DETAILED ACTION
The following is a Final Office Action in response to the Amendment received on 20 August 2026. Claims 1, 2, 4, 8, 10 and 12-15 have been amended. Claim 16-19 have been added. Claims 1-19 are now pending in this application.
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 .
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.
Response to Arguments
Applicant's arguments, see Remarks pages 8-10, filed 20 August 2026, with respect to 35 USC 101 have been fully considered but they are not persuasive.
In response to applicant's argument that the present application does not recite a mathematical relationship, formula, equation, or calculation; Examiner disagrees. As drafted, the limitations “the self-optimizing control process comprises the use of model-based reinforcement learning using Gaussian processes, and wherein imaging one or more components of the process system are imaged in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning, wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process” are mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations. This judicial exception is not integrated into a practical application because claims are directed to abstract ideas of mathematical concepts without significantly more.
Applicant's arguments, see Remarks pages 10-13, filed 20 August 2026, with respect to 35 USC 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of DE 102021210774 A1 to Reiss et al.
Claim Rejections - 35 USC § 112
The amendments to the claims were received on 20 August 2026. These corrections are acceptable.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s), in part, setting one or more actuators in the process system by means of one or more manipulated variable values, whereby one or more operating parameters of the process system are influenced, implementing the setting of the one or more manipulated variable values at least in an operating phase by means of a self-optimizing control process, wherein the self-optimizing control process comprises the use of model-based reinforcement learning using Gaussian processes, and imaging one or more components of the process system in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning, wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process. This judicial exception is not integrated into a practical application because the claims are directed to abstract ideas of mathematical concepts (the use of model-based reinforcement learning using Gaussian processes, and imaging one or more components of the process system in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning, wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are directed to abstract ideas and extra-solution activities that do not have a physical or tangible form, such as mere data gathering, insignificant application, and/or mere instructions to apply a judicial exception.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-12 and 16-18 are directed to a method for operating a process system.
Claim 13, 14 and 19 are directed to a process system.
Claim 15 is directed to method for converting a process system.
Claims 1-19 are directed to at least one of the four statutory categories.
Step 2A, Prong One, Judicial Exception Recited?
Claims 1-19 are directed to mathematical concepts (the use of model-based reinforcement learning using Gaussian processes, and imaging one or more components of the process system in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning, wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process) given the broadest reasonable interpretation.
As per claims 1, 13 and 15, these claims similarly recite the limitations of “the self-optimizing control process comprises the use of model-based reinforcement learning using Gaussian processes, and imaging one or more components of the process system in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning, wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process.” As drafted, these limitations encompass mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claim 2, this claim recites the limitation of “predicting a future behavior of the process system over a specified time horizon by means of the model.” As drafted, this limitation encompasses mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claim 6, this claim recites the limitation of “new control strategies are explored by means of the model in repeated exploration loops.” As drafted, this limitation encompasses mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claim 11, this claim recites the limitation of “the self-optimizing control process also comprises considering a cost function.” As drafted, this limitation encompasses mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
As per claim 16, this claim recites the limitation of “predicting a future behavior of the process system includes controlling the one or more operating parameters of the process system.” As drafted, this limitation encompasses mathematical concepts. Mathematical concepts cover mathematical relationships and mathematical formulas or equations.
Claims 2-12, 14 and 16-19 further elaborate upon the recited abstract ideas in claims 1 and 13.
Claims 1-19 are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-19 are directed to abstract ideas (mathematical concepts).
Step 2A, Prong Two, Integrated into a Practical Application?
The claims recite the following additional limitations:
As per claims 1, 13 and 15, these claims similarly recite the limitations of “setting one or more actuators in the process system by means of one or more manipulated variable values, whereby one or more operating parameters of the process system are influenced, implementing the setting of the one or more manipulated variable values at least in an operating phase by means of a self-optimizing control process, and imaging one or more components of the process system in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning.” As drafted, these limitations encompass no more than an insignificant extra-solution activity.
As per claims 3 and 4, these claims similarly recite the limitations of “the model is used by means of training data obtained in the operating phase.” As drafted, these limitations encompass no more than an insignificant extra-solution activity.
As per claim 5, this claim recites the limitations of “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.” As drafted, these limitations encompass no more than an insignificant extra-solution activity.
As per claims 12 and 14, these claims similarly recite the limitations of “a process system is operated in which a cryogenic separation of component mixtures takes place.” As drafted, these limitations encompass no more than an insignificant extra-solution activity.
Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. See MPEP 2106.05(g).
The additional elements recite insignificant extra-solution activity as pre-solution data gathering and post solution data outputting and do not provide integration into a practical application. The additional claim limitations, claim elements together and claims in their entirety do not provide integration into a practical application. The additional claim limitations, claim elements together and claims in their entirety do not integrate the abstract idea into a practical application or provide an inventive concept (significantly more than the abstract idea). The concept described in the claim(s) is not meaningfully different than those concepts found by the courts to be abstract ideas. As such, the description in the claims describes the concept identified as an abstract idea (data gathering, data outputting and data transmission). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not integrate the exception into a practical application of the exception.
Claims 3-5, 7-10, 12, 14 and 17-19 further elaborate upon the insignificant extra-solution activity in claims 1 and 13.
Dependent claims 2-12, 14 and 16-19 do not provide significant additional elements and do not integrate the abstract ideas into a practical application.
Claims 1-19 do not integrate the recited abstract ideas into a practical application.
Step 2B, Inventive Concept (Significantly More)?
When considered both individually and as an ordered combination, the additional elements and elements of claims 1-19 do not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract ideas into a practical application. The additional elements of outlined in Step 2A performing functions as designed simply accomplish execution of the abstract ideas. The additional limitations identified as insignificant extra-solution activity above are carried over and they also do not provide significantly more.
As per claims 1-19, these claims similarly recite the limitations of “setting one or more actuators in the process system by means of one or more manipulated variable values, whereby one or more operating parameters of the process system are influenced, implementing the setting of the one or more manipulated variable values at least in an operating phase by means of a self-optimizing control process, wherein the self-optimizing control process comprises the use of model-based reinforcement learning using Gaussian processes, and imaging one or more components of the process system in a model by means of one or more Gaussian processes, which model is used in the model-based reinforcement learning, wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process.” As drafted, these limitations encompass mere data gathering and insignificant application to implement abstract ideas and insignificant extra-solution activity. See MPEP 2106.05(d)(II), “Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as mathematical concepts or an idea that could be done by a human analog (i.e., by hand or by merely thinking).”
Considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. Hence, the claims are not patent eligible.
Claims 1-19 are therefore drawn to ineligible subject matter as they are directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 2, 6, 7, 13, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 7162699 B2 (JP 2016502061 A) in view of DE 102021210774 A1 to Reiss et al.
As per claim 1, JP 7162699 B2 reference discloses a method for operating a process system, in which method substance changes (see pages 32-33, “varying the internal gas flow distribution”) and/or substance conversions and/or substance separations are performed, the method comprising: setting one or more actuators (see page 32, “actuator”) in the process system (“heat treatment chamber”) by means of one or more manipulated variable values (“varying the fan speed, varying the differential pressure”), whereby one or more operating parameters (“temperature, humidity”) of the process system (“heat treatment chamber”) are influenced, implementing the setting of the one or more manipulated variable values (“varying the fan speed, varying the differential pressure”) at least in an operating phase (see page 13, “manufacturing phase”) by means of a self-optimizing control process (see page 34, “self-optimizing algorithms”), wherein the self-optimizing control process (“self-optimizing algorithms”) comprises the use of model-based reinforcement learning (“reinforcement learning agent”) using Gaussian processes (see page 19, “Gaussian radial basis functions”), and imaging (see page 3, “images”) one or more components (see page 3, “food being cooked”) of the process system (“heat treatment chamber”) in a model (see page 34, “machine-to-machine trust models”) by means of one or more Gaussian processes (“Gaussian radial basis functions”), which model (“machine-to-machine trust models”) is used in the model-based reinforcement learning (“reinforcement learning agent”), wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process.
The Japan reference does not expressly disclose wherein the one or more Gaussian processes further provide an uncertainty for one or more values predicted by means of the model, and wherein the uncertainty is taken into account in the self-optimizing control process.
The Reiss et al. reference discloses
(see page 10 5th paragraph, “Differences in the expectation value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |Ds),k .sub.t ), can characterize the uncertainty of the first posterior model. In contrast to the first concept, in the second concept each Gaussian process of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ), .sub.kt ), can be conditioned to the known second measurement points, D .sub.t , according to equations (3) and (4). According to various embodiments, the second posterior model may be determined as the mean of the conditioned multiple Gaussian processes. The differences in the expected value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ),k .sub.t ), directly influence the covariance function of the second posterior model, so that the covariance function of the second posterior model also takes into account the uncertainty of the first posterior model.”)
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the Gaussian radial basis functions taught by the Japan reference with the Gaussian processes taught by the Reiss et al. reference.
One of ordinary skill in the art would have been motivated to modify the Gaussian radial basis functions with the Gaussian processes to illustrate differences in the expected value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ),k .sub.t ) that directly influence the covariance function of the second posterior model, so that the covariance function of the second posterior model also takes into account the uncertainty of the first posterior model.
As per claim 2, JP 7162699 B2 reference discloses predicting a future behavior (“future system configuration”) of the process system (“heat treatment chamber”) over a specified time horizon (“time dependent”) by means of the model (“machine-to-machine trust models”).
As per claim 6, JP 7162699 B2 reference discloses new control strategies are explored by means of the model in repeated exploration loops (see page 22, “repeats the cognition perception-action loop”).
As per claim 7, JP 7162699 B2 reference discloses the one or more actuators (“actuator”) are or comprise one or more mass flows and/or valves (“actuator”), the one or more manipulated variable values (“fan speed, differential pressure”) are or comprise manipulated variable values (“fan speed, differential pressure”) of the one or more mass flows and/or valves (“actuator”), and the one or more operating parameters (“temperature, humidity”) are or comprise one or more mass flows and/or substance concentrations and/or temperatures (“temperature, humidity”).
As per claim 13, JP 7162699 B2 reference discloses a process system configured to carry out substance changes (see pages 32-33, “varying the internal gas flow distribution”) and/or substance conversions and/or substance separations, and to set, by means of one or more manipulated variable values (“varying the fan speed, varying the differential pressure”), one or more actuators (see page 32, “actuator”) in the process system (“heat treatment chamber”) and thereby influence one or more operating parameters (“temperature, humidity”) of the process system (“heat treatment chamber”), the process system (“heat treatment chamber”) comprising: a control device (see page 5, “control output device” or page 13, “control unit 1300”) which is configured to carry out the setting of the one or more manipulated variable values (“varying the fan speed, varying the differential pressure”), at least in an operating phase (see page 13, “manufacturing phase”), by means of a self-optimizing control process (see page 34, “self-optimizing algorithms”) and to carry out the self-optimizing control process (“self-optimizing algorithms”) by means of model-based reinforcement learning (“reinforcement learning agent”) using Gaussian processes (see page 19, “Gaussian radial basis functions”), one or more components (“heat treatment chamber”), wherein one or more components (“heat treatment chamber”) is imaged (see page 3, “images”) in a model (see page 34, “machine-to-machine trust models”) by means of one or more Gaussian processes (“Gaussian radial basis functions”), which model (“machine-to-machine trust models”) is used in the model-based reinforcement learning (“reinforcement learning agent”), wherein the one or more Gaussian processes are configured to provide an uncertainty for one or more values predicted by means of the model, and the control device is further configured to take the uncertainty into account in the self-optimizing control process.
The Japan reference does not expressly disclose wherein the one or more Gaussian processes are configured to provide an uncertainty for one or more values predicted by means of the model, and the control device is further configured to take the uncertainty into account in the self-optimizing control process.
The Reiss et al. reference discloses
(see page 10 5th paragraph, “Differences in the expectation value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |Ds),k .sub.t ), can characterize the uncertainty of the first posterior model. In contrast to the first concept, in the second concept each Gaussian process of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ), .sub.kt ), can be conditioned to the known second measurement points, D .sub.t , according to equations (3) and (4). According to various embodiments, the second posterior model may be determined as the mean of the conditioned multiple Gaussian processes. The differences in the expected value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ),k .sub.t ), directly influence the covariance function of the second posterior model, so that the covariance function of the second posterior model also takes into account the uncertainty of the first posterior model.”)
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the Gaussian radial basis functions taught by the Japan reference with the Gaussian processes taught by the Reiss et al. reference.
One of ordinary skill in the art would have been motivated to modify the Gaussian radial basis functions with the Gaussian processes to illustrate differences in the expected value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ),k .sub.t ) that directly influence the covariance function of the second posterior model, so that the covariance function of the second posterior model also takes into account the uncertainty of the first posterior model.
As per claim 15, JP 7162699 B2 reference discloses a method for converting a process system), in which system substance changes (see pages 32-33, “varying the internal gas flow distribution”) and/or substance conversions and/or substance separations are carried out, and which system is configured to set one or more actuators (see page 32, “actuator”) in the process system (“heat treatment chamber”) by means of one or more manipulated variable values (“varying the fan speed, varying the differential pressure”) and thereby influence one or more operating parameters (“temperature, humidity”) of the system (“heat treatment chamber”), wherein in the conversion of the system (“heat treatment chamber”), an existing control process (see page 13, “control unit 1300”), by means of which the one or more control values (“control program or procedure”) are set, is replaced by a self-optimizing control process (see page 34, “self-optimizing algorithms”), the self-optimizing control process (“self-optimizing algorithms”) comprising the use of model-based reinforcement learning (“reinforcement learning agent”) using Gaussian processes (see page 19, “Gaussian radial basis functions”), and wherein one or more components of the process system (“heat treatment chamber”) is imaged (see page 3, “images”) in a model (see page 34, “machine-to-machine trust models”) by means of one or more Gaussian processes (“Gaussian radial basis functions”), which model (“machine-to-machine trust models”) is used in the model-based reinforcement learning (“reinforcement learning agent”), wherein the one or more Gaussian processes provide an uncertainty for one or more values predicted by means of the model, the uncertainty being taken into account in the self-optimizing control process, and in that the replacement of the existing control process (“control unit 1300”) with the self-optimizing control process (“self-optimizing algorithms”) comprises subsequently transferring control functions (“control program or procedure”) of the existing control process (“control unit 1300”) to the self-optimizing control process (“self-optimizing algorithms”),.
The Japan reference does not expressly disclose wherein the one or more Gaussian processes provide an uncertainty for one or more values predicted by means of the model, the uncertainty being taken into account in the self-optimizing control process.
The Reiss et al. reference discloses
(see page 10 5th paragraph, “Differences in the expectation value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |Ds),k .sub.t ), can characterize the uncertainty of the first posterior model. In contrast to the first concept, in the second concept each Gaussian process of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ), .sub.kt ), can be conditioned to the known second measurement points, D .sub.t , according to equations (3) and (4). According to various embodiments, the second posterior model may be determined as the mean of the conditioned multiple Gaussian processes. The differences in the expected value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ),k .sub.t ), directly influence the covariance function of the second posterior model, so that the covariance function of the second posterior model also takes into account the uncertainty of the first posterior model.”)
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to modify the Gaussian radial basis functions taught by the Japan reference with the Gaussian processes taught by the Reiss et al. reference.
One of ordinary skill in the art would have been motivated to modify the Gaussian radial basis functions with the Gaussian processes to illustrate differences in the expected value functions, ƒ̃ .sub.s , of the multiple Gaussian processes, GP((ƒ̃ .sub.s |D .sub.s ),k .sub.t ) that directly influence the covariance function of the second posterior model, so that the covariance function of the second posterior model also takes into account the uncertainty of the first posterior model.
As per claim 16, JP 7162699 B2 reference discloses predicting a future behavior (“future system configuration”) of the process system (“heat treatment chamber”) includes controlling (“control unit 1300”) the one or more operating parameters (“temperature, humidity”) of the process system (“heat treatment chamber”).
Claim(s) 3-6 and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 7162699 B2 (JP 2016502061 A) in view of DE 102021210774 A1 to Reiss et al., and further in view of US Pub. No. 2019/0091859 A1 to Wen et al.
As per claim 3, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the Wen et al. reference, namely: the setting of the one or more manipulated variable values (see [0017], “manipulated variables”) is carried out in a second operating phase (see [0012], “low number of expert demonstrations”) by means of the self-optimizing control process (“optimization process”), wherein the system is operated in a first operating phase (“low number of expert demonstrations”), which precedes the second operating phase (“low number of expert demonstrations”), manually and/or by means of a further control process (“optimization process”), and wherein the model (see [0016], “CAD models”) is first used by means of training data (“training data”) obtained in the first operating phase (“low number of expert demonstrations”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the self-optimizing algorithms taught by Japan and Reiss et al. references with the Deep Inverse Reinforcement Learning (DIRL) algorithms taught by the Wen et al. reference.
One of ordinary skill in the art would have been motivated to further modify the self-optimizing algorithms with the Deep Inverse Reinforcement Learning (DIRL) algorithms to train the reinforcement learning agent various manipulation tasks with significantly reduced human interventions.
As per claim 4, the Wen et al. reference discloses the model (“CAD models”) is subsequently used by means of training data (“training data”) obtained in the second operating phase (“low number of expert demonstrations”), and/or in which the training data (“training data”) in each case comprise operating parameters (see [0018], “parameters”) assigned to specific manipulated variable values (“manipulated variables”).
As per claim 5, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the Wen et al. reference, namely: 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 (see [0021-0026]), 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 (see [0023-0027]).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the self-optimizing algorithms taught by Japan and Reiss et al. references with the Deep Inverse Reinforcement Learning (DIRL) algorithms taught by the Wen et al. reference.
One of ordinary skill in the art would have been motivated to further modify the self-optimizing algorithms with the Deep Inverse Reinforcement Learning (DIRL) algorithms to train the reinforcement learning agent various manipulation tasks with significantly reduced human interventions.
As per claim 6, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the Wen et al. reference, namely: new control strategies are explored by means of the model in repeated exploration loops (see [0013], “automate the generation of the cost functions for a DIRL algorithm using an optimized combination of CAD-based models and expert demonstration”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the self-optimizing algorithms taught by Japan and Reiss et al. references with the Deep Inverse Reinforcement Learning (DIRL) algorithms taught by the Wen et al. reference.
One of ordinary skill in the art would have been motivated to further modify the self-optimizing algorithms with the Deep Inverse Reinforcement Learning (DIRL) algorithms to train the reinforcement learning agent various manipulation tasks with significantly reduced human interventions.
As per claim 9, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the Wen et al. reference, namely: 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 (see [0021], “compare probability distributions of the demonstration trajectories to that of the CAD-based trajectories”), wherein a prediction quality (“adjustments”) is determined on the basis of the comparison (“compare probability distributions”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the self-optimizing algorithms taught by Japan and Reiss et al. references with the Deep Inverse Reinforcement Learning (DIRL) algorithms taught by the Wen et al. reference.
One of ordinary skill in the art would have been motivated to further modify the self-optimizing algorithms with the Deep Inverse Reinforcement Learning (DIRL) algorithms to train the reinforcement learning agent various manipulation tasks with significantly reduced human interventions.
As per claim 10, the Wen et al. reference discloses 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 (see [0023], “local optimal solutions of higher quality”) falls below a specified minimum quality (“local optimal solutions of higher quality”).
As per claim 11, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the Wen et al. reference, namely: the self-optimizing control process also comprises considering a cost function (see [0023], “cost function”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the self-optimizing algorithms taught by Japan and Reiss et al. references with the Deep Inverse Reinforcement Learning (DIRL) algorithms taught by the Wen et al. reference.
One of ordinary skill in the art would have been motivated to further modify the self-optimizing algorithms with the Deep Inverse Reinforcement Learning (DIRL) algorithms to train the reinforcement learning agent various manipulation tasks with significantly reduced human interventions.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 7162699 B2 (JP 2016502061 A) in view of DE 102021210774 A1 to Reiss et al., and further in view of US Pub. No. 2019/0187631 A1 (USPN 10,915,073 B2) to Badgwell et al.
As per claim 8, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the Badgwell et al. reference, namely: assessing the one or more manipulated variable values (see [0027], “manipulated variable”) for their suitability (“suitable states”).
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the self-optimizing algorithms taught by JP 7162699 B2 with the Deep Reinforcement Learning (DRL) agent taught by the Badgwell et al. reference.
One of ordinary skill in the art would have been motivated to further modify the self-optimizing algorithms with the Deep Reinforcement Learning (DRL) agent to provide adaptive tuning of process controllers, such as Proportional-Integral-Derivative (PID) controllers to monitor process controller performance, and if unsatisfactory, improve it by making incremental changes to the tuning parameters for the process controller.
As per claim 17, the Badgwell et al. reference discloses assessing the one or more manipulated variable values for their suitability (see [0037, “suitable change”) includes checking for their plausibility (“overcorrection”), prior to their use to set the one or more actuators (“PID controllers”).
Claim(s) 12, 14, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 7162699 B2 (JP 2016502061 A) in view of DE 102021210774 A1 to Reiss et al., and further in view of WO Pub. No. 2015/158431A1 to ZAPP et al.
As per claim 12, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the ZAPP et al. reference, namely: a process system is operated in which a cryogenic separation of component mixtures (see FIG. 1, “cryogenic air separation process”) takes place.
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the baking and fermentation processes of bread without the need for human intervention taught by JP 7162699 B2 with the integrated process control system of the heat exchanger taught by the ZAPP et al. reference.
One of ordinary skill in the art would have been motivated to further modify the baking and fermentation processes of bread without the need for human intervention with the integrated process control system of the heat exchanger to illustrate Proportional-Integral-Derivative (PID) controllers with significantly reduced human interventions.
As per claim 18, the ZAPP et al. reference discloses an air fractionation plant (see page 3, “cryogenic air separation plant”) is operated as the process system.
As per claim 14, neither the Japan nor Reiss et al. reference expressly discloses the further limitations taught by the ZAPP et al. reference, namely: which is designed in such a way that a cryogenic separation of component mixtures (see FIG. 1, “cryogenic air separation process”) is carried out therein.
Before the invention was filed, it would have been obvious to a person of ordinary skill in the art to further modify the baking and fermentation processes of bread without the need for human intervention taught by JP 7162699 B2 and Reiss et al. references with the integrated process control system of the heat exchanger taught by the ZAPP et al. reference.
One of ordinary skill in the art would have been motivated to further modify the baking and fermentation processes of bread without the need for human intervention with the integrated process control system of the heat exchanger to illustrate Proportional-Integral-Derivative (PID) controllers with significantly reduced human interventions.
As per claim 19, the ZAPP et al. reference discloses system is designed as an air fractionation plant (see page 3, “cryogenic air separation plant”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following reference is cited to further show the state of the art with respect to integrated process control systems:
US 5764515 A to Guerillot et al.
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.
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/CRYSTAL J BARNES-BULLOCK/Primary Examiner, Art Unit 2117 5 October 2026
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