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
1. 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.
2. 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 of this title, 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 1-4, 6-11, 13-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No US 2024/0373345 to Niemela et al. (hereinafter Niemela) in view of CN 115696688
As to claims 1, 8 and 15, Niemela discloses a method of wireless communication, the method comprising:
collecting predicted environmental condition information for a base station of a wireless network (Niemela; [0089]-[0090] discloses a power consumption profile of a base station is a characterization of how much power the base station consumes over a period of time under different conditions. It can be used to understand the energy usage patterns of the base station, which can help to optimize the energy usage of the base station. the power consumption profile may include information about the base station's energy usage during different times of the day, different weather conditions (=environmental condition), different levels of network traffic, and other factors that may affect the energy consumption of the base station);
collecting historical power consumption data (Niemela; [0085] discloses history-based traffic profiles and power consumption profiles may provide a good estimate on the available reserve capacity, they may become obsolete with short notice, if other functions in the system (dynamically) change the base station configuration. Such changes should be taken into account, when decisions for VPP and/or peak shaving are made, such as the amount or duration of battery-based operation or exclusion or inclusion of base station sites for VPP or peak shaving actions)
using the historical power consumption data and the environmental condition information, predicting, at the base station, a power usage profile for the base station, wherein the predicted power usage profile includes power consumed by the plurality of electrically- powered assets (Niemela; [0083]-[0085] discloses Data traffic and power consumption profiles may be made based on the history of measurements, counters or KPIs from base stations, or in some cases directly from the power system. Different applications and functions in base stations, network management system and/or RAN intelligent controller (RIC) may trigger changes that alter data traffic and power consumption profiles in a base station. If a RAN, RIC, NMS application or function changes parameters in a certain base station, it may have impact to the data traffic and/or power consumption profiles of neighboring base stations as well. history-based traffic profiles and power consumption profiles may provide a good estimate on the available reserve capacity, they may become obsolete with short notice, if other functions in the system (dynamically) change the base station configuration. Such changes should be taken into account, when decisions for VPP and/or peak shaving are made, such as the amount or duration of battery-based operation or exclusion or inclusion of base station sites for VPP or peak shaving actions. [0180] discloses by using ESM, data traffic and power consumption can be optimized to match with the available or predicted renewable energy capacity, such as solar panels at the cell site, so that the cell site (base station) may be operated fully with renewable capacity, or the ratio of renewable energy over the total consumption may be optimized).
predicting, at the base station, performance of a secondary power source at the base station (Niemela; [0180] discloses by using ESM, data traffic and power consumption can be optimized to match with the available or predicted renewable energy capacity, such as solar panels at the cell site, so that the cell site (base station) may be operated fully with renewable capacity, or the ratio of renewable energy over the total consumption may be optimized); and
using the predicted power usage profile and the predicted performance of the secondary power source, selecting, at the base station, a power preservation action from the list consisting of:
diverting incoming primary power to recharge a battery, using battery charge in lieu of primary power for at least one of the electrically-powered assets located at the base station, and selectively powering one of the electrically-powered assets while reducing power to another (Niemela; [0134]-[0135] discloses the VPP controller initiates or performs the one or more VPP actions or peak shaving actions associated with at least one base station of the one or more base stations. The VPP action information may be transmitted before or during the one or more VPP actions or peak shaving actions (e.g., in real time as the actions are performed). The one or more virtual power plant actions or peak shaving actions may comprise at least one of: charging one or more batteries of the at least one base station from a power grid, discharging energy from the one or more batteries to the power grid, operating the at least one base station on battery power, operating the at least one base station on power provided by the power grid, or reducing power consumption of the at least one base station).
Niemela discloses of collecting historical power consumption data, but fails to disclose collecting historical power consumption data for a plurality of electrically-powered assets located at the base station. However, CN 115696688 discloses
collecting historical power consumption data for a plurality of electrically-powered assets located at the base station (CN 115696688; Page 2: Contents of the Invention discloses according to the historical power consumption state data of all street lamps of the outdoor illumination area, determining the illumination power consumption peak period of the outdoor illumination area, collecting the real-time power data of all street lamps in the lighting peak period, and combining the power transmission load of the power transmission transformation base station corresponding to the outdoor lighting sheet area, judging whether to switch the power supply mode of the street lamp of the outdoor lighting area, when there is no need to switch the power supply mode, then according to the external environment state information of the area of each street lamp, adjusting the illumination state of the street lamp; when the power supply mode needs to be switched, according to the historical power consumption state data of each street lamp, determining at least one street lamp needed to switch the power supply mode, and then switching the power supply of the corresponding street lamp, based on the historical power consumption state data of all street lamps in the outdoor illumination sheet area)
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings and use the power supply based on the on demand and thus use the limited resources in an effective way.
As to claims 2, 9 and 16, the rejection of claim 1 as listed above is incorporated herein. In addition, Niemela- CN 115696688 discloses wherein the secondary power source comprise at least one power source selected from the list consisting of:
a power harvester, a wind-powered generator, solar-powered generator, and a power saving mode of an electrically-powered asset located at the base station (Niemela; [0180] discloses By using ESM, data traffic and power consumption can be optimized to match with the available or predicted renewable energy capacity, such as solar panels at the cell site, so that the cell site (base station) may be operated fully with renewable capacity, or the ratio of renewable energy over the total consumption may be optimized. Here Niemela is applied for the 3rd alternative solar-powered generator).
As to claims 3, 10 and 17, the rejection of claim 1 as listed above is incorporated herein. In addition, Niemela- CN 115696688 discloses wherein predicting the performance of the secondary power source comprises using the predicted environmental condition information (Niemela; [0180] discloses in legacy systems, when energy saving is done, the available renewable energy capacity (e.g., solar panels) at the cell site is not taken into account in energy saving decisions. By using ESM, data traffic and power consumption can be optimized to match with the available or predicted renewable energy capacity, such as solar panels at the cell site, so that the cell site (base station) may be operated fully with renewable capacity, or the ratio of renewable energy over the total consumption may be optimized); and
wherein predicting the performance of the secondary power source comprises predicting at least one performance selected from the list consisting of:
wind-powered generator performance, solar-powered generator performance, and battery performance (Niemela; [0180] discloses in legacy systems, when energy saving is done, the available renewable energy capacity (e.g., solar panels) at the cell site is not taken into account in energy saving decisions. By using ESM, data traffic and power consumption can be optimized to match with the available or predicted renewable energy capacity, such as solar panels at the cell site, so that the cell site (base station) may be operated fully with renewable capacity, or the ratio of renewable energy over the total consumption may be optimized. Here Niemela is applied for the 3rd alternative solar-powered generator).
As to claims 4, 11 and 18, the rejection of claim 1 as listed above is incorporated herein. In addition, Niemela- CN 115696688 discloses wherein the plurality of electrically-powered assets comprises two different generation cellular technology radios, a tower light, and cooling equipment (Niemela; [0052] discloses In 5G wireless communication networks, access nodes and/or UEs may have multiple radio interfaces, namely below 6 GHz, cmWave and mmWave, and also being integrable with existing legacy radio access technologies)
As to claims 6, 13 and 20, the rejection of claim 1 as listed above is incorporated herein. In addition, Niemela- CN 115696688 discloses wherein the predicted power usage profile, the predicted performance of the secondary power source, and the selected power preservation action is each specific to a time of day, a day of week, and/or a specific date (Niemela; [0070]; [0090] discloses the power consumption profile may include information about the base station's energy usage during different times of the day. Here Niemela is applied for the 1st alternative)
As to claims 7 and 14, the rejection of claim 1 as listed above is incorporated herein. In addition, Niemela- CN 115696688 discloses further comprising: collecting information regarding an event associated with an increased number of wireless network users in a vicinity of the base station, wherein predicting the power usage profile for the base station comprises using the collected information regarding the event (Niemela; [0070]-[0090] discloses the power consumption profile may include information about the base station's energy usage during different times of the day, different weather conditions, different levels of network traffic, and other factors that may affect the energy consumption of the base station)
Claims 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No US 2024/0373345 to Niemela et al. (hereinafter Niemela) in view of CN 115696688 in view of U.S. Publication No US 2024/0214925 to Nocete et al. (hereinafter Nocete)
As to claims 5, 12 and 19, Niemela-CN 115696688 discloses prediction of the power usage profile, the prediction of the performance of the secondary power source, and the selection of the power preservation action, but fails to discloses wherein the prediction of the power usage profile, the prediction of the performance of the secondary power source, and the selection of the power preservation action are performed using machine learning (ML). However, Nocete discloses
wherein the prediction of the power usage profile, the prediction of the performance of the secondary power source, and the selection of the power preservation action are performed using machine learning (ML) (Nocete; [0009] discloses the artificial intelligence cellular base station management program is a Non-Real-Time Radio Access Network Intelligent Controller (Non-RT RIC). In some embodiments, the method further includes adjusting operational parameters of the artificial intelligence cellular base station management program using machine learning. [0007] discloses The process includes determining the cellular base station is subject to a power outage and is being supplied power from backup power storage, preparing a notification of the power outage that is configured to be recognized by an artificial intelligence cellular base station management program operating on a cloud computing network, sending the notification to the cloud computing network so as to be recognized by the artificial intelligence cellular base station management program, receiving a backup power consumption instruction from the cloud computer network in response to the artificial intelligence cellular base station management program evaluating that at least one criterion compelling control of backup power consumption exists, and effectuating a change in backup power consumption at the cellular base station by executing the backup power consumption instruction).
It is obvious for a person of ordinary skilled in the art to combine the teachings before the effective filing date of the invention. One would be motivated to combine the teachings and manage the power outage using machine learning or artificial intelligence.
Conclusion
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/FAISAL CHOUDHURY/Primary Examiner, Art Unit 2478
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