DETAILED ACTION
The instant application having Application No. 18/827392 filed on 09/06/2024 is presented for examination by the examiner.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statement dated 03/04/2026, 12/30/2025, 11/07/2025 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 101
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1-18 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter of abstract ideas.
Step 1: Claim 1 recites “A method for…”; the claim recites a series of steps and therefore is a process. Claim 11 recites “A system…” therefore the claim is a machine.
Step 2A Prong One: Claims 1 and 10 recite the limitations "generating", “detecting …”, “parsing” and "causing the task to be split …" These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "a computing system”, "one or more processor", and “one or more storage device”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, "generating", “detecting …”, “parsing” and "causing the task to be split …" in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to identify the relevant ones (rationale).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements "facilitating", “training” and “displaying”; the limitations are a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
The “one or more hardware processor”, and “One or more storage devices” in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations "facilitating" and “displaying” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....
Regarding claim 2, the limitation “determine the workflow data for the device under test based on the configuration data” is an additional metal process under prong 1. Under prong 2, the “transmit” and “receive” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 2 and 11, under prong 2, the “displaying status indicators for the subtasks” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 3 and 12, under prong 2, the “wherein the status indicators visually distinguish which subtasks have completed processing by the Al agent” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 4 and 13, under prong 2, the “wherein the status indicators visually distinguish which subtasks are currently being processed by the Al agent.” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 5 and 14, under prong 2, the “icons displayed with the subtasks, at least two different types of icons being displayed with at least two different subtasks to visually distinguish different states of processing by the Al agent for the at least two different subtasks” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 6 and 15, the limitation “detecting new user input entered in the input field that, when entered, is used by the computing system to cause the Al agent to modify the subtasks” is an additional metal process under prong 1.
Regarding claims 7 and 16, under prong 2, the “wherein the detected new user input, when entered, is further used to trigger a modification to a manner in which the AI agent will respond during a future interaction with a user based on new user instructions processed by the Al agent” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claims 8 and 17, the limitation “converting the new user input into one or more rules applied by the Al agent during the future interaction” is an additional metal process under prong 1.
Regarding claims 9 and 18, the limitation “converting the new user input into training data that is applied by the AI agent to modify one or more weights or parameters used by the Al agent when determining how to process a prompt during the future interaction.” is an additional metal process under prong 1.
Allowable Subject Matter
Claims 1-18 would be allowable if rewritten to overcome the rejection(s) under 101, set forth in this Office action.
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
Prior arts:
US 2023/0206132 to Zhu
the training configuration interface includes a plurality of training modes for the user to select, and each training mode represents an allocation policy for compute nodes required for training an initial AI model; generating at least one training task based on a selection of the user on the training configuration interface; and performing the at least one training task to train the initial AI model, to obtain an AI model, where the obtained AI model is provided for the user to download or use.
US 2023/0090320 to Mannar
If the resource monitor 236 receives an indication from the interrupt manager 234 that the node 222 has transitioned from an inactive state to an active state while processing an accepted task(s)/sub-task(s), the resource monitor 236 may store that indication and/or transmit all or a portion of the indication to a resource master 238.
US 2022/0391729 to Duford
receive, via a workflow editor interface, a user selection of a first artificial intelligence (AI) agent; receive, via the workflow editor interface, a user selection of a second AI agent; receive, via the workflow editor interface, a selection of a data source to input to the first AI agent; receive, via the workflow editor interface, a selection of data to input to the second AI agent, wherein the data to input to the second AI agent comprises data output by the first AI agent; receive, via the workflow editor interface, a selection of training data for the first AI agent and the second AI agent; train, based on the training data, the first AI agent and the second AI agent; receive, via the workflow editor interface, a selection of a user interface to attach to the first AI agent; activate the first AI agent and the second AI agent; and display the user interface.
US 2022/0383150 to Le
Accordingly, after the on-demand model instantiation system 102 has completed training a particular machine-learning model, the on-demand model instantiation system 102 updates the training status. As shown in FIG. 4I, after the on-demand model instantiation system 102 trains the machine-learning model, the client device 400 refreshes or updates a graphical user interface 402i to include a model status interface 444b. In particular, the client device 400 displays a completed training status 448b within a model instantiation entry 446b as part of the model status interface 444b. The model instantiation entry 446b indicates that the on-demand model instantiation system 102 has completed training the machine-learning model.
US 2022/0043386 to Upadhyay
When trained, the information management system can optionally run the artificial intelligence model on a test set of data files and show a preview of the results to the user (e.g., in a user interface). The user may provide feedback as to whether the trained artificial intelligence model properly classified the data files in the test set, and the information management system can use the feedback to retrain or update the trained artificial intelligence model, if necessary.
US 2022/0036002 to Sriharsha
Via the user interface, the user can label a portion of the newly displayed log and the data field extraction training system can generate another schema based on the labeled portion. The data field extraction training system can then iterate through some or all of the remaining logs in the plurality, attempting to extract data fields from these logs using either schema. If a data field cannot be extracted from a log using either schema, then the data field extraction training system can display this log in the user interface and prompt the user to label the log. The data field extraction training system can repeat this process until some or all of the logs in the plurality are either labeled by the user or successfully resulted in a data field extraction. The labeled logs may form a training data set, and the data field extraction training system can train an artificial intelligence model (e.g., a neural network) to extract a data field using the labeled logs as the training data.
US 2021/0303342 to Dunn
Thus, based on the task intent 220, a task model for accomplishing the task intent 220 may be determined and/or retrieved. As another non-limiting example, semantic and/or textual analysis of a user supplied input, such as “Send flowers to mom for her birthday” may be performed such that the task intent 220, “send flowers,” may be identified. In some examples, natural language processing and/or natural language understanding may be utilized to determine a task intent. Accordingly, a task model 224, specific to the task intent 220, may be retrieved from a storage location 228, such as a database. In examples where a task model does not exist for a determined task intent, a generic task model, or a similar task model, may be utilized based on a similar task intent.
US 2021/0019665 to Gur
These task characteristics are converted to corresponding search criteria for searching the model index 142 to determine if there are existing trained ML models registered in the trained model repository 140 that satisfy the search criteria of the task, such as by performing natural language processing of the task characteristics to extract keywords indicative of search criteria, using mapping data structures to map task characteristics to search criteria, using the task characteristics themselves as search criteria, or the like.
US 2018/0307945 to Haigh
a client device of the user starts the training of an AI model, via a user interface, by sending a call command to send to train the AI model to the AI-model service.
US 2017/0180294 to Miligan
the model builder 224 trains the machine learning model to learn from reactions of a user to previous suggestions and the suggestion generator 230 generates a suggestion based on the machine learning model. The suggestion generator 230 may communicate with the user interface engine 236 to customize a new suggestion tool in a conversation interface based on user actions taken to previous suggestion tools offered in the conversation interface.
The prior art of record does not disclose and/or fairly suggest at least claimed limitations recited in such manners in independent claim 1 "... causing the task to be split into a plurality of subtasks to be performed by the AI agent; displaying a dialog frame that presents the user instructions separately from the instruction input field along with an Al agent response that identifies the subtasks; and displaying a graph that visually identifies a processing flow of the subtasks and that dynamically updates the processing flow to reflect a status of progress for the Al agent performing the subtasks.” and similarly recited in such manners in other independent claim 10.
Conclusion
Any inquiry concerning this communication should be directed to examiner Tuan Dao, whose telephone/fax numbers are (571) 270 3387 and (571) 270 4387, respectively. The examiner can normally be reached on every Monday-Thursday, and the second Friday of the bi-week from 7:30AM to 5:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital, can be reached at (571) 272 4215.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273 8300.
Any inquiry of a general nature of relating to the status of this application or proceeding should be directed to the TC 2100 Group receptionist whose telephone number is (571) 272 2100.
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/TUAN C DAO/ Primary Examiner, Art Unit 2198
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