DETAILED ACTION
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior office action.
All outstanding objections and rejections made in the previous Office Action, and not repeated below, are hereby withdrawn.
The new grounds of rejection set forth below are necessitated by applicant’s amendment filed on 1/16/26. In particular, claim 1 has been amended to include the limitations of claims 2 and 3. Claims 14-16 are new.
The newly introduced limitations and/or the new claims were not present at the time of the preceding action. For this reason, the present action is properly made final.
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 .
Claim Rejections - 35 USC § 101
Claims 1, 4-6 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed a device instructing how to predict a property based on observations without significantly more. The claim(s) recite(s) an abstract idea. See MPEP 2106.05. This judicial exception is not integrated into a practical application because the device (generic computer) do not add a meaningful limitation to the abstract idea because they amount to imply implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the acquisition unit and prediction unit are generic functions, which is a fundamental economic practice and thus grouped as a certain method of organizing human interactions recognized by the court decisions listed in MPEP § 2106.05(d).
In other words, recitation of generic computer components in a claim does not preclude that claim from reciting an abstract idea. In the instant case, the limitations of predicting can be performed in the mind. Thus, the limitations fall within the “Mental Processes” grouping of abstract ideas. Again, the prediction unit and acquisition unit are recited at a high level of generality (that is, there is no meaningful limitation on the structure of the apparatus or the conditions of its operation). The list of variables/parameters/properties measured and predicted are recited in a high level of generatlity. In other words, taken in combination, the list of variables/parameters/properties effectively encompass all ordinary reaction conditions and properties for elastomers. Since the acquisition and prediction steps do not impose any meaningful limits on the physical structure or function of the polymerization, tey are not indicative of integration into a practical application.
Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, 209 USPQ 1 (1981). In Diehr, the claim was directed to the use of the Arrhenius equation (an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78, 209 USPQ at 4. The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products.
However, in the instant case, the claims may be distinguished from those in Diehr, in which a calculation based upon a natural law is used to determine the performance of an actual step in a chemical process (the timing of the opening of a rubber-molding press). In contrast, the claims do not recite any particular steps which are performed or performed differently based on the acquisition and prediction. The claims do not contain any specific instructions as to what changed to make to the polymerization in light of the acquisition and/or prediction. The feeding and polymerization steps are themselves well-understood, routine, and conventional activities in the art, as are elastomers. Further, the abstract idea of predicting properties of an elastomer polymerization are known in the art (see, for example, Jin et al. “Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process” Advances in Polymer Technology 2020, 26 March, pages 1-14).
As seen in the flow chart in MPEP 2106, the answer to step 1, is yes, the claim is directed to a machine (a generic computer device). In step 2A, the answer is yes, the claim is directed towards an abstract idea. In step 2B, the answer is no, the claim does not recite more than the judicial exception. Thus, the claim is not eligible subject matter under 35 U.S.C. 101.
In summary, the claim is directed to a device (generic computer device) that includes a judicial exception, which themselves are known in the art. The claims do not add any limitations which integrate the judicial exception into a practical application.
Claim Rejections - 35 USC § 102
Claim(s) 1, 3-6 and 14-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jin et al. “Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process” Advances in Polymer Technology 2020, 26 March, pages 1-14 (herein Jin).
As to claims 1 and 4-6, Jin discloses (abstract; p. 7, col. 1, par. 1 - p. 8, col. 2, par. 2) the use of a trained "soft sensor" model to predict Mooney viscosity of an elastomer during the manufacturing process. The inputs to the model are different process variables. More formally and using the wording of claim 1, D1 discloses: A prediction device that, in manufacturing of an elastomer, predicts a performance value representing performance of the elastomer in a polymerization tank at a predetermined timing after a raw material is fed and before the manufacturing is terminated (p. 7, col. 1, par. 1), comprising: an acquisition unit that acquires, as a prediction observation value, an observation value observed, in current manufacturing of the elastomer, as a value related to the manufacturing of the elastomer (p. 8, col. 2, pars. 1-2: "The process variables used for soft sensor modeling include temperature in the mixer chamber, motor power, ram pressure, motor speed, and energy” and “delayed and nondelayed variables are obtained as potential input variables") and a prediction unit that predicts the performance value of the elastomer being currently manufactured at the predetermined timing, from the prediction observation value acquired by the acquisition unit, by using a relationship between an observation value acquired in past manufacturing of the same type of elastomer as the elastomer, and a performance value of the elastomer at the predetermined timing in the past manufacturing (p. 8, col. 2, par. 2: “batches are selected from three internal mixers and are further divided into three sets: 822 batches as the training set", the Mooney viscosity is chosen as the input variable). The performance value is any of a Mooney viscosity, a molecular mass, a specific gravity, a hardness, an elongation, a minimum torque, a maximum torque, an induction time to start of curing, an optimal curing time, a tensile strength, a 100% tensile stress, a glass transition point, an initial pyrolysis temperature, and a compression set rate (abstract). Also see p. 5, col. 1, last paragraph and p. 8, col. 2, paragraph 2.
A trained model trained through machine learning is used. See abstract and p. 1-2. Further, paragraph 2 discloses that observations at various time instants are used to predict the value.
As to claim 14, the samples are collected from “all potential input variables at different time instants can be obtained for predicting the final quality variable.” See p. 3, col. 2, par. 2. Also see p. 7, col. 1, par. 1. Further, p. 8, col. 2, par. 2: “batches are selected from three internal mixers and are further divided into three sets: 822 batches as the training set", the Mooney viscosity is chosen as the input variable). Therefore, it is clear from Jin that the selection of a sample used to predicted can be obtained at any time instant (time period after manufacturing starts).
As to claims 15 and 16, as elucidated for paragraph 14 above, the final quality variable is predicted from various time variables. See p. 3, col. 2, para 3 and p. 7, col. 1, par. 1. Further, the reaction is stopped when the desired Mooney Viscosity is achieved.
Claim Rejections - 35 USC § 103
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.
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. “Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process” Advances in Polymer Technology 2020, 26 March, pages 1-14 (herein Jin).
The discussion with respect to Jin set-forth above is incorporated herein by reference.
As to claims 15-16, as elucidated above, Jin discloses that samples are collected from “all potential input variables at different time instants can be obtained for predicting the final quality variable.” See p. 3, col. 2, par. 2. Also see p. 7, col. 1, par. 1. Further, p. 8, col. 2, par. 2: “batches are selected from three internal mixers and are further divided into three sets: 822 batches as the training set", the Mooney viscosity is chosen as the input variable). Further, Jin generally embraces predicting the Mooney Viscosity at various times. Moreover, as seen in figure 3 the system is automated and the abstract describes the desired control of the manufacturing. Further, the deviced outputs a prediction (signal). See figure 2. Therefore, it would have been obvious at the time of the invention to terminate the manufacturing the elastomer using an output signal to an operation unit to stop the polymerization when the Mooney Viscosity is at the desired value because Jin ultimately is measuring the Mooney Viscosity in order to yield the desired MV and one would want to fully automate the system to save on steps.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Applicant argues that incorporating claims 2 and 3 demonstrate SME features. Applicant argues that Jin does not disclose inputting variables and that it is an improvement over the art. Applicant argues that there are a number of steps to acquire the polymer and it is a concrete process.
In response, the examiner disagrees. Jin is appropriate for the reasons given below. Further, as shown in the SME flowchart in MPEP 2106, prior art rejections are not required for the 101 rejection. As elucidated in the 101 rejection, all steps may be performed in the head and all mentions of a computer are generic.
Note that claims 15 and 16 have concrete steps and are not rejected in the 101 rejections.
Applicant also argues that using a trained model that has learned, through machine learning, is a computer implemented aspect of the claimed invention and cannot be performed in the mind.
In response, the reference to machine learning, etc. are generic machine learning and generic computer terms that are eligible. This is similar to Recentive Analytics, INC. v. Fox Corp., No. 23-2437 (Fed. Cir. 2025), wherein it was stated that the use of generic machine learning technology in a new environment does not make the patents eligible.
Applicant argues that the prediction device of claim 1 limits an elapsed time since the start of manufacturing and that Jin does not disclose the input variables are elapsed time.
In response, the examiner disagrees. Jin, as a whole, teaches utilizing measurements at various interviews (time, input) to predict the Mooney viscosity (output). Implicit in this, is one would use the trained machine to predict the Mooney viscosity at predetermined times to then predict when to stop the manufacturing. In other words, one would recognize “all potential input variables at different time instants can be obtained for predicting the final quality variable” (see p. 3, col. 2, par. 2. Also see p. 7, col. 1, par. 1) to be a training conditions, to use to test the and predict the Mooney Viscosity at various times.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK S KAUCHER whose telephone number is (571)270-7340. The examiner can normally be reached M-F 8-6 PM EST.
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/MARK S KAUCHER/Primary Examiner, Art Unit 1764