DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1 and 18-25 have been presented for examination based on the amendment filed on 3/31/2026.
Claims 18-25 are new.
Claims 1 and 18-25 are newly rejected under 35 U.S.C. 101.
This action is made Final.
Response to Argument
Applicant’s argument pertaining to 35 USC 112 are moot in view of cancellation of claims 2-17.
Applicant’s arguments pertaining to rejection under 35 USC 101, specifically in remarks Pgs. 10-31 is considered in making the rejection below. The essence of the rejection is that generic use of machine learning to predict the updated parameter in view disclosure of generic use of machine learning (specification [0175]-[0177]) does not make a well known routine and conventional (WRC) process of modeling a glass melt mixing along with control of actual process in conjunction of modeling, significantly more because the said improvement (use of machine learning) does not disclosure sufficiently how the improvement is brought upon to predict updated design parameters. This opinion takes into consideration Berkheimer Memo and latest Desjardins Memo (use of machine learning). Applicant’s use of communication, user interface, use of encoder, database, modeling (generically as CFD) to control the glass mixing is WRC. This is specifically also mapped below.
Relevant Prior Art of Record
Examiner has reviewed the documents cited by applicant in PTO-1449 and they represent state of art at best and do not constitute to be a prior art.
US PGPUB No. US 202202129761 A1 by Li; Xinghua et al. (Newly cited) teaches glass mixing application/apparatus ([0052]-[0053]) with the program encoded ([0050], [0065]-[0075]).
US Patent No. US 122524302 B2 by Fayerweather; Carl L. et al. (newly cited) teaches similar glass mixing/melting apparatus.
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US Patent No. US 7578144 B2 by Bergman; Richard et al. (previously cited) teaches modeling glass melting, parameter update and manufacturing control based on CFD modeling (See Fig.2 Col.8 onwards).
NPL by Liang Wu et al “Development of a DEM–VOF Model for the Turbulent Free-Surface Flows with Particles and Its Application to Stirred Mixing System” (Industrial & Engineering Chemistry Research 2018 57 (5), 1714-1725 DOI: 10.1021/acs.iecr.7b04833) appears to teach a novel virtual dual grid porosity model to calculate the fluid porosity. The simulated results of single particle sedimentation, the falling of sinking particles, and the floating of buoyant particles agree well to analytical and literatures, which validate the proposed DEM−VOF model. The model designs a mixer with same input parameters as claimed invention as shown in the figure below:
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However this application is not for glass mixing and does not design parameters based on reference design parameters as claimed. More specifically it does not model the mixer in the same manner with equations related to sheer stress equivalence or mixing effect equivalence.
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 and 18-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 1 :
Step 1: the claims 1 is drawn to a method, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial exception identification.
Claim 1
Mapping Under Step 2A Prong 1
1. (Currently Amended) A method for designing a manufacturing and mixing system of a glass substrate, comprising:
selecting a standard mixing system as a reference mixing system, and obtaining a swept height of a blade of a mixer, a pull amount, an inner diameter of a mixing tank, a blade diameter of the mixer, a power of the mixer, a rotation speed of the mixer, and a torque of the mixer of the reference mixing system;
establishing a shear stress equivalence relationship based on the inner diameter of the mixing tank, the blade diameter of the mixer, and the rotation speed of the mixer;
establishing a mixing effect equivalence relationship based on the swept height of the blade of the mixer, the pull amount, the blade diameter of the mixer, the power of the mixer, the rotation speed of the mixer, and the torque of the mixer;
determining, according to the reference mixing system, the shear stress equivalence relationship, and the mixing effect equivalence relationship, an inner diameter of an actual mixing tank, a blade diameter of an actual mixer, and a swept height of a blade of the actual mixer of an actual mixing system to complete a design of the actual mixing system;
when a mixing operation in the actual mixing system is performed, obtaining at least one current mixing parameter and second inspection data corresponding to the at least one current mixing parameter; and
in response to a determination that a fourth matching degree between an actual mixing effect corresponding to the second inspection data and the target mixing effect does not satisfy the preset condition,
determining at least one updated mixing parameter based on the at least one current mixing parameter and the second inspection data by using a parameter adjustment model,
the parameter adjustment model being a machine learning model;
determining an adjustment instruction based on the at least one updated mixing parameter, and adjusting the at least one current mixing parameter of the actual mixing system to the at least one updated mixing parameter to perform mixing;
See step 2A Prong 2 & 2B.
Abstract Idea/Mathematical Concept/Mental Process: The shear stress equivalence relationship recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)).
The establishing may also recites mental process to write formula down based on observation (“based on…”) perform evaluation of the shear stress equivalence (as in MPEP 2106.04(a)(2)(III)(A)). Also see the wherein clause below defining the above relationship as mathematical equation.
Abstract Idea/Mathematical Concept/Mental Process: The a mixing effect equivalence relationship recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)).
The establishing may also recites mental process to write formula down based on observation (“based on…”) perform evaluation of the a mixing effect equivalence relationship (as in MPEP 2106.04(a)(2)(III)(A)). Also see the wherein clause below defining the above relationship as mathematical equation.
Abstract Idea/Mathematical Concept/Mental Process: Determination of actual parameters based on observed mathematical relationship may be mental step of mathematical concept. (as in MPEP 2106.04(a)(2)(III)(A)). This is also rejected below under step 2A Prong 2.
Abstract Idea/Mental Process: Determination of current mixing parameter and second inspection data is observation and is considered a mental step.
Also see under step 2A Prong 2 & 2B.
Abstract Idea/Mathematical Concept/Mental Process: Determining that the comparison does not satisfy the preset condition is a mathematical concept. This can also be considered as mental step where the evaluation (to satisfy the preset condition) is based on observation (a determination that a fourth matching degree between an actual mixing effect corresponding to the second inspection data and the target mixing effect) (as in MPEP 2106.04(a)(2)(III)(A)).
Abstract Idea/Mathematical Concept: The determination of updated mixing parameter is based on a parameter adjustment model and as broadly claimed is considered as generic machine learning model.
Also see Step 2A Prong 2.
See Step 2A Prong 2 and 2B.
See Step 2A Prong 2 and 2B.
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Abstract Idea/Mathematical Concept/Mental Process: The equation recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), mathematical formula/ equations (as in MPEP 2106.04(a)(2)(I)(B) ), and/or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) and is considered as judicial exception.
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Abstract Idea/Mathematical Concept/Mental Process: The equation(s) recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), mathematical formula/ equations (as in MPEP 2106.04(a)(2)(I)(B) ), and/or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) and is considered as judicial exception.
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Abstract Idea/Mathematical Concept/Mental Process: The equation(s) recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), mathematical formula/ equations (as in MPEP 2106.04(a)(2)(I)(B) ), and/or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) and is considered as judicial exception.
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Abstract Idea/Mathematical Concept/Mental Process: The equation recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)), mathematical formula/ equations (as in MPEP 2106.04(a)(2)(I)(B) ), and/or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) and is considered as judicial exception related to computing the various datum.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. This is evident from the fact that the designing aspect is not computer implemented. Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
1. (Currently Amended) A method for designing a manufacturing and mixing system of a glass substrate, comprising:
selecting a standard mixing system as a reference mixing system, and obtaining a swept height of a blade of a mixer, a pull amount, an inner diameter of a mixing tank, a blade diameter of the mixer, a power of the mixer, a rotation speed of the mixer, and a torque of the mixer of the reference mixing system;
establishing a shear stress equivalence relationship based on the inner diameter of the mixing tank, the blade diameter of the mixer, and the rotation speed of the mixer;
establishing a mixing effect equivalence relationship based on the swept height of the blade of the mixer, the pull amount, the blade diameter of the mixer, the power of the mixer, the rotation speed of the mixer, and the torque of the mixer;
determining, according to the reference mixing system, the shear stress equivalence relationship, and the mixing effect equivalence relationship, an inner diameter of an actual mixing tank, a blade diameter of an actual mixer, and a swept height of a blade of the actual mixer of an actual mixing system [1] to complete a design of the actual mixing system;
when a mixing operation in the actual mixing system is performed, obtaining at least one current mixing parameter and second inspection data corresponding to the at least one current mixing parameter; and
in response to a determination that a fourth matching degree between an actual mixing effect corresponding to the second inspection data and the target mixing effect does not satisfy the preset condition,
determining at least one updated mixing parameter based on the at least one current mixing parameter and the second inspection data by using a parameter adjustment model, the parameter adjustment model being a machine learning model;
determining an adjustment instruction based on the at least one updated mixing parameter, and adjusting the at least one current mixing parameter of the actual mixing system to the at least one updated mixing parameter to perform mixing;
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the this is mere data (datum, viz. a swept height of a blade of a mixer, a pull amount, an inner diameter of a mixing tank, a blade diameter of the mixer, a power of the mixer, a rotation speed of the mixer, and a torque of the mixer of the reference mixing system) gathering from reference mixing system.
See Step 2A Prong 1 above.
See Step 2A Prong 1 above.
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
In this case the 3 variables needed to design an actual system (as identified by [1] in limitations ) are interdependent on each other as evident from the equations further claimed below. The claim does not specific how one of them can be selected for a specific actual mixing system. There is no algorithm that selects one value for and steps do not describe how any one value of any one of the three variables is selected.
Under MPEP 2106.05(g), the obtaining aspect is considered as data gathering. Further Under MPEP 2106.05(h) the specific parameters gathered relates to field of use in the field of mixing systems.
See Step 2A Prong 1 as mapped above.
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Here mere use of generic machine learning is an idea of solution. The specification3 does not show how the machine learning model determines at least one updated mixing parameter.
Further in view of Memo “Advance notice of change to the MPEP in light of Ex Parte Desjardins” dated December 5, 2025: In contrast to Desjardins the specification does not identify improvements as to how the machine learning model itself operates and does not disclose improvement in the machine learning itself. See ¶[0175]-[0177]. Mere use of machine learning amount to no more than judicial exception that does not improve on the practical application.
Under MPEP 2106.05(f)(1), determining an adjustment instruction, is generically recited, and no means are claimed or shown how the specific instruction is generated.
Under MPEP 2106.05(g), the obtaining aspect is considered as extra-solution activity.
Further Under MPEP 2106.05(h) the use in the field of mixing systems is simply field of use of generic instructions.
wherein
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See Step 2A Prong 1 above.
Further to complement the idea of solution mapping above, this equation shows that actual mixing system variables N, Dy and DB are dependent on reference mixing system variables values N0, Dy0 and DB0, but there are no steps disclosed how any of N, Dy and DB are picked.
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See Step 2A Prong 1 above.
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See Step 2A Prong 1 above.
Additionally, contributing to idea of solution under this prong:
The selection of swept height of blade of actual mixer H is critical and no algorithm is disclosed how the selection is made for this variable. This variable appears important because then it could be used in next equation to solve for DY. For this reason the equations although tell us how to model the system but not how the value of variable (e.g. H) is solved – expert knowledge?
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See Step 2A Prong 1 above.
Additionally, contributing to idea of solution under this prong:
Notice DY can be used in this equation to solve for DB, but another variable N is also needs solving. Therefore the claims fails to show the steps which variables are solved first and how the value for them is decided (Idea of solution). These intertwined references of equations & variables do not lead to practical application as one cannot determine how the solution to these variables is achieved. Reject
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as mapped in each of the steps above. See MPEP 2106.05(f).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, generally linking the use of the judicial exception to a particular environment of field of use (mixing system) does not integrate the judicial exception into a practical application or provide significantly more than the abstract idea(See MPEP 2106.05(h)) because the steps may lead to novel algorithm to mathematically model the mixing system (therefore is a judicial exception), but it does not lead to steps to designing of the actual model system, as the claim fails to describe the algorithm that leads to selection to model input(s) and how the input(s) is/are optimized to contribute significantly more. The claim does not disclose any limitations on how the inputs and their bounds are selected.
Further in view of Desjardins Memo, the claim as a whole does not (a) improve the function of the machine learning model or (b) is an improvement to computer component or system performance based upon adjustments to parameters of a machine learning model. (as discussed in view of MPEP 2106.05(a)). Further a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome. Here the use of machine learning is generic as discussed in any of the possible models (see specification [0175]-[0177]) that can be employed to generically determine the adjustment. No additional components alone or as a whole lead to technological improvement (as discussed above in view of MPEP 2106.05(a)).
The method claim 1 therefore is considered to be patent ineligible.
Claims 2-17 are cancelled.
Claim 18 recite following limitations and for brevity the steps mapped in one table for purposes of rejection, wherein each limitation is identified with appropriate step it is rejected under.
Claim 18 limitations
Mapping under Step 2A Prong 1 & 2 , and 2B
18. (New) The method of claim 1, wherein at least one initial design parameter of the actual mixing system includes the inner diameter of the actual mixing tank, the blade diameter of the actual mixer and the swept height of the blade of the actual mixer of the actual mixing system, the method further includes:
predicting, based on the at least one initial design parameter, a predicted mixing effect of the actual mixing system when processing a glass melt, and
in response to a determination that a first matching degree between the predicted mixing effect and a target mixing effect satisfies a preset condition, generate a verification instruction;
in response to receiving the verification instruction, initiating a verification action to determine a verification mixing effect, and
determining a second matching degree between the verification mixing effect and the target mixing effect;
in response to a determination that the second matching degree does not satisfy the preset condition, determining at least one optimized design parameter by performing at least one round of update on the at least one initial design parameter;
wherein the verification action includes: sending the manufacturing instruction to the test terminal, and in response to receiving the test instruction, collecting, via the inspection device in real-time, first inspection data when the actual mixing system processes the glass melt;
and determining the verification mixing effect based on the first inspection data;
or in response to a determination that the second matching degree satisfies the preset condition, determining the at least one initial design parameter as the at least one optimized design parameter; and,
establishing a communication connection with an external test system, the external test system comprising a test terminal and an inspection device, the test terminal being configured to send a test instruction and receive a manufacturing instruction;
converting the at least one optimized design parameter into a reference code using an encoder, obtaining a label corresponding to reference melt data from a first database in a memory, storing the reference code and the label in a second database in the memory, the reference melt data being data of the glass melt corresponding to the at least one optimized design parameter; and
determining, based on a user input, initial melt data of a glass melt to be processed and determining a target label from the first database;
determining a target code based on the target label, converting the target code into at least one target design parameter using a decoder corresponding to the encoder; and
generating a design simulation diagram corresponding to the at least one target design parameter based on the at least one target design parameter and a simulation instruction, and displaying the design simulation diagram through a user terminal;
wherein the design simulation diagram is a picture or a modeling model of a mixing vessel generated based on the at least one target design parameter;
andthe user input is obtained through the user terminal and includes at least one of the initial melt data or the simulation instruction.
Under step 2A Prong 1: Mental step where the model is defined based on observation of the parameters as selected for the model.
Alternately, Under step 2A Prong 2 & step 2B, it appears the initial design parameters for actual system are simply entered (by the user?) and is considered as data gathering step/extra-solution activity under MPEP 2106.05(g).
Under step 2A Prong 1: Mental step/ Mathematical concept: predicting can be considered as judgement/opinion/evaluation based on the observation (initial design parameters). This could also be considered as mathematical concept if the predicting involves machine learning4.
Under step 2A Prong 1: Mental step: The generation of verification instruction5 is judgement/evaluation based on observation of the matching.
Under step 2A Prong 1: Mental step: The initiation of verification instruction is done with a processor as a tool. Further Under step 2A Prong 2 and 2B: This would be field of use of the user decision (mental step with process).
Under step 2A Prong 2 and 2B/MPEP 2106.05(g)/(h): This would extra-solution/post-solution activity of data collection to collect verification mixing effect.
Under step 2A Prong 1: The matching is considered as mental step, with processor as generic tool. See MPEP 2106.04(a)(2)(III)(C).
Under step 2A Prong 2 & 2B, it appears this is an idea of solution under MPEP 2106.05(f)(1) as no details of how updating happens in response of matching degree does not satisfy the preset condition.
Under step 2A Prong 1: Mental Step: The limitation “matching degree does not satisfy the preset condition” itself can be considered a mental step.
Under step 2A Prong 2 & 2B: MPEP 2106.05(g) & (h) this is considered a data gathering (receiving / collecting limitation) and simply field (glass melt mixing process) of use. This also might be considered under MPEP 2106.05(d) WRC NPL by Liang Wu et al as cited above in relevant prior art. Also see US 20120008456 A1 (Fig.2), US 7578144 B2 (Fig.2), US 7578144 B2 (Col.1 Lines 51-55; Col.6 Lines 49-67). Also see US 20220212976 A1 (¶[0065]-[0075])
Under step 2A Prong 2 & 2B: MPEP 2106.05 (d): WRC is shown, besides the above cited references, can be shown in US 7578144 B2 (Col.8 Lines 7-28)
Under step 2A Prong 2 & 2B: MPEP 2106.05 (d): WRC is shown, besides the above cited references, can be shown in US 7578144 B2 (circa 2009, Col.8 Lines 7-28) – This is showing model computing variables and then modifying the manufacturing for glass melt operation.
Under step 2A Prong 2 & 2B: MPEP 2106.05 (d): WRC is shown US 20220212976 A1 (¶[0065]-[0075]) teaching encoder to program the glass melting process based on design parameters. Database [0099].
Under step 2A Prong 1: Mental Step: This is a mental step of opinion (creating a target label).
Under step 2A Prong 1: Mental Step: This is a mental step of opinion (using code based on a target label).This can also be analyzed under MPEP 21060.5(d) as WRC US 20220212976 A1 (¶[0065]-[0075]) teaching encoder to program the glass melting process based on design parameters. Database [0099]. See specifically control code for specific heating application6.
Under step 2A Prong 2 & 2B, it appears this is extra-solution to represent information using computer as a tool. Under WRC US 7578144 B2 (circa 2009, Col.8 Lines 7-28 and Col.8 in general shows use of CFD in Example which would make this known).
Under step 2A Prong 2 & 2B, it appears this is extra-solution to represent information using computer as a tool. Under WRC US 7578144 B2 (circa 2009, Col.8 Lines 7-28 and Col.8 in general shows use of CFD in Example which would make this known) – See picture Fig.1.
Under step 2A Prong 2 & 2B, it appears this is extra-solution to represent information using computer as a tool. Under WRC US 7578144 B2 (circa 2009, Col.5 Lines 6-25).
The additional aspect of manufacturing control as disclosed in last 7 limitations is WRC in production/manufacturing of melt glass application. The modeling does not improve the conventional modeling/manufacturing as the novel aspects (use of novel machine learning applied to this field) is neither claimed nor discussed in detail in the specification. See Specification [00175]-[0177]. Therefore under Step 2B the additional elements in view of idea of solution (use of generic machine learning) does not improve the technology or the machine learning aspect.
Claim 19 recite following limitations and for brevity the steps mapped in one table for purposes of rejection, wherein each limitation is identified with appropriate step it is rejected under.
Claim 19 limitations
Mapping under Step 2A Prong 1 & 2 , and 2B
19. (New) the method of claim 18, wherein the at least one round of update includes:
determining an updated mixing effect by performing the verification action based on the verification instruction, the verification instruction comprising at least one updated design parameter, and
the at least one updated design parameter being obtained by updating at least one update parameter to be updated based on an update step length;
in response to determining that a third matching degree between the updated mixing effect and the target mixing effect does not satisfy the preset condition, determining an update instruction and sending the update instruction for a next round of update;
or in response to determining that the third matching degree between the updated mixing effect and the target mixing effect satisfies the preset condition,
determining an abort instruction and sending the abort instruction to stop the update, and determining the at least one updated design parameter as the at least one optimized design parameter.
Under step 2A Prong 1: Mental step where the updated is based on observation of the verification instruction.
Under step 2A Prong 1: Mental step where the updated is based on opinion (step length).
Under step 2A Prong 2 & 2B, it appears this is an idea of solution under MPEP 2106.05(f)(1) as no details of how updating happens in response of matching degree does not satisfy the preset condition.
Under step 2A Prong 1: Mental Step: The limitation “matching degree does not satisfy the preset condition” itself can be considered a mental step. Further simply enumerating update is step length does not teach how it is determined/expert knowledge/arbitrary.
Under step 2A Prong 1: Mental step – simply specifying by user the abort condition.
Claims 20-23 are recite various aspect of the using machine learning (idea of solution) and specifying the inputs to the model (data gathering of design parameters, inspection data, thresholds). The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 24 recites "... determining an updated mixing effect, wherein the updated mixing effect refers to a mixing effect corresponding to a design parameter after at least one round of update is performed; in response to a determination that a third matching degree between the updated mixing effect and the target mixing effect does not satisfy the preset condition, determining an update instruction to perform a next round of update; wherein the update instruction is used to control the update of the design parameter; and determining at least one new updated design parameter and a new updated mixing effect....". This at best contributes to algorithm to update the design parameter and considered mental step/abstract idea. The use of processor as additional element (in the parent claim 1) is at best using it as a tool. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 25 recites "... in response to a determination that the third matching degree does not satisfy the preset condition, when the update round threshold has been reached, an alert is sent to an operator, reminding the operator to replace at least one reference design parameter and re-determine the at least one initial design parameter, including: based on at least one reference design parameter corresponding to a new reference mixing system, establishing an equivalence relationship based on a preset rule, and determining at least one new initial design parameter of the mixing system based on the equivalence relationship....". This at best contributes to algorithm to update the design parameter and considered mental step/abstract idea. See MPEP 2106.04(a)(2)(III)(C). Notifying operator is extra solution activity and field of use. See MPEP 2106.05(g) & (h). The use of processor as additional element (in the parent claim 1) is at best using it as a tool. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Tuesday, May 12, 2026
1 See PE2E Search History L58
2 See PE2E Search History L62
3 See Specification [0175]-[0177] generically enumerates various known machine learning techniques/embodiments, none presented in a manner that would show how the machine learning determining at least one updated mixing parameter based on the at least one current mixing parameter.
4 See Specification [0074] "... The first prediction model may be a model configured to determine the predicted mixing effect and may be a machine learning model. In some embodiments, the first prediction model may be a Convolutional Neural Networks (CNN) model, a Deep Neural Networks (DNN) model, or any models that may realize the same or similar functions...."; [0138], Claims 12/13 as originally filed.
5 See Specification [0027][0028][0032][0065][0066][0092]-[0102][0130]-[0132] at least discuss verification instruction, but none show any specific embodiment of what is the verification instruction.
6 US 20220212976 A1 "... [0066] The control device 315 may be configured to (e.g., “programmed to”, “encoded to”, “designed to”, and/or “made to”) send command signals (e.g., by way of communication line 319) to the laser generator 301 to control a characteristic of the pulsed laser beam 303 to selectively control the heating of a location of a nonuniformity of the molten portion 104..."
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