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
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/THAI NGUYEN/Primary Examiner, Art Unit 2469