DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites “the same type”. There is insufficient antecedent basis for this limitation in the claim. Claim 3 recites “the amount of change”. It is not clear what this is referring to and is therefore indefinite. Claim 3 recites “including a battery voltage and a charging time from the charging data, applying the nearest neighbor algorithm to each battery voltage and each charging time”. Since the claims recite a battery voltage and a charging time, it is not clear what to each battery voltage and each charging time means, since there is only one. Claim 6 recites “the battery state transmitting step”. There is insufficient antecedent basis for this limitation in the claim. Claim 7 recites “a first PLC modem”. PLC is an acronym and is therefore indefinite. Claim 7 recites “the collection gateway”. There is insufficient antecedent basis for this limitation in the claim.
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2023/0258734 A1) in view of Lee (US2024/0110990 A1) and Ropel (US 2024/0149744 A1).Herein Lee (US 2023/0258734 A1) will be referred to as Lee’734.Herein Lee (US 2024/0110990 A1) will be referred to as Lee’990.
With respect to Claim 1 Lee’734 teaches A method for evaluating an electric vehicle battery state, comprising (See Abstract Systems and methods for estimating the state of health (SOH) of a battery are provided. And Fig.1): a data collection step of collecting, as charging data, data exchanged during a charging process from a charger to an electric vehicle (See Para[0007] More specifically, current and voltage may be measured during charging, and the generalized fluctuation-dissipation theorem may be applied to the measured current and voltage to obtain the SOH parameters. For each battery model, an SOH model may be developed as a function of SOH parameters and accumulated charge capacity by training based on reference data. ); a data analysis step of extracting feature information from the charging data and (See Para[0122] The parameter processing module 3110 may obtain SOH parameters) a comparison and diagnosis step of comparing the level analysis data for each item with a reference data set having an average value for the same type of electric vehicles to generate a comparison result, recognizing that an abnormality has occurred in a case where a difference exceeding a preset threshold occurs on the basis of the comparison result, and generating battery state change information based on the comparison result (See Para[0124] The SOH estimation module 3120 may then read the weights of the SOH parameters for the same EV battery model from the database 200 and may apply them to the SOH parameters using Eq. 2. And Para[0137] the analysis module 3140 may compare (S150) the changes in the SOH parameters with the SOH parameters of the representative SOH model. If there is an outlier parameter (S160:Y), the battery may be determined to be abnormal (S180). If there is no outlier parameter, the battery may be considered aged but normal (S170).); a real-time monitoring step of repeating a process of continuously collecting the charging data and cumulatively reflecting the level analysis data for each item in a concerned type of electric vehicle; and (See Para[0133] The remaining models may be weighted and averaged to create a representative SOH model. The weights of SOH parameters may be continuously updated as the reference data set grows, and as a result, the representative SOH model may also be updated over time.) a battery state transmission step of transmitting the battery state change information (See Para[0138]) However Lee’734 is silent to the language of applying a nearest neighbor algorithm to each of a plurality of items included in the feature information to generate level analysis data for each item; to the electric vehicle via the charger. Nevertheless Lee’990 teaches applying a nearest neighbor algorithm to each of a plurality of items included in the feature information to generate level analysis data for each item (See Para[0014] providing an electrode tab disconnection inspection apparatus of a battery cell capable of more accurately detecting whether electrode tabs of the battery cell are disconnected based on a so-called K-nearest neighbor method (a nearest neighbor algorithm).); However Lee’990 is silent to the language of to the electric vehicle via the charger. Nevertheless Ropel teaches to the electric vehicle via the charger. (See Para[0016] a communication link component to establish a bidirectional communication link between a charger unit and a vehicle computer system of a vehicle; a location monitoring component configured to monitor a location of a handheld device connected to the charging station; a receive component configured to receive a message comprising an update to at least one of a charging time and a state-of-health information of a battery pack from the vehicle computer system;) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee’734 and apply a nearest neighbor algorithm to each of a plurality of items included in the feature information to generate level analysis data for each item such as that of Lee’990.. One of ordinary skill would have been motivated to modify Lee’734, because nearest neighbor algorithm is robust to noisy data and can be used with large datasets and will produce accurate results. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee’734 and transmit to the electric vehicle via the charger such as that of Ropel. One of ordinary skill would have been motivated to modify Lee’734, because transmitting to the vehicle would allow the vehicle to know the current status. With respect to Claim 4 Lee’734 teaches The method according to claim 1, wherein the comparison and diagnosis step includes extracting the reference data set having the average value for the same type of electric vehicles as the electric vehicle corresponding to the level analysis data for each item, comparing the level analysis data for each item with the reference data set, recognizing that the abnormality has occurred in a case where the difference between the reference data set and the level analysis data for each item exceeds the preset threshold, and generating battery state change information based on the comparison result between the level analysis data for each item and the reference data set. (See Para[0137] and Fig. 13) With respect to Claim 5 Lee’734 teaches The method according to claim 1, wherein the real-time monitoring step includes continuously collecting the charging data, accumulating a preset number of pieces of data for the same type of electric vehicles to calculate an average value, and reflecting the average value in the reference data set. (See Para[0013]) With respect to Claim 6 Lee’734 teaches The method according to claim 1, wherein the battery state transmitting step includes outputting the battery state change information to a user interface or dashboard within the electric vehicle via the charger. (See Fig. 1)
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2023/0258734 A1) in view of Lee (US2024/0110990 A1) and Ropel (US 2024/0149744 A1) as applied to claim 1 above, and further in view of Sastinsky (US 2020/0055421 A1).
With respect to Claim 2 Lee’734 is silent to the language of The method according to claim 1, wherein the data collection step includes collecting the charging data including at least one of a communication controller identifier (EVCCID), a maximum voltage, and a necessary charging power for the electric vehicle from a collection gateway of the charger. Nevertheless Sastinsky teaches wherein the data collection step includes collecting the charging data including at least one of a communication controller identifier (EVCCID), a maximum voltage, and a necessary charging power for the electric vehicle from a collection gateway of the charger. (See Para[0013] For example, the system can collect or otherwise receive initial battery data from the nameplate or manufacturer of the power cell. For each power cell, these initial battery data can comprise information corresponding to the chemistry parameters of the power cell, the entity name of the producer, the type of battery cell chemistry, time of production (e.g., day, week, month, and/or year), charging and discharging temperatures, stated or estimated calendar life, estimated cycles, c-rate (e.g., a rate at which the battery is discharged relative to its maximum capacity), cell voltage (e.g., minimum and maximum voltages), energy density, depth of discharge, and any other available data from the power cell and power cell producer.) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee’734 and include a maximum voltage such as that of Sastinsky. One of ordinary skill would have been motivated to modify Lee’734, because having more data would improve accuracy.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2023/0258734 A1) in view of Lee (US2024/0110990 A1) and Ropel (US 2024/0149744 A1) as applied to claim 1 above, and further in view of Watanabe (US 2010/0001687 A1). With respect to Claim 7 Lee’734 teaches The method according to claim 1, wherein the data collection step includes receiving, in supplying power to the electric vehicle using a DC power bank of the charger, (See Para[0088]) However Lee’734 is silent to the language of the charging data obtained by collecting data exchanged between the charger and the electric vehicle from the collection gateway of the charger using a first PLC modem. Nevertheless Watanabe teaches the charging data obtained by collecting data exchanged between the charger and the electric vehicle from the collection gateway of the charger using a first PLC modem. (See Para[0052]) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee’734 and use a PLC modem such as that of Watanabe One of ordinary skill would have been motivated to modify Lee’734 because PLC modems are highly reliable and are designed to withstand harsh industrial environments, ensure consistence performance and reliability.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 2023/0258734 A1) in view of Lee (US2024/0110990 A1) and Ropel (US 2024/0149744 A1) as applied to claim 1 above, and further in view of Northrop (US 2020/0108732 A1).
With respect to Claim 8 Lee’734 is silent to the language of The method according to claim 1, wherein the data analysis step includes performing pre-processing for removing missing or inconsistent values among a communication controller identifier (EVCCID), a maximum voltage, and a necessary charging power included in the charging data. Nevertheless Northrop teaches wherein the data analysis step includes performing pre-processing for removing missing or inconsistent values among a communication controller identifier (EVCCID), a maximum voltage, and a necessary charging power included in the charging data. (See Para[0052]) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Lee’734 and perform pre-processing for removing missing or inconsistent values such as that of Northrop. One of ordinary skill would have been motivated to modify Lee’734, to improve accuracy. Examiner notes prior art could not be applied to Clam 3 due to the 35 U.S.C. §112(b) above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSHIHISA ISHIZUKA whose telephone number is (571)270-7050. The examiner can normally be reached M-F 11:00-7:00.
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YOSHIHISA . ISHIZUKA
Examiner
Art Unit 2857
/YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
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