DETAILED ACTION
Notice of 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 27 March 2026 has been entered.
Status of Claims
Claim 1 is amended.
Claims 1-3 are pending.
Claims 1-3 are rejected (Non-Final Rejection).
Related Co-Pending Applications/Patents
Examiner again notifies Applicant that there is a commonly owned patent, U.S. Patent No. 12,094,102 B2 (hereinafter “the ‘102 patent”), that includes a claim 6 that includes a learning model based on mold quality conditions (e.g., defects) and sand information, and a co-pending, commonly owned U.S. patent application, No. 17/550,192, that includes a claim 2 that includes a learning model using a molding condition/property. Examiner will continue to consider double patenting and/or obvious double patenting issues during the prosecution of (e.g., after any claim amendments in) the current application.
Response to Arguments
Applicant’s arguments filed 27 March 2026, at Pages 5-7, with respect to the rejections under 35 U.S.C. § 103 have been fully considered. Specifically, Applicant argues WOWCZUK, alone or in combination with ASAOKA, fails to disclose “molding condition” as recited in claim 1. The arguments regarding the rejections under 35 U.S.C. § 103 challenge certain limitations. These limitations are newly added and were therefore not addressed in the previous rejection; therefore, the arguments are moot. The amendments are newly addressed by the new grounds of rejection under 35 U.S.C. § 103.
Claim Rejections - 35 U.S.C. § 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.
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.
Claims 1-3 are rejected under 35 U.S.C. § 103 as being unpatentable over ASAOKA et al. (U.S. Patent Application Publication No. 2022/0207700 A1), cited in PTO-892 mailed 30 October 2025, in view of SEKO et al. (U.S. Patent Application Publication No. 2018/0311723 A1).
Regarding claim 1, ASAOKA teaches a molding condition deriving device for deriving a mold molding condition (mold information management device, Para. [0009] of ASAOKA; See also), said molding condition deriving device comprising: at least one processor (line controller 6 is an example of a “mold information management device”, Para. [0022] of ASAOKA; See also processor 61 of line controller 6, FIG. 4 & Para. [0046] of ASAOKA); and a primary memory connected to the at least one processor (line controller 6 is an example of a “mold information management device”, Para. [0022] of ASAOKA; See also primary and secondary memories 62, 63 connected to processor 61 of line controller 6, FIG. 4 & Para. [0046] of ASAOKA), the at least one processor being configured to carry out: a collection step of collecting a molding condition of a mold, the molding condition being different than at least one molding condition (mold information may be obtained by adding at least part of the molten metal condition data or the alloy material input history data to the molding history data of the mold, Para. [0055] of ASAOKA; See also squeeze pressure, Para. [0055] of ASAOKA; See also molding history data include the weight of inputted sand, compression ratio, static pressure or squeeze pressure, squeeze time, pressure rising speed, squeeze stroke, mold thickness, aeration pressure, product surface shape, and molding time, in molding of a mold … examples of the molding history data may include data of compactability (CB), moisture, sand temperature, air permeability, and mold strength (pressure resistance) of the foundry sand with which the mold is molded … examples of the molten metal condition data include the weight, temperature, tapping furnace number or charge number, and material number of molten metal poured by the pouring machine 5, time of receipt of molten metal, a time from receipt of molten metal to start of pouring of the molten metal, time from start to end of pouring of molten metal, and the amount of an inputted inoculant. Further, examples of the molten metal condition data may include data regarding properties (including molten metal components, etc.) of molten metal obtained as a result of melting in a furnace … the molten metal condition data may also include, for example, data with regard to molten metal components after inoculation in a casting ladle … the alloy material input history data is, for example, the type, weight, and time at which an alloy material is put in the molten metal, Para. [0057] of ASAOKA; [squeeze pressure is interpreted as corresponding to a mold molding condition (based on Applicant’s original claim 1)]; See also FIGS. 5-7 of ASAOKA and corresponding description), a sand property condition that is a property of sand, which is a material of the mold ([weight of inputted sand and sand temperature (Para. [0057] of ASAOKA) are interpreted as a sand property condition]), and a quality condition of the mold (after it is determined that the identification mark engraved on the mold is readable, the mold information and the identification mark are associated with each other … this can reduce the rate of occurrence of defective casting products whose mold information cannot be traced due to unreadable identification mark, Para. [0062] of ASAOKA; [readability/defect of molded/cast product is interpreted as a quality condition of the mold]; See also mold strength/pressure resistance (Para. [0057] of ASAOKA), which is also interpreted as corresponding to a quality condition of the mold]); and a deriving step of using a learned model learned by a dataset-for-learning to derive the at least one molding condition from the molding condition of the mold, the sand property condition that is a property of sand, which is a material of the mold, and the quality condition of the mold (by associating the identification mark with the shift data, it is possible to trace the production condition etc. of the casting product from the identification mark engraved on the casting product, Para. [0056] of ASAOKA; See also learned model is generated in advance, by causing the machine learning device to read training data and to learn determination of whether identification marks are good or not, Para. [0069] of ASAOKA; See also FIGS. 5-7 of ASAOKA and corresponding description), the dataset-for-learning including the sand property condition, the molding condition, and the quality condition each for learning (the training data includes, for example, image data or profile data of identification marks engraved on molds, and “good” or “not good” results obtained by determination by operator's visual check of the image data or the profile data. Then, the machine learning device is caused to read, as a set, the image data or the profile data of the identification marks together with the “good” or “not good” results of that data, so that the learned model is generated, Para. [0063] of ASAOKA; See also use of the learned model which has been caused to learn (i) identification marks engraved on molds; and (ii) operator's determination results (or data of the identification marks), Para. [0069] of ASAOKA; See also at least squeeze pressure noted above in Para. [0057] of ASAOKA; See also FIGS. 5-7 of ASAOKA and corresponding description), wherein the at least one processor controls the molding machine with use of the molding condition including the at least one molding condition having been outputted from the learned model (molding machine 2 is a device for producing molds M, Para. [0023] of ASAOKA; See also if the result of determining whether the identification mark is good or not in step M12 is “not good” (step M12: NO), the management flow proceeds to step M14 … in step M14, the processor 61 transmits, to the pouring machine 5, an instruction not to pour molten metal into that mold, and the, Para. [0059] of ASAOKA).
Although ASAOKA discloses a molding condition of squeeze pressure, squeeze time and squeeze stroke (Para. [0057] of ASAOKA), ASAOKA does not appear to explicitly disclose the molding condition including at least one of an initial position of a squeeze board, an amount of parting agent applied to a pattern plate, and timing for operating a leveling frame of a molding machine.
SEKO, however, is in the field of filling sand into a mold (for manufacturing by casting) (Para. [0002] of SEKO) and teaches the molding condition including at least one of an initial position of a squeeze board, an amount of parting agent applied to a pattern plate, and timing for operating a leveling frame of a molding machine ([Examiner notes: “at least one of” is disjunctive and interpreted as requiring only one of the following limitations; if Applicant would like to make clear each “condition” is required, “at least one of” could be removed]; SEKO teaches the small diameter plate-shaped portion 30 of the pressing member 27 advances into the kneading sand tank 2, and moves toward the kneaded sand S in the kneading sand tank 2 while sliding inside the kneading sand tank 2 … from the time at which the pressing member 27 starts to move, the moving position of the pressing member 27 is constantly detected by the position detection sensor 5, and the detection result is transmitted to both the control portion 7 and the evaluation portion 8, Para. [0038] of SEKO; [the plate-shaped pressing member is interpreted as corresponding to a squeeze board, and the detected position when starts to move is interpreted as an evaluated molding condition corresponding to an initial position]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention (and the ordinarily-skilled artisan would have been motivated) to modify the mold condition-based learning model of ASAOKA that includes “squeeze” related parameters with the squeeze board condition of SEKO for the purpose of identifying conditions (i.e., the optimum moving speed, position, and pressing force of the piston) to better perform filling, (Para. [0012] of SEKO). See also reducing a rate of defective products (Para. [0007] of ASAOKA).
Regarding claim 2, ASAOKA as modified by SEKO teaches the molding condition deriving device as set forth in claim 1, wherein the learned model is (i) a non-linear function expression specified by a non-linear regression algorithm in which the dataset-for-learning is used or (ii) a learned neural network model that has been constructed by supervised learning in which the dataset-for-learning is used (machine learning device can employ an algorithm of, for example, a model of a neural network such as a convolutional neural network or a recursive neural network, a regression model such as a linear regression model, or a tree model such as a regression tree model, Para. [0065] of ASAOKA; [a regression tree model is interpreted as a non-linear regression model]).
Regarding claim 3, ASAOKA as modified by SEKO teaches the molding condition deriving device as set forth in claim 1, wherein the at least one molding condition is derived in further consideration of a quality condition of a casting to be manufactured with use of the mold (if the determination result of the identification mark is “not good”, the processor 61, for example, issues an alarm and at the same time transmits, to the pouring machine 5, an instruction not to pour molten metal into the mold … in a case where an operator sets a core, the processor 61 specifies the mold and notifies, with an alarm or the like, the operator not to set the core … in a case where the core setting device sets a core, the processor 61 controls the core setting device so that the core setting device will not set the core in the mold … that is, the processor 61 transmits, to the core setting device, an instruction not to set the core in the mold … this control reliably stops production of a defective casting product by using a mold having an unreadable identification mark … therefore, it is possible to reduce the rate of occurrence of defective casting products, as compared to a conventional rate of the occurrence, Para. [0072] of ASAOKA; [the defect/irregularity is interpreted as corresponding to a quality condition]; [Applicant’s specification, at Para. [0040], indicates that presence or absence of a defect is a quality condition of the casting/molding]).
Conclusion
The prior art previously made of record and not relied upon is considered pertinent to applicant's disclosure:
WOWCZUK et al. (U.S. Patent Application Publication No. 2022/0314307 A1), cited in PTO-892 mailed 07/02/2025, discloses a molding condition deriving device for deriving a mold molding condition (the manufacturing system 200 may include one or more system logic devices 210, Para. [0071] of WOWCZUK; See also the programming instructions can include a manufacturing application … “manufacturing application” configured to, among other things, design and/or generate a mold insert, Para. [0072] of WOWCZUK; See also optimize any number of features of the mold insert design in relation to a product mold, Para. [0102] of WOWCZUK; [the logic device that designs and/or generates a mold insert by optimizing features is interpreted to correspond to a deriving device for deriving a mold molding condition]), said molding condition deriving device comprising: at least one processor (processor of system logic devices 210, Para. [0071] of WOWCZUK); and a primary memory connected to the at least one processor (a non-transitory memory housing programming instructions of system logic devices 210, Para. [0071] of WOWCZUK), the at least one processor being configured to carry out: a collection step of collecting a molding condition of a mold, the molding condition being different than at least one molding condition (the data stores 215 can include information obtained from multiple data sources, including third-party data sources, Para. [0074] of WOWCZUK; See also the manufacturing application can generate, look up, or otherwise obtain information from the product specifications and translate this data into mold information that can be used by the manufacturing device 220 to generate the mold insert 225, Para. [0076] of WOWCZUK; [Applicant’s specification, at Paras. [0017] & [0018], indicate that a kind of single sided pattern plate (type) that determines the shape of a mold (shape of casting) is a molding condition; thus, a shape of a mold/casting is interpreted as a molding condition]; Para. [0081] of WOWCZUK indicates that the modeling is based on a product file including product-specific information, such as shape, and Para. [0083] of WOWCZUK indicates that the model should retain depth illusion, depth compression, shape compression, silhouette collapse, object order, and other similar aspects … ensuring that the above features are maintained with a high level of accuracy ensures mold (and therefore product) repeatability), a sand property condition that is a property of sand, which is a material of the mold (in order to accurately determine 415 the resolution, additional information, such as the size of the particulate (for example, foundry sand or casting sand) being used to create the mold insert, can be considered, Para. [0084] of WOWCZUK), and a quality condition of the mold (some or all of the mold materials may be reclaimed and re-used, which will result in equally consistent mold quality, Para. [0119] of WOWCZUK; [based on Para. [0119] of WOWCZUK, the material used for the mold is interpreted as a quality condition of the mold]; See also in order to accurately determine 415 the resolution, additional information, such as the size of the particulate (for example, foundry sand or casting sand) being used to create the mold insert, can be considered, Para. [0084] of WOWCZUK; See also letters and/or decorative features on the pattern may shift in the sand during filling and/or cause breakage of the hardened sand mold upon removal therefrom, resulting in excess metal, that is, imprecise finishes on the letters and decorative features, Para. [0006] of WOWCZUK; [Applicant’s specification, at Para. [0019], indicates that a breakage condition is an example of a mold quality condition]; [thus, whether or not the mold pattern includes letters and/or decorative features is interpreted as a mold quality/breakage condition]; See also the various features of the mold insert may not be perfectly smooth … rather, they can only be as smooth as the size of the particulate being used, Para. [0099] of WOWCZUK; [smoothness is interpreted as a mold quality condition]); and a deriving step of using a learned model learned by a dataset-for-learning (generating 305 a product design comprises using artificial intelligence and/or machine learning systems … however, additional types of technologies may be implemented to generate 305 product designs as would be known to a person having an ordinary level of skill in the art, Para. [0079] of WOWCZUK) to derive the at least one molding condition from the molding condition of the mold ([Examiner Notes that “to derive” is not positively recited and is interpreted as “intended use”]; Nonetheless, WOCZUK teaches in order to accurately determine 415 the resolution, additional information, such as the size of the particulate (for example, foundry sand or casting sand) being used to create the mold insert, can be considered, Para. [0084] of WOWCZUK; [resolution is interpreted as the molding condition that is different than at least one molding condition included in the dataset]; Para. [0081] of WOWCZUK indicates that the modeling is based on a product file including product-specific information, such as shape, and Para. [0083] of WOWCZUK indicates that the model should retain depth illusion, depth compression, shape compression, silhouette collapse, object order, and other similar aspects … ensuring that the above features are maintained with a high level of accuracy ensures mold (and therefore product) repeatability; See also a ledger or other personalized region of a metal product may be discretely designed by the process 400 based on known limitations of the complete metal product (for example, size, shape, material) but without a complete design thereof, Para. [0080]), the sand property condition that is a property of sand, which is a material of the mold (in order to accurately determine 415 the resolution, additional information, such as the size of the particulate (for example, foundry sand or casting sand) being used to create the mold insert, can be considered, Para. [0084] of WOWCZUK), and the quality condition of the mold (some or all of the mold materials may be reclaimed and re-used, which will result in equally consistent mold quality, Para. [0119] of WOWCZUK; [based on Para. [0119] of WOWCZUK, the material used for the mold is interpreted as a quality of the mold condition]; See also in order to accurately determine 415 the resolution, additional information, such as the size of the particulate (for example, foundry sand or casting sand) being used to create the mold insert, can be considered, Para. [0084] of WOWCZUK; See also letters and/or decorative features on the pattern may shift in the sand during filling and/or cause breakage of the hardened sand mold upon removal therefrom, resulting in excess metal, that is, imprecise finishes on the letters and decorative features, Para. [0006] of WOWCZUK; [Applicant’s specification, at Para. [0019], indicates that a breakage condition is an example of a mold quality condition]; [thus, whether or not the mold pattern includes letters and/or decorative features is interpreted as a mold quality/breakage condition]; See also the various features of the mold insert may not be perfectly smooth … rather, they can only be as smooth as the size of the particulate being used, Para. [0099] of WOWCZUK]), the dataset-for-learning including the sand property condition, the molding condition, and the quality condition each for learning (generating 305 a product design comprises using artificial intelligence and/or machine learning systems, Para. [0079] of WOWCZUK; see also discussion and citations in WOWCZUK of above-identified molding condition(s), sand condition and quality condition), wherein the at least one processor controls the molding machine with use of the molding condition including the at least one molding condition having been outputted from the learned model (system logic devices 210 can be a part of a control system for a manufacturing device 220 for mold inserts, such as an additive manufacturing device or 3D printing device, Para. [0071] of WOWCZUK.
MCKIBBEN et al. (U.S. Patent No. 6,505,671) published Jan. 14, 20023, and cited in PTO-892 mailed 07/02/2025, teaches “while the use of the binder provides the benefit of additional strength, it can reduce the user's ability to handle the sand and to form intricate and complex, the temperature and humidity conditions at which the core is produced and stored can cause the core to soften and possibly lose its shape over time.”
ACEDO et al. (EP 3 316 061 A1), and cited in PTO-892 mailed 07/02/2025, teaches process variables may be divided into two large groups: a.- Variables related to the composition of the metal; Chemical composition, inoculation, treatment types, costs; Quality of the potential nucleation mixture : thermal analysis parameters; Pouring: duration of the casting process and temperature; b.- Variables related to the properties of the mould: Sand: type of additives, specific characteristics of the sand, performance of preliminary tests; Moulding: configuration parameters of moulding machine, Para. [0010] of ACEDO; [the sand variables (additive types, characteristics of sand, etc.) are interpreted to correspond to sand property conditions, the Moulding configuration parameters are interpreted to correspond to the molding condition which excludes at least one molding/casting condition]; See also next paragraph of ACEDO: using these variables, measured in real time during the process, an attempt is made to predict the following characteristics: appearance of microporosity or microshrinkage … microshrinkage, also known as secondary contraction, is a defect or irregularity in castings that tends to appear in the cooling stage … specifically, this defect consists of a form of shrinkage involving a large number of very small cavities which they may be distributed across a large area of the casting, Para. [0011] of ACEDO; [the microporosity/microshrinkage (defect/irregularity) is interpreted as corresponding to a quality condition and the “excluded” (to be predicted) molding condition]; [Applicant’s specification, at Para. [0040], indicates that presence or absence of a defect (such as a “cavity”) is a quality condition of the casting/molding]; Regarding learning model dataset, ACEDO teaches “Machine learning methods” … related to prediction… the complete input dataset is ultimately used, both to statistically train the models and to check that they are correct, Para. [0022] of ACEDO. Regarding claim 2, ACEDO teaches: learning of analysis models, in which a prediction process is performed which may be prediction of output variables … Neural Networks, ANN; MLP-BPN, Para. [0022] of ACEDO; [MLP-BPN is interpreted as supervised learning]; See also Machine learning methods” … related to prediction… the complete input dataset is ultimately used, both to statistically train the models and to check that they are correct, Para. [0022] of ACEDO; [checking that they are correct is interpreted to correspond to supervised learning in this context]); Regarding claim 3, ACEDO teaches: using these variables, measured in real time during the process, an attempt is made to predict the following characteristics: appearance of microporosity or microshrinkage … microshrinkage, also known as secondary contraction, is a defect or irregularity in castings that tends to appear in the cooling stage … specifically, this defect consists of a form of shrinkage involving a large number of very small cavities which they may be distributed across a large area of the casting, Para. [0011] of ACEDO; [the microporosity/microshrinkage (defect/irregularity) is interpreted as corresponding to a quality condition and the “excluded” (to be predicted) molding condition]; [Applicant’s specification, at Para. [0040], indicates that presence or absence of a defect (such as a “cavity”) is a quality condition of the casting/molding]).
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JOHN P. HOCKER
Examiner
Art Unit 2189
/JOHN P HOCKER/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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