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 .
Preliminary Amendment filed on 8/29/2023 is acknowledged.
This action is in response to the applicant’s communication filed on 8/29/2023.
Claims 1-20 are pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE102022121954.2, filed on 8/31/2022.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 2, “205”. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim(s) 1-2, 12, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haesendonckx et al. USPGPUB 2012/0226376 A1 (hereinafter Haesendonckx) in view of Appel DE 10165127 B3 (hereinafter Appel).
Regarding claim 1, Haesendonckx teaches a method for minimizing a deviation of a physical parameter of a blow-molded container (Par. [0064] “The measuring device 41 measures the wall thickness or a wall thickness distribution in the area of the blow molded container”) from a target value (Par. [0017] “at least one property of the finished blow molded container is computed and is compared to a desired value” - examiner interprets “desired value” as a target value), the method comprising:
determining a physical parameter of a container assigned to a machine parameter value of a blow molding machine (Par. [0064] “The measuring device 41 measures the wall thickness or a wall thickness distribution in the area of the blow molded container” … “The temperature profile typically concerns the temperature distribution of the preform 1 in its longitudinal direction and/or the temperature distribution between the inner or outer limitations of the preform walls.”);
based on the physical parameter and the target value, determining a change in the machine parameter (Par. [0064] “Taking into consideration the information which has been
taken in, the simulation model 48 makes available the correction values 57 for the temperature profile; Par. [0010] - [0011]” A significant parameter for predetermining the resulting material distribution is the heat distribution realized prior to blow molding in the preforms.” …” through the wall of the preform, a suitable temperature profile from the outside to the inside is predetermined. Basically, it can be assumed that areas of the preform with a lower temperature lead to thicker wall areas of the blown container, and that the warmer areas of the preform are stretched to a greater extent when carrying out the blow molding deformation and, as a result, lead to thinner wall areas of the blow molded container.”);
determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container (Par. [0017] “the parameter influencing the blow molding process is changed in such a way that an eventually remaining deviation is minimized”; Par. [0059] “the simulation model has one or more model outlets 50 which facilitate influencing the regulating behavior. In accordance with an embodiment, the regulating characteristic is changed by means of one of the regulators 42, 43, 45 through the model outlet 50.”; Par. [0061] “regulation of a container manufacture can take place, for example, on the basis of a predetermined pressure pattern for the blow pressure. If in a comparison of the measured values with the values generated by the simulation model, deviations in at least one of the measured parameters are recognized, for example, for each of the product cycles, the starting point for supplying the pre-blowing pressure can be changed and/or it is possible to increase or decrease the speed of the stretching process in a suitable manner”…”It is also being considered to carry out an adjustment or the temperature profile and/or the heating power.”), the process comprising:
determining a deviation from the target value of the physical parameter of a blow-molded container based on the change in the machine parameter value (Par. [0065] “deviation between simulated values for the wall thickness and actually measured process parameters with respect of the wall thickness is evaluated.” - The change in the machine parameter values was based on the previous simulated values); and
determining an adjusted change in the machine parameter value based on the determined deviation of the physical parameter of a blow-molded container from the target value (Par. [0064] “A corresponding deviation 55 is supplied to a difference input of the simulation model 48. Taking into consideration the information which has been taken in, the simulation model 48 makes available the correction values 57 for the temperature profile.”; Par. [0059] “regulating characteristic is changed by means of one of the regulators 42, 43, 45 through the model outlet 50”);
obtaining the optimal machine parameter value (Par. [0064] “simulation model 48 makes available the correction values 57 for the temperature profile”); and
controlling the blow molding machine based on the obtained optimal machine parameter value (Par. [0059] “regulating characteristic is changed by means of one of the regulators 42, 43, 45 through the model outlet 50”).
Haesendonckx does not explicitly teach based on an iteration process, determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container, the iteration process comprising:
a first iteration step for determining a deviation from the target value of the physical parameter of a blow-molded container based on the change in the machine parameter value; and
a second iteration step for determining an adjusted change in the machine parameter value based on the determined deviation of the physical parameter of a blow-molded container from the target value.
However, Appel teaches based on an iteration process, determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container (Par. [0011] - [0016] “suitable software to determine the heat output values for the heating elements and thus a wall thickness- oriented temperature profile. The individual heat output values are stored in a control and regulating device and initially form initial values for the start of operation, which are changed in a control loop depending on the measured container wall thicknesses.” … “The newly obtained values for the temperature profile and/or the blowing parameters are preferably stored and used as new starting values for future wall thickness measurements. An iterative or self-learning process for container production is obtained”), the iteration process comprising:
a first iteration step for determining a deviation from the target value of the physical parameter of a blow-molded container based on the change in the machine parameter value (Par. [0033] “wall thickness of a container is recorded as at least one property immediately after its production and compared with a target value of this at least one recorded property”); and
a second iteration step for determining an adjusted change in the machine parameter value based on the determined deviation of the physical parameter of a blow-molded container from the target value (Par. [0033] “temperature profile generated for heating the preforms and/or at least one blowing parameter depending on the size of the comparison result in the direction of a reduction of this magnitude changed.”).
Haesendonckx and Appel are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to blow-molded container production.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above blow-molded container production method, as taught by Haesendonckx, and incorporate an iterative two-step process for determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container, as taught by Appel.
One of ordinary skill in the art would have been motivated to improve real regulation of container parameters as suggested by Appel (Par. [0016]).
Regarding claim 2, the combination of Haesendonckx and Appel teaches all the limitations of the base claims as outlined above.
Haesendonckx further teaches wherein a predictive model is used to determine the deviation from the target value of the physical parameter of a blow-molded container (Par. [0017] “with the use of a simulation model, based on the measured parameters characterizing the blow molding process, at least one property of the finished blow molded container is computed and is compared to a desired value and that, based upon an eventual deviation between the desired value and the actual value”).
Regarding claim 12, Haesendonckx teaches a blow molding machine for producing containers (Par. [0002] “device for blow molding containers”), comprising:
a sensor device (Par. [0002] “at least one sensor for measuring at least one parameter”); and
a control apparatus (Par. [0002] “sensor is connected to the control device”),
wherein the sensor device is configured to determine a physical parameter of a container (Par. [0012] “measurement of an actual wall thickness in the area of the blown containers can be effected by means of so-called wall thickness sensors”) assigned to a machine parameter value of the blow molding machine and to pass the machine parameter value and the physical parameter to the control apparatus (Par. [0059] “At least one of the measurement values supplied by the sensors 41, 44, 46 is supplied to a simulation model 48” – simulation model is used by the control device),
wherein the control apparatus is configured to:
based on the physical parameter and a target value, determine a change in the machine parameter value (Par. [0019] “based on the measured parameters characterizing the blow molding process, at least one property of the finished blow molded container is computed and is compared to a desired value and that, based upon an eventual deviation between the desired value and the actual value, the parameter influencing the blow molding process is changeable by the control element”);
based on a process, determine an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter (Par. [0017] “the parameter influencing the blow molding process is changed in such a way that an eventually remaining deviation is minimized”; Par. [0059] “the simulation model has one or more model outlets 50 which facilitate influencing the regulating behavior. In accordance with an embodiment, the regulating characteristic is changed by means of one of the regulators 42, 43, 45 through the model outlet 50.”; Par. [0061] “regulation of a container manufacture can take place, for example, on the basis of a predetermined pressure pattern for the blow pressure. If in a comparison of the measured values with the values generated by the simulation model, deviations in at least one of the measured parameters are recognized, for example, for each of the product cycles, the starting point for supplying the pre-blowing pressure can be changed and/or it is possible to increase or decrease the speed of the stretching process in a suitable manner”…”It is also being considered to carry out an adjustment or the temperature profile and/or the heating power.”), wherein the process comprises:
determining a deviation from the target value of the physical parameter of a blow-molded container based on the change in the machine parameter value (Par. [0065] “deviation between simulated values for the wall thickness and actually measured process parameters with respect of the wall thickness is evaluated.” - The change in the machine parameter values was based on the previous simulated values); and
determining an adjusted change in the machine parameter value based on the deviation of the physical parameter from the target value (Par. [0064] “A corresponding deviation 55 is supplied to a difference input of the simulation model 48. Taking into consideration the information which has been taken in, the simulation model 48 makes available the correction values 57 for the temperature profile”; Par. [0059] “regulating characteristic is changed by means of one of the regulators 42, 43, 45 through the model outlet 50);
obtaining the optimal machine parameter value (Par. [0064] “simulation model 48 makes available the correction values 57 for the temperature profile”); and
control the blow molding machine based on the obtained optimal machine parameter value (Par. [0059] “regulating characteristic is changed by means of one of the regulators 42, 43, 45 through the model outlet 50”).
Haesendonckx does not explicitly teach based on an iteration process, determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container, the iteration process comprising:
a first iteration step for determining a deviation from the target value of the physical parameter of a blow-molded container based on the change in the machine parameter value; and
a second iteration step for determining an adjusted change in the machine parameter value based on the determined deviation of the physical parameter of a blow-molded container from the target value.
However, Appel teaches based on an iteration process, determining an optimal machine parameter value for achieving a minimum deviation from the target value of the physical parameter of a blow-molded container (Par. [0011] - [0016] “suitable software to determine the heat output values for the heating elements and thus a wall thickness- oriented temperature profile. The individual heat output values are stored in a control and regulating device and initially form initial values for the start of operation, which are changed in a control loop depending on the measured container wall thicknesses.” … “The newly obtained values for the temperature profile and/or the blowing parameters are preferably stored and used as new starting values for future wall thickness measurements. An iterative or self-learning process for container production is obtained”), the iteration process comprising:
a first iteration step for determining a deviation from the target value of the physical parameter of a blow-molded container based on the change in the machine parameter value (Par. [0033] “wall thickness of a container is recorded as at least one property immediately after its production and compared with a target value of this at least one recorded property”); and
a second iteration step for determining an adjusted change in the machine parameter value based on the determined deviation of the physical parameter of a blow-molded container from the target value (Par. [0033] “temperature profile generated for heating the preforms and/or at least one blowing parameter depending on the size of the comparison result in the direction of a reduction of this magnitude changed.”).
Haesendonckx and Appel are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to blow mold container production.
Regarding claim 14, the combination of Haesendonckx and Appel teaches all the limitations of the base claims as outlined above.
Haesendonckx further teaches wherein the sensor device comprises a sensor configured to determine the physical parameter of the blow-molded container (Par. [0012] “measurement of an actual wall thickness in the area of the blown containers can be effected by means of so-called wall thickness sensors”).
Regarding claim 15, the combination of Haesendonckx and Appel teaches all the limitations of the base claims as outlined above.
Haesendonckx further teaches wherein the sensor is configured to determine a wall thickness, a variable characteristic of the wall thickness, a bottom thickness, a variable characteristic of the bottom thickness, and/or a molecular orientation (Par. [0012] “measurement of an actual wall thickness in the area of the blown containers can be effected by means of so-called wall thickness sensors which operate for example optically, or with the use of sound waves.” –optical and sound wave operated wall thickness sensors can determine wall thickness and variable characteristics of both walls and bottoms).
Claim(s) 3-11, 13, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haesendonckx USPGPUB 2012/0226376 A1 (hereinafter Haesendonckx) in view of Appel DE 10165127 B3 (hereinafter Appel), and further in view of Sun et al. CN 108830376 A (hereinafter Sun).
Regarding claim 3, the combination of Haesendonckx and Appel teaches all the limitations of the base claims as outlined above.
Haesendonckx and Appel do not explicitly teach wherein the predictive model comprises a first neural network.
However, Sun teaches wherein the predictive model comprises a first neural network (Par. [0046] “Through reinforcement learning, we can use the model to automatically select the neural network model to be used in the next moment in order to obtain the maximum reward”).
Haesendonckx and Sun are analogous art because they contain functional similarities. They both relate to predictive models.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above predictive model, as taught by Haesendonckx, and incorporate a neural network, as taught by Sun.
One of ordinary skill in the art would have been motivated to improve learning efficiency as suggested by Sun (Par. [0018]).
Regarding claim 4, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the iteration process is based on a reinforcement learning model (Par. [0046] “Through reinforcement learning, we can use the model to automatically select the neural network model to be used in the next moment in order to obtain the maximum reward.”).
Regarding claim 5, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the reinforcement learning model comprises a first component and a second component, and wherein by an interaction of the first component with the second component (Par. [0005] “Reinforcement learning mainly involves the environment, the agent, and the interactions between them” - first component is the environment. Second component is the agent), the optimal machine parameter for minimizing the deviation of the physical parameter of the blow-molded container from the target value is obtained (Par. [0006] “Reinforcement learning primarily learns strategies by maximizing long-term rewards” - In this case, the long-term reward is minimizing the deviation of the physical parameter of the blow-molded container from the target value as taught by Haesendonckx).
Regarding claim 6, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the first component of the reinforcement learning model comprises the predictive model (Par. [0016] “The target value network is used to calculate the target value by combining the current environmental state, the reward value of the previous action, and the action selected by the neural network model.”).
Regarding claim 7, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the second component of the reinforcement learning model comprises a third neural network (Par. [0078] “third convolutional neural network model contains three convolutional layers and two fully connected output layers.” …” reinforcement learning is used to adjust the selection of different neural networks by applying different rewards under different computational conditions”; Par. [0064] “since one of multiple models can be dynamically selected, the agent can obtain a better reward value, thereby improving learning efficiency”).
Regarding claim 8, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the first iteration step is performed by the first neural network (Par. [0016] “The target value network is used to calculate the target value by combining the current environmental state, the reward value of the previous action, and the action selected by the neural network model.”; Par. [0040] “Each value network is a neural network model.”), and the second iteration step is performed by the third neural network (Par. [0015] “Decision-making process: Based on the current environmental state, a series of Q-values are calculated using the previously determined or preset neural network model” - the action that maximizes the Q-value would result in the desired adjusted change in the machine parameter).
Regarding claim 9, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein obtaining the optimal machine parameter value includes determining an optimal adjusted change based on the minimum deviation from the target value of the physical parameter from a set of deviations from the target value of the
physical parameter (Par. [0015] “allows the selection of the action that maximizes the Q-
value and the corresponding sequence number of the next round of neural network model” – The Q-value in a reinforcement learning model depends on the use case goal. In this case, it would be obvious to one skilled in the art to base the Q-value on the deviation from the target value of the physical parameter.).
Regarding claim 10, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Haesendonckx further teaches wherein the physical parameter of the blow-molded container comprises a wall thickness (Par. [0054] “The immediate input value for the wall thickness regulator 42 is the regulating difference between a predetermined wall thickness and the measured wall thickness”), a variable characteristic of the wall thickness (Par. [0026] properties of the blow molded containers carried out by the simulation model, at least one parameter of the blow molded container, selected from the group including material distribution in the blow molding container, container contours under pressure load, stacking capability of the container, gripping stability of the container, pressure behavior of the container, is simulated– variable characteristics of the bottom thickness would affect these properties as well), a bottom thickness (Par. [0026] “material distribution in the blow molding container” – material distribution in the blow molding container would include a bottom thickness; Fig. 2, Par. [0054] “wall thickness 2 of the container” visually includes the bottom of the container in wall thickness), a variable characteristic of the bottom thickness, and/or a molecular orientation (Par. [0022] “simulation model can take into consideration the expert knowledge concerning material properties”).
Regarding claim 11, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Haesendonckx further teaches wherein, in addition to determining the physical parameter of the container assigned to the machine parameter value of the blow molding machine, the method further comprises determining a disturbance variable, wherein the disturbance variable is an environmental condition and/or a property of a preform (Par. [0024] “at least one parameter characterizing the blow molding process, selected from the group including preform temperature, temperature distribution in the preform, blow pressure sequence, stretching sequence, material properties, material distribution in the preform, material properties in the blow molded container, container contour, ambient parameters” – ambient parameters can account for disturbance variables).
Regarding claim 13, the combination of Haesendonckx and Appel teaches all the limitations of the base claims as outlined above.
Haesendonckx further teaches wherein a predictive model is provided for determining the deviation from the target value of the physical parameter of a container (Par. [0017] “with the use of a simulation model, based on the measured parameters characterizing the blow molding process, at least one property of the finished blow molded container is computed and is compared to a desired value and that, based upon an eventual deviation between the desired value and the actual value”).
Haesendonckx and Appel do not teach wherein:
the iteration process is based on a reinforcement learning model;
the reinforcement learning model comprises a first and a second component;
the first component is the predictive model and comprises a first neural network; and
the second component comprises a second and a third neural network.
However, Sun teaches wherein:
the iteration process is based on a reinforcement learning model (Par. [0046] “Through reinforcement learning, we can use the model to automatically select the neural network model to be used in the next moment in order to obtain the maximum reward.”);
the reinforcement learning model comprises a first and a second component (Par. [0005] “Reinforcement learning mainly involves the environment, the agent, and the interactions between them” - first component is the environment. Second component is the agent);
the first component is the predictive model and comprises a first neural network (Par. [0016] “The target value network is used to calculate the target value by combining the current environmental state, the reward value of the previous action, and the action selected by the neural network model”; Par. [0040] “Each value network is a neural network model.”); and
the second component comprises a second and a third neural network (Par. [0078] “The second convolutional neural network model contains two convolutional layers and two fully connected output layers. The third convolutional neural network model contains three convolutional layers and two fully connected output layers.” … “reinforcement learning is used to adjust the selection of different neural networks by applying different rewards under different computational conditions.”; Par. [0064] “since one of multiple models can be dynamically selected, the agent can obtain a better reward value, thereby improving learning efficiency”).
Haesendonckx and Sun are analogous art because they contain functional similarities. They both relate to predictive models.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above predictive model, as taught by Haesendonckx, and incorporate reinforcement learning neural networks, as taught by Sun.
One of ordinary skill in the art would have been motivated to improve learning efficiency as suggested by Sun (Par. [0018]).
Regarding claim 16, the combination of Haesendonckx and Appel teaches all the limitations of the base claims as outlined above.
Haesendonckx and Appel do not explicitly teach wherein the predictive model is a first neural network.
However, Sun teaches wherein the predictive model is a first neural network (Par. [0046] “Through reinforcement learning, we can use the model to automatically select the neural network model to be used in the next moment in order to obtain the maximum reward”).
Haesendonckx and Sun are analogous art because they contain functional similarities. They both relate to predictive models.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above predictive model, as taught by Haesendonckx, and incorporate a neural network, as taught by Sun.
One of ordinary skill in the art would have been motivated to improve learning efficiency as suggested by Sun (Par. [0018]).
Regarding claim 17, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the first component of the reinforcement learning model is the predictive model (Par. [0016] “The target value network is used to calculate the target value by combining the current environmental state, the reward value of the previous action, and the action selected by the neural network model.”).
Regarding claim 18, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the first component of the reinforcement learning model is the first neural network or comprises the first neural network (Par. [0016] “The target value network is used to calculate the target value by combining the current environmental state, the reward value of the previous action, and the action selected by the neural network model”; Par. [0040] “Each value network is a neural network model.”).
Regarding claim 19, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the second component of the reinforcement learning model consists of a third neural network (Par. [0078] “third convolutional neural network model contains three convolutional layers and two fully connected output layers.” …” reinforcement learning is used to adjust the selection of different neural networks by applying different rewards under different computational conditions”; Par. [0064] “since one of multiple models can be dynamically selected, the agent can obtain a better reward value, thereby improving learning efficiency”).
Regarding claim 20, the combination of Haesendonckx, Appel, and Sun teaches all the limitations of the base claims as outlined above.
Sun further teaches wherein the first iteration step is performed by the predictive model (Par. [0016] “The target value network is used to calculate the target value by combining the current environmental state, the reward value of the previous action, and the action selected by the neural network model.”), and the second iteration step is performed by the third neural network (Par. [0015] “allows the selection of the action that maximizes the Q-value and the corresponding sequence number of the next round of neural network model”).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cowden et al [US 11772317 B2] teaches an inspection system capable of measuring the material distribution of individual blow-molded containers. The measurements are made using a series of emitters and sensors that are located either within or downstream of the blow molder.
Diraddo et al. [Modeling of Membrane Inflation in Blow Molding: Neural Network Prediction of Initial Dimensions from Final Part Specifications, 1993] teaches the use of neural networks in the modeling of the inflation stage of the blow molding process.
Duesterhoeft [DE 10000859 A1] teaches an automatic process for reshaping a thin side wall of a compartment involving measuring deviations between actual and set geometries.
Jebadurai et al. [WO 2020/226921 A1] teaches a computer-based predictive preventative maintenance system that uses a data model to make preventative maintenance predictions for a container-forming apparatus that produces containers.
Conclusion
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/PETER XU/ Examiner, Art Unit 2119 /ZIAUL KARIM/Primary Examiner, Art Unit 2119