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 .
Response to Arguments
Applicant's arguments filed 3/22/2026 have been fully considered but they are not persuasive.
Applicant argues about the definition of the undefined variables in claim 16. Remarks 6. Applicant’s citations to what different variables “can be described as” (Spec. 54) is not definitive enough to put a person on notice about what is definitively included in the scope of the claim.
Applicant argues that Nakagawa does not teach normalization where, “the physical properties of a possible recipe are referred to those common to the various experimental sessions during the normalization procedure.” Remarks 7 citing spec. 42. This is not what applicant claimed. Even if applicant did claim this, this argued element is what Nakagawa’s abstract teaches, “[p]rior to the prediction by the prediction module, a pretreatment is performed to classify the names of the constituent raw materials into one of the identification names used as the input data for learning…” Normalizing names to be consistent through the dataset by classifying the names into a set of ID names, before using the input data as learning data, creates a connection called a shared class and the connection of the old name to the shared class. Normalizing in this way also reduces variability of different names assigned to the same raw materials, by combining disparate names into one class.
Applicant argues that Nakagawa fails to supply “pre-processing the normalized data by data mining to eliminate aberrant data and to add new fictitious ingredients relating to specific categories of actual ingredients.” Remarks 9. Adding fictitious ingredients is not a term of art; and it is taught by Nakagawa’s normalizing step (abstract and p. 5) – where different types of rubber are classified into one class. The new fictitious class is the new fictitious ingredient relating to the specific categories of actual ingredients. The pre-processing of normalized to data to eliminate aberrant data and add new fictitious ingredients relating to categories of actual ingredients is taught by Zhao sec. 4 p. 7 “a contaminated train dataset Xtrain and a clean test dataset Xtest. We perform dimensionality reduction on the contaminated data Xtrain, and obtain projection vectors. Then we calculate the reconstruction error on the test data… A small reconstruction error means that the projection vectors obtained by dimensionality reduction methods contain more favorable information about true data and less negative information about outliers.” Containing less negative information about outliers is eliminating aberrant data. Zhao sec. 2 p. 2 “orthogonal projection of the data onto a lower dimensional linear space called the principal subspace, where the variance of the projected data is maximized…” The projection is the added fictitious data.
Claim Rejections - 35 USC § 112
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.
Claims 10-18 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 10 recites “iterative procedure to perform connections between and to reduce variability of the data…” Claim 10. It is unclear what it means to “perform connections”. Claim 13 includes this language also.
Claim 16 is 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. None of the variables (y, T, R, I, J, yi, yj, -etc.) have been introduced in the claim, so a person would have to guess what they are. The “i” clause is missing a closing parenthesis. The element “the trend of y versus temperature” was not introduced. The element “the properties” to be predicted was not introduced previously.
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.
Claims 10-14 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over JP2020038493A to Nakagawa and Self-Paced Probabilistic Principal Component Analysis for Data with Outliers by Zhao et al.
Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over JP2020038493A to Nakagawa, Self-Paced Probabilistic Principal Component Analysis for Data with Outliers by Zhao et al and US 20200019857 A1 to Wang et al.
Nakagawa teaches claim 10. A computer-implemented method for predicting dynamic properties of a composite to be tested for the production of tire tread compounds, (Nakagawa title “Physical property data prediction method and physical property data prediction device”) the method comprising:
providing a raw data database comprising a dataset of recipes for already existing composites and of corresponding known dynamic properties; (Nakagawa abs “The physical property data is obtained by using physical property data relating to a plurality of vulcanized rubber compositions, an identification name of a raw material in the vulcanized rubber composition, a blending ratio of the raw materials, and information on the processing conditions.”)
normalizing data contained in the raw data database according to an iterative procedure to perform connections between and to reduce variability of the data; (Nakagawa abs “Prior to the prediction by the prediction module, a pretreatment is performed to classify the names of the constituent raw materials into one of the identification names used as the input data for learning based on the material properties of the constituent raw materials.” Nakagawa p. 5 “It is preferable to classify the names of the constituent raw materials into one of the identification names of the raw materials used for the learning data. For example, the diene rubber of synthetic rubber includes styrene butadiene rubber, isoprene rubber, and butadiene rubber. For example, styrene rubber and isoprene rubber include different bonding modes such as cis, trans, vinyl, and the like.” Normalizing is classifying the names into “identification names”. It’s iterative because it goes through all the names. The new connection is the connection between the class and the constituent raw materials.)
(This part of “pre-processing” step is one of the steps in Nakagawa’s p. 5 normalizing step, “It is preferable to classify the names of the constituent raw materials into one of the identification names of the raw materials used for the learning data. For example, the diene rubber of synthetic rubber includes styrene butadiene rubber, isoprene rubber, and butadiene rubber. For example, styrene rubber and isoprene rubber include different bonding modes such as cis, trans, vinyl, and the like.” Adding the weirdly names compounds to the classes of known compounds is Applicant’s adding “new fictitious ingredients” to specific categories.)
training an algorithm based upon automatic learning via the pre-processed data; (Nakagawa abs “identification names used as the input data for learning based on the material properties of the constituent raw materials.” Learning is training.)
applying the trained algorithm to a set of experimental data representative of the recipe of the composite to be tested, for prediction of dynamic properties of the composite to be tested. (Nakagawa p. 4 “The prediction module predicts physical property data of the vulcanized rubber composition to be predicted using the input identification name, compounding ratio, and processing conditions (step S24).” Physical property data is the dynamic properties.)
Nakagawa doesn’t teach eliminating aberrant data and adding new ingredients.
However, Zhao teaches pre-processing the normalized data by data mining to eliminate aberrant data and to add new fictitious ingredients relating to specific categories of actual ingredients. (Zhao sec. 4 p. 7 “a contaminated train dataset Xtrain and a clean test dataset Xtest. We perform dimensionality reduction on the contaminated data Xtrain, and obtain projection vectors. Then we calculate the reconstruction error on the test data… A small reconstruction error means that the projection vectors obtained by dimensionality reduction methods contain more favorable information about true data and less negative information about outliers.” Containing less negative information about outliers is eliminating aberrant data. Zhao sec. 2 p. 2 “orthogonal projection of the data onto a lower dimensional linear space called the principal subspace, where the variance of the projected data is maximized…” The projection is the added fictitious data.)
Nakagawa, Zhao and the claims are all machine learning. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to add PCA “to reduce or eliminate the impact of outliers.” Zhao abs.
Nakagawa teaches claim 11. The method of claim 10, wherein the dynamic properties are a loss module and a storage module of the composite to be tested. (Nakagawa p. 4 “Physical properties data of the vulcanized rubber composition include, for example, rubber elasticity, tan δ…” The spec. para 36 says “tan δ is the loss module…” and “E’ is the storage module… i.e., the elastic portion…”)
Nakagawa teaches claim 12. The method of claim 10, wherein the raw data database comprises data representative of a plurality of experimental measurement sessions. (Nakagawa p. 4 “A plurality of types of material property data are measured, and raw materials can be classified using the measured material property data.”)
Nakagawa teaches claim 13. The method of claim 12, wherein the normalization step provides for an iterative normalization based, at each iteration, upon a recipe that is most repeated in the dataset, to perform connections between and to reduce the variability of the experimental sessions. (Nakagawa’s p. 5 normalizing step, “It is preferable to classify the names of the constituent raw materials into one of the identification names of the raw materials used for the learning data. For example, the diene rubber of synthetic rubber includes styrene butadiene rubber, isoprene rubber, and butadiene rubber.”)
Nakagawa teaches claim 14. The method of claim 10, wherein the iterative normalization step is performed by dividing each of the dynamic properties to be predicted by a corresponding property of iteratively selected composites used as a reference recipe, wherein the reference recipe constitutes the connection between various experimental sessions and enables comparison of the various experimental sessions. (Nakagawa’s p. 5 normalizing step, “It is preferable to classify the names of the constituent raw materials into one of the identification names of the raw materials used for the learning data. For example, the diene rubber of synthetic rubber includes styrene butadiene rubber, isoprene rubber, and butadiene rubber.” The reference recipe in Nakagawa is “diene rubber of synthetic rubber”.)
Nakagawa teaches claim 15. The method of claim 10, wherein (Nakagawa abs. “” )
Nakagawa doesn’t teach physical constraints from a cost function.
However, Wang teaches a weight/penalty logic, applied to calculation of a cost function to be minimized during the training step, imposes physical constraints upon the dynamic properties to be predicted. (Wang abs “A NN is trained using a cost function that places constraints on weights in the NN.” Constraining the weights constrains the output, which is properties to be predicted.)
Wang, Nakagawa and the claims are all directed to machine learning. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to constrain outputs with a cost function because the constraints can act as a key to verify that the NN is “verifiable as the trained NN.” Wang abs. This allows for sharing of verified NN. Id.
Nakagawa teaches claim 17. The method of claim 10, wherein the pre-processing step comprises application of data mining algorithms. (Zhao sec. 4 p. 7 “a contaminated train dataset Xtrain and a clean test dataset Xtest. We perform dimensionality reduction on the contaminated data Xtrain, and obtain projection vectors. Then we calculate the reconstruction error on the test data… A small reconstruction error means that the projection vectors obtained by dimensionality reduction methods contain more favorable information about true data and less negative information about outliers.”)
Nakagawa teaches claim 18. The method of claim 17, wherein the data mining algorithms remove anomalous data and/or execute a principal component analysis (PCA) to add new fictitious ingredients relating to specific categories of actual ingredients. (Zhao title “Probabilistic Principal Component Analysis for Data with Outliers…” Zhao sec. 4 p. 7 “a contaminated train dataset Xtrain and a clean test dataset Xtest. We perform dimensionality reduction on the contaminated data Xtrain, and obtain projection vectors. Then we calculate the reconstruction error on the test data… A small reconstruction error means that the projection vectors obtained by dimensionality reduction methods contain more favorable information about true data and less negative information about outliers.” Containing less negative information about outliers is eliminating aberrant data. Zhao sec. 2 p. 2 “orthogonal projection of the data onto a lower dimensional linear space called the principal subspace, where the variance of the projected data is maximized…” The projection is the added fictitious data.)
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.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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