CTFR 18/295,018 CTFR 73675 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 the claimed amendment filed on March 24, 2026, in which claims 1-20 are presented for further examination. Response to Arguments Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of a new ground of rejection necessitated by amendment. After further reviewed Applicant’s arguments in light of the original specification, it is conceivable that the amended claims 1, 8 and 15 as a whole does not improve upon the traditional multi-round to produce a plurality of optimized machine learning models that are then deployed into a digital assistant system because claims 1, 8 and 15 do not involved in any practical transformation when determining a periodic checkpoint has not reached in the hyperparameter tuning process and when determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score (see Enfish, LLC v. Microsoft Corp., 822 F.3d 1327. See also McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299). Therefore, the 35 USC 101 rejection set forth in the last office action is hereby sustained. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more. Step 1, Statutory Category: Claims 1-8 are directed to a method Claims 9-14 are directed to a computer system. Claims 15-20 are directed to a non-transitory computer readable medium. Therefore, claims 1-20 fall into at least one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. Step 2A, Prong One (Judicial exception recited): The limitation “creating a plurality of machine learning models” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. One can manually with the aid of pen and paper create a plurality of machine learning models. The limitation “evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating a domain score for each domain” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. One can manually with the aid of pen and paper evaluate a machine learning model to assign a domain weight that indicates an importance of a dataset The limitation “calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical process. One can manually with the aid of pen and paper calculate the hyperparameter values for trial and domain score based on the machine learning model evaluation. The limitation “determining whether a periodic checkpoint has been reached in the hyperparameter tuning process” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. One can manually with the aid of pen and paper determine whether a periodic checkpoint has been reached in the hyperparameter tuning process. The limitation “updating at least one domain weight for a domain of the plurality of domains and recalculating, using the hyperparameter objective function, the current trial objective score based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. One can manually with the aid of pen and paper update the domain weight and recalculate the hyperparameter values for trial and domain score. The limitation “determining whether the machine learning algorithm has reached convergence based on the recalculated current trial objective score” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. One can manually with the aid of pen and paper determine if the machine learning algorithm reached a threshold convergence. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea. At Step 2A, Prong Two (Integrated into a practical application): This judicial exception is not integrated into a practical application. The claim recites the following additional elements: That the method is "implemented by a computing system" is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. The limitation “retrieving a machine learning algorithm” amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)) and does not provide integration into a practical application. The limitation “initializing a machine learning algorithm with a set of hyperparameter values; and accessing a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset” amount to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)) and does not provide integration into a practical application. The limitation “deploying the plurality of machine learning models” represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05 (g)). The limitation “one or more processors; and one or more non-transitory computer-readable media” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). At Step 2B (claim provides an inventive concept): The conclusions for the mere implementation using a computer, mere field of use, and using generic computer components (machine learning i.e. ML) as a tool are carried over and do not provide significantly more. With respect to the “retrieving ….; initializing…; and accessing … " identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. With respect to the “training … and deploying ….” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334; i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". With respect to the “one or more processors; and one or more non-transitory computer-readable media” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). - similarly, the current invention recites “a computer program product, the computer program product comprising: one or more non-transitory computer-readable storage media and program instructions collectively stored on the one or more non-transitory computer-readable storage media, the program instructions executable by a processor to cause the processor to initiate operations.” The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible. Accordingly, claim 1 is directed to an abstract idea. The remaining independent claims 8 and 15 fall short the 35 USC 101 requirement under the same rationale. The dependent claims 2-7, 10-14 and 16-20 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea. Claim 2 recites “wherein in response to determining the periodic checkpoint has been reached: comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing; retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 3 recites “wherein in response to determining the periodic checkpoint has been reached: determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in the M domains; retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 4 recites “storing the current trial objective score to a database, wherein the database comprises a plurality of trial objective scores from current and prior trials of the hyperparameter tuning process, the domain weight associate with each domain, the domain score for each domain from current and prior trials of the hyperparameter tuning process, and the set of hyperparameter values and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing the current trial objective score to a database, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. Claim 5 recites “determining the periodic checkpoint has been reached, comprises determining a trial counter or timer is equal to or exceeds a trial counter or timer threshold; determining the periodic checkpoint has not been reached, comprises determining the trial counter or the timer is less than the trial counter or the timer threshold; in response to determining the periodic checkpoint has not been reached, determining whether the machine learning algorithm has reached convergence based on the current trial objective score; in response to determining the machine learning algorithm has not reached convergence based on the current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the current trial objective score, the trial objective scores from the prior trials of the hyperparameter tuning process, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 6 recites “wherein: in response to determining the periodic checkpoint has been reached, recalculating, using the hyperparameter objective function, at least one of the trial objective scores from the prior trials of the hyperparameter tuning process based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; in response to determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the recalculated current trial objective score, the trial objective scores from the prior trials including the at least one recalculated trial objective score from a prior trial, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 7 recites “wherein all of the trial objective scores from the prior trials of the hyperparameter tuning process are recalculated based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight, and the new set of hyperparameters is determined based on the recalculated current trial objective score, the recalculated trial objective scores from the prior trials, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 9 recites “wherein in response to determining the periodic checkpoint has been reached: comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing; retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 10 recites “wherein in response to determining the periodic checkpoint has been reached: determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in the M domains; retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 11 recites “storing the current trial objective score to a database, wherein the database comprises a plurality of trial objective scores from current and prior trials of the hyperparameter tuning process, the domain weight associate with each domain, the domain score for each domain from current and prior trials of the hyperparameter tuning process, and the set of hyperparameter values and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing the current trial objective score to a database, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. Claim 12 recites “determining the periodic checkpoint has been reached, comprises determining a trial counter or timer is equal to or exceeds a trial counter or timer threshold; determining the periodic checkpoint has not been reached, comprises determining the trial counter or the timer is less than the trial counter or the timer threshold; in response to determining the periodic checkpoint has not been reached, determining whether the machine learning algorithm has reached convergence based on the current trial objective score; in response to determining the machine learning algorithm has not reached convergence based on the current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the current trial objective score, the trial objective scores from the prior trials of the hyperparameter tuning process, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 13 recites “wherein: in response to determining the periodic checkpoint has been reached, recalculating, using the hyperparameter objective function, at least one of the trial objective scores from the prior trials of the hyperparameter tuning process based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; in response to determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the recalculated current trial objective score, the trial objective scores from the prior trials including the at least one recalculated trial objective score from a prior trial, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 14 recites “wherein all of the trial objective scores from the prior trials of the hyperparameter tuning process are recalculated based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight, and the new set of hyperparameters is determined based on the recalculated current trial objective score, the recalculated trial objective scores from the prior trials, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 16 recites “wherein in response to determining the periodic checkpoint has been reached: comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing; retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 17 recites “wherein in response to determining the periodic checkpoint has been reached: determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in the M domains; retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 18 recites “storing the current trial objective score to a database, wherein the database comprises a plurality of trial objective scores from current and prior trials of the hyperparameter tuning process, the domain weight associate with each domain, the domain score for each domain from current and prior trials of the hyperparameter tuning process, and the set of hyperparameter values and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. This additional element is recited at a high level of generality and would function in its ordinary capacity for storing the current trial objective score to a database, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. Claim 19 recites “determining the periodic checkpoint has been reached, comprises determining a trial counter or timer is equal to or exceeds a trial counter or timer threshold; determining the periodic checkpoint has not been reached, comprises determining the trial counter or the timer is less than the trial counter or the timer threshold; in response to determining the periodic checkpoint has not been reached, determining whether the machine learning algorithm has reached convergence based on the current trial objective score; in response to determining the machine learning algorithm has not reached convergence based on the current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the current trial objective score, the trial objective scores from the prior trials of the hyperparameter tuning process, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Claim 20 recites “wherein: in response to determining the periodic checkpoint has been reached, recalculating, using the hyperparameter objective function, at least one of the trial objective scores from the prior trials of the hyperparameter tuning process based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; in response to determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the recalculated current trial objective score, the trial objective scores from the prior trials including the at least one recalculated trial objective score from a prior trial, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process”. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-35 Claim s 1-20 provisionally rejected on the ground of non-statutory double patenting as being unpatentable over claim s 1-20 of co-pending Application No. 18/197,224 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 under examination are obvious, respectively, by claims 1-20 of the reference co-pending application. Every limitations in the instant application under examination claims are recited in the conflicting reference patent claims, and the differences or additional limitations between the claims are highlighted below by underlining and bolding all limitations. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the independent claim 1 of the instant application so that the hyperparameter objective function comprises a domain score for each domain of the plurality of domains, and wherein the domain score is calculated based on a of instance-level changes in prediction correctness between the machine learning algorithm during a given trial and a baseline model, in order to minimize instance level regression during a target-based hyper parameter tuning, improve overall performance of the model and improve performance of the computing system running the model, thereby increasing speed or efficiency of an underlying computing device and reducing a processing requirement or memory usage of the underlying computing device. Note, such deviation would not interfere with the functionality of the claims that are already patented, and would achieve the same end result. Please, see the comparison table below: Application Co-pending Application 1. A computer-implemented method comprising: a plurality of machine learning models by: retrieving a machine learning algorithm; initializing the machine learning algorithm with a set of hyperparameter values; accessing a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset; for each trial of a hyperparameter tuning process: training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs the plurality of machine learning models comprising a machine learning model for each domain; evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating a domain score for each domain; calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain; determining whether a periodic checkpoint has been reached in the hyperparameter tuning process; in response to determining the periodic checkpoint has been reached, updating at least one domain weight for a domain of the plurality of domains and recalculating, using the hyperparameter objective function, the current trial objective score based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight ; and determining whether the machine learning algorithm has reached convergence based on the recalculated current trial objective score; and in response to determining the machine learning algorithm has reached convergence, the plurality of machine learning models for use in a digital assistant system. 1. A computer-implemented method comprising: initializing a machine learning algorithm with a set of hyperparameter values; accessing a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain of the plurality of domains comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset, wherein the hyperparameter objective function comprises a domain score for each domain of the plurality of domains, and wherein the domain score is calculated based on a of instance-level changes in prediction correctness between the machine learning algorithm during a given trial and a baseline model; for each trial of a hyperparameter tuning process: training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs a plurality of machine learning models comprising a machine learning model for each domain; evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating the domain score for each domain; calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain; and determining whether the machine learning model has reached convergence based on the current trial objective score; and in response to determining the machine learning model has reached convergence, providing at least one of the plurality of machine learning models. Claims 2-7 are rejected for incorporating the deficiency of their respective base claims by dependency. Application Co-pending Application 2. The computer-implemented method of claim 1, wherein in response to determining the periodic checkpoint has been reached: comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing; retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value. 3. The computer-implemented method of claim 1, wherein in response to determining the periodic checkpoint has been reached: determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in the M domains; retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value. 4. The computer-implemented method of claim 1, further comprising storing the current trial objective score to a database, wherein the database comprises a plurality of trial objective scores from current and prior trials of the hyperparameter tuning process, the domain weight associate with each domain, the domain score for each domain from current and prior trials of the hyperparameter tuning process, and the set of hyperparameter values and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 5. The computer-implemented method of claim 4, wherein: determining the periodic checkpoint has been reached, comprises determining a trial counter or timer is equal to or exceeds a trial counter or timer threshold; determining the periodic checkpoint has not been reached, comprises determining the trial counter or the timer is less than the trial counter or the timer threshold; in response to determining the periodic checkpoint has not been reached, determining whether the machine learning algorithm has reached convergence based on the current trial objective score; in response to determining the machine learning algorithm has not reached convergence based on the current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the current trial objective score, the trial objective scores from the prior trials of the hyperparameter tuning process, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 6. The computer-implemented method of claim 4, wherein: in response to determining the periodic checkpoint has been reached, recalculating, using the hyperparameter objective function, at least one of the trial objective scores from the prior trials of the hyperparameter tuning process based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; in response to determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the recalculated current trial objective score, the trial objective scores from the prior trials including the at least one recalculated trial objective score from a prior trial, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 7. The computer-implemented method of claim 6, wherein all of the trial objective scores from the prior trials of the hyperparameter tuning process are recalculated based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight, and the new set of hyperparameters is determined based on the recalculated current trial objective score, the recalculated trial objective scores from the prior trials, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 2. The computer-implemented method of claim 1, wherein the hyperparameter objective function is formulated to normalize instance level improvements and instance level regressions over a total number of instances to obtain an improvement score and a regression score, and wherein the domain score for each domain is calculated based on the improvement score and the regression score. 3. The computer-implemented method of claim 2, wherein the improvement score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are correctly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are incorrectly predicted by a baseline machine learning model, and (iii) a total count of the number of instances within the at least one evaluation dataset, and wherein the regression score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are correctly predicted by a baseline machine learning model, and (iii) the total count of the number of instances within the at least one evaluation dataset. 4. The computer-implemented method of claim 1, wherein the hyperparameter objective function is formulated to exclude unstable instances, which are instances within the at least one evaluation dataset that are determined to yield prediction results that differ from one another using a same machine learning model. 5. The computer-implemented method of claim 4, wherein excluding unstable instances comprises: (i) subtracting a count of the excluded unstable instances from the number of instances within the at least one evaluation dataset that are correctly predicted by the machine learning model during the given trial, and (ii) subtracting the count of the excluded unstable instances from the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial. 6. The computer-implemented method of claim 1, wherein the hyperparameter objective function is formulated to include a parameter for an acceptable regression ratio, m, for one or more of the plurality of domains. 7. The computer-implemented method of claim 6, wherein the parameter is defined based on a regression score, and if the regression score is less that the acceptable regression ratio, m, then the regression score is set to zero, otherwise the regression score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are correctly predicted by a baseline machine learning model, and (iii) the total count of the number of instances within the at least one evaluation dataset. With respect to claim 8, one having ordinary skill in the art before the effective filing date of the claimed invention to modify the independent claim 8 of the instant application so that the hyperparameter objective function comprises a domain score for each domain of the plurality of domains, and wherein the domain score is calculated based on a of instance-level changes in prediction correctness between the machine learning algorithm during a given trial and a baseline model, in order to minimize instance level regression during a target-based hyper parameter tuning, improve overall performance of the model and improve performance of the computing system running the model, thereby increasing speed or efficiency of an underlying computing device and reducing a processing requirement or memory usage of the underlying computing device. Note, such deviation would not interfere with the functionality of the claims that are already patented, and would achieve the same end result. Please, see the comparison table below: Application Co-pending 8. A system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: creating a plurality of machine learning models by: retrieving a machine learning algorithm; initializing the machine learning algorithm with a set of hyperparameter values; obtaining a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset; for each trial of a hyperparameter tuning process: training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs the plurality of machine learning models comprising a machine learning model for each domain; evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating a domain score for each domain; calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associate with each domain; determining whether a periodic checkpoint has been reached in the hyperparameter tuning process; in response to determining the periodic checkpoint has been reached, updating at least one domain weight for a domain of the plurality of domains and recalculating, using the hyperparameter objective function, the current trial objective score based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; and determining whether the machine learning algorithm has reached convergence based on the recalculated current trial objective score; and in response to determining the machine learning algorithm has reached convergence , deploying the plurality of machine learning models for use in a digital assistant system. 8. A system comprising: one or more processors; and one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising; initializing a machine learning algorithm with a set of hyperparameter values; accessing a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain of the plurality of domains comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset, wherein the hyperparameter objective function comprises a domain score for each domain of the plurality of domains, and wherein the domain score is calculated based on a quantification of instance-level changes in prediction correctness between the machine learning algorithm during a given trial and a baseline model ; for each trial of a hyperparameter tuning process: training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs a plurality of machine learning models comprising a machine learning model for each domain; evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating the domain score for each domain; calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain; and determining whether the machine learning model has reached convergence based on the current trial objective score; and in response to determining the machine learning model has reached convergence, providing at least one of the plurality of machine learning models. Claims 9-14 are rejected for incorporating the deficiency of their respective base claims by dependency. Application Co-pending 9. The system of claim 8, wherein in response to determining the periodic checkpoint has been reached, comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing; retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value. 10. The system of claim 8, wherein in response to determining the periodic checkpoint has been reached, determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in M domains; retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value. 11. The system of claim 8, wherein the operations further comprise storing the current trial objective score to a database, wherein the database comprises a plurality of trial objective scores from current and prior trials of the hyperparameter tuning process, the domain weight associate with each domain, the domain score for each domain from current and prior trials of the hyperparameter tuning process, and the set of hyperparameter values and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 12. The system of claim 11, wherein: determining the periodic checkpoint has been reached, comprises determining a trial counter or timer is equal to or exceeds a trial counter or timer threshold; determining the periodic checkpoint has not been reached, comprises determining the trial counter or the timer is less than the trial counter or the timer threshold; in response to determining the periodic checkpoint has not been reached, determining whether the machine learning algorithm has reached convergence based on the current trial objective score; in response to determining the machine learning algorithm has not reached convergence based on the current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the current trial objective score, the trial objective scores from the prior trials of the hyperparameter tuning process, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 13. The system of claim 11, wherein: in response to determining the periodic checkpoint has been reached, recalculating, using the hyperparameter objective function, at least one of the trial objective scores from the prior trials of the hyperparameter tuning process based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; in response to determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the recalculated current trial objective score, the trial objective scores from the prior trials including the at least one recalculated trial objective score from a prior trial, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 14. The system of claim 13, wherein all of the trial objective scores from the prior trials of the hyperparameter tuning process are recalculated based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight, and the new set of hyperparameters is determined based on the recalculated current trial objective score, the recalculated trial objective scores from the prior trials, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 9. The system of claim 8, wherein the hyperparameter objective function is formulated to normalize instance level improvements and instance level regressions over a total number of instances to obtain an improvement score and a regression score, and wherein the domain score for each domain is calculated based on the improvement score and the regression score. 10. The system of claim 9, wherein the improvement score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are correctly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are incorrectly predicted by a baseline machine learning model, and (iii) a total count of the number of instances within the at least one evaluation dataset, and wherein the regression score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are correctly predicted by a baseline machine learning model, and (iii) the total count of the number of instances within the at least one evaluation dataset. 11. The system of claim 8, wherein the hyperparameter objective function is formulated to exclude unstable instances, which are instances within the at least one evaluation dataset that are determined to yield prediction results that differ from one another using a same machine learning model. 12. The system of claim 11, wherein excluding unstable instances comprises: (i) subtracting a count of the excluded unstable instances from the number of instances within the at least one evaluation dataset that are correctly predicted by the machine learning model during the given trial, and (ii) subtracting the count of the excluded unstable instances from the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial. 13. The system of claim 8, wherein the hyperparameter objective function is formulated to include a parameter for an acceptable regression ratio, m, for one or more of the plurality of domains. 14. The system of claim 13, wherein the parameter is defined based on a regression score, and if the regression score is less that the acceptable regression ratio, m, then the regression score is set to zero, otherwise the regression score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are correctly predicted by a baseline machine learning model, and (iii) the total count of the number of instances within the at least one evaluation dataset. With respect to claim 15, one having ordinary skill in the art before the effective filing date of the claimed invention to modify the independent claim 15 of the instant application so that the hyperparameter objective function comprises a domain score for each domain of the plurality of domains, and wherein the domain score is calculated based on a of instance-level changes in prediction correctness between the machine learning algorithm during a given trial and a baseline model, in order to minimize instance level regression during a target-based hyper parameter tuning, improve overall performance of the model and improve performance of the computing system running the model, thereby increasing speed or efficiency of an underlying computing device and reducing a processing requirement or memory usage of the underlying computing device. Note, such deviation would not interfere with the functionality of the claims that are already patented, and would achieve the same end result. Please, see the comparison table below: Application Co-pending Application 15. A computer-program product tangibly embodied in one or more non-transitory machine-readable media, including instructions configured to cause one or more data processors to perform the following operations: creating a plurality of machine learning models by: retrieving a machine learning algorithm; initializing a machine learning algorithm with a set of hyperparameter values; obtaining a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset; for each trial of a hyperparameter tuning process: training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs the plurality of machine learning models comprising a machine learning model for each domain; evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating a domain score for each domain; calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associate with each domain; determining whether a periodic checkpoint has been reached in the hyperparameter tuning process; in response to determining the periodic checkpoint has been reached, updating at least one domain weight for a domain of the plurality of domains and recalculating, using the hyperparameter objective function, the current trial objective score based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; and determining whether the machine learning algorithm has reached convergence based on the recalculated current trial objective score; and in response to determining the machine learning algorithm has reached convergence, deploying the plurality of machine learning models for use in a digital assistant system. 15. One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising; initializing a machine learning algorithm with a set of hyperparameter values; accessing a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain of the plurality of domains comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset, wherein the hyperparameter objective function comprises a domain score for each domain of the plurality of domains, and wherein the domain score is calculated based on quantification of instance-level changes in prediction correctness between the machine learning algorithm during a given trial and a baseline model ; for each trial of a hyperparameter tuning process: training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs a plurality of machine learning models comprising a machine learning model for each domain; evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating the domain score for each domain; calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain; and determining whether the machine learning model has reached convergence based on the current trial objective score; and in response to determining the machine learning model has reached convergence, providing at least one of the plurality of machine learning models. Claims 16-20 are rejected for incorporating the deficiency of their respective base claims by dependency. Application Co-pending Application 16. The computer-program product of claim 15, wherein in response to determining the periodic checkpoint has been reached, comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing; retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value. 17. The computer-program product of claim 15, wherein in response to determining the periodic checkpoint has been reached, determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in M domains; retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain; and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value. 18. The computer-program product of claim 15, wherein the operations further comprise storing the current trial objective score to a database, wherein the database comprises a plurality of trial objective scores from current and prior trials of the hyperparameter tuning process, the domain weight associate with each domain, the domain score for each domain from current and prior trials of the hyperparameter tuning process, and the set of hyperparameter values and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 19. The computer-program product of claim 18, wherein: determining the periodic checkpoint has been reached, comprises determining a trial counter or timer is equal to or exceeds a trial counter or timer threshold; determining the periodic checkpoint has not been reached, comprises determining the trial counter or the timer is less than the trial counter or the timer threshold; in response to determining the periodic checkpoint has not been reached, determining whether the machine learning algorithm has reached convergence based on the current trial objective score; in response to determining the machine learning algorithm has not reached convergence based on the current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the current trial objective score, the trial objective scores from the prior trials of the hyperparameter tuning process, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 20. The computer-program product of claim 18, wherein: in response to determining the periodic checkpoint has been reached, recalculating, using the hyperparameter objective function, at least one of the trial objective scores from the prior trials of the hyperparameter tuning process based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight; in response to determining the machine learning algorithm has not reached convergence based on the recalculated current trial objective score, determining a new set of hyperparameters to be used in a subsequent trial of the hyperparameter tuning process; and the new set of hyperparameters is determined based on the recalculated current trial objective score, the trial objective scores from the prior trials including the at least one recalculated trial objective score from a prior trial, the set of hyperparameter values, and all other sets of hyperparameter values from prior trials of the hyperparameter tuning process. 16. The one or more non-transitory computer-readable media of claim 15, wherein the hyperparameter objective function is formulated to normalize instance level improvements and instance level regressions over a total number of instances to obtain an improvement score and a regression score, and wherein the domain score for each domain is calculated based on the improvement score and the regression score. 17. The one or more non-transitory computer-readable media of claim 16, wherein the improvement score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are correctly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are incorrectly predicted by a baseline machine learning model, and (iii) a total count of the number of instances within the at least one evaluation dataset, and wherein the regression score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are correctly predicted by a baseline machine learning model, and (iii) the total count of the number of instances within the at least one evaluation dataset. 18. The one or more non-transitory computer-readable media of claim 15, wherein the hyperparameter objective function is formulated to exclude unstable instances, which are instances within the at least one evaluation dataset that are determined to yield prediction results that differ from one another using a same machine learning model. 19. The one or more non-transitory computer-readable media of claim 18, wherein excluding unstable instances comprises: (i) subtracting a count of the excluded unstable instances from the number of instances within the at least one evaluation dataset that are correctly predicted by the machine learning model during the given trial, and (ii) subtracting the count of the excluded unstable instances from the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial. 20. The one or more non-transitory computer-readable media of claim 15, wherein the hyperparameter objective function is formulated to include a parameter for an acceptable regression ratio, m, for one or more of the plurality of domains, and wherein the parameter is defined based on a regression score, and if the regression score is less that the acceptable regression ratio, m, then the regression score is set to zero, otherwise the regression score is calculated based on: (i) the number of instances within the at least one evaluation dataset that are incorrectly predicted by the machine learning model during the given trial, (ii) the number of instances within the at least one evaluation dataset that are correctly predicted by a baseline machine learning model, and (iii) the total count of the number of instances within the at least one evaluation dataset . This is a provisional non-statutory double patenting rejection because the patentably indistinct claims have not in fact been patented. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-3, 8-10 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Qi et al. (US 2021/0304055 A1) ("Qi") (cited in an IDS) in view of Rebuffi et al., "Learning multiple visual domains with residual adapters," arXiv:1705.08045v5 [cs.CV] 27 Nov 2017 ("Rebuffi") . As to claim 1, Qi discloses a computer-implemented method ([[0007]: "The system/apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.") comprising: a plurality of machine learning models by retrieving a machine learning algorithm (see [0005]: "executing an initial AutoML process on the machine learning model based on a plurality of datasets comprising a plurality of domains of data elements, utilizing the initially configured AutoML logic) ; initializing the machine learning algorithm with a set of hyperparameter values (see [[0037]: "For example, an initial set of hyperparameter values for N parameters of a computer model may be set as beta=(param1, param2, paramN), along with an initial upper bound and lower bound for each of these hyperparameter values." The computer model is a "machine learning computer model" ([0018])); accessing a hyperparameter objective function that is defined at least in part on a plurality of domains of a search space that is associated with the machine learning algorithm, wherein the search space comprises training datasets and evaluation datasets with each domain comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset (see [0035]: "As shown in FIG. 1, the AutoML process 110, comprises multiple stages of logic 112-116 which are repeatedly executed until the hyperparameter value settings for the ML model 140 are optimized, e.g., a particular time constraint is met, a particular amount of improvement in the performance of the model, e.g., a loss or error, is equal to or less than a particular threshold, a particular amount of improvement in performance is not able to be achieved, or any other suitable stopping criteria for the AutoML process 110." See also [0040]- [0041], the performance measure corresponds to a "hyperparameter objective function." It is defined on a plurality of domains, since the data used in the training includes a plurality of domains. A "plurality of domains" is disclosed in [0005]: "executing an initial AutoML process on the machine learning model based on a plurality of datasets comprising a plurality of domains of data elements, utilizing the initially configured AutoML logic." See also [0041]: "The one or more input datasets 130 may comprise data in a variety of different domains, e.g., domains D1-D4."] wherein the search space comprises training datasets and evaluation datasets with each domain comprising a subdivision of the search space with at least one training dataset and at least one evaluation dataset, and wherein the hyperparameter objective function comprises [...]; [[0042]: "In the case of training data, the labels of the data in the datasets 130 further comprise a ground truth classification or output that is a correct output of a properly trained ML model given the corresponding input data. This ground truth output may be compared to the actual output generated by the ML model 140 to evaluate a performance of the ML model 140 with regard to the correct output, e.g., a loss function may be used to calculate an error in the ML model 140 output" [0047]: " trained on one or more input datasets, e.g., one or more datasets 132-138 having labeled domains, to recognize patterns of content indicative of particular domains and thereby classify data elements of the input datasets into a plurality of predefined domains, e.g., domains D1-D4, where an input dataset 132 may comprise data elements of various domains D1-D4 such that it represents a mixed domain dataset." In regards to the limitation of an "evaluation dataset" for each domain, the above part of [0042] teaches that the dataset is both the "training dataset" and the "evaluation dataset." See also [0041], which refers to "evaluate the performance of the ML model 140, the AutoML process 110 comprises a second stage 114" with the use of the input datasets); for each trial of a hyperparameter tuning process (see [0020] and [0066], process may be repeated for each subsequent new dataset received such that the learning of the hyperparameter sampling configuration parameters is continuously or periodically updated): training the machine learning algorithm for each domain using the at least one training dataset associated with each domain and the set of hyperparameter values, wherein the training outputs the plurality of machine learning models comprising a machine learning model for each domain (see [0060]: "As shown in FIG. 2, for each of these datasets, a learned value for the hyperparameter param1 is determined through the AutoML process, e.g., AutoML process 110 in FIG. 1." As noted above, training is described in [0042]: "In the case of training data, the labels of the data in the datasets 130 further comprise a ground truth classification or output that is a correct output of a properly trained ML model given the corresponding input data. This ground truth output may be compared to the actual output generated by the ML model 140 to evaluate a performance of the ML model 140 with regard to the correct output." Furthermore, as illustrated in FIG. 2, the same domains appear across multiple workspaces, each of which has a corresponding learned value. The limitation of a plurality of machine learning models is described because as shown in FIG. 2, different learned values correspond to different machine learning models); evaluating the machine learning model for each domain using the at least one evaluation dataset associated with each domain and the set of hyperparameter values, wherein the evaluating comprises generating a domain score for each domain (see [0058]: "This initial set of hyperparameter sampling configuration parameters 210 are used to perform an initial AutoML process, such as described previously with regard to FIG. 1, and thereby generate for performance metrics for each sampled set of hyperparameter values which are then used to identify a particular setting of hyperparameter values that provide a best, or optimum, performance of the ML model when the ML model is configured with the selected set of hyperparameter values. As shown in FIGS. 2-4, the value of hyperparameter param 1 that provides the best performance of the ML model based on the evaluation of the performance metrics generated by this initial AutoML operation is the "learned" value for the hyperparameter." That is, for each workspace in FIG. 2, the performance is calculated in order to derive the optimal value that is learned for a particular hyperparameter); “determining whether a periodic checkpoint has been reached in the hyperparameter tuning process (see [0019]-[0020], determine whether that particular set of values for the size hyperparameters provides an improvement in the performance of the ML model, the ML model must be configured with the selected set of hyperparameters and run on one or more training data to generate output results which can then be used to determine the performance of the ML model, e.g., accuracy of the ML model as determined from the loss function of the ML model and the ground truth of the training data); in response to determining the periodic checkpoint has been reached, updating at least one domain weight for a domain of the plurality of domains (see [0050]-[0054], updating the lower and upper bounds in the entries based on the new learned default value); and recalculating, using the hyperparameter objective function, the current trial objective score based on the domain score for each domain and the domain weight associate with each domain including the at least one updated domain weight (see [0054], a new learned default value is generated for each domain in the input dataset, the values in the corresponding entries of the hyperparameter sampling configuration data structure 126 for the hyperparameter are updated in the manner previously described above by resetting the default values in the entries to be the new learned default value and recalculating and updating the lower and upper bounds in the entries based on the new learned default value. It should be appreciated that this process may be repeated for each subsequent input dataset that is processed by the ML model such that the hyperparameter sampling configuration data is continuously or periodically updated which improves the subsequent AutoML process 110 by continuously refining the default values and lower/upper bounds of the range of possible hyperparameter values which are sampled for inclusion in sets of hyperparameters during the AutoML process); calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain (see [0040]: "the performance of the ML model 140, configured with the hyperparameter values corresponding to the selected set of hyperparameter values (beta*i), is evaluated with regard to one or more performance metrics of interest, e.g., accuracy of the output of the ML model 140 relative to a ground truth as may be measured by a loss function); and determining whether the machine learning algorithm has reached convergence based on the recalculated current trial objective score (see [0035]: "As shown in FIG. 1, the AutoML process 110, comprises multiple stages of logic 112-116 which are repeatedly executed until the hyperparameter value settings for the ML model 140 are optimized, e.g., a particular time constraint is met, a particular amount of improvement in the performance of the model, e.g., a loss or error, is equal to or less than a particular threshold, a particular amount of improvement in performance is not able to be achieved, or any other suitable stopping criteria for the AutoML process); and in response to determining the machine learning algorithm has reached convergence, the plurality of machine learning models for use in a digital assistant system (see [0035]: "Once the AutoML process 110 completes, a set of learned hyperparameter values are generated that provide an optimum performance of the ML model 140 during training of the ML model 140 and/or runtime deployment of the ML model 140. That is, this set of learned hyperparameters are learned from the AutoML process 110 and may be used to configure an instance of the ML model for training database and/or in a runtime environment which processes new workloads using the configured model). Qi does not explicitly disclose “calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain”. Meanwhile Rebuffi discloses the claimed “calculating, using the hyperparameter objective function, a current trial objective score based on the domain score for each domain and a domain weight associated with each domain” (see page 4, paragraph 4: "Performance is measured in terms of a single scalar score S determined as in the decathlon discipline. Performing well at this metric requires algorithms to perform well in all tasks, compared to a minimum level of baseline performance for each. In detail, S is computed as follows: [See equations in expression (1)] where Ed is the average test error for each domain The coefficient ad is set to 1, 000 (Eᵐᵃₓd)⁻⁷ᵈ so that a perfect result receives a score of 1,000 (10,000 in total)." That is, referring to equation 1, admax Edᵐᵃₓ - Ed}ʳᵈ constitutes a domain score for domain d. Note that as further shown in the formula for Ed, this measures the test error, which is measured over the number of instances in the evaluation set Ddtcst] and thus the related limitation of use of "the domain score for each domain" for evaluating and for calculation of a current trial objective score "based on the domain score for each domain and a domain weight associated with each domain" [As noted above, the individual scores Ed (or max {0, Edᵐᵃₓ - Ed}) are used to compute the performance scalar score S and is based on the domain weight ad. This scalar score is analogous to the performance metric disclosed in the base reference.] Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Qi with the teachings of Rebuffi, in order to implement a measure of performance that evaluates a model for multiple-domain learning, in a way that assesses whether a method can successfully learn to perform well in several different domains at the same time. As to claim 2, the combination of Qi and Rebuffi discloses the invention as claimed. In addition, Qi, discloses the claimed “comparing the domain score for each domain to a domain score threshold; identifying at least one domain score that is less than the domain score threshold based on the comparing (see [0019], assisting with hyperparameter tuning, it can be appreciated that the process of identifying optimized hyperparameter settings is still a time consuming and computation intensive process); retrieving a domain weight for a domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain (see [0064]-[0065], retrieve the hyperparameter sampling configuration parameters for the domains represented in the dataset ); and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value (see [0066]-[0067], update the corresponding entries in the domain specific hyperparameter sampling configuration data structure 240 . This process may be repeated for each subsequent new dataset 310 received such that the learning of the hyperparameter sampling configuration parameters is continuously or periodically updated). As to claim 3, the combination of Qi and Rebuffi discloses the invention as claimed. In addition, Qi, discloses the claimed “determining a subset of the plurality of machine learning models in M domains of the plurality of domains having a poorest performance based on the evaluating; identifying at least one domain score associated with a domain that is in the M domains (see [0019], assisting with hyperparameter tuning, it can be appreciated that the process of identifying optimized hyperparameter settings is still a time consuming and computation intensive process); retrieving a domain weight for the domain associated with the identified at least one domain score, wherein the retrieved domain weight is the at least one domain weight for the domain(see [0064]-[0065], retrieve the hyperparameter sampling configuration parameters for the domains represented in the dataset ); and updating the at least one domain weight for the domain, wherein the updating comprises adjusting a value for the at least one domain weight based on a predefined adjustment value (see [0066]-[0067], update the corresponding entries in the domain specific hyperparameter sampling configuration data structure 240 . This process may be repeated for each subsequent new dataset 310 received such that the learning of the hyperparameter sampling configuration parameters is continuously or periodically updated). As to claims 8-10, claims 8-10 are system for performing the method of claims 1-3 above. They are rejected under the same rationale. As to claims 15-17, claims 15-17 are computer program product having storing therein instructions for executing the method of claims 1-3 above. They are rejected under the same rationale . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 4-7, 11-14 and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 6301571B1 (involved in testing and classifying people/system based on knowledge states and functionality states by putting relevant questions and deciding the remedy to be provided with respect to responses received for the questions). US20220027757A1 (involved in expressing hyperparameter tuning process of a model (152) based on type of the model, dimensions of a training dataset, associated loss function of the model and associated computational constraints of model by using a computer processor. A set of optimal hyper-rectangles is identified based on calculated local variability and a calculated best function value by using the computer processor. A point is calculated as representative for each identified potentially optimal hyper rectangle to locally search over the identified set of potentially optimal hyper-rectangles by using the computer processor. Hyper-rectangles in the identified set of optimal hyper-rectangles is divided into smaller hyper-rectangles based on each calculated point. A globally converged hyper-rectangle utilized from smaller hyper-rectangles is calculated by using the computer processor). Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex). 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, Ann J Lo can be reached at (571)272-9767. 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. /JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 May 26, 2026 Application/Control Number: 18/295,018 Page 2 Art Unit: 2159
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