DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. The following action is in response to the original filing of 02/21/2023. Claims 1- 1 8 are pending and have been considered below. Claim Objections Claims 4 and 13 objected to because of the following informalities: each claim cites “the app” however the respective parent claims refer to an “application”. Appropriate correction is required. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(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. Claim s 1 -18 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Nagaraja et al., US 2021/0334700 A1 published 10/28/2021 [“NAGARAJA”]. Regarding claim 1, NAGARAJA discloses an artificial intelligence-based application creation interface (¶21-24: domain based model generation framework and interface for creating a custom AI model) providing method performed through a communication connection between a system and a terminal (¶115, Fig. 1: model generation system generates and transmits the UI to the model generation interface which accepts the user inputs and transmits the results back to the model generation system, i.e. the communication is between a system and a terminal where each interface is transmitted from the model generation system to the interface and received information is then received from the interface terminal by the generation system) , the artificial intelligence-based application creation interface providing method comprising: a) transmitting, by the system, a first interface including a customizing information input portion applied to an artificial intelligence-based application to be created to the terminal and receiving customizing information set by the terminal from the terminal through the first interface (¶132-135: first interface provides interfaces for the user to provide customization information regarding the custom AI model application to be built, ex. domain and sub-domain information Fig. 3, Fig. 4A-C) ; b) uploading learning data and pre-processing information required to generate an artificial intelligent model applied to the application when artificial intelligence model information applied to the application is missing from the customizing information, transmitting a second interface including a function unit for pre-processing the learning data to the terminal, receiving the learning data and the pre-processing information required to generate the artificial intelligence model applied to the application from the terminal through the second interface, and generating a training dataset by pre-processing the learning data according to the pre-processing information by using the system (¶132, ¶136-138: when the customization requires additional upload of data, a second interface provides interfaces for the user to provide required learning data and pre-processing information to generate a dataset for training in model generation, Fig. 3, Fig. 5A-5C) ; and c) providing, by the system, a third interface including a download portion of the artificial intelligence model applied to the application to the terminal to output a certain result for a preset input based on the training dataset (¶139-142: subsequent to the data pre-processing, a third interface provides interfaces for generating and downloading the custom AI model and performing a model output). Regarding claim 2, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 1, wherein the first interface includes a first template to a fourth template required to create the application (¶132-134: first interface includes at least four interfaces to create the custom AI model application, Fig. 3, Fig. 4A-4C) . Regarding claim 3, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 1, wherein the second interface includes a fifth template to a tenth template required to generate a training dataset by pre-processing the learning data (¶132 -133 , ¶137-138: second interface includes at least four interfaces to generate the training dataset from the pre-processed learning data, Fig. 3, Fig. 5A-5C) . Regarding claim 4, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 1, wherein the third interface generates the artificial intelligence model applied to the app, trains the artificial intelligence model, and includes an eleventh template to a fifteenth template required to download the artificial intelligence model applied to the application ( ¶132-133, ¶137-138: third interface includes at least four interfaces to download the custom AI model application and perform and output, Fig. 6A-D) . Regarding claim 5, NAGARAJA discloses artificial intelligence-based application creation interface providing method of claim 2, wherein a) includes: a-1) transmitting the first template including an application name input portion, an application description input portion, and an artificial intelligence model information input portion applied to the application to the terminal, and receiving application name information, application description information, and artificial intelligence model information through the first template by using the system (¶132-133: tag tab provides description information of the custom AI model application ) ; a-2) transmitting the second template including an application configuration information input portion required to create the application to the terminal, and receiving application configuration information through the second template by using the system (¶134-136: select domain and subdomain tab provides configuration information regarding domain and subdomain) ; a-3 ) transmitting the third template including an artificial intelligence model training degree input portion and an artificial intelligence model training result checking portion to the terminal, and receiving artificial intelligence model training degree information through the third template by using the system (¶119: selection of subdomain provided through meta-learning, transfer learning or architecture search) ; and a-4) providing, by the system, the fourth template including an application link providing portion required to provide the created application based on a-1) to a-3) (¶132: get started tab to provide the creation information) . Regarding claim 6, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 3, wherein b) includes: b-1) transmitting the fifth template including a learning data upload portion, a data name input portion, a data description input portion, and a data type input portion required to generate the training dataset to the terminal, and receiving learning data information, training data name information, training data description information, and learning data type information through by using the system (¶137-138: data prep tab provides uploaded data file and data type to generate the training data) ; and b-2) providing, by the system, a different template according to a type of the training dataset received from the terminal (¶137-138: based on data type selection) . Regarding claim 7, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 6, wherein b-2) includes: transmitting the sixth template including an image learning data upload portion required to upload the image learning data to the terminal when a type of the learning data is an image dataset, and receiving the image learning data through the sixth template by using the system (¶137-138: upload file tab for image upload ) ; and transmitting the seventh template including an image pre-processing portion required to label, add, or remove the image learning data to the terminal, and receiving image pre-processing information through the seventh template by using the system (¶138: following upload, providing data cleaning process interface) . Regarding claim 8, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 6, wherein b-2) includes: transmitting the eighth template including a comma separated value (CSV) learning data upload portion required to upload CSV learning data to the terminal when a type of the learning data is CSV data of a CSV format, and receiving the CSV learning data through the eighth template by using the system (¶137-138: upload file tab for tabular data, ¶143 ) ; transmitting the ninth template including a CSV information correction portion for information of the CSV learning data and a CSV learning data pre-processing portion required to pre-process the CSV learning data to the terminal, and receiving CSV pre-processing information through the ninth template by using the system (¶138: following upload, providing data cleaning interface for tabular data) ; and transmitting the tenth template including a graph providing portion required to visualize and display the CSV learning data generated through pre-processing to the terminal by using the system (¶143: csv tabular dataset graph visualization, Fig. 8A-8D) . Regarding claim 9, NAGARAJA discloses the artificial intelligence-based application creation interface providing method of claim 4, wherein c) includes: c-1) transmitting the eleventh template including an artificial intelligence model name input portion for inputting basic information of the artificial intelligence model, an artificial intelligence model description input portion, a training data upload portion, and a training data sample selection portion to the terminal, and receiving name information, description information, and training data information of the artificial intelligence model through the eleventh template by using the system (¶139-140: tab interfaces provided for model naming and information and uploading training data sets) ; c-2) transmitting the twelfth template including a training information selection portion required to select a training and verification data ratio of the training data to the terminal, and receiving the training and verification data ratio through the twelfth template by using the system (¶140: AI model parameters tab for providing model parameters for training including targeted column for training) ; c-3) transmitting the thirteenth template including a layer generation portion to generate a layer constituting the artificial intelligence model to the terminal, and receiving layer information through the thirteenth template by using the system (¶140: AI model parameters tab for providing model parameters for training , ¶121: examples of layer parameters ) ; c-4) transmitting the fourteenth template including a parameter setting portion required to set a parameter necessary for training the artificial intelligence model to the terminal, and receiving parameter information through the fourteenth template by using the system (¶140 : performance parameters tab for providing necessary parameters for training ) ; and c-5) transmitting, to the terminal, the fifteenth template including an artificial intelligence model training portion required to train the artificial intelligence model according to the parameter information, a download portion required to store the trained artificial intelligence model and required for the terminal to download the artificial intelligence model, and a real-time monitoring portion required to provide a training situation of the stored artificial intelligence model in real time by using the system (¶140-143: tabs provided for training the model, downloading the trained custom AI model and monitoring of the model, ¶161: real-time) . Regarding claims 10-18, claims 10-18 recite limitations similar to claims 1-9, respectively, and are similarly rejected. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Polleri ; Alberto et al. US 11562267 B2 CHATBOT FOR DEFINING A MACHINE LEARNING (ML) SOLUTION D’Silva ; Kenneth et al. US 11681504 B1 AUTOMATED APPLCIATION BUILDER USING CONFIGURATION FILES Kwan; Yuk Lun Patrick et al. US 12112287 B1 AUTOMATED ESTIMATION OF RESOURCES RELATED TO TESTING WITHIN A SERVICE PROVIDER NETWORK No; Jamie J. et al. US 20140108971 A1 APPLICATION BUILDER Meinders ; Christine US 20190318262 A1 TOOL FOR DESIGNING ARTIFICIAL INTELLIGENCE SYSTEMS Siracusa ; Michael R. et al. US 20200380301 A1 TECHNIQUES FOR MACHINE LANGUAGE MODEL CREATION Marlin; Todd et al. US 20210158221 A1 METHODS AND SYSTEMS FOR FACILITATING ANALYSIS OF A MODEL Rand; Chaim et al. 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"Saga: An Open Source Platform for Training Machine Learning Models and Community-driven Sharing of Techniques." 2019 International Conference on Content-Based Multimedia Indexing (CBMI) . IEEE, 2019. Patil, Prithviraj Sanjay, et al. "Development of AMES: automated ML expert system." 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) . Vol. 1. IEEE, 2022. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ANDREW L TANK whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1692 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Thursday 9a-6p . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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