1874) (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art."). Additionally, the Examiner notes, obviousness can be shown in a predictable art when a difference between the claimed ranges is virtually negligible absent any showing of unexpected results or criticality. In re Brandt, 886 F. 3d 1171, 1177, 126 USPQ2d 1079, 1082 (Fed. Cir. 2018). The instant specification fails to explicitly establish the instantly claimed frequency range as critical and it is unclear if any unexpected results are achieved by covering such a range. Since the particulates of Tudor hit the inside wall of the pipe and create noise within a certain range of frequencies that will be detected by the acoustic sensor so as to enable the determination of the concentration thereof in the fluid, as is instantly claimed and disclosed by Applicant, it does not appear that such would be considered an unexpected result of providing for the frequency range instantly claimed, and, as such, the determination of optimal frequency range to cover would be achievable through routine experimentation in the art.
With respect to dependent claim 17, Tudor discloses wherein the hydrodynamic acoustic noise spectra are measured at a surface of a subterranean well ([0032]).
With respect to dependent claims 18 and 19, Tudor discloses wherein the particulates injected include sands added to fracture fluids ([0003]; [0033]), and, further, wherein the amount thereof is determined (abstract). Although silent to the type of proppant pack created therewith, and, thus, wherein the fracturing treatment creates a homogeneous or heterogeneous proppant pack in the fracture as instantly claimed, since Applicant claims the only two alternatives which are possible for creating a proppant pack in a fracture, i.e., homogeneous or heterogeneous, it is the position of the Office that it would have been obvious to one having ordinary skill in the art to try to create either when conducting the method of Tudor as a fracturing treatment since such types of proppant packs are chosen from a finite list of possibilities known to one having ordinary skill in the art for creating a proppant pack, and, would thus be expected to yield predictable results of providing for enhanced production therefrom.
With respect to dependent claim 20, Heidari et al. teaches adjusting the proppant concentration in real-time in response to the determined proppant concentration ([0040]).
Response to Arguments
Applicant’s arguments, with respect to the rejections of claims as set forth in the previous office action have been fully considered and are persuasive in view of Applicant’s amendments to each of the independent claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of further consideration of Applicant’s amendments.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Angela M DiTrani Leff whose telephone number is (571)272-2182. The examiner can normally be reached Monday-Friday, 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Doug Hutton can be reached at 5712724137. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Angela M DiTrani Leff/Primary Examiner, Art Unit 3674
ADL
04/09/26