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 .
Response to Arguments
Applicant's arguments filed 8/11/26 have been fully considered but they are not persuasive.
The applicant argues “…Instead, the vehicle-mounted controller is a component
"configured to send a charging request and make an appointment for charging" (see, paragraph [0080] of Zhou '338). A controller for requesting charging appointments is not sufficient to teach or suggest a radio unit, when read in light of Applicant's Specification. Even if arguendo, the vehicle mounted controller could be construed as a radio head (which Applicant does not concede), Zhou '338 does not teach or suggest "determining a decision about delivery of power from the local battery to" the vehicle mounted controller (alleged radio head) for a future time window. Instead, Zhou '338 discusses how it is concerned with "issuing a scheduling instruction to each charging station according to the scheduling strategy, to realize orderly charging of electric vehicles in the entire charging region." In other words, Zhou '338 discuses a method for optimizing electric vehicle charging scheduling strategy, which would include charging a battery of the vehicle, not "a decision about delivery of power from the local battery to a radio head. Therefore, Zhou '338 discusses delivery of power to a battery of a vehicle and does not discuss delivery of power from a local battery to a radio head, much less in the future.” See page 8 of applicant’s remarks. The examiner respectfully disagrees.
Applicant’s Publication state “A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio.” Paragraph [0111]. As the broadest reasonable interpretation, in line with applicant’s publication and the claims presented, the examiner has interpreted the radio head as a transceiver used with network nodes capable of being charged (in other words, receiving power from a local battery as described in the claims). According to Zhou 338’, the charging request is sent by the electric vehicle, and in particular, a vehicle-mounted control system of the electric vehicle sends the charging request within a network as shown in figure 4. See paragraph [0054]. The electric vehicle comprises the vehicle-mounted control system therefore, it can be interpreted as the electric vehicle as the radio head for receiving charging (in other words, the electric vehicle/radio head is the transceiver communicating in the network for receiving charges from charging stations).
Zhou 338’ discloses issuing scheduling instructions to each charging station for charging an electric vehicle based on a charging optimization scheduling model which includes considering maximum power that can be provided by the regional power grid in the time period (therefore, considered as a decision about delivery of power is determined). See paragraphs [0009]-[0011] and [0018]. Zhou 338’ discloses "determining a decision about delivery of power from the local battery to" the vehicle mounted controller/electric vehicle (i.e., radio head) for a future time window. Zhou 338’ discloses charging the electric vehicle/radio head with power from a local battery/charging station including making a decision about delivery of power from the local battery to a radio head.
The applicant argues “Moreover, Zhou '338 does not teach or suggest a decision being based on a differentiation of power data statistics including and average power and peak power demands covering a defined time period for a radio head. On page 4 of the Office Action, the Office acknowledges that Zhou '338 "does not specifically teach peak power." To reject this feature, the Office merely points out that the Background of Zhou '338 mentions peak power. However, Zhou '338 merely discusses that "charging behaviors will lead to a sharp increase in charging demand during peak hours of the power grid" and there is no mention that the peak hours are used as part of a differentiation of power data statistics, much less forming a decision based on such a differentiation.” See page 9 of applicant’s remarks. The examiner respectfully disagrees.
Zhou 338’ discloses when large-scale electric vehicles are connected to a power grid, the randomness of their charging behaviors will lead to a sharp increase in charging demand during peak hours of the power grid (i.e., peak hours used as part of power data statistics), which will affect the stability and safety of the power grid. A charging process of the electric vehicles can be controlled. Therefore, adopting a reasonable and effective charging coordination strategy to guide orderly charging of the electric vehicles can reduce the load fluctuation of the power grid. See paragraph [0003]. Therefore, Zhou 338’ discloses peak hours used as part of a differentiation of power data statistics for making decisions for charging electric vehicles providing a controlled, orderly charging process for electric vehicles. As such, Zou 338’ discloses a decision being based on a differentiation of power data statistics including and average power and peak power demands covering a defined time period for a radio head as required in the claims.
The applicant argues “Lastly, Zhou '338 does not teach or suggest that the decision is based on "time and location dependent cost data of charging and/or discharging the local battery and power grid utilization for the future time window," as recited in claim 1. Instead, Zhou '338 discusses how "a target charging station for a to-be-charged electric vehicle is determined with a minimum traveling cost as a target, SO that a data transmission distance is reduced, and the electric vehicle user is timely assisted in selecting the target charging station and completing a charging appointment" (see, paragraph [0034] of Zhou '338). Therefore, Zhou '338 is concerned with the travel cost for a vehicle to travel to a charging station, not "cost data of charging and/or discharging the local battery," as claimed. As such, Zhou '338 cannot reasonably be construed to teach or suggest the features of claim 1.” See page 9 of applicant’s remarks. The examiner respectfully disagrees.
Paragraph [0056] of Zhou 338’ states “The traveling cost as including… W.sub.i,j is a time the electric vehicle i needs to wait at the charging station j; T.sub.i.sup.start is an expected charging start moment of the electric vehicle i… U.sub.i,j.sup.cs is determined based on a using condition of the charging station j when the electric vehicle i sends the charging request”. In other words, Zhou 338’ teaches traveling cost including waiting time at the charging station, expected charging start moment and a using condition of the charging station all considered as “cost data of charging the local battery” as required in the claims.
Paragraph [0057] of Zhou 338’ states “It is not difficult to see that an electric vehicle traveling cost function takes into account a traveling time of the electric vehicle heading for the charging station, a waiting time at the charging station, and a time from the charging station to the destination, which not only reduces time cost of the electric vehicle user, but also reduces congestion of the charging station.” In other words, Zhou 338’ teaches traveling cost including traveling time to/from charging station and waiting time at the charging station all considered as “time and location dependent cost data of charging the local battery” as required in the claims.
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.
Claim(s) 1, 4-5, 10, 13-15, 20-21 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (Zhou 338’), U.S. Publication No. 2022/0024338.
Regarding Claim 1, Zhou 338’ discloses a method performed by a computing
device (i.e., cloud platform; see figure 4) in a communication system for management of delivery of power (in other words, receiving an electric vehicle charging scheduling strategy obtained by the cloud platform which solves the charging optimization scheduling model based on a particle swarm algorithm, and issuing a scheduling instruction to each charging station according to the scheduling strategy, to realize orderly charging of electric vehicles in the entire charging region; see paragraph [0011]) to at least one radio head (i.e., vehicle-mounted controller; see figure 4) from a local battery (charging station; see figure 4) located proximate to the at least one radio head (shown in figure 4), the method comprising:
determining a decision about delivery of power from the local battery to the at least one radio head for a future time window, the decision made by a machine learning model based on (i) differentiation of output power data statistics comprising average power (i.e., P.sub.avg represents an average load of the regional power grid in a cycle T as described in paragraph [0066]) demands over a set of past time windows covering a defined time period for the at least one radio head (in other words, the current basic load prediction result in S2 is obtained by the deep learning model constructed by the cloud platform based on basic load data, environmental data, and date data of the regional power grid in a historical time period. The test set is input into the prediction model to obtain the prediction result, and a root mean square error and an average absolute percentage error are used to evaluate the prediction result. If a prediction error is lower than a predetermined error requirement, the current deep learning model is used to predict basic load of the regional power grid; or otherwise, parameters of the current deep learning model are optimized; see paragraphs [0061]-[0063]), and (ii) time and location dependent cost data of charging and/or discharging the local battery and power grid utilization for the future time window (i.e., the target charging station for the to-be-charged electric vehicle is determined with a minimum traveling cost as a target… a nearest edge computing unit receives the charging request, applies to the cloud platform to obtain all charging station information in the charging region, determines the target charging station for the to-be-charged electric vehicle with a minimum traveling cost as a target, and makes the charging appointment… after the to-be-charged electric vehicle establishes the charging appointment with the target charging station, the electric vehicle charging data in the subinterval served by each edge computing unit is uploaded to the charging optimization scheduling model pre-trained by the cloud platform, wherein the charging optimization scheduling model is established by the cloud platform based on the current basic load prediction result; see paragraphs [0053]-[0055] and [0058]); and
outputting the decision about delivery of power from the local battery to the at least one radio head for the future time window (i.e., the electric vehicle charging scheduling strategy obtained by the cloud platform which solves the charging optimization scheduling model based on the particle swarm algorithm is received, and the scheduling instruction is issued to each charging station according to the scheduling strategy, to realize orderly charging of the electric vehicles in the entire charging region; see paragraphs [0071]-[0072]).
Although, Zhou 338’ does not specifically teach peak power as directly mentioned in the claim, paragraph [0003] of Zhou 338’ does state when large-scale electric vehicles are connected to a power grid, the randomness of their charging behaviors (in other words, the output power data statistics is considered) will lead to a sharp increase in charging demand during peak hours of the power grid. Therefore, it can be said that “peak” hours of the power grid (i.e., peak power) is considered when determining a decision about delivery of power from the local battery to the at least one radio head for a future time window. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Zhou 338’s teaching for providing stability and safety of the power grid (see paragraph [0003] of Zhou).
Regarding Claim 14, Zhou 338’ discloses a computing device (i.e., cloud
platform; see figure 4) in a communication system for management of delivery of power (in other words, receiving an electric vehicle charging scheduling strategy obtained by the cloud platform which solves the charging optimization scheduling model based on a particle swarm algorithm, and issuing a scheduling instruction to each charging station according to the scheduling strategy, to realize orderly charging of electric vehicles in the entire charging region; see paragraph [0011]) to at least one radio head (i.e., vehicle-mounted controller; see figure 4) from a local battery (charging station; see figure 4) located proximate to the at least one radio head (shown in figure 4), the computing device comprising:
at least one processor (i.e., processor; see paragraph [0085]);
at least one memory (i.e., memory; see paragraph [0086]) connected to the at least one processor and storing program code that is executed by the at least one processor to perform operations (i.e., one or more programs, wherein the one or more programs are stored on the memory and configured to be executed by the one or more processors; see paragraph [0087]) comprising:
determine a decision about delivery of power from the local battery to the at least one radio head for a future time window, the decision made by a machine learning model based on (i) differentiation of power output data statistics comprising average power (i.e., P.sub.avg represents an average load of the regional power grid in a cycle T as described in paragraph [0066]) demands over a set of past time windows covering a defined time period for the at least one radio head (in other words, the current basic load prediction result in S2 is obtained by the deep learning model constructed by the cloud platform based on basic load data, environmental data, and date data of the regional power grid in a historical time period. The test set is input into the prediction model to obtain the prediction result, and a root mean square error and an average absolute percentage error are used to evaluate the prediction result. If a prediction error is lower than a predetermined error requirement, the current deep learning model is used to predict basic load of the regional power grid; or otherwise, parameters of the current deep learning model are optimized; see paragraphs [0061]-[0063]), and (ii) time and location dependent cost data of charging and/or discharging the local battery and power grid utilization for the future time window (i.e., the target charging station for the to-be-charged electric vehicle is determined with a minimum traveling cost as a target… a nearest edge computing unit receives the charging request, applies to the cloud platform to obtain all charging station information in the charging region, determines the target charging station for the to-be-charged electric vehicle with a minimum traveling cost as a target, and makes the charging appointment… after the to-be-charged electric vehicle establishes the charging appointment with the target charging station, the electric vehicle charging data in the subinterval served by each edge computing unit is uploaded to the charging optimization scheduling model pre-trained by the cloud platform, wherein the charging optimization scheduling model is established by the cloud platform based on the current basic load prediction result; see paragraphs [0053]-[0055] and [0058]); and
output the decision about delivery of power from the local battery to the at least one radio head for the future time window (i.e., the electric vehicle charging scheduling strategy obtained by the cloud platform which solves the charging optimization scheduling model based on the particle swarm algorithm is received, and the scheduling instruction is issued to each charging station according to the scheduling strategy, to realize orderly charging of the electric vehicles in the entire charging region; see paragraphs [0071]-[0072]).
Although, Zhou 338’ does not specifically teach peak power as directly mentioned in the claim, paragraph [0003] of Zhou 338’ does state when large-scale electric vehicles are connected to a power grid, the randomness of their charging behaviors (in other words, the output power data statistics is considered) will lead to a sharp increase in charging demand during peak hours of the power grid. Therefore, it can be said that “peak” hours of the power grid (i.e., peak power) is considered when determining a decision about delivery of power from the local battery to the at least one radio head for a future time window. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Zhou 338’s teaching for providing stability and safety of the power grid (see paragraph [0003] of Zhou).
Regarding Claim 20, Zhou 338’ discloses a computer program product
comprising a non-transitory storage medium including program code to be executed by processing circuitry (i.e., one or more programs, wherein the one or more programs are stored on the memory and configured to be executed by the one or more processors; see paragraph [0087]) of a computing device (i.e., cloud platform; see figure 4) in a communication system for management of delivery of power (in other words, receiving an electric vehicle charging scheduling strategy obtained by the cloud platform which solves the charging optimization scheduling model based on a particle swarm algorithm, and issuing a scheduling instruction to each charging station according to the scheduling strategy, to realize orderly charging of electric vehicles in the entire charging region; see paragraph [0011]) to at least one radio head (i.e., vehicle-mounted controller; see figure 4) from a local battery (charging station; see figure 4) located proximate to the at least one radio head (shown in figure 4), whereby execution of the program code causes the computing device to perform operations comprising:
determine a decision about delivery of power from the local battery to the at least one radio head for a future time window, the decision made by a machine learning model based on (i) differentiation of output power data statistics comprising average power (i.e., P.sub.avg represents an average load of the regional power grid in a cycle T as described in paragraph [0066]) demands over a set of past time windows covering a defined time period for the at least one radio head (in other words, the current basic load prediction result in S2 is obtained by the deep learning model constructed by the cloud platform based on basic load data, environmental data, and date data of the regional power grid in a historical time period. The test set is input into the prediction model to obtain the prediction result, and a root mean square error and an average absolute percentage error are used to evaluate the prediction result. If a prediction error is lower than a predetermined error requirement, the current deep learning model is used to predict basic load of the regional power grid; or otherwise, parameters of the current deep learning model are optimized; see paragraphs [0061]-[0063]), and (ii) time and location dependent cost data of charging and/or discharging the local battery and power grid utilization for the future time window (i.e., the target charging station for the to-be-charged electric vehicle is determined with a minimum traveling cost as a target… a nearest edge computing unit receives the charging request, applies to the cloud platform to obtain all charging station information in the charging region, determines the target charging station for the to-be-charged electric vehicle with a minimum traveling cost as a target, and makes the charging appointment… after the to-be-charged electric vehicle establishes the charging appointment with the target charging station, the electric vehicle charging data in the subinterval served by each edge computing unit is uploaded to the charging optimization scheduling model pre-trained by the cloud platform, wherein the charging optimization scheduling model is established by the cloud platform based on the current basic load prediction result; see paragraphs [0053]-[0055] and [0058]); and
output the decision about delivery of power from the local battery to the at least one radio head for the future time window (i.e., the electric vehicle charging scheduling strategy obtained by the cloud platform which solves the charging optimization scheduling model based on the particle swarm algorithm is received, and the scheduling instruction is issued to each charging station according to the scheduling strategy, to realize orderly charging of the electric vehicles in the entire charging region; see paragraphs [0071]-[0072]).
Although, Zhou 338’ does not specifically teach peak power as directly mentioned in the claim, paragraph [0003] of Zhou 338’ does state when large-scale electric vehicles are connected to a power grid, the randomness of their charging behaviors (in other words, the output power data statistics is considered) will lead to a sharp increase in charging demand during peak hours of the power grid. Therefore, it can be said that “peak” hours of the power grid (i.e., peak power) is considered when determining a decision about delivery of power from the local battery to the at least one radio head for a future time window. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Zhou 338’s teaching for providing stability and safety of the power grid (see paragraph [0003] of Zhou).
Regarding Claims 4 and 23, Zhou 338’ further discloses wherein the decision
comprises one of the following for the future time window (i) deliver power from the local battery to the at least one radio head during the future time window, the future time window comprising a period of peak power consumption at the at least one radio head (see paragraph [0003]), (ii) charge the local battery during the future time window, and (iii) deliver no power from the local battery to the at least one radio head during the future time window.
Regarding Claim 5, Zhou 338’ further discloses wherein the decision is made by the machine learning model based on (i) inputting power data and the local battery and cost data to the machine learning model, and (ii) for the future time window, determining a minimized total cost for delivery of power to the at least one radio head when constrained by a plurality of constraints for the future time window (see paragraphs [0055]-[0056]).
Regarding Claim 10, Zhou 338’ further discloses wherein the outputting the decision comprises outputting the decision to control the delivery of power to the at least one radio head based on the decision (see paragraphs [0071]-[0072]).
Regarding Claim 13, Zhou 338’ further discloses wherein the computing device is located at one of proximate the local battery and a cloud-based location (see paragraph [0008]).
Claim(s) 2, 15 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou 338’ in view of Li et al. (Li), U.S. Publication No. 2019/0041937.
Regarding Claims 2, 15 and 21, Zhou 338’ discloses the method, computing
device and computer program product as described above. Zhou 338’ fails to disclose wherein the output power data statistics comprise at least (i) an average power demand of the at least one radio head over the set of past time windows, and (ii) a peak power demand of the at least one radio head over the set of past time windows. Li discloses wherein the output power data statistics comprise at least (i) an average power demand of the at least one radio head over the set of past time windows (see paragraph [0033]), and (ii) a peak power demand of the at least one radio head over the set of past time windows (see paragraph [0033]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Li’s invention with Zhou 338’s invention for providing sufficient power (see paragraph [0003] of Li).
Claim(s) 3, 9 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou 338’ in view of Zhou et al. (Zhou 646’), U.S. Publication No. 2017/0337646.
Regarding Claims 3 and 22, Zhou 338’ discloses the method and computing
device as described above. Zhou 338’ fails to disclose wherein the time and location dependent cost data comprises a first cost for charging the local battery in the future time window, and a second cost for using power from a power grid for an average power demand at the at least one radio head in the future time window. Zhou 646’ discloses wherein the time and location dependent cost data comprises a first cost for charging the local battery in the future time window (see paragraph [0059]), and a second cost for using power from a power grid for an average power demand at the at least one radio head in the future time window (see paragraph [0059]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Zhou 646’s invention with Zhou 338’s invention to minimize the operation and maintenance cost, and the emission cost of the whole microgrid (see paragraph [0003] of Zhou 646’).
Regarding Claim 9, Zhou 338’ discloses the method as described above. Zhou 338’ fails to disclose wherein the at least one radio head comprises a plurality of radio heads, and further comprising: dividing the defined time period into the set of time windows; and determining the decision per radio head per time window in the set of time windows. Zhou 646’ discloses wherein the at least one radio head comprises a plurality of radio heads, and further comprising: dividing the defined time period into the set of time windows; and determining the decision per radio head per time window in the set of time windows (see paragraph [0060]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Zhou 646’s invention with Zhou 338’s invention to minimize the operation and maintenance cost, and the emission cost of the whole microgrid (see paragraph [0003] of Zhou 646’).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou 338’ in view of Roy et al. (Roy), U.S. Patent No. 11,270,243.
Regarding Claim 11, Zhou 338’ discloses the method as described above.
Zhou 338’ fails to disclose wherein: the output power data statistics is offline data; and the determining and the outputting are performed during training of the machine learning model using the offline data. Roy discloses wherein: the output power data statistics is offline data, and the determining and the outputting are performed during training of the machine learning model using the offline data (see col. 22, lines 4-26). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Roy’s invention with Zhou 338’s invention to support optimum energy usage, risk mitigation, and grid fortification (see abstract of Roy).
Regarding Claim 12, Zhou 338’ discloses the method as described above.
Zhou 338’ fails to disclose wherein: the output power data statistics is online data; the machine learning model is deployed in the communication system; and the determining and the outputting are performed by the deployed machine learning model. Roy discloses wherein: the output power data statistics is online data; the machine learning model is deployed in the communication system; and the determining and the outputting are performed by the deployed machine learning model (see col. 22, lines 4-26). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to consider Roy’s invention with Zhou 338’s invention to support optimum energy usage, risk mitigation, and grid fortification (see abstract of Roy).
Allowable Subject Matter
Claims 6-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for 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 SHANTELL LAKETA HEIBER whose telephone number is (571)272-0886. The examiner can normally be reached on M-F from 9am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy, can be reached at telephone number (571)272-0886. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANTELL L HEIBER/Primary Examiner, Art Unit 2645
September 23, 2026
