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 .
DETAILED ACTION
Claims 1-11 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the best mode contemplated by the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s) has not been disclosed. Evidence of concealment of the best mode is based upon “In this regard, the above method provides for creating the process parameter model with the aid of a quality model. The data-based process parameter model is trained on the basis of the quality model.” [para 0016] and “The core of the method is the linking of process parameter model 11 with the quality model 12.” [para 0043]. Since the independent claim 1 does not contain this feature, it fails under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the best mode contemplated by the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventors have not been disclosed. Claim 7 should therefore be included in the subject matter of claim 1.
Claim 1 defines a quality model with which is “… designed to specify the quality of the resulting component … on the basis of … one or more predefined state variables … and … one or more process parameters …”. However, claim 1 also defines the step of “… training a data-based process parameter model to output one or more process parameters based on one or more measurement variables captured by a sensor and/or one or more predefined state variables by optimizing the quality.”
It is unclear: (a) whether the “one or more predefined state variables” correspond to the same variable; (b) how can “one or more predefined state variables” constitute the current state of the element; (c) as a result of the “and/or” and “or” conjunctions, the number of possible alternatives in the claims makes the sought scope of protection unclear.
It is unclear what the difference is between a quality function and a quality model.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Masuda et al (US Pub. 2020/0033842; hereinafter Masuda).
As per claim 1, Masuda discloses a computer-implemented method for providing a process parameter model for parameterizing one or more process steps of a production process for manufacturing a component [Abstract; para 0002; generating a first learning model for estimating grinding quality of a workpiece], the method comprising:
providing a quality model for determining a quality, the quality model configured to specify the quality of the resulting component directly or with the aid of a predefined quality function based on one or more predefined measurement variables and/or one or more predefined state variables, which each specify a property of a pre-product or intermediate product of the component being manufactured and/or a production device for performing a process step and/or at least one environmental condition, and based on one or more process parameters which control a corresponding one of the process steps [Abstract; para 56-57; “The machine learning device 100 includes elements 101a, 101b, and 101c functioning in a first learning phase 101 that generates the first learning model and elements 102a and 102b that function in an estimation phase 102 (generally also referred to as an “inference phase”) that estimates grinding quality. The machine learning device 100 includes an element 101a that acquires first learning input data, an element 101b that acquires first supervision data, and an element 101c that generates a first learning model, as the elements functioning in the first learning phase 101.”; “First learning input data which is acquired by the element 101a is input data which is used for machine learning and examples thereof include operation command data, actual operation data, first measured data (data indicating the states of the structural members), and second measured data (data associated with a grinding region).”]; and
training a data-based process parameter model to output one or more process parameters based on the basis of one or more measurement variables captured by a sensor and/or one or more predefined state variables by optimizing the quality [Abstract; para 101; “The third learning model is a model indicating a correlation between the first data relationship before adjustment and the second data relationship after adjustment. The third learning model generating unit 220 learns a method of adjustment from the unadjusted operation command data on the first workpiece W (i.e., the operation command data on the first workpiece W before adjustment) to the adjusted operation command data on the second workpiece W (i.e., the operation command data on the second workpiece W after adjustment) such that the grinding quality data on the second workpiece W after adjustment is better than the grinding quality data on the first workpiece W before adjustment, that is, such that the incentive increases.”].
As per claim 2, Masuda discloses wherein the process parameters comprise a constant control variable for a process step, a time course of a control variable for a process step, a control parameter of a control for a process step, and/or a target manipulated variable for control for a process step [para 0002, 0010, 0021-0022, 0025-0028; commands are control parameters of a control for a process step].
As per claim 3, Masuda discloses wherein; the quality model comprises a physical model, a heuristic or data-based model and is configured to evaluate properties of the manufactured component and/or costs of the production process based on an initial situation, which is specified by the at least one measurement variable and/or the at least one state variable and the at least one process parameter, which characterizes the performance of the production process, to evaluate properties of the manufactured component and/or costs of the production process, and the quality is determined with the aid of the quality function with respect to the properties of the manufactured component and/or the costs of the production process [para 0059; “The first learning model which is generated by the element 101c is a model (a function) for estimating grinding quality of a workpiece W by performing supervised learning of the machine learning, based on the first learning input data and the first supervision data. Here, the first learning model may be generated by applying unsupervised learning for the purpose of classification of grinding quality. Here, when supervised learning is applied, it is possible to acquire grinding quality with high accuracy.”; para 0056-0057].
As per claim 4, Masuda discloses wherein; the quality model comprises a data-based model, training datasets are determined to train the quality model, the training datasets are determined by training data points, which are determined by varying values of the one or more measurement variables and/or one or more state variables and varying values of the process parameters within respectively predefined allowable value ranges, with respectively assigned qualities as labels, the qualities resulting in each case from at least one property of the manufactured component and/or costs of the production process with the aid of the quality function, and the quality model is trained with the training datasets [Fig. 3; para 0059; the variation of data, as defined is considered to be inherent for the use of data in order to generate the first learning model; see also table 1].
As per claim 5, Masuda discloses wherein; the quality model comprises a data-based model, training datasets are determined to train the quality model, the training datasets are determined by training data points, which are determined by varying values of the one or more measurement variables and/or one or more state variables and varying values of the process parameters within respectively predefined allowable value ranges, each assigned with at least one property of the manufactured component and/or costs of the production process as labels, from which the quality can be determined with the aid of the quality function, and the quality model is trained with the training datasets [Fig. 3; para 0059; the variation of data, as defined is considered to be inherent for the use of data in order to generate the first learning model; see also table 1].
As per claim 6, Masuda discloses wherein: the properties of the manufactured component have geometric dimensions along with dimensional tolerances, a surface quality of the manufactured component, an electrical property of the manufactured component and a robustness of the manufactured component, and the cost of the production process includes wear of a tool, a duration of the production process, comprises an energy expenditure of the production process and a material expenditure [para 0002-0003; surface quality (for example a surface roughness)].
As per claim 7, Masuda discloses wherein training data points are used by varying the one or more measurement variables and/or the one or more predefined state variables within their respective value ranges to train the data-based process parameter model based on a loss function which is determined by the quality resulting from the application of the quality model [para 0101-0102; It would be obvious for a routineer in the art to consider a loss function; this is a standard optimization; “… such that the grinding quality data on the second workpiece W after adjustment is better than the grinding quality data on the first workpiece W before adjustment …”; ].
As per claim 8, Masuda discloses wherein the process parameter model is used prior to production of a component in order to parameterize the one or more process steps, based on at least one measurement variable of a property captured by a sensor or state of one or more pre- products, a property or state of one or more production devices for the one or more process steps and/or one or more environmental conditions with the aid of the trained process parameter model [Though Masuda relates only to the adjustment of the command, it would be obvious to a routineer in the art that the system defined in Masuda should also be used to determine the starting commands of the production process].
As per claim 9, Masuda discloses a device for performing the method according to claim 1 [Fig. 1].
As per claim 10, Masuda discloses wherein a computer program product comprises instructions which, when the computer program product is executed by at least one data processing device, cause the data processing device to perform the method of claim 1 [para 0061; the control device generates an NC program].
As per claim 11, Masuda discloses a non-transitory machine-readable storage medium comprising instructions which, when executed by at least one data processing device, cause the data processing device to perform the method according to claim 1 [para 0061; inherent to generate an NC program as it has to be stored in a machine-readable storage medium].
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-5600758 discloses methods for conducting a process in an automatically controlled system preset the system at the beginning of each process run based on at least one process parameter. The process parameter is precomputed with a model of the process which is supplied with input values.
US-20210247744 discloses manufacturing process control using constrained reinforcement machine learning.
US-20200166909 discloses a method for real-time adaptive control of manufacturing process wherein a machine learning algorithm providing output values to adjust the manufacturing process control parameters in a real-time.
US-20220090912 discloses a trained machine learning based measurement model is employed to directly estimate values of parameters of interest based on raw measurement data.
N. EP-3970905 discloses a method for calculating process parameters, which are optimized for processing a workpiece with specific material properties by means of a laser machine.
O. CN-113987938 discloses a process parameter optimization method, model training method, device, device, storage medium and computer program product, relating to the technical field of artificial intelligence, specifically to the deep learning field of industrial large data, which can be applied to the technical parameter optimization scene.
P. CN-113537503 discloses a model training method of incremental optimization; the method is acted on a plurality of edge AI device node through the dispatching master station.
Q. CN-105550387 discloses a method and a system for constructing a process model on the basis of parameterization.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SURESH K SURYAWANSHI whose telephone number is (571)272-3668. The examiner can normally be reached M-F 8:00-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M Lo can be reached at 5712729774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SURESH SURYAWANSHI/Primary Examiner, Art Unit 2116
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