DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendments filed August 07, 2025, have been entered. Accordingly, claims 1-14 are currently pending.
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 1-2 and 6-8 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
A patent may be obtained for “any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof.” 35 U.S.C. § 101. The Supreme Court has held that this provision contains an important implicit exception: laws of nature, natural phenomena, and abstract ideas are not patentable. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014); Gottschalk v. Benson, 409 U.S. 63, 67 (1972) (“Phenomena of nature, though just discovered, mental processes, and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.”). Notwithstanding that a law of nature or an abstract idea, by itself, is not patentable, the application of these concepts may be deserving of patent protection. Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1293-94 (2012). In Mayo, the Court stated that “to transform an unpatentable law of nature into a patent eligible application of such a law, one must do more than simply state the law of nature while adding the words ‘apply it.” Mayo, 132 S. Ct. at 1294 (citation omitted).
In Alice, the Supreme Court reaffirmed the framework set forth previously in Mayo “for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of these concepts.” Alice, 134 S. Ct. at 2355. The first step in the analysis is to “determine whether the claims at issue are directed to one of those patent-ineligible concepts.” Id. If the claims are directed to a patent-ineligible concept, then the second step in the analysis is to consider the elements of the claims “individually and ‘as an ordered combination” to determine whether there are additional elements that “transform the nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo, 132 S. Ct. at 1298, 1297). In other words, the second step is to “search for an ‘inventive concept’-i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Id. (brackets in original) (quoting Mayo, 132 S. Ct. at 1294). The prohibition against patenting an abstract idea “cannot be circumvented by attempting to limit the use of the formula to a particular technological environment or adding insignificant post-solution activity.” Bilski v. Kappos, 561 U.S. 593, 610-11 (2010) (citation and internal quotation marks omitted). The Court in Alice noted that “[s]imply appending conventional steps, specified at a high level of generality,’ was not ‘enough’ [in Mayo] to supply an ‘inventive concept.” Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1300, 1297, 1294).
Examiners must perform a Two-Part Analysis for Judicial Exceptions. In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter.
Claims 1-2 and 6-8 are directed to a system and a non-transitory computer readable medium. As such, the claimed invention falls into the broad categories of invention. However, even claims that fall within one of the four subject matter categories may nevertheless be ineligible if they encompass laws of nature, physical phenomena, or abstract ideas. See Diamond v. Chakrabarty, 447 U.S. at 309.
In Step 2A, it must be determined whether the claimed invention is ‘directed to’ a judicially recognized exception. According to the specification, the invention is directed to “generation of personalized study plans for students to learn about topics of study.” (par. 1).
Independent claim 1 recites the following (with emphasis):
A method for operating a natural gas liquids (NGL) plant, the method comprising:
(a) obtaining upstream flow volumes, input flows, and operating conditions of a refinery complex including the NGL plant for a first time period and a second time period;
(b) determining one or more features to extract from the upstream flow volumes, input flows, and operating conditions for each of the first time period and the second time period;
(c) extracting the one or more features from the upstream flow volumes, input flows, and operating conditions of the refinery complex upstream of the NGL plant to form a first feature vector for the first time period and a second feature vector for the second time period;
(d) processing the first feature vector and the second feature vector using a machine learning model, the machine learning model being trained with labeled data representing incoming feed gas of the refinery complex upstream of the NGL plant, the labeled data associating upstream flow volumes, input flows, and operating conditions with incoming feed gas volumes; and
(e) determining, based on the processing, predicted incoming feed gas volumes of the NGL plant.
The underlined portions of claim 1 generally encompass the abstract idea, with substantially identical features in claim 3, but claim 3 includes further steps make the claim fall outside of 35 USC § 101. Claims 2-10 and 12-20 further define the abstract idea such as by defining the training data provided. Under prong 2, the claimed invention encompasses an abstract idea in the form of certain methods of organizing human activity and/or mental processes.
Obtaining upstream flow volumes, input flows, and operating conditions of a refinery complex including the NGL plant for a first time period and a second time period as a means of training a machine learning model is basic to the learning process. Computers have assisted in the preparation of data by obtaining, determining, extracting and processing one or more features for years. The CRM and method in the instant application simply seek to automate this activity using general purpose computers recited at a high level of generality to implement models that mimic the thought process of a plant operator. Using iteration to generate a new operational data for the operator to predict the incoming feed gas volumes of the NGL plant is managing personal behavior of the plant operator. Therefore, the claims are directed to the abstract concept sub-grouping of certain methods of organizing human activity.
In addition, the claims also recite a mental process. But for the recitation of the recitation of computer readable medium storing instructions executed by a computer and systems configured to implement software, nothing precludes the recitations from practically being performed in the mind or with the aid of pen and paper. For example, a plant operator may write/collect upstream flow volumes, input flows, and operating conditions of a refinery complex, including the NGL plant, for a first and second time period, then determine one or more features to extract from this data for each period and (c) extracts these features to form a first and second feature vector. Further iterating and rerunning the process based upon continuing information (the teaching of the machine learning model is merely an implementation of a mental process. Finally, the operator can use a computer to process these feature vectors using a machine learning model, which has been trained with labeled data and determine the predicted incoming feed gas volumes of the NGL plant based on the model's processing. If a claim, under its broadest reasonable interpretation, covers performance of recitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Therefore, under prong 2, the claimed invention encompasses an abstract idea in the form of certain methods of organizing human activity and/or mental processes.
Under prong 2, the instant claims do not integrate the abstract idea into a practical application. In other words, the claims do not (1) improve the functioning of a computer or other technology, (2) effect a particular treatment or prophylaxis for a disease or medical condition (3) are not applied with any particular machine, (4) do not effect a transformation of a particular article to a different state, and (5) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim, as a whole, is more than a drafting effort designed to monopolize the exception, the claims are directed to the judicially recognized exception of an abstract idea. See MPEP §§ 2106.05(a)-(c), (e)-(h).
While certain physical elements (i.e., elements that are not an abstract idea) are present in the claims, such features do not affect an improvement in any technology or technical field and are recited in generic (i.e., not particular) ways. Similarly, the abstract idea does not improve the functioning of these physical elements. In recent cases, the CAFC has made it clear that the term “practical application” means providing a technical solution to a technical problem in computers or networks per se. To be patent-eligible, the claimed invention must improve the computer as a computer. Applicant’s invention does not meet these requirements. Of note, the claims do not recite any specifics about the makeup of the models, such as a specific type of neural network or machine learning, the features that are selected and/or details of how the features are trained for the model, the number of layers of the network, the weights provided for the neurons at the input layer, or any other significant details of the models, such that the models recite a specific machine learning model. In addition, the specification and claims provide no description of any improvement to the technology of the models or machine learning. As a result, the claimed method amounts to more than a non-specific machine learning model implemented by a general-purpose computer. This does not improve the computer qua computer. Instead, Applicant’s invention uses general purpose computers as a tool to implement the abstract idea. As such, the claims are not eligible under Section 101.
Step 2B requires that if the claim encompasses a judicially recognized exception, it must be determined whether the claimed invention recites additional elements that amount to significantly more than the judicial exception. The additional elements or combination of elements other than the abstract idea per se amounts to no more than: a system having a computer and memory configured to perform the abstract idea.
These elements amount to generic, well-understood and conventional computer components. The use of computers to implement models and run software represents well-understood, routine, conventional activity previously known to the industry and/or constitutes extra-solution activity. As demonstrated by Berkheimer v. HP, such computer functions cannot save an otherwise ineligible claim under §101. In short, each step does no more than require a generic computer to perform generic computer functions recited at a high level of generality.
Considered as an ordered combination, only generic computer components are present. Viewed as a whole, the claims simply recite the concept of making judgments by a generic computer. The claims do not, for example, purport to improve the functioning of the computer itself. Nor do they effect an improvement in any other technology or technical field. Instead, the claims at issue amount to nothing significantly more than an instruction to apply the abstract idea using some unspecified, generic computer. Under relevant court precedents, that is not enough to transform an abstract idea into a patent-eligible invention.
As a result, claims 1-2 and 6-8 are not patent eligible.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 7-10 and 14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lolla et al. (WO 2022236222 A1).
In re Claim 1, Lolla discloses a method for operating a natural gas liquids (NGL) plant (See Fig. 2 and Fig. 5; [0073]), the method comprising:
(a) obtaining upstream flow volumes (202), input flows ([0031]: input variables such as input gas feed), and operating conditions (See [0031]: current operating conditions; [0078-0079]) of a refinery complex ([0030]: process variable data measured from the LNG facility) including the NGL plant (Fig. 2: 200 LNG plant) for a first time period (([0024]: first time period) and a second time period ([0024]: second time period);
(b) determining one or more features ([0002]: key performance indicators (KPI), i.e., features, in the production of LNG, such as production quantity, volume, flow rate, or value) to extract from the upstream flow volumes (202), input flows ([0031]: KPI based on inputs), and operating conditions ([0031]: KPI based on operating conditions) for each of the first time period ([0024]: first time period) and the second time period ([0024]: second time period);
(c) extracting the one or more features ([0002]: key performance indicators (KPI), i.e., features, in the production of LNG, such as production quantity, volume, flow rate, or value) from the upstream flow volumes (202), input flows ([0031]: KPI based on inputs), and operating conditions ([0031]: KPI based on operating conditions) of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200) to form
a first feature vector ([0058]: one of the vectors (the process variables) for the first time period ([0024]: first time period) and
a second feature vector (another of the vectors (the process variables) for the second time period ([0024]: first time period);
(d) processing the first feature vector and the second feature vector using a machine learning model ([0058]: By applying the vectors to a machine learning model the model may learn to determine a KPI based on input process variables, resulting in a machine-learned model), the machine learning model being trained with labeled data ([0058]: the training dataset may be applied to the machine learning model ) representing incoming feed gas of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200), the labeled data ([0058]) associating upstream flow volumes (202), input flows ([0031]: input variables such as input gas feed), and operating conditions (See [0031]: current operating conditions; [0078-0079]) with incoming feed gas volumes (102); and
(e) determining, based on the processing ([00119]: the CPU 1202 may execute machine-level instructions for performing processing according to the operational flow described.), predicted incoming feed gas volumes (102; See [0045]) of the NGL gas plant (200).
In re Claim 2, Lolla discloses wherein obtaining upstream flow volumes (202), input flows (102; [0031]: input variables such as input gas feed), and operating conditions (See [0053]) of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200)comprises obtaining condensate (132; See [0044]), ambient temperature ([0078-0079]), and H2S readings ([0037]: acid gas, i.e., hydrogen sulfide; [0053]: process variable data from a sensor).
In re Claim 3, Lolla discloses a method for operating a natural gas liquids (NGL) plant (See Fig. 2 and Fig. 5; [0073]), the method comprising:
(a) obtaining upstream flow volumes (202), input flows ([0031]: input variables such as input gas feed), and operating conditions (See [0031]: current operating conditions; [0078-0079]) of a refinery complex ([0030]: process variable data measured from the LNG facility) including the NGL plant (Fig. 2: 200 LNG plant) for a first time period (([0024]: first time period) and a second time period ([0024]: second time period);
(b) determining one or more features ([0002]: key performance indicators (KPI), i.e., features, in the production of LNG, such as production quantity, volume, flow rate, or value) to extract from the upstream flow volumes (202), input flows ([0031]: KPI based on inputs), and operating conditions ([0031]: KPI based on operating conditions) for each of the first time period ([0024]: first time period) and the second time period ([0024]: second time period);
(c) extracting the one or more features ([0002]: key performance indicators (KPI), i.e., features, in the production of LNG, such as production quantity, volume, flow rate, or value) from the upstream flow volumes (202), input flows ([0031]: KPI based on inputs), and operating conditions ([0031]: KPI based on operating conditions) of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200) to form
a first feature vector ([0058]: one of the vectors (the process variables) for the first time period ([0024]: first time period) and
a second feature vector (another of the vectors (the process variables) for the second time period ([0024]: first time period);
(d) processing the first feature vector and the second feature vector using a machine learning model ([0058]: By applying the vectors to a machine learning model the model may learn to determine a KPI based on input process variables, resulting in a machine-learned model), the machine learning model being trained with labeled data ([0058]: the training dataset may be applied to the machine learning model ) representing incoming feed gas of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200), the labeled data ([0058]) associating upstream flow volumes (202), input flows ([0031]: input variables such as input gas feed), and operating conditions (See [0031]: current operating conditions; [0078-0079]) with incoming feed gas volumes (102); and
(e) determining, based on the processing ([00119]: the CPU 1202 may execute machine-level instructions for performing processing according to the operational flow described.), predicted incoming feed gas volumes (102; See [0045]) of the NGL gas plant (200).
(f) controlling operation of compressors (224,236) of the NGL plant (200) based on the predicted incoming feed gas volumes received by the NGL plant (input variables such as input gas feed; See also [0053]: control system of the LNG production process operating based on process variables which include incoming feed gas volumes);
wherein obtaining upstream flow volumes (Lolla 202), input flows (Lolla [0031]: input variables such as input gas feed), and operating conditions (See Lolla [0053]) of the NGL plant (Lolla 200) comprises obtaining condensate (Lolla 132; See [0044]), ambient temperature ([Lolla 0078-0079]), and H2S readings (Lolla [0037]: acid gas, i.e., hydrogen sulfide; [0053]: process variable data from a sensor). In re Claim 7, Lolla discloses further comprising periodically updating feature vectors based on new upstream flow volumes, input flows, and operating conditions of the NGL ([0069-0070]: he machine-learned model may be updated based on the process variables which includes flow volumes, input flows and operating conditions of NGL).
In re Claim 8, Lolla discloses further comprising repeating step (e) while plant operations are continuing ([0052]: acts in the flow chart 300 may be iterative or one or more acts may be repeated).
In re Claim 9, Lolla discloses applying a supervised machine learning model ([0069-0070]: a machine learning model with supervised learning) to the upstream flow volumes (202), input flows ([0031]: input variables such as input gas feed), and operating conditions (See [0031]: current operating conditions; [0078-0079]) of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200) for the first time period ([0024]: first time period),
the upstream flow volumes (202), input flows ([0031]: input variables such as input gas feed), and operating conditions (See [0031]: current operating conditions; [0078-0079]) being associated with incoming feed gas volumes (102) to develop the machine learning model ([0058]: a machine-learned model) predicting incoming feed gas volumes (102; See [0045]) based on a subset of features ([0070]: the machine-learned be generated by training the machine learning model on the subset of process variables) from the upstream flow volumes, input flows, and operating conditions of the refinery complex upstream of the NGL plant (See [0030] and NGL of 200) | (input variables such as input gas feed; See also [0053]: control system of the LNG production process operating based on process variables which include incoming feed gas volumes); and
controlling operation of compressors (224,236) of the NGL plant (200) based on the predicted incoming feed gas volumes (input variables such as input gas feed; See also [0053]: control system of the LNG production process operating based on process variables which include incoming feed gas volumes).
In re Claim 10, Lolla discloses wherein obtaining upstream flow volumes (Lolla 202), input flows (Lolla [0031]: input variables such as input gas feed), and operating conditions (See Lolla [0053]) of the NGL plant (Lolla 200) comprises obtaining condensate (Lolla 132; See [0044]), ambient temperature ([Lolla 0078-0079]), and H2S readings (Lolla [0037]: acid gas, i.e., hydrogen sulfide; [0053]: process variable data from a sensor).
In re Claim 14, Lolla as modified teaches further comprising periodically updating feature vectors based on new upstream flow volumes, input flows, and operating conditions of the NGL plant (Lolla [0069-0070]: he machine-learned model may be updated based on the process variables which includes flow volumes, input flows and operating conditions of NGL).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4-6 and 11-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lolla et al. (WO 2022236222 A1) in view of Espie (US 20220397119 A1).
In re Claim 4, Lolla does not explicitly teach, wherein controlling operation of compressors of the NGL plant based on the predicted incoming feed gas volumes comprises shutting down at least one compression train if the capacity of running compression trains exceeds the predicted incoming feed gas by the capacity of at least one compression train.
However, Espie teaches wherein controlling operation of compressors (201, 202) based on the predicted incoming feed gas volumes ([0144]: during periods when demand exceeds production, the process may comprise: reducing the pressure of the compressed gas) comprises shutting down at least one compression train ([0065]: to put said compressor into shutdown mode in which said compressor produces no net compressed gas) if the capacity of running compression trains exceeds the predicted incoming feed gas ([0133]: when to stabilize electricity demands and fluctuations due to excess feed and production) by the capacity of at least one compression train ([0065-0066]: while simultaneously loading the remaining centrifugal compressors to maximum capacity and conserving electricity and operating the system in a more efficient manner).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have taken the teachings of Lolla and to have modified them by controlling operation of compressors of the NGL plant of Lolla based on the predicted incoming feed gas volumes of Lolla comprises shutting down at least one compression train if the capacity of running compression trains exceeds the predicted incoming feed gas by the capacity of at least one compression train as taught by Espie, in order to conserve electricity so that the electricity can be used in other parts of the process to produce feed gas and as part of a downstream process for consuming compressed gas (See Espie [0024]), without yielding unpredictable results.
In re Claim 5, Lolla as modified teaches wherein controlling operation of compressors (Espie 201, 202) of the NGL plant (Lolla 200) based on the predicted incoming feed gas volumes (Lolla 102) comprises starting up at least one compression train (Espie 201) if the capacity of running compression trains (Espie 201, 202) is less than the predicted incoming feed gas by the capacity of at least one compression train (Espie [0153]).
In re Claim 6, Lolla does not explicitly teach, further comprising evaluating starting a second train by assessing whether reducing the recycle rate can provide needed additional capacity.
However, Espie teaches further comprising evaluating starting a second train (Espie 202) by assessing whether reducing the recycle rate ([0280-0282]: Each recycle system removes compressed gas from the product end and, after suitable pressure reduction feeds it to the feed end of the associated compressor; Each centrifugal is electrically connected to a control system, indicated by reference numeral 40 that monitors the amount of gas flow to the multistage compression system and accordingly controls the load of the compressor) can provide needed additional capacity (Espie [0300-0301]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have taken the teachings of Lolla and to have modified them by comprising evaluating starting a second train by assessing whether reducing the recycle rate can provide needed additional capacity, in order to ensure the load of the final (n) compressor is maintained above its anti-surge control point (See Espie [0301]) and conserve electricity so that the electricity can be used in other parts of the process to produce feed gas and as part of a downstream process for consuming compressed gas (See Espie [0024]), without yielding unpredictable results.
In re Claim 11, Lolla does not explicitly teach, wherein controlling operation of compressors of the NGL plant based on the predicted incoming feed gas volumes comprises shutting down at least one compression train if the capacity of running compression trains exceeds the predicted incoming feed gas by the capacity of at least one compression train.
However, Espie teaches wherein controlling operation of compressors (201, 202) based on the predicted incoming feed gas volumes ([0144]: during periods when demand exceeds production, the process may comprise: reducing the pressure of the compressed gas) comprises shutting down at least one compression train ([0065]: to put said compressor into shutdown mode in which said compressor produces no net compressed gas) if the capacity of running compression trains exceeds the predicted incoming feed gas ([0133]: when to stabilize electricity demands and fluctuations due to excess feed and production) by the capacity of at least one compression train ([0065-0066]: while simultaneously loading the remaining centrifugal compressors to maximum capacity and conserving electricity and operating the system in a more efficient manner).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have taken the teachings of Lolla and to have modified them by controlling operation of compressors of the NGL plant of Lolla based on the predicted incoming feed gas volumes of Lolla comprises shutting down at least one compression train if the capacity of running compression trains exceeds the predicted incoming feed gas by the capacity of at least one compression train as taught by Espie, in order to conserve electricity so that the electricity can be used in other parts of the process to produce feed gas and as part of a downstream process for consuming compressed gas (See Espie [0024]), without yielding unpredictable results.
In re Claim 12, Lolla as modified teaches wherein controlling operation of compressors (Espie 201, 202) of the NGL plant (Lolla 200) based on the predicted incoming feed gas volumes (Lolla 102) comprises starting up at least one compression train (Espie 201) if the capacity of running compression trains (Espie 201, 202) is less than the predicted incoming feed gas by the capacity of at least one compression train (Espie [0153]).
In re Claim 13, Lolla does not explicitly teach, further comprising evaluating starting a second train by assessing whether reducing the recycle rate can provide needed additional capacity.
However, Espie teaches further comprising evaluating starting a second train (Espie 202) by assessing whether reducing the recycle rate ([0280-0282]: Each recycle system removes compressed gas from the product end and, after suitable pressure reduction feeds it to the feed end of the associated compressor; Each centrifugal is electrically connected to a control system, indicated by reference numeral 40 that monitors the amount of gas flow to the multistage compression system and accordingly controls the load of the compressor) can provide needed additional capacity (Espie [0300-0301]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have taken the teachings of Lolla and to have modified them by comprising evaluating starting a second train by assessing whether reducing the recycle rate can provide needed additional capacity, in order to ensure the load of the final (n) compressor is maintained above its anti-surge control point (See Espie [0301]) and conserve electricity so that the electricity can be used in other parts of the process to produce feed gas and as part of a downstream process for consuming compressed gas (See Espie [0024]), without yielding unpredictable results.
Response to Arguments
The Remarks of August 07, 2025, have been fully considered but are not persuasive for the reasons below.
Applicant argues
On Page 1 ¶3-4 of the Remarks, that the examiner has not shown that claims 1 and 3 are anticipated by Lolla because there is allegedly no basis in the prior art for using flow volumes, input flows, and operating conditions of a refinery complex upstream of its NGL plant to determine predicted incoming feed gas volumes of the NGL plant as required by the claims. Applicant appears to suggest that one of ordinary skill in the art would not recognize from the teaching of Jones that a model is used to predict incoming feed gas volumes of the NGL plant. This is not persuasive.
Contrary to Applicant' s assertion, the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
Moreover, Lolla does in fact indicate disclose using flow volumes, input flows, and operating conditions. Specifically, Lolla discloses in ¶2 key performance indicators (KPI) includes flow volumes, input flows and other operating conditions (See also [0031-0032]. Lolla [0032] teaches that a model may be trained using process variable data from an upstream process, such as a well, and configured to estimate KPIs for such a facility. Therefore, the rejections of Claims 1 and 3 are maintained.
On Page 2¶1 of the Remarks, that the examiner has not shown that claims 1 3 are obvious over Lolla in view of Espie because there is allegedly no basis in the prior art for using flow volumes, input flows, and operating conditions of a refinery complex upstream of its NGL plant to determine predicted incoming feed gas volumes of the NGL plant as required by the claims. Applicant appears to suggest that one of ordinary skill in the art would not recognize from the teaching of Espie that compressor adjustments appear to be based on actual feed gas volumes. This is not persuasive.
Contrary to Applicant' s assertion, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Second, 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). Therefore, the rejection of Claims 1-14 is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM M ADENIJI whose telephone number is (571)272-5939. The examiner can normally be reached 8:00-5:00 PM.
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/IBRAHIM A. MICHAEL ADENIJI/Examiner, Art Unit 3763
/JOEL M ATTEY/Primary Examiner, Art Unit 3763