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 .
Claim Status
Claims 1-5 are currently pending and examined on the merits.
Priority
The instant application claims foreign priority to Application CN202210364271X filed on 4/7/2022, in China. At this point in examination, the effective filing date of claims 1-5 is 4/7/2022.
Information Disclosure Statement
No Information Disclosure Statement has been filed herein.
Claim Objections
Claims 1 are objected to because of the following informalities:
In claim 1, fourth to last line, "is represents" should read "represents".
In claim 4, line 6, there is a double space between “of” and “dissolved”, should be corrected to single space.
There are typographical errors. Appropriate correction is required.
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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-5 are directed to a method (process) for determining a quantity of a calcium line fed into molten steel based on a minimum Gibbs free energy principle. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1 and 4 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
S21, calculating a minimum Gibbs free energy of the molten steel based on the minimum Gibbs free energy principle using a formula (1) expressed as follows:
m
i
n
.
G
s
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
m
G
m
θ
+
R
T
l
n
a
m
+
n
s
l
a
g
G
s
l
a
g
θ
+
R
T
l
n
a
s
l
a
g
+
n
s
o
l
i
d
×
G
s
o
l
i
d
θ
(1)
where
m
i
n
.
G
s
represents the minimum Gibbs free energy of the molten steel,
G
i
θ
represents a standard molar Gibbs free energy of a composition
i
of the molten steel,
a
i
represents an activity value of the composition
i
, the composition
i
comprises a solid-phase inclusion, a liquid-phase inclusion and a liquid-phase steel, m represents elements of the liquid-phase steel,
n
represents a number of moles,
R
represents a gas constant,
T
represents a temperature of the molten steel, slag represents the liquid-phase inclusion of the molten steel, solid is the solid-phase inclusion of the molten steel, and
c
represents the number of compositions of the molten steel (i.e., mental processes, mathematical concepts);
S22, calculating Gibbs free energies of the solid-phase inclusion, the liquid-phase inclusion, and the liquid-phase steel, where the Gibbs free energy of the solid-phase inclusion is calculated based on a formula (2) expressed as follows:
m
i
n
.
G
s
o
l
i
d
=
n
s
o
l
i
d
G
s
o
l
i
d
θ
=
n
A
l
2
O
3
G
A
l
2
O
3
θ
+
n
C
a
O
∙
6
A
l
2
O
3
G
C
a
O
∙
6
A
l
2
O
3
θ
+
n
C
a
O
∙
2
A
l
2
O
3
G
C
a
O
∙
2
A
l
2
O
3
θ
+
n
C
a
O
G
C
a
O
θ
+
n
C
a
S
G
C
a
S
θ
(2)
where the Gibbs free energy of the liquid-phase inclusion is calculated based on a formula (3) expressed as follows:
m
i
n
.
G
s
l
a
g
=
n
A
l
2
O
3
G
A
l
2
O
3
θ
+
R
T
l
n
a
A
l
2
O
3
+
n
C
a
O
G
C
a
O
θ
+
R
T
l
n
a
C
a
O
(3)
where the Gibbs free energy of the liquid-phase steel is calculated based on a formula (4) expressed as follows:
m
i
n
.
G
F
e
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
A
l
G
A
l
θ
+
R
T
l
n
x
A
l
γ
A
l
+
n
C
a
G
C
a
θ
+
R
T
l
n
x
C
a
γ
C
a
+
n
O
G
O
θ
+
R
T
l
n
x
O
γ
O
+
n
S
G
S
θ
+
R
T
l
n
x
S
γ
S
(4)
where
C
represents the number of the elements of the liquid-phase steel,
x
represents a molar fraction of the elements in the liquid-phase steel, and
γ
represents an activity coefficient of the elements in the liquid-phase steel (i.e., mental processes, mathematical concepts);
S23, calculating activity values of compositions of the solid-phase inclusion and activity values of compositions of the liquid-phase inclusion, wherein each of the activity values of the compositions of the solid-phase inclusion is 1, and the activity values of the compositions of the liquid-phase inclusion is calculated based on formulas (5) and (6) expressed as follows:
a
A
l
2
O
3
=
(
-
3.9367
*
m
A
l
2
O
3
4
+
8.1721
*
m
A
l
2
O
3
3
-
3.7817
*
m
A
l
2
O
3
2
+
0.57821
*
m
A
l
2
O
3
-
0.0145
(5)
a
C
a
O
=
(
-
6.4181
*
m
A
l
2
O
3
4
+
13.8441
*
m
A
l
2
O
3
3
-
8.1761
*
m
A
l
2
O
3
2
+
0.2823
*
m
A
l
2
O
3
+
1.0129
(6)
where
a
A
l
2
O
3
represents an activity value of a composition
A
l
2
O
3
of the liquid-phase inclusion,
a
C
a
O
represents an activity value of a composition
C
a
O
of the liquid-phase inclusion,
m
A
l
2
O
3
represents a mass fraction of the composition
A
l
2
O
3
of the liquid-phase inclusion (i.e., mental processes, mathematical concepts);
S24, determining the contents of the inclusions in the molten steel, by substituting the formulas (2) to (6) into the formula (1), adding a constraint condition, in which an input variable is the composition information of the molten steel when the contents of the inclusions in the molten steel are calculated, and solving the substituted formula (1) (i.e., mental processes, mathematical concepts);
determining the required quantity of the calcium of the molten steel on a condition that the inclusions in the molten steel are controlled in a liquid phase region (i.e., mental processes);
S3, predicting a yield rate of the calcium during the calcium treatment process (i.e., mental processes);
S4, determining a length of the fed calcium line according to the required quantity of calcium of the molten steel, the yield rate of the calcium, and the parameter information of the calcium treatment process, wherein the length of the fed calcium line is calculated based on a formula (7) expressed as follows:
L
=
W
×
(
n
C
a
T
-
n
C
a
O
×
M
C
a
η
×
β
×
μ
×
M
F
e
(7)
where
L
represents the length of the fed calcium line with a unit of meter,
W
represents a weight of the molten steel with a unit of ton;
n
C
a
T
represents the required quantity of calcium of the molten steel with a unit of %;
n
C
a
O
represents a calcium content of the molten steel before the calcium treatment process with a unit of %;
M
C
a
represents a molar mass of calcium with a unit of gram per mole (g/mol);
M
F
e
is represents a molar mass of iron with a unit of g/mol;
η
represents the yield rate of the calcium with a unit of %;
β
represents a content of calcium of the calcium line with a unit of %; and
μ
represents a weight per meter of the calcium line with a unit of gram per meter (g/m) (i.e., mental processes).
Dependent claim 4 further recites:
wherein the predicting the yield rate of the calcium during the calcium treatment process comprises: predicting the yield rate of the calcium according to one of a neural network model and a content of oxygen in the liquid-phase steel (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Dependent claims 2-3 and 5 recite information further limiting the judicial exceptions indicated above.
Therefore, claims 1 and 4 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claim 1 recites the additional non-abstract elements of data gathering:
S1, obtaining, from a factory database, composition information of the molten steel before a calcium treatment process and parameter information of the calcium treatment process (claim 1).
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-5 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claim 1 contains additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claim 1: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
[Step 2B: NO]
Therefore, claims 1-5 are patent ineligible under 35 U.S.C. § 101.
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.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (Metallurgical and Materials Transactions B, 2022, 53, 1396-1410), in view of Koukkari (VTT Research Notes, 2009, 1-150), Luo et al. (11th International Symposium on High-Temperature Metallurgical Processing, 2020, 253-259), Zhao et al. (ISIJ International, 2015, 55(10), 2115-2124), Abraham et al. (Journal of Iron and Steel Research International, 2018, 25, 133-145), Kumar et al. (Ironmaking & Steelmaking, 2019, 46(5), 454-462), and The Experts at ASK Chemical (Evaluating Cored-Wire Practice to Automate Iron Treatment, Inoculation, Foundry Management & Technology, 1 December 2018, 1-3, https://www.foundrymag.com/ask-the-expert/article/21931901/evaluating-cored-wire-practice-to-automate-iron-treatment-inoculation).
With respect to claim 1:
Regarding the recited S2, performing thermodynamic calculation on the composition information of the molten steel based on the minimum Gibbs free energy principle to obtain contents of inclusions in the molten steel and a required quantity of calcium of the molten steel, specifically comprising: S21, calculating a minimum Gibbs free energy of the molten steel based on the minimum Gibbs free energy principle using a formula (1) expressed as follows:
m
i
n
.
G
s
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
m
G
m
θ
+
R
T
l
n
a
m
+
n
s
l
a
g
G
s
l
a
g
θ
+
R
T
l
n
a
s
l
a
g
+
n
s
o
l
i
d
×
G
s
o
l
i
d
θ
(1)
where
m
i
n
.
G
s
represents the minimum Gibbs free energy of the molten steel,
G
i
θ
represents a standard molar Gibbs free energy of a composition
i
of the molten steel,
a
i
represents an activity value of the composition
i
, the composition
i
comprises a solid-phase inclusion, a liquid-phase inclusion and a liquid-phase steel, m represents elements of the liquid-phase steel,
n
represents a number of moles,
R
represents a gas constant,
T
represents a temperature of the molten steel, slag represents the liquid-phase inclusion of the molten steel, solid is the solid-phase inclusion of the molten steel, and
c
represents the number of compositions of the molten steel, Wu et al. discloses using a minimum Gibbs free energy method to model
C
O
2
injection into Fe-C melts, which is an effective way of analyzing the thermodynamic equilibrium state of this multiphase closed system. The Gibbs free minimization is presented in Equation 5 as
m
i
n
.
G
s
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
O
2
G
m
,
O
2
θ
+
R
T
l
n
p
O
2
+
n
C
O
2
G
m
,
C
O
2
θ
+
R
T
l
n
p
C
O
2
+
n
C
O
G
m
,
C
O
θ
+
R
T
l
n
p
C
O
+
n
C
G
m
,
C
θ
+
R
T
l
n
a
C
+
n
O
G
m
,
O
θ
+
R
T
l
n
a
O
+
n
F
e
G
m
,
F
e
θ
+
R
T
l
n
a
F
e
+
n
F
e
O
G
m
,
F
e
O
θ
+
R
T
l
n
a
F
e
O
(pg. 1400, col. 1-2, last paragraph). This teaches calculating a total minimum Gibbs free energy expanded into separate phase components for Fe-C melts.
Wu et al. does not disclose calculating a minimum Gibbs free energy of molten steel.
However, Koukkari et al. discloses that multi-phase Gibbsian methods are practical in predicting slag window conditions in alloy refining and steelmaking (pg. 57, para. 2, lines 1-2). This teaches that minimum Gibbs free energy could be applied to molten steel.
Regarding the recited S22, calculating Gibbs free energies of the solid-phase inclusion, the liquid-phase inclusion, and the liquid-phase steel, where the Gibbs free energy of the solid-phase inclusion is calculated based on a formula (2) expressed as follows:
m
i
n
.
G
s
o
l
i
d
=
n
s
o
l
i
d
G
s
o
l
i
d
θ
=
n
A
l
2
O
3
G
A
l
2
O
3
θ
+
n
C
a
O
∙
6
A
l
2
O
3
G
C
a
O
∙
6
A
l
2
O
3
θ
+
n
C
a
O
∙
2
A
l
2
O
3
G
C
a
O
∙
2
A
l
2
O
3
θ
+
n
C
a
O
G
C
a
O
θ
+
n
C
a
S
G
C
a
S
θ
(2)
where the Gibbs free energy of the liquid-phase inclusion is calculated based on a formula (3) expressed as follows:
m
i
n
.
G
s
l
a
g
=
n
A
l
2
O
3
G
A
l
2
O
3
θ
+
R
T
l
n
a
A
l
2
O
3
+
n
C
a
O
G
C
a
O
θ
+
R
T
l
n
a
C
a
O
(3)
where the Gibbs free energy of the liquid-phase steel is calculated based on a formula (4) expressed as follows:
m
i
n
.
G
F
e
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
A
l
G
A
l
θ
+
R
T
l
n
x
A
l
γ
A
l
+
n
C
a
G
C
a
θ
+
R
T
l
n
x
C
a
γ
C
a
+
n
O
G
O
θ
+
R
T
l
n
x
O
γ
O
+
n
S
G
S
θ
+
R
T
l
n
x
S
γ
S
(4)
where
C
represents the number of the elements of the liquid-phase steel,
x
represents a molar fraction of the elements in the liquid-phase steel, and
γ
represents an activity coefficient of the elements in the liquid-phase steel, Wu et al. discloses using a minimum Gibbs free energy method to model
C
O
2
injection into Fe-C melts, which is an effective way of analyzing the thermodynamic equilibrium state of this multiphase closed system. The Gibbs free minimization is presented in Equation 5 as
m
i
n
.
G
s
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
O
2
G
m
,
O
2
θ
+
R
T
l
n
p
O
2
+
n
C
O
2
G
m
,
C
O
2
θ
+
R
T
l
n
p
C
O
2
+
n
C
O
G
m
,
C
O
θ
+
R
T
l
n
p
C
O
+
n
C
G
m
,
C
θ
+
R
T
l
n
a
C
+
n
O
G
m
,
O
θ
+
R
T
l
n
a
O
+
n
F
e
G
m
,
F
e
θ
+
R
T
l
n
a
F
e
+
n
F
e
O
G
m
,
F
e
O
θ
+
R
T
l
n
a
F
e
O
(pg. 1400, col. 1-2, last paragraph). This teaches calculating a minimum Gibbs free energy including separate phase components for Fe-C melts.
Wu et al. does not disclose calculating Gibbs free energies of the solid-phase inclusion, the liquid-phase inclusion, and the liquid-phase steel.
However, Koukkari et al. discloses a characteristic multi-phase description of a steel melt, where all solids are assumed as stoichiometric phases, the slag phase contains all major elements of the system and is described as a quasi-chemical oxide (slag-liquid) phase, and the liquid iron contains all key elements, their interaction parameters are described with the thermodynamic Wagner model (pg. 54, para. 2). Also, further discloses that multi-phase Gibbsian methods are practical in predicting slag window conditions in alloy refining and steelmaking (pg. 57, para. 2, lines 1-2). This teaches that minimum Gibbs free energy could be applied to molten steel for multiple phases.
Regarding the recited S24, determining the contents of the inclusions in the molten steel, by substituting the formulas (2) to (6) into the formula (1), adding a constraint condition, in which an input variable is the composition information of the molten steel when the contents of the inclusions in the molten steel are calculated, and solving the substituted formula (1), Wu et al. discloses using a minimum Gibbs free energy method to model
C
O
2
injection into Fe-C melts, which is an effective way of analyzing the thermodynamic equilibrium state of this multiphase closed system. The Gibbs free minimization is presented in Equation 5 as
m
i
n
.
G
s
=
∑
i
=
1
c
n
i
G
i
=
∑
i
=
1
c
n
i
G
m
,
i
θ
+
R
T
l
n
a
i
=
n
O
2
G
m
,
O
2
θ
+
R
T
l
n
p
O
2
+
n
C
O
2
G
m
,
C
O
2
θ
+
R
T
l
n
p
C
O
2
+
n
C
O
G
m
,
C
O
θ
+
R
T
l
n
p
C
O
+
n
C
G
m
,
C
θ
+
R
T
l
n
a
C
+
n
O
G
m
,
O
θ
+
R
T
l
n
a
O
+
n
F
e
G
m
,
F
e
θ
+
R
T
l
n
a
F
e
+
n
F
e
O
G
m
,
F
e
O
θ
+
R
T
l
n
a
F
e
O
(pg. 1400, col. 1-2, last paragraph). Also, further discloses constraint condition equations representing matter conservation of the elements in the
C
O
2
-Fe-C system (pg. 1400-1401, col. 2, last paragraph). This teaches calculating a total minimum Gibbs free energy expanded into separate phase components for Fe-C melts, including constraint conditions.
Wu et al. does not disclose solving the substituted formula (1) for molten steel.
However, Koukkari et al. discloses that multi-phase Gibbsian methods are practical in predicting slag window conditions in alloy refining and steelmaking (pg. 57, para. 2, lines 1-2). This teaches that minimum Gibbs free energy and constraint conditions could be applied to molten steel.
Wu et al. and Koukkari et al. do not disclose S1, obtaining, from a factory database, composition information of the molten steel before a calcium treatment process and parameter information of the calcium treatment process.
However, Luo et al. discloses a precise calcium treatment software that builds a database based on actual molten steel composition (pg. 255-256, para. 2, lines 1-7). Also, further discloses the operation interface of the calcium treatment software in Figures 2-3, which includes parameter input (pg. 256, para. 2, lines 1-5; Fig. 2-3). This teaches a database comprising composition information of molten steel before a calcium treatment process and parameter information of the calcium treatment process.
Wu et al., Koukkari et al., and Luo et al. do not disclose S23, calculating activity values of compositions of the solid-phase inclusion and activity values of compositions of the liquid-phase inclusion, wherein each of the activity values of the compositions of the solid-phase inclusion is 1, and the activity values of the compositions of the liquid-phase inclusion is calculated based on formulas (5) and (6) expressed as follows:
a
A
l
2
O
3
=
(
-
3.9367
*
m
A
l
2
O
3
4
+
8.1721
*
m
A
l
2
O
3
3
-
3.7817
*
m
A
l
2
O
3
2
+
0.57821
*
m
A
l
2
O
3
-
0.0145
(5)
a
C
a
O
=
(
-
6.4181
*
m
A
l
2
O
3
4
+
13.8441
*
m
A
l
2
O
3
3
-
8.1761
*
m
A
l
2
O
3
2
+
0.2823
*
m
A
l
2
O
3
+
1.0129
(6)
where
a
A
l
2
O
3
represents an activity value of a composition
A
l
2
O
3
of the liquid-phase inclusion,
a
C
a
O
represents an activity value of a composition
C
a
O
of the liquid-phase inclusion,
m
A
l
2
O
3
represents a mass fraction of the composition
A
l
2
O
3
of the liquid-phase inclusion.
However, Zhao et al. discloses calculating the activities of
A
l
2
O
3
and
C
a
O
expressed in Equations 6 and 7 as
a
[
A
l
2
O
3
]
i
n
c
l
u
s
i
o
n
=
(
-
5.2566
m
a
s
s
%
A
l
2
O
3
3
+
10.9145
m
a
s
s
%
A
l
2
O
3
2
-
6.1234
[
m
a
s
s
%
A
l
2
O
3
]
+
1.0438
and
a
[
C
a
O
]
i
n
c
l
u
s
i
o
n
=
(
-
11.4516
m
a
s
s
%
A
l
2
O
3
3
+
28.0160
m
a
s
s
%
A
l
2
O
3
2
-
22.8360
[
m
a
s
s
%
A
l
2
O
3
]
+
6.2724
, respectively (pg. 2119-2120, col. 2, para. 2). This teaches calculating activity values of compositions of the solid-phase and liquid-phase inclusions.
Wu et al., Koukkari et al., Luo et al., and Zhao et al. do not disclose determining the required quantity of the calcium of the molten steel on a condition that the inclusions in the molten steel are controlled in a liquid phase region.
However, Abraham et al. discloses that nonmetallic inclusions are present when high levels of calcium have been added to molten steel and are liquid throughout processing. Also, further discloses that the end results of an optimized calcium treatment are that the alumina is modified to form liquid calcium aluminate and sulfur is tied up as CaS, which will precipitate on the calcium aluminate inclusions (pg. 140, col. 2, para. 2; pg. 140, col. 2, para. 8, lines 1-4). This teaches determining a quantity of calcium for molten steel so that the inclusions in the molten steel are controlled in liquid phase.
Wu et al., Koukkari et al., Luo et al., Zhao et al., and Abraham et al. do not disclose S3, predicting a yield rate of the calcium during the calcium treatment process.
However, Kumar et al. discloses estimating calcium recovery from plant data using Equation 3 presented as %Ca recovery =
(
m
F
i
n
a
l
C
a
-
m
I
n
i
t
i
a
l
C
a
)
m
A
d
d
e
d
C
a
×
100
, where
m
F
i
n
a
l
C
a
and
m
I
n
i
t
i
a
l
C
a
are the mass of Ca (kg) in the bath after and before Ca-treatment, respectively, and
m
A
d
d
e
d
C
a
is the mass of Ca (kg) added through cored wire (pg. 456, col. 1, para. 5, lines 1-6). This teaches predicting calcium yield during the calcium treatment process.
Wu et al., Koukkari et al., Luo et al., Zhao et al., Abraham et al., and Kumar et al. do not disclose S4, determining a length of the fed calcium line according to the required quantity of calcium of the molten steel, the yield rate of the calcium, and the parameter information of the calcium treatment process, wherein the length of the fed calcium line is calculated based on a formula (7) expressed as follows:
L
=
W
×
(
n
C
a
T
-
n
C
a
O
×
M
C
a
η
×
β
×
μ
×
M
F
e
(7)
where
L
represents the length of the fed calcium line with a unit of meter,
W
represents a weight of the molten steel with a unit of ton;
n
C
a
T
represents the required quantity of calcium of the molten steel with a unit of %;
n
C
a
O
represents a calcium content of the molten steel before the calcium treatment process with a unit of %;
M
C
a
represents a molar mass of calcium with a unit of gram per mole (g/mol);
M
F
e
is represents a molar mass of iron with a unit of g/mol;
η
represents the yield rate of the calcium with a unit of %;
β
represents a content of calcium of the calcium line with a unit of %; and
μ
represents a weight per meter of the calcium line with a unit of gram per meter (g/m).
However, The Experts at ASK Chemical discloses a machine that takes as input sulfur content before and after treatment, the meters of wire fed, the quantity of magnesium per meter of wire, and additional inputs that vary according to the current practice, and calculates the length of wire necessary to feed into metal (pg. 2-3, para. 4-7). This teaches determining a length of the fed calcium line.
It would have been prima facie obvious to one of ordinary skill in the art to combine the Gibbs free minimization principle disclosed by Wu et al. with the application of Gibbs free energy to molten steel disclosed by Koukkari et al., the factory database disclosed by Luo et al., activity value formulas disclosed by Zhao et al., determining quantity of calcium in molten steel disclosed by Abraham et al., predicting calcium yield rate disclosed by Kumar et al., and determining length of fed calcium line disclosed by The Experts at ASK Chemical. One would be motivated to make this combination because Koukkari et al. discloses that multi-phase Gibbsian methods are flexible enough to incorporate new elements into the system, allowing for assessment of new grades including rare earth components (pg. 57, para. 2, lines 2-4). This means multi-phase Gibbs free energy are flexible to molten steel analysis. Luo et al. discloses that the online software is beneficial to improve stability and accuracy of the inclusion modification by calcium treatment during the production process of high-quality steels (pg. 257, para. 1). This means that the database built from this software will improve stability and accuracy of the Gibbs free minimization of molten steel. Zhao et al. discloses that thermodynamic calculations of various calcium aluminates and the precipitated CaS were performed to explain the formation mechanism for better controlling, and the production of clean steel involves controlling of nonmetallic inclusions (pg. 2115, col. 1, para. 1, lines 1-2; pg. 2116, col. 1, para. 1, lines 3-6). This means the activity value formulas are necessary components to the Gibbs free minimization of molten steel for controlling inclusions. Abraham et al. discloses that an optimized calcium treatment would result in alumina modified to form liquid calcium aluminate and sulfur tied up as CaS (pg. 140, col. 2, para. 8, lines 1-4). This means determining quantity of calcium in molten steel where the inclusions are controlled in liquid phase leads to optimal calcium treatment. Kumar et al. discloses that calcium recovery is a critical factor to be adjusted alongside calcium addition for optimum calcium levels in the bath (pg. 455, col. 1, para. 2, lines 1-4; pg. 455, col. 2, para. 1, lines 13-25). This means calcium yield will be an important adjustment in calcium treatment. The Experts at ASK Chemical discloses the process of calculating the length of wire necessary to feed into metal is automated and brings accuracy to the sequence of adding magnesium and inoculation materials (pg. 2, para. 4). This means calculating the length of fed calcium line will be efficient in calcium treatment. There is a likelihood of success, since all teachings are of thermodynamics in alloys or experimentation with alloys, which are well known techniques in the field of metallurgy.
With respect to claim 2:
Luo et al., Zhao et al., Abraham et al., and Kumar et al. do not disclose wherein the constraint condition is expressed by formulas (8)-(11) as follows:
∑
n
C
a
=
n
[
C
a
]
+
n
C
a
O
+
n
C
a
S
+
n
C
A
2
+
n
C
A
6
(8)
∑
n
A
l
=
n
[
A
l
]
+
2
n
A
l
2
O
3
+
4
n
C
A
2
+
12
n
C
A
6
(9)
∑
n
O
=
n
[
O
]
+
3
n
A
l
2
O
3
+
7
n
C
A
2
+
19
n
C
A
6
+
n
C
a
O
(10)
∑
n
S
=
n
[
C
a
S
]
+
n
C
a
S
(11)
where
∑
n
C
a
represents a total number of moles of calcium in the molten steel,
n
[
C
a
]
represents a number of moles of dissolved calcium in the liquid-phase steel,
n
[
A
l
]
represents a number of moles of dissolved aluminum in the liquid-phase steel,
n
[
O
]
represents a number of moles of dissolved oxygen in the liquid-phase steel,
n
[
S
]
represents a number of moles of dissolved sulfur in the liquid-phase steel,
n
C
a
O
represents a number of moles of CaO in the inclusions,
n
C
a
S
represents a number of moles of CaS in the inclusions,
n
A
l
2
O
3
represents a number of moles of
A
l
2
O
3
in the inclusions,
n
C
A
6
represents a number of moles of CaO
∙
6
A
l
2
O
3
in the inclusions, and
n
C
A
2
represents the number of moles of CaO
∙
2
A
l
2
O
3
in the inclusions.
However, Wu et al. discloses constraint condition equations representing matter conservation of the elements in the
C
O
2
-Fe-C system (pg. 1400-1401, col. 2, last paragraph). This teaches constraint conditions as part of calculating a total minimum Gibbs free energy expanded into separate phase components for Fe-C melts.
Wu et al. does not disclose constraint conditions for molten steel.
Koukkari et al. discloses that multi-phase Gibbsian methods are practical in predicting slag window conditions in alloy refining and steelmaking (pg. 57, para. 2, lines 1-2). This teaches that minimum Gibbs free energy and constraint conditions could be applied to molten steel.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (Metallurgical and Materials Transactions B, 2022, 53, 1396-1410), Koukkari (VTT Research Notes, 2009, 1-150), Luo et al. (11th International Symposium on High-Temperature Metallurgical Processing, 2020, 253-259), Zhao et al. (ISIJ International, 2015, 55(10), 2115-2124), Abraham et al. (Journal of Iron and Steel Research International, 2018, 25, 133-145), Kumar et al. (Ironmaking & Steelmaking, 2019, 46(5), 454-462), and The Experts at ASK Chemical (Evaluating Cored-Wire Practice to Automate Iron Treatment, Inoculation, Foundry Management & Technology, 1 December 2018, 1-3, https://www.foundrymag.com/ask-the-expert/article/21931901/evaluating-cored-wire-practice-to-automate-iron-treatment-inoculation) as applied to claims 1-2 above, in view of Zhang et al. (Metallurgical and Materials Transactions B, 2018, 49, 1841-1859), referred to as Zhang [A], Huang et al. [CN102206732A], and Zhang et al. (Results in Physics, 2019, 16, 1-5), referred to as Zhang [B].
Wu et al., Koukkari et al., Luo et al., Zhao et al., Abraham et al., Kumar et al., and The Experts at ASK Chemical are applied to claims 1-2 above.
With respect to claim 3:
Wu et al., Koukkari et al., Zhao et al., Abraham et al., Kumar et al., and The Experts at ASK Chemical do not disclose wherein the parameter information of the calcium treatment process comprises: a content of Carbon (C) of the molten steel, a content of Silicon (Si) of the molten steel, a content of Manganese (Mn) of the molten steel, a content of Phosphorus (P) of the molten steel, a content of Sulphur (S) of the molten steel, a content of Calcium (Ca) of the molten steel, a content of Aluminum (Al) of the molten steel, a total content of dissolved oxygen in the molten steel, a content of dissolved oxygen in the liquid-phase steel, the temperature of the molten steel, the weight per meter of the calcium line, the content of calcium of the calcium line, and the weight of the molten steel.
However, Luo et al. discloses a precise calcium treatment software and its operation interface in Figures 2-3, which includes parameter inputs comprising of Carbon (C), Silicon (Si), Manganese (Mn), Phosphorus (P), Sulphur (S), Calcium (Ca), Aluminum (Al), and temperature (pg. 256, para. 2, lines 1-5; Fig. 2-3). This teaches parameter information of the calcium treatment process comprising: a content of Carbon (C) of the molten steel, a content of Silicon (Si) of the molten steel, a content of Manganese (Mn) of the molten steel, a content of Phosphorus (P) of the molten steel, a content of Sulphur (S) of the molten steel, a content of Calcium (Ca) of the molten steel, a content of Aluminum (Al) of the molten steel, and the temperature of the molten steel.
Luo et al. does not disclose a total content of dissolved oxygen in the molten steel and a content of dissolved oxygen in the liquid-phase steel.
Zhang [A] discloses total oxygen in molten steel and dissolved oxygen in liquid steel (pg. 1857, Table VIII). This teaches a total content of dissolved oxygen in molten steel and a content of dissolved oxygen in liquid-phase steel.
Luo et al. and Zhang [A] do not disclose the weight per meter of the calcium line and the content of calcium of the calcium line.
Huang et al. discloses weight of calcium wire per meter as 70
±
3g and calcium content of calcium wire (pg. 3, Example 1). This teaches weight per meter of calcium line and calcium content of calcium line.
Luo et al., Zhang [A], and Huang et al. do not disclose the weight of the molten steel.
Zhang [B] discloses a weight of molten steel as 100 g (pg. 2, col. 2, para. 3). This teaches the weight of the molten steel.
It would have been prima facie obvious to one of ordinary skill in the art to combine the parameters disclosed by Luo et al. with the parameters disclosed by Zhang [A], Huang et al., and Zhang [B]. One would be motivated to combine parameters because Zhang [A] discloses that thermodynamic prediction was performed to predict the composition of inclusions considering the effect of the total oxygen and the total calcium and was validated by measurement (pg. 1858, col. 1, para. 3, lines 6-9). This means the total dissolved oxygen content and content of dissolved oxygen are reliable parameters. Huang et al. discloses significant improvement of calcium yield in their methods (pg. 3, para. 2). This means that the measurements of the calcium line are critical parameters for improving calcium yield. Zhang [B] discloses that the thermodynamic database software Factsage7.0 was used to study the influence of calcium, oxygen, and sulfur contents on tin removal to provide a theoretical basis for calcium treatment of tin removal in the steelmaking process (pg. 1, col. 2, para. 5). This means the weight of molten steel used as part of the software for the study is an important parameter for calcium treatment. There is a likelihood of success, since all methods deal with thermodynamics in alloys or calcium treatment, which are well known techniques in the field of metallurgy.
Claim 4 recites wherein the predicting the yield rate of the calcium during the calcium treatment process comprises: predicting the yield rate of the calcium according to one of a neural network model and a content of oxygen in the liquid-phase steel; wherein predicting the yield rate of the calcium according to the content of dissolved oxygen in the liquid-phase steel is expressed as a formula (12) as follows:
y
=
50000
*
x
o
+
10
(12)
where
x
o
represents the content of dissolved oxygen in the liquid-phase steel, and
y
represents the yield rate of the calcium predicted according to the content of dissolved oxygen in the liquid-phase steel.
Claim 5 recites wherein the neural network model is one of a shallow neural network model and a deep neural network model.
Claims 4 and 5 are free of the art.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571)270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
Prosecution Insights