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 .
Response to Arguments
1. Applicant’s arguments filed 4-20-26 have been fully considered but are moot in view of the new ground(s) of rejection(s).
Furthermore, the double patenting rejections have been overcome with the proposed claim amendments.
However, upon reviewing the prior art, the claim amendments do not overcome the prior art of record of Belghoul in view of Ottersten. In particular, as stated below in the claims rejections, Belghoul (See [0077], [0087]-[0094], [0102]-[0110], [0119]-[0120], [0141]-[0148], and [0162]-[0170] discloses of at least architecture and parameter configurations of at least timing information of when to monitor for the packages wherein architecture is construed to be that of any component needed to send and/or receive data as well as to reduce uplink and radio resource expenditures. It is further disclosed that the configuration and/or parameter settings are included in the indication that are multicast. Moreover, Ottersten (See [0017]-[0028], [0118], [0143], [0157]-[0169], [0173]-[0178], [0188]-[0198], [0204]-[0210], and [0245]-[0254]) also teaches of the combination of architecture and parameter configurations that define the behavior of the UEE.
The Examiner suggests detailing and specifically pointing out which architecture configurations and parameters are included and how they specifically define or influence the behavior in order to advance prosecution.
Claim Objections
2. Claims 4, 10-11, and 15 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.
Claim Rejections - 35 USC § 103
3. 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 of this title, 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.
4. Claims 1-3, 5-8, 12-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Belghoul et al., US 2019/0132708 in view of Ottersten et al., US 2021/0345134.
Regarding claim 1, Belghoul teaches of a method performed by a network entity associated with a wireless communication system (See [0060]-[0061] network entities being of wireless communication), the method comprising:
determining a first configuration of a first deep neural network, DNN, for processing, in a receiver processing chain of each user equipment in a targeted group of user equipments, UEs, broadcast or multicast communications transmitted over the wireless communication system to each UE in the targeted group of UEs (See [0077], [0087]-[0094]);
communicating the first configuration of the first DNN to each UE in the targeted group of UEs (See [0119]-[0120] and [0141]-[0144] which discloses sending the configuration/parameters of at least first iterative first DNN to each of the UE’s in the group in the location);
forming, at the network entity, a common DNN of the network entity using a second configuration complementary to the determined first configuration, the DNN trained to perform an operation and configured to perform processing complementary to the first DNN of each UE in the targeted group of UEs (See [0119]-[0120] and [0141]-[0144] the network controller/entity is a common network entity to the user end equipment’s, a second DNN which is an updated baseline DNN which is complementary to the first DN configuration in that it is updated and retrained with updated parameters, thereby being complimentary and performs an operation of the updating/retraining and transmission);
transmitting to each UE in the targeted group of UE’s, a neural network formation configuration indicating a modification to the common DNN, the neural network formation configuration (See [0077], [0087]-[0094], [0102]-[0109], [0119]-[0120], [0141]-[0148], and [0162]-[0170] which discloses of multicasting to each of the targeted UE’s the machine learning model with updated parameters and configuration settings and in particular at [0102]-[0103] explicitly states that the configuration and/or parameter settings are included in the indication that are multicast) including:
a combination of architecture configurations and parameter configurations that define or influence a behavior of each respective UE-side DNN (See [0077], [0087]-[0094], [0102]-[0110], [0119]-[0120], [0141]-[0148], and [0162]-[0170] discloses of at least architecture and parameter configurations of at least timing information of when to monitor for the packages wherein architecture is construed to be that of any component needed to send and/or receive data. The Examiner suggests detailing and specifically pointing out which architecture configurations and parameters are included and how they define or influence the behavior),
and
processing the broadcast or multicast communications using the common DNN to direct the broadcast or multicast communications to each UE of the targeted group of UEs using the wireless communication system (See [0060]-[0061]; [0119]-[0120] and [0141]-[0144] processing the broadcast by transmission of the updated DNN to the UE’s wirelessly).
Belghoul is silent with respect to where the common network-entity DNN is in a transmitter processing chain of the network entity and trained to perform an operation within the transmitter processing chain and wherein the processing of the communications uses the transmitter processing chain.
However, in the same field endeavor, Ottersten teaches of the common network-entity DNN is in a transmitter processing chain of the network entity and trained to perform an operation within the transmitter processing chain and wherein the processing of the communications uses the transmitter processing chain (See [0116]-[0121]; [0152]-[0179]; [0188]-[0198] which discloses of using the DNN in the transmission process of the network entity which is trained to perform operations in processing of the communications taking into account network conditions).
It would have been obvious to one of ordinary skill in the art before the time effective filing date of the claimed invention to have modified the teachings of Belghoul to have incorporated the teachings of Ottersten for the mere benefit of applying additional DNN models to the entire process consisting of updating the DNN and the operation of transmitting the DNN providing for a more efficient overall process.
Regarding claim 2, the combination teaches the method as recited in claim 1, wherein the determining of the first configuration comprises:
determining the first configuration of the DNN based, at least in part, on at least one characteristic of the targeted group of UEs (See Belghoul, [0102], [0109] and [0110]-[0111]; [0119]-[0120]; and [0142]-[0144]; Ottersten, [0216]-[0224]).
Regarding claim 3, the combination teaches the method as recited in claim 2, wherein the at least one characteristic comprises at least one of:
an estimated location of at least one user equipment, UE, in the targeted group of UEs; or at least one UE capability of the at least one UE in the targeted group of UEs (See Belghoul, [0087]-[0093], [0102] and [0109] location; [0110]-[0111] capabilities of at least battery; Ottersten, [0216]-[0224]).
Regarding claim 5, the combination teaches the method as recited in claim 1, wherein the determining of the first configuration further comprises at least one of:
determining a gradient version of the first configuration for at least one UE in the targeted group of UEs based on processing capabilities of the at least one UE (See Belghoul, [0120] gradient aggregation of the updated parameters; Ottersten, [0216]-[0224]); or
determining the first configuration of the DNN based, at least in part, on one or more quality-of-service, QoS, requirements (See Ottersten, [0205]-[0209], [0231],and [0236] quality metrics in the ML model).
Regarding claim 6, combination teaches the method as recited in claim 1, wherein the determining of the first configuration further comprises at least one of:
determining the first configuration of the DNN based, at least in part, on transmitting broadcast or multicast communications based on a network slice configuration; or
determining the first configuration based, at least in part, on fulfilling one or more content requirements of the broadcast or multicast communications, the one or more content requirements comprising one or more of a quality requirement, a resolution requirement, or a frames-per-second requirement (See Belghoul, [0119]-[0120] and [0141]-[0144]; Ottersten, [0205]-[0209], [0231],and [0236] quality metrics/requirements in the ML model).
Regarding claim 7, the combination teaches the method as recited in claim 1, wherein the determining of the first configuration comprises:
determining an end-to-end machine-learning configuration, E2E ML configuration, as the configuration of the DNN (See Belghoul, [0119]-[0120] and [0141]-[0144] which discloses of distributing the ML configuration to specific multiples devices with updated parameters); and
determining a partitioning to the E2E ML configuration that distributes the E2E ML configuration across multiple devices (See Belghoul, [0119]-[0120] and [0141]-[0144] which discloses of distributing the ML configuration to specific multiples devices with updated parameters, thereby being a form of partitioning of the ML configuration since it is distributed to different devices depending on the updated parameter uploads; Ottersten, [0085] partitioning the tasks).
Regarding claim 8, the combination teaches the method as recited in claim 1, further comprising:
receiving feedback from at least one UE of the targeted group of UEs (See Belghoul, [0119]-[0120] and [0141]-[0144] feedback of updated parameters; Ottersten, [0216]-[0224] signaling messaging of capabilities and characteristics);
determining the modification to the common DNN based on the feedback (See Belghoul, [0119]-[0120] and [0141]-[0144]);
transmitting the indication of the modification to the targeted group of UEs (See Belghoul, [0119]-[0120] and [0141]-[0144] feedback of updated parameters; Ottersten, [0216]-[0224] the notification being of at least the ML model being transmitted);
updating the common DNN with the modification to form a modified common DNN (See analysis of claim 1; Belghoul, [0119]-[0120] and [0141]-[0144] updated DNN model; See Ottersten, [0116]-[0121]; [0152]-[0179]; [0188]-[0198]); and
processing the broadcast or multicast communications using the modified common DNN in the transmitter processing chain to direct the broadcast or multicast communications to each UE in the targeted group of UEs using the wireless communication system (See analysis of claim 1; Belghoul, [0119]-[0120] and [0141]-[0144] updated DNN model; See Ottersten, [0116]-[0121]; [0152]-[0179]; [0188]-[0198]).
Regarding claim 12, the combination teaches the method as recited in claim 1, wherein the operation that the common DNN is trained to perform includes:
transmitter-side forward error correction; transmitter-side convolutional encoding; end-to-end encoding; and/or end-to-end modulating (See Ottersten, [0101], [0106], [0112], [0138], [0146], [0155], [0164], and [0232] modulation schemes and error rates based on traffic needs and performance).
Regarding claim 13, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 1.
Regarding claim 14, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 2.
Regarding claim 16, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 5.
Regarding claim 17, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 6.
Regarding claim 18, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 7.
Regarding claim 19, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 8.
Regarding claim 20, the claim has been analyzed and rejected for the same reasons set forth in the rejection of claim 1.
Conclusion
5. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ricky Chin whose telephone number is 571-270-3753. The examiner can normally be reached on M-F 8:30-6:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Bruckart can be reached on 571-272-3982. The fax phone number for the organization where this application or proceeding is assigned is 703-872-9306.
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/Ricky Chin/
Primary Examiner
AU 2424
(571) 270-3753
Ricky.Chin@uspto.gov
Prosecution Insights