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 January 27, 2026 amends claims 1, 9-10, 12, and 19. Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed on January 27, 2026 regarding the newly presented claim limitations have been fully considered and are moot as shown in the rejections that follow. The amended independent claims, which necessitate a new ground of rejection, are taught by Heino and newly cited reference, Xia et al. (CN 114928077 A), as shown in detail in the rejections that follow.
Claim Objections
Claims 9-10 are objected to because of the following informalities: The words “consisting a dividend and a divisor” should be changed to “consisting of a dividend and a divisor” to correct typographical or grammatical errors. Appropriate corrections are required.
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, 5, 7-8, 12, 16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Heino et al. (US 2025/0128635) in view of Xia et al. (CN 114928077 A) (English language translation attached).
Regarding claim 1, Heino teaches an apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to: receive electric vehicle charging point (EVCP) data associated with a plurality of EVCPs; (see Heino at [0021] which discloses that the computer device or computer system is configured to receive charging event data from a plurality of charge stations over a data communication network; see Heino at [0078] which discloses that as used herein, a computer device refers to any electronic device comprising a processor, such as a general purpose central processing unit (CPU), a specific purpose processor or a microcontroller; see Heino at [0079] which discloses that the systems and methods described herein may be embodied by a computer program or a plurality of computer programs, which may exist in a variety of forms, both active and inactive, in a single computer system or across multiple computer systems, that for example, they may exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats for performing some of the steps, and that any of the above may be embodied on a computer readable medium, which include storage devices and signals, in compressed or uncompressed
form.)
determine one or more clustering features for each of the plurality of EVCPs based on the EVCP data, wherein the one or more clustering features comprises a duration parameter, a predefined status parameter, a charging gap parameter, or a combination thereof; (see Heino at [0031] which discloses that the present invention has the advantage that it provides more reliable charging time estimates and that reliable forecasting of charging event duration improves user experience by enabling the client to better plan his/her schedules, when either duration of the charging event or queuing time to access a crowded charging station is known in advance; see Heino at [0044] which discloses that some exemplary factors that are known to reduce the reliability of charging time forecasting are energy consumed by the electric vehicle during the charging event, operating condition of the battery of the electric vehicle and available charging power from the charging station, that during the charging event, the electric vehicle may consume energy for example on warming or cooling, among other things, that some electric vehicles warm or cool the battery for improving charging efficiency, which on the other hand consumes energy, that need for warming or cooling the battery depends at least partly on ambient temperature, and that likewise, using any other electrical equipment of the vehicle, such as air conditioning, lights, entertainment, or navigation during the charging event affects duration of the charging event. Examiner maps any one of the exemplary factors such as energy consumed by the electric vehicle during the charging event, operating condition of the battery of the electric vehicle, available charging power from the charging station, ambient temperature, and the use of other electric equipment of the vehicle such as air conditioning, lights, entertainment, or navigation during the charging event, to the duration parameter.)
generate, using a first model, one or more clusters from among the plurality of EVCPs based on the one or more clustering features, wherein each of the one or more clusters comprises at least one of the plurality of EVCPs; (see Heino at [0039] which discloses that charging stations 120 collect charging event data on charging events; see Heino at [0041] which discloses that data obtained from the data collection is clustered; see Heino at [0065] in conjunction with Fig. 4 which discloses that sample data of the data collection is clustered and filtered in the step 41 and that by clustering we mean that the sample data is divided into a plurality of data clusters, each data cluster comprising sample data concerning a determined electric vehicle cluster and thus, only samples in the data collection that are relevant for a selected vehicle cluster are used; see Heino at [0066] which further discloses that data in a data cluster may be filtered by removing one or more samples from the cluster, if found to deviate significantly from other samples in the same data cluster, and that this may concern for example a sample that indicates a clearly higher or lower charging energy, charging power, charging voltage and/or state of charge (SoC) than any other samples in the cluster; Examiner notes that the preceding examples correspond to clustering features. Furthermore, see Heino at [0067-0068] in conjunction with Fig. 4 which discloses generating data clusters, such as training data 42, validation data 43, and test data 44. Examiner notes that the clustering of data (i.e., at step 41) to generate training data 42, validation data 43, and test data 44 collectively corresponds to generating, using a first model, one or more clusters based on the one or more clustering features.)
and train a second model to predict availability data associated with each EVCP within each of the one or more clusters based on a data point associated with one or more EVCPs within said cluster (see Heino at [0070] which disclose that the training data 42 is used for training a plurality of forecasting models; see Heino at [0073] which discloses that the test data 44 is used in the step 48 to test performance of the selected forecasting model, and values or labels generated by the forecasting model are stored to enable tracking how performance of the selected charging time forecasting model evolves over time. Examiner maps forecasting model to the second model. Examiner notes that the collected data corresponds to at least one data point associated with one or more EVCPs with said cluster.)
Heino does not expressly disclose wherein the one or more clustering features indicate one or more characteristics of the plurality of EVCPs, which in a related art Xia teaches (see Xia, at the Abstract, which discloses determining the fast charging station cluster constraint according to the charging and discharging condition of each electric vehicle in the quick charging station cluster, and a two-stage target function and operation constraint condition. Examiner notes that either of the fast charging station cluster constraint or the two-stage operation constraint condition corresponds to one or more characteristics of the plurality of EVCPs.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heino to include wherein the one or more clustering features indicate one or more characteristics of the plurality of EVCPs, as taught by Xia.
One would have been motivated to make such a modification to improve the economic benefit of the power grid, as suggested by Xia at page 12.
Regarding claim 5, the modified Heino teaches the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to: receive vehicle information of a vehicle associated with the geographic area, wherein the vehicle information comprises location data and battery charge data; and identify one or more EVCPs from the one or more clusters for the vehicle based on the vehicle information and the availability data (see Heino at [0009] which discloses that patent application CN110010987A discloses a method for predicting residual charging time of electric vehicle based on big data and that battery data uploaded by vehicles is divided randomly to train multiple machine learning models, among which the best prediction model is selected; see Heino at [0044] which discloses that an estimate of the effects of energy consumption by the electric vehicle during the charging event is facilitated by information on total net battery capacity of the EV, which can be obtained from the EV itself or based on vehicle or vehicle cluster identification from a separate, possibly external EV data source; see Heino at [0045] which further discloses that at least one parameter indicating operating conditions of the battery, such as internal temperature of the battery and/or ambient temperature may be included in the charging event data table 20; also see Heino at [0076] which discloses that upon detecting initiation of a new charging event, the charging station identifies the vehicle or a vehicle cluster and uses this information for forecasting charging time for the initiated charging event.)
Regarding claim 7, the modified Heino teaches the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to: generate, using the first model, the one or more clusters based on one or more additional clustering features, wherein the one or more additional clustering features comprise a record parameter, a utilization parameter, or a combination thereof (see Heino at [0044] which discloses that some exemplary factors that are known to reduce the reliability of charging time forecasting are energy consumed by the electric vehicle during the charging event, operating condition of the battery of the electric vehicle and available charging power from the charging station, that during the charging event, the electric vehicle may consume energy for example on warming or cooling, among other things, that some electric vehicles warm or cool the battery for improving charging efficiency, which on the other hand consumes energy, that need for warming or cooling the battery depends at least partly on ambient temperature, and that likewise, using any other electrical equipment of the vehicle, such as air conditioning, lights, entertainment, or navigation during the charging event affects duration of the charging event. Examiner maps any one of the exemplary factors such as energy consumed by the electric vehicle during the charging event to the recited utilization parameter.)
Regarding claim 8, the modified Heino teaches the apparatus of claim 1, wherein the EVCP data comprises geographic area data, weather condition associated with a geographic area, and one or more events associated with the geographic area of each of the plurality of EVCPs (see Heino at [0012] which discloses that models for charging time forecasting, using machine learning utilizes current weather conditions; see Heino at [0043] which discloses charging event data obtained from each charging event preferably comprises a plurality of charging event data arrays, wherein each data array preferably comprises an individual identifier of the ongoing charging event (txID), a time stamp (sampleTime) preferably formatted according to ISO-8601 standard, an identifier of an EV cluster ( clusterld), a target charging level of the battery (targetSoc ), a current charging level of the battery ( currentSoc ), current charging voltage of the battery (voltage), current charging current of the battery (current), the current theoretical maximum charging power of the charging station (evseMaxPower ), amount of energy or power already charged during the ongoing charging event (chargedEnergy ), time lapsed during the ongoing charging event (chargeTime), an estimate of total net battery capacity of the electric vehicle (batteryEstimate). Heino at [0043] further discloses that additional pieces of data that are not shown in the FIG. 2 but may comprise in the data array received from the charging station are for example ambient temperature (ambientTemp), pin temperature (pin Temp), battery temperature (battery Temp) and/or other available pieces of system component temperature information. Pin temperature refers to temperature of pins of a plug that combines to the socket of the EV; also, see Heino at [0044] in conjunction with Fig. 2 which discloses the data table 20 illustratively depicting vehicle cluster identification or ClusterId. Examiner notes that weather data is associated with a geographical location.)
Independent claim 12 recites a method that performs the steps recited in the apparatus of claim 1. The cited portions of the prior art used in the rejection of claim 1 teach the corresponding limitations recited in the method of claim 12. Therefore, claim 12 is rejected for the same reasons as stated for claim 1 above.
Claim 16 recites a method that performs the steps recited in the apparatus of claim 5. The cited portions of the prior art used in the rejection of claim 5 teach the corresponding limitations recited in the method of claim 16. Therefore, claim 16 is rejected for the same reasons as stated for claim 5 above.
Claim 18 recites a method that performs the steps recited in the apparatus of claim 7. The cited portions of the prior art used in the rejection of claim 7 teach the corresponding limitations recited in the method of claim 18. Therefore, claim 18 is rejected for the same reasons as stated for claim 7 above.
Independent claim 19 recites a computer programmable product comprising a non-transitory computer readable medium that performs the steps recited in the apparatus of claim 1. The cited portions of the prior art used in the rejection of claim 1 teach the corresponding limitations recited in the computer programmable product comprising a non-transitory computer readable medium of claim 19. Therefore, claim 19 is rejected for the same reasons as stated for claim 1 above.
Claims 2-4, 9-10, 13-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Heino et al. (US 2025/0128635) in view of Xia et al. (CN 114928077 A) and further in view of Liu et al. (US 2022/0101097).
Regarding claim 2, modified Heino does not expressly disclose the apparatus of claim 1, wherein the computer program code instructions are configured to, when executed, cause the apparatus to: receive ground-truth availability data associated with each of the plurality of EVCPs; determine a confidence score of the second model based on the availability data and the ground-truth availability data; and update, using the first model, the one or more clusters based on the confidence score which in a related art Liu teaches (see Liu at [0093] which discloses that 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; Examiner maps error MAPE (mean absolute percentage error) to confidence score as it represents a confidence value or number. Examiner maps forecasting data to availability data and actual data to ground-truth availability data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heino to include wherein the computer program code instructions are configured to, when executed, cause the apparatus to: receive ground-truth availability data associated with each of the plurality of EVCPs; determine a confidence score of the second model based on the availability data and the ground-truth availability data; and update, using the first model, the one or more clusters based on the confidence score, as taught by Liu.
One would have been motivated to make such a modification to calculate a forecasting error, as suggested by Liu at [0093].
Regarding claim 3, the modified Heino teaches the apparatus of claim 2, wherein, to update the one or more clusters, the computer program code instructions are configured to, when executed, cause the apparatus to: compare the confidence score with a confidence threshold; and responsive to the confidence score failing to satisfy the confidence threshold, generate, using the first model, a plurality of updated clusters by increasing a number of the one or more clusters (see Liu at [0093] which discloses that 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; see Liu at [0094-0095] which discloses the equation for MAPE which is a function of n, wherein n is the number of times. Examiner notes that n, which corresponds to a number, is increased.)
Regarding claim 4, the modified Heino teaches the apparatus of claim 3, wherein the computer program code instructions are configured to, when executed, cause the apparatus to: re-train the second model to predict the availability data associated with each EVCP within each of the plurality of updated clusters based on a data point associated with one or more EVCPs within said updated cluster (see Liu at [0093] which discloses that 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.)
Regarding claim 9, Heino does not expressly disclose the apparatus of claim 1, wherein the duration parameter is a ratio consisting a dividend and a divisor, wherein the dividend is a number of first charging events from among a plurality of second charging events that occurred at each of the plurality of EVCPs, and wherein the divisor is a number of the plurality of second charging events, wherein each of the first charging events is less than a predefined duration which in a related art Liu teaches (see Liu at [0094] which discloses calculation of the mean absolute percentage error MAPE of the forecast data is:
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Examiner notes that the MAPE corresponds to a ratio, where the dividend of the ratio is the difference between the actual load value and the forecasted load value, and the divisor is the actual load value of n events. Examiner notes that n corresponds to the number of the plurality of second charging events. Examiner notes that each event has a predefined duration as it occurs at a time i.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heino to include wherein the duration parameter is a ratio consisting a dividend and a divisor, wherein the dividend is a number of first charging events from among a plurality of second charging events that occurred at each of the plurality of EVCPs, and wherein the divisor is a number of the plurality of second charging events, wherein each of the first charging events is less than a predefined duration, as taught by Liu.
One would have been motivated to make such a modification to calculate a forecasting error, as suggested by Liu at [0093].
Regarding claim 10, Heino does not expressly disclose the apparatus of claim 1, wherein the predefined status parameter is a ratio consisting a dividend and a divisor, wherein the dividend is a number of one or more events that occurred at each of the plurality of EVCPs, and a number of status changes for each of the plurality of EVCPs, wherein each of the one or more events is defined as a status other than a charging status, which in a related art Liu teaches (see Liu at [0094] which discloses calculation of the mean absolute percentage error MAPE of the forecast data is:
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See Liu at [0095] which discloses that n is the number of times. Examiner notes that the MAPE corresponds to a ratio, where the dividend of the ratio is the difference between the actual load value and the forecasted load value, and the divisor is the actual load value of n events. Examiner notes that n (number of times) corresponds to the number of status changes associated with the plurality of second charging events. Examiner notes that each event has a predefined duration as it occurs at a time i. Examiner has shown a teaching based on a broadest reasonable interpretation of the claimed language.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heino to include wherein the predefined status parameter is a ratio consisting a dividend and a divisor, wherein the dividend is a number of one or more events that occurred at each of the plurality of EVCPs, and a number of status changes for each of the plurality of EVCPs, wherein each of the one or more events is defined as a status other than a charging status, as taught by Liu.
One would have been motivated to make such a modification to calculate a forecasting error, as suggested by Liu at [0093].
Claims 13-15 are directed toward a method that performs the steps recited in the apparatus of claims 2-4. The cited portions of the prior art used in the rejections of claims 2-4 teach the corresponding limitations recited in the method of claims 13-15. Therefore, claims 13-15 are rejected for the same reasons as stated for claims 2-4 above.
Claim 20 is directed toward a computer programmable product that performs the steps recited in the apparatus of claim 2. The cited portions of the prior art used in the rejection of claim 2 teach the corresponding limitations recited in the computer programmable product of claim 20. Therefore, claim 20 is rejected for the same reasons as stated for claim 2 above.
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Heino et al. (US 2025/0128635) in view of Xia et al. (CN 114928077 A) and further in view of Huh (US 2022/0402477).
Regarding claim 6, the modified Heino does not expressly disclose the apparatus of claim 5, wherein the computer program code instructions are configured to, when executed, cause the apparatus to: generate navigation instructions for the vehicle from a starting location to one of the one or more EVCPs from the one or more clusters which in a related art, Huh teaches (see Huh at [0054] which discloses that for example, when the residual SOC value of the battery 41 is low, the display device 30 may display guiding information to leave the exhaust gas emission restriction zone or move to an electric vehicle 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 Heino to include wherein the computer program code instructions are configured to, when executed, cause the apparatus to: generate navigation instructions for the vehicle from a starting location to one of the one or more EVCPs from the one or more clusters, as taught by Huh.
One would have been motivated to make such a modification to guide the vehicle to an electric vehicle charging station with the residual SOC value of the battery is low, as suggested by Huh at [0054].
Claim 17 is directed toward a method that performs the steps recited in the apparatus of claim 6. The cited portions of the prior art used in the rejection of claim 6 teach the corresponding limitations recited in the method of claim 17. Therefore, claim 17 is rejected for the same reasons as stated for claim 6 above.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Heino et al. (US 2025/0128635) in view of Xia et al. (CN 114928077 A) and further in view of Losinger et al. (DE 102019127054) (English translation previously provided).
Regarding claim 11, Heino does not expressly disclose the apparatus of claim 1, wherein the charging gap parameter is an average duration between two consecutive charging events that occurred at each of the plurality of EVCPs, which in a related art, Losinger teaches (see Losinger at page 13 which discloses the average duration between two charging processes related to fast charging of electric vehicles.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heino to include wherein the charging gap parameter is an average duration between two consecutive charging events that occurred at each of the plurality of EVCPs, as taught by Losinger.
One would have been motivated to make such a modification to show the course of a utility value for the typical duration between two events of primary use of an energy store, as suggested by Losinger at page 13.
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.
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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.
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/ROY RHEE/Examiner, Art Unit 3664
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