DETAILED ACTION
Applicant’s response filed on 06/12/2026 has been entered and made of record.
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 Status
Claims 52, 63 and 71 are amended.
Claims 55 and 66 are canceled.
No new claim is/are added.
Claims 52-54, 57-65 and 67-71 are pending for examination.
Applicant’s Argument
Applicant’s arguments (remark pages 6-9), filed on 06/12/2026, with respect to claims 52-54, 57-65 and 67-71 have been considered but are moot in view of the new ground of rejection below which better address the claimed invention as amended.
This Office Action is made Final.
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 (i.e., changing from AIA to pre-AIA ) 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 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.
The factual inquiries 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 52-53, 56, 63-64 and 71 are rejected under 35 U.S.C. 103 as being unpatentable over Osterling et al. (US 20110317606 A1), hereinafter “Osterling”, in view of Dimpflmaier et al. (US 20110136462 A1), hereinafter “Dimpflmaier”.
Per claim 52, 63 and 71:
Regarding claim 71, Osterling teaches ‘A traffic load prediction node’ (Osterling: [FIG.3]; [0032]: “the RBS 300 comprises an REC”; [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period”; [0059]: “the prediction needs to also take into account the circuit switched (CS) traffic”; [0060]: “The prediction will thus include the CS traffic+the HSDPA power needs+possible margin”; traffic load prediction node (REC)); ‘comprising: a processor’ (existence of a processor for RBS (REC) is implied); ‘a memory’ (existence of a memory for RBS(REC) is implied);’coupled to the processor’ (this is implied); ‘said memory containing instructions executable by said processor’ (this is implied);
‘whereby said traffic load prediction node is operative to:
determine prediction information of traffic load to be transmitted by a radio node’ (Osterling: [FIG.3]; [0032]: “the RBS 300 comprises an REC 305 and an RE”, RE (radio node); [0060]: “The prediction will thus include the CS traffic+the HSDPA power needs+possible margin”; [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period … The prediction is signalled to the RE Control 320 from the MAC scheduler 310 in order to adjust the biasing, i.e. the output power level of the PA”; [0039]: “the MAC scheduler 310 informs the control unit 320 of the output power level of the power amplifier and of the level of a predefined error parameter in the power amplifier for the upcoming transmission period, and the control unit 320 sets the PA 325 accordingly. The signal sent from MAC scheduler 310 to the control unit 320 for the control of the PA 325 can comprise a set of values which are valid for the complete upcoming transmission period, or it may state a more fine-grained resolution, such as the output power level and the level of a predefined error parameter, such as the EVM, per OFDM symbol within the upcoming transmission period”; [0040]: “signals from the MAC scheduler 310 of the RBS 300 of the invention to the REC 320 or to the PA 325 will suitably be sent over an interface between the REC 305 and the RE”; [0043]-[0046]: “the MAC 310 monitors the load in the cell, and determines the number of SFs that need to be "active” … optimize the power consumption of the SFs which carry traffic”; [0058]: “If the MAC scheduler 310 predicts that the QoS contracts of the UEs in the cell cannot be met due to limits on the maximum output power of the PA 325, it sends a request to the RE Control 320 for an increase the maximum output power to the radio”, send prediction information of traffic load to RE (radio node));
‘send a message comprising the prediction information of traffic load to the radio node’ (discussed in element above);
‘wherein the message further comprises at least one parameter of traffic load prediction comprising at least one of prediction granularity, prediction duration’ (these are optional);
‘radio band information’ (Osterling: [0048]: “The MAC scheduler 310 informs the RE Control 320 of the required output power and EVM for the next sub frame and the RE Control 220 then configures the PA 325 to act accordingly, e.g. by setting its bias and clipping thresholds accordingly”, message comprises parameters such as required output power and EVM for RE to set its bias based on traffic load prediction; [0056]: “The prediction is signalled to the RE Control 320 from the MAC scheduler 310 in order to adjust the biasing, i.e. the output power level of the PA”). However, Osterling fails to expressly teach radio band information;
‘traffic load prediction usage, average value of traffic load prediction, variance value of traffic load prediction, or prediction window’ (these are optional).
However, Dimpflmaier in the same field of endeavor teaches bias of PA would depend on radio frequency band such as the higher the radio frequency band, the lower the bias (Dimpflmaier: [FIG.2]: the higher the frequency, the lower the bias; [0038]: “Different power amplifiers that are used to transmit signals at different radio-frequency bands may be biased at different bias voltage levels, as shown in FIG. 2”; [FIG.4]: four “FREQUENCY BAND (MHz)” => “1900”, “1800”, “900”, “850”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dimpflmaier’s teaching with that of Osterling to include the radio band information with the required output power and EVM in order to specify which radio band to adjust the bias.
Regarding claim 52, claim 52 recites the method implemented by the traffic load prediction node of claim 71 (see rejection of claim 71 above).
Regarding claim 63, Osterling teaches ‘A method’ (Osterling: [0001]: “a method”); ‘performed by a radio node’ (Osterling: [FIG.3]: [0032]: “the RBS 300 comprises an REC 305 and an RE”, RE (radio node));
‘comprising:
receiving a message comprising prediction information of traffic load to be transmitted by the radio node from a traffic load prediction node (Osterling: [0060]: “The prediction will thus include the CS traffic+the HSDPA power needs+possible margin”; [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period … The prediction is signalled to the RE Control 320 from the MAC scheduler 310 in order to adjust the biasing, i.e. the output power level of the PA”; [0058]: “If the MAC scheduler 310 predicts that the QoS contracts of the UEs in the cell cannot be met due to limits on the maximum output power of the PA 325, it sends a request to the RE Control 320 for an increase the maximum output power to the radio”; RE (radio node) receives prediction information of traffic load);
‘at least one parameter of traffic load prediction comprising at least one of prediction granularity, prediction duration’ (these are optional);
‘radio band information’ (Osterling: [0048]: “The MAC scheduler 310 informs the RE Control 320 of the required output power and EVM for the next sub frame and the RE Control 220 then configures the PA 325 to act accordingly, e.g. by setting its bias and clipping thresholds accordingly”, message comprises parameters such as required output power and EVM for RE to set its bias based on traffic load prediction; [0056]: “The prediction is signalled to the RE Control 320 from the MAC scheduler 310 in order to adjust the biasing, i.e. the output power level of the PA”). However, Osterling fails to expressly teach radio band information;
‘traffic load prediction usage, average value of traffic load prediction, variance value of traffic load prediction, or prediction window’ (these are optional);
‘adjusting at least one parameter of power amplifier based on the prediction information of traffic load and the at least one parameter of traffic load prediction’ (Osterling: [FIG.3]; [0039]: “the MAC scheduler 310 informs the control unit 320 of the output power level of the power amplifier and of the level of a predefined error parameter in the power amplifier for the upcoming transmission period, and the control unit 320 sets the PA 325 accordingly”; message comprises parameters such as required output power and EVM for RE to set its bias based on traffic load prediction).
However, Dimpflmaier in the same field of endeavor teaches bias of PA would depend on radio frequency band such as the higher the radio frequency band, the lower the bias (Dimpflmaier: [FIG.2]: the higher the frequency, the lower the bias; [0038]: “Different power amplifiers that are used to transmit signals at different radio-frequency bands may be biased at different bias voltage levels, as shown in FIG. 2”; [FIG.4]: four “FREQUENCY BAND (MHz)” => “1900”, “1800”, “900”, “850”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dimpflmaier’s teaching with that of Osterling to include the radio band information with the required output power and EVM in order to specify which radio band to adjust the bias.
Per claim 53 and 64:
Regarding claim 53, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Osterling teaches ‘wherein the prediction information of traffic load is determined by an artificial intelligence model’ (this is optional);
‘the prediction information of traffic load is determined based on at least one parameter of traffic load prediction’ (Osterling: [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period, where the length of the time period of the prediction”, duration of prediction).
Regarding claim 64, combination of Osterling and Dimpflmaier teaches the method according to claim 63 (discussed above).
Osterling teaches ‘wherein the prediction information of traffic load is determined by an artificial intelligence model’ (this is optional);
‘the prediction information of traffic load is determined based on at least one parameter of traffic load prediction’ (Osterling: [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period, where the length of the time period of the prediction”, duration of prediction).
Regarding claim 56, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Osterling teaches ‘wherein the prediction information of traffic load is used to adjust at least one parameter of power amplifier’ (Osterling: [0048]: “The MAC scheduler 310 informs the RE Control 320 of the required output power and EVM for the next sub frame and the RE Control 220 then configures the PA 325 to act accordingly, e.g. by setting its bias and clipping thresholds accordingly”).
Claims 54 and 65 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Osterling and Dimpflmaier, in view of Yang et al. (US 20230164657 A1), hereinafter “Yang”.
Per claim 54 and 65:
Regarding claim 54, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Combination of Osterling and Dimpflmaier does not expressly teach ‘determining confidence level for the prediction information of traffic load, wherein the message further comprises confidence level for the prediction information of traffic load’.
However, Yang in the same field of endeavor teaches load prediction information includes a confidence level of the load prediction information which is outputted when neural network model outputs load prediction information (Yang: [0011]: “the load prediction information further includes a confidence level of the load prediction information”; [0191]: “network device may determine, by using the neural network model, the load prediction information corresponding to the first cell. A confidence level is an inherent attribute of the neural network, and can be used to indicate a reliability degree of prediction information output by the neural network. Therefore, when outputting the load prediction information corresponding to the first cell, the neural network model may also output a confidence level corresponding to the load prediction information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yang’s teaching with that of combination of Osterling and Dimpflmaier to determine confidence level for the prediction information of traffic load, wherein the message further comprises confidence level for the prediction information of traffic load in order to indicate a reliability degree of prediction information (see reference quotes in element above).
Regarding claim 65, combination of Osterling and Dimpflmaier teaches the method according to claim 63 (discussed above).
Osterling teaches ‘wherein the message further comprises confidence level for the prediction information of traffic load, and the at least one parameter of power amplifier is adjusted further based on the confidence level for the prediction information of traffic load’ (Osterling: [0060]: “The prediction will thus include the CS traffic+the HSDPA power needs+possible margin”; [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period … The prediction is signalled to the RE Control 320 from the MAC scheduler 310 in order to adjust the biasing, i.e. the output power level of the PA”). However, combination of Osterling and Dimpflmaier fails to expressly teach confidence level for the prediction information.
However, Yang teaches load prediction information includes a confidence level of the load prediction information which is outputted when neural network model outputs load prediction information (Yang: [0011]: “the load prediction information further includes a confidence level of the load prediction information”; [0191]: “network device may determine, by using the neural network model, the load prediction information corresponding to the first cell. A confidence level is an inherent attribute of the neural network, and can be used to indicate a reliability degree of prediction information output by the neural network. Therefore, when outputting the load prediction information corresponding to the first cell, the neural network model may also output a confidence level corresponding to the load prediction information”) and select resources based on confidence level (Yang: [0039]: “radio access network device may further select, based on the confidence level of the load prediction information, a first cell corresponding to load prediction information with a highest confidence level”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yang’s teaching with that of combination of Osterling and Dimpflmaier for the message to include confidence level for the prediction information of traffic load and adjust the biasing of PA based on the confidence level for the prediction information of traffic load in order to indicate a reliability degree of prediction information (see reference quotes in element above).
Claims 57, 61 and 67 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Osterling and Dimpflmaier, in view of Tang et al. (US 20130210479 A1), hereinafter “Tang”.
Per claim 57 and 67:
Regarding claim 57, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Combination of Osterling and Dimpflmaier does not expressly teach ‘receiving radio component configuration information from the radio node’.
However, Tang in the same field of endeavor teaches RRU (radio node) sends configuration information such as production version, input power, output power and link gain to data analysis unit to achieve real-time improvement on radio frequency performance by power adjustment (Tang: [FIG.1]: “BBU”, “RRU”; [0039]: “FIG. 3 is a block diagram of a processing flow of carrying out real-time improvement on a radio frequency performance by way of power adjustment”; [0054]: “the RRU collects the power information comprising the input power, output power and link gain as well as the production version information by using the detection unit, and then uploads the power information and the production version information to the data analysis unit”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tang’s teaching with that of combination of Osterling and Dimpflmaier for the traffic load prediction node to receive radio component configuration information from the radio node in order to achieve real-time improvement on radio frequency performance by power adjustment (see reference quotes in element above).
Regarding claim 67, combination of Osterling and Dimpflmaier teaches the method according to claim 63 (discussed above).
Combination of Osterling and Dimpflmaier does not expressly teach ‘sending radio component configuration information to the traffic load prediction node’.
However, Tang teaches RRU (radio node) sends configuration information such as production version, input power, output power and link gain to data analysis unit to achieve real-time improvement on radio frequency performance by power adjustment (Tang: [FIG.1]: “BBU”, “RRU”; [0039]: “FIG. 3 is a block diagram of a processing flow of carrying out real-time improvement on a radio frequency performance by way of power adjustment”; [0054]: “the RRU collects the power information comprising the input power, output power and link gain as well as the production version information by using the detection unit, and then uploads the power information and the production version information to the data analysis unit”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tang’s teaching with that of combination of Osterling and Dimpflmaier for the radio node to send radio component configuration information to the traffic load prediction node in order to achieve real-time improvement on radio frequency performance by power adjustment (see reference quotes in element above).
Regarding claim 61, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Combination of Osterling and Dimpflmaier does not expressly teach ‘determining whether an update of an artificial intelligence model for determining the prediction information of traffic load is needed; and when the update of the artificial intelligence model is needed, updating the artificial intelligence model for determining the prediction information of traffic load’.
However, Tang teaches data analysis unit determine whether needs to adjust the learning model (an artificial intelligence model) based on power information from RRU and adjust the learning model when needed (Tang: [FIG.3]: “The difference value between the output power in the learning model and the expected output power is within a certain control range” -> “No” -> “The data analysis unit adjusts the learning mode according to the collected power information, until the output power and the expected output power of the RRU are within a certain control range”; [0010]: “provide a real-time improvement method and a real-time improvement apparatus for a distributed network radio frequency performance, in which the link adjustment on the element node is determined by establishing a power model of an element node and adjusting a model parameter so as to carry out the real-time adjustment on the radio frequency performances”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Tang’s teaching with that of combination of Osterling and Dimpflmaier for traffic load prediction node to determine whether an update of an artificial intelligence model for determining the prediction information of traffic load is needed; and when the update of the artificial intelligence model is needed, updating the artificial intelligence model for determining the prediction information of traffic load in order to improve radio frequency performance (see reference quotes in element above).
Claims 58 and 68 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Osterling and Dimpflmaier, in view of Sasaki et al. (US 20240155431 A1), hereinafter “Sasaki”.
Per claim 58 and 68:
Regarding claim 58, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Combination of Osterling and Dimpflmaier does not expressly teach ‘receiving a traffic load prediction request from the radio node; and sending a traffic load prediction response to the radio node’.
However, Sasaki in the same field of endeavor teaches a control unit transmits a request for prediction and receive a response about prediction (Sasaki: [0049]: “control function unit 130 transmits a request for the prediction/estimation result to the data store group 150 (S117). Then, the design/control function unit 130 acquires, as a response, the prediction/estimation result”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sasaki’s teaching with that of combination of Osterling and Dimpflmaier for the traffic load prediction node to receive a traffic load prediction request from the radio node and send a traffic load prediction response to the radio node in order to calculate an optimal wireless parameter based on the prediction result (Sasaki: [0049]: “control function unit 130 calculates an optimum wireless parameter and an optimum reflector design value on the basis of the acquired prediction/estimation result”).
Regarding claim 68, combination of Osterling and Dimpflmaier teaches the method according to claim 63 (discussed above).
Combination of Osterling and Dimpflmaier does not expressly teach ‘sending a traffic load prediction request to the traffic load prediction node; and receiving a traffic load prediction response from the traffic load prediction node’.
However, Sasaki teaches a control unit transmits a request for prediction and receive a response about prediction (Sasaki: [0049]: “control function unit 130 transmits a request for the prediction/estimation result to the data store group 150 (S117). Then, the design/control function unit 130 acquires, as a response, the prediction/estimation result”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sasaki’s teaching with that of combination of Osterling and Dimpflmaier for the radio node to send a traffic load prediction request to the traffic load prediction node and receive a traffic load prediction response from the traffic load prediction node in order to calculate an optimal wireless parameter based on the prediction result (Sasaki: [0049]: “control function unit 130 calculates an optimum wireless parameter and an optimum reflector design value on the basis of the acquired prediction/estimation result”).
Claims 59, 69 and 60 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Osterling and Dimpflmaier, in view of Griffioen et al. (US 20190313346 A1), hereinafter “Griffioen”.
Per claim 59 and 69:
Regarding claim 59, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Osterling teaches ‘receiving a feedback message comprising power amplifier status from the radio node’ (Osterling: [FIG.3]: “PA” of RE (radio node); [0012]: “The control unit is arranged to set the output power level in the power amplifier”). However, combination of Osterling and Dimpflmaier fails to expressly teach receiving a feedback about PA status from the radio node;
‘optimizing a determination method of the prediction information of traffic load based on the power amplifier status’ (Osterling: [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period”; [0060]: “The prediction will thus include the CS traffic+the HSDPA power needs+possible margin”). However, combination of Osterling and Dimpflmaier fails to expressly teach ‘optimizing a determination method’.
However, Griffioen in the same field of endeavor teaches RE (radio node) report and update a power rating of a Power Amplifier based on current operating conditions to REC for learning optimal operating parameters by Artificial Neural Network to configure the maximum carrier power (Griffioen: [FIG.2]: “REC”, “RE”; [FIG.5]; [0036]: “the REC 16 configures the maximum carrier power based on a power rating of a Power Amplifier (PA) reported by the RE … When the REC 16 computes the carrier power, it uses the PA power rating to limit the maximum carrier power in the baseband signal sent to the RE”; [FIG.5]; [0048]: “The RE 18 provides the over power subscription adjustment to the REC 16 for the one or more carriers (step 102). In some embodiments, providing the over power subscription adjustment to the REC 16 comprises sending a Common Public Radio Interface (CPRI) message to the REC. The RE 18 may then optionally update the over power subscription adjustment for the one or more carriers based on current operating conditions (step 104). In some embodiments, this process returns to step 102 to again provide the values to the REC 16 and continue to update the over power subscription adjustment as operating conditions change. In some embodiments this over power subscription adjustment is updated over time as a Bayesian or probabilistic Artificial Neural Network (ANN) learns optimal operating parameters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Griffioen’s teaching with that of combination of Osterling and Dimpflmaier for the traffic load prediction node to receive a feedback message comprising power amplifier status from the radio node and optimize a determination method of the prediction information of traffic load based on the power amplifier status in order to achieve optimal operating parameters (see reference quotes in element above).
Regarding claim 69, combination of Osterling and Dimpflmaier teaches the method according to claim 63 (discussed above).
Osterling teaches ‘sending a feedback message comprising power amplifier status to the traffic load prediction node’ (Osterling: [FIG.3]: “PA” of RE (radio node); [0012]: “The control unit is arranged to set the output power level in the power amplifier”). However, combination of Osterling and Dimpflmaier fails to expressly teach sending a feedback about PA status to the traffic load prediction node.
However, Griffioen in the same field of endeavor teaches RE (radio node) report and update a power rating of a Power Amplifier based on current operating conditions to REC for learning optimal operating parameters by Artificial Neural Network to configure the maximum carrier power (Griffioen: [FIG.2]: “REC”, “RE”; [FIG.5]; [0036]: “the REC 16 configures the maximum carrier power based on a power rating of a Power Amplifier (PA) reported by the RE … When the REC 16 computes the carrier power, it uses the PA power rating to limit the maximum carrier power in the baseband signal sent to the RE”; [FIG.5]; [0048]: “The RE 18 provides the over power subscription adjustment to the REC 16 for the one or more carriers (step 102). In some embodiments, providing the over power subscription adjustment to the REC 16 comprises sending a Common Public Radio Interface (CPRI) message to the REC. The RE 18 may then optionally update the over power subscription adjustment for the one or more carriers based on current operating conditions (step 104). In some embodiments, this process returns to step 102 to again provide the values to the REC 16 and continue to update the over power subscription adjustment as operating conditions change. In some embodiments this over power subscription adjustment is updated over time as a Bayesian or probabilistic Artificial Neural Network (ANN) learns optimal operating parameters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Griffioen’s teaching with that of combination of Osterling and Dimpflmaier for the radio node to send a feedback message comprising power amplifier status to the traffic load prediction node in order to achieve optimal operating parameters (see reference quotes in element above).
Regarding claim 60, combination of Osterling, Dimpflmaier and Griffioen teaches the method according to claim 59 (discussed above).
Combination of Osterling and Griffioen teaches ‘optimizing an artificial intelligence model for determining the prediction information of traffic load based on the power amplifier status’ (Osterling: [0056]: “the MAC scheduler 310 predicts the maximum output power requirement (average+PAPR margin) for an upcoming time period”; [0060]: “The prediction will thus include the CS traffic+the HSDPA power needs+possible margin”. Griffioen: [FIG.5]; [0048]: “this process returns to step 102 to again provide the values to the REC 16 and continue to update the over power subscription adjustment as operating conditions change. In some embodiments this over power subscription adjustment is updated over time as a Bayesian or probabilistic Artificial Neural Network (ANN) learns optimal operating parameters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Griffioen’s teaching of continue updating value based on operating conditions with that of combination of Osterling and Dimpflmaier in order to learn optimal operating parameters (see reference quotes in element above).
Claims 62 and 70 are rejected under 35 U.S.C. 103 as being unpatentable over combination of Osterling and Dimpflmaier, in view of Hu et al. (US 20210219161 A1), hereinafter “Hu”.
Per claim 62 and 70:
Regarding claim 62, combination of Osterling and Dimpflmaier teaches the method according to claim 52 (discussed above).
Combination of Osterling and Dimpflmaier teaches ‘sending a message indicating stopping traffic load prediction for a radio band to the radio node’ (Osterling: [FIG.3]; [0039]: “the MAC scheduler 310 informs the control unit 320 of the output power level of the power amplifier and of the level of a predefined error parameter in the power amplifier for the upcoming transmission period, and the control unit 320 sets the PA 325 accordingly”. Dimpflmaier: [FIG.4]: four “FREQUENCY BAND (MHz)” => “1900”, “1800”, “900”, “850”; [FIG.2]: the higher the frequency, the lower the bias; [0038]: “Different power amplifiers that are used to transmit signals at different radio-frequency bands may be biased at different bias voltage levels, as shown in FIG. 2”). However, combination of Osterling and Dimpflmaier fails to expressly teach sending a message indicating stopping traffic load prediction to the radio node.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dimpflmaier’s teaching of radio band with that of Osterling to include the radio band information with the required output power and EVM in order to specify which radio band to adjust the bias.
However, Hu in the same field of endeavor teaches message indicating prediction engine has stopped (Hu: [0228]: “The ‘predStop’ is a per UE flag sent by the prediction algorithm core, indicating the prediction engine has stopped for this UE. When predStop is received, this UE shall be removed”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hu’s teaching with that of combination of Osterling and Dimpflmaier for the traffic load prediction node to send a message indicating stopping traffic load prediction for a radio band to the radio node in order to inform radio node to take proper action when prediction is stopped (see reference quotes in element above).
Regarding claim 70, combination of Osterling and Dimpflmaier teaches the method according to claim 63 (discussed above).
Combination of Osterling and Dimpflmaier teaches ‘receiving a message indicating stopping traffic load prediction for a radio band from the traffic load prediction node; and stopping adjusting at least one parameter of power amplifier for the radio band’ (Osterling: [FIG.3]; [0039]: “the MAC scheduler 310 informs the control unit 320 of the output power level of the power amplifier and of the level of a predefined error parameter in the power amplifier for the upcoming transmission period, and the control unit 320 sets the PA 325 accordingly”. Dimpflmaier: [FIG.4]: four “FREQUENCY BAND (MHz)” => “1900”, “1800”, “900”, “850”; [FIG.2]: the higher the frequency, the lower the bias; [0038]: “Different power amplifiers that are used to transmit signals at different radio-frequency bands may be biased at different bias voltage levels, as shown in FIG. 2”). However, combination of Osterling and Dimpflmaier fails to expressly teach receiving a message indicating stopping traffic load prediction from the traffic load prediction node; and stopping adjusting at least one parameter of power amplifier.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Dimpflmaier’s teaching of radio band with that of Osterling to include the radio band information with the required output power and EVM in order to specify which radio band to adjust the bias.
However, Hu teaches message indicating prediction engine has stopped (Hu: [0228]: “The ‘predStop’ is a per UE flag sent by the prediction algorithm core, indicating the prediction engine has stopped for this UE. When predStop is received, this UE shall be removed”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hu’s teaching with that of combination of Osterling and Dimpflmaier for the radio node to receive a message indicating stopping traffic load prediction for a radio band from the traffic load prediction node; and stop adjusting at least one parameter of power amplifier for the radio band in order to inform radio node to take proper action when prediction is stopped (see reference quotes in element above).
Conclusion
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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUOXING FAN whose telephone number is (703)756-1310. The examiner can normally be reached Monday - Friday 9:00 am - 5:30 pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yemane Mesfin can be reached at (571)272-3927. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G.F./Examiner, Art Unit 2462
/YEMANE MESFIN/Supervisory Patent Examiner, Art Unit 2462
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