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 Amendment
Applicant’s amendment filed on December 13, 2025 amends claims 1, 6, 9, 14, 17, and adds new claims 18-23. Claims 1-23 are pending.
Response to Arguments
Applicant's arguments filed on December 13, 2025 regarding the newly presented claim limitations have been fully considered and are unpersuasive and/or moot as shown in the rejections that follow. The newly presented limitations, which necessitate a new ground(s) of rejection, are taught by the combination of Liu and Kunz as shown in the rejections that follow.
In the Remarks regarding the rejections under 35 U.S.C. 103, Applicant characterizes Liu and the claimed invention by alleging that: “The Applicant is of the opinion that Liu is in a different field from the claimed invention. Liu discloses a method for forecasting electric vehicle charging load on a power system, especially the distribution network. In other words, Liu uses a statistical approach to answer a question: What kind of load is it expected on the power system (power distribution network) on a specific day? In other words, the forecasted load represents a total load on the power distribution network that is caused by charging of any electric vehicles during the forecasted day. Liu utilizes normalizing data related to a historical date and clustering the normalized data. The clustering is performed for constructing similar daily load set of a date to be forecast.” In response, the Examiner notes that the embodiments disclosed by Liu support a teaching of one or more limitations recited in the claimed invention. Examiner notes that the combination of Liu’s clustering model in with Kunz’ use of a plurality of configuration parameters of a plurality of charging stations may be used to perform operation scheduling of power transmission and facilitate the forecasting of the electric charging load events throughout the distribution network including the charge stations.
Applicant further argues that Liu is different from the claimed invention because Liu forecasts a charging load on a power system while the claimed invention forecasts a charging time for an individual charging event. Applicant alleges that: The claimed invention thus looks for an answer to a totally different question: How long will it take to fully charge the battery of the electric vehicle that is currently coupled to the charging dispenser?” Therefore, Liu also fails to disclose or suggest: "applying the charging time forecasting model on a charging event of an electric vehicle comprised in the selected electric vehicle cluster" as recited in claim 1, and "a charging station configured to apply the charging time forecasting model on a charging event of an electric vehicle comprised in the selected electric vehicle cluster" as recited in claim 9.”
Examiner disagrees. Examiner notes that Liu in combination with Kunz teaches what is recited in each of claims 1 and 9. Examiner directs the Applicant to Liu, at [0003], for example, which discloses that electric vehicle charging load forecasting is the foundation of improving the power grid regulation and control ability, as well as carrying out orderly charging and discharging. Examiner notes that Liu (see [0005], [0014], and [0057], for example) further discloses that accurately forecasting electric vehicle charging load by way of utilizing a clustering model which facilitates the deployment of the power supply and demand, realizes the effective power supply, lays a foundation for a rational planning and operation of the power grid, and provides decision-making basis for the planning management and operation scheduling of the power transmission and distribution network; the present disclosure may adjust and promote the implementation of demand response and the calling of load-side resources. Examiner notes that load-side resources correspond to electrical charging stations that provide the demand response that satisfy the electrical load demand requirements of electric vehicles that are charging at the charging stations. Furthermore, see Liu at [0095] which discloses that yi, is the actual load value at time i,
y
^
I, is the forecast load value at time i, and n is the number of times. Examiner notes that operation scheduling as well as the adjustment and implementation of a demand response and the calling of load-side resources, for example, corresponds to the application of Liu’s clustering model to one or more charging events associated with electric vehicles comprised in a selected electric vehicle cluster. Examiner notes that the operation scheduling, adjustment, and implementation of a demand response ultimately comprises or includes a forecasting of the individual charging events as denoted by the load values at charging times indicated by the variable, i. Kunz discloses that a configuration data set with a plurality of configuration parameters is stored in a charging station and based on this fixed configuration, the charging control module controls each charging process. Thus, the combination of Liu and Kunz teaches the amended claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1, at the first clause, recites “wherein the charging event data is received from a plurality of charge stations, and wherein the charging event data comprises at least one of operating conditions of the charging station, configuration of the charging station, and temperatures of components of the charging station”. It is unclear what “the charging station” is referring to as there is no prior antecedent basis of a previous recitation of a charging station. In addition, since the foregoing clause recites a plurality of charge stations, it is further unclear which of the plurality of charge stations the charging station is referring to. It is further unclear whether the term, charge station(s), is supposed to be the same as the term, charging station(s). Examiner recommends changing all instances of “charge station(s)” to “charging station(s)” or vice-versa. There are a number of antecedent basis issues which require corrections. Based on the foregoing issues, it is unclear what the claim is directed to.
Each of claims 2-8 and 18-20 depend on claim 1. Since claims 2-8 and 18-20 fail to resolve the deficiencies of claim 1, they are also rejected under 35 U.S.C. 112(b), second paragraph for the same reasons as stated above.
Claim 9, at the first clause, recites “to receive charging event data from a plurality of charge stations over a data communication network, the charging event data comprising at least one of operating conditions of the charging station, configuration of the charging station, and temperature of components of the charging station,”. It is unclear what “the charging station” is referring to as there is no prior antecedent basis of a previous recitation of a charging station. In addition, since the foregoing clause recites a plurality of charge stations, it is further unclear which of the plurality of charge stations the charging station is referring to. Examiner recommends changing all instances of “charge station(s)” to “charging station(s)” or vice-versa. Furthermore, regarding claim 9, the preamble recites a plurality of charging stations while the first clause recites a plurality of charge stations. There is an antecedent basis issue which requires correction as it is unclear whether “a plurality of charge stations” is referring to “a plurality of charging stations” or another set of charge or charging stations. It is further unclear whether the term, charge station(s), is supposed to be the same as the term, charging station(s). There are a number of antecedent basis issues which require corrections. Based on the foregoing issues, it is unclear what the claim is directed to.
Each of claims 10-16 and 21-23 depend on claim 9. Since claims 10-16 and 21-23 fail to resolve the deficiencies of claim 9, they are also rejected under 35 U.S.C. 112(b), second paragraph for the same reasons as stated above.
Claim 18 recites “providing the selected charging time forecasting model for the plurality of charging stations over the data communication network, and applying, by a charging station, the charging time forecasting model …”. It is unclear whether a charging station is referring to the plurality of charging stations or another charging station. If “a charging station” is referring to “the plurality of charging stations”, Examiner suggests Applicant to recite “providing the selected charging time forecasting model for the plurality of charging stations over the data communication network, and applying, by a charging station of the plurality of charging stations, the charging time forecasting model …”.
Claim 21 recites “to provide the charging time forecasting model to the plurality of charging stations over the data communication network, and wherein the charging station is configured to: apply the charging time forecasting model …”. It is unclear whether the charging station is referring to the plurality of charging stations. If “the charging station” is referring to “the plurality of charging stations”, Examiner suggests Applicant to recite “to provide the charging time forecasting model to the plurality of charging stations over the data communication network, and wherein the charging station of the plurality of charging stations is configured to: apply the charging time forecasting model …”.
Appropriate amendments are required to correct the foregoing issues. Applicant is requested to provide support from the specification for any amendments made. No new matter should be added. Due to the number of antecedent basis issues, the Applicant is requested to review the claims to obviate the presence of additional antecedent basis issues.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 2022/0101097) in view of Kunz et al. (US 2021/0300200).
Regarding claim 1, Liu teaches a method of forecasting charging time of electric vehicles, comprising: (see Liu at the Abstract which disclose that the present disclosure relates to a method for clustering forecasting of the electric vehicle charging load, comprising the following steps: collecting electric vehicle charging load data on a historical date and weather information data related to that historical date; preprocessing and then normalizing the collected data to obtain a new data set; performing fuzzy C-means clustering on the normalized data, and taking an actual load measurement point as a fuzzy clustering index to construct a similar daily load set of the date to be forecast; according to the similar daily load set, constructing and training a least-square SVM (support vector machine) forecasting model; inputting load values at the same time in three days ahead of the date to be forecast and the weather information data related to the three days into the trained least-square SVM forecasting model, and outputting a forecast load; see Liu at [0002] which discloses that the present disclosure relates to the technical field of automatic control of power systems, and particularly to a method and a device for clustering forecasting of electric vehicle charging load. Examiner notes that forecasting of the electric vehicle charging load corresponds to forecasting the charging time of electric vehicles. See Liu at [0003] which discloses that with the large-scale grid-connected operation of electric vehicles, the impact due to the growth of electric vehicle charging load on the power system, especially the distribution network, has become increasingly prominent, that on one hand, the change of electric vehicle charging load leads to the fluctuation of the line load rate and the decrease of power supply reliability, thus increasing the difficulty of the distribution network upgrading and reconstruction, and that it is urgent to accurately forecast the electric vehicle charging load. Examiner notes that the charging time may be determined from the electrical charging load, which is calculated by way of multiplying an electric charger’s power rating by the expected number of charging hours (i.e., charging time.)).
receiving charging event data over a data communication network, wherein the charging event data is received [from a plurality of charge stations, and wherein the charging event data comprises at least one of operating conditions of the charging station, configuration of the charging station, and temperatures of components of the charging station] (see Liu at [0008] and [0067] which discloses collecting electric vehicle charging load data on a historical date and weather information data related to that historical date; see Liu at [0057] which discloses planning management and operation scheduling performed using a distribution network. Examiner maps collecting to receiving.)
filtering received charging event data, wherein the filtering is configured to discard at least one piece of the charging event data or disable use of at least one piece of the charging event data, (see Liu at [0038] which discloses that preprocessing of the collected data includes: filling up missing data and correcting abnormal data. Examiner notes that the correcting of abnormal data corresponds to disabling the use of uncorrected data or the at least one piece of charging event data.)
processing the remaining charging event data after said filtering to generate a data collection comprising sample data, (see Liu at [0048] which discloses a data processing module, used for preprocessing and then normalizing the collected data to obtain a new data set. Examiner maps normalizing the collected data to processing the remaining charging event data.)
obtaining from the data collection a data cluster representing sample data associated with a selected electric vehicle cluster, (see Liu at [0049] which discloses an acquisition module of similar daily load set of the date to be forecast, used for performing fuzzy C-means clustering on the normalized data; see Liu at [0055], for example, which discloses one or more processing units execute the method for clustering forecasting of the electrical vehicle charging load; see Liu at [0057] which discloses that the present disclosure takes into consideration the factors affecting the charging load, and adopts the forecasting model based on clustering and LS-SVM (least-square support vector machine forecasting model) to effectively improve the accuracy of the forecasting of the electric vehicle charging load.)
generating a charging time forecasting model on basis of the obtained data cluster, wherein the charging time forecasting model is generated by a machine learning method comprising: (see Liu at [0014] which disclose that a fuzzy C-means clustering model is constructed, and the measured data points of a daily load curve are taken as the characteristic quantity for fuzzy clustering; see Liu at [0018] which discloses that a method of solving the fuzzy C-means clustering model by the alternating optimization strategy; see Liu at [0036] which further discloses that in the formula, yi is the actual load value at time i,
y
^
I, is the forecast load value at time i, and n is the number of times. Examiner notes that the fuzzy C-means clustering model corresponds to a time forecasting model generated by a machine learning method.)
training a plurality of charging time forecasting models based on a first portion of the data cluster, (see Liu at [0012] which discloses inputting load values at the same time in three days ahead of the date to be forecast and the weather information data related to the three days into the trained least-square SVM forecasting model, and outputting a forecast load; see Liu at [0024] which discloses constructing and training a least-square SVM (support vector machine) forecasting model. Examiner notes that data associated with load values at the same time in three days ahead of the date to be forecast and the weather information data related to the three days corresponds to a first portion of the data cluster.)
selecting the best performing charging time forecasting model among the plurality of trained charging time forecasting models based on an evaluation performed on basis of a second portion of the data cluster, and (see Liu at [0049] which discloses that an acquisition module of similar daily load set of the date to be forecast, used for performing fuzzy C-means clustering on the normalized data, and taking an actual load measurement point as a fuzzy clustering index to construct a similar daily load set of the date to be forecast; see Liu at [0083] which discloses constructing and training; see Liu at [0092-0093] which further discloses that according to the obtained similar daily load set of the date to be forecast, constructing and training a least-square SVM (support vector machine) forecasting model: The similar daily load set and related weather information data are taken as the input variables for the least square SVM model to obtain the forecasting data as the output variable, and the forecasting data is compared with the actual data to calculate the forecasting error; the training ends if the error MAPE (i.e., mean absolute percentage error) is less than a threshold value; otherwise the parameters are corrected and the process returns to fuzzy C-means clustering again, so as to retrain the forecasting model of the least square SVM and continuously optimize the forecasting model. Examiner notes that an evaluation of the forecasting model is performed on a daily basis using actual load measurements to obtain a daily load set to optimize or allow selection of the best performing model corresponds to selecting the best performing charging time forecasting model among the plurality of trained charging time forecasting models based on an evaluation performed on the basis of the second portion of the data cluster. Examiner has shown a teaching based on a broadest reasonable interpretation of the claimed language.)
testing the selected charging time forecasting model based on a third portion of the data cluster, and (see Liu at [0099] which discloses that in the formula Yn+j, yn, yn+1 are loads at time points n+j, n, and n+1, respectively; see Liu, at [0117] in conjunction with Table 1, which discloses that in order to compare the effectiveness of the forecasting model, the load data of non-working days in April acts as the test set firstly, and then a forecasting result comparison is performed between a BP neural network and an LS-SVM model. Examiner notes that different loads at different time points n+j correspond to the use of different portions of the data cluster.)
applying the charging time forecasting model on a charging event of an electric vehicle comprised in the selected electric vehicle cluster, (see Liu at the Abstract, for example, which discloses collecting electric vehicle charging load data on a historical date and weather information data related to that historical date; preprocessing and then normalizing the collected data to obtain a new data set; performing fuzzy C-means clustering on the normalized data, and taking an actual load measurement point as a fuzzy clustering index to construct a similar daily load set of the date to be forecast; according to the similar daily load set, constructing and training a least-square SVM (support vector machine) forecasting model; inputting load values at the same time in three days ahead of the date to be forecast and the weather information data related to the three days into the trained least-square SVM forecasting model, and outputting a forecast load. Also, see Liu at [0005], [0014], and [0057], for example, which further discloses that accurately forecasting electric vehicle charging load by way of utilizing a clustering model which facilitates the deployment of the power supply and demand, realizes the effective power supply, lays a foundation for a rational planning and operation of the power grid, and provides decision-making basis for the planning management and operation scheduling of the power transmission and distribution network and that the present disclosure may adjust and promote the implementation of demand response and the calling of load-side resources. Furthermore, see Liu at [0095] which discloses that yi, is the actual load value at time i, y ̂i, is the forecast load value at time i, and n is the number of times. Examiner notes that operation scheduling as well as the adjustment and implementation of a demand response and the calling of load-side resources, for example, corresponds to the application of Liu’s clustering model to one or more charging events associated with electric vehicles comprised in a selected electric vehicle cluster. Examiner notes that the operation scheduling, adjustment, and implementation of a demand response ultimately comprises or includes a forecasting of the individual charging events as denoted by the load values at charging times indicated by the variable, i.
Liu does not expressly disclose from a plurality of charge stations, and wherein the charging event data comprises at least one of operating conditions of the charging station, configuration of the charging station, and temperatures of components of the charging station which in a related art Kunz teaches (see Kunz at [0005] which discloses that a configuration data set with a plurality of configuration parameters is stored in the charging station and based on this fixed configuration, the charging control module controls each charging process. Also, see Kunz at [0018-0020] which discloses that upon receipt of a configuration request message from a charging station, the multiplex module checks a time specification of the configuration request message, i.e., a time specification associated with the configuration request message, and evaluates the time specification and the timing dependency of the stored charging configuration data sets and that the charging control module of the charging station controls the charging process with the electric vehicle according to the at least one charging configuration parameter of the received configuration data set, as previously described. Also, see Kunz at Fig. 1, for example, which illustratively depicts a plurality of charging stations 104.1, 104.2. Examiner notes that a time specification associated with a configuration message provided by a charging station corresponds to a configuration of the charging station.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu to include receiving from a plurality of charge stations, and wherein the charging event data comprises at least one of operating conditions of the charging station, configuration of the charging station, and temperatures of components of the charging station, as taught by Kunz.
One would have been motivated to make such a modification to control the charging process with the electric vehicle according to the at least one charging configuration parameter, as suggested by Kunz at [0020].
Regarding claim 2, the modified Liu teaches the method according to claim 1, wherein the step of processing comprises obtaining samples of the filtered charging event data, and- pre-processing the obtained samples, wherein the pre-processing comprises at least one of: removing effects of dynamical changes in the obtained samples caused by parallel charging events, and enhancing samples in the data collection by adding therein enhancement data determined based on data comprised in the obtained samples (see Liu at [0074] which discloses that n is the number of samples; see Liu at [0097-0098] which discloses that preprocessing of the collected data includes: filling up missing data and correcting abnormal data, the method of which includes using a linear interpolation method to process the missing data including adopting a horizontal processing method to identify and correct abnormal data. Examiner maps one or more of the foregoing methods to enhancing samples in the data collection by adding therein enhancement data based on data comprised in the obtained samples.)
Regarding claim 3, the modified Liu teaches the method according to claim 1, wherein the filtering received charging event data comprises discarding or disabling data based on: determining that charging apparatus configuration associated with a piece of charging event data is not known, discarding overrepresented charging event data such that resulting charging event data represents charging events evenly spread over the year, discarding charging event data associated with preselected charging station power configurations, and/or determining that the charging event data is associated to a charging event involving amount of charging energy and/or charging time that is below a predetermined threshold (Examiner notes that Applicant has used the phrase “and/or” in the instant claim. The Patent Trial and Appeal Board (PTAB) has held that use of the phrase “and/or” within a claim is not indefinite. According to the PTAB, “and/or” is not wrong, but it’s not preferred verbiage (see Ex Parte Gross, Appeal No. 2011-004811). Nevertheless, during patent examination, the pending claims must be given their broadest reasonable interpretation (BRI) consistent with the specification (see MPEP § 2111; Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005)). Based upon this guidance from the MPEP and the Federal Circuit Court of Appeals, the Examiner interprets the phrase “and/or” under its broadest reasonable interpretation of “or” for purposes of examination of the instant Application. See Kunz at [0005] which discloses that a configuration data set with a plurality of configuration parameters is stored in the charging station and based on this fixed configuration, the charging control module controls each charging process; see Liu at [0038] which discloses that preprocessing of the collected data includes filling up missing data and correcting abnormal data. Examiner notes that being able to fill up missing data requires determining that charging apparatus configuration associated with a piece of charging event data is not known.)
Regarding claim 4, the modified Liu teaches the method according to claim 1, wherein the steps of obtaining the data cluster and generating the charging time forecasting model are repeated for a plurality of different data clusters and a charging time forecasting model for each of the plurality of different data clusters (see Liu at [0022-0023], for example, which discloses that the iteration ends, or otherwise returns to calculate a new clustering center for continuing the iteration, and the sample category is determined according to the principle of maximum subordination. Examiner maps iteration to wherein the steps of obtaining the data cluster and generating the charging time forecasting model are repeated. Examiner notes that calculating a new clustering center for continuing the iteration corresponds to repeating the iteration for plurality of different data clusters and a charging time forecasting model for each of the plurality of different data clusters.)
Regarding claim 5, the modified Liu teaches the method according to claim 1, wherein an obtained data cluster is rejected from being used for charging time forecasting model generation if sample data therein is determined to be unreliable (see Liu at [0034] which discloses that the similar daily load set and related weather information data are taken as the input variables for the least square SVM model to obtain the forecasting data as the output variable, and the forecasting data is compared with the actual data to calculate the forecasting error; the training ends if the error MAPE is less than a threshold value; otherwise the parameters are corrected and the process returns to fuzzy C-means clustering again, so as to retrain the forecasting model of the least square SVM and continuously optimize the forecasting model. Examiner notes that the comparison of forecasted data with the actual data to calculate a forecasting error and ending training if the error MAPE (mean absolute percentage error) is less than a threshold value corresponds to rejecting the forecasted data and continuing the training to obtain better forecasted data until the error MAPE is less than the threshold value. The foregoing teaches that the sample data therein is determined to be unreliable until the MAPE is less than the threshold value.)
Regarding claim 6, the modified Liu teaches the method according to claim 1, wherein the obtained data cluster is filtered to remove one or more samples from the data cluster that are determined to deviate significantly from the majority of samples in the data cluster (see Liu at [0100 - 0101] which discloses adopting a horizontal processing method to identify and correct abnormal data and that in the formula, y(d,t) and y(d,t-1) are load values at the times t and t-1 on the dth day, respectively, and Θ1 and Θ2 are the threshold values; Θ1 and Θ2 reflect the change of load, which may be selected manually according to historical experience. Examiner notes that identifying and correcting abnormal data and manually selecting the load corresponds to wherein the obtained data cluster is filtered to remove one or more samples from the data cluster that are determined to deviate significantly from the majority of samples in the data cluster.)
Regarding claim 7, the modified Liu teaches the method according to claim 1, wherein the plurality of charging time forecasting models are trained using a regression-based machine learning method such as one of linear regression, ridge regression, neural network regression, lasso regression, random forest, KNN model, support vector machines (SVM), gaussian regression, polynomial regression and decision tree regression such as Gradient Boosting Decision Tree (GBDT) (see Liu at [0024-0025] which discloses that according to the obtained similar daily load set of the date to be forecast, constructing and training a least-square SVM (support vector machine) forecasting model in which the regression estimation function is:
PNG
media_image1.png
42
142
media_image1.png
Greyscale
.
Regarding claim 8, the modified Liu teaches the method according to claim 1, wherein results of the testing the selected charging time forecasting model are stored along with the respective charging time forecasting model for tracking performance of the charging time forecasting model over time (see Liu at [0117] in conjunction with Table 1 which discloses the load data of non-working days in April which acts as the test set; see Liu at [0118-0122] which discloses forecasting the electrical vehicle charging load on April 25th and 26th respectively using three scenarios; see Kunz at [0012] which discloses that as used herein, an electric vehicle means a vehicle that is at least partially electrically operable and comprises a rechargeable electrical storage device; see Kunz at [0016] which discloses that in the configuration memory module a plurality of time dependent configuration data sets is stored indirectly and/or directly.)
Regarding claim 18, the modified Liu teaches the method according to claim 1, further comprising: providing the selected charging time forecasting model for the plurality of charging stations over the data communication network, (see Liu at [0092-0093] which further discloses that according to the obtained similar daily load set of the date to be forecast, constructing and training a least-square SVM (support vector machine) forecasting model: The similar daily load set and related weather information data are taken as the input variables for the least square SVM model to obtain the forecasting data as the output variable, and the forecasting data is compared with the actual data to calculate the forecasting error; the training ends if the error MAPE (i.e., mean absolute percentage error) is less than a threshold value; otherwise the parameters are corrected and the process returns to fuzzy C-means clustering again, so as to retrain the forecasting model of the least square SVM and continuously optimize the forecasting model. Examiner notes that continuously optimizing the forecasting model allows for selecting a charging time forecasting model for the plurality of charging stations over the distribution or data communication network.)
The modified Liu teaches an electric vehicle comprised in the selected electric vehicle cluster (see Liu at [0002] which discloses that the present disclosure relates to the technical field of automatic control of power systems, and particularly to a method and a device for clustering forecasting of electric vehicle charging load; see Liu at [0049] which discloses an acquisition module of similar daily load set of the date to be forecast, used for performing fuzzy C-means clustering on the normalized data; see Liu at [0055], for example, which discloses one or more processing units execute the method for clustering forecasting of the electrical vehicle charging load.)
Claims 9-16 are directed toward a system that performs the steps recited in methods of claims 1-8. The cited portions of the reference(s) used in the rejections of claims 1-8 teach the steps recited in the systems of claims 9-16. Therefore, claims 9-16 are rejected under the same rationale used in the rejections of claims 1-9.
Claim 17 depends on claim 1. Therefore claim 17 is rejected based on its dependency and under the same rationale used in the rejection of claim 1.
Claims 18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 2022/0101097) in view of Kunz et al. (US 2021/0300200) and further in view of Shi et al. (CN 106447129 A) (English translation attached).
The modified Liu does not expressly disclose and applying, by a charging station, the charging time forecasting model to provide, in real time, a charging time forecast during a charging event of an electric vehicle comprised in the selected electric vehicle cluster which in a related art Shi teaches (see Shi at the Abstract which discloses a real-time prediction server in real time for calculating the prediction queue of each charging time and that a real-time prediction server sends the user intelligent terminal of each charging station [a] forecast time of starting charging and the waiting time of prediction is returned.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu to include applying, by a charging station, the charging time forecasting model to provide, in real time, a charging time forecast during a charging event of an electric vehicle comprised in the selected electric vehicle cluster, as taught by Shi.
One would have been motivated to make such a modification to save the time of driver and improve the usage efficiency, as suggested by Shi at the Abstract.
Claim 21 is directed toward a system that performs the steps recited in method of claim 18. The cited portions of the reference(s) used in the rejection of claim 18 teach the steps recited in the system of claim 21. Therefore, claim 21 is rejected under the same rationale used in the rejection of claim 18.
Claims 19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 2022/0101097) in view of Kunz et al. (US 2021/0300200) and further in view of Schriever (DE 102020205865 A1) (English translation attached).
Regarding claim 19, the modified Liu does not expressly disclose the method according to claim 1, further comprising: estimating energy consumption by the electric vehicle during the charging event to improve reliability of the charging time forecasting which in a related art, Schriever teaches (see Schriever at the Abstract which discloses that: The invention relates to a driver assistance system for a battery-powered motor vehicle as an information display (4). According to the invention, the driver assistance system has the following: a vehicle-side measuring device (5) for determining a usable energy charged in the battery system (2) during a current charging process as electrochemically stored energy and for outputting an assigned useful energy signal value, a vehicle-side measuring device (8) for determining an electrical energy fed in from a charging station (1) during the current charging process and for outputting an assigned charging energy signal value, a computing unit (7) to which the useful energy signal value and the assigned charging energy signal value are fed and which is obtained by dividing the useful energy - Signal value a charging efficiency (η) is calculated by the charging energy signal value and/or a charging loss energy (LVE) is calculated by subtracting the useful energy signal value from the charging energy signal value, and a vehicle monitor (11) on which the calculated value of the charging efficiency (η) and/or the value of the energy loss (LVE) are displayed. Examiner notes that determining the charging efficiency (η) and/or charging loss energy (LVE) corresponds to estimating energy consumption by the electric vehicle during the charging event to improve reliability of the charging time forecasting. For example, the value of the charging efficiency may be used to improve the reliability of the charging time forecasting. Examiner has shown a teaching based on a broadest reasonable interpretation in light of the specification (see the published patent application (US 2025/0128635) at [0044] which discloses improving charging efficiency and/or determining loss of energy, for example.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu to include estimating energy consumption by the electric vehicle during the charging event to improve reliability of the charging time forecasting, as taught by Schriever.
One would have been motivated to make such a modification to monitor charging efficiency or energy loss, as suggested by Schriever at the Abstract.
Claim 22 is directed toward a system that performs the steps recited in method of claim 19. The cited portions of the reference(s) used in the rejection of claim 19 teach the steps recited in the system of claim 22. Therefore, claim 22 is rejected under the same rationale used in the rejection of claim 19.
Subject Matter Not Taught by Art of Record
Examiner notes that the prior art of record does not appear to teach each and every feature of claim 20 and claim 23.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROY RHEE whose telephone number is 313-446-6593. The examiner can normally be reached M-F 8:30 am to 5:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant may contact the Examiner via telephone or 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, Kito Robinson, can be reached on 571-270-3921. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, one may visit: https://patentcenter.uspto.gov. In addition, more information about Patent Center may be found at https://www.uspto.gov/patents/apply/patent-center. Should you have questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ROY RHEE/Examiner, Art Unit 3664