Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-15 are pending and examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) received on 4/10/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 11-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 2 recites the broad recitation “wherein a future behavior of the process system is predicted over a specified time horizon by means of the neural network”, and the claim also recites “in particular in the context of controlling the one or more operating parameters of the process system”, which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claim 11 recites the limitation "the determined prediction quality" in Ln. 3. There is insufficient antecedent basis for this limitation in the claim. As claim 10 provides sufficient antecedent basis for the limitation “the determined prediction quality”, claim 11 has been examined as dependent from claim 10.
Examiner’s Note: to avoid a 112(b) rejection for this claim in subsequent rounds of prosecution, the Examiner suggests amending claim 11 to depend from claim 10. If the Applicant does not wish for claim 11 to depend from claim 10, claim 11 can be amended to change “the determined prediction quality” to “a determined prediction quality”.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 12 recites the broad recitation “in which method a process system is operated in which a cryogenic separation of component mixtures takes place”, and the claim also recites “wherein in particular an air fractionation plant is operated as the process system”, which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Regarding claim 13, the preamble is drawn to a process. However, the claim itself is drawn to a system, as no steps are recited, only components, i.e. a control device. Further, claim 14, which depends from claim 13, is drawn to a system. Therefore, it is unclear if the claim is drawn to a process or a system. Further clarification is needed. For purposes of compact prosecution, the Examiner has treated claim 13 as drawn to a process system.
Further regarding claim 13, the claim recites the limitation "the manipulated variable values" in Lns. 1-2. There is insufficient antecedent basis for this limitation in the claim. For purposes of compact prosecution, the above limitation has been examined as, “one or more manipulated variable values”.
Further regarding claim 13, Lns. 2-3 recite, “by means of one or more…”, but does not specify what element is present in an amount of one or more. Is this limitation referring to the previously recited manipulated variable values, or something else? Further clarification is needed.
Examiner’s Note: for purposes of compact prosecution, the above limitation has been examined as referring to the previously recited manipulated variable values. Further, if this interpretation is correct, the Examiner suggests deleting the words “by means of one or more” and changing “by means of the manipulated variable values” to “by means of one or more manipulated variable values” to overcome the above 112(b) rejection.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claim 14 recites the broad recitation “which is designed in such a way that a cryogenic separation of component mixtures is carried out therein”, and the claim also recites “and is designed in particular as an air fractionation plant”, which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The subject matter eligibility test for the claims is shown below:
Subject Matter Eligibility Test, Step 1
Independent claim 1 and its dependent claims are drawn to a method. Independent claim 13 and its dependent claim are drawn to a system (see the rejection of claim 13 in the Claim Rejections-35 USC 112 section in the instant Office action above for the explanation of why claim 13 has been treated as a system). Independent claim 15 is drawn to a method. All claims are therefore drawn to a statutory category.
Subject Matter Eligibility Test, Step 2A Prong One
In Step 2A Prong One, it is determined if the claims recite an abstract idea, law of nature, or natural phenomenon. Independent claims 1, 13, and 15 each recite setting one or more values using a model-based deep reinforcement learning and the consideration of a cost function. This cost function is a mathematical equation, which falls under the mathematical concepts group of abstract ideas. See MPEP 2106.04(a). All independent claims therefore recite an abstract idea.
Subject Matter Eligibility Test, Step 2A Prong Two
In step 2A Prong Two, it is determined if the claims recite additional elements that integrate the judicial exception into a practical application. Independent claim 1 further recites i) one or more actuators, ii) a model-based deep reinforcement learning process, and iii) a neural network. Independent claim 13 recites i) the one or more actuators, ii) the model-based deep reinforcement learning process, and iii) the neural network as in claim 1, and additionally recites iv) a control device configured to carry out the setting of one or more manipulated variable values. Independent claim 15 recites i) the one or more actuators, ii) the model-based deep reinforcement learning, and iii) the neural network as in claim 1. The dependent claims also recite: predicting a future behavior of a process system based on the neural network (claim 2), including an initial phrase of operating the system that is used to train the neural network (claim 3), further training the neural network in a second phase following the initial phase and/or assigning operating parameters to specific manipulated variable values (claim 4), taking consumption parameters into account with the cost function (claim 5), using past values to determine prediction values for specifying the one or more manipulated variable values (claim 6), exploring new control strategies in repeated loops by the neural network (claim 7), using valves or mass flows as the one or more actuators, and mass flows and/or concentrations and/or temperatures as the operating parameters (claim 8), assessing manipulated variable values for suitability (claim 9), comparing prediction values for future instant to actual values when that future instant has occurred to determine prediction quality (claim 10), adapting or replacing the self-optimizing control process if a prediction quality falls below a specified minimum quality (claim 11), applying the method to a system where a cryogenic separation of component mixtures takes place (claim 12), and a system where cryogenic separation of component mixtures takes place (claim 14). Other than the limitations present in claims 12 and 14, the limitations recited in the dependent claims merely further describe the limitations already present in the independent claims, and do not recite additional features. The recited limitations do not actually apply the mathematical equation-type abstract idea judicial exceptions into a practical application. Rather, the model-based deep reinforcement learning and neural network are stated at a high level of generality. There are no particular details about a particular model-based deep reinforcement learning or neural network, or how the model-based deep reinforcement learning or neural network work to provide a self-optimizing control process. These components are used to generally apply the abstract idea. The claims invoke a general model-based deep reinforcement learning/neural network as a tool for performing the self-optimizing control process in combination with the cost function, rather than purporting to improve the technology or a computer. See MPEP 2106.05(f). Therefore, the limitations of the model-based deep reinforcement learning/neural network are nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers. Additionally, the other limitations present in the claims, i.e. i) one or more actuators, iv) a control device configured to carry out the setting of one or more manipulated variable values, and the system where a cryogenic separation of component mixtures takes place (present in claims 12 and 14), generally link the use of the judicial exception to a laboratory environment.
Subject Matter Eligibility Test, Step 2B
In step 2B, it is determined if the claim recites additional elements that amount to significantly more than the judicial exception. In this case, the claims recite one or more actuators comprising one or more mass flows and/or valves, a self-optimizing control process comprising the use of model-based deep reinforcement learning, a neural network, a control device, and a system that is operated in which a cryogenic separation of component mixtures takes place. These generically recited elements are nothing more than well-understood, routine, and conventional components that are well-known in the art, particularly as all claims have been rejected over the prior art. Further, the application of the abstract idea-type judicial exception into a laboratory environment is nothing more than generally linking the mental process judicial exception to a particular technological environment or field of use. See MPEP 2106.05(d) and 2106.05(e).
Further, with regards to the generically recited actuator(s), control process comprising model-based deep reinforcement learning, neural network, control device, and cryogenic separation system being nothing more than well-understood, routine, and conventional components that are well-known in the art, the following prior art is relied upon to show that the above elements are well-understood, routine, and conventional:
Badgwell et al. (US Pub. No. 2019/0187631; hereinafter Badgwell; already of record on the IDS received 4/10/2023) teaches one or more actuators comprising one or more mass flows and/or valves ([0032]), a self-optimizing control process comprising the use of model-based deep reinforcement learning, a neural network, and a control device ([0003], [0028], the reinforcement learning agent acts as a model, [0060]).
Okada (US Pub. No. 2018/0218262; already of record on the IDS received 4/10/2023) teaches a self-optimizing control process comprising the use of model-based deep reinforcement learning, a neural network, and a control device ([0007], [0028], [0084]).
Zapp et al. (Translation of WO Pub. No. 2015/158431; hereinafter Zapp) teaches a system that is operated in which a cryogenic separation of component mixtures takes place (Pg. 4 4th Para.).
Claims 2-12 and 14 are rejected as depending on a claim rejected under 35 U.S.C. 101 without including additional elements sufficient to make the claims subject matter 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 6-7, 13, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Okada.
Regarding claim 1, Okada discloses a method for operating a process system ([0001], [0027]-[0034]), in which method one or more actuators in the process system are set by means of one or more manipulated variable values, whereby one or more operating parameters of the process system are influenced ([0027]-[0034]), wherein the setting of the one or more manipulated variable values is carried out at least in a process phase by means of a self-optimizing control process, wherein the self-optimizing control process comprises the use of model-based deep reinforcement learning and the consideration of a cost function ([0027]-[0034], [0084]), and wherein one or more components of the process system are represented in a model by means of a neural network, wherein the neural network represents a behavior of the process system and is used in the model-based deep reinforcement learning ([0027]-[0034], [0084]).
Regarding claim 2, Okada discloses the method according to Claim 1, wherein a future behavior of the process system is predicted over a specified time horizon by means of the neural network, in particular in the context of controlling the one or more operating parameters of the process system ([0049], [0064]).
Regarding claim 6, Okada discloses the method according to Claim 1, in which method one or more actual values of the one or more operating parameters are acquired for one or more past instants at which one or more prediction values for the one or more operating parameters are determined for one or more future instants using the one or more actual values by means of the self-optimizing control process, and in which the one or more manipulated variable values are specified by means of one or more setpoint values for the one or more operating parameters and by means of the one or more prediction values by means of the self-optimizing control process ([0107]-[0109]).
Regarding claim 7, Okada discloses the method according to Claim 1, in which method new control strategies are explored by means of the neural network in repeated exploration loops ([0107]-[0109]).
Regarding claim 13, Okada discloses a process system configured to set by means of one or more manipulated variable values one or more actuators in the process system and thereby influence one or more operating parameters of the process system ([0001], [0027]-[0034]), wherein a control device is provided which is configured to carry out the setting of the one or more manipulated variable values, at least in a process phase, by means of a self-optimizing control process and to carry out the self-optimizing control process by means of model-based deep reinforcement learning and the consideration of a cost function ([0027]-[0034], [0084]), one or more components of the process system being represented in a model by means of a neural network, the neural network representing a behavior of the process system and being used in the model-based deep reinforcement learning ([0027]-[0034], [0084]).
Regarding claim 15, Okada discloses a method for converting a process system, which system is configured to set one or more actuators in the process system by means of one or more manipulated variable values and thereby influence one or more operating parameters of the system ([0001], [0025]-[0034], see especially [0025]-[0031], which describes traditional optimal control, and how their invention provides an improvement over traditional optimal control), wherein in the conversion of the system, an existing control process, by means of which the one or more control values are set, is replaced by a self-optimizing control process, the self-optimizing control process comprising the use of model-based deep reinforcement learning and the consideration of a cost function ([0027]-[0034], [0084]), and one or more components of the process system being represented in a model by means of a neural network, the neural network representing a behavior of the process system and being used in the model-based deep reinforcement learning ([0027]-[0034], [0084]), and in that the replacement of the existing control process with the self-optimizing control process comprises subsequently transferring control functions of the existing control process to the self-optimizing control process (during replacement, control functions will intrinsically switch from an existing control process to a new process).
Claims 1, 3-7, 10-11, 13, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wen et al. (US Pub. No. 2019/0091859; hereinafter Wen; already of record on the IDS received 4/10/2023).
Regarding claim 1, Wen discloses a method for operating a process system, in which method one or more actuators in the process system are set by means of one or more manipulated variable values ([0002], [0016]-[0023]), whereby one or more operating parameters of the process system are influenced ([0012]), wherein the setting of the one or more manipulated variable values is carried out at least in a process phase by means of a self-optimizing control process, wherein the self-optimizing control process comprises the use of model-based deep reinforcement learning and the consideration of a cost function ([0012]-[0016]), and wherein one or more components of the process system are represented in a model by means of a neural network, wherein the neural network represents a behavior of the process system and is used in the model-based deep reinforcement learning ([0023]-[0027]).
Regarding claim 3, Wen discloses the method according to Claim 1, in which method the setting of the one or more manipulated variable values is carried out in a second process phase by means of the self-optimizing control process, wherein the system is operated in a first operating phase, which precedes the second operating phase, manually and/or by means of a further control process, and wherein the neural network is first trained by means of training data obtained in the first operating phase ([0012], [0024]-[0026], [0030], see Figs. 2-3).
Regarding claim 4, Wen discloses the method according to Claim 3, in which method the neural network is subsequently trained by means of training data obtained in the second operating phase, and/or in which the training data in each case comprise operating parameters assigned to specific manipulated variable values ([0012], [0024]-[0026], [0030], see Figs. 2-3).
Regarding claim 5, Wen discloses the method according to Claim 1, in which method consumption parameters are taken into account by means of the cost function and are assessed with respect to respective target parameters ([0017]-[0020]).
Regarding claim 6, Wen discloses the method according to Claim 1, in which method one or more actual values of the one or more operating parameters are acquired for one or more past instants at which one or more prediction values for the one or more operating parameters are determined for one or more future instants using the one or more actual values by means of the self-optimizing control process, and in which the one or more manipulated variable values are specified by means of one or more setpoint values for the one or more operating parameters and by means of the one or more prediction values by means of the self-optimizing control process ([0022]-[0025]).
Regarding claim 7, Wen discloses the method according to Claim 1, in which method new control strategies are explored by means of the neural network in repeated exploration loops ([0022]-[0025]).
Regarding claim 10, Wen discloses the method according to Claim 1, in which method the one or more prediction values for the one or more operating parameters for the one or more future instants are compared to real values later obtained at these instants, wherein a prediction quality is determined on the basis of the comparison ([0021], [0023]).
Regarding claim 11, Wen discloses the method according to Claim 10, in which method an adaptation of the self-optimizing control process is performed or the self-optimizing control process is replaced by a different control process if the determined prediction quality falls below a specified minimum quality ([0021], [0023]).
Regarding claim 13, Wen discloses a process system configured to set by means of one or more manipulated variable values one or more actuators in the process system and thereby influence one or more operating parameters of the process system ([0002], [0016]-[0023]), wherein a control device is provided which is configured to carry out the setting of the one or more manipulated variable values, at least in a process phase, by means of a self-optimizing control process and to carry out the self-optimizing control process by means of model-based deep reinforcement learning and the consideration of a cost function ([0012]-[0016]), one or more components of the process system being represented in a model by means of a neural network, the neural network representing a behavior of the process system and being used in the model-based deep reinforcement learning ([0023]-[0027]).
Regarding claim 15, Wen discloses a method for converting a process system, which system is configured to set one or more actuators in the process system by means of one or more manipulated variable values and thereby influence one or more operating parameters of the system ([0002], [0016]-[0023], see also [0012], [0024]-[0026], [0030], Figs. 2-3, which show that an expert demonstration may be used as training data, i.e. the process system is initially operated without applying the deep inverse reinforcement learning algorithm), wherein in the conversion of the system, an existing control process, by means of which the one or more control values are set, is replaced by a self-optimizing control process, the self-optimizing control process comprising the use of model-based deep reinforcement learning and the consideration of a cost function ([0012]-[0016]), and one or more components of the process system being represented in a model by means of a neural network, the neural network representing a behavior of the process system and being used in the model-based deep reinforcement learning ([0023]-[0027]), and in that the replacement of the existing control process with the self-optimizing control process comprises subsequently transferring control functions of the existing control process to the self-optimizing control process (during replacement, control functions will intrinsically switch from an existing control process to a new process, see also [0012], [0024]-[0026], [0030], Figs. 2-3, which show that an expert demonstration may be used as training data, i.e. the process system is initially operated without applying the deep inverse reinforcement learning algorithm).
Claims 1, 8-9, 13, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Badgwell.
Regarding claim 1, Badgwell discloses a method for operating a process system ([0007]), in which method one or more actuators in the process system are set by means of one or more manipulated variable values, whereby one or more operating parameters of the process system are influenced ([0028]-[0032]), wherein the setting of the one or more manipulated variable values is carried out at least in a process phase by means of a self-optimizing control process, wherein the self-optimizing control process comprises the use of model-based deep reinforcement learning and the consideration of a cost function ([0028]-[0036], a reward function acts as a cost function when the reward is negative based on poor/undesirable performance, [0057]-[0060]), and wherein one or more components of the process system are represented in a model by means of a neural network, wherein the neural network represents a behavior of the process system and is used in the model-based deep reinforcement learning ([0057]-[0060]).
Regarding claim 8, Badgwell discloses the method according to Claim 1, in which method the one or more actuators are or comprise one or more mass flows and/or valves, the one or more manipulated variable values are or comprise manipulated variable values of the one or more mass flows and/or valves, and the one or more operating parameters are or comprise one or more mass flows and/or substance concentrations and/or temperatures ([0032], [0049], [0063]).
Regarding claim 9, Badgwell discloses the method according to Claim 1, in which method the one or more manipulated variable values are assessed for their suitability prior to their use to set the one or more actuators ([0032], [0049], [0063]).
Regarding claim 13, Badgwell discloses a process system configured to set by means of one or more manipulated variable values one or more actuators in the process system and thereby influence one or more operating parameters of the process system ([0007], [0028]-[0032]), wherein a control device is provided which is configured to carry out the setting of the one or more manipulated variable values, at least in a process phase, by means of a self-optimizing control process and to carry out the self-optimizing control process by means of model-based deep reinforcement learning and the consideration of a cost function ([0028]-[0036], a reward function acts as a cost function when the reward is negative based on poor/undesirable performance, [0057]-[0060]), one or more components of the process system being represented in a model by means of a neural network, the neural network representing a behavior of the process system and being used in the model-based deep reinforcement learning (0057]-[0060]).
Regarding claim 15, Badgwell discloses a method for converting a process system, which system is configured to set one or more actuators in the process system by means of one or more manipulated variable values and thereby influence one or more operating parameters of the system ([0007], [0028]-[0032], see also [0003]-[0006], which discusses conventional use of PID controllers and the desire to improve on these systems), wherein in the conversion of the system, an existing control process, by means of which the one or more control values are set, is replaced by a self-optimizing control process, the self-optimizing control process comprising the use of model-based deep reinforcement learning and the consideration of a cost function ([0028]-[0036], a reward function acts as a cost function when the reward is negative based on poor/undesirable performance, [0057]-[0060]), and one or more components of the process system being represented in a model by means of a neural network, the neural network representing a behavior of the process system and being used in the model-based deep reinforcement learning ([0057]-[0060]), and in that the replacement of the existing control process with the self-optimizing control process comprises subsequently transferring control functions of the existing control process to the self-optimizing control process (during replacement, control functions will intrinsically switch from an existing control process to a new process).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Badgwell, as applied to claims 1, 8-9, 13, and 15 above, in view of Zapp.
Regarding claim 12, Badgwell discloses the method according to Claim 1.
Badgwell fails to explicitly disclose that in the method a process system is operated in which a cryogenic separation of component mixtures takes place, wherein in particular an air fractionation plant is operated as the process system.
Zapp is in the analogous field of controlling process systems (Zapp Pg. 4 4th Para.), and teaches a method of controlling a process system that is operated in which a cryogenic separation of component mixtures takes place, particularly an air fractionation plant (Zapp Pg. 4 4th Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method of Badgwell with the teachings of Zapp so that in the method a process system is operated in which a cryogenic separation of component mixtures takes place, wherein in particular an air fractionation plant is operated as the process system. The motivation would have been to apply the teachings of Badgwell, which provide for improved automatic tuning of controllers to reduce or minimize the amount of required manual intervention (Badgwell [0006]), into a cryogenic separation environment as in Zapp (Zapp Pg. 4 4th Para.), thereby optimizing the process of cryogenic separation and minimizing the need for oversight.
Regarding claim 14, Badgwell discloses the system according to Claim 13.
Badgwell fails to explicitly disclose that the system is designed in such a way that a cryogenic separation of component mixtures is carried out therein, and is designed in particular as an air fractionation plant.
Zapp is in the analogous field of controlling process systems (Zapp Pg. 4 4th Para.), and teaches controlling a process system that is designed in such a way that a cryogenic separation of component mixtures takes place, particularly designed as an air fractionation plant (Zapp Pg. 4 4th Para.). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the system of Badgwell with the teachings of Zapp so that the system is designed in such a way that a cryogenic separation of component mixtures is carried out therein, and is designed in particular as an air fractionation plant. The motivation would have been to apply the teachings of Badgwell, which provide for improved automatic tuning of controllers to reduce or minimize the amount of required manual intervention (Badgwell [0006]), into a cryogenic separation environment as in Zapp (Zapp Pg. 4 4th Para.), thereby optimizing the process of cryogenic separation and minimizing the need for oversight.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to John McGuirk whose telephone number is (571)272-1949. The examiner can normally be reached M-F 8am-530pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Capozzi can be reached at (571) 270-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN MCGUIRK/Examiner, Art Unit 1798