DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 12/28/2023 for application number 18/574,792.
Claims 1-17 are pending.
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 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.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it is directed to a “data carrier signal.” Signals are non-statutory subject matter. In re Nuijten, 500 F.3d 1346, 1356-57, 84 U.S.P.Q.2d 1495, 1502 (Fed. Cir. 2007).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
In claims 1-17, the exemplary claim language of "particularly" and “preferably,” (which are used throughout the claims) renders the claims indefinite because it is unclear if the limitations that follow are required by the claim or not. See MPEP § 2173.05(d). For prior art purposes, the Examiner is assuming the limitations are not required.
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.
Claim(s) 1-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Johnson et al. (US 6,332,961 B1).
In reference to claim 1, Johson discloses a method for machine learning a detection of at least one irregularity in a plasma system, particularly an RF powered plasma processing system (method for detecting arcing in RF plasma system, col. 3, line 40 – col. 4, line 6), comprising: input signal each related to an analog signal of a power delivery system for the plasma system and/or to another characteristic of the power delivery system and/or of the plasma system (analog signal of power source is received and converted into digital, col. 7, lines 27-59), the at least one input signal having at least one irregularity feature indicative of the irregularity in the plasma system (signal includes features of arcing, col. 6, line 47 – col. 7, line 16), Performing a machine learning procedure wherein the at least one input signal having the at least one irregularity feature is processed by a programmable circuit (plasma controller can be an FPGA, col. 9, line 17-26) to train the detection of the irregularity in the plasma system (neural network on plasma controller is trained to detect arcing, col. 10, lines 34-48).
In reference to claim 2, Johson discloses the method according to claim 1, wherein the programmable circuit is configured as a programmable integrated circuit, preferably a digital signal processor (DSP), a complex programmable logic device (CPLD) or a field programmable gate array (FPGA) (FPGA, col. 9, line 17-26).
In reference to claim 3, Johson discloses the method according to claim 1, wherein the programmable circuit carries out, at least partially, a detection procedure, particularly comprising an application of a neural network, preferably a pattern recognition or pattern matching using the neural network, or an algorithm, for identifying the at least one irregularity feature (neural network used to detect arcing, col. 10, lines 34-48), and wherein the machine learning procedure comprises: Performing, by the circuit, the processing of the at least one input signal using the detection procedure and at least one configurable parameter of the detection procedure, particularly weights of the neural network or parameters of the algorithm, wherein a configuration of the at least one parameter is varied, particularly modified, for each processing of the input signal to obtain respective processing results, Determining at least one parameter result, particularly comprising a selection of the varied configurations, as a training result of the machine learning based on the processing results (neural network trained by modifying parameters over training iterations, and the trained neural network is then used to predict and detect errors, col. 11, lines 5-41).
In reference to claim 4, Johson discloses the method according to claim 3, wherein the machine learning procedure comprises repeated processing steps, in each of which the same at least one input signal is processed by the programmable circuit, but using the different varied configurations of the parameter, to obtain the respective processing results assigned to the used configurations, wherein the evaluation of the varied configurations is performed by comparing each of the obtained processing results with a reference result (neural network trained by modifying parameters over training iterations and comparing to known outputs, col. 11, lines 5-41).
In reference to claim 5, Johson discloses the method according to claim 4, wherein the evaluation of the varied configurations depends on the matching of the processing results with the reference result, wherein at least one configuration with the highest evaluation is selected from the varied configurations as the at least one determined parameter result (weights of neural network are modified until optimized, i.e. the weights are correctly outputting desired results, col. 11, lines 5-41).
In reference to claim 6, Johson discloses the method according to claim 4, wherein the reference result is a predetermined indication of the at least one irregularity (reference is known output, or known arc, col. 11, lines 5-41).
In reference to claim 7, Johson discloses the method according to claim 1, wherein each of the at least one input signals is related to a radio-frequency signal used for supplying power to the plasma system and/or to another characteristic of the power delivery system and/or of the plasma system (signal of power source in RF plasma system, col. 7, lines 27-59), and the irregularity is specific to an arc that occurs in the plasma processing system or is specific to a probability for an occurrence of the arc (irregularity is arcing, col. 6, line 47 – col. 7, line 16).
In reference to claim 8, Johson discloses the method according to claim 1, wherein the machine learned detection of the at least one irregularity is used for arc detection and/or arc prevention and/or arc management (detecting and preventing arcs, col. 3, line 40 – col. 4, line 6; col. 11, lines 5-41).
In reference to claim 9, Johson discloses the method according to claim 1, wherein the machine learning procedure provides an iterative determination of a configuration of at least one parameter of a detection procedure for the detection, particularly a configuration of at least one weight of a neural network or of at least one parameter of an algorithm, particularly the neural network or the algorithm being implemented at least partly by the programmable circuit (neural network is trained iteratively, col. 11, lines 5-41), wherein the determined configuration is afterwards used for the detection procedure in field operation of the power delivery system, comprising: Outputting a warning information when the irregularity has been detected by the detection procedure (neural network is deployed in use and warns when arc is predicted or detected, col. 10, lines 34-48).
In reference to claim 10, Johson discloses the method according to claim 1, wherein the providing the at least one input signal (210) comprises: Recording and converting an analog signal of the power delivery system (1) in the form of a radio-frequency signal and/or another characteristic of the power delivery system and/or of the plasma system to obtain a digital representation of the analog signal and/or of the another characteristic (analog signal of power source is received and converted into digital, col. 7, lines 27-59), Providing the digital representation of the analog signal and/or of the another characteristic as the at least one input signal to the programmable circuit (digital signal is provided to neural network, col. 10, lines 34-48).
In reference to claim 13, Johson discloses the method according to claim, wherein the machine learning procedure is configured as a training procedure for parameterization of the programmable circuit (neural network is trained, col. 11, lines 5-41), wherein exactly the same circuit or another circuit with the same parameterization is usable for the detection of the at least one irregularity during a field operation of the power delivery system, particularly for arc detection and/or arc prevention and/or arc management (neural network can then be deployed for arcing detection / prevention, col. 10, lines 34-48).
In reference to 14, Johson discloses the method according to claim 13, wherein in the field operation, upon detection of the at least one irregularity, at least one of the following actions is initiated individually or in combination: At least a partly or a complete switch off of the power delivery system a switch off of the power delivery system without subsequent restart of the power delivery system, a temporary switch off of the power delivery system with subsequent restart of the power delivery system, a temporary reduction of the output power of the power delivery system, a temporary modification of an output frequency of the power delivery system a temporary modification of at least one adjustable element in the impedance matching network (in response to detecting or predicting arc, action of temporary switch off of power or power variation is performed, col. 18, line 35 – col. 19, line 6).
In reference to 15, Johson discloses the method according to claim 1, in that the detection of the irregularity comprises a probabilistic detection, particularly for detecting an arc before it occurs in the plasma processing system, preferably for the determination of a probability of the occurrence of the arc and/or for arc prevention (detection is a prediction that arcing is imminent or likely to occur).
In reference to claim 16, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a signal associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The remaining references also teach machine learning for detecting problems in plasma power systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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