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 .
● This action is responsive to the following communication: Response to restriction requirement filed on 7/28/2026.
● Claims 1-21 are currently pending; claims 19-21 have been withdrawn due to non-elected invention.
Election/Restrictions
● Applicant’s election without traverse of Group I (claims 1-18) in the reply filed on 7/28/2026 is acknowledged.
Information Disclosure Statement
● The information disclosure statement (IDS) submitted on 9/5/2024 compliances with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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)(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) 1-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mansour et al (US 202500005062).
Regarding claim 1, Mansour discloses one or more processors (server 102, fig. 1) comprising:
one or more circuits to:
receive, using a first graphical user interface (GUI, figs. 2A/2B), data associated with a system prompt (figs. 2A/2B/19C) including at least a first string of text (fig. 19C);
receive, using the first GUI or a second GUI (figs. 2A/2B/19C), data associated with a task prompt including at least a second string of text (see fig. 19C for plurality of string of texts) configured to cause a large language model (LLM, pars. 66-80) to generate an output;
generate a model prompt comprising a third string of text (figs. 19C shows example of prompts with plurality of text strings) based at least on the first string of text and the second string of text; and
provide the model prompt (tasks prompts, model prompts known as prompt engineering are taught in pars. 165-172) to the LLM to cause the LLM to generate the output, the output including an answer (LLM generate output answers, pars. 374-382) that is determined based at least on a context associated with the third string of text (string of texts, fig. 19C, also see 24-27 for more details with respect to string of texts).
Regarding claim 2, Mansour further discloses the one or more processors of claim 1, wherein the one or more circuits are to: receive data associated with an indication of the LLM from among a plurality of LLMs (LLMs, pars. 66-80), where individual LLMs of the plurality of LLMs are trained using at least partially different training datasets or training parameters (each LLM using different training data or dataset, par. 241); and provide the model prompt to the LLM to cause the LLM to generate the output based at least on the indication of the LLM.
Regarding claim 3, Mansour further discloses the one or more processors of claim 1, wherein the one or more circuits are to: receive data associated with one or more example strings of text, wherein the model prompt is further generated based at least on the one or more example strings of text (string of texts, fig. 24, 27A-27B, par. 58-62).
Regarding claim 4, Mansour further discloses the one or more processors of claim 1, wherein the one or more circuits are to: receive data associated with a dataset identifier; and receive a dataset (training dataset, par. 72) based at least on the dataset identifier (par. 184), the dataset including one or more example strings of text (figs. 27A-27B), wherein the model prompt is further generated based at least on the dataset.
Regarding claim 5, Mansour further discloses the one or more processors of claim 1, wherein the one or more circuits are to: receive data associated with a seed, the seed including a random number (random number, par. 70), wherein the model prompt (figs. 27A-27B) is further generated based at least on the seed.
Regarding claim 6, Mansour further discloses the one or more processors of claim 1, wherein the one or more circuits that receive the data associated with a task prompt (task prompts, figs. 12A-12D, pars. 20, 63) are to: receive the data associated with the task prompt (fig. 19C) from an endpoint (par. 91), the endpoint associated with display of at least one of the first GUI or the second GUI (figs. 12A-12D).
Regarding claim 7, Mansour further discloses the one or more processors of claim 1, wherein the one or more circuits are to: receive the data associated with the system prompt from a first device of a plurality of devices (devices 104/106, fig. 1); generate data associated with a project template (project template, pars. 110, 118, 121) based at least on the system prompt (system prompt, par. 54), the data configured to prepopulate one or more fields of the first graphical user (user interface as shown in fig. 2A) interface based at least on the system prompt; and store the data associated with the project template in a database (database, par. 95), the database accessible by a second device of the plurality of devices (fig. 1).
Regarding claim 8, Mansour further discloses the one or more processors of claim 7, wherein the one or more circuits are to: receive a request for the project template (project template, pars. 110, 118, fig. 25) from the second device of the plurality of devices; determine the data associated with the project template based at least on the request; and provide the data associated with the project template to the second device (second device, 104/106, fig. 1).
Regarding claim 9, Mansour further discloses the one or more processors of claim 1, wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system implemented using a robot; an aerial system; a medical system; a boating system; a smart area monitoring system; a system for performing deep learning operations; a system for performing simulation operations; a system for generating or presenting virtual reality (VR) content, augmented reality (AR) content, or mixed reality (MR) content; a system for performing digital twin operations; a system implemented using an edge device; a system incorporating one or more virtual machines (VMs); a system for generating synthetic data; a system implemented at least partially in a data center; a system for performing conversational artificial intelligence (AI) operations; a system for performing generative AI operations; a system implementing language models; a system implementing large language models (LLMs, pars. 66-69); a system implementing vision language models (VLMs); a system for hosting one or more real-time streaming applications; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; or a system implemented at least partially using cloud computing resources.
Regarding claims 10-18 recite limitations that are similar and in the same scope of invention as in those in claims 1-9 above; therefore, claims 10-18 are rejected for the same rejection rationale/basis as described in claims 1-9.
Conclusion
● The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(1) US 12647381 B1 to Kamra et al, teaches systems and methods for multi-channel Artificial Intelligence (AI) architectures include receiving data representing a communication, such as a document. A format associated with the document may be determined. Once the format associated with the document is determined, a preprocessing model configured to process data associated with the format may be used with the data to generate text data representing the document.
(2) US 12586578 B1 to Shi et al, teaches techniques for generating an executable API call for an LLM-generated request, where the executable API call is usable to cause a component to generate a potential response to a user input, are described. In some embodiments, the system receives a user input and uses a language model to generate a request for a component to provide a potential response to the user input. The system uses the request, an API description corresponding to the component, and other information not available to the language model during processing to generate an executable API call corresponding to the request. The system can execute the executable API calls (in a system-determined order or concurrently) to cause the corresponding components to generate potential responses to the user input.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIERRY L PHAM whose telephone number is (571)272-7439. The examiner can normally be reached M-F, 11-6.
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, Hai Phan can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THIERRY L PHAM/Primary Examiner, Art Unit 2654
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