DETAILED ACTION This office action is in response to the application filed on 9/21/2023. Claim(s) 1-20 is/are pending and are examined. 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. Information Disclosure Statement PTO-1449 The Information Disclosure Statement(s) submitted by applicant on 9/21/2023 has/have been considered. The submission is in compliance with the provisions of 37 CFR § 1.97. Form PTO-1449 signed and attached hereto. Examiner’s Note – Medium Instant specification ¶ 60 discloses that a medium is not a signal . Examiner’s Note – Allowable Subject Matter Claims 8 and 18 overcome the prior art and would otherwise be allowable if incorporated into the base claim along with any intervening claims. Claim Rejections - 35 USC § 102 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. (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 , 9, 11, and 19- 20 , is/are rejected under AIA 35 U.S.C. 102(a)( 1 ) as being anticipated by Johnson (US 2021/0304003 A1) . Regarding claim s 1 , 11, and 20 , Johnson teaches: “ A method, in a data processing system, for selecting an artificial intelligence (AI) computer model for processing an input ( Johnson, ¶ 164-165 and 173 teaches processor, memory and medium to execute method steps ) , the method comprising: generating at least one distribution of first characteristics of previous input data processed by the data processing system ( Johnson, ¶ 40, 43, and 45 describes skill bots which are models trained to respond to different user language inputs. Johnson, ¶ 62 and 77-89 teaches the process for training the skill bot models including a series of weighted metrics ) ; analyzing current input data to generate second characteristics of the current input data ( Johnson, Fig. 1, element 110, ¶ 58-60 teaches receiving a user language input which is put into a classifier to determine the intent of the user input ) ; comparing the second characteristics of the current input data to the at least one distribution of first characteristics to generate at least one similarity metric ( Johnson, ¶ 75-76 teaches determining a confidence score that the intent of the user input matches a given skill bot, based on a threshold. When the threshold is exceeded, the skill bot model is selected ) ; processing, by an Al computer model selection engine, the at least one similarity metric to select an Al computer model from a plurality of different Al computer models, wherein the processing of the at least one similarity metric comprises evaluation of the at least one similarity relative to one or more threshold values ( Johnson, ¶ 75-76 teaches determining a confidence score that the intent of the user input matches a given skill bot, based on a threshold. When the threshold is exceeded, the skill bot model is selected ) ; and processing the current input data by the selected Al computer model to generate a result of processing the current input data ( Johnson, Fig. 1, element 112, ¶ 33-34 and 45 the skill bot processes the request and the response is returned to the user ) ”. Regarding claim s 9 and 19 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above ) , wherein the previous input data comprises previous natural language content, the current input data comprises current natural language content ( Johnson, Fig. 1, element 110, ¶ 58-60 teaches receiving a user language input which is put into a classifier to determine the intent of the user input ) , and the plurality of AI computer models are AI computer models that determine intent of the current natural language content ( Johnson, Fig. 1, element 110, ¶ 58-60 teaches receiving a user language input which is put into a classifier to determine the intent of the user input. Johnson, ¶ 40, 43, and 45 describes skill bots which are models trained to respond to different user language inputs. Johnson, ¶ 62 and 77-89 teaches the process for training the skill bot models including a series of weighted metrics ) ”. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Sriharsha (US 2022/0036177 A1) . Regarding claim s 2 and 12 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above ) , wherein the previous input data comprises previous natural language content ( Johnson, Fig. 1, element 110, ¶ 58-60 teaches receiving a user language input which is put into a classifier to determine the intent of the user input ) ”. Johnson does not, but in related art, Sriharsha teaches: “ the first characteristics comprise regular expressions present in the previous natural language content, and wherein the second characteristics comprise regular expressions present in the current input data ( Sriharsha, ¶ 672, 674, 719 and 722 teaches receiving and matching language input based on regular expressions which are able to merge similar content ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Sriharsha, to modify the model selection system of Johnson to include the method to implement regex as taught in Sriharsha . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Claim(s) 3-4 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Polleri (US 2023/0237348 A1) . Regarding claim s 3 and 13 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above ) ”. Johnson does not, but in related art, Polleri teaches: “ wherein each distribution in the at least one distribution corresponds to a cluster of input data which correlates characteristics of the input data with performance of Al computer models ( Polleri , ¶ 81-83, 94, and 117 teaches grouping models and measuring them based on performance criteria ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Polleri , to modify the model selection system of Johnson to include the method to measure performance of models as taught in Polleri . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Regarding claim s 4 and 14 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above ) , wherein processing the at least one similarity metric to select an Al computer model comprises identifying an Al computer model that corresponds to a distribution, in the at least one distribution, to which the current input data is most similar as indicated by the at least one similarity metric ( Johnson, ¶ 75-76 teaches determining a confidence score that the intent of the user input matches a given skill bot, based on a threshold. When the threshold is exceeded, the skill bot model is selected ) ”. Johnson does not, but in related art, Polleri teaches: and provides a performance and cost that meets specified selection criteria ( Polleri , ¶ 81-83, 94, and 117 teaches grouping models and measuring them based on performance criteria. Polleri , ¶ 283 teaches cost as a selection criteria ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Polleri , to modify the model selection system of Johnson to include the method to measure performance and cost of models as taught in Polleri . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Ensing (US 2024/0386667 A1) . Regarding claim s 5 and 15 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above )”. Johnson does not, but in related art, Ensing teaches: “ wherein the plurality of different Al computer models comprise a rules-based Al computer model, a shallow classifier machine learning computer model, and a large scale deep learning Al computer model ( Ensing , ¶ 56 teaches a combination of models including a rules base model, a standard classifier and a deep learning model ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Ensing , to modify the model selection system of Johnson to include the method to utilize multiple types of models as taught in Ensing . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Claim(s) 6 -7 and 16 -17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Durvasula (US 2023/0177441 A1) . Regarding claim s 6 and 16 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above ) ”. Johnson does not, but in related art, Durvasula teaches: “ obtaining cost metrics for each of the different Al computer models, wherein the cost metrics specify costs related to asset management for operating the corresponding Al computer model ( Durvasula , Fig. 7, ¶ 59, and 92-93 teaches determining the cost and computational load for various models ) ; and generating one or more constraints, based on the cost metrics, for selection of the different Al computer model, wherein the constraints include constraints on at least one of hardware costs, wherein the processing of the at least one similarity metric to select the Al computer model from the plurality of different Al computer models is further based on the one or more constraints ( Durvasula , Fig. 7, ¶ 59, and 92-93 teaches determining the cost for implementing a model and computational load for various models ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Durvasula , to modify the model selection system of Johnson to include the method to measure performance and cost of models as taught in Durvasula . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Regarding claim s 7 and 17 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above ) ”. Johnson does not, but in related art, Durvasula teaches: “ obtaining one or more selection constraints for selecting the Al computer model from the plurality of Al computer models, wherein the constraints specify performance characteristics of Al computer models to optimize, wherein the processing of the at least one similarity metric comprises selecting the Al computer model from the plurality of Al computer models based on which Al computer models have characteristics meeting at least one of the one or more selection constraints ( Durvasula , Fig. 7, ¶ 59, and 92-93 teaches determining the cost for implementing a model and computational load for various models ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Durvasula , to modify the model selection system of Johnson to include the method to measure performance and cost of models as taught in Durvasula . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnson in view of Jade (US 11,501,547 B1 . Regarding claim s 10 , Johnson teaches: “ The method of claim 1 ( Johnson teaches the limitations of the parent claims as discussed above )”. Johnson does not, but in related art, Jade teaches: “ wherein the current input data is natural language input to a conversation bot application, and wherein the second characteristics of the current input data include a determination of a level of complexity of the natural language input ( Jade Col. 50 Ln. 51 – Col. 51 Ln. 2 teaches the system determining the complexity of the language for model selection ) ”. Before applicant’s earliest effective filing it would have been obvious to one of ordinary skill in the art, having the teachings of Johnson and Jade , to modify the model selection system of Johnson to include the method to measure complexity as taught in Jade . The motivation to do so constitutes applying a known technique to known devices and/or methods ready for improvement to yield predictable results. Conclusion In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: See PTO-892. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen T Gundry whose telephone number is (571) 270-0507. The examiner can normally be reached Monday-Friday 9AM-5PM (EST). 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 Amir Mehrmanesh can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 270-3351 . 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. /STEPHEN T GUNDRY/ Primary Examiner, Art Unit 2435