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 .
This Action is non-final and is in response to the claims filed December 29, 2023. Claims 1-20 are currently pending, of which claims 1-20 are currently rejected.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea(s) without significantly more.
As per claim 1, at Step 1, the claim is directed to a statutory category of invention (method/process). At Step 2A, Prong 1, the claim has been replicated below and the abstract ideas identified accordingly:
A method for automatically optimizing parameters of a safety-critical system of interconnected subsystems, comprising:
selecting a representative sample data set for optimization of parameters of interconnected subsystems of a complex system using a hybrid data analytics approach (mental process – evaluation and judgment);
breaking down a complexity of optimizing the complex system into interconnected subsystems by determining an order of the interconnected subsystems for optimization (mental process – evaluation and judgment);
based on the order of the interconnected subsystems, exploring a parameter space of each of the interconnected subsystems to define an optimal configuration of the parameters of the interconnected subsystems for optimizing performance of the interconnected subsystems (mental process – evaluation and judgment);
determining an overall optimal setting for the complex system of the interconnected subsystems based on an optimal configuration of the parameters of the interconnected subsystems (mental process – evaluation and judgment); and
incorporating compliance to certification of a safety-critical system in the overall optimal setting for the complex system (mental process – evaluation and judgment).
That is, a user/operator can assess the various features of the system and rank them accordingly to determine what is optimal. This can be based on experience with the system and its subsystems, or other knowledge. There is no actual change made to the subsystem, nor is there anything actuated based on the optimization. The claimed features are all evaluations that can be performed in the human mind with the assistance of pen and paper.
At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s).
As per claim 2, the claim is directed to the abstract idea of evaluating configurations for optimal performance (mental process – evaluation and judgment with the assistance of pen and paper). At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s).
As per claim 3, the claim is directed to the abstract idea of evaluating configurations for optimal performance (mental process – evaluation and judgment with the assistance of pen and paper). At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s).
As per claim 4, the claim is directed to the abstract idea of evaluating configurations for optimal performance (mental process – evaluation and judgment with the assistance of pen and paper). At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s).
As per claim 5, the claim is directed to the abstract idea of evaluating configurations for optimal performance (mental process – evaluation and judgment with the assistance of pen and paper). At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s). Machine learning is merely an “apply it” scenario (or an equivalent) with the judicial exception, or are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
As per claim 6, the claim is directed to the abstract idea of evaluating configurations for optimal performance (mental process – evaluation and judgment with the assistance of pen and paper). At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s).
As per claim 7, the claim is directed to the abstract idea of evaluating configurations for optimal performance (mental process – evaluation and judgment with the assistance of pen and paper). At Step 2A Prong 2 and Step 2B, there are no additional elements that integrate the abstract idea into a practical application, nor are there any additional elements that amount to significantly more than the judicial exception(s).
As per claims 8-14, the claims are directed to an apparatus that implements the same features as the method of claims 1-7, respectively, and are therefore rejected for at least the same reasons therein. Regarding the additional elements of the memory and processor as part of the apparatus, these are merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Therefore, at both Steps 2A, Prong 2 and Step 2B, these additional elements are not integrated into a practical application, nor do they amount to significantly more than the judicial exception(s).
As per claims 15, 16, and 17-20, the claims are directed to a computer-readable medium that implements the same features as the method of claims 1, 2, and 4-7, respectively, and are therefore rejected for at least the same reasons therein. Regarding the additional elements of the computer-readable media that is executed by a processor, these are merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Therefore, at both Steps 2A, Prong 2 and Step 2B, these additional elements are not integrated into a practical application, nor do they amount to significantly more than the judicial exception(s).
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.
Claims 2, 3, 5, 9, 10, 12, 16, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites “promising performance” and “promising” is a relative term which renders the claim indefinite. The term “promising” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what makes a performance promising, how that is calculated, and what the metes and bounds of “promising” are. Claims 9 and 16 are rejected for at least the same reasons as claim 2.
Claim 5 recites “no group in the groups is overrepresented” and “overrepresented” is a relative term which renders the claim indefinite. The term “overrepresented” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what makes a group overrepresented, how that determination is made, and what the metes and bounds of “overrepresented” are. Claims 12 and 18 recite similar language and are rejected for at least the same reasons as claim 5.
Claims 3 and 10 are rejected based on their dependency from an above-rejected claim.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 8, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abhulimen (U.S. Publication No. 2012/0317058) and further in view of Höfig (U.S. Publication No. 2016/0171506).
As per claim 1, Abhulimen teaches a method for automatically optimizing parameters of a safety-critical system of interconnected subsystems, comprising:
selecting a representative sample data set for optimization of parameters of interconnected subsystems of a complex system using a hybrid data analytics approach (See Abhulimen paras. [0011], [0068], [0120], and [0159]: inputs to neural networks with actual outputs compared to desired outputs, as well as weight errors generated for the hazards. This is used in the risk analysis of the subsystems of the complex system to identify faults and risk events therein);
breaking down a complexity of optimizing the complex system into interconnected subsystems by determining an order of the interconnected subsystems for optimization (See Abhulimen para. [0067]: systems and subsystems arranged in a hierarchy with various data outputs);
based on the order of the interconnected subsystems, exploring a parameter space of each of the interconnected subsystems to define an optimal configuration of the parameters of the interconnected subsystems for optimizing performance of the interconnected subsystems (See Abhulimen paras. [0077] and [0273]: breaking down parameters in the safety index. “The fitted weighted hazard rate parameters of actual risk observations are matched with randomly skewed hazard surrogates generated by Monte Carlo simulation of the true parameters using a weight structure that represents intrinsic risk and safety ratings”);
determining an overall optimal setting for the complex system of the interconnected subsystems based on an optimal configuration of the parameters of the interconnected subsystems (See Abhulimen paras. [0068-69] and [0122]: ranking weights to determine operating conditions and environment in a neural network model. This includes three fundamental parameters; paras. [0418]: limits of safety and unsafe positions can be determined, and “[t]hese criteria can be an important tool for Safety operators to mark the limit of design or operation. Any factor that tends to push safety function above or below absolute 1 should be minimized”).
However, while Abhulimen teaches safety management systems, Abhulimen does not explicitly certify its optimal settings.
Höfig teaches incorporating compliance to certification of a safety-critical system in the overall optimal setting for the complex system (See Höfig para. [0035]: recertification of safety critical system when functionality is altered).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the subsystems and optimization of Abhulimen with the configurations and validation of Höfig. One would have been motivated to combine these references because both references disclose designing and monitoring subsystem configurations, as well as safety management systems, and Höfig enhances the optimization of Abhulimen by ensuring that “the complex safety critical system may be recertified automatically at runtime to provide a safe operation of the safety critical system”.
As per claim 8, the claim is directed to an apparatus that implements the same features as the method of claim 1, and is therefore rejected for at least the same reasons therein. Furthermore, Abhulimen teaches an apparatus for providing automated optimization for safety-critical system of interconnected subsystems, comprising: a memory storing computer-readable instructions; and a processor connected to the memory, wherein the processor is configured to execute the computer-readable instructions to perform operations to implement said method(s) (See Abhulimen paras. [1230-1231]).
As per claim 15, the claim is directed to a computer-readable medium that implements the same features as the method of claim 1, and is therefore rejected for at least the same reasons therein. Furthermore, Abhulimen teaches a non-transitory computer-readable media having computer-readable instructions stored thereon, which when executed by a processor causes the processor to perform operations comprising said method(s) (See Abhulimen paras. [1230-1231]).
Claim(s) 5, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abhulimen/Höfig as applied above, and further in view of Weider et al. (U.S. Publication No. 2020/0380309; hereinafter “Weider”).
As per claim 5, Abhulimen/Höfig further teaches the method of claim 1, wherein the selecting the representative sample data set for optimization of the parameters of the interconnected subsystems of the complex system using the hybrid data analytics approach includes:
performing statistical analysis for each of the groups…and wherein the performing the statistical analysis for each of the groups includes sampling data of each of the groups using machine learning, and including, in the representative sample data set, one or more of data of the groups exhibiting nominal behavior, trends in the data of the groups, or anomalous behavior in the data of the groups (See Abhulimen paras. [0273] and [0399]: monitoring trends in groups and behaviors of the risk and safety systems using Markov chain process).
However, while Abhulimen/Höfig teaches machine learning and statistical analysis (See Abhulimen para. [0159]), Abhulimen/Höfig does not explicitly teach clustering or unsupervised learning with balancing sample data.
Weider teaches applying clustering or unsupervised learning to further classify the representative sample data set into groups according to similarities in the representative sample data set (See Weider paras. [0055]: “user may also specify a desired threshold of similarity to a desired distribution for the corrected dataset. The desired threshold may be the same or it may be different for each identified feature.”); and
balancing the representative sample data set to ensure that no group in the groups is overrepresented in the representative sample data set (See Weider paras. [0043] and [0058]: “the input dataset may be trimmed to select a subset of the dataset that represents a more balanced distribution”. Additionally, “correction of bias and/or imbalance in data may include identifying feature values that stand out as uncharacteristic or unusual as these values could indicated problems that occurred during data collection. In one implementation, any indication that certain groups or characteristics may be under or overrepresented relative to their real-world prevalence can point to bias or imbalance in data”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the machine learning of Abhulimen/Höfig with the clustering and balancing of Weider. One would have been motivated to combine these references because both references disclose machine learning data sets, and Weider improves upon the machine learning of Abhulimen/Höfig by reducing and correcting bias, which can harm the learning process and introduce significant negative implications (See Weider paras. [0003] and [0019]).
As per claim 12, the claims are directed to an apparatus that implements the same features as the method of claim 5, and is therefore rejected for at least the same reasons therein.
As per claim 18, the claim is directed to a computer-readable medium that implements the same features as the method of claim 5, and is therefore rejected for at least the same reasons therein.
Allowable Subject Matter
Examiner notes that should Applicant cure the issues under 35 U.S.C. §101, claims 3, 4, 6, 7, 10, 11, 13, 14, 17, 19, and 20 would be allowable over the prior art of record. Each of these dependent claims are directed to separate features that are not taught by the prior art of record.
For example, claims 2, 9, and 16 are directed towards local and global optimization configurations. While Abhulimen teaches iteratively adjusting weight factors through each layer and comparing real (local) outputs to desired (global) outputs, Abhulimen does not teach the validations and the specific regions of interest as claimed, nor does Abhulimen teach the validation on leftover sample data not used in the optimization (See Abhulimen para. [0159]).
As an additional example, claims 6, 13, and 19 are directed towards ranking and adjusting parameters. The closest prior art of record is Bennet (U.S. 2002/0151989), which discloses process variables and ranking them based on the variables that yield the largest effect (See Bennet para. [0069]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Klicos whose telephone number is (571)270-5889. The examiner can normally be reached Mon-Fri 9:00 AM-5:00 PM.
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/NICHOLAS KLICOS/Primary Examiner, Art Unit 2118