DETAILED ACTION
This action is responsive to the following communication: Amendment filed 12/11/25. This action is made final.
Claims 1-10 are pending in the case. Claims 1, 4, 6 and 9 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice as to Grounds of Rejection and Pre-AIA or AIA Status
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 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 § 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With regard to claims 1, 4, 6 and 9, are directed to a method, a method, a control device and a control device respectively that are directed to providing training data, training, optimizing, controlling a controllable system. These limitations perform certain methods of organizing human activity without significantly more. This judicial exception is not integrated into a practical application because these steps which may be practically performed in the human mind using observation, evaluation, judgment, and opinion.
As recited in independent claims
“providing training data for training…” is collecting data;
“training, by a machine learning method …” is analyzing data;
“subsequently, after the training, optimizing one parameter based on a non-differential cost function can be performed mentally or by using a paper and pencil.
The eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The only additional element are “the trained deep-learning-based machine learning model”, “sensor data”, “processor”, which at best is mere instructions to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Claims 2, 3, 5, 6, 7 and 10 do not include elements that that amount to significantly more than the abstract idea and also are rejected under the same rationale.
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 of this title, 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.
Claims 1-2, 4-7, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Bai et al (US Patent Application Publication 2023/0274540 A1 hereinafter Bai) in view of Selvam et al (US Patent Application Publication 2020/0126417 A1 hereinafter Selvam).
With regard to claims 1, 6, Bai teaches a method, a control device respectively, for training a deep-learning-based machine learning algorithm, the method comprising the following steps:
providing training data for training the deep-learning-based machine learning algorithm, the training data including sensor data < training data can be provided including sensor data para 0113>;
training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data <machine learning models and deep neural networks can be used based on training/sensor data para 0112-0113>; and
subsequently, after the training, optimizing at least one parameter of the trained deep-learning-based machine learning algorithm < parameter can be optimized 0114>
Bai does not appear to explicitly disclose the limitation crossed out above.
In the same field of endeavor Selvam teaches optimizing at least one parameter based on a non-differential cost function <model can be trained, decisions can be optimized based upon fixed cost and variable cost para 0042-0046, fig 3>.
Accordingly, it would have been obvious before the effective filing date to one of ordinary skill in the art, having the teachings of Bai, Selvam before him/her before the effective filing date of the claimed invention, to modify the teachings of Bai to include the teachings of Selvam, in order to obtain optimizing model parameters per different criterion. One would have been motivated to make such a combination because it provides to control different parameters under different constraints.
With regard to claims 2, 7, these claims depend upon claims 1 and 6 respectively, which are rejected above. Bai does not appear to explicitly disclose limitations of this claim.
In the same filed of endeavor, Selvam teaches wherein the training of the deep-learning-based machine learning algorithm by the machine learning method includes training the deep-learning-based machine learning algorithm based on a differentiable cost function <model can be trained, decisions can be optimized based upon fixed cost and variable cost para 0042-0046, fig 3>.
With regard to claims 4, 9, Bai teaches a method, a control device respectively for controlling a controllable system, the method comprising the following steps:
providing a deep-learning-based machine learning algorithm for controlling a controllable system, wherein the deep-learning-based machine learning algorithm has been trained by <autonomous vehicle distance can be controlled by controlling brakes para 0022, machine learning models and deep neural networks can be used based on training/sensor data para 0112-0113>:
providing training data for training the deep-learning-based machine learning algorithm, the training data including sensor data < training data can be provided including sensor data para 0113>>;
training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data, <machine learning models and deep neural networks can be used based on training/sensor data para 0112-0113> and
subsequently, after the training, optimizing at least one parameter of the trained deep-learning-based machine learning algorithm < parameter can be optimized 0114>
controlling the controllable system based on the deep-learning-based machine learning algorithm <autonomous vehicle distance can be controlled by controlling brakes para 0022>;
Bai does not appear to explicitly disclose the limitation crossed out above.
In the same field of endeavor Selvam teaches optimizing at least one parameter based on a non-differential cost function <model can be trained, decisions can be optimized based upon fixed cost and variable cost para 0042-0046, fig 3>.
Accordingly, it would have been obvious before the effective filing date to one of ordinary skill in the art, having the teachings of Bai, Selvam before him/her before the effective filing date of the claimed invention, to modify the teachings of Bai to include the teachings of Selvam, in order to obtain optimizing model parameters per different criterion. One would have been motivated to make such a combination because it provides to control different parameters under different constraints.
With regard to claims 5, 10, these claims depend upon claims 4 and 9 respectively, which are rejected above. In addition, Bai teaches wherein the controllable system is an automatic distance control of an autonomously driving motor vehicle <autonomous vehicle distance can be controlled by controlling brakes para 0022>.
Claims 3, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Bai in view of Selvam in view of Saito (US Patent Application Publication 2021/0287081 A1 hereinafter Saito).
With regard to claims 3, 8, these claims depend upon claims 1 and 6 respectively, which are rejected above. Bai, Selvam do not appear to explicitly disclose limitations of this claim.
In the same filed of endeavor, Saito further teaches wherein the optimizing of the at least one parameter of the trained deep-learning-based machine learning algorithm based on the non-differentiable cost function includes optimizing the trained deep-learning-based machine learning algorithm based on temperature scaling <temperature scaling can be used to optimize the confidence of the machine learning model para 0047, fig 3>.
Accordingly, it would have been obvious before the effective filing date to one of ordinary skill in the art, having the teachings of Bai, Selvam, Saito before him/her before the effective filing date of the claimed invention, to modify the teachings of Bai, Selvam to include the teachings of Satio, in order to obtain optimizing confidence of the model. One would have been motivated to make such a combination because it improves the prediction of the outcome of the model with higher reliability.
Response to Arguments
Applicant's remarks filed on 12/11/25 have been considered but are not persuasive.
With regard to 101 rejection, examiner has further explained the rational for the rejection see the rejection above in the Office Action. Therefore, 101 rejection is maintained.
Regarding the previous rejection of the claim 1 under 35 USC 103a, Applicant argues on page 8 that the combination of Bai and Selvan fail to disclose “subsequently, after the training, optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.” because Bai does not teach that optimization is performed “subsequently, after the training”, because the optimization is performed as part of training.
The Office respectfully disagrees, noting that the references as a whole teach the recited claim language. the other earlier sentence from spec "optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function," As taught by Bai (para 0112-0014) that optimization is sone after the training step and is an iterative process. In addition, applicant's own spec shows that the cost function is still part of training (via the training unit applying the cost function) see para 0065-0067 of the instant application as published. See also para 0024, 0034. Applicant argument that optimization occurs after all training is complete is not supported by the specification of the instant application and is not claimed as such.
Therefore, the references have been reasonably interpreted as teaching the recited claim language.
The same rational applies to other independent claims 4, 6 and 9.
Applicant does not provide additional remarks for dependent claims and therefore, counter-asserts the rationale set forth above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANIL K BHARGAVA whose telephone number is (571)270-3278. The examiner can normally be reached Monday - Friday 8:30 am - 5:00 pm.
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, Adam Queler can be reached at 571-272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANIL K BHARGAVA/Primary Examiner, Art Unit 2172
Prosecution Insights