Prosecution Insights
Last updated: April 19, 2026
Application No. 18/774,224

DETERMINING A MACHINE-LEARNING ARCHITECTURE FOR NETWORK SLICING

Non-Final OA §DP
Filed
Jul 16, 2024
Examiner
NGUYEN, THAI
Art Unit
2469
Tech Center
2400 — Computer Networks
Assignee
Google LLC
OA Round
1 (Non-Final)
85%
Grant Probability
Favorable
1-2
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

Grants 85% — above average
85%
Career Allow Rate
659 granted / 776 resolved
+26.9% vs TC avg
Moderate +15% lift
Without
With
+14.7%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
22 currently pending
Career history
798
Total Applications
across all art units

Statute-Specific Performance

§101
8.3%
-31.7% vs TC avg
§103
41.9%
+1.9% vs TC avg
§102
10.8%
-29.2% vs TC avg
§112
30.0%
-10.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 776 resolved cases

Office Action

§DP
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 . Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b). Claims 1-8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-8 of US Patent 12,075,346, hereafter Patent’346, in view of Ganguli et al (USPN 20220103614), with provisional application 62810091 filed 2/25/2019. Although the claims at issue are not identical, they are not patentably distinct from each other because (see below). Regarding claim 1 of instant application, claim 1 of Patent’346 discloses A method performed by a user equipment, the method comprising: (see claim 1 line 1) executing a first application associated with a first requested quality-of-service level; (see claim 1 lines 2-3) selecting a first machine-learning architecture based on the first requested quality -of-service level; (see claim 1 lines 4-5) transmitting, to a network-slice manager of a wireless network, a first machine-learning architecture request message to request permission to use the first machine-learning architecture; (see claim 1 lines 6-8) receiving, from the network-slice manager, a first machine-learning architecture response message that grants permission to use the first machine-learning architecture based on a first network slice, (see claim 1 lines 9-11) wirelessly communicating data for the first application using the first machine-learning architecture (see claim 1 lines 12-13) Patent’346 does not expressly disclose “the first machine-learning architecture forming a portion of at least one first end- to-end machine-learning architecture associated with the first network slice, the at least one first end-to-end machine-learning architecture being a distributed machine-learning architecture that is configured to process wireless communication signals and is formed by the first machine- learning architecture implemented by the user equipment, a machine-learning architecture implemented by a base station, and a machine-learning architecture implemented by an entity of a core network; the first machine-learning architecture being configured to compute an output based on an input using coefficients determined by the user equipment”. Ganguli discloses the first machine-learning architecture forming a portion of at least one first end- to-end machine-learning architecture associated with the first network slice, (end-to-end of distributed machine learning architecture comprising compute nodes such as UE with UE having its own machine learning model [0182, 0188-0197], FIGs. 1, 12, 13, 15, provisional application [0176, 0041, 0035], FIG. 13 the at least one first end-to-end machine-learning architecture being a distributed machine-learning architecture that is configured to process wireless communication signals (end-to-end architecture includes UE/RAN/MEC capable of processing wireless signals [0006, 0033], FIGs. 3A, 3B, provisional application [0057, 0059], FIG. 3A and is formed by the first machine-learning architecture implemented by the user equipment, a machine-learning architecture implemented by a base station, and a machine-learning architecture implemented by an entity of a core network; and (distributed machine-learning architecture implemented on UE (FIGs. 3A 3B #302) with machine learning driven applications, gNB (FIGs. 3A 3B #308), and UPF/5G core [0063, 0084], provisional application [0057, 0061-0063], FIGs. 3A, 3B the first machine-learning architecture being configured to compute an output based on an input using coefficients determined by the user equipment. (UE based machine learning inference using locally computed heuristics [0029, 0043, 0044], FIG. 13 Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement “the first machine-learning architecture forming a portion of at least one first end- to-end machine-learning architecture associated with the first network slice, the at least one first end-to-end machine-learning architecture being a distributed machine-learning architecture that is configured to process wireless communication signals and is formed by the first machine- learning architecture implemented by the user equipment, a machine-learning architecture implemented by a base station, and a machine-learning architecture implemented by an entity of a core network; the first machine-learning architecture being configured to compute an output based on an input using coefficients determined by the user equipment” as taught by Ganguli into Patent’346’s system with the motivation to a distributed machine learning architecture involving UE, RAN, and 5G core (Ganguli, paragraph [0057, 0061-0063], FIGs. 3A, 3B). Regarding claim 2 of instant application, claim 2 of Patent’346 discloses similar limitations. Regarding claim 3 of instant application, claim 3 of Patent’346 discloses similar limitations. Regarding claim 4 of instant application, claim 4 of Patent’346 discloses similar limitations. Regarding claim 5 of instant application, claim 5 of Patent’346 discloses similar limitations. Regarding claim 6 of instant application, claim 6 of Patent’346 discloses similar limitations. Regarding claim 7 of instant application, claim 7 of Patent’346 discloses similar limitations. Regarding claim 8 of instant application, claim 8 of Patent’346 discloses similar limitations. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ottersten et al (WO 2020080989 A1) FIG. 7 Any inquiry concerning this communication or earlier communications from the examiner should be directed to THAI NGUYEN whose telephone number is (571)270-7632. The examiner can normally be reached M-F campus 10:30-5pm, telework 6pm-8pm| Telework count days. 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, Ian N Moore can be reached at (571)272-3085. 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. /THAI NGUYEN/Primary Examiner, Art Unit 2469
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Prosecution Timeline

Jul 16, 2024
Application Filed
Mar 20, 2026
Non-Final Rejection — §DP (current)

Precedent Cases

Applications granted by this same examiner with similar technology

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Patent 12592760
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Patent 12587998
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2y 5m to grant Granted Mar 24, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
85%
Grant Probability
99%
With Interview (+14.7%)
2y 11m
Median Time to Grant
Low
PTA Risk
Based on 776 resolved cases by this examiner. Grant probability derived from career allow rate.

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