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 is a Non-final action for application number 18/586,361 in response to an amendment filed on 06/01/2026; the original application filed on 02/23/2024.
Claims 1-13 and 20 are currently pending and have been considered below.
Claims 1 and 20 are independent claims.
Claims 14-19 are restricted and applicant elected claims 1-13 and 20.
Election/Restrictions
Applicant’s election without traverse of claims 1-13 and 20 in the reply filed on 06/01/2026 is acknowledged.
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, 3-9, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Monajemi et al. (US 2020/0296739 A1) in view of Bega et al. (US 2024/0046148 A1).
Regarding claims 1 and 20, a device, [Figure 6, computing device], comprising:
a processor, [Figure 6, Ref # 602],
at least one network interface controller configured to provide access to a network, [Figure 6, Ref # 612], and a memory communicatively coupled to the processor, [Figure 6, Ref # 604], wherein the memory comprises a network management logic that is configured to:
gather a plurality of input data, [Figure 3, At 310, upon the occurrence of a particular trigger event or at a predetermined time (e.g., periodically at regular time intervals), the EDCA selection agent 152 obtains network measurements, such as the measurements 140, from the BSS 132, (Monajemi et al., Paragraph 38)],
process the input data through one or more machine-learning-based models, [At 330, the client behavior predictor 156 computes the probabilities of the client SU/MU modes and provides these probabilities as the client mode predictions 158 to the EDCA selection agent 152. The probabilities (i.e., the client mode predictions 158) are computed using the set of optimal SU/MU EDCA parameters, (Monajemi et al., Paragraph 40)],
derive a plurality of data transmission settings, [At 350, it is determined whether the potential BSS 132 performance is satisfactory. In some implementations, the potential BSS performance is determined to be satisfactory when the optimal EDCA parameters are predicted to cause at least a predetermined number or percentage of the plurality of clients to remain in MU mode (i.e., not switch from MU mode to SU mode when the optimal EDCA parameters are deployed by the AP to the clients of the BSS), (Monajemi et al., Paragraph 42)],
and transmit the plurality of data transmission settings to at least one network device, [if the potential BSS 132 performance is satisfactory, then the settings (i.e., the optimal parameters from 320) are deployed at 360 by announcing the new set of parameters (i.e., the optimal parameters from 320) to the BSS 132. Thus, if the BSS operation after the predicted mode switches is satisfactory, the newly optimal set of parameters is transferred to the AP to be deployed to the clients 130a, 130b, 130c, . . . 130n of the BSS 132, (Monajemi et al., Paragraph 43)],
Bega et al. teaches receiving (S61) a first machine learning model message including a first machine learning inference model, (Bega et al., Paragraph 65),
It would have been obvious to one of ordinary skill in the art at the time of the invention was made to modify Monajemi et al. by transmitting one or more beacon frames, (Bega et al., Paragraph 65),in order to provide for machine learning model renewal, (Bega et al., Paragraph 65).
Regarding claim 3, the device of claim 1, wherein input data comprises at least one of: telemetry data, historical data, or parameter data, [input parameters (e.g., the measurements 140) include but are not limited to, client's recent scheduling history (resources allocated and frequency), (Monajemi et al., Paragraph 47)].
Regarding claim 4, the device of claim 3, wherein telemetry data may comprise at least one of: collision rates, transfer success rates, background noise, a quantity of devices being serviced, one or more applications being utilized, quality of service policies, or interference, [input parameters (e.g., the measurements 140) include but are not limited to, client's recent scheduling history (resources allocated and frequency), (Monajemi et al., Paragraph 47)].
Regarding claim 5, the device of claim 1, wherein the one or more machine-learning-based models is an inference model, [receiving a first machine learning model message including a first machine learning inference model, (Bega et al., Paragraph 13)].
Regarding claim 6, the device of claim 5, wherein the network management logic is further configured to receive the inference model prior to processing the input data, [receiving a first machine learning model message including a first machine learning inference model, (Bega et al., Paragraph 13)].
Regarding claim 7, the device of claim 6, wherein the inference model is received in response to a request transmitted by the device, [receiving a first machine learning model message including a first machine learning inference model, (Bega et al., Paragraph 13)].
Regarding claim 8, the device of claim 1, wherein the plurality of data transmission settings are associated with an enhanced distributed channel access (EDCA) method, [Figure 5, Ref # 510].
Regarding claim 9, the device of claim 8, wherein the EDCA method is configured to parse transmitted data into two or more categories, [Once opted-out, the client would then individually contend for the medium. Such opt out algorithms may be different based on the individual client implementations, and are functions of the client's buffer, traffic categories, scheduling resources allocated, UL MU-MIMO capability (as clients that support UL MU-MIMO are likely to be scheduled more frequently), and/or medium contention levels, (Monajemi et al., Paragraph 29)].
Regarding claim 13, the device of claim 1, wherein network management logic is further configured to transmit data to the at least one network device utilizing an enhanced distributed channel access (EDCA) method, [Figure 5, Ref # 510].
Allowable Subject Matter
Claims 2 and 10-12 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 Shukri Taha whose telephone number is 571-270-1921. The examiner can normally be reached on 8:30am-5pm Mon-Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joseph Avellino can be reached on 571-272-3905.
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/SHUKRI TAHA/ Primary Examiner, Art Unit 2478
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