Prosecution Insights
Last updated: August 17, 2026
Application No. 18/466,271

CONTINUOUS RESULT COLLECTION SYSTEM OF LICENSE-INDEPENDENT CFD SIMULATION AND DATA-DRIVEN MACHINE LEARNING FOR HYBRID MODELING

Non-Final OA §103§112
Filed
Sep 13, 2023
Priority
Oct 07, 2022 — RE 10-2022-0129117
Examiner
JEAN GILLES, JUDE
Art Unit
Tech Center
Assignee
Korea Electronics Technology Institute
OA Round
1 (Non-Final)
93%
Grant Probability
Favorable
1-2
OA Rounds
0m
Est. Remaining
95%
With Interview

Examiner Intelligence

Grants 93% — above average
93%
Career Allowance Rate
875 granted / 943 resolved
+32.8% vs TC avg
Minimal +2% lift
Without
With
+2.5%
Interview Lift
resolved cases with interview
Typical timeline
2y 4m
Avg Prosecution
12 currently pending
Career history
947
Total Applications
across all art units

Statute-Specific Performance

§101
13.3%
-26.7% vs TC avg
§103
33.2%
-6.8% vs TC avg
§102
19.4%
-20.6% vs TC avg
§112
4.5%
-35.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 943 resolved cases

Office Action

§103 §112
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 Office Action is in reply to communication filed on 09/13/2023. Claimed priority is granted from foreign application KR10-2022-0129117 filed on 10/07/2022. 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 1-5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Peters et al. (US 2022/0034542 A1) in view of Gillbert et al. (US 2021/0319363 A1) Regarding claim 1, Peters teaches the invention substantially as claimed. Peters discloses a continuous result collection system of license-independent computational fluid dynamics (CFD) simulation and data-driven machine learning for hybrid modeling (par. 0008-0012, 0039-0043, and 0136-0143), the system comprising: a user input processor configured to receive a 3D resource, a CFD simulation result, and structured data (0009-0012, and 0039-0043; 00139-0140); and a framework processor configured to establish a database by accumulating data received from the user input processor and to perform hybrid modeling of the CFD simulation and a machine learning model (0139-0143). However, Peters does not expressly disclose a framework processor configures to establish a database by accumulating data received from the user input processor, as recited in the claim. While Peters processes accumulation inputs and analytics information, it does not explicitly describe accumulating the received user input data into a database that serves as the bases for subsequent machine-learning training and repeated model refinement. Gillberg teaches this feature. Specifically, Gillberg discloses a server arrangement configured to receive structured input information, accumulate the received information into stored datasets, establish and maintain training datasets, and repeatedly utilize the accumulated datasets for machine-learning training and optimization. See par. 0022-0026, describing the receipt of structured input information and establishment of stored datasets, and par. 0036-0044, describing repeated evaluation, optimization, and training using the accumulated datasets. Gillberg teaches the limitation of “the framework processor configured to establish a database by accumulating data received from the user input processor...”. Accordingly, it would have been obvious for an ordinary skill in the art, at the time of the before the effective filing date of the claimed invention, to modify the CFD simulation and machine-learning framework of Peters by incorporating Gulberg’s data accumulation and training database architecture in order to organize, store, and reuse simulation inputs and generated CFD results for repeated machine-learning training and model refinement, Incorporating Gulberg’s database architecture would have predictably improved the efficiency of model development, enabled continuous retraining as additional simulation data became available, reduces redundant data preparation, and enhanced the accuracy, scalability, and maintainability of the hybrid CFD and machine-learning modeling framework. The proposed modification merely applies Gulberg’s known data management techniques to Peters’ known CFD simulation environment according to their established functions to obtain the predictable benefit of improved machine-learning training and data management. Regarding claims 2-5 and 7 the combination teaches: 2. The system of claim 1, wherein the user input processor comprises a 3D resource input processor, and is configured to receive the 3D resource including at least one of 3D geographic information, location information, a 3D shape, or 3D metadata (Peters, 0039-0043, 0139; fig. 8; Gillberg, par. 0022-0026, par. 0051-0052). 3. The system of claim 1, wherein the framework processor is configured to receive a CFD simulation performance result in a license holding state and to store the result in a CFD result database and a visualization toolkit (VTK) database (Peters, 0139-0143; Gillberg, par. 0022-0026, and 0036-0044; VTK is interpreted as being merely as one well-known visualization database format, among other available visualization formats application that may be desirable). 4. The system of claim 3, wherein the framework processor comprises a processing service providing processor configured to provide a CFD simulation interface, and is configured to transmit the CFD simulation performance result through an abstract function implementation and CFD simulation execution function existing in an interface of the processing service providing processor (Peters, fig. 8, par. 0136-0143; Gillberg, par. 0022-0026). 5. The system of claim 4, wherein the processing service providing processor is configured to periodically re-execute a CFD and to transmit an alarm to a user in case of an execution failure (Peters, 0141-0143; Gillberg, 0036-0044; alarm is implicit and can be an obvious engineering addition). 7. The system of claim 1, wherein the framework processor is configured to provide an interface for transferring data previously managed by a user with a certain file or a certain database (Peters, 0136-0143 and Gillberg, par. 0022-0026). Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. “Continuous result collection system”, is functional and indefinite. The specification does not provide information to dictate otherwise. “License-independent”, is a functional limitation with unclear scope. Hybrid modeling” is vague and does not define how the CFD simulation and machine learning model are combined. The issues mentioned above are just exemplary. Applicant is advised to review the entire application, claims and specifications, for errors and correct the same. Allowable Subject Matter Claims 6, 8 and 9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. CONCLUSION Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jude Jean-Gilles whose telephone number is 571-272-3914. The examiner can normally be reached on Mon-Fri, from 9:00AM-5:00PM. 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, Tonia Dollinger can be reached on 571-272-4170. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JUDE JEAN GILLES/Primary Examiner, Art Unit 2459 July 27, 2026
Read full office action

Prosecution Timeline

Sep 13, 2023
Application Filed
Jul 30, 2026
Non-Final Rejection mailed — §103, §112 (current)

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Prosecution Projections

1-2
Expected OA Rounds
93%
Grant Probability
95%
With Interview (+2.5%)
2y 4m (~0m remaining)
Median Time to Grant
Low
PTA Risk
Based on 943 resolved cases by this examiner. Grant probability derived from career allowance rate.

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