DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Election/Restrictions
Applicant's election with traverse of Group II (claims 7-14) in the reply filed on 11/11/25 is acknowledged. The traversal is on the ground(s) that the two groups of inventions include common special technical features that are not disclosed in the prior art. This is not found persuasive because the features listed in the remarks that applicant argues are common special technical features are not in the independent claims, and thus not common between all the claims of each group. The common features, shared between the independent claims, is known as discussed in the previous Office Action. Furthermore, the additional features of the dependent claims are known in the prior art, as discussed in the rejections below.
The requirement is still deemed proper and is therefore made FINAL.
Claim Objections
Claim 7 is objected to because of the following informalities: in line 1, “m a foundry” appears to be a typographical error of --in a foundry--. Appropriate correction is required.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: paragraph [0049], other engines 220. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: fig 4C, reference 406, figure 4D reference 410. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 11-12 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 11, in line 4, the claim ends with “; and” and therefore it is not clear whether the claim is intending to introduce another limitation or is intended to be ended. Note that each claim must end with a period. See MPEP 608.01 (m).
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) 7-8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary (WO 2014/132269 A2) in view of Ogura et al (US 2018/0056375, previously cited) and Eirich (US 4,569,025).
Regarding claim 7, Chowdhary teaches a system for optimization of compactibility of sand in a foundry (abstract, optimization of sand, p.13, parameters include compactability), the system comprising:
a compactibility controller (p.19, fig 2, system 200);
testing a sample of sand to obtain compactibility data associated with the sample (p.13, storing data pertaining to a set of primary sand parameters, including compactability index);
providing a supporting data related to at least one parameter of the sand, additives and environment of the operation unit from where the sand sample is collected (p.13-14, storing data pertaining to primary and secondary set data, primary set data includes green compression strength, moisture content, active clay, inert fines content, LOI, permeability index, wet tensile strength, volatile matter content, grain fineness number, oolitics content, pH value, secondary set parameters include return sand temperature, core sand infiltration, green tensile strength, flowability, mouldability, sieve distribution, grain shape, bentonite, gelling time, swelling capacity, etc);
one or more processors (p.19, processor module 250 to store and retrieve data for the purpose of processing and analysis) communicably coupled with the compactibility controller, the compactibility tester and the one or more sensors, and configured to:
predict, based on the sand compactibility data obtained from the compactibility tester and the supporting data, compactibility of the sand at one or more of the different stages of the sand molding and casting operations of the foundry (p.19, correlates the parameter values, primary set of sand parameters, secondary set of sand parameters, rejection times, for each instance data is input, p.20 pattern forming 260 to determine at least a pattern based on the correlations of the values, parameters, rejections, p.20, prescriptive-predictive module 270 generates a predictive/prescriptive solution); and
perform, at the compactibility controller, when the estimated compactibility is found to be unacceptable, an adjustment to compactibility set point to achieve a desired compactibility at one or more of the different stages of the sand molding and casting operations of the foundry (p.33, significant parameter predictor module suggests the input parameter values that can be considered, p.34, parameter predictor module initiates internal computations of the parameters and vales contributing to rejection, displays optimal values predicted, p.48, foundry user to act on predictions to achieve process consistency).
Chowdhary teaches of storing data pertaining to primary and secondary sand parameters, but is not specific to how said data is obtained, such as through the use of a compactibility tester to obtain the sand parameter including compactibility and using sensors to obtain the secondary set of sand and casting parameters.
Ogura et al teaches managing a casting process based on measured properties of molding sand (abstract). The method is based on properties of the molding sand, comprising steps of measuring the properties of the molding sand, determining if the measured properties comply with predetermined properties, and if found to be unacceptable, adjusting the process to use a molding sand whose properties do comply (abstract). Ogura et al teaches measuring the properties of the molding sand by a device 30 (paragraph [0027]), including measuring the properties of compressive strength, tensile strength, shear strength, water content, permeability, compactability, and temperature (paragraph [0033]).
It would have been obvious to one of ordinary skill in the art to combine the teachings of Chowdhary and Ogura et al to arrive at the claimed invention, as the features of measuring primary parameters such as compactibility and secondary parameters, using testers/sensors are well known in the art. All the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would yield nothing more than predictable results to one of ordinary skill in the art. KSR, 550 U.S. at 416, 82 USPQ2d at 1395. MPEP 2143(I)(A).
The combination above suggests sampling the sand before molding, but is quiet to sampling from an operation unit associated with different stages of sand molding and casting operations of the foundry.
Eirich teaches a method of preparing foundry sand by measuring moisture and compressibility (abstract), including measuring the actual compressibility in a first stage early in the processing of the foundry sand, adding moisture to bring the foundry sand to a predetermined value in accordance with the assumed set of characteristics, and making a second measurement of the compressability in a second stage as a countercheck near the conclusion of the processing (abstract). Eirich teaches that this ascertains the causes for changing properties of the sand, and that the difference in measurement being found can be used as a correcting factor to adjust the first measuring stage (col 2 lines 1-25).
It would have been obvious to one of ordinary skill in the art to modify the combination to include sampling during additional stages, as taught in Eirich, to provide additional data points that can ascertain the causes for the change in sand properties, so that the initial measuring stage can be calibrated.
Regarding claim 8, the combination teaches wherein the one or more processors are configured to evaluate, at the processor, the compactibility data obtained from the compactibility tester and the predicted compactibility to determine one or more attributes associated with optimization of the compactibility of the sand, and wherein the adjustment to the compactibility set point is carried out based on the determined one or more attributes; wherein the one or more attributes associated with optimization of the compactibility of the sand includes at least one of amount of water, sand mixing time and amount of additives (Ogura, paragraph [0059], properties that are measured and used for determination such as compactability, can be adjusted by changing the amount of water or bentonite added to the sand muller).
Regarding claim 10, the combination teaches wherein the supporting data related to the at least one parameter of the sand, additives and environment of the operation unit is selected from at least one of green compression strength of sand, compactibility index, moisture content, active clay, inert fines content, loss on ignition percent, permeability index, wet tensile strength, volatile matter content, grain fineness number, American Foundry Society grain fineness number, oolitics content, pH value of the sand, humidity of the operation unit, temperature of the operation unit, recycled sand temperature, recycled sand moisture content, quality of sand additives and quantity of sand additives (Chowdhary, p.13-14, storing data pertaining to primary and secondary set data, primary set data includes green compression strength, moisture content, active clay, inert fines content, LOI, permeability index, wet tensile strength, volatile matter content, grain fineness number, oolitics content, pH value, secondary set parameters include return sand temperature, core sand infiltration, green tensile strength, flowability, mouldability, sieve distribution, grain shape, bentonite, gelling time, swelling capacity, etc).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary as modified by Ogura and Eirich as applied to claim 8 above, and further in view of Karunakar et al (“Controlling green sand mould properties using artificial neural networks and genetic algorithms - A comparison”).
Regarding claim 9, the combination is quiet to wherein one or more processors are configured to evaluate the compactibility data and the predicted compactibility using genetic algorithm model.
Karunakar et al teaches that optimum formulations of the green sand mixture has remained as a critical problem for the foundrymen for several years (abstract). Karunakar et al teaches measuring mould properties and feedback to an artificial neural network (abstract, first investigation) and a genetic algorithm (abstract, second investigation), recognizing that the genetic algorithm was found to produce considerably accurate results compared to the artificial neural network (p.65, Conclusion). Karunakar et al further teaches that properties like flowability and compactibility can also be incorporated (p.65, Conclusion).
It would have been obvious to one of ordinary skill in the art to modify the combination such that the process evaluates the data using a genetic algorithm model, as Karunakar et al teaches tthat the genetic algorithm model was found to produce considerably accurate results compared to the artificial neural network, and can incorporate properties such as compactibility (Karunakar et al, p.65 Conclusions).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary as modified by Ogura and Eirich as applied to claim 7 above, and further in view of Noone et al (US 2020/0166909).
Regarding claim 11, the combination teaches wherein the one or more processors, while predicting the compactibility of the sand, are configured to use a machine learning model (Chowdhary, p.58, self-learning system that automatically learns and updates itself) that implements a linear regression methodology (Chowdhary, p.27), but is quiet to the machine learning model is operable using any of a support vector regression model, ridge regression and a lasso regression.
Noone et al teaches machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of manufacturing processes (abstract). Noone et al teaches that the invention is applicable to a wide variety of manufacturing process, including casting processes (paragraph [0005], [0076]) and molding processes (paragraph [0005], [0092]). Noone et al teaches the machine learning algorithms for adaptive process control may comprise a supervised learning algorithm (paragraph [0238-0239]) such as support vector machines that are used for classification and regression analysis of object defect classification data or manufacturing process control (paragraph [0246]).
It would have been obvious to one of ordinary skill in the art to modify the combination such that the machine learning model is operable using a support vector regression model, as Noone et al teaches that the support vector regression is known (paragraph [0246]) and can be applicable to casting and molding processes (paragraph [0005], [0076], [0092]).
All the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would yield nothing more than predictable results to one of ordinary skill in the art. KSR, 550 U.S. at 416, 82 USPQ2d at 1395. MPEP 2143(I)(A).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary as modified by Ogura, Eirich, and Noone et al as applied to claim 11 above, and further in view of David (US 2016/0148850).
Regarding claim 12, the combination teaches a gaussian process optimization (Noone et al, paragraph [0005], Gaussian process regression algorithm), but is quiet to an internal k-fold cross validation.
However, use of k-fold cross validation is well known. David teaches techniques for measuring and/or compensating for process variations in manufacturing processes (abstract). David teaches the use of machine learning algorithms, data mining, and predictive analytics make the handling of large data sets manageable (paragraph [0130]). David teaches that the training dataset can be partitioned into training, testing, and validation portions to ensure a robust model that is not over-fit or over-biased (paragraph [0113]), and that techniques such as k-fold cross validation can be employed during the model building phase to ensure that the model is not over-fit to any given training set (paragraph [0113]).
It would have been obvious to one of ordinary skill in the art to modify the combination to include k-fold cross validation, as David teaches that k-fold cross validation is a known technique applied during the model building phase to ensure that the model is not over-fit to any given training set (David, paragraph [0113]).
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chowdhary as modified by Ogura and Eirich as applied to claim 7 above, and further in view of Klein et al (US 2008/0263096).
Regarding claim 13, the combination is quiet to wherein the sand compactibility data obtained from the compactibility tester by testing of the sample of sand from one or more operation units includes a timestamp, wherein the timestamp is related to time at which the sample is obtained from the operation unit or time at which the sand sample is tested.
However, obtaining timestamp data associated with a time of measurement by sensors is well known. Klein et al teaches a method and system for managing data quality (abstract). Klein et al teaches that data items may be ordered by timestamp values associated with the time of obtaining the data items from sensors (paragraph [0030], [0076]).
It would have been obvious to one of ordinary skill in the art to modify the combination to further include a timestamp, such as for measurements taken during the different stages of measurement as taught in Eirich, as timestamps are well known and that Klein et al teaches that data items can be grouped or ordered in association by their timestamps (paragraph [0030], [0076]).
Regarding claim 14, the combination teaches wherein the one or more processors, while predicting the compactibility of the sand, are configured to:
pre-process the compactibility data and the supporting data, to remove one or more missing attributes present therein (Chowdhary, p.23, data cleaning module to check for empty or null entries, excluding data point win which value is missing); and
align the compactibility data, and the supporting data, based on one or more timestamps present therein, to obtain collective datasets grouped based on similar timestamps (Klein, paragraph [0030], [0076], data items may be ordered by time stamp values associated with the time of obtaining the data items from sensors).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACKY YUEN whose telephone number is (571)270-5749. The examiner can normally be reached 9:30 - 6:00.
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/JACKY YUEN/
Examiner
Art Unit 1735
/KEITH WALKER/Supervisory Patent Examiner, Art Unit 1735