DETAILED ACTION
This action is in response to the applicant’s reply filed on October 13, 2025. Claims 1-20 are pending and addressed below.
Response to Amendment
In response to the applicant’s submission of new Figure 2 to correct reference character “300”, the objection to the drawings have been withdrawn.
In response to the Applicant’s amendments to claims 1, 14, and 20 to clarify that “control data includes actions performed by the separator” the rejections of claims 1-20 under 35 USC 112(a) have been withdrawn.
In response to the applicant’s amendments to claims 1, 14, and 20 to further clarify the claims and correct antecedent basis, the rejections of claims 1-20 under 35 USC 112(b) have been withdrawn.
Response to Arguments
Applicant's arguments filed October 13, 2025 with respect to claims 1-20 under 35 USC 101 have been fully considered but they are not persuasive.
Regarding the Examiner's analysis under Step 2A, Prong 1 (whether the claims recite a judicial exception), the Applicant has argued that independent claim 1 does not recite a judicial exception as it is not directed to a law of nature, a natural phenomenon, or an abstract idea and as such is patent eligible under at least Prong One of Step 2A.
As stated in the current rejection the limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of generic computing elements (i.e. the mobile computing device, server, and separator) does not take the claim out of the mental process grouping, nor does the recitation of “continuously” as a human could in theory make continuous calculations. Thus, the claim recites an abstract idea.
Second, applicant argues that “not all methods of organizing human activity are abstract idea” and because claim 1 does not recite “fundamental economic principles or practices, commercial or legal interactions” or “managing personal behavior and relationships or interactions between people” it is not a method of organizing a method of human activity. However, as indicated in the current rejection the limitations of claim 1, as drafted, could also represent a process that, under its broadest reasonable interpretation, represents a commercial interaction (steps/processes for optimizing well production). The recitation of generic computing devices, does not preclude the abstract steps recited above from practically being performed by a human. Thus, the claim recites an abstract idea.
Regarding the Examiner's analysis under Step 2A, Prong 2 (whether the claims recite any additional elements).
First, the Applicant has argued that claim 1 recites a particular machine that is integral to the
claim. Specifically, the applicant has indicated that claim 1 explicitly requires a mobile computing device,
a server different from the mobile computing device, a separator, a plurality of pressure sensors, and a wired or wireless communication link, and that one of ordinary skill would recognize these items as a particular machine. However, the addition of "a mobile computing device", "a server different from the mobile computing device", ”a separator”, “a plurality of pressure sensors”, and “a wired or wireless communication link” are enough to make the claim eligible because the "a mobile computing device", "a server different from the mobile computing device", ”a separator”, “a plurality of pressure sensors”, and “a wired or wireless communication link” are recited at a high level of generality and are merely invoked as tools to perform the abstract ideas (more details in the rejection presented below which has been previously presented).
Second, the Applicant has indicated that claim 1 integrates any potential judicial exception into a practical application because the recited elements reflect an improvement to a technical field as claim 1 is directed to "optimizing the technical field of oil and gas well production through a solution of automated pressure", however the Applicant has not presented specific arguments as to how this is an improvement to the technical field. Further, the argument that optimizing the technical field of oil and gas well production through a solution of automated pressure is not persuasive, as the "improvement" is to the abstract idea itself, the abstract idea being optimizing the technical field of oil and gas well production.
Third, the Applicant argues that claim 1 integrates any potential judicial exception into a practical application because is recites "Effecting a transformation or reduction of a particular article to a different state or thing" as oil and gas wells convert underground petrochemicals into stored petrochemical products. This assertion merely acts to link the use of the abstract idea to a particular technological environment and does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Regarding the Examiner's analysis under Step 2B, applicant has argued that claim 1 is eligible
under Step 2B of the subject matter eligibility as claim 1 recites additional elements that amount to significantly more than the exception itself.
First, Applicant indicates that claim 1 reflects an improvement to a technical filed and thus the claims are eligible under Step 2B. However, as discussed above the claims do not reflect an improvement to a technical field.
Second, Applicant argues that claim 1 is eligible under Step 2B because it recites a combination of features that are not well-understood, routine, conventional activity in the field, in particular well production optimization because the combination of the steps operates in a non-conventional and non-generic way to optimize well production.
However, the Applicant has not presented any arguments as to how this the combination of features are not well-understood, routine, conventional activity in the field, other than submitted that one or ordinary skill in the art would not recognize this as such. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, is not enough to qualify as "significantly more" when recited in a claim with a judicial exception (as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Third, Applicant argues that claim 1 is eligible under Step 2B because the three references used in a 103 rejection against the independent claim 1, Babic, US 2019/0345797 (hereinafter Babic), Hearn, US 2009/0200020 (hereinafter Hearn) and Wynn, US 2006/0254777 (hereinafter Wynn), are eligible under 35 USC 101. Therefore any perceived similarity between independent claim 1 and these references strongly implies that independent claim 1 also meets the requirements of 35 USC 101. However, the use of particular references in a rejection against the claims does not determine the eligibility of said claims under 35 USC 101 and that the standard for determining said eligibility has been set forth in the current rejection under 35 USC 101 as laid out below.
For at least these reasons, claim 1 does not satisfy the requirements of 35 USC 101. Regarding
claims 14 and 20-26, the arguments as presented above with respect to claim 1 are equally applicable to claims 14 and 20-26.
Applicant's arguments filed October 13, 2025 with respect to claims 1-20 under 35 USC 103 have been fully considered but they are not persuasive.
The Applicant has argued that Babic fails to disclose “receiving third-party compliance data at a mobile computing device” and ‘creating optimized well-production control data based at least in part on the user input data and the third -party compliance data”. Specifically, the Applicant points out that the term “compliance” is entirely absent from Babic. Further applicant asserts that Hearn also does not disclose “compliance” or its equivalent and that Wynn merely states “The owners and operators of oil and gas well production operations will see an increase in sales and will be in compliance with Environmental Protection Agency requirements”.
Babic clearly discloses receiving data from a third-party system, such as a management module, and that received data can be used to manage well production (par [0078], [0088]). Although Babic does not specific the use of “compliance data” related to environmental regulations or an environmental regulatory body, in the current rejection Babic has been modified by Hearn and Wynn. Wynn discloses vapor emission thresholds set by the Environmental Protection Agency (EPA) for the amount of gas that can be released from a holding tank into the atmosphere through a release valve on the top of the tank (par [0005]). This would be considered “compliance data”. Modification of the third-party data of Babic and Hearn with the vapor emission thresholds as disclosed by Wynn would have yielded the predictable results of maximized production within the third-party regulatory constraints of the vapor emission thresholds, as these vapor emission thresholds are set by the EPA and are mandatory guidelines which must be followed during well production (Wynn, par [0005]-[0006]).
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).
Further, Applicant argues that there is no disclosure or suggestions to indicate that the actions of “creating optimized well-production control data based at least in part on the user input data and the third-party compliance data” and “wherein the well production output data is analyzed to further optimize well production” are performed by the “mobile computing device”. However Babic clearly discloses the use of computing devices including server computers 102 and client computing devices 104. The computing devices 102/104 have similar hardware and structure and may include laptop computers or tablets (par [0056]-[0063]). Clearly a laptop and /or tablet would be considered a mobile device.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the USPTO’s eligibility analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1, 14 and 20 are all directed to a method (process). As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites “receiving user input data; receiving third-party compliance data, wherein the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank; creating optimized well-production control data based at least in part on the user input data and the third-party compliance data, wherein the well-production control data includes actions performed by a separator; transmitting the optimized well-production control data…; executing a well-production plan based at least in part on the optimized well-production control data,…; converting the two or more of analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to a respective two or more digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data,…; analyzing the at least two of digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data, wherein the at least two of digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data are compared against each other to create digital differential pressure range data, monitoring and maintain digital differential pressure range data for continued well production optimization; creating well-production output data based at least in part on the digital differential pressure range data; transmitting the well-production output data…; receiving the well-production output data…; and analyzing the well production output data to further optimize well production.”
Claim 14 recites “receiving user input data; receiving third-party compliance data, wherein the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank; creating optimized well-production control data based at least in part on the user input data and the third-party compliance data, wherein the well-production control data includes actions performed by a separator; transmitting the optimized well-production control data…; executing a well-production plan based at least in part on the optimized well-production control data,…; converting the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively; analyzing the digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data, wherein the digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data are compared against each other to create digital differential pressure range data; monitoring and maintaining the digital differential pressure range data for continued well production optimization; creating well-production output data based at least in part on the digital differential pressure range data; transmitting the well-production output data…; and receiving the well-production output data, and analyzing the well production output data is analyzed to further optimize well production, and wherein production is maximized within the third-party regulatory constraints.”
Claim 20 recites “receive user input data; electronically receive,…third-party compliance data, wherein the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank; create optimized well-production control data based at least in part on the user input data and the third-party compliance data, wherein the well-production control data includes actions performed be a separator; transmitting the optimized well-production control data…; continuously, execute a well-production plan based at least in part on the optimized well production control data…; convert the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively; analyze the digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data, wherein the digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data are compared against each other to create digital differential pressure range data, monitor and maintain the digital differential pressure range data for continued well production optimization; create well-production output data based at least in part on the digital differential pressure range data; transmitting the well-production output data…; receive the well-production output data; analyze the well production output data to further optimize well production, and store the well-production output data in a database.”
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of generic computing elements does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
In addition, these limitations, as drafted, could also represent a process that, under its broadest reasonable interpretation, represents a commercial interaction (steps/processes for optimizing well production) and are therefore a method of organizing human activity. More specifically, other than recitation of generic computing devices, nothing in the claim element precludes the abstract steps recited above from practically being performed by a human. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 1, 14, and 20 also recites the additional elements of: a mobile computing device, a separator, a server, a plurality of pressure sensors, and a wireless or wired communication link.
The mobile computing device and the server, which act to send, receive, and process data and the separator are additional elements whose functions are recited at a high level of generality and are merely invoked as tools to perform the abstract idea.
The plurality of pressure sensors acts to collect various forms of data for processing, and thus amount to pre-solution activity.
The characterization of the communication link as wireless or wired merely indicates a field of use or technological environment in which to apply a judicial exception.
The characterization of the data as digital merely acts to link the use of the abstract idea to a particular technological (i.e. computing) environment.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the mobile computing device and the server amount to mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
With respect to the communication between the server and the mobile computing device, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Regarding the plurality of pressure sensors, the specification demonstrates the well-understood, routine, conventional nature of this additional element as it describes the additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). For example, the specification does not even give an example of what type of sensors would be used (strain gauge, piezoelectric, manometers, etc.), and the drawings simply have a black box indicating the myriad of sensors that may be used (see Figure 2).
Regarding the separator the specification demonstrates the well-understood, routine, conventional nature of this additional element as it describes the additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). For example, the specification does not even give an example of what type of separator would be used (horizontal, vertical, spherical, etc.), and the drawings provide no additional details (see Figure 2).
As discussed above, the characterization of the communication link as wireless or wired and the characterization of the data as digital amounts to merely indicating a field of use or technological environment in which to apply a judicial exception, which does not amount to significantly more than the exception itself. (see MPEP 2106.05(h)).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
Claims 2, 5-8, and 15 recites the additional elements of: a database, a wellhead compressor, a sales line compressor, a pumping unit controller. The database, a wellhead compressor, a sales line compressor, a pumping unit controller, are additional elements whose functions are recited at a high level of generality and are merely invoked as tools to perform the abstract idea.
Claims 3, 9-13, 16, and 18-19 the recitation of the specific variables and data limitations are insufficient as “merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from §101 undergirds the information-based category of abstract ideas (See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016)). Similar to claims 1, 14, and 20, this recitation does not provide a practical application of the abstract idea, and is not significantly more.
Claims 4 and 17 limitations of “wherein production is shut in when the pre-defined alert conditions exceed one or more predefined safety thresholds”, the specification demonstrates the well-understood, routine, conventional nature of these additional elements as it describes the additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a).
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.
Claim(s) 1-4, 7-10, 13-19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Babic, US 2019/0345797 (hereinafter Babic) in view of Hearn, US 2009/0200020 (hereinafter Hearn) and Wynn, US 2006/0254777 (hereinafter Wynn).
Claims 1 and 14: Babic discloses a method of optimizing well production through automated pressure monitoring (system and method for controlling and/or optimizing remote oil and gas production systems, abstract) comprising:
with a mobile computing device (computing devices 102/104, may be laptop computer or tablet, [0056]-[0062]) and configured to:
receiving user input data (customizable algorithm submodule 342 collects data of the well in real time including the well parameters, well operation parameters, conditions, requirements, and/or the like, step 364, par [0088]);
receiving third-party compliance data (management module receives parameters from third-party systems 322, par [0078], customizable algorithm submodule 342 collects data of the well in real time including the well parameters, well operation parameters, conditions, requirements, and/or the like, step 364, par [0088]);
creating optimized well-production control data based at least in part on the user input data and the third-party compliance data (determines an optimization target such as a target production rate, step 366, par [0088]);
with a plurality of sensors (sensors 142 and controllers 146), collecting analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data (sensors 142 monitor operation conditions of each oil-production device 106, drives 144 for operating the oil-production device 106, par [0065], sensors 142 obtains wellhead data and downhole data such as pressures, par [0082], parameters may include pressure downhole and/or at surface such as the tubing head pressure (THP), pump intake pressure downhole and/or at surface, pump discharge pressure downhole and/or at surface, one or more economical thresholds, operational requirements and parameters, flow line pressure, tank indication, par [0089]-[0090]);
executing a well-production plan at the server based at least in part on the optimized well-production control data, the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data (sensors 142 monitor operation conditions of each oil-production device 106, drives 144 for operating the oil-production device 106, par [0065], sensors 142 obtains wellhead data and downhole data such as pressures, par [0082], parameters may include pressure downhole and/or at surface such as the tubing head pressure (THP), pump intake pressure downhole and/or at surface, pump discharge pressure downhole and/or at surface, one or more economical thresholds, operational requirements and parameters, flow line pressure, tank indication, par [0089]-[0090]);
converting the two or more of analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively, at the server (data collection layer 316 collects data obtained by the oil-production devices 106, which collects data from sensors 142, drives 144, and controllers 146, and sends the collected data to a data historian module 318, par [0074]-[0075]);
analyzing the digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data at the server, wherein a digital differential pressure range data includes a differential pressure range for the digital tubing pressure less digital sales line pressure and upper and lower control points for the digital tank pressure data (customizable algorithm submodule 342 collects data of the well in real time including well parameters, well operation parameters, conditions, requirements, and/or the like, step 364, and determines an optimization target such as a target production rate, step 366, par [0088], [0091], customizable algorithm submodule 342 conducts a small-step optimization to the well based on the analyzing and diagnosing result, step 374, customizable algorithm submodule 342 commands the well to adjust its production parameters to increase its production rate by a predetermined rate-increment that is generally smaller than what is required for adjusting the current production rate to the target production rate, par [0093], which is considered a range); and
the digital tubing pressure data, digital sales line pressure data and digital tank pressure data are compared against historical data concerning the digital tubing pressure data, digital sales line pressure data and digital tank pressure data to create the digital differential pressure range data (determining whether the production well is suitable for optimization based on one or more conditions comprising at least one of historical pump efficiency data, historical THT data, the one or more economical thresholds, basic sediment and water, and THP, par [0014]-[0015]), and wherein the digital differential pressure range data (required adjustments for the difference between the target production rate and the current production rate, par [0093]-[0094]) is monitored and maintained at the server for continued well production optimization (customizable algorithm submodule 342 determines an optimization target such as a target production rate, step 366, par [0088], [0091], then conducts a small-step optimization to the well based on the analyzing and diagnosing result, step 374, par [0094]);
creating well-production output data at the server based at least in part on the digital differential pressure range data (customizable algorithm submodule 342 then conducts a small-step optimization to the well based on the analyzing and diagnosing result, step 374, par [0093]);
transmitting the optimized well-production control data to a server different from the mobile computing device via a communication link (computers 102, one or more client-computing devices 104, and one or more oil-production devices 106 are functionally interconnected by a network 110 via suitable wired and wireless networking connections, par [0053]); and
receiving the well-production output data at a mobile computing device, wherein the well-production output data is analyzed to further optimize well production (after the small-step optimization, the customizable algorithm submodule 342 checks if the optimization target has been reached, step 376 if the process 360 loops back to step 364 for further optimization); and
storing the well-production output data in a database (optimization results are stored into the database 344 under the control of the configuration submodule 328, and are also sent to an output submodule 346, par [0080]).
Babic fails to disclose the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank, wherein the well-production control data includes actions performed at a separator, the plurality of pressure sensors collects analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data; converting the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively, at the server; analyzing the digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data at the server, wherein a digital differential pressure range data includes a differential pressure range for the digital tubing pressure less digital sales line pressure and upper and lower control points for the digital tank pressure data, and the digital tubing pressure data, digital sales line pressure data and digital tank pressure data are compared against historical data concerning the digital tubing pressure data, digital sales line pressure data and digital tank pressure data to create the digital differential pressure range data, and wherein the digital differential pressure range data is monitored and maintained at the server for continued well production optimization.
Hearn discloses a method of optimizing well production through automated pressure monitoring (see abstract) including
wherein the well-production control data includes actions performed by a separator (separator 24) (controller 80 optimizes operation of plunger lift system 100, which includes separator 24, par [0029])
executing a well-production plan at the server (controller 80) based at least in part on the optimized well production control data, wherein a plurality of pressure sensors collects analog tubing pressure data (tubing pressure sensor 53), analog casing pressure data (casing pressure sensors 55), and analog sales line pressure data (sales line pressure sensor 57) (controller 80 monitors conditions of the well 12 to optimize operation based on monitored conditions, par [0028]-[0029]);
converting the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively, at the server (controller 80 processes inputs from the sensors 51, 53, 55, 57 to optimize the well, par [0029]-[0030]);
analyzing the at least two of digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data at the server (controller 80 calculates any combination of tubing pressure, casing pressure, sales line pressure, and pressure differential therebetween, par [0030]), wherein a digital differential pressure range data includes a differential pressure range for the digital tubing pressure less digital sales line pressure (controller 80 calculates the pressure differential between the casing pressure and the sales pressure, par [0030]) and upper and lower control points for the digital tank pressure data (pressure differential is at least equal to the pre-selected ON pressure differential, the controller 80 initiates the on-cycle, or "on time" 2-1, par [0030], Fig 2, controller 80 will end the on cycle when the pressure differential between the casing pressure and the tubing pressure meets a certain condition, i.e., OFF condition, par [0036], Fig 2), and the digital tubing pressure data, digital sales line pressure data and digital tank pressure data are compared against historical data concerning the digital tubing pressure data, digital sales line pressure data and digital tank pressure data to create the digital differential pressure range data (daily production rate of the completed cycle is compared to the daily production rate of the previous cycle, controller will optimize the well operating conditions depending on whether the production increased or decreased from the previous cycle, par [0045], Fig 2);
analyzing the digital tubing pressure data, digital casing pressure data, and digital sales line pressure data, wherein the digital tubing pressure data, digital casing pressure data, and digital sales line pressure data are compared against each other to create digital differential pressure range data (controller 80 calculates any combination of tubing pressure, casing pressure, sales line pressure, and pressure differential therebetween, par [0030]), and wherein the digital differential pressure range data is monitored and maintained at the server for continued well production optimization (pressure differential is used to optimize well production by controlling OFF and ON cycles by meeting certain ON and OFF conditions, par [0030]-[0036]); and
adjusting operational parameters for subsequent cycles in order to optimize well production (par [0047]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic to optimize well production based on pressure monitoring of the tubing pressure, casing pressure, and sales line pressure as disclosed by Hearn, as this modification would have allowed for the monitoring and adjustment of well operations in order to improve the well production while improving efficient of the production from the well (Hearn, par [0013]).
Babic and Hearn are silent as to the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank, executing the well-production plan at the server based at least in part on the tank pressure data and including analyzing digital tank pressure data at the server and including the comparison of digital tank pressure to create digital differential pressure range data.
Wynn discloses the Environmental Protection Agency (EPA) has set mandatory guidelines for the amount of gas that can be released from a holding tank into the atmosphere through a release valve on the top of the tank (par [0005]). A vapor recovery apparatus (10) is used reducing the amount of gas being released into the atmosphere from the holding tank during operation of oil and gas well production (par [0006]-[0007]). A pressure sensor (26) collects tank pressure data (sensor 26 monitors the pressure of gas in the holding tank 16, par [0017]) and sends a signal to a controller that correlates to the pressure and thus signals the controller (24) when the holding tank (16) has reached a predetermined pressure. At a first predetermined pressure, the controller (24) actuates a vapor recovery apparatus (10) such that it is ready to pump gas from the holding tank (16) when a second predetermined pressure is reached. At second predetermined pressure, a compressor pumps gas from the holding tank (16), compresses the gas, and forces the pressurized gas into the sales line (14) (par [0020]-[0022]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the third-party compliance data of Babic and Hearn with the vapor emission thresholds as disclosed by Wynn such that the production is maximized within the third-party regulatory constraints of the vapor emission thresholds, as these vapor emission thresholds are set by the EPA and are mandatory guidelines which must be followed during well production (Wynn, par [0005]-[0006]). Additionally, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic and Hearn to include executing the well-production plan at the server based at least in part on the tank pressure data as disclosed by Wynn, as Babic and Hearn both discloses tanks as part of the well production system (Babic, par [0090], Hearn, par [0028]), and one of ordinary skill in the art would understand that monitoring the tank pressure, which is part of the well production system, would be necessary when optimizing well production within the EPA mandatory guidelines for vapor emission thresholds (Wynn, par [0005]-[0006]).
Claim 20: Babic discloses a system of optimizing well production through automated pressure monitoring (system and method for controlling and/or optimizing remote oil and gas production systems, abstract) comprising:
a plurality of pressure sensor (sensors 142 and controllers 146),;
a server (the system comprises one or more server computers 102, par [0052]);
a wireless communication link (computers 102, one or more client-computing devices 104, and one or more oil-production devices 106 are functionally interconnected by a network 110 via suitable wired and wireless networking connections, par [0053]);
with a mobile computing device different from the server (computers 102, one or more client-computing devices 104, and one or more oil-production devices 106 are functionally interconnected by a network 110 via suitable wired and wireless networking connections, par [0053], computing devices 102/104, may be laptop computer or tablet, [0056]-[0062]) and configured to:
receive user input data (customizable algorithm submodule 342 collects data of the well in real time including the well parameters, well operation parameters, conditions, requirements, and/or the like, step 364, par [0088]);
electronically receive, via wireless communication link, third-party compliance data (management module receives parameters from third-party systems 322, par [0078], customizable algorithm submodule 342 collects data of the well in real time including the well parameters, well operation parameters, conditions, requirements, and/or the like, step 364, par [0088]);
create optimized well-production control data based at least in part on the user input data and the third-party compliance data (determines an optimization target such as a target production rate, step 366, par [0088]);
with a plurality of sensors (sensors 142 and controllers 146), collecting analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data (sensors 142 monitor operation conditions of each oil-production device 106, drives 144 for operating the oil-production device 106, par [0065], sensors 142 obtains wellhead data and downhole data such as pressures, par [0082], parameters may include pressure downhole and/or at surface such as the tubing head pressure (THP), pump intake pressure downhole and/or at surface, pump discharge pressure downhole and/or at surface, one or more economical thresholds, operational requirements and parameters, flow line pressure, tank indication, par [0089]-[0090]);
wherein the server is configured to, continuously
execute a well-production plan based at least in part on the optimized well-production control data, the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data (sensors 142 monitor operation conditions of each oil-production device 106, drives 144 for operating the oil-production device 106, par [0065], sensors 142 obtains wellhead data and downhole data such as pressures, par [0082], parameters may include pressure downhole and/or at surface such as the tubing head pressure (THP), pump intake pressure downhole and/or at surface, pump discharge pressure downhole and/or at surface, one or more economical thresholds, operational requirements and parameters, flow line pressure, tank indication, par [0089]-[0090]);
convert the two or more of analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively (data collection layer 316 collects data obtained by the oil-production devices 106, which collects data from sensors 142, drives 144, and controllers 146, and sends the collected data to a data historian module 318, par [0074]-[0075]);
analyze the digital tubing pressure data, digital casing pressure data digital sales line pressure data and digital tank pressure data at the server, wherein a digital differential pressure range data includes a differential pressure range for the digital tubing pressure less digital sales line pressure and upper and lower control points for the digital tank pressure data (customizable algorithm submodule 342 collects data of the well in real time including well parameters, well operation parameters, conditions, requirements, and/or the like, step 364, and determines an optimization target such as a target production rate, step 366, par [0088], [0091], customizable algorithm submodule 342 conducts a small-step optimization to the well based on the analyzing and diagnosing result, step 374, customizable algorithm submodule 342 commands the well to adjust its production parameters to increase its production rate by a predetermined rate-increment that is generally smaller than what is required for adjusting the current production rate to the target production rate, par [0093], which is considered a range); and
the digital tubing pressure data, digital sales line pressure data and digital tank pressure data are compared against historical data concerning the digital tubing pressure data, digital sales line pressure data and digital tank pressure data to create the digital differential pressure range data (determining whether the production well is suitable for optimization based on one or more conditions comprising at least one of historical pump efficiency data, historical THT data, the one or more economical thresholds, basic sediment and water, and THP, par [0014]-[0015]), and wherein the digital differential pressure range data (required adjustments for the difference between the target production rate and the current production rate, par [0093]-[0094]) is monitored and maintained at the server for continued well production optimization (customizable algorithm submodule 342 determines an optimization target such as a target production rate, step 366, par [0088], [0091], then conducts a small-step optimization to the well based on the analyzing and diagnosing result, step 374, par [0094]);
create well-production output data based at least in part on the digital differential pressure range data (customizable algorithm submodule 342 then conducts a small-step optimization to the well based on the analyzing and diagnosing result, step 374, par [0093]);
transmitting the optimized well-production control data to a server different from the mobile computing device via a communication link (computers 102, one or more client-computing devices 104, and one or more oil-production devices 106 are functionally interconnected by a network 110 via suitable wired and wireless networking connections, par [0053]); and
receive the well-production output data the well-production output data is analyzed to further optimize well production (after the small-step optimization, the customizable algorithm submodule 342 checks if the optimization target has been reached, step 376 if the process 360 loops back to step 364 for further optimization); and
store the well-production output data in a database (optimization results are stored into the database 344 under the control of the configuration submodule 328, and are also sent to an output submodule 346, par [0080]).
Babic fails to disclose the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank, wherein the well-production control data includes actions performed at a separator, the plurality of pressure sensors collects analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data; converting the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively, at the server; analyzing the digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data at the server, wherein a digital differential pressure range data includes a differential pressure range for the digital tubing pressure less digital sales line pressure and upper and lower control points for the digital tank pressure data, and the digital tubing pressure data, digital sales line pressure data and digital tank pressure data are compared against historical data concerning the digital tubing pressure data, digital sales line pressure data and digital tank pressure data to create the digital differential pressure range data, and wherein the digital differential pressure range data is monitored and maintained at the server for continued well production optimization.
Hearn discloses a method of optimizing well production through automated pressure monitoring (see abstract) including
wherein the well-production control data includes actions performed by a separator (separator 24) (controller 80 optimizes operation of plunger lift system 100, which includes separator 24, par [0029])
executing a well-production plan at the server (controller 80) based at least in part on the optimized well production control data, wherein a plurality of pressure sensors collects analog tubing pressure data (tubing pressure sensor 53), analog casing pressure data (casing pressure sensors 55), and analog sales line pressure data (sales line pressure sensor 57) (controller 80 monitors conditions of the well 12 to optimize operation based on monitored conditions, par [0028]-[0029]);
converting the analog tubing pressure data, analog casing pressure data, analog sales line pressure data, and analog tank pressure data to digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data, respectively, at the server (controller 80 processes inputs from the sensors 51, 53, 55, 57 to optimize the well, par [0029]-[0030]);
analyzing the at least two of digital tubing pressure data, digital casing pressure data, digital sales line pressure data and digital tank pressure data at the server (controller 80 calculates any combination of tubing pressure, casing pressure, sales line pressure, and pressure differential therebetween, par [0030]), wherein a digital differential pressure range data includes a differential pressure range for the digital tubing pressure less digital sales line pressure (controller 80 calculates the pressure differential between the casing pressure and the sales pressure, par [0030]) and upper and lower control points for the digital tank pressure data (pressure differential is at least equal to the pre-selected ON pressure differential, the controller 80 initiates the on-cycle, or "on time" 2-1, par [0030], Fig 2, controller 80 will end the on cycle when the pressure differential between the casing pressure and the tubing pressure meets a certain condition, i.e., OFF condition, par [0036], Fig 2), and the digital tubing pressure data, digital sales line pressure data and digital tank pressure data are compared against historical data concerning the digital tubing pressure data, digital sales line pressure data and digital tank pressure data to create the digital differential pressure range data (daily production rate of the completed cycle is compared to the daily production rate of the previous cycle, controller will optimize the well operating conditions depending on whether the production increased or decreased from the previous cycle, par [0045], Fig 2);
analyzing the digital tubing pressure data, digital casing pressure data, and digital sales line pressure data, wherein the digital tubing pressure data, digital casing pressure data, and digital sales line pressure data are compared against each other to create digital differential pressure range data (controller 80 calculates any combination of tubing pressure, casing pressure, sales line pressure, and pressure differential therebetween, par [0030]), and wherein the digital differential pressure range data is monitored and maintained at the server for continued well production optimization (pressure differential is used to optimize well production by controlling OFF and ON cycles by meeting certain ON and OFF conditions, par [0030]-[0036]); and
adjusting operational parameters for subsequent cycles in order to optimize well production (par [0047]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic to optimize well production based on pressure monitoring of the tubing pressure, casing pressure, and sales line pressure as disclosed by Hearn, as this modification would have allowed for the monitoring and adjustment of well operations in order to improve the well production while improving efficient of the production from the well (Hearn, par [0013]).
Babic and Hearn are silent as to the third-party compliance data comprises at least one of an environmental regulation related to vapor emission thresholds of a gas or oil storage tank, or an environmental regulatory body rule or regulation related to vapor emission thresholds of a gas or oil storage tank, executing the well-production plan at the server based at least in part on the tank pressure data and including analyzing digital tank pressure data at the server and including the comparison of digital tank pressure to create digital differential pressure range data.
Wynn discloses the Environmental Protection Agency (EPA) has set mandatory guidelines for the amount of gas that can be released from a holding tank into the atmosphere through a release valve on the top of the tank (par [0005]). A vapor recovery apparatus (10) is used reducing the amount of gas being released into the atmosphere from the holding tank during operation of oil and gas well production (par [0006]-[0007]). A pressure sensor (26) collects tank pressure data (sensor 26 monitors the pressure of gas in the holding tank 16, par [0017]) and sends a signal to a controller that correlates to the pressure and thus signals the controller (24) when the holding tank (16) has reached a predetermined pressure. At a first predetermined pressure, the controller (24) actuates a vapor recovery apparatus (10) such that it is ready to pump gas from the holding tank (16) when a second predetermined pressure is reached. At second predetermined pressure, a compressor pumps gas from the holding tank (16), compresses the gas, and forces the pressurized gas into the sales line (14) (par [0020]-[0022]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the third-party compliance data of Babic and Hearn with the vapor emission thresholds as disclosed by Wynn such that the production is maximized within the third-party regulatory constraints of the vapor emission thresholds, as these vapor emission thresholds are set by the EPA and are mandatory guidelines which must be followed during well production (Wynn, par [0005]-[0006]). Additionally, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic and Hearn to include executing the well-production plan at the server based at least in part on the tank pressure data as disclosed by Wynn, as Babic and Hearn both discloses tanks as part of the well production system (Babic, par [0090], Hearn, par [0028]), and one of ordinary skill in the art would understand that monitoring the tank pressure, which is part of the well production system, would be necessary when optimizing well production within the EPA mandatory guidelines for vapor emission thresholds (Wynn, par [0005]-[0006]).
Claims 2 and 15: Babic, Hearn and Wynn discloses the well-production output data is stored in a database (Babic, optimization results are stored into the database 344 under the control of the configuration submodule 328, and are also sent to an output submodule 346, par [0080]).
Claim 3: Babic, Hearn and Wynn discloses the well-production output data comprises alert data if the well production pressure monitoring system reports pre-defined alert conditions (Babic, although the well meets the requirements for optimization to increase the production rate, the optimization to the well is terminated, an alert is sent to the user for investigation, the well is not disabled in order to maintain production, par [0104], [0115]).
Claim 4: Babic, Hearn and Wynn discloses production is shut in when the pre-defined alert conditions exceed one or more pre-defined safety thresholds (Hearn, when an increase in pressure differential is detected, the controller initiates the off cycle, off cycle starts with a mandatory shut-in period to allow the plunger to fall back into the well, par [0020]).
Claim 7: Babic, Hearn and Wynn discloses the method is used with a pumping unit controller (Babic, rod pump controller (RPC), Fig 3, par [0065]).
Claim 8: Babic discloses the method is used with a flowmeter (Babic, system 100 may optimize wells based on flow rate, par [0144], which necessarily requires a flow meter).
Claim 9: Babic, Hearn and Wynn are silent as to the detection of flow line leaks based on the digital differential pressure.
It would have been obvious to none of ordinary skill in the art, before the effective filing date of the invention, to utilize the flow line pressure (Babic, par [0090]) to detect of the existence of flowline leaks in the output data, as one of ordinary skill in the art would understand that a reduction in flow-line pressure could be indicative of the presence of a leak. Further, it is well-known to utilize flow-line pressure in optimizing oil/gas production (Babic, par [0005]) and optimization of an oil/gas well may include target flow rates of flow rate limitations, as well as well integrity (Babic, par [0144], claim 2).
Claim 10: Babic, Hearn and Wynn discloses optimization parameters may include pump discharge pressure (Babic, par [0089]).
Babic, Hearn and Wynn is silent as to the well-production output data detects over pressurization of the discharge.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to include detection over pressurization of the discharge as pump discharge pressure is a monitored optimization parameter and one of ordinary skill would understand that monitoring over pressurization of said discharge would contribute to the optimization of the wellbore production.
Claim 13: Babic, Hearn and Wynn is silent as to the well-production output data regulates the tank pressure.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic to include the well-production output data regulates the tank pressure as Babic discloses tanks as part of the well production system (Babic, par [0090]) and one of ordinary skill in the art would understand that the tank pressure would affect the differential pressure would affect optimizing well production.
Claim 15: Babic, Hearn and Wynn, discloses the well-production output data is stored in a database (Babic, optimization results are stored into the database 344 under the control of the configuration submodule 328, and are also sent to an output submodule 346, par [0080]).
Claim 16: While claim 16 further limits the well production output data of base claim 14, it recites “the well-production output data comprises alert data if the well production pressure monitoring system reports pre-defined alert conditions”. Hence, claim 16 is met by Babic, as modified by Hearn, since the well out-put data is taught by Babic as modified by Hearn and the alert data is only required if the well production pressure monitoring system reports pre-defined alert conditions.
However, Babic, Hearn and Wynn, discloses the well-production output data comprises alert data if the well production pressure monitoring system reports pre-defined alert conditions (Hearn, when an increase in pressure differential is detected, the controller initiates the off cycle, off cycle starts with a mandatory shut-in period to allow the plunger to fall back into the well, par [0020]).
Claim 17: Babic, Hearn and Wynn, discloses production is shut in when the pre-defined alert conditions exceed one or more pre-defined safety thresholds (Hearn, when an increase in pressure differential is detected, the controller initiates the off cycle, off cycle starts with a mandatory shut-in period to allow the plunger to fall back into the well, par [0020]).
Claim 18: Babic, Hearn and Wynn, discloses flow-line pressure may be used in optimizing oil/gas production (par [0005],[0090]).
Babic, Hearn and Wynn are silent as to the well-production output data detects the existence of flowline leaks.
It would have been obvious to none of ordinary skill in the art, before the effective filing date of the invention, to include detection of the existence of flowline leaks in the output data, as this modification would have provided an indication as to a potential leakage in the flowlines. Further, flow-line pressure is used in optimizing oil/gas production. One of ordinary skill would understand how to utilize flow-line pressure data to determine the existence of flowline leaks.
Claim 19: Babic, Hearn and Wynn, discloses optimization parameters may include pump discharge pressure (par [0089]).
Babic, Hearn and Wynn is silent as to the well-production output data detects over pressurization of the discharge.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to include detection over pressurization of the discharge as pump discharge pressure is a monitored optimization parameter and one of ordinary skill would understand that monitoring over pressurization of said discharge would contribute to the optimization of the wellbore production.
Claim(s) 5 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Babic, Hearn and Wynn as applied to claim 1, and further in view of Elmer, US 2017/0051588 (hereinafter Elmer).
Claim 5: Babic, Hearn and Wynn is silent as to the method is used with a wellhead compressor.
Elmer discloses a gas compression optimization system (100A) includes a compressor configured to pump incompressible fluid into the gas injection line (135) at the wellhead (150) (Fig 1A, par [0027], [0056]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic to further include the use of a wellhead compressor of Elmer, as this modification would have facilitated injection of compressed gas into the annular region (125) (Fig 1A, par [0056]) in response to differential pressure signals (Elmer, abstract).
Claim 11: Babic, Hearn and Wynn is silent as to the method is silent as to the well-production output data detects the existence of low oil pressure in the compressor.
Elmer discloses a gas compression optimization system (100A) includes a compressor configured to pump incompressible fluid into the gas injection line (135) at the wellhead (150) (Fig 1A, par [0027], [0056]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention to include the existence of low oil pressure in the compressor in the well-production output data, as one of ordinary skill in the art would understand that this would provide data on the operating condition of the compressor thereby helping to avoid compressor failure.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Babic, Hearn and Wynn as applied to claim 1, and further in view of Ricotta, US 2018/0274347 (hereinafter Ricotta).
Claim 6: Babic, Hearn and Wynn are silent the method is used with a sales line compressor.
Ricotta discloses a system and method for on-site treating and separating of a hydrocarbon liquid stream (abstract). The system include a sales line compressor (48) for use in a natural gas pipeline (68) (Fig 3A-2, par [0119]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the method of Babic to further include a sales line compressor as disclosed by Ricotta, as this modification would have facilitated controlling differential pressures within the system.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Babic, Hearn, and Wynn as applied to claim 1, and further in view of Gruner, US 8,366,114 (hereinafter Gruner).
Claim 12: Babic, Hearn and Wynn are silent the well-production output data detects the existence of stuffing box over pressurization.
Gruner discloses common causes of packing failure in stuffing boxes may include over pressuring the packing which effects packing integrity thereby creating a leakage path (col 1, ln 46-52).
It would have been obvious to none of ordinary skill in the art, before the effective filing date of the invention, to include the existence of stuffing box over pressurization in the output data as this modification would have provided an indication as to a potential leakage path at the stuffing box (Gruner, col 1, ln 46-52).
Conclusion
Claims 1-20 are rejected. No claims are allowed.
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 CAROLINE N BUTCHER whose telephone number is (571)272-1623. The examiner can normally be reached Monday-Friday 10-6 pm EST.
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/CAROLINE N BUTCHER/Primary Examiner, Art Unit 3676