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 .
Election/Restrictions
Applicant's arguments with respect to the restriction requirement have been fully considered and are persuasive. The restriction requirement has been withdrawn. Accordingly, claims 1-18 will be examined on the merits.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d)
Claim Objections
Claim 1 is objected to because of the following informalities: In claim 1, the last limitation appears to have a typo "to control the operation of . It is assumed “that” was meant to be “the”
Appropriate correction is required.
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.
Claims 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Applicants Admitted Prior Art (hereinafter AAPA) in view of Zubarev (US 20200401111 A1) (hereinafter AAPA in view of Zubarev).
Regarding claims 1, 12 and 14, AAPA discloses an apparatus for controlling a process for producing a substance, wherein the process is performed at least partly by utilizing at least two coupled chemical reactors, wherein the at least two coupled reactors produce the same substance in parallel and are operated such that they are coupled with respect to at least one operation parameter, wherein the apparatus comprises;
the at least two reactors,(Applicant’s Admitted Prior Art: pg. 1, ln. 1-15 parallel reactors that are coupled with respect to at least one operation parameter, a temperature, pg. 1 ln. 13 input substance, ln. 20-21 to maximize the output of the produced chemical. Pg.2 ln.1-2, being dependent on the experience of the human operator.);
AAPA does not disclose expressly the use of machine learning models to perform the optimization of the two reactors.
Zubarev recognizes in ¶22-23 the laborious nature of the human experimentalist and the advantages of machine learning models to facilitate efficient and automatic control of chemical reactors. Fig. 11 shows a process of using historical chemical reactor data to train machine learning models to simulate the efficient operation of multiple reactors by iteratively checking if a threshold (the efficiency of the process) is being breached and updating the data to better match the threshold expectations (¶64 Control of one or more reactors, (this is read as being capable of having a model for parallel reactors) ¶65 following control parameters such as catalysts and temperature, ¶66 train a model based on the desired/recommended outcomes determined by difference in historical and new data. ¶67 generate recommended control settings. ¶68 control the reactors according to the recommendations. ¶69-70 determine the properties of the chemical reactions taking place in the reactor, checking if it is the desired result).
Zubarev further discloses optimizing the model to better control the chemical reactors to obtain the desired results. The models measure the reactions for the expected results and update the training data to be more accurate to reflect the desired results. (¶71 determine an update (optimization) to the training data to be implemented in retrain models. Then repeat the previous steps if needed (further optimizing the datasets and models to achieve the desired results). At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to adapt the modeling of Zubarev and AAPA to train models that can simulate the efficiency and optimize the results of the chemical reactors without the need of human intervention allowing the autonomous control of multiple chemical reactors.
The suggestion/motivation for doing so would have been to minimize the production time/increase the efficiency of the chemical reactors without the need of human intervention (Zubarev ¶22-23 “Manual synthesis of the members of such classes can be laborious and/or can rely solely on the discretion of the human experimentalist… the present invention can be directed to computer processing systems, computer-implemented methods, apparatus and/or computer program products that facilitate the efficient, effective, and autonomous (e.g., without direct human guidance) control of one or more chemical reactors through the use of one or more generative machine learning models.” ¶43 “Thereby, the production time of discovering and/or synthesizing one or more polymers can be reduced and/or minimized by the autonomous nature of the system 100.”)
Regarding claim 2, the limitations of claim 1 are discussed above, wherein the simulated efficiency model for a reactor refers to a trained machine learning based model, wherein the simulated efficiency model is trained based on historical training data comprising the at least one coupled operation parameter and a corresponding efficiency of a respective reactor, wherein a trained simulated efficiency model is trained such that it can provide a simulated efficiency for the respective reactor based on an at least one coupled operation parameter (AAPA in view of Zubarev discloses “At 1002, the method 1000 can comprise collecting (e.g., via data collection component 114), by a system 100 operatively coupled to a processor 120, training data regarding one or more past chemical reactor 108 operations. For example, the training data can include, but is not limited to the following control settings implemented during one or more chemical reactions previously performed by the one or more chemical reactors 108: chemical reactants, monomers, catalysts, co-catalysts, values of reactor parameters, initiators, retention time, temperature, flow rate, pressure, the order of component addition/mixing, exposure to ultraviolet light and/or other radiation, a combination thereof, and/or the like. (Zubarev ¶57)”).
Regarding claim 3, the limitations of claim 1 are discussed above, wherein the apparatus further comprises an evaluation unit adapted to regularly evaluate the simulated efficiency model of a respective reactor, wherein the evaluation unit is adapted to receive a determined current efficiency of the respective reactor and to compare the determined current efficiency with the simulated efficiency provided by the simulated efficiency model of the reactor for the currently used at least one coupled operation parameter, wherein the optimization unit is adapted to adapt during the optimization the simulated efficiency model based on the comparison (AAPA in view of Zubarev discloses determining the current process characteristics and updating the models based on the comparison of the current process to the expectations (Zubarev Fig 11 and ¶64-71)).
Regarding claim 4, the limitations of claim 3 are discussed above, wherein the simulated efficiency model refers to a trained machine learning based model and wherein the adapting of the simulated efficiency model based on the comparison refers to a retraining of the simulated efficiency model if the comparison indicates a difference between the determined current efficiency and the simulated efficiency lying above a predetermined threshold (AAPA in view of Zubarev discloses updating the models based on the current characteristics not meeting expectations (Zubarev Fig 11 and ¶64-71)).
Regarding claim 5, the limitations of claim 4 are discussed above, wherein the retraining comprises utilizing retraining data comprising the currently used at least one coupled operation parameter and determined current efficiency of the respective reactor and/or additional historical data of at least one coupled operation parameters used in the past and corresponding determined efficiencies from a time period with operation conditions corresponding to the current operation conditions of the reactor (AAPA in view of Zubarev discloses “At 1114, the method 1100 can comprise updating (e.g., via update component 702), by the system 100, the training data based on the one or more recommended chemical reactor 108 control settings. Further, the updating at 1114 can comprise updating one or more training datasets 122 based on the one or more measurements and/or detections generated at 1110. In various embodiments, the updating at 1114 can facilitate one or more iterations of the training conducted at 1104.” (Zubarev ¶71 ), Where the data was based on temperature (Zubarev ¶69) or historical data (Zubarev ¶65 )).
Regarding claim 8, the limitations of claim 1 are discussed above, wherein the at least one coupled operation parameter refers to at least one of an inflow of a substance into the at least two reactors, a pressure in the at least two reactors, a temperature in the at least two reactors, an amount of a catalyst in the at least two reactors, and an amount of a reactant in the at least two reactors (AAPA in view of Zubarev discloses a temperature as a control parameter taken by a thermometer (Zubarev ¶69) ).
Regarding claim 9, the limitations of claim 1 are discussed above. A production system for producing a substance, wherein the production system comprises: at least two chemical reactors adapted to perform at least parts of the process for producing the substance, wherein the at least two coupled reactors are adapted to be operated such as to produce the substance in parallel and such that they are coupled with respect to at least one operation parameter, an operation unit adapted to provide operation control signals to the at least two chemical reactors to control the at least two chemical reactors such as to produce the same substance in parallel and such that they are coupled with respect two at least one operation parameter, and an apparatus according to claim 1 adapted to provide a control signal indicative of the determined at least one optimized coupled operation parameter to the operation unit, wherein the operation unit is adapted to control the at least two reactors based on the control signal such that an overall output of the substance of the at least two reactors is optimized (AAPA in view of Zubarev discloses coupled reactors producing an optimized output based on at least one control parameter. This limitation covers substantially the same limitations as claim 1, See Claim 1 arguments for citations of an optimized reactor).
Regarding claims 10, 13, 15, 16 and 18, the limitations of claims 1 and 2 are discussed above. A training apparatus for training a simulated efficiency model, wherein the training apparatus comprises: a training data providing unit for providing historical training data, wherein the training data comprises a plurality of data sets comprising the at least one coupled operation parameter and a corresponding efficiency of a respective reactor, a trainable simulated efficiency model providing unit for providing a trainable simulated efficiency model, and a training unit for training the trainable simulated efficiency model by applying the trainable simulated efficiency model to the provided historical training data until the trainable simulated efficiency model is trained to determine a simulated efficiency for the respective reactor based on an at least one coupled operation parameter (AAPA in view of Zubarev discloses training a model on historic training data based on previous control data and chemical characteristics until the model meets expectations based on threshold (Zubarev Fig. 11 and ¶64-71)).
Regarding claims 11 and 17, the limitations of claims 10 and 16 are discussed above, wherein the training apparatus is further adapted to retrain a trained simulated efficiency model based on retraining data comprising a currently used at least one coupled operation parameter and determined current efficiency of the respective reactor and/or additional historical data of at least one coupled operation parameters used in the past and corresponding determined efficiencies from a time period with operation conditions corresponding to current operation conditions of the respective reactor (AAPA in view of Zubarev discloses training a model on historic training data based on previous control data and chemical characteristics and being able to repeat the process iteratively (retrain) the model until the model meets expectations based on threshold (Zubarev Fig. 11 and ¶64-71)).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over AAPA in view of Zubarev further in view of Kravaris US 20240018451 A1 filed 2021-11-16.
AAPA in view of Zubarev discloses the apparatus according to claim 3, wherein the apparatus further comprises a unit adapted to determine based on current process data indicative of a current state of the process for producing the product a current efficiency for a respective reactor and to provide the determined current efficiency for the respective reactor to the evaluation unit (Zubarev Fig 11, ¶64-71).
AAPA in view of Zubarev does not disclose expressly a soft sensor or wherein the current efficiency is determined based on process data comprising at least one of a volume flow of a substance at an inlet of the respective reactor, a reaction capability of a substance at the inlet of the respective reactor, a mass flow of a substance at the inlet of the respective reactor, a mass flow of a substance at the outlet of the respective reactor, and a weight fraction of a substance at the outlet of the respective reactor.
Kravaris discloses a software sensor that determines a mass flow of the substance. (¶32 “In one or more embodiments, the first-principles model (230) outputs substrate concentrations based on the input parametrization data (203). Input parametrization data (203) are estimates of any of the parameters or variables associated with the gas feed, solid/liquid feed, biomass, additional chemical concentrations, feed flow rates of any of the substrates, growth rate of the substrates, volume, product concentration, etc. Input gather D (202) may be the materials retained by the reactor in order to perform the chemical process as described above, along with online sensor measurements such as temperature and feed flow rates.”)
AAPA in view of Zubarev and Kravaris are analogous art because they are from the same field of endeavor of efficient chemical reactor operation.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to use the software sensor of Kravaris in AAPA in view of Zubarev to determine a mass flow of the substance.
The suggestion/motivation for doing so would have been to maximize amount or profitability Kravaris “¶47 The output gathers B (213) of the hybrid model represent optimal conditions or condition candidates of the chemical process that result in maximization of the product amount and/or profitability.”
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine AAPA in view of Zubarev and Kravaris for the benefit of determining a mass flow with a software sensor to obtain the invention as specified in the claim 6 and 7.
Conclusion
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/C.D.C./Examiner, Art Unit 2115
/PAUL B YANCHUS III/ Primary Examiner, Art Unit 2115
September 15, 2026
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