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 .
This office action is in response to amendments filed on April 15, 2024.
Claim 11 has been amended.
Claim 12 as been canceled.
Claims 1-11 are pending.
Information Disclosure Statement
As required by M.P.E.P. 609(C), the applicant’s submissions of the Information Disclosure Statements dated April 11, 2024, January 24, 2025, and February 24, 2025 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609, a copy of the PTOL-1449 initialed and dated by the examiner is attached to the office action.
Claim Objections
Claim 2 is objected to because of the following informalities: The term “value” is misspelled as “vale”. Appropriate correction is required.
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.
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 2-5, 7-8, and 10 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.
Regarding claim 2, the claim recites the limitation "the range" in line 4 of the claim. There is insufficient antecedent basis for this limitation in the claim. For the purposes of examination the limitation is interpreted as “a range”.
Regarding claim 3, the claim recites the limitations "the output pressure" in line 2 of the claim, "the output flow" in line 2 of the claim, "the shooting distance" in line 3 of the claim, "the gun head rotation speed" in lines 3-4 of the claim, "the nozzle size" in line 4 of the claim, and "the work table rotation speed” in lines 4-5 of the claim. There is insufficient antecedent basis for these limitations in the claim. For the purposes of examination the limitations are interpreted as "an output pressure", "an output flow", "a shooting distance", "a gun head rotation speed", "a nozzle size", and "a work table rotation speed”.
Regarding claim 4, the claim depends on claim 3 and does not address the antecedent basis issue. As such, claim 4 is similarly rejected under the same rationale.
Regarding claim 5, the claim recites the limitation "the regression analysis method" in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim. For the purposes of examination the limitation is interpreted as “a regression analysis method”.
Regarding claim 7, the claim recites the limitations "the radius" in line 2 of the claim, "the nozzle size" in line 3 of the claim, and “the mesh number” in line 3 of the claim. There is insufficient antecedent basis for these limitations in the claim. For the purposes of examination the limitations are interpreted as "a radius", "a nozzle size", and “a mesh number”.
Regarding claim 8, the claim depends on claim 7 and does not address the antecedent basis issue. As such, claim 8 is similarly rejected under the same rationale.
Regarding claim 10, the claim recites the limitations "the experimental parameters" and “the rubber crumb chemical activity” in line 2 of the claim. There is insufficient antecedent basis for these limitations in the claim. For the purposes of examination the limitation is interpreted as “experimental parameters” and “rubber crumb chemical activity”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable Tan (CN117162335A) in further view of Behrooz et al. (US11915419B1) and Sato (US20250264871A1).
Regarding claim 1, Tan teaches a method for generating processing parameters of tires to achieve the desired properties of rubber crumb, adapted to establish in a software program and executed in the following steps after read by a computer (the present invention adopts the following technical solution: a vulcanized rubber ultra-fine powder production line based on the water explosion method, including ... intelligent control system ... The present invention outputs ultra-high-pressure water whose pressure and flow rate can be intelligently adjusted … The powder particle size and the pressure and flow rate of the jet form a collaborative algorithm to achieve intelligent control of the rotation speed, pressure, flow rate, and charging amount corresponding to the target particle size powder product)([09], [036], and [038]; processing parameters (e.g., flow rate) are generated based on target properties of rubber crumb (i.e., powder)):
establishing a predictive processing model through a waterjet tire destructing processing module (an intelligent control model map library is formed through an experimental data model based on comprehensive balance indicators of high-value product granularity and supply based on market demand )([037]; a predictive processing (i.e., control) model is created), comprising the following steps:
… performing data analysis on said waterjet data … establishing said predictive processing model according to said … waterjet data (Achieve algorithmic intelligent control of particle size and yield targets based on a mathematical model library formed by experimental collection)([035]; experimental data is analyzed to create the control model); and
outputting a processing suggestion parameter through a waterjet technology parameter optimization module and said predictive processing model (The entire production line of the present invention adopts the intelligent control mode of intelligent controller ... combines the three output indicators corresponding to particle size, output and energy consumption cost with the rotation speed control ... and the water pressure flow rate ... The data model in the system issues various automatic control instructions to realize intelligent control of the entire production line)([037]; suggestion parameters (i.e., controls) are outputted based on the control model and optimization of energy cost).
Although Tan discloses of inputting waterjet data (Achieve algorithmic intelligent control of particle size and yield targets based on a mathematical model library formed by experimental collection)([035]). Tan differs from the claim in that Tan fails to teach inputting data from a database to perform data normalization such that the model is established according to the normalized data.
However, inputting data from a database to perform data normalization such that a model is established according to the normalized data is taught by Behrooz (At block 308, the process 300 involves obtaining another set of training samples. This second set of training samples is for training a second prediction model and can also be stored in the datastore 110 ... At block 310, the process 300 involves applying the normalization model 114 trained in block 304 to the second set of training samples ... to generate normalized training samples ... At block 312, the process 300 involves training the second prediction model using the normalized training samples generated at block 310 … the technology presented herein applies to any type of input data, such as time-series data ... or structured data)(column 11 lines 22-39 and column 13 lines 15-17).
The examiner notes Tan and Behrooz teach using models for predictive processing. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Tan to include the inputting of Behrooz such that data is inputted from a database to perform data normalization such that the model is established according to the normalized data. One would be motivated to make such a combination to provide the advantage of reducing computational complexity and memory usage of a training process (Behrooz; column 5 lines 11-15).
Although Tan-Behrooz disclose training a predictive model (Behrooz - Machine learning algorithms build and train the machine learning models based on training data that include training inputs and outputs corresponding to the training inputs)(column 1 lines 26-28). Tan-Behrooz differs from the claim in that Tan-Behrooz fails to teach training the model to predict chemical activity level.
However, training a model to predict chemical activity level is taught by Sato (it is possible to control the state of the first processed product obtained by the first process, which is a chemical reaction ... The second model may be, for example, a machine learning model created by a machine learning ... may be a mathematical model created based on ... chemical laws ... During operation of the continuous processes ... the control device 30 ... monitors whether the acquired yield of the substance C matches the target yield of the substance C ... the second model inputs the yield of substance C produced by the first process estimated by the first model (78%) and the parameters of the current condition list of the second process, and outputs the modified value of the parameter)([0010], [0050], [0075], and [0076]).
The examiner notes Tan, Behrooz, and Sato teach using models for predictive processing. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Tan-Behrooz to include the training of Sato such that a model to predict chemical activity level is trained. One would be motivated to make such a combination to provide the advantage of ensuring a yield is within range ([0076]; Sato).
Regarding claim 9, Tan-Behrooz-Sato teach the method according to claim 1, further comprising the following step: adjusting said prediction processing model through a waterjet tire destructing model fine-tuning module (Behrooz - Machine learning algorithms build and train the machine learning models based on training data that include training inputs and outputs corresponding to the training inputs)(column 1 lines 26-28; in machine learning a model is continually adjusted).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable Tan, Behrooz, Sato, and in further view of “How to adjust scaled scikit-learn Logicistic Regression coeffs to score a non-scaled dataset?” (publicly accessible August 8, 2016); hereinafter referred to as Dataset.
Regarding claim 5, Tan-Behrooz-Sato teach the method as applied above, wherein data is normalized (Behrooz - The normalization model is configured to normalize the input data to the prediction model to remove variations in the input data irrelevant to the prediction task and highlight features utilized by the prediction model)(column 3 lines 51-55). Tan-Behrooz-Sato differs from the claim in that Tan-Behrooz-Sato fails to teach using regression analysis to normalize data.
However, using regression analysis to normalize data is taught by Dataset (I am currently using Scikit-Learn's LogisticRegression to build a model ... You have to divide by the scaling you applied to normalise the feature ... Here's an example using pandas and sklearn LinearRegression ... We now normalise all our variables ... We can do the regression again on this normalised data)(pages 1-3).
The examiner notes Tan, Behrooz, Sato, and Dataset teach processing data. As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Tan-Behrooz-Sato to include the using of Dataset such that a regression analysis is used to normalize data. One would be motivated to make such a combination to provide the advantage of improving understanding of relationships between variables.
Allowable Subject Matter
Claims 6 and 11 are allowed.
Claims 2-4, 7-8, and 10 would be allowable if rewritten to overcome the 35 U.S.C. 112 and in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider the reference fully when responding to this action. The document cited therein and enumerated below teaches a method and apparatus for processing a tire to create rubber crumbs.
US5115983
US5794861
US6601788B2
US20020096583A1
US20110163190A1
US20110168818A1
US20120223167A1
WO2003057442A1
WO2013170357A1
KR102694280B1
CN118634973B
CN1923486A
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Yongjia Pan whose telephone number is (571)270-1177. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/YONGJIA PAN/Primary Examiner, Art Unit 2118
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