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 .
DETAILED ACTION
2. The action is responsive to the communications filed on 2/19/2026. Claims 1-18 are pending in the case. Claims 1, 7, 9 are amended. Claims 1, 7, 9 are independent claims. Claims 1-18 are rejected.
Summary of claims
3. Claims 1-18 are pending,
Claims 1, 7, 9 are amended,
Claims 1, 7, 9 are independent claims,
Claims 1-18 are rejected.
Response to Arguments
4. Applicant’s arguments, see Remarks, filed on 2/19/2026, with respect to the rejection(s) of claim(s) 1-18 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
Applicant argued on pages 9-14 that the cited references including Bonada and Yagi did not teach the newly added limitations in claims 1 and 7, such as, “wherein the predecessor production parameters and the at least one predecessor weight value from a time series across at least two predecessor products produced earlier in time on the same or a substantially similar injection molding device; and wherein the ML model is trained to output the predicted weight value of the product produced via the injection molding device during the production of the product, based on production parameters at least one of recorded and determined during production of the product and the predecessor time series.” Examiner respectfully submits that Bonada discloses receiving operation parameters from a plurality of injection molding machine sensors about performance of a plurality of injection cycles ([0022]), for each cycle, the Production Control System analyses plurality of machine parameters and compares them with the generated extended mold model by means of AI solutions allows for a near real time prediction of the quality of the part ([0060]), please note the plurality of cycles may include current cycles and previous cycles so both the current data and the historical data are utilized to build the AI model for optimizing a production process. An analogous art of monitoring and optimizing an injection process, Rella is cited to specifically disclose predecessor production data (Rella: [0006], [0012]) are collected and recorded with time stamp (Rella: [0013], [0025]), that is, the predecessor production data are recorded in time series and they are included in building a machine learning model to optimize an injection process.
Applicant argued on page 14 that Bonada and Yagi fail to teach “continuing the production sequence for producing the product with the changed control variables”, as amended in claim 9. Examiner respectfully submits that Bonada disclose a real-time optimizing process (Bonada: [0060]), an enhanced AI model including both mold cavity parameters and injection molding machine parameters is modelled and encoded that can be exported and updated (Bonada: [0051]), that is, the parameters are dynamically updated in the continually running production.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Francesc Bonada Bo et al (US Publication 20200230857 A1, hereinafter Bonada), and in view of Daisuke Yagi et al (US Publication 20240042665 A1, hereinafter Yagi), and Johannes Rella (US Publication 20210197432 A1, hereinafter Rella).
As for independent claim 1, Bonada discloses: A computer-implemented method for training a machine learning (ML) model via an ML method (Bonada: [0002]-[0004], Industry 4.0 has emerged as the perfect booster for process monitoring and optimization techniques, aiming for Cyber Physical Systems enabling novel approaches based on Artificial Intelligence (AI), Machine learning and Data Mining … These data enable advanced analysis that can drive to a change of paradigm of the Plastic Injection Molding (PIM) monitoring and control, from the Statistical Process Control (SPC) to the use of AI and Machine Learning techniques that allow for a much more detailed and accurate study of the process), the trained ML model being configured to determine a predicted weight value of a product produced (Bonada: [0022], Embodiments of the present invention provide a computer implemented method for generating a mold model for production predictive control; [0053], the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.)) of the injected given parts of the mold) via an injection molding device (Bonada: Abstract, A computer implemented method for generating a mold model for production predictive control and computer program products thereof), the method comprising: recording and/or determining first production parameters of the injection molding device during production of a first product; recording and/or determining predecessor production parameters of the injection molding device during the production of at least one predecessor product (Bonada: [0053], first, the computing device, step 201, receives the first group of parameters about the performance of injection cycles in the first injection molding machine. Preferably, the method works with the cycle evolution of the hydraulic pressure, screw position, screw speed and rotational speed, not limitative as other type of available parameters in the injection molding machine can be also used. Then, at step 202, the computing device receives the second group of parameters relating to the mold cavity. Preferably, pressure and temperature evolution of the cavity and the mold along the injection cycle. At that point, step 203, the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.) of the injected given parts of the mold), and at least one predecessor weight value of each at least one predecessor product (Bonada: [0053], the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.)); recording and/or determining a first weight value for the first product (Bonada: [0053], the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.)); and training the ML model, via a supervised learning method (Bonada: [0050], provides a machine learning supervised approach that performs a training phase were a computing device, or server, (not shown in the figures), having one or more processors and at least one memory or database, learns and establishes hidden correlations to obtain, once a mold has been inserted in a first injection molding machine (preferred machine), injected given parts, also known as molded pieces; [0061], PCS relays on supervised Machine Learning algorithms, meaning that a training phase where the raw data from the cycles plus the quality controls must be provided to the system in order to learn the hidden correlations that allow creating the extended mold model), with the first product parameters, the further product parameters, the at least one predecessor weight value, and the first weight value (Bonada: [0053], first, the computing device, step 201, receives the first group of parameters about the performance of injection cycles in the first injection molding machine. Preferably, the method works with the cycle evolution of the hydraulic pressure, screw position, screw speed and rotational speed, not limitative as other type of available parameters in the injection molding machine can be also used. Then, at step 202, the computing device receives the second group of parameters relating to the mold cavity. Preferably, pressure and temperature evolution of the cavity and the mold along the injection cycle. At that point, step 203, the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.) of the injected given parts of the mold).
Bonada discloses generating a mold model for production predictive control, but does not clearly disclose determine a predicted weight value of a product produced, in an analogous art of optimizing injection molding model, Yagi discloses: the trained ML model being configured to determine a predicted weight value of a product produced (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model);
Bonada and Yagi are analogous arts because they are in the same field of endeavor, optimizing injection molding model. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Bonada using the teachings of Yagi to include generating a predicted value of the weight of the molded article. It would provide Bonada’s method with enhanced capabilities of monitoring and managing specific operation parameter to further optimize injection molding model.
Further, Bonada does not specially discloses using the predecessor production parameters, in another analogous art of optimizing injection molding model, Rella discloses: wherein the predecessor production parameters and the at least one predecessor weight value from a time series across at least two predecessor products produced earlier in time on the same or a substantially similar injection molding device; and wherein the ML model is trained to output the predicted weight value of the product produced via the injection molding device during the production of the product, based on production parameters at least one of recorded and determined during production of the product and the predecessor time series (Rella: [0006], [0012], predecessor production data are collected and recorded with time stamp (Rella: [0013], [0025]), that is, the predecessor production data are recorded in time series and they are included in building a machine learning model to optimize an injection process);
Bonada and Rella are analogous arts because they are in the same field of endeavor, optimizing injection molding model. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Bonada using the teachings of Rella to include monitoring and recording predecessor production data with time stamp. It would provide Bonada’s method with enhanced capabilities of tracking previous operation parameter to further optimize injection molding model.
As for claim 2, Bonada-Yagi discloses: wherein at least one of (i) at least one of the production parameters, the predecessor production parameters are at least one of recorded and determined at least in part via at least one of sensors of the injection molding device and control variables for the injection molding device (Bonada: Abstract, receiving first parameters about molding machine sensors; Yagi: [0137], In the performance evaluation, a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) and (ii) the first weight value of at least one of the first product and the at least one predecessor weight value of the at least one predecessor product is at least one of recorded and determined utilizing a weighing apparatus (Bonada: [0053], the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.); Yagi: [0137], In the performance evaluation, a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model).
As for claim 3, Bonada-Yagi discloses: wherein at least one of the production parameters, the further production parameters, the first weight value, the at least one predecessor weight value is at least one of recorded and determined at least in part via a computer-implemented simulation of the injection molding device .
As for claim 4, Bonada-Yagi discloses: wherein at least one of the production parameters, the further production parameters, the first weight value, the at least one predecessor weight value is at least one of recorded and determined at least in part via a computer-implemented simulation of the injection molding device (Bonada: [0016], simulation tools; Yagi: [0120], the molding condition and the material lots to be changed in the trial molding may be determined based on, for example, experimental design, or may be determined based on a result obtained by CAE simulation).
As for claim 5, Bonada-Yagi discloses: wherein at least one of the first weight value and the at least one predecessor weight value are each assigned to a finished product removed or removable from the injection molding device (Bonada: [0011], an injection molding system that optimizes the injection molding process by removing selected articles from a plurality of articles produced cyclically).
As for claim 6, Bonada-Yagi discloses: wherein at least one of the first weight value and the at least one predecessor weight value are configured as a time series of individual weight values (Bonada: [0035], The reception of the first, second and also of the third group of parameters can be made at different periods of time or alternatively at the same time; [0053], The output of the DCT has exactly the same number of coefficient as time stamps on the time domain data; [0059], The Production Control System (PCS) is the module in charge of monitoring and control the machine/mold performance in soft real time for ensuring an optimal productivity performance. The PCS evaluates the performance of the mold at injection cycle time, ensuring full traceability and a prediction of the quality of the injected part or the presence of the defects for which the system has been trained; Yagi: [0104], variation of the time series data is different for each lot even under the same molding condition, and it can be confirmed that the time series data of the pressure sensor is affected by the material information unique to the material).
As per claim 7, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As for claim 8, Bonada-Yagi discloses: wherein the ML model is further trained utilizing the production parameters and a product weight of the manufactured product (Bonada: [0001], process monitoring and optimization techniques applied to the manufacturing industry; [0043], present invention applies novel AI and Machine Learning techniques applied to the plastic manufacturing process).
As for independent claim 9, Bonada discloses: A control method for controlling production of a product via an injection molding device (Bonada: Abstract, A computer implemented method for generating a mold model for production predictive control and computer program products thereof), the method comprising:
starting a production sequence for producing the product with the injection molding device utilizing starting control variables for the injection molding device (Bonada: [0059], The Production Control System (PCS) is the module in charge of monitoring and control the machine/mold performance in soft real time for ensuring an optimal productivity performance. The PCS evaluates the performance of the mold at injection cycle time, ensuring full traceability and a prediction of the quality of the injected part or the presence of the defects for which the system has been trained);
recording and/or determining current production parameters during the production sequence (Bonada: Abstract, receiving first parameters about molding machine sensors); determining a product predicted weight value using a computer-implemented method comprising: recording and/or determining production parameters of the injection molding device during production of the product (Bonada: Abstract, receiving first parameters about molding machine sensors);
recording and/or determining predecessor production parameters of the injection molding device during production of at least one predecessor product, and each at least one predecessor weight value of the at least one predecessor product (Bonada: [0053], first, the computing device, step 201, receives the first group of parameters about the performance of injection cycles in the first injection molding machine. Preferably, the method works with the cycle evolution of the hydraulic pressure, screw position, screw speed and rotational speed, not limitative as other type of available parameters in the injection molding machine can be also used. Then, at step 202, the computing device receives the second group of parameters relating to the mold cavity. Preferably, pressure and temperature evolution of the cavity and the mold along the injection cycle. At that point, step 203, the method performs an iterative classification step which includes classifying each injection cycle considering the received first and second group of parameters and quality or characteristics (defects, weight, dimension, etc.) of the injected given parts of the mold); and determining the predicted weight value of the product utilizing a trained machine learning (ML) model, the production parameters, [the predecessor production parameters], the at least one predecessor weight value, and the at least one current production parameter as the production parameters (Bonada: [0002]-[0004], Industry 4.0 has emerged as the perfect booster for process monitoring and optimization techniques, aiming for Cyber Physical Systems enabling novel approaches based on Artificial Intelligence (AI), Machine learning and Data Mining … These data enable advanced analysis that can drive to a change of paradigm of the Plastic Injection Molding (PIM) monitoring and control, from the Statistical Process Control (SPC) to the use of AI and Machine Learning techniques that allow for a much more detailed and accurate study of the process); … and continuing the production sequence for producing the product with the changed control variables (Bonada: [0060], a real-time optimizing process; [0051], an enhanced AI model including both mold cavity parameters and injection molding machine parameters is modelled and encoded that can be exported and updated, that is, the parameters are dynamically updated in the continually running production);
Bonada discloses generating a mold model for production predictive control, but does not clearly disclose determine a predicted weight value of a product produced, and Bonada does not clearly disclose utilizing a deviation of the product predicted weight value from a product reference weight value, in an analogous art of optimizing injection molding model, Yagi discloses: determining a product predicted weight value (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) … determining changed control variables utilizing a deviation of the product predicted weight value from a product reference weight value (Yagi: [0153], In order to quantitatively evaluate the deviation between the predicted value and the target value, the training and optimization system 4 generates the molding condition closest to the target weight as the optimal condition by calculating absolute values of differences between the predicted value and the target value and sorting the absolute values of the differences in ascending order); and continuing the production sequence for producing the product with the changed control variables (Yagi: [0156], the weight of the molded article molded under the reference condition in the injection molding process 55 using the second material and the weight distribution of the molded article molded under the optimization condition generated for the second material).
Bonada and Yagi are analogous arts because they are in the same field of endeavor, optimizing injection molding model. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Bonada using the teachings of Yagi to include generating a predicted value of the weight of the molded article and using a deviation of the product predicted weight value in the further production process. It would provide Bonada’s method with enhanced capabilities of monitoring and managing specific operation parameter to further optimize injection molding model.
Further, Bonada does not specially discloses using the predecessor production parameters, in another analogous art of optimizing injection molding model, Rella discloses: the predecessor production parameters (Rella: [0006], [0012], predecessor production data are collected and recorded with time stamp (Rella: [0013], [0025]), that is, the predecessor production data are recorded in time series and they are included in building a machine learning model to optimize an injection process);
Bonada and Rella are analogous arts because they are in the same field of endeavor, optimizing injection molding model. Therefore, it would have been obvious to one with ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Bonada using the teachings of Rella to include monitoring and recording predecessor production data with time stamp. It would provide Bonada’s method with enhanced capabilities of tracking previous operation parameter to further optimize injection molding model.
As for claim 10, Bonada-Yagi discloses: wherein the control method is performed or is performable in real time (Bonada: [0059], The Production Control System (PCS) is the module in charge of monitoring and control the machine/mold performance in soft real time for ensuring an optimal productivity performance; [0062], receives the real-time cycle data of the machine and mold sensors).
As for claim 11, Bonada-Yagi-Rella discloses: A control system for controlling an injection molding device which is configured to produce a product, wherein the control system is configured to control the injection molding device via the control method as claimed in claim 9 (Bonada: [0001], process monitoring and optimization techniques applied to the manufacturing industry; [0043], present invention applies novel AI and Machine Learning techniques applied to the plastic manufacturing process).
As for claim 12, Bonada-Yagi discloses: A control system for controlling an injection molding device which is configured to produce a product, wherein the control system is configured to control the injection molding device via the control method as claimed in claim 10 (Bonada: [0001], process monitoring and optimization techniques applied to the manufacturing industry; [0043], present invention applies novel AI and Machine Learning techniques applied to the plastic manufacturing process).
As for claim 13, Bonada-Yagi discloses: wherein the control system is configured to perform the control method in real time (Bonada: [0059], The Production Control System (PCS) is the module in charge of monitoring and control the machine/mold performance in soft real time for ensuring an optimal productivity performance; [0062], receives the real-time cycle data of the machine and mold sensors).
As for claim 14, Bonada-Yagi discloses: wherein the control system comprises an edge device which configured to determine at least one (i) the product predicted weight value (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) and (ii) the changed control variables (Yagi: [0003], measuring a change in material property during the injection molding process as a change in control signal from the controller, and adjusting the pressure based on the change in control signal, it is possible to control the injection molding condition (pressure) in consideration of the change in material property without depending on the operator).
As for claim 15, Bonada-Yagi discloses: wherein the control system comprises an edge device which configured to determine at least one (i) the product predicted weight value (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) and (ii) the changed control variables (Yagi: [0003], measuring a change in material property during the injection molding process as a change in control signal from the controller, and adjusting the pressure based on the change in control signal, it is possible to control the injection molding condition (pressure) in consideration of the change in material property without depending on the operator).
As for claim 16, Bonada-Yagi discloses: wherein the control system comprises a programmable logic controller configured to control the injection molding device via a control method; and wherein the programmable logic controller comprises an application module configured to determine at least one of (i) the product predicted weight value (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) and (ii) changed control variables (Yagi: [0003], measuring a change in material property during the injection molding process as a change in control signal from the controller, and adjusting the pressure based on the change in control signal, it is possible to control the injection molding condition (pressure) in consideration of the change in material property without depending on the operator).
As for claim 17, Bonada-Yagi discloses: wherein the control system comprises a programmable logic controller configured to control the injection molding device via a control method; and wherein the programmable logic controller comprises an application module configured to determine at least one of (i) the product predicted weight value (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) and (ii) changed control variables (Yagi: [0003], measuring a change in material property during the injection molding process as a change in control signal from the controller, and adjusting the pressure based on the change in control signal, it is possible to control the injection molding condition (pressure) in consideration of the change in material property without depending on the operator).
As for claim 18, Bonada-Yagi discloses: wherein the control system comprises a programmable logic controller configured to control the injection molding device via a control method; and wherein the programmable logic controller comprises an application module configured to determine at least one of (i) the product predicted weight value (Yagi: [0137], a predicted value of the weight of the molded article is calculated by inputting the explanatory variables of the first data set and the second data set to the trained regression model) and (ii) changed control variables (Yagi: [0003], measuring a change in material property during the injection molding process as a change in control signal from the controller, and adjusting the pressure based on the change in control signal, it is possible to control the injection molding condition (pressure) in consideration of the change in material property without depending on the operator).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118