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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 20claimed 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, 8-10, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (WO 2021/007019, hereinafter Wang) in view of Pezeshki et al. (US 2021/0243631, hereinafter Pezeshki).
Regarding claim 1, Wang discloses a method comprising: using a reinforcement learning (RL) model (para 0037; machine-learned module uses reinforcement learning)
to select a plurality of Coordination Groups (CGs) (Para 0004; 0010; machine learning enables the network-optimization controller to evaluate gradients from a group of entities within the cellular network to determine the optimized network- configuration parameter that optimizes performance for these entities as a group);
collecting a plurality of performance data sets for the network device, wherein each respective performance data set corresponds to a respective CG selection by the network device (para 0010; 0030; 0065; 0072; The performance-metric and network-configuration-parameter selector determines performance metrics. Example types of performance metrics include spectrum efficiency, network capacity, cell- edge capacity, packet latency, jitter, total network interference, signal-to-interference-plus-noise ratio (SINR), received signal strength indication (RSSI), reference signal received power (RSRP), reference signal received quality (RSRQ), bit-error rate (BER), packet-error rate, transmit power headroom, and transmit power);
predicting one or more parameters for the RL model using a machine learning (ML) model, wherein the ML model is trained based on the plurality of performance data sets
(para 0001; 0028; 0072; analyzing data to make the estimation/prediction).
Wang does not explicitly disclose that the selection is made for a network device to join in a network environment; and executing the RL model to select one or more CGs, from the plurality of CGs, based on the predicted one or more parameters.
In an analogous art, Pezeshki discloses that the selection is made for a network device to join in a network environment (para 0080; adding to the group); and executing the RL model to select one or more CGs, from the plurality of CGs, based on the predicted one or more parameters (para 0009; 0027-0029; 0077; group is selected based on the predicted characteristics). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s method/system by having Pezeshki’s disclosure in order to achieve the greater efficiency of a communication system.
Regarding claim 8, Wang discloses a system comprising: one or more computer processors (para 0010; one or more processors); and one or more memories collectively containing one or more programs (para 0010; memory), which, when executed by the one or more computer processors, perform operations, the operations comprising the method steps of claim 1 (para 0049).
Regarding claim 15, Wang discloses one or more non-transitory computer-readable media containing, in any combination, computer program code (para 0045; program stored in the memory), which, when executed by a computer system (para 0049), performs operations comprising the method steps of claim 1.
Regarding claims 2, 9, and 16, Wang discloses wherein the each respective performance data set for the network device comprises at least one of: (i) an Received Signal Strength Indicator (RSSI) value; (ii) a Signal-to-Noise Ratio (SNR) value; (iii) a channel utilization; (iv) an access delay; or (v) a timeslot allocated to the network device (para 0030; RSSI; SINR).
Regarding claims 3, 10, and 17, Wang does not explicitly disclose wherein the predicted one or more parameters for the RL model comprise at least one of: (i) a maximum number of CGs the network device can join; (ii) one or more CGs to avoid based on historical performance data; (iii) a frequency of joining or opting out of CGs; (iv) an RSSI threshold for joining a CG; or (v) a SNR threshold for joining a CG.
In an analogous art, Pezeshki discloses wherein the predicted one or more parameters for the RL model comprise at least one of: (i) a maximum number of CGs the network device can join; (ii) one or more CGs to avoid based on historical performance data; (iii) a frequency of joining or opting out of CGs; (iv) an RSSI threshold for joining a CG; or (v) a SNR threshold for joining a CG (para 0098; removing group/avoiding group based on the estimated characteristics/historical performance; and 0027;0043-0044; 0146; thresholds for joining/RSSI). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s method/system by having Pezeshki’s disclosure in order to achieve the greater efficiency of a communication system.
Claims 4-5, 11-12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang/Pezeshki in view of Comsa et al. (US 2019/0124667, hereinafter Comsa).
Regarding claims 4, 11, and 18, Wang does not explicitly disclose
wherein using the RL model to select the plurality of CGs for the network device to join in the network environment, comprises: measuring signal strength data between the network device and each respective network device within a first CG, of the plurality of CGs; and upon determining the signal strength data exceeds a defined threshold, providing a positive reward for joining the first CG, of the plurality of CGs, to the RL model.
In an analogous art, Wang discloses wherein using the RL model to select the plurality of CGs for the network device to join in the network environment, comprises: measuring signal strength data between the network device and each respective network device within a first CG, of the plurality of CGs (para 0043 ; 0094– comparing with threshold); and upon determining the signal strength data exceeds a defined threshold, joining the first CG, of the plurality of CGs, to the RL model (para 0080-0081; adding to the group).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s method/system by having Pezeshki’s disclosure in order to achieve the greater efficiency of a communication system.
Wang/Pezeshki does not explicitly disclose providing a positive reward if signal data exceeds the threshold.
In an analogous art, Cosma discloses providing a positive reward if signal data exceeds the threshold (para 0046-0047; comparing with threshold and determining a reward based on the comparison).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang’s method/system by having Pezeshki’s disclosure in order to allocate resource to selected groups to improve the quality of service.
Regarding claims 5, and 12, Wang discloses wherein the signal strength data comprises at least one of an RSSI value or a SNR value (para 0030; rssi and sinr).
Claims 6-7, 13-14 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang/Pezeshki in view of Oshea et al. (US 2023/0342590).
Regarding claims 6, 13, and 19, Wang discloses further comprising training the ML model using an RSSI value and an access delay, wherein the ML model learns to correlate the RSSI value to the access delay (para 0030; RSSI, latency).
Wang/Pezeshki does not explicitly disclose using the values as input and output.
In an analogous art, Oshea discloses using the values as input and output in machine learning (para 0009; 0013 and 0023). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang/Pezeshki’s method/system by having Oshea’s disclosure in order to generate reports by analyzing different parameters to improve resource allocation.
Regarding claims 7, 14, and 20, Wang discloses training the ML model using an RSSI value, and a timeslot allocated to the network device for each CG selection, wherein the ML model learns to correlate the RSSI values to the timeslot (para 0030 and 0043; RSSI and slot).
Wang/Pezeshki does not explicitly disclose using the values as input and output.
In an analogous art, Oshea discloses using the values as input and output in machine learning (para 0009; 0013 and 0023). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang/Pezeshki’s method/system by having Oshea’s disclosure in order to generate reports by analyzing different parameters to improve resource allocation.
Conclusion
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMINA CHOUDHRY whose telephone number is (571)270-7102. The examiner can normally be reached on Monday to Thursday (7:30 a.m. to 5.00p.m.).
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/SAMINA F CHOUDHRY/Primary Examiner, Art Unit 2462