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: Amendment filed Jun. 19, 2026. This Action is made Final.
Claims 1-20 are pending in the case. Claims 1 and 11 are independent claims.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 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.
Claims 1-3, 6-13, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sarah et al. (hereinafter Sarah) U.S. Patent Publication No. 2022/0035878 in view of Kottler et al. (hereinafter Kottler) U.S. Patent Publication No. 2024/0038344.
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 110a 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 model 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 model, wherein the request is of a second format that is different from the first format (see e.g., Fig. 3 Para [24]-[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 110a provides the ML config. 105 (e.g., search parameters and/or supernet) to the MLAS function 200.”);
requesting the particular Al model (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.”);
Sarah does not expressly show the features discussed below. However, Kottler teaches requesting that the particular AI model generate a result in response to the request (see e.g. Fig. 25A 25B Para [53]-[55] – “an orchestrator engine, providing a context driven workflow, processes (at block 2502) structured information of the patient information and medical image (e.g., a result of a scan of a portion of a body) to determine whether the patient information should be sent to one of the AI programs … The determined AI program is optimized to process medical images generated according to the determined characteristic of the methodology of the scan by a scanner to generate the structured result of a plurality of AI programs … At block 2516, the AI program processes the medical image and the patient data to provide a structured result as output. “); modifying the result based on a configuration parameter associated with the particular AI model (see e.g. Fig. 19 Para [44] – “As shown in FIG. 19, the orchestrator 1901, 1900 may determine where to forward the images/reports 1912, 1914, 1916 before processing by the AI engine 1902, 1904 and after processing by the AI engine, a second level orchestration 1900. This provides the context driven workflow to use the context of the content of the information to determine the workflow and add further structure to assist in decision making. “Postprocessing of AI output is performed by a downstream orchestrator) and providing at least portion of the modified result in response to the specification (see e.g. Fig. 1B Para [56] – “A determination is made (at block 2524) whether the structured result from the AI program indicates whether the medical image is of a low or high quality. If (at block 2524) the quality is low, then the orchestrator engine forwards (at block 2526) a request to a radiology technician that the medical image has low quality and request the radiology technician to acquire a high quality medical image for the patient. The orchestrator engine may further forward (at block 2528) the image and patient information to the radiologist indicating that the medical image is of a low quality. If (at block 2524) the image is of high quality, then the structured result may be forwarded (at block 2530) with the patient information to present to the radiologist to evaluate the medical image. “). Both Sarah and Kottler are directed to AI model selection methods. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Sarah and Kottler in front of them to modify the system of Sarah to include the above feature. The motivation to combine Sarah and Kottler comes from Kottler. Kottler 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 2, the modified Sarah teaches the modifying the specification includes determining that the second format is associated with the particular Al model (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, the modified 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, the modified Sarah teaches selecting the particular Al model 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, the modified 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, the modified 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 model in the second format (see e.g., Paras [22]-[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, the modified 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, the modified Sarah teaches modifying the specification includes generating a prompt based on a prompt configuration parameter for the selected particular Al model (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 12 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 13 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 16 is rejected for the similar reasons discussed above with respect to claim 6.
Claim 17 is rejected for the similar reasons discussed above with respect to claim 7.
Claim 18 is rejected for the similar reasons discussed above with respect to claim 8.
Claim 19 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 Kottler and further in view of Grois (hereinafter Grois) U.S. Patent Publication No. 2009/0030800.
With respect to dependent claim 4, Sarah-Kottler 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 Para [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 further modify the modified 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 14 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 5.
Conclusion
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 PEIYONG WENG whose telephone number is (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.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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