DETAILED ACTION 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 § 102 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. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1 – 6 , 8 – 13, 15 - 20 is/are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Van Der Linden et al (US 2024/0194341, hereafter Van Der Linden) . As per claim 1 , Van Der Linden discloses a computer-implemented method for improving performance of a machine learning (ML) based algorithm used to provide a user experience to a user via a media device (¶ 100) , comprising: selecting, by at least one computer processor, a first set of hyperparameter values; implementing a first iteration of the ML based algorithm based on the first set of hyperparameter values; utilizing the first iteration of the ML based algorithm to provide a first user experience to the user via the media device (¶ 101 and 108) ; determining a response of the user to the first user experience; selecting, by a hyperparameter tuning ML model and based at least on the response of the user, a second set of hyperparameter values, wherein the hyperparameter tuning ML model comprises one of a contextual multi-arm bandit (CMAB) model or a reinforcement learning (RL) model; implementing a second iteration of the ML based algorithm based on the second set of hyperparameter values; and utilizing the second iteration of the ML based algorithm to provide a second user experience to the user via the media device (¶ 111 - 114) . As per claim 2 , Van Der Linden discloses t he computer-implemented method of claim 1, wherein implementing the first iteration of the ML based algorithm based on the first set of hyperparameter values comprises controlling a training process in accordance with the first set of hyperparameter values to generate a first iteration of an ML model used by the ML based algorithm (¶ 108 - 111) , and wherein implementing the second iteration of the ML based algorithm based on the second set of hyperparameter values comprises controlling the training process in accordance with the second set of hyperparameter values to generate a second iteration of the ML model used by the ML based algorithm (¶ 111 - 115) . As per claim 3 , t he computer-implemented method of claim 1, wherein implementing the first iteration of the ML based algorithm based on the first set of hyperparameter values comprises selecting a first ML model to be used by the ML based algorithm from among a set of candidate ML models based on the first set of hyperparameter values (¶ 108 - 111) , and wherein implementing the second iteration of the ML based algorithm based on the second set of hyperparameter values comprises selecting a second ML model to be used by the ML based algorithm from among the set of candidate ML models based on the second set of hyperparameter values (¶ 115) . As per claim 4 , Van Der Linden discloses t he computer-implemented method of claim 1, wherein the hyperparameter tuning ML model comprises the CMAB model and selecting the second set of hyperparameter values comprises: selecting, by the CMAB model and based at least on context information and the response of the user, the second set of hyperparameter values (¶ 114) . As per claim 5 , Van Der Linden discloses t he computer-implemented method of claim 1, wherein the hyperparameter tuning ML model comprises the RL model and selecting the second set of hyperparameter values comprises: selecting, by the RL model and based at least on state information and the response of the user, the second set of hyperparameter values (¶ 114) . As per claim 6 , Van Der Linden discloses t he computer-implemented method of claim 1, wherein selecting the second set of hyperparameter values comprises: selecting whether to increment or decrement each hyperparameter value in the first set of hyperparameter values by a fixed number of discrete steps (¶ 108) . Regarding claim 8 , arguments analogous to those presented for claim 1 are applicable for claim 8. Regarding claim 9 , arguments analogous to those presented for claim 2 are applicable for claim 9 . Regarding claim 10 , arguments analogous to those presented for claim 3 are applicable for claim 10 . Regarding claim 11 , arguments analogous to those presented for claim 4 are applicable for claim 11 . Regarding claim 12 , arguments analogous to those presented for claim 5 are applicable for claim 12 . Regarding claim 13 , arguments analogous to those presented for claim 6 are applicable for claim 13 . Regarding claim 15 , arguments analogous to those presented for claim 1 are applicable for claim 15 . Regarding claim 16 , arguments analogous to those presented for claim 2 are applicable for claim 16 . Regarding claim 17 , arguments analogous to those presented for claim 3 are applicable for claim 17 . Regarding claim 1 8 , arguments analogous to those presented for claim 4 are applicable for claim 1 8. Regarding claim 19 , arguments analogous to those presented for claim 5 are applicable for claim 19 . Regarding claim 20 , arguments analogous to those presented for claim 6 are applicable for claim 20 . Claim Rejections - 35 USC § 103 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. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim (s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Van Der Linden in view of Faust et al (US 202 1/0325894, Faust ) . As per claim 7 , Van Der Linden discloses t he computer-implemented method of claim 1 . However, Van Der Linden does not explicitly teach wherein selecting the second set of hyperparameter values comprises: determining, by the hyperparameter tuning ML model and based at least on the response of the user, a deterministic policy or a stochastic policy; and selecting the second set of hyperparameter values based on the deterministic policy or the stochastic policy. In the same field of endeavor, Faust teaches wherein selecting the second set of hyperparameter values comprises: determining, by the hyperparameter tuning ML model and based at least on the response of the user, a deterministic policy or a stochastic policy; and selecting the second set of hyperparameter values based on the deterministic policy or the stochastic policy (¶ 7 ) . Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention was effectively filed to modify the invention of Van Der Linden in view Faust . The advantage is selection of optimal hyperparameters. Regarding claim 14 , arguments analogous to those presented for claim 7 are applicable for claim 14. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT CHIKAODILI E ANYIKIRE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1445 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 8 am - 4:30 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, FILLIN "SPE Name?" \* MERGEFORMAT David Czekaj can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-7327 . 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. /CHIKAODILI E ANYIKIRE/ Primary Examiner, Art Unit 2487