DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
REQUIREMENT FOR UNITY OF INVENTION
As provided in 37 CFR 1.475(a), a national stage application shall relate to one invention only or to a group of inventions so linked as to form a single general inventive concept (“requirement of unity of invention”). Where a group of inventions is claimed in a national stage application, the requirement of unity of invention shall be fulfilled only when there is a technical relationship among those inventions involving one or more of the same or corresponding special technical features. The expression “special technical features” shall mean those technical features that define a contribution which each of the claimed inventions, considered as a whole, makes over the prior art.
The determination whether a group of inventions is so linked as to form a single general inventive concept shall be made without regard to whether the inventions are claimed in separate claims or as alternatives within a single claim. See 37 CFR 1.475(e).
When Claims Are Directed to Multiple Categories of Inventions:
As provided in 37 CFR 1.475 (b), a national stage application containing claims to different categories of invention will be considered to have unity of invention if the claims are drawn only to one of the following combinations of categories:
(1) A product and a process specially adapted for the manufacture of said product; or
(2) A product and a process of use of said product; or
(3) A product, a process specially adapted for the manufacture of the said product, and a use of the said product; or
(4) A process and an apparatus or means specifically designed for carrying out the said process; or
(5) A product, a process specially adapted for the manufacture of the said product, and an apparatus or means specifically designed for carrying out the said process.
Otherwise, unity of invention might not be present. See 37 CFR 1.475 (c).
Restriction is required under 35 U.S.C. 121 and 372.
This application contains the following inventions or groups of inventions which are not so linked as to form a single general inventive concept under PCT Rule 13.1.
In accordance with 37 CFR 1.499, applicant is required, in reply to this action, to elect a single invention to which the claims must be restricted.
Group I: Claims 1-7 and 14-17, directed to a method of optimizing manufacturing conditions for a polyarylene sulfide resin composition using machine learning algorithm.
Group II: Claims 8-13 and 18-20, directed to a method for manufacturing polyarylene sulfide resin composite.
The groups of inventions listed above do not relate to a single general inventive concept under PCT Rule 13.1 because, under PCT Rule 13.2, they lack the same or corresponding special technical features for the following reasons:
Groups I and II lack unity of invention because the two groups have no common technical features; that is, Group I is entirely a data processing method requiring no manufacturing and Group II is a production method for a polymer composite involving no data processing. To the extent that both groups are concerned with manufacturing of polyarylene sulfide composite under conditions such as temperature, composition and shear speed, such is known in the art, for example, US 2010/0004375 (cited in the IDS dated 4/10/2024), Para. [0028], showing manufacturing of polyarylene sulfide composite under conditions of composition, shear rate, and temperature, and therefore does not constitute a special technical feature.
The search and examination of these two groups would require search and examination of art specific to machine learning with respect to Group I and art relating to manufacture of resin composites with respect to Group II. There is expected to be little overlap in the art searched for each of these groups.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Election/Restrictions
During a telephone conversation between Primary Examiner Kregg Brooks and Attorney of Record James Armstrong on 3/19/2025, a provisional election was made without traverse to prosecute the invention of Group I, claims 1-7 and 14-17. Affirmation of this election must be made by applicant in replying to this Office action. Claims 8-13 and 18-20 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Status of the Claims
Claims 1-20 are currently pending.
Claims 8-13 and 18-20 are withdrawn.
Claims 1-7 and 14-17 are under consideration in this action.
Priority
The instant application is 371 of PCT/JP2021/005897, filed 2/17/2021, which claims priority to U.S. Provisional Application number 62/982,759, filed 2/28/2020, and Japanese Application number 2020-159132, filed 9/23/2020, as reflected in the filing receipt mailed 12/15/2022. Acknowledgment is made of applicant' s claim for domestic benefit and foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-7 and 14-17 is 2/28/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/25/2022, 4/10/2024, and 2/17/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS’s have been considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 recites the phrase “the manufacturing conditions data including manufacturing conditions items of at least … during melt kneading whereas the measured characteristics data including a … specified by the manufacturing conditions data” in lines 4-9 of the claim. The “whereas” in the phrase should be corrected to “and” to improve the clarity of the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 14-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the phrase “executing a machine learning algorithm using a data set … to find out which of the plurality of items … is highly important for changes in a characteristic value for a target item …” in lines 3-12 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The claim uses broad functional language of “a machine learning algorithm” and “to find out” without disclosing the specific structure, network architecture, or training methodology of the machine learning algorithm. With regards to the specific structure, the Specification (Para. [0014]) discloses that the machine learning algorithm is a random forest-based algorithm. However, it is unclear whether this is the intended machine learning algorithm, and therefore the metes and bounds of the invention are not clearly defined. Additionally, the claim language describes a desired result (i.e., “to find out which of the plurality of items…”), rather than the technical steps required to achieve the result, leaving the scope of the invention unclear. Clarification of the metes and bounds of the claim through clearer claim language is respectfully requested. Claims 2-7 and 14-17 are also rejected due to their dependency from claim 1.
Claim 1 also recites the phrase “to find out which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is highly important for changes in a characteristic value for a target item” in lines 9-11 of the claim. The term “highly important” in claim 1 is a relative term which renders the claim indefinite. The term “highly important” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification (see at least Para. [0013]-[0014], [0035], and [0051]-[0052]) reiterates the claim language without providing a definition for “highly important”. Additionally, it is unclear what method is used to classify changes as highly important. The specification (see, for example, Para. [0034]-[0035]) reiterates the claim language, stating that the machine learning algorithm “determines highly critical items HC, items highly important for improvements in characteristics”. However, it is unclear whether the highly critical/highly important items are determined using for example, a threshold or a specific number of output values/scores. This rejection can be overcome by amendment of claim 1 to clarify the definition and steps necessary to classify an item as highly important. Claims 2-7 and 14-17 are also rejected due to their dependency from claim 1.
Claim 1 also recites the phrase “changes in a characteristic value for a target item for improved characteristics of the polyarylene sulfide resin composite”. The term “improved characteristics” in claim 1 is a relative term which renders the claim indefinite. The term “improved characteristics” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The specification (see, for example, Para. [0047]) discloses that the measured characteristics data may include values that can be targets for improved characteristics, such as heat resistance at high temperatures and elastic modulus at high temperatures. However, it is unclear what “improved” means, or what parameters define the improvement in the recited characteristics. This rejection can be overcome by amendment of claim 1 to clarify the definition of improved characteristics. Claims 2-7 and 14-17 are also rejected due to their dependency from claim 1.
Claim 2 recites the limitation “the algorithm determines the item highly important for changes in the characteristic value for the target item for improved characteristics by calculating importance of each of the plurality of items included in the manufacturing conditions data and the measured characteristics data”. The terms “highly important” and “improved characteristics” in claim 2 are relative terms which renders the claim indefinite. The terms “highly important” and “improved characteristics” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Similar to claim 1, the specification (see at least Para. [0013]-[0014], [0035], and [0051]-[0052] for “highly important” and Para. [0013]-[0014] and [0047] for “improved characteristics”) reiterates the claim language but does not provide a definition for “highly important” or “improved characteristics”. For example, the claim does not define any parameters to determine if a change is highly important or if a characteristic is improved in any way. The metes and bounds of the claim are therefore not defined. This rejection can be overcome by amendment of claim 2 to define “highly important” and “improved characteristics”. Claims 3-7 and 15-17 are also rejected due to their dependency on claim 2.
Claim 4 recites the limitation “the manufacturing conditions item in the second class includes internal temperatures of a kneader of the production system” in lines 3-4 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The “second class”, as defined in claim 3, is not to be controlled by the production system. This is contrary to the recited limitation in claim 4, because the internal temperatures of the kneader are controlled by production system. For the purposes of compact prosecution, the “second class” will be interpreted as the “first class”, since the “first class”, as recited in claim 3, is controlled by a production system. This rejection can be overcome by amendment of claim 4 to clarify the appropriate class for the internal temperatures of the kneader of the production system. Claims 5, 15, and 17 are also rejected due to their dependency from claim 4.
Claims 6 and 14-15 recite the phrase “the machine learning algorithm is executed with the item with high calculated importance as a new objective variable to find out which item is highly important for changes in a characteristic value for the new objective variable” in lines 3-5 of the claims. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. Similar to claim 1, the claim recites broad functional language of “a machine learning algorithm” and “to find out” without disclosing the specific structure, network architecture, or training methodology of the machine learning algorithm. With regards to the specific structure, the Specification (Para. [0014]) discloses that the machine learning algorithm is a random forest-based algorithm. However, it is unclear whether this is the intended machine learning algorithm, and therefore the metes and bounds of the invention are not clearly defined. Additionally, the claim language describes a desired result (i.e., “to find out which item is highly important…”), rather than the technical steps required to achieve the result, leaving the scope of the invention unclear. Clarification of the metes and bounds of the claim through clearer claim language is respectfully requested.
Claims 6 and 14-15 also recite the phrase “with high calculated importance as a new objective variable to find out which item is highly important for changes in a characteristic value” in lines 3-5 of the claims. The terms “high calculated importance” and “highly important” in claims 6 and 14-15 are relative terms which renders the claim indefinite. The terms “high calculated importance” and “highly important” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The Specification (see at least Para. [0018] and [0054]) reiterates the claim language but does not provide a definition for “high calculated importance” or “highly important”. For example, the claim does not define any steps or parameters to determine which items have a high calculated importance compared to other items, or what parameters classify a change as highly important. The metes and bounds of the claim are therefore not defined. This rejection can be overcome by amendment of claims 6 and 14-15 to define “high calculated importance” and “highly important”.
Claims 7 and 16-17 recite the phrase “a regression operation using the data set is performed with the item with a high calculated level of the importance as an analytical axis to estimate correspondence between changes in a characteristic value for the item with a high level of the importance and changes in the characteristic value for the objective variable” in lines 3-7 of the claims. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. The claim recites broad functional language of “a regression operation” and “to estimate correspondence between changes” without disclosing the specific structure or steps required to perform the estimation using the regression operation. With regards to the specific structure, the Specification (Para. [0056]) discloses that the regression operation is a support vector regression with items determined to be highly critical items. However, it is unclear whether this is the intended regression operation to perform the estimation, and therefore the metes and bounds of the invention are not clearly defined. Additionally, the claim language describes an intended result (i.e., “to estimate correspondence between changes…”), rather than the technical steps required to achieve the result, leaving the scope of the invention unclear. Clarification of the metes and bounds of the claim through clearer claim language is respectfully requested.
Claims 7 and 16-17 also recite the phrase “with the item with a high calculated level of importance …” in lines 3-4 of the claims. The term “high calculated level of importance” in claims 7 and 16-17 is a relative term which renders the claim indefinite. The term “high calculated level of importance” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The Specification (see at least Para. [0019] and [0056]) reiterates the claim language but does not provide a definition for “high calculated level of importance”. For example, the claim does not define any steps or parameters to determine which items have a high calculated importance compared to other items. The metes and bounds of the claim are therefore not defined. This rejection can be overcome by amendment of claims 7 and 16-17 to define “high calculated level of importance”.
Applicant is kindly reminded that any amendment must find adequate support in the Specification as originally filed.
Claim Rejections - 35 USC § 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 1-7 and 14-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-7 and 14-17 are directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mathematical concept (i.e., using a machine learning algorithm with input data; it is noted that the machine learning algorithm is disclosed as a random forest algorithm (see Specification Para. [0014])) and a mental process (i.e., an evaluation to determine which item is most important to improve the characteristics of the resin composite) in “executing a machine learning algorithm using a data set including manufacturing conditions data and measured characteristics data, the manufacturing conditions data including manufacturing conditions items of at least ingredients for the polyarylene sulfide resin composite, mixing conditions, and polymer melt temperature during melt kneading whereas the measured characteristics data including a characteristic value item of at least impact resistance of the polyarylene sulfide resin composite when produced under manufacturing conditions specified by the manufacturing conditions data, to find out which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is highly important for changes in a characteristic value for a target item for improved characteristics of the polyarylene sulfide resin composite selected as an objective variable”.
Claim 2 recites a mathematical concept (i.e., using a random forest algorithm) in “wherein the machine learning algorithm is a random forest-based algorithm”; and a mathematical concept (i.e., the random forest algorithm calculates the importance of the input variables; see Specification Para. [0049]-[0051]) in “wherein the algorithm determines the item highly important for changes in the characteristic value for the target item for improved characteristics by calculating importance of each of the plurality of items included in the manufacturing conditions data and the measured characteristics data”.
Claim 3 recites a mental process (i.e., an evaluation of the input manufacturing conditions data) in “wherein the manufacturing conditions items included in the manufacturing conditions data include at least one in a first class, which is to be controlled by a production system with which the polyarylene sulfide resin composite is manufactured, and at least one in a second class, which is not to be controlled by the production system”.
Claim 4 recites a mental process (i.e., an evaluation of the input manufacturing conditions data) in “wherein the manufacturing conditions item in the second class includes internal temperatures of a kneader of the production system, at which a polyarylene sulfide resin is kneaded, at a plurality of points”.
Claim 5 recites a mental process (i.e., an evaluation of the input manufacturing conditions data) in “wherein of the internal temperatures of the kneader at a plurality of points, which are manufacturing conditions items in the second class, an upstream one, which is on a side where raw materials for the polyarylene sulfide resin composite are introduced into the kneader, has a higher level of the importance than a downstream one, which is on a side where the kneaded polyarylene sulfide resin composite is extruded”.
Claims 6, 14, and 15 recite a mathematical concept (i.e., executing the random forest algorithm with a new target/objective variable) in “wherein the machine learning algorithm is executed with the item with high calculated importance as a new objective variable to find out which item is highly important for changes in a characteristic value for the new objective variable”.
Claims 7, 16, and 17 recite a mathematical concept (i.e., performing a regression operation) in “wherein a regression operation using the data set is performed with the item with a high calculated level of the importance as an analytical axis to estimate correspondence between changes in a characteristic value for the item with a high level of the importance and changes in the characteristic value for the objective variable”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, steps recited in claim 1 involve nothing more than instructions for a user to input manufacturing conditions and characteristic data, execute a machine learning algorithm, and identify highly important items based on the output. The step reciting “executing a machine learning algorithm” is, under the BRI, performed using mathematical operations. The instant Specification (see for example Para. [0062]) discloses that the random forest algorithm is used to classify and analyze data. Additionally, since there are no specifics in the methodology, the identification of highly important items, is something that under BRI, one could perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing a calculation using a computer as a tool, and merely looking at data and making a determination. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)).
In the instant case, independent claim 1 does not recite any additional elements. Dependent claims 2-7 and 14-17 also do not recite any additional elements. As such, claims 1-7 and 14-17 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). As described in Step 2A, Prong Two above, the claims do not recite any additional elements.
Since the claims do not contain any additional elements, the claimed judicial exceptions are not transformed into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-7 and 14-17 are not patent eligible.
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.
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.
Claims 1-7 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Menon et al. (Hierarchical Machine Learning Model for Mechanical Property Predictions of Polyurethane Elastomers From Small Datasets. Front. Mater. 6: 1-12 (2019); published 5/7/2019) in view of Matsuo (Japanese Application JP 2008-163112 A; published 7/17/2008; English translation provided in the IDS dated 7/25/2022).
Regarding claim 1, Menon et al. teaches a machine learning model for predicting the mechanical properties of polymers (Title, Abstract). Menon et al. further teaches a schematic of the machine learning approach used, depicting three layers (i.e., executing a machine learning algorithm) (Pg. 3, Fig. 2). Menon et al. further teaches that the training set consisted of samples, all of which were prepared by reacting a bifunctional diisocyanate with either a bifunctional or trifunctional polyol at NCO:OH indices of 1.0, 1.2, or 1.5. The reactions were carried out at room temperature in 8ml of dichloromethane as a solvent under the presence of DBTDL as a catalyst. Films were cast from the synthesized polymers and were left to dry at room temperature for 24 h and then again dried in a vacuum oven for 24 h at 60°C to remove any residual solvent (i.e., using a data set including the manufacturing conditions data) (Pg. 2, Col. 2, Para. 3 – Pg. 3, Col. 1, Para. 1). Menon et al. further teaches that stress-at-break and strain-at-break were measured for all polymers in a universal testing machine (i.e., using a data set including measured characteristics data) (Pg. 3, Col. 1, Para. 2). Menon et al. further teaches that a random forest (RF) regression model was fitted between the latter and the mechanical responses (stress-at-break, strain-at-break, and Tan δ). The feature importance values from the RF model are shown in Figure 6 (i.e., to find out which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is highly important for changes in a characteristic value for a target item for improved characteristics) (Pg. 8, Col. 2, Para. 4 and Pg. 7, Fig. 6).
Regarding claim 2, Menon et al. teaches the ensemble averaged decision tree from the random forest (RF) model trained to predict strain-at-break, stress-at-break, and tan δ (i.e., the machine learning algorithm is a random forest-based algorithm) (Pg. 9, Fig. 8). Menon et al. further teaches an exemplary feature importance plot from the random forest model for the strain-at-break, stress-at-break, and tan δ mechanical responses. For example, in the stress-at-break case, the two most important features are "NH_AperCO_A" and "NH_W" (i.e., the algorithm determines the item highly important for changes in the characteristic value for the target item for improved characteristics by calculating importance of each of the plurality of items included in the manufacturing conditions data and the measured characteristics data) (Pg. 7, Fig. 6).
Regarding claims 6, 14, and 15, Menon et al. teaches the feature importance plots from several objective variables: strain-at-break, stress-at-break, and tan δ. The features that are most important change when the objective variable changes. For example, "CO_W" is the most important feature for strain-at-break, and "NH_W" is the most important feature for stress-at-break (i.e., the machine learning algorithm is executed with the item with high calculated importance as a new objective variable to find out which item is highly important for changes in a characteristic value for the new objective variable) (Pg. 7, Fig. 6).
Regarding claims 7, 16, and 17, Menon et al. teaches the use of linear regression to compare the predicted vs. actual mechanical responses for strain-at-break, stress-at-break, and tan δ (Pg. 8, Fig. 7). Though not explicitly taught by Menon et al., it would be obvious to one of ordinary skill in the art to perform a regression with any variables in the dataset to ensure accuracy and predictive capability (i.e., a regression operation using the data set is performed with the item with a high calculated level of the importance as an analytical axis to estimate correspondence between changes in a characteristic value for the item with a high level of the importance and changes in the characteristic value for the objective variable) (Pg. 8, Col. 2, Para. 2).
Menon et al. does not teach the method for a polyarylene sulfide resin composite (claim 1); the manufacturing conditions data including manufacturing conditions items of at least ingredients for the polyarylene sulfide resin composite, mixing conditions, and polymer melt temperature during melt kneading (claim 1); whereas the measured characteristics data including a characteristic value item of at least impact resistance of the polyarylene sulfide resin composite when produced under manufacturing conditions specified by the manufacturing conditions data (claim 1); the manufacturing conditions items included in the manufacturing conditions data include at least one in a first class, which is to be controlled by a production system with which the polyarylene sulfide resin composite is manufactured, and at least one in a second class, which is not to be controlled by the production system (claim 3); the manufacturing conditions item in the second class includes internal temperatures of a kneader of the production system, at which a polyarylene sulfide resin is kneaded, at a plurality of points (claim 4); and of the internal temperatures of the kneader at a plurality of points, which are manufacturing conditions items in the second class, an upstream one, which is on a side where raw materials for the polyarylene sulfide resin composite are introduced into the kneader, has a higher level of the importance than a downstream one, which is on a side where the kneaded polyarylene sulfide resin composite is extruded (claim 5).
Regarding claim 1, Matsuo teaches a method of producing polyarylene sulfide resin composite with excellent impact resistance and bending strength (Para. [0001]). Matsuo further teaches that the polyarylene sulfide resin composition is composed of (a) the polyarylene sulfide resin and (b) the thermoplastic elastomer particles having a mass average particle diameter of 0.1 mm to 3.0 mm. The thermoplastic elastomer particles (b) are melt-kneaded at a ratio of 0.1% by mass to 2.0% by mass with respect to all the compounding components (i.e., manufacturing conditions items of at least ingredients for the polyarylene sulfide resin composite) (Para. [0009]). Matsuo further teaches that during production, the polyarylene sulfide resin (a) and the thermoplastic elastomer particles (b) are dry-blended by a mixing device before being melt-kneaded, and then melt-kneaded. It is preferable to put it in the water and melt-knead it because the thermoplastic elastomer particles (b) can be mixed well (i.e., manufacturing conditions items of at least mixing conditions) (Para. [0011]). Matsuo further teaches that when the extruder is used for melt-kneading, a temperature gradient is provided from the inlet of the compounding component of the polyarylene sulfide resin composition to the discharge port from which the polyarylene sulfide resin composition is melt-kneaded and then discharged. The temperature inside the melting cylinder of the portion having a length of two-fifths from the charging port to the discharging port with respect to the total length in the melting cylinder of the extruder in the axial direction is in the range of 330°C to 370°C (i.e., manufacturing conditions items of polymer melt temperature during melt kneading) (Para. [0015]). Matsuo further teaches that the polyarylene sulfide resin composition generated has excellent impact resistance and bending strength (i.e., whereas the measured characteristics data including a characteristic value item of at least impact resistance of the polyarylene sulfide resin composite when produced under manufacturing conditions specified by the manufacturing conditions data) (Para. [0005]).
Regarding claim 3, Matsuo teaches that the method of mixing the polyarylene sulfide resin (a) and the thermoplastic elastomer particles (B) uses a mixing device such as a Nauta mixer, a tumbler, or a Henshell mixer. For example, the operating conditions for mixing using the Nauta mixer are that the rotation speed of the screw installed inside the Nauta mixer is in the range of 50 rpm to 80 rpm and the revolution speed is in the range of 1.5 rpm to 2.5 rpm (i.e., the manufacturing conditions items included in the manufacturing conditions data include at least one in a first class, which is to be controlled by a production system with which the polyarylene sulfide resin composite is manufactured) (Para. [0012]). Matsuo further teaches that the thermoplastic elastomer particles (b) which are used have a mass average particle diameter of 0.1 mm to 3.0 mm (i.e., the manufacturing conditions items included in the manufacturing conditions data include at least one in a second class, which is not to be controlled by the production system) (Para. [0009]).
Regarding claim 4, Matsuo teaches that when the extruder is used for melt-kneading, a temperature gradient is provided from the inlet of the compounding component of the polyarylene sulfide resin composition to the discharge port from which the polyarylene sulfide resin composition is melt-kneaded and then discharged (Para. [0015]). Matsuo further teaches an example where five heaters are used to provide a temperature gradient in the melting cylinder. Five heaters, each having a length obtained by dividing the total length in the melting cylinder in the axial direction into five equal parts, are attached to the outer shell of the cylinder. The first heater, the second heater, the third heater, the fourth heater, and the fifth heater are arranged in this order from the input port to the discharge port. Examples thereof include a method of setting first and second heaters in the range of 330°C to 360°C, the third heater in the range of 320°C to 330°C, and the fourth and fifth heaters in the range of 280°C to 320°C (i.e., the manufacturing conditions item in the second class includes internal temperatures of a kneader of the production system, at which a polyarylene sulfide resin is kneaded, at a plurality of points) (Para. [0016]).
Regarding claim 5, Matsuo teaches that by providing the temperature gradient in the axial direction in the melting cylinder, the compounding components are rapidly melted in the vicinity of the inlet of each compounding component to improve the dispersibility, and the heat due to shear heat generation is improved in the vicinity of the discharging port. Deterioration can be avoided, and the homogeneity of the polyarylene sulfide resin composition and the mechanical strength of the molded product are further improved (i.e., of the internal temperatures of the kneader at a plurality of points, which are manufacturing conditions items in the second class, an upstream one, which is on a side where raw materials for the polyarylene sulfide resin composite are introduced into the kneader, has a higher level of the importance than a downstream one, which is on a side where the kneaded polyarylene sulfide resin composite is extruded) (Para. [0015])
Therefore, regarding claims 1-7 and 14-17, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model used to predict mechanical properties of Menon et al. with the manufacturing conditions and characteristics data for polyarylene sulfide resin composites of Matsuo because the model of Menon et al. is advantageous for predicting qualitative responses of polymer products with regards to formulation and processing variables (Menon et al., Pg. 11, Col. 2, Para. 1). As such, inputting the manufacturing data of the polyarylene sulfide resin composite disclosed by Matsuo into the machine learning algorithm of Menon et al. will be advantageous to predict the mechanical properties (e.g., improved impact resistance of the polyarylene sulfide resin composite; see Matsuo, Para. [0005]). One of ordinary skill in the art would be able to combine the teachings of Menon et al. with Matsuo with reasonable expectation of success due to the same nature of the problem to be solved, since both incorporate a method for optimizing mechanical properties of polymers. Therefore, regarding claims 1-7 and 14-17, the instant invention is prima facie obvious (MPEP § 2142).
Conclusion
No claims allowed.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687