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 .
DETAILED ACTION
This communication is a Final office action in merits. Claims 65-89, after amendment, are presently pending. Claims 65-73, 79-83, 85-87, after restriction election, have been elected and considered below.
Restriction Election
Applicant has elected Species I, namely claims 65-73, 79-83, 85-87, without traverse, for further examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/3/2022, 6/7/2023, 10/32023, 3/14/2024, 5/17/2024, 5/14/2025, and 8/6/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 65, 79-81, 85 are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0021470 A1, Callard et al. (hereinafter Callard).
1-64. (Cancelled)
As to claim 65, Callard discloses an apparatus of an access point (AP) node of a network, the apparatus including an interconnect interface to connect the apparatus to one or more components of the AP node (Fig 1), and a processor to:
perform local update operations for a machine learning (ML) model of a radio resource management (RRM) optimization problem (Fig 4; pars 0008-0009, 0012-0013, 0015-0016, 0018-0019, training a local ML model with local data with respect to RRC (radio resources control) performance/optimization), the local update operations comprising: obtaining channel measurements (h) for wireless links between the AP node and user equipment (UE) devices (pars 0009, 0013, 0016, 0019, 0049-0050, obtaining channel measurement including PM, CQI, RSRP, etc.);
updating first parameters of the ML model based on the channel measurements (pars Figs 5-6; pars 0008-0009, 0012-0013, 0016, 0019, 0097-0098, 0110, 0112, updating ML model’s parameters based on a channel measurement);
determining a RRM decision for an uplink transmission from a particular UE device to the AP node based on the ML model with the updated first parameters (pars 0013, 0031, 0040, 0088, 0092, management decision for transmitting from a client to RAN node (e.g. uplink) based on measurement and parameter setting); and
causing the RRM decision to be transmitted to the particular UE device (Fig 6; pars 0047, 0112, 0114, the RRC configuration information being sent to the UE from the gNB based on performance/quality measurement, such configuration includes SINR, BER requirement between UE and the gNB), the RRM decision to be transmitted to and performed by the particular UE device for the uplink data transmission from the particular UE device to the AP node (Fig 6; pars 0031, 0047, 0052, 0112, 0114, uplink communication from UE to the network (gNB) being configured based on RRC message).
Although Callard teaches above limitations in more than one embodiment, consider Callard’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Callard’s teachings in the more than one embodiment together and provide predictable ML training for RRM optimization.
As to claim 79, Callard discloses the apparatus of claim 65, wherein the ML model is a neural network (NN) (Callard: pars 0008, 0012, 0015, 0019, a neural network).
As to claim 80, Callard discloses the apparatus of claim 65, wherein the AP node is a base station of a cellular network (Callard: Fig 4; pars 0027, 0045-0046, a base station of a cellular network).
As to claim 81, it is a method claim necessitated claim 65. Rejection of claim 65 is therefore incorporated herein.
As to claim 85, it recites a non-transitory CRM with instructions executed to perform functions and features of claim 81. Rejection of claim 81 is therefore incorporated herein.
Claims 66-71, 73, 83, 86 are rejected under 35 U.S.C. 103 as being unpatentable over Callard in view of US 2018/0357566 A1, Liu et al. (hereinafter Liu).
As to claim 66, Callard discloses the apparatus of claim 65, wherein the processor is further to perform global update operations for the ML model, the global update operations comprising:
updating second parameters of the ML model; causing the updated second parameters to be transmitted to one or more aggregator nodes of the network (Callard: Fig 5; pars 0063, 0080, 0110); and obtaining updated third parameter of the ML model from one or more aggregator nodes of the network (pars 0045-0047, 0054, 0060, BNC, NodeB/eNB/gNB, RNC, etc.).
Callard does not expressly disclose obtaining updated third parameter of the ML model based on the updated global primal parameters.
Liu, in the same or similar field of endeavor, further teaches parameters of the ML model being updated second parameters (pars 0004-0006, 0038).
Therefore, consider Callard and Liu’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of endeavor, to incorporate Liu’s teachings in Callard’s apparatus to utilize global primal parameters for ML model update.
As to claim 67, Callard as modified discloses the apparatus of claim 66, wherein the processor is further to perform additional rounds of the local operations based on the updated second parameters (Liu: pars 0004, 0006, 0038-0043).
As to claim 68, Callard as modified discloses the apparatus of claim 66, wherein the one or more aggregator nodes of the network include a central node of the network or another AP node (Callard: pars 0045-0047, 0054, 0060, aggregator nodes including other base stations, BNC, NodeB/eNB/gNB, RNC etc.).
As to claim 69, Callard as modified discloses the apparatus of claim 66, wherein the RRM optimization problem is a primal-dual optimization problem and the ML model comprises primal parameters and dual parameters (Liu: pars 0004, 0006, 0038-0043).
As to claim 70, Callard as modified discloses the apparatus of claim 69, wherein the first parameters of the ML model include local primal parameters (θi,xi) and local dual parameters (λi,µi) of the ML model (Liu: pars 0004-0006, 0038, local primal parameters and local dual parameters).
As to claim 71, Callard as modified discloses the apparatus of claim 69, wherein the second parameters of the ML model include global primal parameters (pji) of the ML model, and the third parameters of the ML model include global dual parameters (vji) of the ML model (Liu: pars 0004-0006, 0038, global primal parameters and global dual parameters).
74-78. (Withdrawn)
As to claim 82, it is rejected with the same reason as set forth in claim 66.
84. (Withdrawn)
As to claim 86, it is rejected as the same reason as set forth in claim 82.
88-89. (Withdrawn)
Claims 72, 83, 87 are rejected under 35 U.S.C. 103 as being unpatentable over Callard in view of US 2018/0357566 A1, Liu et al. (hereinafter Liu) and further in view of CN 103369599, Zhang et al. see google translation attached for citation (hereinafter Zhang).
As to claim 72, Callard as modified discloses the apparatus of claim 69, but does not expressly teach wherein the dual parameters are Lagrange variables corresponding to constraints of the RRM optimization problem. Zhang, in the same or similar field of endeavor, additionally teaches Lagrange variables may be utilized for wireless network resources optimization (claim 1; page 3, pars 9-11; page 6, 9-12). Therefore, consider Callard as modified and Zhang’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Zhang’s teachings in Callard as modified’s apparatus to decompose optimization problem to a number of sub-problems of optimization.
As to claim 83, it is rejected with the same reason as set forth in claims 69-72.
As to claim 87, it is rejected as the same reason as set forth in claim 83.
Claim 73 is rejected under 35 U.S.C. 103 as being unpatentable over Callard in view of Liu and further in view of US 2020/0280863 A1, Oioffi et al. (hereinafter Oioffi).
As to claim 73, Callard as modified discloses the apparatus of claim 66, wherein the second parameters indicate expected power outputs for transmitters in the network (Callard: par 0049) but does not expressly teach the third parameters indicate sensitivities of receivers to other transmitters. Oioffi, in the same or similar filed of endeavor, further teaches parameters to be updated may include a reasonable range of transmit power (e.g. expected transmit power) (pars 0226-0229) and sensitivities of receivers to other transmitters (pars 0282, 0285). Therefore, consider Callard as modified and Oioffi’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Oioffi’s teachings in Callard as modified’s apparatus to for the machine learning model to include the transmit power and receive sensitivity as parameters to be monitored and optimized as they are closely related to radio resources management and optimization in a wireless communication network.
Response to Arguments
Applicant’s arguments have been considered but they are not persuasive. Applicant argues that Callard does not disclose or teach the ML model being updated. Examiner would like to point out that the triggering event as taught by Callard provides ML parameters update based on measurement results upon triggering (timer, measurement, etc.). Applicant also argues Callard does not disclose or teach the RRM decision being made based on the measurement and transmitted to UE for uplink communication. As indicated in merit rejection above (previous citations and additional citations to further clarify the matter), Callard does teach RRC configuration from the network to any particular UE. RRC configuration typically includes channel allocation, uplink transmission rate, among others to assure the uplink communication maintaining performance requirements (based on BER, SINR, CQI, etc.) which relies on the link measurement between network edge device (gNB) and the mobile device (UE), and often including resources/channel optimization among UEs covered in the network as well.
Above rationale also applies to dependent claims depending from the base claim.
Applicant is encouraged to further clarify and differentiate claimed invention from prior or record.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Examiner’s Note
Examiner has cited particular column, line number, paragraphs and/or figure(s) in the reference(s) as applied to the claims for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the reference(s) in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUN SHEN whose telephone number is (571)270-7927. The examiner can normally be reached on Mon-Fri 8:30-5:50 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUN SHEN/
Primary Examiner, Art Unit 2662