DETAILED ACTION
Applicant’s response filed 05/26/2026 has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied.
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 .
Status of the Claims
Claims 1-20 are currently pending.
Claims 8-13 and 18-20 are withdrawn from further consideration under 37 CFR 1.142(b), as being drawn to a non-elected invention.
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.
Claim Objections
The objection to claim 1 is withdrawn in view of Applicant’s amendments to the claims filed 05/26/2026 (Applicant’s Remarks, Pg. 8).
Claim Rejections - 35 USC § 112(b)
Maintained Rejections
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.
These rejections are maintained from the previous Office action. Any newly recited portion is necessitated by claim amendment.
Claim 1 recites the limitation “executing a machine learning algorithm using a data set … to determine which of the plurality of items …is considered 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 lack of clarity. The claim uses broad functional language of “a machine learning algorithm” and “to determine” without disclosing, for example, 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, since the claim recites “a machine learning algorithm”, it is unclear what the metes and bounds of the claim are to use any machine learning algorithm other than the random forest algorithm disclosed in the Specification. Additionally, the claim language describes a desired result (i.e., to determine 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 recites the limitation “to determine which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered 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” in lines 9-12 of the claim. The term "considered important" in claim 1 is a relative term which renders the claim indefinite. The term "considered 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 "considered important". Additionally, it is unclear what method is used to classify changes as 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, or any other parameter used to designate any number of items as being “considered important”. 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" in lines 11-12 of the claim. 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 important for changes in the characteristic value for the target item for improved characteristics by calculating an importance level of each of the plurality of items included in the manufacturing conditions data and the measured characteristics data” in lines 4-7 of the claim. The terms "important" and "improved characteristics" in claim 2 are relative terms which renders the claim indefinite. The terms "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. Analogous to claim 1, the Specification (see at least Para. [0013][0014], [0035], and [0051 ]-[0052] for "important" and Para. [0013]-[0014] and [0047] for "improved characteristics") reiterates the claim language but does not provide a definition for "important" or "improved characteristics". For example, the claim does not define any parameters to determine if a change is 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 "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 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 “items in a second class”, as defined in claim 3, is not controlled by the production system. This is contrary to the recited limitation in claim 4, because it appears the internal temperature of the kneader is controlled by the production system, as internal temperatures are process parameters for the production system. This suggests that the internal temperatures are part of the first class, and not the second class. Clarification through clearer claim language is respectfully requested. 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 using the item with a high calculated importance level as a new objective variable to determine which item is considered important for changes in a characteristic value for the new objective variable” in lines 3-5 of the claim. The metes and bounds of the claim are rendered indefinite due to the lack of clarity. Analogous to claim 1, the claims recite broad functional language of “a machine learning algorithm” and “to determine” without disclosing, for example, 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, since the claim recites “a machine learning algorithm”, it is unclear what the metes and bounds of the claim are to use any machine learning algorithm other than the random forest algorithm disclosed in the Specification. Additionally, the claim language describes a desired result (i.e., to determine 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 6 and 14-15 also recite the phrase “with a high calculated importance level as a new objective variable to determine which item is considered important for changes in a characteristic value” in lines 3-5 of the claims. The terms "a high calculated importance level" and "considered important" in claims 6 and 14-15 are relative terms which renders the claim indefinite. The terms "a high calculated importance level" and "considered 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 "a high calculated importance level" or "considered important". For example, the claim does not define any steps or parameters to determine which items have a high calculated importance level compared to other items, or what parameters classify a change to be considered as 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 "a high calculated importance level" and "considered important".
Claims 7 and 16-17 recite the phrase “a regression operation using the data set is performed using item with a high calculated importance level as an analytical axis to estimate correspondence between changes in a characteristics value for the item with a high importance level 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 importance level…” in lines 3-4 of the claims. The term "high calculated importance level" in claims 7 and 16-17 is a relative term which renders the claim indefinite. The term "high calculated importance level" 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 importance level". 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 importance level".
Applicant is kindly reminded that any amendment must find adequate support in the Specification as originally filed.
Response to Arguments under 35 U.S.C. 112(b)
Applicant’s arguments filed 05/26/2026 have been fully considered but they are not persuasive.
1. Applicant argues that a broad claim is not indefinite merely because it encompasses a wide scope of subject matter provided the scope is clearly defined. MPEP 2173.04 explicitly states "a genus claim that covers multiple species is broad, but is not indefinite because of its breadth, which is otherwise clear." Here, claim 1 recites a genus, machine learning algorithm, wherein a random forest algorithm is a species. "machine learning algorithm" in the context of the claim as a whole is not so broad a genus that it is impossible to tell which species would be covered. Claim 1 covers any and all possible machine learning algorithms configured to or capable of performing the claimed process. One skilled in the art would certainly be able to ascertain the metes and bounds of claim 1, in particular because claim 1 explicitly defines the function of the machine learning algorithm, i.e., determining which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered 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. Accordingly, claim 1 is clear and not indefinite in this respect (Applicant’s Remarks, Pg. 9).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP § 2173.05(g) recites:
Notwithstanding the permissible instances, the use of functional language in a claim may fail "to provide a clear-cut indication of the scope of the subject matter embraced by the claim" and thus be indefinite. In re Swinehart, 439 F.2d 210, 213 (CCPA 1971). For example, when claims merely recite a description of a problem to be solved or a function or result achieved by the invention, the boundaries of the claim scope may be unclear. Halliburton Energy Servs., Inc. v. M-I LLC, 514 F.3d 1244, 1255, 85 USPQ2d 1654, 1663 (Fed. Cir. 2008) (noting that the Supreme Court explained that a vice of functional claiming occurs "when the inventor is painstaking when he recites what has already been seen, and then uses conveniently functional language at the exact point of novelty") (quoting General Elec. Co. v. Wabash Appliance Corp., 304 U.S. 364, 371 (1938)); see also United Carbon Co. v. Binney & Smith Co., 317 U.S. 228, 234, 55 USPQ 381 (1942) (holding indefinite claims that recited substantially pure carbon black "in the form of commercially uniform, comparatively small, rounded smooth aggregates having a spongy or porous exterior"). Further, without reciting the particular structure, materials or steps that accomplish the function or achieve the result, all means or methods of resolving the problem may be encompassed by the claim. Ariad Pharmaceuticals., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1353, 94 USPQ2d 1161, 1173 (Fed. Cir. 2010) (en banc). See also Datamize LLC v. Plumtree Software Inc., 417 F.3d 1342, 75 USPQ2d 1801 (Fed. Cir. 2005) where a claim directed to a software based system for creating a customized computer interface screen recited that the screen be "aesthetically pleasing," which is an intended result and does not provide a clear cut indication of scope because it imposed no structural limits on the screen. Unlimited functional claim limitations that extend to all means or methods of resolving a problem may not be adequately supported by the written description or may not be commensurate in scope with the enabling disclosure, both of which are required by 35 U.S.C. 112(a) and pre-AIA 35 U.S.C. 112, first paragraph. In re Hyatt, 708 F.2d 712, 714, 218 USPQ 195, 197 (Fed. Cir. 1983); Ariad, 598 F.3d at 1340, 94 USPQ2d at 1167. For instance, a single means claim covering every conceivable means for achieving the stated result was held to be invalid under 35 U.S.C. 112, first paragraph because the court recognized that the specification, which disclosed only those means known to the inventor, was not commensurate in scope with the claim.
As currently recited, the limitation of “executing a machine learning algorithm using a data set … to determine which of the plurality of items … is considered important for changes in a characteristic value for a target item” recites a positive step of “executing a machine learning algorithm using a data set” with an intended result of “to determine which of the plurality of items is … considered important…”. Analogous to Datamize LLC v. Plumtree Software Inc., which did not provide a clear cut indication of scope because it imposed no structural limits on the screen, the intended result of the machine learning algorithm does not provide a clear indication of the scope because it does not impose any structural limits on the machine learning algorithm to determine the output. Accordingly, the limitation recites a description of the result to be achieved without providing the structure necessary to achieve the result. Therefore, the claim is indefinite and this argument is not persuasive.
2. Applicant also argues that claim 1 is also amended to remove the term "highly" preceding "important”. Claim 1 as amended recites that the algorithm determines which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered important when selecting the target item as an objective variable. While the claim is amended to remove "highly," Applicant notes it would have been understood by one skilled in the art that "highly" while a relative term, is relative to the other items in the dataset, as in those that are "highly important" have a higher calculated importance level than those with a lower calculated importance level. Accordingly, claim 1 is clear and not indefinite in this respect (Applicant’s Remarks, Pg. 9).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP § 2173.05(b)(I) recites:
"Claim language employing terms of degree has long been found definite where it provided enough certainty to one of skill in the art when read in the context of the invention." Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1370, 112 USPQ2d 1188, 1192-93 (Fed. Cir. 2014) (citing Eibel Process Co. v. Minnesota & Ontario Paper Co., 261 U.S. 45, 65-66 (1923) (finding ‘substantial pitch’ sufficiently definite because one skilled in the art ‘had no difficulty … in determining what was the substantial pitch needed’ to practice the invention)). Thus, when a term of degree is used in the claim, the examiner should determine whether the specification provides some standard for measuring that degree. Hearing Components, Inc. v. Shure Inc., 600 F.3d 1357, 1367, 94 USPQ2d 1385, 1391 (Fed. Cir. 2010); Enzo Biochem, Inc., v. Applera Corp., 599 F.3d 1325, 1332, 94 USPQ2d 1321, 1326 (Fed. Cir. 2010); Seattle Box Co., Inc. v. Indus. Crating & Packing, Inc., 731 F.2d 818, 826, 221 USPQ 568, 574 (Fed. Cir. 1984). If the specification does not provide some standard for measuring that degree, a determination must be made as to whether one of ordinary skill in the art could nevertheless ascertain the scope of the claim (e.g., a standard that is recognized in the art for measuring the meaning of the term of degree). For example, in Ex parte Oetiker, 23 USPQ2d 1641 (Bd. Pat. App. & Inter. 1992), the phrases "relatively shallow," "of the order of," "the order of about 5mm," and "substantial portion" were held to be indefinite because the specification lacked some standard for measuring the degrees intended.
As described in MPEP § 2173.05(b)(I) and in the rejection above, the Specification does not provide, for example, any definition or parameters to consider an item “important”. While the Specification (see, for example, Para. [0034]-[0035]) does indicate that the machine learning algorithm "determines highly critical items HC, items highly important for improvements in characteristics", it does not provide an example indicating the correlation between items that are output and which of the items is considered important from said output (e.g., a specific number of items or by a threshold). Analogous to Ex parte Oetiker, 23 USPQ2d 1641 (Bd. Pat. App. & Inter. 1992), where the phrases "relatively shallow," "of the order of," "the order of about 5mm," and "substantial portion" were held to be indefinite because the specification lacked some standard for measuring the degrees intended, the instant Specification does not provide any standard for “considered important”. Accordingly, this phrase is indefinite and this argument is not persuasive.
3. Applicant also argues that as to the recitation "improved characteristics" in claim 1, Applicant respectfully submits the claim term is clear as it is written and would be understood by the skilled person when read in light of the claim as a whole. Claim 1. The technical problem being solved by the invention, and the process of claim 1 is that a melt kneading process involves a myriad of parameters to control as manufacturing conditions, and the individual parameters interact in a complicated manner, and thus there is a need for an improved process of determining the right parameter values to give for boosting the impact resistance of the polyarylene sulfide resin composite. (Published Application, [0010]). The process of claim 1 provides a means whereby the manufacturer can optimize the control parameters without guess work (Id. at [0011]). In this context (and even without it) it is clear than an improved characteristic is one that is improved over a prior iteration, or improved over one wherein the process of claim 1 had not been performed, for example. The method relates to a process for optimizing the manufacture of the polyarylene sulfide resin by strategically controlling the control parameters. Thus, the process determines which of the input parameters are responsible for doing the improving of the selected characteristic. Accordingly, claim 1 is clear and not indefinite in this respect (Applicant’s Remarks, Pg. 10).
It is respectfully submitted that this is not persuasive for the following reasons:
Analogous to argument (2) above, and as described in MPEP § 2173.05(b)(I), the Specification does not provide, for example, any definition or parameters for the composite to have “improved characteristics”. While Applicant provides several exemplary of ways that the characteristics can be improved, these are not recited in the claim, and it is therefore unclear what baseline to compare to when evaluating whether or not a characteristic is improved over, for example, a previous iteration. Additionally, as described in argument (1), “to determine which of the plurality of items is … considered important…” is an intended result, as there are no positive steps for measuring and/or comparing the improved characteristics over, for example, a previous iteration. Accordingly, this phrase is indefinite and this argument is not persuasive.
4. Applicant also argues that as to the rejection of claim 4, it is unclear to Applicant how the Office has determined that the temperature of the kneader is controlled by the production system. The rejection points to no evidence where this is the case so as to cause any contradiction between claims 3 and 4. In fact, paragraphs [0066]-[0067] of the published application explicitly state: "The second manufacturing conditions data CD2 is a collection of measured values obtained through actual measurement, i.e., values obtained uncontrolled ... That is, the manufacturing conditions items in the second class (second manufacturing conditions data CD2) include temperatures of the kneader, at which the polyarylene sulfide is kneaded, at multiple points. The temperatures of the kneader at multiple points include internal temperatures of the kneader, specifically polymer melt temperatures or resin temperatures." (emphasis added). Unless the Office can show in the record that the temperature of the kneader is actually an item of the first class based on the documents as filed, claim 4 in its current form is correct and no contrary to the definition of the item of the second class as provided in claim 3. Claim 4 is therefore clear (Applicant’s Remarks, Pg. 10).
It is respectfully submitted that this is not persuasive for the following reasons:
Based on Applicant’s argument, and the description provided in Specification Para. [0066]-[0067], it appears there is ambiguity in the definition of “controlled by the production system”. Based on the claims, Examiner has interpreted the internal temperature of the kneader of the production system as a process parameter which is controlled by the production system. This interpretation would indicate that the internal temperatures should be part of the first class, not the second class, according to claim 3. However, based on the Applicant’s argument, the rejection has been amended to distinguish that the ambiguity of the class label for the internal temperatures arises from the interpretation of “controlled by the production system”. Examiner therefore suggests amendment of claim 4 to clarify this ambiguity, and accordingly the rejection of claim 4 is maintained.
5. As to claims 6, 7, 16, and 17, Applicant respectfully submits that the claim language is clear at least for the same reasons presented above with respect to claim 1. Additionally, Applicant submits that the term "high level of importance" is sufficiently clear so as to allow one skilled in the art to ascertain the requisite degree. As explained above, the purpose of the invention is to determine which control parameters are responsible for improving the desired characteristic. The level of importance is a calculated value. Thus a "high" level of importance is one above a particular threshold so as to be selected for use by the algorithm, as in it is one that has an impact on improving the desired characteristic. That is to say, it has a level of importance that is higher than the calculated importances of the other parameters considered by the process. This would be clear to one skilled in the art (Applicant’s Remarks, Pg. 11).
It is respectfully submitted that this is not persuasive for the following reasons:
For the same reasons as disclosed in the rejection and arguments (2) and (3) above, claims 6, 7, 16, and 17 recite phrases that are indefinite because the Specification lacked some standard for measuring the degrees intended (see MPEP § 2173.05(b)(I)). Accordingly, this argument is not persuasive and the rejection of claims 6, 7, 16, and 17 is maintained.
Claim Rejections - 35 USC § 101
Maintained Rejections
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)).
Any newly recited portion is necessitated by claim amendment.
Framework with which to evaluate Subject Matter Eligibility as outlined in MPEP § 2106:
Step 1: Are the claims directed to a process, machine, manufacture or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework as it pertains to the instant claims:
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 of the output of the algorithm) 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, and 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 determine which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered 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 important for changes in the characteristic value for the target item for improved characteristics by calculating an importance level 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 item 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 item 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, an upstream point, which is on a side where raw materials for the polyarylene sulfide resin composite are introduced into the kneader, has a higher importance level than a downstream point, 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 using the item with a high calculated importance level as a new objective variable to determine which item is considered 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 using the item with a high calculated importance level as an analytical axis to estimate correspondence between changes in a characteristic value for the item with a high importance level 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 in 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, the 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 determine 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 determination of 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.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 05/26/2026 have been fully considered but they are not persuasive.
1. Applicant argues that claim 1 recites method for optimizing manufacturing conditions for a polyarylene sulfide resin composite, the method comprising executing a machine learning algorithm, which clearly does not recite a mathematical step/equation which falls under mental process grouping of § 101 (Applicant’s Remarks, Pg. 12).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP § 2106.04(a)(2)(I)(C) recites:
“A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
As described in Step 2A, Prong One above, the limitation of executing a machine learning algorithm using a data set … to determine which of the plurality of items…is considered important for changes in a characteristic value for a target item recites a mathematical concept. The Specification (see at least Para. [0058]) discloses that a random forest algorithm is used to calculate the importance of every one of the control, measured, and characteristics variables included in the data set. Therefore, given its broadest reasonable interpretation in light of the Specification, the limitation equates to a mathematical calculation of using a random forest algorithm to determine an output. This argument is thus not persuasive.
2. Applicant also argues that The USPTO's guidance under prong one (Step 2A) of the Alice/Mayo framework provides that eligibility rejections are to be applied only to claims that recite subject matter within the defined categories of judicial exceptions, and even then, a rejection would only be applied if the claim does not integrate the recited exception into a practical application. (See 2019 PEG at 53.) The 2025 Memo reiterates this, but reminds examiners "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s) ... The mental process grouping is not without limits. Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping" (2025 Memo at 2, emphasis added). Here, the steps of the process of claim 1 cannot be performed in the human mind (Applicant’s Remarks, Pg. 12).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in Step 2A, Prong One and argument (1) above, the limitation of executing a machine learning algorithm using a data set … to determine which of the plurality of items…is considered important for changes in a characteristic value for a target item recites a mathematical concept, not a mental process. Unlike the mental processes grouping of abstract ideas, limitations recited mathematical concepts are not required to be performed in the human mind (see MPEP § 2106.04(a)). This argument is thus not persuasive.
3. Applicant also argues that step two (Step 2B) of the Alice/Mayo framework (i.e., Does the claim recite additional elements that amount to significantly more than the judicial exception?) is applied only when step one (Step 2A) fails to show patent eligibility. However, an analysis under Step 2B is not necessary in this instance (although Applicant nevertheless reserves the right to present such an analysis) as the claims of the present application are patent-eligible under Step 2A for at least the reasoning set forth herein (Applicant’s Remarks, Pg. 13).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in arguments (1) and (2) above, the claim recites judicial exceptions, and as such requires subsequent analysis under Step 2A Prong Two (If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application) and Step 2B (If the claims do not integrate the judicial exception, do the claims provide an inventive concept). As described in the rejection above, the claims do not recite any additional elements and are therefore not integrated into a practical application under Step 2A, Prong Two, nor do they provide an inventive concept under Step 2B. Therefore, the Alice/Mayo framework was correctly applied to the instant claims and this argument is not persuasive.
4. Applicant also argues that as an initial matter, Applicant respectfully requests that the claims be considered as a whole when assessing patent eligibility under § 101, since claims, when considered as a whole, may be patent eligible, e.g., as providing an inventive concept. Claim 1 clearly does not fall within the "Mental processes" groupings of abstract ideas, as enumerated in the 2019 PEG or the 2024 PEG, or the 2025 Memo. Moreover, the Examiner's attention is respectfully directed to the 2019 PEG Update which provides: "While a claim limitation to a process that 'can be performed in the human mind, or by a human using a pen and paper' qualifies as a mental process, a claim limitation that 'could not, as a practical matter, be performed entirely in a human's mind' (even if aided with pen and paper) would not qualify as a mental process." (2019 Update at p. 9.). Accordingly, claim 1 is not directed to a mental process. (Applicant’s Remarks, Pg. 13).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in Step 2A, Prong One, and arguments (1) and (2) above, the limitation of executing a machine learning algorithm using a data set … to determine which of the plurality of items…is considered important for changes in a characteristic value for a target item recites a mathematical concept, not a mental process. This argument is thus not persuasive.
5. Applicant also argues that assuming arguendo that the recitations of claim 1 are directed to an abstract idea (which Applicant submits they are not), Applicant additionally points out that under the 2019 PEG, the recitations of claim 1 are clearly "integrated into a practical application", and thus, satisfy Step 2A, Prong II This is because the recitations of claim 1, as currently presented, are directed to improvements to a process for optimizing the manufacture of the polyarylene sulfide resin composite. (Published application, [0010]) (Applicant’s Remarks, Pg. 13-14).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP § 2106.04(d)(II) recites:
The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
The limitation of executing a machine learning algorithm using a data set … to determine which of the plurality of items…is considered important for changes in a characteristic value for a target item… has been identified as a judicial exception in Step 2A, Prong One above. The integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Thus, the recited limitation is not considered as an improvement in process for optimizing the manufacture of the polyarylene sulfide resin composite. This argument is thus not persuasive.
6. Applicant also argues that the Office's November 2, 2016 memorandum to the Patent Examining Corps titled "Recent Subject Matter Eligibility Decisions" explains that an indication that a claim is directed to an improvement in computer-related technology may include "(1) a teaching in the specification about how the claimed invention improves a computer or other technology" and "(2) a particular solution to a problem or a particular way to achieve a desired outcome defined by the claimed invention, as opposed to merely claiming the idea of a solution or outcome". (Id. at pp. 2-3.). This is particularly reiterated in the 2025 Memo wherein the Office states "[i]n computer-related technologies, examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field." The application-as-filed provides "[a]fter extensive research to solve the above problems, the inventors found a machine learning algorithm-based analysis performed using a data set based on acquired data, described below, provides a way to determine the right control parameters." (Published Application, [0015]). Accordingly, when evaluating a claim as a whole, examiners should not dismiss additional elements as mere "generic computer components" without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system. (See, e.g., Ex Parte Desjardins; MPEP § 2106.05(a)). As such, Examiners are expected to consider existing precedent like Enfish, as discussed in MPEP § 2106, in addition to these updates when assessing eligibility under 35 U.S.C. § 101, particularly when evaluating claims related to machine learning or artificial intelligence. (See, December 5, 2025 Memorandum "Advance notice of change to the MPEP in light of Ex Parte Desjardins") (Applicant’s Remarks, Pg. 14).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in Step 2A, Prong Two and argument (5) above, claim 1 does not recite any additional elements. Improvements in computer-related technologies, using (1) or (2), indicated above by Applicant, can only be achieved by additional elements in the claim. While the instant Specification discloses that the method was used to determine the right control parameters, this improvement is recited in the machine learning limitation, and as the machine learning limitation recites a mathematical concept, it is therefore not considered as an improvement (see MPEP § 2106.04(d)(II)).
Additionally, claim 1 does not recite any generic computer components. No evidence has been presented to suggest that the alleged generic computer components have been altered in any way, nor that any alleged generic computer components are changed by the limitations recited in claim 1 (see MPEP § 2106.05(a)(I)). Unlike the instant case, the claims in Enfish were not directed to an abstract idea. It was the Specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility (see MPEP § 2106.05(a)(I)). This is different from the instant case, which recites mathematical concepts (see Step 2A, Prong One and arguments (1)-(2) above). This argument this thus not persuasive.
To assist Applicant, the Examiner suggests a few exemplary paths towards overcoming the rejection of the claims under 35 U.S.C. 101. One suggestion to integrate the claims into a practical application would be to manufacture the polyarylene sulfide resin composite with improved characteristics, as predicted by the model. Another suggestion is to show technological improvements to the computer system by showing that machine learning algorithm/random forest algorithm changes the operation of the computing system. It is noted that the Examiner provides these suggestions as merely exemplary, and the status of the claims under 35 U.S.C. 101 will be re-assessed upon any amendment.
Claim Rejections - 35 USC § 103
Maintained Rejections
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; previously recited) in view of Matsuo (Japanese Application JP 2008-163112 A; published 7/17/2008; English translation provided in the IDS dated 7/25/2022; previously recited).
This rejection is maintained from the previous Office action.
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 determine which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered 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 important for changes in the characteristic value for the target item for improved characteristics by calculating an importance level 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 using the item with a high calculated importance level as a new objective variable to determine which item is considered 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 using the item with a high calculated importance level as an analytical axis to estimate correspondence between changes in a characteristic value for the item with a high importance level 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).
Response to Arguments under 35 U.S.C. 103
Applicant’s arguments filed 05/26/2026 have been fully considered but they are not persuasive.
1. Applicant argues that the rejection alleges that Menon teaches a machine learning model for predicting the mechanical properties of polymers (Office Action, p. 20). First, Menon relates specifically polyurethane elastomers, not polyarylene sulfide resin composites. Although Menon discloses, for example, "[a]fter generating the set of middle layer variables, a random forest regression model was fitted between the latter and the mechanical responses (stress-at-break, strain-at-break, and Tanδ)," and using a random forest algorithm, one having ordinary skill in the art would not be able to infer parameters considered important nor improved characteristics recited in claim 1. (See, Menon, p. 8). The same can be said for the details of the machine learning algorithm and the specific high importance parameters and improved characteristics recited in the dependent claims. At best, Menon merely visualizes the feature importance but it does not describe any other items with high importance among the explanatory variables (Applicant’s Remarks, Pg. 16).
It is respectfully submitted that this is not persuasive for the following reasons:
While Examiner agrees that the algorithm of Menon et al. relates to polyurethane products, Menon et al. also discloses that the algorithm can be used in future work for other polymer systems and bio based materials (e.g., the polyarylene sulfide resin composites of claim 1) (see Menon et al., Pg. 11, Col. 2, Para. 1). For the specified polymers, Menon et al. includes both manufacturing conditions data (Pg. 2, Col. 2, Para. 3 – Pg. 3, Col. 1, Para. 1) and measured characteristics data (Pg. 3, Col. 1, Para. 2). To use the algorithm in future work, as described by Menon et al. above, the manufacturing conditions data and measured characteristics data for the polyarylene sulfide resin composites could be incorporated. With regards to the machine learning algorithm, Menon et al. teaches a random forest algorithm that predicts feature importance values for several different items (Pg. 8, Col. 2, Para. 4 and Pg. 7, Fig. 6). Since the instant claims do not provide, for example, a definition of which values are considered to be important (see 112(b) Rejections above), the feature values disclosed by Menon et al., and those items with the highest feature importance values are interpreted to be the items considered important for changes, as disclosed in instant claim 1. Therefore, the random forest algorithm disclosed by Menon et al. teaches the limitation of executing a machine learning algorithm using a data set including the manufacturing conditions data and measured characteristics data to determine which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered important for changes in a characteristic value for a target item for improved characteristics as disclosed in instant claim 1. This argument is thus not persuasive.
2. Applicant also argues that the present invention as claimed differs from Menon in that it newly utilizes items of high importance among the explanatory variables as the dependent variable. This is not contemplated in Menon. Menon does not disclose determining which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered 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. Since Menon even when combined with Matsuo does not disclose the recitations of claim 1, no prima facie case of obviousness exists (Applicant’s Remarks, Pg. 16).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in the rejection, and argument (1) above, with regards to the random forest algorithm, Menon et al. does disclose the limitation of determining which of the plurality of items included in the manufacturing conditions data and the measured characteristics data is considered important for changes in a characteristic value for a target item for improved characteristics. When viewed in combination with Matsuo (disclosing the method for a polyarylene sulfide resin composite; 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; and 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), the algorithm determines which of the plurality of items…is considered important for changes in a characteristic value for a target item for improved characteristics of the polyarylene sulfide resin composite.
With regards to “newly utilizes items of high importance among the explanatory variables as the dependent variable”, this appears to equate to the limitations in claims 6, 14, and 15, reciting “the machine learning algorithm is executed using the item with a high calculated importance level as a new objective variable to determine which item is considered important for changes in a characteristic value for the new objective variable”, not the limitations recited in claim 1. Regardless, Menon et al. discloses that the features that are most important change when the objective variable changes, as shown by the plots from three different objective variables in Fig. 6. 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 (Menon et al., Pg. 7, Fig. 6). Therefore, Menon et al. does disclose this limitation, and this argument is not persuasive.
Conclusion
No claims allowed.
THIS ACTION IS MADE FINAL. 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.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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