DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Amendment filed on 05/27/2026. Claims 1-20 are pending in the case. Claims 1 and 16 are independent claims.
Response to Arguments
Applicant's amendment to claim 15 and argument regarding the objection to claim 15 is persuasive. Accordingly, this objection is hereby withdrawn.
Applicant's amendments and arguments regarding the 35 U.S.C. § 101 rejections are persuasive. Accordingly, these rejections are hereby withdrawn.
Applicant's prior art arguments have been fully considered but are moot in view of the new grounds of rejection presented below.
Claim Rejections - 35 U.S.C. § 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.
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 are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1, 5-9, 11, 12, 14-16, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Piovoso et al. (Int’l. Pat. App. Pub. No. WO-2001024991-A1, hereinafter Piovoso) in view of Zhu et al. (Zhu, Chang-Hao, and Jie Zhang. "Developing soft sensors for polymer melt index in an industrial polymerization process using deep belief networks." International Journal of Automation and Computing 17, no. 1 (2020): 44-54., hereinafter Zhu) and Leong et al. (Int’l. Pat. App. Pub. No. WO-2022132050-A1, hereinafter Leong).
As to independent claim 1, Piovoso teaches:
A method comprising (Title and abstract):
applying,… while operating a polymer extruder to produce a first polymer extrudate, a… model to an input data set to output… a predicted… property value for the first polymer extrudate (Page 1, line 46 to page 2, line 2, "a computer-monitored process for controlling the properties of a material extruded in a continuous extrusion process. The improvement resides in the utilization of the product property information that can be present in variability of signals such as pressure, temperature, and amperage, and others normally acquired in an extrusion process." Controlling extrusion based on predicted properties derived from process signals. Page 2, lines 4 and 5, "monitored continuous extrusion process for controlling the properties of an extruded material". Page 4, line 22, "Point measurements.").
Piovoso does not appear to expressly teach machine learning model and predicted melt property.
Zhu teaches machine learning model and predicted melt property (Abstract, "soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN)").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
Piovoso does not appear to expressly teach in real time and wherein the input data set is constructed in real-time while operating the polymer extruder from time-series real-time extruder data generated by the polymer extruder.
Leong teaches in real time and wherein the input data set is constructed in real-time while operating the polymer extruder from time-series real-time extruder data generated by the polymer extruder (Page 10, lines 29 and 30, "The extrusion data is collected automatedly and in real-time during the extrusion process." Page 10, lines 32 and 33, "The automated collection of extrusion data in real-time enables the materials database 310 to be built in a fast and cost-effective manner." Figure 1, polymer extrudate 200).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the extrusion data processing techniques of Leong to mitigate time consuming processes and reduce the time lag between the manual sample collection and analysis of the samples (see Leong at Page 2, lines 4 and 5).
As to dependent claim 5, Piovoso further teaches operating the polymer extruder to form the first polymer extrudate (Page 2, line 15, "extruded material." Page 1, line 9, "polymeric materials."); and generating the time-series real-time extruder data during the operating, wherein the time-series real-time extruder data corresponds to a plurality of operating parameters of the polymer extruder at a first point in time (Page 1, line 19, "time-series analyses." Page 2, lines 4 and 5, "monitored continuous extrusion process for controlling the properties of an extruded material").
Zhu further teaches receiving or retrieving the time-series real-time extruder data (Page 48, right column, "estimation of MI can be obtained by soft sensors"); and wherein the input data set is constructed after receiving or retrieving (Tables 1 and 2 training data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
As to dependent claim 6, Piovoso further teaches ii) a first plurality of operating data points for a plurality of operating parameters of the polymer extruder corresponding to when the sample was collected (Page 6, line 8, "regular time intervals"); and iii) a first plurality of delta values corresponding to a difference between the first plurality of operating data points and a second plurality of operating data points of the polymer extruder, wherein the second plurality of operating data points corresponds to a previous sample that was collected from the polymer extruder before the sample was collected (Page 9, lines 26 and 27, "difference between the desired composition and the estimated one").
Zhu further teaches training the machine learning model with a training data set (Tables 1 and 2 training data); and the training data set comprises, for each sample of polymer extrudate obtained from the polymer extruder: i) a measured melt property value for the sample (Page 48, right column, "The melt index of polymer").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
As to dependent claim 7, Piovoso further teaches each sample is collected over a first interval of time, wherein each of the first plurality of operating data points is an average value for a time-series data set for one of the plurality of operating parameters collected over the first interval of time, wherein the average value is based on the first interval of time (Page 9, lines 23 and 24, "average of the predicted composition for each of the six runs is used as the product composition").
As to dependent claim 8, Piovoso further teaches each sample is collected at a point in time, wherein each of the first plurality of operating data points is a raw data value for a time-series data set for one of the plurality of operating parameters at the point in time (Page 2, lines 10 and 11, "the variable digital signals are transmitted to a control computer which calculates setpoints").
As to dependent claim 9, Zhu further teaches the measured melt property value is scaled on a scale of -1 to 1 based on a resin grade of the sample (Page 47, left column, "input data are commonly normalized to zero mean").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
As to dependent claim 11, Zhu further teaches the predicted melt property value is scaled on a scale of -1 to 1, the method further comprising: unscaling the predicted melt property value to produce a predicted unscaled melt property value (Page 47, left column, "input data are commonly normalized to zero mean").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
As to dependent claim 12, Zhu further teaches the machine learning model is supervised (Abstract, "the training of DBN contains a supervised training phase").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
As to dependent claim 14, Piovoso further teaches the first polymer extrudate is a homopolymer or copolymer of one or more olefin monomers (Page 13, line 34, "copolymer").
As to dependent claim 15, Zhu further teaches measured melt property value is a melt flow (MF) value, a melt index (MI2) value, a melt index (MI5) value, or a high load melt index (HLMI) value (Equation 13, MI2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
As to independent claim 16, Piovoso teaches
A melt property value prediction computer having one or more processors and a memory having instructions stored thereon that cause the one or more processors to (Title and abstract. Paragraphs 13-15):
apply,… while a polymer extruder is operated to produce a first polymer extrudate, a… model to an input data set to output… a predicted… property value for the first polymer extrudate (Page 1, line 46 to page 2, line 2, "a computer-monitored process for controlling the properties of a material extruded in a continuous extrusion process. The improvement resides in the utilization of the product property information that can be present in variability of signals such as pressure, temperature, and amperage, and others normally acquired in an extrusion process." Controlling extrusion based on predicted properties derived from process signals. Page 2, lines 4 and 5, "monitored continuous extrusion process for controlling the properties of an extruded material". Page 4, line 22, "Point measurements.").
Piovoso does not appear to expressly teach machine learning model and predicted melt property.
Zhu teaches machine learning model and predicted melt property (Abstract, "soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN)").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
Piovoso does not appear to expressly teach in real time and wherein the input data set is constructed in real-time while operating the polymer extruder from time-series real-time extruder data generated by the polymer extruder.
Leong teaches in real time and wherein the input data set is constructed in real-time while operating the polymer extruder from time-series real-time extruder data generated by the polymer extruder (Page 10, lines 29 and 30, "The extrusion data is collected automatedly and in real-time during the extrusion process." Page 10, lines 32 and 33, "The automated collection of extrusion data in real-time enables the materials database 310 to be built in a fast and cost-effective manner." Figure 1, polymer extrudate 200).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the extrusion data processing techniques of Leong to mitigate time consuming processes and reduce the time lag between the manual sample collection and analysis of the samples (see Leong at Page 2, lines 4 and 5).
As to dependent claim 19, Piovoso further teaches ii) a first plurality of operating data points for a plurality of operating parameters of the polymer extruder corresponding to when the sample was collected (Page 6, line 8, "regular time intervals"); and iii) a first plurality of delta values corresponding to a difference between the first plurality of operating data points and a second plurality of operating data points of the polymer extruder, wherein the second plurality of operating data points corresponds to a previous sample that was collected from the polymer extruder before the sample was collected (Page 9, lines 26 and 27, "difference between the desired composition and the estimated one").
Zhu further teaches train the machine learning model with a training data set (Tables 1 and 2 training data); and wherein the training data set comprises, for each sample of polymer extrudate obtained from the polymer extruder: i) a measured melt property value for the sample (Page 48, right column, "The melt index of polymer").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the machine learning polymer melt index techniques of Zhu to give accurate estimations of MI (see Zhu at page 45 left column).
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Piovoso in view of Zhu, Leong, and Siddiqui et al. (U.S. Pat. App. Pub. No. 2020/0293952, hereinafter Siddiqui).
As to dependent claim 13, the rejection of claim 1 is incorporated.
Piovoso does not appear to expressly teach the machine learning model is a gradient-boosting decision tree model.
Siddiqui teaches the machine learning model is a gradient-boosting decision tree model (Paragraph 3, "gradient boosting decision tree").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the extrusion control of Piovoso to include the gradient boosting decision tree techniques of Siddiqui to improve accuracy and speed with fewer resources consumed (see Siddiqui at paragraphs 18-21).
Allowable Subject Matter
Claims 2-4, 10, 17-18, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Casey R. Garner/Primary Examiner, Art Unit 2123
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