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 .
Status of the Application
2. Claim 1-20 have been examined in this application. This communication is the first action on the merits.
Drawings
3. The drawings filed on 12/8/23 are acceptable for examination proceedings.
Claim Rejections - 35 USC § 112
4. 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.
5. Claim 1, and 13 recites the limitation "and controlling the mechanical system using a predictive controller that determines control commands changing a state of the operation of the mechanical system using the probability of at least some of the predicted values of the disturbance".
The claimed limitation includes the “controlling using a predictive controller….”, the limitation includes controlling using predictive controller, control command, and using some of the predicted values of the disturbance. The claimed scope is indefinite due to variety of options claimed in a way to read and understand the limitation difficult and unclear. The limitation needs to re-write in a way to make it understandable and definite.
Claim 2-12 and 14-20 are also rejected under 112(b) rejection due to their direct/indirect dependency over the claim 1, and 13, respectively.
Remarks: Claim should be re-written as below:
determining a control commands by a predictive controller based on the probability of at least some of the predicted values of the disturbance;
and controlling the mechanical system by changing a state of the operation of the mechanical system based on the control command generated by the predictive controller.
Pertinent Art Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Min (Pub: 2022/0222520) disclose applications of Machine Learned and Artificial Intelligence models for use solving a problem of generating optimal samples for achieving a target outcome, and leverages a variational autoencoder (VAE) model to generate as realistic samples of a data space as possible (Para. [0001]).
Qiu (Pub: 2022/0100877) disclose a method, apparatus, and electronic device for preventing model theft during classification using a service classification model. The service classification model is a deep neural network for predicting a category of a service object, including an input layer, multiple intermediate layers, and an output layer that are sequentially stacked. The method includes: obtaining feature information of a service object; inputting the feature information into the service classification model for initial prediction to obtain a category corresponding to a maximum initial prediction probability as a target category; determining, based on the target category, disturbance data corresponding to a target layer selected in advance from the input layer, the multiple intermediate layers, and the output layer; inputting the feature information into the service classification model for subsequent prediction, including adding the corresponding disturbance data to data to be input into the target layer; and outputting the target category and a maximum subsequent prediction probability (Abstract).
Llani (Pub: 2021/0096518) disclose a method for controlling a plant that exhibits nonlinear dynamics. The method includes training a neural network model during an offline learning period using historical plant data representing a plurality of different historical states of the plant. The method further includes using the neural network model during online operation of the plant to generate a linear predictor as a function of a current state of the plant, the linear predictor defining a linearization of the nonlinear dynamics localized at the current state of the plant. The method further includes controlling equipment that operate to affect the current state of the plant by performing a predictive control process that uses the linear predictor to generate values of one or more manipulated variables provided as inputs to the equipment (Para. [0014]).
Drees (Pub: 2019/0041811) disclose a database, a trust region identifier configured to perform a cluster analysis technique to identify trust regions, and a regression model predictor configured to utilize a regression model technique to calculate a regression model prediction. The building management system further includes a distance metric calculator configured to calculate a distance metric, an artificial neural network model predictor configured to utilize an artificial neural network model technique to calculate an artificial neural network model prediction, and a combined prediction calculator configured to determine a combined prediction based on the distance metric, the regression model prediction, and the artificial neural network model prediction (Para. [0004]).
Allowable Subject Matter
The claim 1 and its dependent claim 2-12, independent claim 13 and its dependent claim 14-20 are allowable once the outstanding 35 U.S.C 112(b) rejection is overcome as discussed above.
For independent claim 1, none of the prior art on record taken either alone or in obvious combination disclose “collecting a deep generative decoder model defining a mapping from a latent space of latent representations of time-series values of the disturbance affecting the mechanical system over the time horizon to a measurement space of the partial observations of the disturbance; determining, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance; sampling the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon; decoding the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations.”
Independent claim 13 also recites the same allowable subject matter as claim 1.
Conclusion
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/JIGNESHKUMAR C PATEL/Primary Examiner, Art Unit 2116