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 § 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 1-14 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.
Regarding Claim 1, it recites “A computer-implemented method for controlling a technical system, wherein a) reading in training data . . .” For grammatical agreement, the preamble should recite “comprising” instead of “wherein” because it is followed by a set of steps that make up the method. The claim further recites “a) reading in training data, a respective training dataset.” It is unclear what the training dataset is respective to because no other training dataset (or any other element) is mentioned. Similarly, the claim later recites “e) respectively controlling the technical system by the selected control agents . . .” Here, the term “respectively” is awkward and confusing. There is only one technical system, but multiple control agents. The term “respectively” suggests a one-to-one correspondence, which is impossible given a single technical system. Do the control agents sequentially (or successively) control the technical system? Do the control agents somehow work together to control the technical system? The arrangement is unclear. Finally, the claim recites “f) repeating method steps b) to e) using the augmented training data.” The term “the augmented training data” lacks antecedent basis, so it cannot be determined what training data it refers to.
Regarding Claim 2, it recites “a second machine learning module is trained, or is trained using the training data” (line 2). It is unclear what it means for the machine learning module to be “trained, or trained using the training data.” What is the distinction between “trained” and “trained using the training data”? How would a machine learning system be trained without training data? The first instance of “trained” should be clarified to specify how is differs from “trained using the training data.”
Regarding Claim 8, it recites “a respective training dataset” (line 3). It is unclear what the training dataset is respective to. No other training datasets are recited, and no elements corresponding to the training dataset(s) are recited. Similarly, line 10 recites “the respective control agent.” It cannot be determined what the control agent is respective to.
Regarding Claims 3-7 and 9-14, they are rejected as being dependent on a rejected base claim.
Allowable Subject Matter
Claims 1-14 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action. None of the prior art of record teaches all of the limitations of independent claim 1. Murugesan et al. (U.S. 2022/0026864) teaches a building management and control system that compares policies and prediction models with one another and selects the best performing model to control HVAC, electrical, lighting, and other building systems. But it does not train a machine learning module or use such a module to reproduce a resulting performance value of the policies/prediction models and select instances of the controllers, and does not capture further datasets and add them to training data. German Patent application DE102016224207A1, cited by the International Search Report supplied by the applicant, trains control models to control a technical system, but does not train a machine learning module to reproduce performance values and to select instances of the control models.
Conclusion
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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