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 .
Allowable Subject Matter Discussion
Claims 19, 29, and 33, but for the 35 USC 101 and 35 USC 112(b) rejections, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 19:
a heat conduction calculation unit configured to calculate a temperature history inside the rail by the thermal treatment by using the heat transfer coefficient calculated by the heat transfer coefficient calculation unit as a boundary condition, 0038-22B
a microstructure calculation unit configured to predict a microstructure inside the rail considering phase transformation, from the temperature distribution inside the rail based on the temperature history calculation calculated by the heat conduction calculation unit, 0038-22C
a hardness calculation unit configured to calculate the hardness inside the rail from a microstructure distribution inside the rail based on the microstructure prediction inside the rail calculated by the microstructure calculation unit. 0038 22D
Claim 27
a hardness prediction model generation unit configured to generate a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the plurality of sets of data for learning; 0038- 24
a hardness prediction unit configured to predict the hardness of the rail after the thermal treatment process, based on information on the hardness inside the rail with respect to a set of cooling condition data sets set as cooling conditions of the thermal treatment process, by using a measured value measured by the thermometer and the hardness prediction model. 0038-26
Claim 29
a heat transfer coefficient calculation unit configured to calculate a heat transfer coefficient of the rail surface during thermal treatment using the cooling facility, 0038 22A
a heat conduction calculation unit configured to calculate a temperature history inside the rail by the thermal treatment by using the heat transfer coefficient calculated by the heat transfer coefficient calculation unit as a boundary condition, 0038 22B
a microstructure calculation unit configured to predict a microstructure inside the rail considering phase transformation, from the temperature distribution inside the rail based on the temperature history calculation calculated by the heat conduction calculation unit, 0038 22C and
a hardness calculation unit configured to calculate the hardness inside the rail from a microstructure distribution inside the rail based on the microstructure prediction inside the rail calculated by the microstructure calculation unit. 0038 22D
Claim 30:
a hardness prediction unit configured to predict hardness inside the rail by the hardness prediction device for the heat hardened rail according to claim 27, before a start of cooling of the rail in the cooling facility; and
an operating condition resetting unit configured to reset, when the hardness inside the rail predicted by the hardness prediction unit is out of a target hardness range, operating conditions of the cooling facility such that the predicted hardness inside the rail falls within the target hardness range. 0038
Claim 33
a heat transfer coefficient calculation unit configured to calculate a heat transfer coefficient of a rail surface during thermal treatment using the cooling facility, 0038
a heat conduction calculation unit configured to calculate a temperature history inside the rail by the thermal treatment by using the heating transfer coefficient calculated by the heat transfer coefficient calculation unit as a boundary condition, 0038
a microstructure calculation unit configured to predict a microstructure inside the rail considering phase transformation, from the temperature distribution inside the rail based on the temperature history calculation calculated by the heat conduction calculation unit, 0038
a hardness calculation unit configured to calculate the hardness inside the rail from a microstructure distribution inside the rail based on the microstructure prediction inside the rail calculated by the microstructure calculation unit 0038, figure 7
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
Claims 19 and 27-33 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. Claims that depend upon a rejected claim inherit the indefiniteness.
Claim limitations “ unit” (see above Interpretation) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Here, the specification generally describes the units in terms of function; however, the specification does not map the corresponding units to a particular hardware such as a processor or controller. Because software lacks an ascertainable structure, each unit is ambiguous. For purposes of examination, each unit is interpreted as a function realized via software or hardware.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17-19, 23-29, and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
The claim(s) recite(s) a mental process directed to generating in advance a hardness prediction model using the cooling condition data set as at least input data; predicting the hardness of the rail after the thermal treatment process, based on information on the hardness inside the rail with respect to a set of cooling condition data sets, claims 1, 18, 23, 27, MPEP 2106.04(d).
Claims 19, 29, and 33 recite mathematical relationships directed to heat conduction calculation unit configured to calculate a temperature history inside the rail by the thermal treatment by using the heat transfer coefficient calculated by the heat transfer coefficient calculation unit as a boundary condition, a microstructure calculation unit configured to predict a microstructure inside the rail considering phase transformation, from the temperature distribution inside the rail based on the temperature history calculation calculated by the heat conduction calculation unit, and a hardness calculation unit configured to calculate the hardness inside the rail from a microstructure distribution inside the rail based on the microstructure prediction inside the rail calculated by the microstructure calculation unit, MPEP 2106.04(a)(2).
This judicial exception is not integrated into a practical application because the acquiring, by using an internal hardness model (claims 17, 23, 27, 30 ) computing output data (claims 18, 24, 28 ), and measuring a surface temperature (claim 27) represent insignificant extra solution activity, MPEP 2106.05(g)
Each of the units represent mere instructions to apply the abstract idea, see multiple units of claims 27 , 29, and 33, MPEP 2106.05(f)
The heat hardened rails and associated cooling facility (claims 17-18, 23, 25, and 27) and database , units, and thermometer (claim 27) are recited generally so as to generically link the abstract idea to the field of steel manufacturing, MPEP 2106.05(h).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because each of the units represent mere instructions to apply the abstract idea while the insignificant extra solution activity is well understood, conventional, and routine, MPEP 2106.05(d), infra applied prior art.
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.
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.
Claim(s) 17-18, 20-28, 30-32, and 34-36 are rejected under 35 U.S.C. 103 as being unpatentable over Lainati et al. (JP2015523467, see machine translation) in view over Fujioka et al. (JP 2005315703, see machine translation)
Claim 17.
Laiati et al. ‘467 teaches a hardness prediction method for a heat hardened rail, of predicting, after a thermal treatment process in which a rail having a temperature equal to or higher than an austenite region temperature is forcibly cooled in a cooling facility, hardness of the rail (e.g. as interpreted, the austenite region is not accorded patentable weight because the limitation is not recited in the claim body and is not necessary to “give life, meaning, and vitality” to the claim, see MPEP 2111.02), the method comprising:
acquiring, by using an internal hardness computing model (pages 6/16-10/16 e.g., see application of model for receiving desired, final mechanical properties for calculating control parameters, as per ‘467, and see the internal hardness computing model developed using machine learning of ‘703 described below. Here, ‘467 teaches a model for regulating cooling means while obtaining the claimed plurality of data sets) that is a physical model of performing computing by using a cooling condition data set having at least a surface temperature of the rail before a start of cooling (e.g. as interpreted, a model is implemented for acquiring preliminary temperatures, external conditions, and hardness values, each of these values are applied as training data for developing the prediction model described below. Here, see acquiring temperature before start cooling ->e.g. “Measuring the surface temperature of the rail upstream of the cooling module and comparing the measured temperature with the temperature calculated by the model;”) and operating conditions of the cooling facility for the forced cooling as input data ((pages 6/16-10/16 e.g. “Supplying the model with a plurality of parameters relating to the rail to be heat-treated”) and using hardness inside at least a rail head portion of the rail after the forced cooling as output data ((pages 8/16-10/16 e.g. In step 101, these settings are fed into various embedded process models (computerized control means 15 being the host) that work together to provide the best cooling strategy. Several numerical, mechanical and metallurgical embedded models are used: -> Mechanical properties, and see corresponding mechanical properties as hardness, “The hardness of the rail is in the range of 400 to 550 HB in the case of a high-performance bainite structure, and is in the range of 320 to 440 HB in the case of a fine pearlite microstructure, see “homogeneous” as reading on internal rail hardness) , a plurality of sets of data for learning (see Fujioka, ABSTRACT, pages 5/18-9/18, ‘703 for learning ,see above data sets capable of being used for learning, infra below application of learning methods for predicting hardness composed of the cooling condition data set and the hardness output data (‘467, pages 6-10 e.g. see additional content as “The control means includes a model which receives parameters relating to the rail entering the cooling system and values defining the intended final mechanical properties of the rail, the model comprising the intended order to obtain mechanical properties, drive parameters of the cooling means are supplied,” see also “In step 101, these settings are fed into various embedded process models (computerized control means 15 being the host) that work together to provide the best cooling strategy. Several numerical, mechanical and metallurgical embedded models are used ” (emphasized added), see associated mechanical properties as hardness “this is very important for obtaining excellent mechanical properties that are homogeneously distributed in the final product in terms of hardness, toughness and ductility,” see also FIG. 3 is a diagram showing temperature transition over a rail cross section during a controlled cooling process based on a method for obtaining a microstructure of fine pearlite. It is a figure which shows the hardness value in a various measurement point regarding the high performance bainite rail obtained based on the method of this invention”)
However, Lainati ‘ 467 does not expressly teach the limitations of the machine learning using the plurality of data sets for generating in advance a hardness prediction model described below. Fujioka ‘703 teaches the limitations of the machine learning using the plurality of data sets for generating in advance a hardness prediction model described below.
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of ‘703, namely using machine learning to develop a model configured to predict steel hardness, to the teachings of ‘467, namely applying an internal hardness model for achieving target, hardness properties by controlling cooling, would achieve an expected and predictable result of applying machine learning for training an internal hardness prediction model for controlling manufacturing parameters including but not limited to forced cooling. ‘703 is in the same field of endeavor and reasonably pertinent to a problem of training a model for predicting hardness.
The combination of cited prior art, supra above, teaches:
generating in advance a hardness prediction model (e.g. see trained model for predicting hardness based on input condition as ‘generating in advance’) using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the acquired plurality of sets of data for learning (e.g. see application of neural networks/prediction models for relating input data to output predictions, supra above “plurality of data” where input temperature and conditions are related to output hardness, see ‘703 for developing neural model , i.e., as applied in combination, a prediction/neural model for predicting hardness is a function of input data describing rail temperature and conditions, see ‘703 “To provide a steel material quality prediction method using a neural network model capable of predicting a steel plate material with high accuracy from the actual values or predicted values of steel components and production conditions. According to a metallurgical mathematical model from actual or predicted values of steel components and production conditions, during reheating, during cooling to the start of rolling, during each pass during rolling, and during cooling after rolling, the microstructure Change, solid solution / precipitation state of element, and metallographic state are calculated sequentially, and finally obtained after cooling metal structure state and element solid solution / precipitation state, and calculated from these metal structure state and solid solution / precipitation state after cooling The material, steel components, and actual or predicted values of manufacturing conditions are used as input items of the neural network model, and the steel material is predicted by the model, see also learning data ->“. The neural network model in the embodiment uses a three-layer model using the back propagation method, and the number of units in the intermediate layer is 15. Next, the metallurgical mathematical model described in Non-Patent Document 1 was used. As the data used for learning, 100 examples of data relating to steel materials in the component ranges shown in Table 1 and the manufacturing condition ranges shown in Table 2 were used. In learning, the measured values are used as data for the material to be predicted, and the values calculated by the metallurgical formula model are used for the metal structure state, element solid solution / precipitation state, and the material to be calculated by the metallurgical formula model. Used as data.”
predicting the hardness of the rail after the thermal treatment process, based on information on the hardness inside the rail (‘467 e.g. “This is very important for obtaining excellent mechanical properties that are homogeneously distributed (e.g. as interpreted, including depth) in the final product in terms of hardness, toughness and ductility,”, pages 6-10)
with respect to a set of cooling condition data sets set as cooling conditions of the thermal treatment process, obtained by using the hardness prediction model (e.g. e.g. see application of neural networks/prediction models for relating input data to output predictions, supra above “plurality of data” where input temperature and conditions are related to output hardness, see ‘703 for developing predictive model , i.e., as applied in combination, a prediction model for predicting hardness is a function of input data describing rail temperature and conditions, see “To provide a steel material quality prediction method using a neural network model capable of predicting a steel plate material with high accuracy from the actual values or predicted values of steel components and production conditions. According to a metallurgical mathematical model from actual or predicted values of steel components and production conditions, during reheating, during cooling to the start of rolling, during each pass during rolling, and during cooling after rolling, the microstructure Change, solid solution / precipitation state of element, and metallographic state are calculated sequentially, and finally obtained after cooling metal structure state and element solid solution / precipitation state, and calculated from these metal structure state and solid solution / precipitation state after cooling The material, steel components, and actual or predicted values of manufacturing conditions are used as input items of the neural network model, and the steel material is predicted by the model, see also learning data ->“. The neural network model in the embodiment uses a three-layer model using the back propagation method, and the number of units in the intermediate layer is 15. Next, the metallurgical mathematical model described in Non-Patent Document 1 was used. As the data used for learning, 100 examples of data relating to steel materials in the component ranges shown in Table 1 and the manufacturing condition ranges shown in Table 2 were used. In learning, the measured values are used as data for the material to be predicted, and the values calculated by the metallurgical formula model are used for the metal structure state, element solid solution / precipitation state, and the material to be calculated by the metallurgical formula model. Used as data.”
Claim 18.
The hardness prediction method according to claim 17, wherein output data computed using the internal hardness computing model is a hardness distribution in at least a region from a rail surface to a depth set in advance (‘467 pages 6/16-10/16 e.g. “The change in properties can be made very precisely so that the formation of regions with too high or too low hardness is avoided and any unwanted microstructure (e.g martensite) is avoided,” see also “This is very important for obtaining excellent mechanical properties that are homogeneously distributed (e.g. as interpreted, including depth) in the final product in terms of hardness, toughness and ductility,”
Claim 23.
Lainati, as modified, supra claim 17, teaches a method of generating a hardness prediction model for obtaining, after a rail having a temperature equal to or higher than an austenite region temperature is forcibly cooled in a cooling facility, hardness of the rail from a cooling condition data set having at least a surface temperature of the rail before a start of cooling in the cooling facility and operating conditions of the cooling facility for the forced cooling, the method comprising:
acquiring, by using an internal hardness computing model that is a physical model for performing computing by using the cooling condition data set as input data and using hardness inside at least a rail head portion of the rail after the forced cooling as output data, a plurality of sets of data for learning composed of the cooling condition data set and the hardness output data; supra claim 17 and
generating in advance a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the acquired plurality of sets of data for learning, supra claim 17
Claim 24. The method according to claim 23, wherein output data computed using the internal hardness computing model is a hardness distribution in at least a region from a rail surface to a depth set in advance. Supra claim 18
Claim 25. The method according to claim 23, wherein the hardness prediction model is a neural network model, a random forest, or a model learned by SVM regression, supra claim 17
Claim 26. A method of manufacturing a heat hardened rail comprising:
the thermal treatment method for the heat hardened rail according to claim 20, supra claim 20
Claim 27. A hardness prediction device for a heat hardened rail, which predicts, after a thermal treatment process in which a rail having a temperature equal to or higher than an austenite region temperature is forcibly cooled in a cooling facility, hardness of the rail, the device comprising:
a database configured (‘467, Figure 2-16 pages 6/16-10/16) to store a plurality of sets of data for learning computed using an internal hardness computing model that is a physical model for performing computing by using a cooling condition data set having at least a surface temperature of the rail before a start of cooling and operating conditions of the cooling facility for the forced cooling as input data and using hardness inside at least a rail head portion of the rail after the forced cooling as output data , and composed of the cooling condition data set and the hardness output data; supra claim 17
a hardness prediction model generation unit configured (e.g. each “unit” is interpreted as a software function realized using a processor, module, or controller, i.e.. unit) to generate a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the plurality of sets of data for learning, supra claim 17
a thermometer configured to measure the surface temperature of the rail before the start of cooling (6/16-10/16 ‘467 e.g. “thermal sensors”)
a hardness prediction unit configured to ((e.g. each “unit” is interpreted as a software function realized using a processor, module, or controller, i.e.. unit) predict the hardness of the rail after the thermal treatment process, based on information on the hardness inside the rail with respect to a set of cooling condition data sets set as cooling conditions of the thermal treatment process, by using a measured value measured by the thermometer and the hardness prediction model, supra claim 17
Claim 28. The hardness prediction device according to claim 27, wherein output data computed using the internal hardness computing model is a hardness distribution in at least a region from a rail surface to a depth set in advance. Supra claim 18
Claim 30. A thermal treatment device for a heat hardened rail having a thermal treatment process in which a rail having a temperature equal to or higher than an austenite region temperature is forcibly cooled in a cooling facility, the device comprising:
a hardness prediction unit configured to ((e.g. each “unit” is interpreted as a software function realized using a processor, module, or controller, i.e.. unit) predict hardness inside the rail by the hardness prediction device ((e.g. each “unit/device” is interpreted as a software function realized using a processor, module, or controller, i.e.. unit) for the heat hardened rail according to claim 27, before a start of cooling of the rail in the cooling facility; supra claim 17
an operating condition resetting unit configured ((e.g. each “unit” is interpreted as a software function realized using a processor, module, or controller, i.e.. unit) to reset, when the hardness inside the rail predicted by the hardness prediction unit is out of a target hardness range, operating conditions of the cooling facility such that the predicted hardness inside the rail falls within the target hardness range, supra claim 17
Claim 31. The thermal treatment device according to claim 30, wherein the operating conditions of the cooling facility to be reset include at least one operating condition among an injection pressure, an injection distance, an injection position, and an injection time of a cooling medium injected toward the rail in the cooling facility (e.g. “ The method of the present invention is characterized in that a substantial amount of austenite is transformed into a selected bainite or pearlite microstructure while the rail is still undergoing a cooling step. This ensures the acquisition of high-performance bainite or fine pearlite microstructure. In order to correctly implement the controlled cooling pattern required for the rail along all heat treatments, flexible cooling systems typically include multiple adjustable multi-means nozzles, but water, air, It is not limited to a mixture of water and air. The nozzle can be adjusted for on / off conditions, pressure, flow rate, and type of coolant depending on the chemical composition of the rail and the final mechanical properties required by the rail user,” see also “ The control means 15 controls the heat treatment of the rail by controlling the parameters (flow rate, cooling medium temperature, cooling medium pressure) of each nozzle of each cooling module and the approach speed of the rail. In other words, the flow rate, the pressure, the number of nozzles to be used, the position of the nozzles, and the cooling efficiency of each nozzle group (N1, N2, N3, N4.N5, N6) can be set individually. . Therefore, any module 12. n can also be controlled and managed alone or in combination with one or more modules. The cooling strategy (eg, heating rate, cooling rate, temperature profile) is predetermined according to the properties of the final product.,”
Claim 32. A manufacturing facility for a heat hardened rail comprising:
the thermal treatment device for the heat hardened rail according to claim 30 (e.g. “method of heat treating a high temperature rail to obtain the desired microstructure with improved mechanical properties,The method includes an active cooling phase, in which the cooling phase includes:Quenching the rail from the austenite temperature and then slowly cooling the rail to maintain the target transformation temperature between the specified values;The cooling process is performed by a plurality of cooling modules (‘ 467 pages 6/16-10/16 element 12.n), “Each cooling module includes a plurality of means for spraying a cooling medium onto the rail during the active cooling phase. In the method Each cooling module is provided with multiple cooling sections, Each cooling section is located in one plane transverse to the rail when the rail is located inside the heat treatment system; Each cooling section is at least One cooling means located above the head of the rail; Two cooling means located on each side of the head of the rail; One cooling means located below the bottom of the rail,Each cooling means controls the cooling rate of the rail so that the amount of austenite transformed in the rail is 50% or more at the rail surface and 20% or more at the central portion of the rail head. Drive, It is solved by a method characterized by this.”)
Claim 34. A thermal treatment method for a heat hardened rail having a thermal treatment process in which a rail having a temperature equal to or higher than an austenite region temperature is forcibly cooled in a cooling facility, the method comprising:
measuring a surface temperature of the rail before a start of cooling; supra claim 17
predicting hardness inside the rail by using the measured surface temperature of the rail by the hardness prediction method for the heat hardened rail according to claim 18, before the start of cooling of the rail in the cooling facility; supra claim 17
resetting, when the predicted hardness inside the rail is out of a target hardness range, operating conditions of the cooling facility such that the predicted hardness inside the rail falls within the target hardness range (467’ e.g. see modifying/resettomg in light of “Modifying the driving parameters of the cooling means if the difference between the measured temperature and the calculated temperature is greater than a predetermined value. The cooling means is a mixture of air and water sprayed by the cooling means provided around the cross section of the rail, and separately controls the amount of air sprayed and the amount of water sprayed. The surface temperature of the rail entering the first cooling module is between 750 and 1000 ° C., and the surface temperature of the rail coming out of the last cooling module is between 300 and 650 ° C.The rail is cooled by the cooling means at a rate between 0.5 and 70 ° C./second.”)
Claim 35. A thermal treatment method for a heat hardened rail having a thermal treatment process in which a rail having a temperature equal to or higher than an austenite region temperature is forcibly cooled in a cooling facility, the method comprising:
measuring a surface temperature of the rail before a start of cooling; supra claim 17
predicting hardness inside the rail by using the measured surface, temperature of the rail by the hardness prediction method for the heat hardened rail according to claim 19, before the start of cooling of the rail in the cooling facility; supra claim 17
resetting, when the predicted hardness inside the rail is out of a target hardness range, operating conditions of the cooling facility such that the predicted hardness inside the rail falls within the target hardness range, supra claims 17, 34
Claim 36. The thermal treatment method according to claim 21,
wherein the cooling facility has a plurality of cooling zones disposed along a longitudinal direction of the rail to be cooled (e.g. “Each cooling module 12. Each n includes a plurality of cooling sections arranged in a row. Each cooling section includes a plurality of nozzles located in the same plane defined by the rail cross-section. FIG. 3 is a cross-sectional view of the rail 6. From this figure, a configuration example of a plurality of nozzles belonging to the same one cooling section can be seen. In this embodiment, the one cooling section includes six nozzles located around the cross section of the rail 6. One nozzle N1 is arranged above the head of the rail, two nozzles N2 and N3 are arranged on each side of the head of the rail, and two optional nozzles N4 and N5 are It is arranged on each side of the abdomen of the rail, and the last nozzle N6 is arranged below the bottom of the rail 6.”)
the resetting of the operating conditions of the cooling facility is executed individually for each of the cooling zones, supra claim 24 for modifying.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Claim 1 relevancy
JP2004133415 JP2015523467 JP 09227942 JPW202014157198 JP7148024 JP2014038595 JP2023127326 JP2021168863
Claim 19 relevancy
JP5962290 (coefficient)
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117