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. DETAILED ACTION This action is responsive to the following communication: Preliminary Amendment filed Oct. 13 , 202 5 . Claims 1-20 are pending in the case. Claims 1 and 11 are independent 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. Claims 1- 3 , 6-13, 16 - 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sarah et al. (hereinafter S arah ) U.S. Patent Publication No. 20 22 /0 035878 . With respect to independent claim 1 , Sarah teaches a method comprising: receiving a specification indicative of a particular artificial intelligence (AI) solution, wherein the specification is of a first format (see e.g., Para [ 22 ] [23 ] - “ the MLASI 110 a includes application programming interfaces (APIs) to access the other subsystems of system 100 , manages the ML model and parameter updates (e.g., new or updated ML config. 105 ), and calls the supported ML operations library (e.g., as indicated by the ML config. 105 ). “ ) ; selecting a particular Al service that is associated with the particular Al solution (see e.g., Fig. 3 Para [ 22 ] - [ 25 ] [62][63] - “ After the user inputs the various ML parameters, the system 100 finds ML architecture candidates and displays them to the user via the GUI 300 who can then select and download the ML architecture which best fits their needs using the graphical object 307 . In this example, the GUI 300 displays a graph 330 indicating the Pareto frontier (referred to as “Pareto frontier 330 ”) of the discovered ML architectures. In this example, the Pareto frontier 330 is represented by the displayed points in the graph 330 . Each point in the graph 330 may represent a respective ML model that is downloadable by the user. ”) ; modifying the specification to generate a request for the particular Al service, wherein the request is of a second format that is different from the first format (see e.g., Fig. 3 Para [ 2 4 ] - [ 26 ] – The ML model may utilize clustering regression, natural language and the like to provide a smaller subset of data (format) for the desired goal . “a relatively large reference ML model (referred to herein as a “super-network” or “ supernet ”) may be provided instead of, with, or in the ML config. 105 from which the system 100 is to discover a smaller ML model (referred to herein as a “sub-network” or “subnet”” “ Furthermore, the ML config. 105 may be an information object, file, electronic document, etc., in any suitable form or format such as, for example, a suitable mark-up language document (e.g., HyperText Markup Language (HTML), Extensible Markup Language (XML), AI Markup Language (AIML), JavaScript Object Notation (JSON), etc.), a columnar file format (e.g., Hierarchical Data Format (HDF) including HDF4, HDF5, etc. ” The system may receive a search parameter where the formatted data can be tabular text, HTML, Java, python and the like. ““ the ML config. 105 may include HPI. The HPI may include configuration, specifications, technical details, characteristics, and/or other aspects of a desired hardware platform and/or individual hardware components on which the user intends to deploy an ML model. ““ The ML config. 105 can also include an appropriately formatted dataset (or a reference to such a dataset). Here, an appropriately formatted dataset refers to a dataset that corresponds to the provided supernet , and/or the specified AI/ML task and/or AI/ML domain. For example, a dataset that would be used for the NLP domain would likely be different than a dataset used for the computer vision domain. ” “ At step 2 , the MLASI 110 a provides the ML config. 105 (e.g., search parameters and/or supernet ) to the MLAS function 200 . ” ) ; requesting that the particular Al service generate a result in response to the request (see e.g., Para [ 63 ] - “ After the user inputs the various ML parameters, the system 100 finds ML architecture candidates and displays them to the user via the GUI 300 who can then select and download the ML architecture which best fits their needs using the graphical object 307 . In this example, the GUI 300 displays a graph 330 indicating the Pareto frontier (referred to as “Pareto frontier 330 ”) of the discovered ML architectures.” ) ; modifying the result based on a configuration parameter associated with the particular Al service ( see e.g., Para [ 63 ] [64] - “ the GUI 300 includes a toggle graphical object 325 (also referred to as “top-1 object 325 ”, “top-1 325 ”, or the like) that allows the user to switch between views of only ML architectures having top-1 accuracy and all discovered/generated ML models. Top-1 accuracy refers to a level of ML model prediction accuracy where a model answer with the highest probability is the expected answer. ” ) ; and providing at least a portion of the modified result in response to the specification (see e.g., Para [ 63][64] ) . With respect to dependent claim 2 , Sarah teaches the modifying the specification includes determining that the second format is associated with the particular Al service (see e.g., Para [ 24 ] - [ 26 ] [62] - “ a relatively large reference ML model (referred to herein as a “super-network” or “ supernet ”) may be provided instead of, with, or in the ML config. 105 from which the system 100 is to discover a smaller ML model (referred to herein as a “sub-network” or “subnet”). ”) . With respect to dependent claim 3 , Sarah teaches the specification is based on an input directed to a user interface, and wherein providing at least the portion of the modified result includes updating the user interface to include the at least the portion of the modified result (see e.g., Fig. 3 Para [ 64 ]) . With respect to dependent claim 6 , Sarah teaches selecting the particular Al service includes selecting a particular trained LLM, of a plurality of trained LLMs, based on the particular Al solution (see e.g., Para [ 19 ] [24] ) . With respect to dependent claim 7 , Sarah teaches generating the result via the particular trained LLM (see e.g., Para [ 19 ][ 64 ] – The examiner notes that there is no limitation regarding the type of trained machine learning model. ) . With respect to dependent claim 8 , Sarah teaches the modifying the specification includes searching an internal repository to identify internal context data for the particular Al solution and including at least a portion of the internal context data in the request for the particular Al service in the second format ( see e.g., P aras [2 2 ] - [25], [41], [62] - The first ML architecture search may need utilize a second search where the parameter may be the same based on the clustering, regression, subset of data (format) for the desired goal . ) . With respect to dependent claim 9 , Sarah teaches performing one or more checks on the result, wherein the one or more checks include a content moderation check, a trustworthiness check, or a hallucination check (see e.g., Para [ 70 ] - [ 71 ]- the ML model utilize the training data set (moderation check) based on the parameters to generate a prediction from the ML algorithm . ) . With respect to dependent claim 10 , Sarah teaches modifying the specification includes generating a prompt based on a prompt configuration parameter for the selected particular Al service (see e.g., Para [ 24 ][ 25 ] [41][62] - consist of an text input box/prompt) may be the same based on the clustering, regression, subset of data (format) for the desired goal ) . Claim 11 is rejected for the similar reasons discussed above with respect to claim 1. Claim 1 2 is rejected for the similar reasons discussed above with respect to claim 2. Claim 1 3 is rejected for the similar reasons discussed above with respect to claim 3. Claim 1 6 is rejected for the similar reasons discussed above with respect to claim 6. Claim 1 7 is rejected for the similar reasons discussed above with respect to claim 7. Claim 1 8 is rejected for the similar reasons discussed above with respect to claim 8. Claim 1 9 is rejected for the similar reasons discussed above with respect to claim 9. Claim 20 is rejected for the similar reasons discussed above with respect to claim 1. Claims 4 -5 and 14 -15 are rejected under 35 U.S.C. 103 as being unpatentable over Sarah in view of Grois (hereinafter Grois ) U.S. Patent Publication No. 2009/00 30800 . With respect to dependent claim 4 , Sarah does not expressly show the user interface includes a chat conversation with a virtual agent, wherein the input is directed to within the chat conversation, and wherein at least the portion of the modified result is within the chat conversation. However, Grois teaches the above feature (see e.g. Fig. 1B P ara [56] [ 57 ] – “a Virtual Assistant 125, and of advertising by using the same, according to a preferred embodiment of the present invention. The User Interface of the search engine comprises a Virtual Assistant means 125 (one or more software and/or hardware components or units) providing a user with a natural communication environment and helping said user to obtain the most appropriate search results for his one or more search queries. It is assumed, for example, that the user conducts a textual or voice (by providing queries by voice) search for a query "tennis courts". The user receives a number of relevant search results 120, such as "Tennis courts in California" and etc. Virtual Assistant 125 can discuss with the user the received search results for obtaining the optimal search result. “ ). Both Sarah and Grois are directed to GUI navigation systems . Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Sarah and Grois in front of them to modify the system of Sarah to include the above feature. The motivation to combine Sarah and Grois comes from Grois . Grois discloses the motivation to provide virtual agent in a user interface so that a user can navigate data/results in a more natural way and more appropriate results can be presented to a user (see e.g. para [ 3 ]). With respect to dependent claim 5 , the modified Sarah teaches accessing a virtual agent that is configured to perform a workflow and directing the virtual agent to add to the workflow the specification of the particular Al solution ( Sarah does not expressly teaches this feature. However, Grois expressly teaches that a virtual agent can be implemented in the user interface to assist a user to automatically add search criteria based on user’s “wishes” - see e.g., Grois Para [ 62 ] - [ 65 ]-“ In addition, prior to conducting the search the user can discuss with Virtual Assistant 125 what he is interested (what he wishes) to find, and Virtual Assistant 125 helps said user to obtain the most appropriate search results based on user's interests (wishes). ” Therefore, it would have been obvious to include the above feature. The motivation to combine Sarah and Grois comes from Grois . Grois discloses the motivation to provide virtual agent in a user interface so that a user can navigate data/results in a more natural way and more appropriate results can be presented to a user (see e.g. para [3]) ) . Claim 1 4 is rejected for the similar reasons discussed above with respect to claim 4. Claim 1 5 is rejected for the similar reasons discussed above with respect to claim 5. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT PEIYONG WENG whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1660 . The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell , can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://portal.uspto.gov/external/portal. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /PEI YONG WENG/ Primary Examiner, Art Unit 2141