DETAILED ACTION
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
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.
Claim(s) 19-24, 31, and 35-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over MYRON (US 20220256474 A1) in view of Langer (US 20120064849 A1).
Regarding claim 19, MYRON teaches a transmission power method (MYRON teaches optimizing operation parameters such as the "transmission power of the individual access points such as base stations". Adjusting transmission power involves a power amplifier, para. 0007-9), the method comprising:
in accordance with a predicted traffic condition associated with a future time (MYRON teaches "forecasting the network demand at the base station in the network during a second time interval". The projected network demand corresponds to a predicted traffic condition for a future time, para. 0044, fig. 3, 306), determining PA related parameters for the future time (MYRON teaches determining an adjustment to the transmission power (a PA related parameter) based on the "difference between a projected network demand and the baseline reference level at the second time interval", para. 0045, fig. 3, 308) and applying the determined PA related parameters when the future time comes (MYRON teaches “adjusting the transmission power of the base station by a predetermined increment during the second time interval based at least on the difference", para. 0045, fig. 3, 310).
MYRON is silent to teaching that wherein the method is a digital predistortion (DPD) method for a power amplifier (PA).
In the same field of endeavor, Langer teaches a transmission power method is a digital predistortion (DPD) method for a power amplifier (PA) (Langer teaches a method for optimizing the power consumption and operating point of a power amplifier (PA), para. 0012-16, 0036-39, fig. 4,5, PA 406).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the teachings of MYRON with the teachings of Langer to proactively optimize the power efficiency and operating parameters of a base station's power amplifier for expected future traffic conditions.
MYRON teaches modulating and optimizing operation parameters, specifically the transmission power of a base station, by forecasting network demand for a future time interval. This demand forecasting enables the network provider to decrease resource usage by adjusting transmission power levels up or down based on the projected demand. However, MYRON focuses on overall transmission power adjustments and lacks specific details regarding how to optimize the internal operating point of the power amplifier itself based on the changing signal characteristics.
Langer teaches a method for improving the power consumption of a transmission chain by dynamically varying the operating point of a power amplifier (PA). Langer achieves this by changing the PA's bias voltages (such as supply voltage and quiescent voltage) based on the characteristics of the transmitted signal's modulation scheme, specifically relying on the number of resource blocks and channel bandwidth being utilized. Langer utilizes a lookup table (LUT) to store and retrieve predetermined bias voltages corresponding to these specific resource block combinations to optimize current consumption while ensuring Adjacent Channel Leakage Ratio (ACLR) performance meets system requirements.
A skilled artisan would be motivated to incorporate Langer's PA bias voltage optimization techniques into MYRON's predictive network management system. The motivation for this combination would be to use MYRON's forecasted network demand to anticipate the specific number of resource blocks that will be required in a future time interval, and proactively utilize Langer's lookup table to determine and apply the optimal PA bias voltages for that anticipated traffic load. This combination would allow the base station to precisely minimize the power amplifier's current consumption for the forecasted traffic load, ensuring maximum energy savings and hardware efficiency without degrading the linearity and signal quality (ACLR) when the future transmission occurs.
Regarding Claim 20, the combination of MYRON and Langer teaches the method of claim 19, wherein the predicted traffic condition is predicted by an Artificial Intelligence (AI) module (MYRON teaches that a demand forecasting module is configured to "generate a prediction model for network demand at individual base stations", para. 0034, fig. 2, demand forecasting model 212)
Regarding Claim 21, the combination of MYRON and Langer teaches the method of claim 19, wherein the future time comprises a time slot in the future (MYRON teaches forecasting network demand for a "second time interval" and for a "specified time period (e.g., weekday morning, weekday afternoon, evenings, weekends, holidays, etc.)", para. 0033-36).
Regarding Claim 22, the combination of MYRON and Langer teaches the method of claim 19, wherein the PA related parameters comprise DPD coefficients and a PA biasing configuration, and the PA biasing configuration comprises at least one of a voltage drain (Vdd) of the PA, a voltage gate (Vgg) of the PA and a load impedance of the PA (MYRON teaches a PA biasing configuration comprising a supply voltage (Vcc) and a quiescent voltage (Vcq) provided to the power amplifier, para. 0032-36).
Regarding Claim 23, the combination of MYRON and Langer teaches the method of claim 19, wherein the predicted traffic condition comprises at least one of a mean power level, a maximum power level, a minimum power level, a variance of power level, a physical resource block (PRB) utilization ratio and a confidence level (MYRON teaches analyzing Key Performance Indicators (KPIs) to "calculate an average, a mean, a maximum value, a minimum value" to determine a baseline reference level for demand forecasting, para. 0033-36) (Langer teaches evaluating transmission characteristics based on the number of resource blocks (RBs) and channel bandwidth utilized, para. 0042-48).
Regarding Claim 24, the combination of MYRON and Langer teaches the method of claim 19, wherein determining the PA related parameters for the future time comprises: looking up in a mapping table with the predicted traffic condition as an index to find an item in the mapping table whose traffic condition value matches the predicted traffic condition, wherein the mapping table comprises a plurality of items and each item comprises a traffic condition and PA related parameters; and determining the PA related parameters in the found item as the PA related parameters for the future time (Langer teaches utilizing a mapping table, specifically a lookup table (LUT). The LUT stores a plurality of items (rows) where each item corresponds to a specific combination of current transmission characteristics (number of RBs and channel bandwidth) and PA related parameters (supply voltage and quiescent voltage). Langer also teaches using the transmission characteristics to output the corresponding bias voltages from the table to determine the PA operating point, para. 0032-42,47).
Regarding Claim 31, the combination of MYRON and Langer teaches the method of claim 20, wherein the Al module is trained with historical traffic conditions and is updated with actual traffic conditions (MYRON teaches a demand forecasting module configured to generate a prediction model. MYRON teaches that this model utilizes historical information (historical traffic conditions) regarding KPIs. Furthermore, it teaches analyzing network performance following adjustments via an analysis module that provides feedback to indicate "the difference between the projected network demand and the actual network demand to continuously refine the prediction model". This maps to updating the module with actual traffic conditions, para. 0040-41).
Regarding Claim 35, MYRON teaches a transmission power apparatus, comprising:
a communication interface; a processor; and a memory coupled to the processor, said memory containing instructions executable by said processor (MYRON, fig. 2, memory 208, processor 204, para. 0028-29), whereby the DPD apparatus is configured to:
in accordance with a predicted traffic condition associated with a future time (MYRON teaches "forecasting the network demand at the base station in the network during a second time interval". The projected network demand corresponds to a predicted traffic condition for a future time, para. 0044, fig. 3, 306), determine PA related parameters for the future time (MYRON teaches determining an adjustment to the transmission power (a PA related parameter) based on the "difference between a projected network demand and the baseline reference level at the second time interval", para. 0045, fig. 3, 308) and apply the determined PA related parameters when the future time comes (MYRON teaches “adjusting the transmission power of the base station by a predetermined increment during the second time interval based at least on the difference", para. 0045, fig. 3, 310).
MYRON is silent to teaching that wherein the method is a digital predistortion (DPD) apparatus for a power amplifier (PA).
In the same field of endeavor, Langer teaches a transmission power apparatus is a digital predistortion (DPD) apparatus for a power amplifier (PA) (Langer teaches a method for optimizing the power consumption and operating point of a power amplifier (PA), para. 0012-16, 0036-39, fig. 4,5, PA 406).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the teachings of MYRON with the teachings of Langer to proactively optimize the power efficiency and operating parameters of a base station's power amplifier for expected future traffic conditions.
MYRON teaches modulating and optimizing operation parameters, specifically the transmission power of a base station, by forecasting network demand for a future time interval. This demand forecasting enables the network provider to decrease resource usage by adjusting transmission power levels up or down based on the projected demand. However, MYRON focuses on overall transmission power adjustments and lacks specific details regarding how to optimize the internal operating point of the power amplifier itself based on the changing signal characteristics.
Langer teaches a method for improving the power consumption of a transmission chain by dynamically varying the operating point of a power amplifier (PA). Langer achieves this by changing the PA's bias voltages (such as supply voltage and quiescent voltage) based on the characteristics of the transmitted signal's modulation scheme, specifically relying on the number of resource blocks and channel bandwidth being utilized. Langer utilizes a lookup table (LUT) to store and retrieve predetermined bias voltages corresponding to these specific resource block combinations to optimize current consumption while ensuring Adjacent Channel Leakage Ratio (ACLR) performance meets system requirements.
A skilled artisan would be motivated to incorporate Langer's PA bias voltage optimization techniques into MYRON's predictive network management system. The motivation for this combination would be to use MYRON's forecasted network demand to anticipate the specific number of resource blocks that will be required in a future time interval, and proactively utilize Langer's lookup table to determine and apply the optimal PA bias voltages for that anticipated traffic load. This combination would allow the base station to precisely minimize the power amplifier's current consumption for the forecasted traffic load, ensuring maximum energy savings and hardware efficiency without degrading the linearity and signal quality (ACLR) when the future transmission occurs.
Regarding claims 36-38, the dependent claims are interpreted and rejected for the same reasons as set forth above in claims 20-22.
Claim(s) 25 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over MYRON and Langer as applied to claim 24 above, and further in view of Wu (US 20200259465 A1)
Regarding Claim 25, the combination of MYRON and Langer teaches the method of claim 24.
The combination of MYRON and Langer is silent to teaching that wherein each item comprised in the mapping table further comprises a radio identity and/or a branch identity.
In the same field of endeavor, WU teaches a method wherein each item comprised in the mapping table further comprises a radio identity and/or a branch identity (Wu discloses a mapping table (coefficient database) where values include a "part 'signature', which represents substantially invariant characteristics and which may be unique to the electronic parts of the transmit chain". This unique signature maps to a radio identity or branch identity. Para. 0056-58).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the predictive network demand forecasting of MYRON with the digital predistortion (DPD) mapping table and database of Wu.
MYRON teaches a system that dynamically optimizes operation parameters, specifically adjusting the transmission power of a base station for a future time interval based on forecasted network demand. While MYRON effectively scales power usage to conserve resources during low traffic or boost capacity during high demand, rapidly altering transmission power shifts the operating point and load conditions of the base station's power amplifier. This shifting naturally induces non-linear distortions and spectral regrowth that severely degrade transmission signal quality.
Wu addresses power amplifier non-linearities by utilizing a DPD coefficient database (a mapping table) that stores pre-calibrated, converged DPD coefficients corresponding to specific system characteristics and load conditions. Wu’s look-up table approach allows the power amplifier system to rapidly select and apply the correct DPD coefficients to compensate for non-linear behavior without the significant computational delay and transient errors associated with iteratively converging an adaptive DPD algorithm in real-time.
The motivation to combine these teachings would be to guarantee continuous signal linearity and prevent transient distortion spikes during the proactive power adjustments dictated by MYRON. By incorporating Wu's DPD mapping table into MYRON's predictive system, a skilled artisan could use the forecasted traffic condition and its corresponding anticipated power state as an index to pre-fetch the optimal, pre-converged DPD parameters from the database. This combination allows the base station to seamlessly and efficiently scale its power output for anticipated network traffic while completely bypassing the latency of real-time DPD calculation. As a result, the combined system ensures pristine transmission quality and optimal power amplifier linearity at the exact moment the future traffic demand arrives, preventing any momentary drop in Quality of Service (QoS) during dynamic network load variations.
Regarding Claim 26, the combination of MYRON and Langer teaches the method of claim 24.
The combination of MYRON and Langer is silent to teaching that wherein the mapping table is created by: applying test signals with a set of predetermined traffic conditions to the PA under a set of predetermined PA biasing configurations; for each predetermined traffic condition in the set and each predetermined PA biasing configuration, obtaining converged DPD coefficients; and creating an item in the mapping table with a corresponding predetermined traffic condition, predetermined PA biasing configuration and converged DPD coefficients.
In the same field of endeavor, Wu teaches a method wherein the mapping table is created by: applying test signals with a set of predetermined traffic conditions to the PA under a set of predetermined PA biasing configurations; for each predetermined traffic condition in the set and each predetermined PA biasing configuration, obtaining converged DPD coefficients; and creating an item in the mapping table with a corresponding predetermined traffic condition, predetermined PA biasing configuration and converged DPD coefficients (Wu teaches creating the mapping table during a calibration (pre-run) stage where a specific PA is tested and "loaded with a physical device ... to scan loading effects" (applying test signals under predetermined configurations/conditions), and DPD coefficients are estimated and stored for those conditions, para. 0063).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the predictive network demand forecasting of MYRON with the digital predistortion (DPD) mapping table and database of Wu.
The motivation to combine these teachings would be to guarantee continuous signal linearity and prevent transient distortion spikes during the proactive power adjustments dictated by MYRON. By incorporating Wu's DPD mapping table into MYRON's predictive system, a skilled artisan could use the forecasted traffic condition and its corresponding anticipated power state as an index to pre-fetch the optimal, pre-converged DPD parameters from the database. This combination allows the base station to seamlessly and efficiently scale its power output for anticipated network traffic while completely bypassing the latency of real-time DPD calculation. As a result, the combined system ensures pristine transmission quality and optimal power amplifier linearity at the exact moment the future traffic demand arrives, preventing any momentary drop in Quality of Service (QoS) during dynamic network load variations.
Claim(s) 27-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over MYRON and Langer as applied to claim 24 above, and further in view of Braithwaite (US 20050009479 A1).
Regarding claim 27, the combination of MYRON and Langer teaches the method of claim 24.
The combination of MYRON and Langer is silent to teaching that wherein, after applying the determined PA related parameters, if DPD performance meets a first predetermined criteria, the method further comprises updating the mapping table with actual traffic condition and PA related parameters.
In the same field of endeavor, Braithwaite teaches a method wherein, after applying the determined PA related parameters, if DPD performance meets a first predetermined criteria, the method further comprises updating the mapping table with actual traffic condition and PA related parameters (Braithwaite teaches checking the residual distortion (DPD performance). If the distortion correction is adequate (meets a first predetermined criteria), "the predistortion parameter setting along with the current attributes are combined to form to a new element within the set" (updating the mapping table with actual conditions). Para. 0067-80).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the network demand forecasting of MYRON with the digital predistortion (DPD) parameter lists of Braithwaite to proactively prevent transient distortion during dynamic power adjustments.
MYRON teaches forecasting network demand for a future time interval and adjusting the overall transmission power of a base station based on that projected demand. While this effectively scales power usage to conserve resources during low traffic or boost capacity during high demand, dynamically and abruptly altering transmission power shifts the operating conditions of the base station's RF power amplifier.
Braithwaite teaches a digital predistortion (DPD) system that uses adjustable parameters to compensate for non-linear gain and distortion in an RF power amplifier. Braithwaite explicitly notes that abrupt changes in operating conditions (such as input power levels, DC supply, or temperature) can cause transient periods where output distortion exceeds allowable spectral mask specifications because real-time adaptive controllers take time to converge on new parameters. To avoid this transient distortion, Braithwaite utilizes a self-generating predistortion parameter list (a look-up table) that stores successful, pre-converged DPD parameters indexed by these specific operating conditions.
A skilled artisan would be motivated to incorporate Braithwaite's DPD parameter lists into MYRON's predictive network management system. The motivation for this combination would be to proactively retrieve and apply the optimal DPD coefficients from the look-up table that correspond to the specific anticipated transmission power level dictated by the forecasted traffic condition. By combining these teachings, the system can seamlessly scale its transmission power for anticipated network loads while bypassing the convergence delay of real-time adaptive DPD algorithms. This ensures continuous signal linearity, prevents transient distortion spikes, and maintains strict compliance with spectral emission requirements at the exact moment the base station dynamically shifts its power in response to network demand.
Regarding claim 28, the combination of MYRON, Langer and Braithwaite teaches the method of claim 27, wherein the updating is performed when a PA efficiency meets a second predetermined criteria (Braithwaite updates the table based on distortion correction quality, para. 0076-80).
Regarding claim 29, the combination of MYRON and Langer teaches the method of claim 24.
The combination of MYRON and Langer is silent to teaching that wherein, after applying the determined PA related parameters, if DPD performance doesn't meet a first predetermined criteria, the method further comprises: changing the DPD coefficients until the DPD performance meets the first predetermined criteria; and updating the mapping table with actual traffic condition and PA related parameters.
In the same field of endeavor, Braithwaite teaches a method wherein, after applying the determined PA related parameters, if DPD performance doesn't meet a first predetermined criteria, the method further comprises: changing the DPD coefficients until the DPD performance meets the first predetermined criteria; and updating the mapping table with actual traffic condition and PA related parameters (Braithwaite teaches that if the distortion correction is not adequate (doesn't meet the first predetermined criteria), the adaptive controller computes predistortion parameter corrections iteratively "until the distortion is reduced sufficiently" (meets criteria). Once reduced, the setting and current attributes "are stored in the predistortion parameter list" (updating the mapping table), para. 0065-67).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the network demand forecasting of MYRON with the digital predistortion (DPD) parameter lists of Braithwaite to proactively prevent transient distortion during dynamic power adjustments.
A skilled artisan would be motivated to incorporate Braithwaite's DPD parameter lists into MYRON's predictive network management system. The motivation for this combination would be to proactively retrieve and apply the optimal DPD coefficients from the look-up table that correspond to the specific anticipated transmission power level dictated by the forecasted traffic condition. By combining these teachings, the system can seamlessly scale its transmission power for anticipated network loads while bypassing the convergence delay of real-time adaptive DPD algorithms. This ensures continuous signal linearity, prevents transient distortion spikes, and maintains strict compliance with spectral emission requirements at the exact moment the base station dynamically shifts its power in response to network demand.
Regarding claim 30, the combination of MYRON, Langer and Braithwaite teaches the method of claim 29, wherein, after applying the determined PA related parameters, if a PA efficiency doesn't meet a second predetermined criteria, the method further comprises: changing the PA biasing configuration until the PA efficiency meets the second predetermined criteria and the DPD performance meets the first predetermined criteria; and updating the mapping table with actual traffic condition and PA related parameters (Braithwaite, iterative flag, para. 0065-66).
Claim(s) 32-34 is/are rejected under 35 U.S.C. 103 as being unpatentable over MYRON and Langer as applied to claim 23 above, and further in view of Yousefi'zadeh (US 20170359752 A1).
Regarding claim 32, the combination of MYRON and Langer teaches the method of claim 23.
The combination of MYRON and Langer is silent to teaching that wherein the applying is performed when the confidence level meets a third predetermined criteria.
In the same field of endeavor, Yousefi'zadeh teaches a method wherein the applying is performed when the confidence level meets a third predetermined criteria (Yousefi'zadeh teaches predicting traffic/load parameters using machine learning, evaluating the prediction error (RMSE), and applying the new parameters subject to a "safety margin of 1%" to ensure cells do not exceed congestion thresholds. Under BRI, utilizing an error-based safety margin to gate the application of parameters operates functionally as requiring a confidence level to meet a third predetermined criteria. Para. 0072-78,118-128).
Therefore, a person of ordinary skill in the art at the time of the invention would have been motivated to combine the teachings of MYRON with the teachings of Yousefi'zadeh to enhance the accuracy, reliability, and precision of proactive network demand forecasting and power optimization.
MYRON teaches a system for optimizing base station operation parameters by forecasting future network demand and adjusting the transmission power based on that projected demand. To achieve this, MYRON utilizes a demand forecasting module that generates prediction models based on general Key Performance Indicators (KPIs), such as bandwidth utilization, data transmission rates, and network capacity.
Yousefi'zadeh teaches a highly advanced approach to predicting and mitigating 4G LTE cellular network congestion by utilizing a Multi-Layer Perceptron Deep Learning (MLPDL) model. Specifically, Yousefi'zadeh accurately predicts congestion breakpoints using a standardized metric—Physical Resource Block (PRB) utilization (e.g., predicting when a tower will hit an 80% utilization threshold). Furthermore, Yousefi'zadeh evaluates the prediction error (RMSE) of its learning algorithms and applies a "safety margin" (functionally a confidence level) before adjusting cell power parameters to ensure that non-congested cells do not accidentally exceed their congestion thresholds due to prediction errors.
A skilled artisan would be motivated to incorporate the specific deep learning (MLPDL) modeling, the precise PRB utilization metrics, and the safety margin evaluations of Yousefi'zadeh into the demand forecasting module of MYRON. The motivation for this combination would be to significantly improve the accuracy and statistical reliability of the network demand forecasts.
By utilizing Yousefi'zadeh's standardized LTE resource metrics and error-based safety margins, the combined system could more precisely anticipate exact congestion breakpoints and ensure the predictive model is statistically reliable before making critical transmission power adjustments. This prevents network degradation, handover failures, or dropped calls that could result from erroneous traffic predictions, thereby allowing the network to dynamically scale power usage with high confidence and minimal risk to user quality of service.
Regarding claim 33, the combination of MYRON, Langer and Yousefi'zadeh teaches the method of claim 32, wherein the applying is not performed when the confidence level doesn't meet the third predetermined criteria, and wherein the method further comprises:
if DPD performance doesn't meet a first predetermined criteria, changing the DPD coefficients until the DPD performance meets the first predetermined criteria; and updating the mapping table with actual traffic conditions and PA related parameters; AND if the DPD performance meets the first predetermined criteria, updating the mapping table with actual traffic conditions and PA related parameters (Yousefi'zadeh teaches that application relies on the safety margin/confidence criteria. Para. 0072-78,118) (MYRON teaches updating table with KPI. Para. 0035).
Regarding claim 34, the combination of MYRON, Langer and Yousefi'zadeh teaches the method of claim 33, wherein the method further comprises:
if a PA efficiency doesn't meet a second predetermined criteria, changing the PA biasing configuration until the PA efficiency meets the second predetermined criteria and the DPD performance meets the first predetermined criteria; and updating the mapping table with actual traffic conditions and PA related parameters; AND if the PA efficiency meets the second predetermined criteria and the DPD performance meets the first predetermined criteria, updating the mapping table with actual traffic conditions and PA related parameters (Yousefi'zadeh teaches that application relies on the safety margin/confidence criteria. Para. 0072-78,118) (MYRON teaches updating table with KPI. Para. 0035).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Astrom: US20160261295A1, Blume: US20130203434A1, Leipold: US20200169223A1 teach transmission power systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEN WU HUANG whose telephone number is (571)272-7852. The examiner can normally be reached Mon-Fri 10-6.
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, Wesley Kim can be reached at (571) 272-7867. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WEN W HUANG/Primary Examiner, Art Unit 2648
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