CTNF 18/708,366 CTNF 81709 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-20-aia AIA 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. 07-21-aia AIA 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 the power data and the 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]) . 07-21-aia AIA 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) . 07-21-aia AIA 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’) . 07-21-aia AIA 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 power data 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 power data 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 power data 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 power data 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 12-151-08 AIA 07-43 12-51-08 Claim s 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 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 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /SHANTELL L HEIBER/Primary Examiner, Art Unit 2645 May 7, 2026 Application/Control Number: 18/708,366 Page 2 Art Unit: 2645 Application/Control Number: 18/708,366 Page 3 Art Unit: 2645 Application/Control Number: 18/708,366 Page 4 Art Unit: 2645 Application/Control Number: 18/708,366 Page 5 Art Unit: 2645 Application/Control Number: 18/708,366 Page 6 Art Unit: 2645 Application/Control Number: 18/708,366 Page 7 Art Unit: 2645 Application/Control Number: 18/708,366 Page 8 Art Unit: 2645 Application/Control Number: 18/708,366 Page 9 Art Unit: 2645 Application/Control Number: 18/708,366 Page 10 Art Unit: 2645 Application/Control Number: 18/708,366 Page 11 Art Unit: 2645 Application/Control Number: 18/708,366 Page 12 Art Unit: 2645 Application/Control Number: 18/708,366 Page 13 Art Unit: 2645
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