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 Application filed on 05/16/2023. Claims 1-20 are pending in the case. Claims 1 and 16 are independent claims.
Claim Objections
Claim 15 is objected to because of the following informalities:
Claim 15 recites acronyms “MF,” “MI2,” “MI5,” and “HLMI” without first defining what the acronyms stand for.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-15 are directed towards the statutory category of a process. Claims 16-20 are directed towards the statutory category of a machine.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
A method comprising (mental process):
applying, while a polymer extruder produces a first polymer extrudate, a… model to an input data set to output a predicted melt property value for the first polymer extrudate, wherein the input data set comprises a raw value data point for each of a plurality of operating parameters of the polymer extruder at a first point in time (mental process and/or mathematical concept).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
the input data set further comprises: a delta value for each of the plurality of operating parameters, wherein the delta value is a difference between the raw value data point at the first point in time and a previous raw value data point for each of the plurality of operating parameters of the polymer extruder at a second point in time (mental process and/or mathematical concept).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
the input data set further comprises: a measured melt property value for a sample of a second polymer extrudate obtained before the first point in time (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
the measured melt property value is scaled on a scale of -1 to 1 based on a resin grade of the sample (mental process and/or mathematical concept).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
generating time-series real-time extruder data during the operating, wherein the time-series real-time extruder data corresponds to the plurality of operating parameters of the polymer extruder at the first point in time (mental process); and
constructing the input data set after receiving or retrieving (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
operating the polymer extruder to form the first polymer extrudate (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
receiving or retrieving the time-series real-time extruder data (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
operating the polymer extruder to form the first polymer extrudate (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); and
receiving or retrieving the time-series real-time extruder data (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
training the… model with a training data set (mental process – high level machine learning); 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; 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; 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 (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
machine learning model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
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 (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 8:
2A Prong 1: This claim is directed to a judicial exception.
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 (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 9:
2A Prong 1: This claim is directed to a judicial exception.
the measured melt property value is scaled on a scale of -1 to 1 based on a resin grade of the sample (mental process and/or mathematical concept).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 10:
2A Prong 1: This claim is directed to a judicial exception.
the plurality of operating parameters comprises i) counts measured in a master feed line of the polymer extruder, ii) a fluff feed rate, iii) a speed of a drive motor of the polymer extruder, iv) one or more temperatures in one or more zones of a screw portion of the polymer extruder, v) one or more temperatures of polymer in the one or more zones of the screw portion, vi) a pressure in one or more zones of the screw portion, vii) one or more temperatures in one or more zones of a molten flow portion of the polymer extruder, viii) a temperature for at least one bearing of a gear pump of the polymer extruder, ix) a temperature of an oil of the gear pump, x) an amperage of the gear pump, xi) a speed of the gear pump, xii) a suction pressure of the gear pump, xiii) a discharge pressure of the gear pump, xiv) a differential pressure of a screenpack of a die plate assembly of the polymer extruder, xv) a temperature of a die plate the die plate assembly, xvi) a temperature of polymer in the die plate, xvii) a pressure in the die plate, xviii) a speed of a pelletizer of the polymer extruder, xix) a ratio of power to amperage of the gear pump, or xx) combinations thereof (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 11:
2A Prong 1: This claim is directed to a judicial exception.
the predicted melt property value is scaled on a scale of -1 to 1, the method further comprising (mental process and/or mathematical concept):
unscaling the predicted melt property value to produce a predicted unscaled melt property value (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 12:
2A Prong 1: This claim is directed to a judicial exception.
the machine learning model is supervised (mental process – high level machine learning).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 13:
2A Prong 1: This claim is directed to a judicial exception.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
the machine learning model is a gradient-boosting decision tree model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
the machine learning model is a gradient-boosting decision tree model (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)).
With respect to claim 14:
2A Prong 1: This claim is directed to a judicial exception.
the first polymer extrudate is a homopolymer or copolymer of one or more olefin monomers (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 15:
2A Prong 1: This claim is directed to a judicial exception.
measured melt property value is a MF value, a MI2 value, a MI5 value, or a HLMI value (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The remaining claims 16-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claims 1-15 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more.
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 1-12 and 14-20 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).
As to independent claim 1, Piovoso teaches:
A method comprising (Title and abstract):
applying, while a polymer extruder produces a first polymer extrudate, a… model to an input data set to output a predicted… property value for the first polymer extrudate, wherein the input data set comprises a raw value data point for each of a plurality of operating parameters of the polymer extruder at a first point in time (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).
As to dependent claim 2, Piovoso further teaches the input data set further comprises: a delta value for each of the plurality of operating parameters, wherein the delta value is a difference between the raw value data point at the first point in time and a previous raw value data point for each of the plurality of operating parameters of the polymer extruder at a second point in time (Page 2, line 29, " degree of variation in the data." Signals are continuously measured and digitized for process monitoring and control. Process variable correlate with extrudate characteristics for control purposes.).
As to dependent claim 3, Zhu further teaches the input data set further comprises: a measured melt property value for a sample of a second polymer extrudate obtained before the first point in time (Figure 8, melt index in (a) D201 and (b) D204).
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 4, Piovoso 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").
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 time-series real-time extruder data during the operating, wherein the time-series real-time extruder data corresponds to the plurality of operating parameters of the polymer extruder at the 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 constructing the input data set 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 10, Piovoso further teaches the plurality of operating parameters comprises i) counts measured in a master feed line of the polymer extruder, ii) a fluff feed rate, iii) a speed of a drive motor of the polymer extruder, iv) one or more temperatures in one or more zones of a screw portion of the polymer extruder, v) one or more temperatures of polymer in the one or more zones of the screw portion, vi) a pressure in one or more zones of the screw portion, vii) one or more temperatures in one or more zones of a molten flow portion of the polymer extruder, viii) a temperature for at least one bearing of a gear pump of the polymer extruder, ix) a temperature of an oil of the gear pump, x) an amperage of the gear pump, xi) a speed of the gear pump, xii) a suction pressure of the gear pump, xiii) a discharge pressure of the gear pump, xiv) a differential pressure of a screenpack of a die plate assembly of the polymer extruder, xv) a temperature of a die plate the die plate assembly, xvi) a temperature of polymer in the die plate, xvii) a pressure in the die plate, xviii) a speed of a pelletizer of the polymer extruder, xix) a ratio of power to amperage of the gear pump, or xx) combinations thereof (Page 4, line 20 "feed rate". Page 6, line 20, "extruder screw speed". Page 7, line 3, "barrel temperature". Page 4, line 23, "measurement of pressure". Page 2, line 2, "amperage". Etc.).
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 MF value, a MI2 value, a MI5 value, or a 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 produces a first polymer extrudate, a… model to an input data set to output a predicted… property value for the first polymer extrudate, wherein the input data set comprises a raw value data point for each of a plurality of operating parameters of the polymer extruder at a first point in time (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).
As to dependent claim 17, Piovoso further teaches the input data set further comprises: a delta value for each of the plurality of operating parameters, wherein the delta value corresponds to a difference between the raw value data point at the first point in time and a previous raw value data point for each of the plurality of operating parameters of the polymer extruder at a second point in time (Page 2, line 29, " degree of variation in the data." Signals are continuously measured and digitized for process monitoring and control. Process variable correlate with extrudate characteristics for control purposes.).
As to dependent claim 18, Zhu further teaches the input data set further comprises: a measured melt property value for a sample of a second polymer extrudate obtained before the first point in time (Figure 8, melt index in (a) D201 and (b) D204).
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 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).
As to dependent claim 20, Piovoso further teaches the plurality of operating parameters comprises i) counts measured in a master feed line of the polymer extruder, ii) a fluff feed rate, iii) a speed of a drive motor of the polymer extruder, iv) one or more temperatures in one or more zones of a screw portion of the polymer extruder, v) one or more temperatures of polymer in the one or more zones of the screw portion, vi) a pressure in one or more zones of the screw portion, vii) one or more temperatures in one or more zones of a molten flow portion of the polymer extruder, viii) a temperature for at least one bearing of a gear pump of the polymer extruder, ix) a temperature of an oil of the gear pump, x) an amperage of the gear pump, xi) a speed of the gear pump, xii) a suction pressure of the gear pump, xiii) a discharge pressure of the gear pump, xiv) a differential pressure of a screenpack of a die plate assembly of the polymer extruder, xv) a temperature of a die plate the die plate assembly, xvi) a temperature of polymer in the die plate, xvii) a pressure in the die plate, xviii) a speed of a pelletizer of the polymer extruder, xix) a ratio of power to amperage of the gear pump, or xx) combinations thereof (Page 4, line 20 "feed rate". Page 6, line 20, "extruder screw speed". Page 7, line 3, "barrel temperature". Page 4, line 23, "measurement of pressure". Page 2, line 2, "amperage". Etc.).
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Piovoso in view of Zhu 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).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Leong et al. (Int’l. Pat. App. Pub. No. WO-2022132050-A1) teaches a system and method for processing extrusion data during an extrusion process performed by an extruder under predefined extrusion parameters. The system comprises: a set of pre-extrusion instruments for measuring, during the extrusion process, pre-extrusion properties of an extrudate within the extruder; a set of post-extrusion instruments for measuring, during the extrusion process, post-extrusion properties of the extrudate after exiting the extruder; and a processor for collecting the extrusion data comprising the extrusion parameters and the measured pre-extrusion and post-extrusion properties of the extrudate, wherein the processor is configured for building a materials database using the extrusion data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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.
Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Casey R. Garner/Primary Examiner, Art Unit 2123