CTNF 18/765,600 CTNF 87778 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION 2. This action is responsive to the Application filed on 7/8/2024. A filing date 7/8/2024 is acknowledged. Claims 1-19 are pending in this application. Claims 1, 11 and 15 are independent claims. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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 of this title, 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA 3. Claim s 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mohamed Ahmed Soliman (US Publication 20220402781 A1, hereinafter Soliman), in view of Scott Love (US Publication 20120024758 A1, hereinafter Love), and Samusldeen Adewake Salu et al (US Publication 20180195010 A1, hereinafter Salu) . As for independent claim 1, Soliman discloses: A method for automating wash water rates into a desalter (Abstract, Methods and systems are provided for desalting wash water treatment and recycling processes and control of those processes ) comprises the steps of: collecting historical ([0057], A predictive model is created through experiment or through artificial intelligence based on the historical data, which correlates values such as salt-in-crude and BSW measurements with the operations variables) dry crude rates ([0009], recording a dry crude basic sediment and water (BSW) measurement from a dry crude monitor operable to measure characteristics of the dry crude stream) ; collecting historical wash water rates ([0019], a desalting unit wash water flow rate) ; collecting historical salt content amounts ([0058], the salt content of a desalting unit dry crude stream) , where the historical dry crude rates, historical wash water rates, and historical salt content amounts are from a historical time period ([0057], A predictive model is created through experiment or through artificial intelligence based on the historical data, which correlates values such as salt-in-crude and BSW measurements with the operations variables) ; determining a percent wash water from the historical wash water rates and historical dry crude rates ([0010], PTB is the PTB value in pounds of salt per one thousand barrels of the dry crude stream coming from a desalter vessel or a dehydrator vessel; BSW is the dry crude basic sediment and water measurement in volume percent; TDS is the wash water dissolved solids measurement in mg/L of the wash water coming from a desalter or a dehydrator) ; … developing a salt content predictive equation from the salt content versus percent wash water curve ([0057], processes information from monitors and from predetermined values or database information, such as by calculating the PTB value of second desalter unit dry crude stream 570 using information from second desalter unit dry crude monitor 572, second desalter unit wash water monitor 576, and Equation 1. Machine learning and artificial intelligence algorithms designed to manage operations of the desalting systems can be used to evaluate information from sensors or monitors and alter operating controls through Controller) ; programming the salt content predictive equation and the percent wash water equation into a distributed control system ([0020], an artificial intelligence algorithm predicting when the PTB value for the desalting unit dry crude stream will exceed the predetermined PTB value based upon the one or more operations monitor measurements, and the artificial intelligence algorithm provides changes to the one or more system operations control. The artificial intelligence is also used to update and maintain the predictive model parameters) ; and regulating a current wash water rate based on an expected salt content determined from the salt content predictive equation ([0057], processes information from monitors and from predetermined values or database information, such as by calculating the PTB value of second desalter unit dry crude stream 570 using information from second desalter unit dry crude monitor 572, second desalter unit wash water monitor 576, and Equation 1. Machine learning and artificial intelligence algorithms designed to manage operations of the desalting systems can be used to evaluate information from sensors or monitors and alter operating controls through Controller); Soliman discloses predicting treatment of wash water but does not disclose a salt content versus percent wash water curve, in an analogous art of desalting process, Love discloses: developing a salt content versus percent wash water curve using the historical salt content amounts and percent wash water (Love: [0050], curve of volume percent versus droplet diameter) ; Soliman and Love are analogous arts because they are in the same field of endeavor, desalting process. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Soliman using the teachings of Love to include developing a curve of volume percent versus another volume data. It would provide Soliman’s method with enhanced capabilities of calculating desalting data more efficiently. Further, Soliman does not clearly disclose developing a salt content predictive equation, Salu discloses: developing a salt content predictive equation (Salu: [0061], In the algorithm, the rate of wash water required for a desalter crude throughput is given by: Qww =( Pww*Qc )/100); Soliman and Salu are analogous arts because they are in the same field of endeavor, desalting process. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Soliman using the teachings of Salu to include developing an equation for the rate of wash water required for a desalter crude. It would provide Soliman’s method with enhanced capabilities of calculating desalting data more efficiently. As for claim 2, Soliman-Love-Salu discloses: where the salt content amounts are measured in pounds per thousand barrels (PTB) (Soliman: [0010], PTB is the PTB value in pounds of salt per one thousand barrels of the dry crude stream coming from a desalter vessel or a dehydrator vessel) . As for claim 3, Soliman-Love-Salu discloses: wash water rates, and salt content amounts from a plurality of historical time periods are collected such that one salt content versus percentage wash water curve is developed for each historical time period (Soliman: ([0057], A predictive model is created through experiment or through artificial intelligence based on the historical data, which correlates values such as salt-in-crude and BSW measurements with the operations variables) . As for claim 4, Soliman-Love-Salu discloses: where the salt content predictive equation is a best fit polynomial of the salt content amount percent wash water curve (Salu: [0061], In the algorithm, the rate of wash water required for a desalter crude throughput is given by: Qww =( Pww*Qc )/100) . As for claim 5, Soliman-Love-Salu discloses: the step of regulating the current the wash water rate comprises maintaining the current wash water rate when the expected salt content meets a specified target (Soliman: [0003], In a second stage, crude oil is dehydrated and desalted to separate emulsified water and salt to meet certain basic sediment and water (BSW) specifications. In a third stage, crude oil is stabilized and sweetened to meet hydrogen sulfide (H.sub.2S) and Reid Vapor Pressure (RVP) specifications) . As for claim 6, Soliman-Love-Salu discloses: where the step of regulating the current wash water rate comprises modifying the current wash water rate when the expected salt content does not meet a specified target (Soliman: [0011], controlling operation of the desalting unit based upon the PTB value and a predetermined PTB value by adjusting a wastewater feed stream flow rate for the wastewater feed stream such that wash water generation is reduced in comparison to a volume of wash water generated from controlling the operation of the desalting unit without the calculation) . As for claim 7, Soliman-Love-Salu discloses: where the step of regulating the current wash water rate based on the expected salt content further comprises the steps of: reading a current dry crude rate from a crude flow transmitter (Soliman: [0009], recording a dry crude basic sediment and water (BSW) measurement from a dry crude monitor operable to measure characteristics of the dry crude stream) ; reading the current wash water rate from a water flow transmitter (Soliman: [0019], a desalting unit wash water flow rate) ; determining a current percent wash water using the percent wash water equation and the current dry crude rate and current wash water rate (Soliman: [0010], PTB is the PTB value in pounds of salt per one thousand barrels of the dry crude stream coming from a desalter vessel or a dehydrator vessel; BSW is the dry crude basic sediment and water measurement in volume percent; TDS is the wash water dissolved solids measurement in mg/L of the wash water coming from a desalter or a dehydrator) ; determining an expected salt content from the salt content predictive equation (Soliman: [0057], processes information from monitors and from predetermined values or database information, such as by calculating the PTB value of second desalter unit dry crude stream 570 using information from second desalter unit dry crude monitor 572, second desalter unit wash water monitor 576, and Equation 1. Machine learning and artificial intelligence algorithms designed to manage operations of the desalting systems can be used to evaluate information from sensors or monitors and alter operating controls through Controller) ; and comparing the expected salt content to a specified target, where the action on the current wash water rate is selected from the group consisting of maintaining the current wash water rate and modifying the current wash water rate (Soliman: [0017], comparing the PTB value to the predetermined PTB value programmed within the controller, and adjusting one or more system operations control through the controller such that the PTB value nears without meeting the predetermined PTB value) . As for claim 8, Soliman-Love-Salu discloses: comprising the step of sampling the salt content exiting the desalter (Love: [0049], Four sets of samples at various stages, including a feed sample (1-qt), product sample (1-qt), water outlet (4 oz.), chilled feed sample (2 oz) and chilled product sample (2 oz) for water DSD analysis were taken during the approximately 1 hour 30 minutes of run time) . As for claim 9, Soliman-Love-Salu discloses: comprising the step of developing a plurality of salt content versus percentage wash water curves, where each salt content versus percentage wash water curve represents a different time period (Love: [0050], curve of volume percent versus droplet diameter) . As for claim 10, Soliman-Love-Salu discloses: comprising the step of selecting the salt content versus percentage wash water curve in the distributed control system from the plurality of salt content versus percentage wash water curves, where the step of selecting is performed manually (Soliman: [0057], The machine learning and artificial intelligence algorithms assist in predicting the next off-spec crude incident or transformer short circuit, can automatically propose setpoint changes for controllers and notify operators of inefficient operations) . As for independent claim 11, Soliman discloses: A method for automating wash water injection rates in a desalter (Soliman: Abstract, Methods and systems are provided for desalting wash water treatment and recycling processes and control of those processes ) , the method comprising the steps of: reading a current dry crude rate from a crude flow transmitter (Soliman: [0009], recording a dry crude basic sediment and water (BSW) measurement from a dry crude monitor operable to measure characteristics of the dry crude stream) ; reading a current wash water rate from a water flow transmitter (Soliman: [0019], a desalting unit wash water flow rate) ; determining a current percent wash water from the current dry crude rate and current wash water rate ([0010], PTB is the PTB value in pounds of salt per one thousand barrels of the dry crude stream coming from a desalter vessel or a dehydrator vessel; BSW is the dry crude basic sediment and water measurement in volume percent; TDS is the wash water dissolved solids measurement in mg/L of the wash water coming from a desalter or a dehydrator) ; determining an expected salt content [using the current percent wash water and a salt content versus percent wash water curve] (Soliman: [0057], processes information from monitors and from predetermined values or database information, such as by calculating the PTB value of second desalter unit dry crude stream 570 using information from second desalter unit dry crude monitor 572, second desalter unit wash water monitor 576, and Equation 1. Machine learning and artificial intelligence algorithms designed to manage operations of the desalting systems can be used to evaluate information from sensors or monitors and alter operating controls through Controller) ; comparing the expected salt content to a specified target; modifying the current wash water rate by adjusting a flow control valve to produce a modified wash water rate when the expected salt content does not meet the specified target; (Soliman: [0017], comparing the PTB value to the predetermined PTB value programmed within the controller, and adjusting one or more system operations control through the controller such that the PTB value nears without meeting the predetermined PTB value) . Soliman discloses predicting treatment of wash water but does not disclose a salt content versus percent wash water curve, in an analogous art of desalting process, Love discloses: using the current percent wash water and a salt content versus percent wash water curve (Love: [0050], curve of volume percent versus droplet diameter) ; Soliman and Love are analogous arts because they are in the same field of endeavor, desalting process. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Soliman using the teachings of Love to include developing a curve of volume percent versus another volume data. It would provide Soliman’s method with enhanced capabilities of calculating desalting data more efficiently. Further, Soliman does not clearly disclose maintaining the modified wash water rate, Salu discloses: and maintaining the modified wash water rate for a run time (Salu: [0079], If the current value is at set point, then the system maintains the current wash water rate for a predetermined period of time); Soliman and Salu are analogous arts because they are in the same field of endeavor, desalting process. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Soliman using the teachings of Salu to include maintaining the optimized set value. It would provide Soliman’s method with enhanced capabilities of making the process reliable. As for claim 12, Soliman-Love-Salu disclose: where determining the current percent wash water comprises determining the percent wash water according to the following P=W×100/C where P is the percent wash water, W is the current wash water rate in thousands of barrels per day (MBD), and C is current dry crude in thousands of barrels per day (MBD) (Salu: [0061], In the algorithm, the rate of wash water required for a desalter crude throughput is given by: Qww =( Pww*Qc )/100) . As for claim 13, Soliman-Love-Salu disclose: where the step of determining an expected salt content further comprises the steps of: collecting historical dry crude rates; collecting historical wash water rates; collecting historical salt content amounts, where the historical dry crude rates, historical wash water rates, and historical salt content amounts are from a historical time period; determining a percent wash water from the historical wash water rates and historical dry crude rates; developing a salt content versus percent wash water curve using the historical salt content amounts and percent wash water; determining a best fit polynomial of the salt content versus percent wash water curve; and using the best fit polynomial to determine the expected salt content ([0057], A predictive model is created through experiment or through artificial intelligence based on the historical data, which correlates values such as salt-in-crude and BSW measurements with the operations variables) . As for claim 14, Soliman-Love-Salu disclose: further comprising the step of programming the best fit polynomial and the percent wash water equation into a distributed control system, where the distributed control system controls the flow control valve (Soliman: [0019], a desalting unit control valve) . As per claim 15, it recites features that are substantially same as those features claimed by claims 11 and 14, thus the rationales for rejecting claims 11 and 14 are incorporated herein. As for claim 16, Soliman-Love-Salu disclose: where the desalter is in the absence of an online salt analyzer (Soliman: [0057], The machine learning and artificial intelligence algorithms assist in predicting the next off-spec crude incident or transformer short circuit, can automatically propose setpoint changes for controllers and notify operators of inefficient operations, by monitoring salinity and water content from inlet crude monitor 204, salinity and water content from dehydrator dry crude monitor 432, salinity and water content from first desalter unit dry crude monitor 556, salinity and water content from second desalter unit dry crude monitor 572, flow rate from dehydrator wash water monitor 428, flow rate from first desalter unit wash water monitor 544, flow rate from second desalter unit wash water monitor 576, level from dehydrator level control 422, level from first desalter unit level control 540, level from second desalter unit level control 568, and electricity flow to electric coalescers) . As for claim 17, Soliman-Love-Salu disclose: where the run time is the residence time of the desalter (Love: [0062], the average residence time) . As for claim 18, Soliman-Love-Salu disclose: where the specified target is between 9.6 pounds per thousand barrels and 9.8 pounds per thousand barrels (Soliman: [0010], PTB is the PTB value in pounds of salt per one thousand barrels of the dry crude stream coming from a desalter vessel or a dehydrator vessel) . As for claim 19, Soliman-Love-Salu disclose: further comprising the step of using a best fit polynomial of the salt content versus percent wash water curve to determine the expected salt content (Love: [0050], curve of volume percent versus droplet diameter) . Examiner’s Note Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action . Lopez (US Publication 20180371876) CONTROLLING HIGH-PRESSURE PRODUCTION TRAP SEPARATION EFFICIENCY It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300. 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. /Hua Lu/ Primary Examiner, Art Unit 2118 Application/Control Number: 18/765,600 Page 2 Art Unit: 2118 Application/Control Number: 18/765,600 Page 3 Art Unit: 2118 Application/Control Number: 18/765,600 Page 4 Art Unit: 2118 Application/Control Number: 18/765,600 Page 5 Art Unit: 2118 Application/Control Number: 18/765,600 Page 6 Art Unit: 2118 Application/Control Number: 18/765,600 Page 7 Art Unit: 2118 Application/Control Number: 18/765,600 Page 8 Art Unit: 2118 Application/Control Number: 18/765,600 Page 9 Art Unit: 2118 Application/Control Number: 18/765,600 Page 10 Art Unit: 2118 Application/Control Number: 18/765,600 Page 11 Art Unit: 2118 Application/Control Number: 18/765,600 Page 12 Art Unit: 2118 Application/Control Number: 18/765,600 Page 13 Art Unit: 2118 Application/Control Number: 18/765,600 Page 14 Art Unit: 2118 Application/Control Number: 18/765,600 Page 15 Art Unit: 2118 Application/Control Number: 18/765,600 Page 16 Art Unit: 2118
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