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 .
This office action is in response to application filed 7/8/2022. Claims 1-4 are pending. Claim priority date: 6/17/2022
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 1-4 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. In claim 1, the scope of multiple terms or phrases are unclear:
Step 1. What is “a coaxiliaty error source”? What is the function(s) to which the error source is the input? Step 2. What is an individual in “real number coding of individuals” and what is “a corresponding evolution algebra”? Step 3. How is the probability distribution related to the selection of individuals from low to high according to the fitness? The claim may be amended to clarify that selection from low to high is based on the normalization of fitness. Step 5. Is the optimal offspring individuals generated from step 3? With respect to “decoding”, is GA representation of the weights and bias considered as the encoding? Step 6. The term ‘swarm’ is introduced, but not clear how it is related to other claim limitations. The claim may be amended to describe how the swarm affects other concepts (e.g., hyperparameters, GA optimization). Step7. Are the ranges determined by the swarm?
Step 8. Is the introduced “a” genetic algorithm same as or different from that described in steps 2 – step 5? Is the MSE different from the “error” in step 5? Step 9. Regarding “into the BP neural network for training”, does the training use the input given in step 1 as training data? Step 10. The scope of the term “comprehensively” is not well defined. A metric may be provided.
In claim 2, terms, such as genes and chromosomes, lack antecedent basis. The term “better than” is indefinite as a comparison criterion is not provided. In claim 3, the terms pbest and gbest are not defined. In claim 4, it is not clear how a “sample” is generated and whether the sample is related to the input, individuals or population.
Because of plurality of ambiguity in claim language, the scope of the claim cannot be ascertained. Clarification or correction is needed.
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.
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 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1. The claim recites a process.
Step 2a, prong 1. The claim recites an abstract idea. The steps, generating an initial population of genetic algorithm solutions, taking an error between a predicted value and an actual value of the BP neural network as a fitness function, and selecting the individuals from low to high, according to the fitness function performing crossover and mutation operations to generate a new population, initializing a particle swarm, comprising a swarm size, and a position and of each particle, introducing a genetic algorithm to optimize the BP neural network, and performing solving with MSE as an objective function to find an optimal hyperparameter combination, introducing the optimal initial weight and threshold and the optimal hyperparameter combination into the BP neural network for training, fall under math concepts. The step determining solution ranges of three hyperparameters, comprising a maximum number of times of training, a learning rate, and a regularization coefficient, falls under mental processing.
Step 2a, prong 2. This judicial exception is not integrated into a practical application. The additional element, a predicting a coaxiality of parts of rotary equipment, a genetic algorithm-particle swarm optimization-back propagation (GA-PSO-BP) neural
network, and performing data preprocessing with a coaxiality error source as an input. The coaxiality equipment is included merely to indicate a data source and does not apply judicial exception beyond generally linking to a particular technological environment (MPEP 2106.05(h)). The genetic algorithm-particle swarm optimization-back propagation (GA-PSO-BP) neural network is a math model. Adding an abstract idea (e.g., a math model) to another abstract idea does not make the claim non-abstract (RecogniCorp). The step performing data preprocessing with a coaxiality error source as an input is an extra solution data collection activity.
Step 2b. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The inclusion of rotary equipment does not improve the functioning of a computer or other technology as it is merely used as a tool to provide data, as broadly recited. Neither does inclusion of the GA-PSO-BP model, a math concept, improves the functioning of a computer. The step performing data preprocessing as an input is a well understood or WURC activity (Umbrajkaar: e.g., Abstract, preparing to collect the vibration signals). Therefore, claim 1 is not eligible.
Dependent claims recite further claim limitations, in claim 2, the crossover operation (math concept), in claim 3, swarm optimization (math concept), in claim 4, MSE definition (math concept). Claims 2-4 are not eligible.
Examiner Note
Claims 1-4 do not have prior art rejection. Reference Umbrajkaar et al. teaches
analyzing shaft misalignment based on vibration signals using machine learning approaches. Jiang et al. teaches optimizing RNN based on gravitational search, comprising multiple features of GA and PSO. Jin et al. teaches generating a design parameter model by training a BP using genetic algorithm and particle swarm algorithm for optimization. Combination of references does not expressly disclose claim limitations of claim 1, such as introducing a genetic algorithm to optimize the BP neural network, and performing solving with MSE as an objective function to find an optimal hyperparameter combination and introducing the optimal initial weight and threshold and the optimal hyperparameter combination into the BP neural network for training.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure, e.g., reference Tran et al. teaches supply chain management based on anomaly detection using neural networks and refence Chen et al. teaches resource capacity management for multiple users with multiple host controllers. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LiWu Chang whose telephone number is (571)270-3809. The examiner can normally be reached on M-F. 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, Miranda Huang can be reached on (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LI WU CHANG/Primary Examiner, Art Unit 2124 August 13, 2025