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 final action for application number 18/586,361 in response to an amendment filed on 09/09/2026; the original application was 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 1 and 20 have been amended.
Claims 14-19 are withdrawn.
Response to Arguments
Applicants’ arguments with respect to claims 1-13 and 20 have been considered but are moot in view of the new ground(s) of rejection.
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 1-9, 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 Naik et al. (US 2024/0104426 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)],
Monajemi et al. fails to explicitly teach transmitting one or more beacon frames to indicate availability of optimization,
Naik et al. teaches that an access point may advertise use cases for which the access point supports machine learning. Exemplary use cases include enhanced distributed channel access (EDCA) optimization, interference estimation, rate adaptation, channel state information (CSI) enhancement, and traffic classification, Advertising may refer to broadcasting or otherwise communicating a message, (Naik et al., Paragraph 95),
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 to indicate availability of optimization, (Naik et al., Paragraph 95), in order to establish or maintain a communication link with the WLAN, (Naik et al., Paragraph 95).
Regarding claim 2, the device of claim 1, wherein the one or more beacon frames is modified to indicate a capacity for high throughput operation, [This can help the controlled entity achieve higher throughput, lower latency, or improve the overall network efficiency depending on the machine learning use case, (Naik et al., Paragraph 49)].
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, [Signaling may be introduced to indicate whether the use of machine learning inference is allowed for a particular interval of time. Signaling may also be introduced to indicate what portion of data can be used for deriving machine learning inference, (Naik et al., Paragraph 44)].
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, [An access point may dynamically enable or disable machine learning inference in its BSS, (Naik et al., Paragraph 164)].
Regarding claim 7, the device of claim 6, wherein the inference model is received in response to a request transmitted by the device, [wherein the at least one processor is configured to transmit information indicating at least one of: a first time interval for when machine learning inference is allowed, a second time interval for when machine learning inference is recommended, and a third time interval for when machine learning inference is mandatory, (Naik et al., Paragraph 220)].
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 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
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 replying 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 case, however, will the statutory period for replying expire later than SIX MONTHS from the mailing date of this final action.
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 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
