DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 12/23/2025 has been entered. Claims 1, 3-7, 9, 11-15 and 17-19 have been amended. Claims 2 and 10 have been canceled. Claims 1, 3-9, and 11-20 remain pending in the application.
Response to Arguments
Regarding Applicant’s arguments, on page 9-15 of the remark filed on 12/23/2025, on the newly amended limitations of independent claim 1 “wherein each data model of the plurality of data models is configured to receive, as input data, measurements from all of the plurality of electric vehicle supply equipment except one electric vehicle supply equipment, the one electric vehicle supply equipment is different for each data model, and identify one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment generating erroneous data based on the comparing of each of the plurality of predicted electric current values to the actual electric current value.”, arguments are persuasive.
Therefore, the 35 U.S.C. 103 rejection over Choi et al. (U.S Pub. No. 20210101502) and Elangovan et al. (U.S Pub. No. 20220292388) further in view of Kim et al. (U.S Pub. No. 20240039320), has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. § 103 in view of the following prior art: Chase et al. (U.S Pub. No. 20200388167) and Spalt et al. (U.S Pub. No. 20210373518) in conjunction with Choi et al. (U.S Pub. No. 20210101502) and Kim et al. (U.S Pub. No. 20240039320)). Please refer to the 35 U.S.C. 103 section below for a detailed explanation.
For the reasons stated above and the new ground(s) of rejection under 35 U.S.C. 103 below, Examiner respectfully disagrees with Applicant’s argument, see Applicant’s Remarks Page 9-15, regarding allowance of the application. Examiner asserts that claims 1, 3-9 and 11-20 are rejected for the reasons stated above in conjunction with the new ground(s) of rejection under 35 U.S.C. 103 below.
Conclusion: Choi- Chase-Spalt-Kim teaches the aforementioned limitations of independent claims and 1, 9 and 17 rendering the claim limitations obvious before the effective date of the claimed invention.
Claim Objections
Claim 6 and 15 are objected to because of the following informalities:
In regards to Claim 6, the applicant recites the limitation “current value by greater than a threshold value”, this is a typographically error and a grammatically not a proper sentence. Appropriate correction is required.
In regards to Claim 15, the applicant recites the limitation “electric current value by less than the threshold value”, this is a typographically error and a grammatically not a proper sentence. Appropriate correction is 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.
Claims 1, 3, 9, 11 and 17, is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”) Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”) and Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”) further in view of Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”)
In regards to Clam 1, Choi teaches a controller for detecting erroneous data generated at an electric vehicle charging station, the electric vehicle charging station including a plurality of electric vehicle supply equipment for charging electric vehicles, the controller comprising: (Par. (0007-0009); predicting failure in electric car charging) (Par. (0035); plurality of electric vehicle chargers with plurality of electric cars), (Par. (0051); processor for controlling components)
Choi does not explicitly teach a memory configured to store a plurality of data models that predict a current at a point of common coupling drawn by the electric vehicle charging station from a utility, wherein each data model of the plurality of data models is configured to receive, as input data, measurements from all of the plurality of electric vehicle supply equipment except one electric vehicle supply equipment, the one electric vehicle supply equipment is different for each data model, and wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; and a processor configured to: generate a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, each generated utilizing a corresponding data model of the plurality of data models and representing a prediction of the electric current at the point of common coupling based on the input data received by the corresponding data model; measure an actual electric current value at the point of common coupling; compare each of the plurality of predicted electric current values to the actual electric current value; and identify one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment generating erroneous data based on the comparing of each of the plurality of predicted electric current values to the actual electric current value.
Wherein Chase teaches wherein each data model of the plurality of data models is configured to receive, as input data, measurements from all of the plurality of electric vehicle supply equipment ((Par. (0025, 0027-0028 and 0029); measurements (itinerary with state of charge and payload characteristics of electrical vehicle i.e. refuel, current state of charge etc.) from all plurality of electric vehicle supply equipment (multiple vertiports)), (Par. (0018 and 0020-0021); from all plurality of electric vehicle supply equipment (multiple vertiports corresponding to ariel electric vehicles recharge and refuel), (Par. (0049 0076-0080); machine learning models collect and receives vehicle characteristics such as metrics, state of charge, powers etc.; models receive and determine itinerary and state of charge, current power, payload and other data))
except one electric vehicle supply equipment, the one electric vehicle supply equipment is different for each data model, and ((Par. (0077, 0080-0081 and 0073-0074); machine learning models reject itinerary with electric charge data but one or models accept other itinerary )), (Par. (0025, 0027-0028 and 0029); first electric vehicle charging measurements (itinerary with state of charge and payload characteristics of electrical vehicle i.e. refuel, current state of charge etc.) from the first electric vehicle supply equipment (vertiports)), (Par. (0018 and 0020-0021); from the first electric vehicle supply equipment (vertiports corresponding to ariel electric vehicles recharge and refuel), (Par. (0077, 0080-0081 and 0073-0074); machine learning models reject itinerary with electric charge data but one or models accept other itinerary ))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi to incorporate the teaching of Chase to utilize the above feature because of the analogous concept of charging of electric vehicles associated with data models, with the motivation of implementing data models to accurately depict electric and power saving techniques to ensure quality and give accurate prediction result to in return benefit the electric vehicle technologies and meet demands of electric vehicle charging station in housing and commercial areas. (Chase Par. (0003-0006))
Choi and Chase do not explicitly teach each generated utilizing a corresponding data model of the plurality of data models and representing a prediction of the electric current at the point of common coupling based on the input data received by the corresponding data model; compare each of the plurality of predicted electric current values to the actual electric current value; and identify one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment generating erroneous data based on the comparing of each of the plurality of predicted electric current values to the actual electric current value, a memory configured to store a plurality of data models that predict a current at a point of common coupling drawn by the electric vehicle charging station from a utility, wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; and a processor configured to: generate a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, measure an actual electric current value at the point of common coupling;
Wherein Spalt teaches each generated utilizing a corresponding data model of the plurality of data models and representing a prediction of the electric current at the point of common coupling based on the input data received by the corresponding data model; ((Par. (0095); models output a prediction of out variables corresponding to electric vehicle charging point and utility grid)), (Par. (0044); first model and second model (collection of multiple model predicting variables)), (Par. (0052-0053 and 0070); at a point of coupling (consumer site 119 (electric vehicle charging point) is coupled to utility grid/ distribution power point))
compare each of the plurality of predicted electric current values to the actual electric current value; and (Par. (0534); obtain samples of electricity usage and received sample signals contain electric voltage)), (Par. (0110-0112); matching real time input with predicted values of sample signals of electricity))
identify one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment generating erroneous data based on the comparing of each of the plurality of predicted electric current values to the actual electric current value ((Par. (0110-0112); matching real time input with predicted values of sample signals of electricity to determine faulty anomalies or failure), (Par. (0083-0085 and 0103); determine and remove erroneous data with prediction results; error checks based on predicted outputs and actual value of signal of electricity))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi and Chase to incorporate the teaching of Spalt to utilize the above feature because of the analogous concept of charging of electric vehicles associated with data models, with the motivation of using machine learning models to predict values and determine accurate reading of electricity for electric vehicle and use the models to enhance and train to deliver better results. (Spalt Par. (0011 and 0034))
Choi, Chase and Spalt do not explicitly teach a memory configured to store a plurality of data models that predict a current at a point of common coupling drawn by the electric vehicle charging station from a utility, wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; and a processor configured to: generate a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, measure an actual electric current value at the point of common coupling;
Wherein Kim teaches a memory configured to store a plurality of data models that predict a current at a point of common coupling drawn by the electric vehicle charging station from a utility, (Par. (0077-0079); load prediction model collecting and storing data coupled to power supply of apartment and chargers; calculating prediction values of voltage), (Par. (0199); plurality of electric vehicle supply (individual loads corresponding to electric vehicle chargers L1-L3)), (Par. (0217); predict voltage)
wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; and (Figure 2 labels 210, 400, 40, L1, L2 and L3, EV1; point of coupling (line 40) represents node (210, 400) connecting plurality of electric vehicle supply (Electric vehicles with plurality of chargers)), (Par. (0213-0214); plurality of electric vehicle supply (plurality of individual loads) connected to one node)), (Par. (0199); plurality of electric vehicle supply (individual loads corresponding to electric vehicle chargers L1-L3))
a processor configured to: generate a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, (Par. (0179-0180 and 0189-0190); calculating prediction voltage with numeric values and electric vehicle chargers and charging with prediction voltage)
measure an actual electric current value at the point of common coupling; (Par. (0074); electric data is measure corresponding to currents))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase and Spalt to incorporate the teaching of Kim to utilize the above feature because of the analogous concept of charging of electric vehicles using data models to determine values, with the motivation of utilizing data models to predict voltage with electric vehicles. This assures users that issues with the charging of their electrical vehicle will be mitigated and breaches, threats, error and any faults that may hinder the experience and charging of the vehicles will be limited. By having a data model to calculate differences and predict values help protect the sensors of the electrical vehicles in the future and the ability to spot errors in charging station becomes more enhanced and effective as compared to traditional ways. (Kim Par. (0003-0010))
In regards to Claim 11, the combination of Choi, Chase, Spalt and Kim teach the method of claim 9, Spalt further teaches to compare each of the predicted electric current values to the actual electric current value, the processor is configured to: (Par. (00534); obtain samples of electricity usage and received sample signals contain electric voltage)), (Par. (0110-0112); matching real time input with predicted values of sample signals of electricity))
calculate a plurality of difference values, each representing a difference between one of the plurality of predicted electric current values and the actual electric current value; and (Par. (0111); matching input associated with electric samples/signals, plurality of differences between one of the plurality of predicted electric current values (exact match compared to match within tolerance and predicted values))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase and Kim to incorporate the teaching of Spalt for the reasons discussed in independent claim 1 stated above.
In regards to Claim 9, Choi teaches a method of detecting erroneous data generated at an electric vehicle charging station, the electric vehicle charging station including a plurality of electric vehicle supply equipment for charging electric vehicles, the method comprising: (Par. (0007-0009); predicting failure in electric car charging) (Par. (0035); plurality of electric vehicle chargers with plurality of electric cars), (Par. (0051); processor for controlling components)
Choi does not explicitly teach identifying a plurality of data models that predict at a point of common coupling, electric current drawn by the electric vehicle charging station from a utility, wherein each data model of the plurality of data models ignores is configured to receive, as input data, measurements from all a different one of the plurality of electric vehicle supply equipment but one electric vehicle supply equipment, the one electric vehicle supply equipment is different for each data model, and wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; generating a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, each generated utilizing a corresponding data model different one of the plurality of data models and representing a prediction of the electric current at the point of common coupling based on the input data received by the corresponding data model; measuring an actual electric current value at the point of common coupling; identifying one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment, generating erroneous data based on the comparing of the plurality of predicted electric current values to the actual electric current value.
Wherein Chase teaches wherein each data model of the plurality of data models ignores is configured to receive, as input data, measurements from all a different one of the plurality of electric vehicle supply equipment but one electric vehicle supply equipment, the one electric vehicle supply equipment is different for each data model, and (Par. (0025, 0027-0028 and 0029); first electric vehicle charging measurements (itinerary with state of charge and payload characteristics of electrical vehicle i.e. refuel, current state of charge etc.) from the first electric vehicle supply equipment (vertiports)), (Par. (0018 and 0020-0021); from the first electric vehicle supply equipment (vertiports corresponding to ariel electric vehicles recharge and refuel), (Par. (0077, 0080-0081 and 0073-0074); machine learning models reject itinerary with electric charge data but one or models accept other itinerary ))
the one electric vehicle supply equipment is different for each data model, and (Par. (0025); plurality of different models))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi to incorporate the teaching of Chase to utilize the above feature because of the analogous concept of charging of electric vehicles associated with data models, with the motivation of implementing data models to accurately depict electric and power saving techniques to ensure quality and give accurate prediction result to in return benefit the electric vehicle technologies and meet demands of electric vehicle charging station in housing and commercial areas. (Chase Par. (0003-0006))
Choi and Chase do not explicitly teach identifying a plurality of data models that predict at a point of common coupling, electric current drawn by the electric vehicle charging station from a utility, and wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; generating a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, each generated utilizing a corresponding data model different one of the plurality of data models and representing a prediction of the electric current at the point of common coupling based on the input data received by the corresponding data model; measuring an actual electric current value at the point of common coupling; comparing each of the plurality of predicted electric current values to the actual electric current value; and identifying one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment, generating erroneous data based on the comparing of the plurality of predicted electric current values to the actual electric current value.
Wherein Spalt teaches each generated utilizing a corresponding data model different one of the plurality of data models and representing a prediction of the electric current at the point of common coupling based on the input data received by the corresponding data model; ((Par. (0095); models output a prediction of out variables corresponding to electric vehicle charging point and utility grid)), (Par. (0044); first model and second model (collection of multiple model predicting variables)), (Par. (0052-0053 and 0070); at a point of coupling (consumer site 119 (electric vehicle charging point) is coupled to utility grid/ distribution power point))
comparing each of the plurality of predicted electric current values to the actual electric current value; and (Par. (0534); obtain samples of electricity usage and received sample signals contain electric voltage)), (Par. (0110-0112); matching real time input with predicted values of sample signals of electricity))
identifying one or more electric vehicle supply equipment, of the plurality of electric vehicle supply equipment, generating erroneous data based on the comparing of the plurality of predicted electric current values to the actual electric current value. ((Par. (0110-0112); matching real time input with predicted values of sample signals of electricity to determine faulty anomalies or failure), (Par. (0083-0085 and 0103); determine and remove erroneous data with prediction results; error checks based on predicted outputs and actual value of signal of electricity))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi and Chase to incorporate the teaching of Spalt to utilize the above feature because of the analogous concept of charging of electric vehicles associated with data models, with the motivation of using machine learning models to predict values and determine accurate reading of electricity for electric vehicle and use the models to enhance and train to deliver better results. (Spalt Par. (0011 and 0034))
Choi, Chase and Spalt do not explicitly teach identifying a plurality of data models that predict at a point of common coupling, electric current drawn by the electric vehicle charging station from a utility, and wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; generating a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, each generated utilizing a different one of the plurality of data models; measuring an actual electric current value at the point of common coupling;
Wherein Kim teaches identifying a plurality of data models that predict at a point of common coupling, electric current drawn by the electric vehicle charging station from a utility, and (Par. (0077-0079); load prediction model collecting and storing data coupled to power supply of apartment and chargers; calculating prediction values of voltage), (Par. (0199); plurality of electric vehicle supply (individual loads corresponding to electric vehicle chargers L1-L3)), (Par. (0217); predict voltage)
wherein the point of common coupling represents a node electrically connecting the plurality of electric vehicle supply equipment to the electric utility; (Figure 2 labels 210, 400, 40, L1, L2 and L3, EV1; point of coupling (line 40) represents node (210, 400) connecting plurality of electric vehicle supply (Electric vehicles with plurality of chargers)), (Par. (0213-0214); plurality of electric vehicle supply (plurality of individual loads) connected to one node)), (Par. (0199); plurality of electric vehicle supply (individual loads corresponding to electric vehicle chargers L1-L3))
generating a plurality of predicted electric current values of the electric current drawn by the electric charging station from the electric utility, each generated utilizing a different one of the plurality of data models; (Par. (0179-0180 and 0189-0190); calculating prediction voltage with numeric values and electric vehicle chargers and charging with prediction voltage), (Par. (0175); different data models (machine learning with three prediction models))
measuring an actual electric current value at the point of common coupling; (Par. (0074); electric data is measure corresponding to currents))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase and Spalt to incorporate the teaching of Kim to utilize the above feature because of the analogous concept of charging of electric vehicles using data models to determine values, with the motivation of utilizing data models to predict voltage with electric vehicles. This assures users that issues with the charging of their electrical vehicle will be mitigated and breaches, threats, error and any faults that may hinder the experience and charging of the vehicles will be limited. By having a data model to calculate differences and predict values help protect the sensors of the electrical vehicles in the future and the ability to spot errors in charging station becomes more enhanced and effective as compared to traditional ways. (Kim Par. (0003-0010))
In regards to Claim 11, the combination of Choi, Chase, Spalt and Kim teach the method of claim 9, Splat further teaches wherein comparing each of the predicted electric current values to the actual electric current value includes: (Par. (00534); obtain samples of electricity usage and received sample signals contain electric voltage)), (Par. (0110-0112); matching real time input with predicted values of sample signals of electricity))
calculating a plurality of difference values, each representing a difference between one of the plurality of predicted electric current values and the actual electric current value; and (Par. (0111); matching input associated with electric samples/signals, plurality of differences between one of the plurality of predicted electric current values (exact match compared to match within tolerance and predicted values))
comparing each of the plurality of difference values to a threshold value. (Par. (0084); samples of electricity corresponding to electrical vehicle must meet predetermined threshold), (Par. (0096-0098, 0110); input of electric samples that is matched is compared to meet threshold))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase and Kim to incorporate the teaching of Spalt for the reasons discussed in independent claim 1 stated above.
In regards to Claim 17, Choi teaches a controller for detecting erroneous data generated at an electric vehicle charging station, the electric vehicle charging station including a first electric vehicle supply equipment for charging electric vehicles and a second electric vehicle supply equipment for charging the electric vehicles, the controller comprising: (Par. (0007-0009); predicting failure in electric car charging) (Par. (0035); plurality of electric vehicle chargers with plurality of electric cars),
at least one processor configured to: (Par. (0051); processor for controlling components)
Choi does not explicitly teach identify a first data model that predicts a first electrical current value at a point of common coupling between the electric vehicle charging station and an electric grid, wherein the first data model is trained to consider first electric vehicle charging measurements from the first electric vehicle supply equipment but not second electric vehicle charging measurements from the second electric vehicle supply equipment, the first electric current value represents a prediction of electric current drawn by the electric vehicle charging station based on the first electric vehicle charging measurements from the first electric vehicle supply equipment, and wherein the point of common coupling represents a node electrically connecting the first and second electric vehicle supply equipment to the electric grid; identify a second data model that predicts a second electrical value at the point of common coupling, wherein the second data model is trained to consider the second electric vehicle charging measurements from the second electric vehicle supply equipment and but not the first electric vehicle charging measurements from the first electric vehicle supply equipment, the second electric current value represents a prediction of electric current drawn by the electric vehicle charging station based on the second electric vehicle charging measurements from the second electric vehicle supply equipment; generate, utilizing the first data model and the second data model, prediction of the first electrical current value and a prediction of the second electrical current value at the point of common coupling; measure an actual electrical current value at the point of common coupling; and
Wherein Chase teaches wherein the first data model is trained to consider first electric vehicle charging measurements from the first electric vehicle supply equipment but not second electric vehicle charging measurements from the second electric vehicle supply equipment, (Par. (0025, 0027-0028 and 0029); first electric vehicle charging measurements (itinerary with state of charge and payload characteristics of electrical vehicle i.e. refuel, current state of charge etc.) from the first electric vehicle supply equipment (vertiports)), (Par. (0018 and 0020-0021); from the first electric vehicle supply equipment (vertiports corresponding to ariel electric vehicles recharge and refuel), (Par. (0077, 0080-0081 and 0073-0074); machine learning models reject itinerary with electric charge data but one or models accept other itinerary ))
wherein the second data model is trained to consider the second electric vehicle charging measurements from the second electric vehicle supply equipment and trained to ignore but not the first electric vehicle charging measurements from the first electric vehicle supply equipment, ((Par. (0077, 0080-0081 and 0073-0074); the second data model (one or more machine learning models reject itinerary with electric charge data but one or models accept other itinerary )), (Par. (0018 and 0020-0021); from the first electric vehicle supply equipment (vertiports corresponding to ariel electric vehicles recharge and refuel)
wherein the second data model is trained to consider the second electric vehicle charging measurements from the second electric vehicle supply equipment and trained to ignore the first electric vehicle charging measurements from the first electric vehicle supply equipment; and (Par. (0077, 0080-0081 and 0073-0074); the second data model (one or more machine learning models reject itinerary with electric charge data but one or models accept other itinerary )), (Par. (0018 and 0020-0021); from the first electric vehicle supply equipment (vertiports corresponding to ariel electric vehicles recharge and refuel),
Wherein Choi and Chase do not explicitly teach the first electric current value represents a prediction of electric current drawn by the electric vehicle charging station based on the first electric vehicle charging measurements from the first electric vehicle supply equipment, and the second electric current value represents a prediction of electric current drawn by the electric vehicle charging station based on the second electric vehicle charging measurements from the second electric vehicle supply equipment; wherein the point of common coupling represents a node electrically connecting the first and second electric vehicle supply equipment to the electric grid; identify a first data model that predicts a first electrical current value at a point of common coupling between the electric vehicle charging station and an electric grid, identify a second data model that predicts a second electrical current value at the point of common coupling; generate, utilizing the first data model and the second data model, a prediction of the first electrical current value and a prediction of the second electrical current value at the point of common coupling; measure an actual electrical current value at the point of common coupling; and identify, based on the first electrical current value, the second electrical current value, and the actual electrical current value, one or more of the first electric vehicle supply equipment or the second electric vehicle supply equipment generating the erroneous data.
Wherein Spalt teaches the first electric current value represents a prediction of electric current drawn by the electric vehicle charging station based on the first electric vehicle charging measurements from the first electric vehicle supply equipment, and (Par. (0092 and 0095); prediction ant output variables representing electric current (voltage associated with samples) drawn by the electric vehicle charging station (electric vehicle charging point)), (Par. (0103-0105); based on the first electric vehicle charging measurements from the first electric vehicle supply equipment (prediction of electric/voltage samples with trained model based on electric vehicle charging point))
the second electric current value represents a prediction of electric current drawn by the electric vehicle charging station based on the second electric vehicle charging measurements from the second electric vehicle supply equipment; (Par. (0053-0054); the second electric current value represents (plurality of samples of electricity usage)), (Par. (0092 and 0095); represents a prediction of electric current drawn by the electric vehicle charging station (samples correspond to electric charge point/ electric vehicle charging point and prediction outputs)), (Par. (0099 and 0103); prediction values based on samples)) , (Par. (0051-0052, 0054 and 0070); in the second electric vehicle charging measurements (samples of electricity usage) from the second electric vehicle supply equipment plurality of electric vehicle supply equipment (electric vehicle charging points associated with consumer 119n with power/distribution supply))
generate, utilizing the first data model and the second data model, a prediction of the first electrical current value and a prediction of the second electrical current value at the point of common coupling; (Par. (0095); models output a prediction of out variables corresponding to electric vehicle charging point and utility grid)), (Par. (0044); first model and second model (collection of multiple model predicting variables)), (Par. (0052-0053 and 0070); at a point of coupling (consumer site 119 (electric vehicle charging point) is coupled to utility grid/ distribution power point))
identify, based on the first electrical current value, the second electrical current value, and the actual electrical current value, one or more of the first electric vehicle supply equipment or the second electric vehicle supply equipment generating the erroneous data. (((Par. (0110-0112); matching real time input with predicted values of sample signals of electricity to determine faulty anomalies or failure), (Par. (0083-0085 and 0103); determine and remove erroneous data with prediction results; error checks based on predicted outputs and actual value of signal of electricity))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi and Chase to incorporate the teaching of Spalt to utilize the above feature because of the analogous concept of charging of electric vehicles associated with data models, with the motivation of using machine learning models to predict values and determine accurate reading of electricity for electric vehicle and use the models to enhance and train to deliver better results. (Spalt Par. (0011 and 0034))
Choi, Chase and Spalt do not explicitly teach wherein the point of common coupling represents a node electrically connecting the first and second electric vehicle supply equipment to the electric grid; identify a first data model that predicts a first electrical current value at a point of common coupling between the electric vehicle charging station and an electric grid, identify a second data model that predicts a second electrical current value at the point of common coupling; measure an actual electrical current value at the point of common coupling; and
Wherein Kim teaches identify a first data model that predicts a first electrical current value at a point of common coupling between the electric vehicle charging station and an electric grid, ((Figure 2 labels 210, 400, 40, L1, L2 and L3, EV1; point of coupling (line 40) represents node (210, 400) connecting plurality of electric vehicle supply (Electric vehicles with plurality of chargers)), (Par. (0213-0214); plurality of electric vehicle supply (plurality of individual loads) connected to one node)), (Par. (0199); plurality of electric vehicle supply (individual loads corresponding to electric vehicle chargers L1-L3))
identify a second data model that predicts a second electrical current value at the point of common coupling, (Par. (0175); plurality of machine learning prediction models) , (Par. (0077-0079); load prediction model collecting and storing data coupled to power supply of apartment and chargers; calculating prediction values of voltage), (Par. (0199); plurality of electric vehicle supply (individual loads corresponding to electric vehicle chargers L1-L3)), (Par. (0217); predict voltage)
generate, utilizing the first data model and the second data model, predictions of the first electrical current value and the prediction of the second electrical current value at the point of common coupling; ((Par. (0179-0180 and 0189-0190); calculating prediction voltage with numeric values and electric vehicle chargers and charging with prediction voltage)
measure an actual electrical current value at the point of common coupling; and (Par. (0074); electric data is measure corresponding to currents))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase and Spalt to incorporate the teaching of Kim to utilize the above feature because of the analogous concept of charging of electric vehicles using data models to determine values, with the motivation of utilizing data models to predict voltage with electric vehicles. This assures users that issues with the charging of their electrical vehicle will be mitigated and breaches, threats, error and any faults that may hinder the experience and charging of the vehicles will be limited. By having a data model to calculate differences and predict values help protect the sensors of the electrical vehicles in the future and the ability to spot errors in charging station becomes more enhanced and effective as compared to traditional ways. (Kim Par. (0003-0010))
Claims 6, 14 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”), Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”), Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”) and Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”) further in view of Althoff et al. (U.S Pub. No. 20230391350, hereinafter referred to as “Althoff”)
In regards to Claim 6, the combination of Choi, Chase, Spalt and Kim do not explicitly teach wherein the processor is further configured to: determine that the erroneous data is generated when at least one of the plurality of predicted electric current values deviates from the actual electric current value by greater than a threshold value.
Wherein Althoff teaches wherein the processor is further configured to: determine that the erroneous data is generated when at least one of the plurality of predicted electric current values deviates from the actual electric current value by greater than a threshold value. (Par. (0030); error in the set of errors greater than pre-defined amount), (Par. (0088) control value E is greater than threshold value), (Par. (0096); greater than threshold value)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt and Kim to incorporate the teaching of Althoff to utilize the above feature because of the analogous concept of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison towards a threshold value for users operating the electric vehicle to identify discrepancies and issues that could prevent damage to the system. . (Althoff Par. (0001-0003))
In regards to Claim 14, the combination of Choi, Chase, Spalt and Kim do not explicitly teach further comprises: determining that the erroneous data is generated when at least one of the plurality of difference values is greater than a threshold value.
Wherein Althoff teaches further comprises: determining that the erroneous data is generated when at least one of the plurality of difference values is greater than a threshold value. (Par. (0030); error in the set of errors greater than pre-defined amount), (Par. (0088) control value E is greater than threshold value), (Par. (0096); greater than threshold value)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt and Kim to incorporate the teaching of Althoff to utilize the above feature because of the analogous concept of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison towards a threshold value for users operating the electric vehicle to identify discrepancies and issues that could prevent damage to the system. . (Althoff Par. (0001-0003))
In regards to Claim 18, the combination of Choi, Chase, Spalt and Kim teach the controller of claim 17, Kim further teaches the at least one processor is further configured to: calculate a first difference between the prediction of the first electrical value and the actual electrical value; ((Par. (0087); calculate a prediction voltage that includes difference between maximum and minimum demand), (Par. (0122-0128); difference between maximum and minimum values are selected)
calculate a second difference between the prediction of second electrical value and the actual electrical value; and ((Par. (0087); calculate a prediction voltage that includes difference between maximum and minimum demand), (Par. (0122-0128); difference between maximum and minimum values are selected), (Par. (0214 and 0219-0222); second electric value (plurality of voltages with numerical values calculated)), (Par. (0058-0060); comparing voltages to voltage recommended))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase and Spalt to incorporate the teaching of Kim to utilize the above feature because of the analogous concept of charging of electric vehicles using data models to determine values, with the motivation of utilizing data models to predict voltage with electric vehicles. This assures users that issues with the charging of their electrical vehicle will be mitigated and breaches, threats, error and any faults that may hinder the experience and charging of the vehicles will be limited. By having a data model to calculate differences and predict values help protect the sensors of the electrical vehicles in the future and the ability to spot errors in charging station becomes more enhanced and effective as compared to traditional ways. (Kim Par. (0003-0010))
Choi, Chase, Spalt and Kim do not explicitly teach determine that the erroneous data is generated when one or more of the first difference and the second difference is greater than a threshold value.
Wherein Althoff teaches determine that the erroneous data is generated when one or more of the first difference and the second difference is greater than a threshold value. ((Par. (0096); determining exposure and error that is compared to a threshold), (Par. (0086); plurality of difference values and threshold value (set of errors corresponding to threshold) (Par. (0051); electric vehicle)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt and Kim to incorporate the teaching of Althoff to utilize the above feature because of the analogous concept of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison towards a threshold value for users operating the electric vehicle to identify discrepancies and issues that could prevent damage to the system. (Althoff Par. (0001-0003))
Claims 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”), Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”), Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”), Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”) and Althoff et al. (U.S Pub. No. 20230391350, hereinafter referred to as “Althoff”) further in view of Javaid et al. (U.S Pub. No. 20220161786, hereinafter referred to as “Javaid”)
In regards to Claim 4, the combination of Choi, Chase, Spalt, and Kim teach the controller of claim 1, Choi further teaches the controller of claim 3, wherein the processor is further configured to: identify a minimum difference value of the plurality of difference values; and (Par. (0071-0073); failure of vehicle charging corresponding to minimum value of 0 and value of 1 to determine failure)
Choi, Chase, Spalt, Kim and Althoff do not explicitly teach identify the one or more electric vehicle supply equipment generating the erroneous data is being generated based on a comparison between each of the plurality of difference values and m times the minimum difference value.
Wherein Javaid teaches identify the one or more electric vehicle supply equipment generating the erroneous data is being generated based on a comparison between each of the plurality of difference values and m times the minimum difference value. (Par. (0048); determining risk in vehicle by comparing and multiplying a value with minimum difference 0.2 and 0.4), (Par. (0079); one or more electric vehicle supply (all electric engine connected to supply)), (Par. (0196); power supply for electric vehicles)) (Examiner Note: As the claim is presented there is no indication of what the variable “m” represents therefore it will be broadly and reasonably interpreted that m is referring to a multiplication step between differences of values)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt Kim and Althoff to incorporate the teaching of Javaid to utilize the above feature because of the analogous concept of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison towards a difference a specific minimum value ,users in the network will be able to identifies issues pertaining to the vehicle more effectively with this specific formula. This creates high assurances for users in the error detection system and mitigate damage to vehicles in traffic or charging. (Javaid Par. (0028-0029))
In regards to Claim 12, the combination of Choi, Chase, Spalt and Kim teach the method of claim 9, Choi further teaches the method of claim 11, further comprises: identifying a minimum difference value of the plurality of difference values; and (Par. (0071-0073); failure of vehicle charging corresponding to minimum value of 0 and value of 1 to determine failure)
Choi, Chase, Spalt Kim and Althoff do not explicitly teach identifying the one or more electric vehicle supply equipment generating the erroneous data by comparing each of the plurality of difference values with m times the minimum difference value.
Wherein Javaid teaches identifying the one or more electric vehicle supply equipment generating the erroneous data by comparing each of the plurality of difference values with m times the minimum difference value. (Par. (0048); determining risk in vehicle by comparing and multiplying a value with minimum difference 0.2 and 0.4), (Par. (0196); identifying the one or more electric vehicle supply equipment (power supply of electric vehicle)) (Examiner Note: As the claim is presented there is no indication of what the variable “m” represents therefore it will be broadly and reasonably interpreted that m is referring to a multiplication step between differences of values)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, Kim and Althoff to incorporate the teaching of Javaid to utilize the above feature because of the analogous concept of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison towards a difference a specific minimum value ,users in the network will be able to identifies issues pertaining to the vehicle more effectively with this specific formula. This creates high assurances for users in the error detection system and mitigate damage to vehicles in traffic or charging. (Javaid Par. (0028-0029))
Claims 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”), Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”), Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”), Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”) Althoff et al. (U.S Pub. No. 20230391350, hereinafter referred to as “Althoff”) and Javaid et al. (U.S Pub. No. 20220161786, hereinafter referred to as “Javaid”) further in view of Larsson et al. (U.S Pub. No. 20170080819, hereinafter referred to as “Larsson”)
In regards to Claim 5, the combination of Choi, Chase, Spalt, Kim, Althoff and Javid do not explicitly teach wherein the processor is further configured to: determine that the erroneous data is generated upon determining that at least p samples of the plurality of predicted electric current values, in a time window of size s for deviate from the actual electric current value by greater than a threshold value.
Wherein Larsson teaches wherein the processor is further configured to: determine that the erroneous data is generated upon determining that at least p samples of the plurality of predicted electric current values, in a time window of size s for deviate from the actual electric current value by greater than a threshold value. (Par. (0009); fault in battery of vehicle by comparing current charging period with current value to threshold value)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, Kim, Althoff and Javaid to incorporate the teaching of Larsson to utilize the above feature because of the analogous concept of charging battery supply of electric vehicle communication and detecting error within the data, with the motivation of implementing a time-based verification as a form of comparison to detect forged entities or errors within data recorded. This creates an enhanced feature for electric vehicles and concerns with battery power or charging outputs because only authentic readings of sensors would correspond to data associated with specific time windows. This compares maintains the integrity of the system as whole and reports accurate readings on electric vehicles in transit. (Larsson Par. (0003-0005))
In regards to Claim 13, the combination of Choi, Chase, Spalt, Kim, Althoff and Javid do not explicitly teach wherein determining whether the erroneous data is being generated upon determining that at least p samples of the plurality of predicted electric current values, in a time window size s deviate from the actual electric current value by greater than a threshold value.
Wherein Larsson teaches wherein determining whether the erroneous data is being generated upon determining that at least p samples of the plurality of predicted electric current values, in a time window size s deviate from the actual electric current value by greater than a threshold value. (Par. (0009); fault in battery of vehicle by comparing current charging period with current value to threshold value)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, Kim, Althoff and Javaid to incorporate the teaching of Larsson to utilize the above feature because of the analogous concept of charging battery supply of electric vehicle communication and detecting error within the data, with the motivation of implementing a time-based verification as a form of comparison to detect forged entities or errors within data recorded. This creates an enhanced feature for electric vehicles and concerns with battery power or charging outputs because only authentic readings of sensors would correspond to data associated with specific time windows. This compares maintains the integrity of the system as whole and reports accurate readings on electric vehicles in transit. (Larsson Par. (0003-0005))
Claims 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”), Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”), Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”), Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”)and Althoff et al. (U.S Pub. No. 20230391350, hereinafter referred to as “Althoff”) further in view of Kwon et al. (U.S Pub. No. 20200254882, hereinafter referred to as “Kwon”)
In regards to Claim 7, the combination of Choi, Chase, Spalt, Kim and Althoff do not explicitly teach wherein the processor is further configured to: identify the one or more of electric vehicle supply equipment generating the erroneous data based on which of the plurality of difference values is less than the threshold value.
Wherein Kwon teaches wherein the processor is further configured to: identify the one or more of electric vehicle supply equipment generating the erroneous data based on which of the plurality of difference values is less than the threshold value. (Par. (0029); determining fault of electric vehicle when a sensed value is less than a predetermined threshold) (Par. (0025); charging of electrical vehicle)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, Kim, and Althoff to incorporate the teaching of Kwon to utilize the above feature because of the analogous concept of charging battery supply of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison that is below a certain threshold as a way for identifying users operating the charging stations of electric vehicles that reporting are with error, possibly spoofed or issues of concern may arise. This creates a notification system to users by detecting based on the threshold accurate readings for charging stations of electric vehicle and in return leads to content consumers. (Kwon Par. (0003-0006))
In regards to Claim 15, the combination of Choi, Chase, Spalt, Kim and Althoff do not explicitly teach identifying the one or more of the plurality of electric vehicle supply equipment where the erroneous data is being generated based on which of the plurality of predicted electric current values deviates from the actual electric current value by less than the threshold value.
Wherein Kwon teaches identifying the one or more of the plurality of electric vehicle supply equipment where the erroneous data is being generated based on which of the plurality of predicted electric current values deviates from the actual electric current value by less than the threshold value. (Par. (0029); determining fault of electric vehicle when a sensed value is less than a predetermined threshold) (Par. (0025); charging of electrical vehicle)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, Kim, and Althoff to incorporate the teaching of Kwon to utilize the above feature because of the analogous concept of charging battery supply of electric vehicle communication and detecting error within the data, with the motivation of implementing a comparison that is below a certain threshold as a way for identifying users operating the charging stations of electric vehicles that reporting are with error, possibly spoofed or issues of concern may arise. This creates a notification system to users by detecting based on the threshold accurate readings for charging stations of electric vehicle and in return leads to content consumers. (Kwon Par. (0003-0006))
Claims 8, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”), Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”), Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”),and Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”) further in view of Liu et al. (U.S Pub. No. 20200282854, hereinafter referred to as “Liu”)
In regards to Claim 8, the combination of Choi, Chase, Spalt and Kim do not explicitly teach wherein: the erroneous data comprises data spoofing generated by a cyberattack on the electric vehicle charging station.
Wherein Liu teaches wherein: the erroneous data comprises data spoofing generated by a cyberattack on the electric vehicle charging station. (Par. (0025); detecting spoofing on electric vehicle charging station), (Par. (0049); spoofed data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, and Kim, to incorporate the teaching of Liu to utilize the above feature because of the analogous concept of detecting possible spoofing and malware to securely protected the integrity of data in electric vehicles. This prevents security breaches, tampering or falsification associated with the data and protects consumer from harm or damage to charging stations of the electric vehicle. By having a detection mechanism put into place users can be assured cyber threats and impacts on power drawn from charging station will not be affected and their products are safeguarded. (Liu Par. (0003-0004))
In regards to Claim 16, the combination of Choi, Chase, Spalt and Kim do not explicitly teach wherein: the erroneous data comprises data spoofing generated by a cyberattack on the electric vehicle charging station.
Wherein Liu teaches wherein: the erroneous data comprises data spoofing generated by a cyberattack on the electric vehicle charging station. (Par. (0025); detecting spoofing on electric vehicle charging station), (Par. (0049); spoofed data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, and Kim, to incorporate the teaching of Liu to utilize the above feature because of the analogous concept of detecting possible spoofing and malware to securely protected the integrity of data in electric vehicles. This prevents security breaches, tampering or falsification associated with the data and protects consumer from harm or damage to charging stations of the electric vehicle. By having a detection mechanism put into place users can be assured cyber threats and impacts on power drawn from charging station will not be affected and their products are safeguarded. (Liu Par. (0003-0004))
In regards to Claim 20, the combination of Choi, Chase, Spalt and Kim do not explicitly teach wherein: the erroneous data comprises data spoofing generated by a cyberattack on the electric vehicle charging station.
Wherein Liu teaches wherein: the erroneous data comprises data spoofing generated by a cyberattack on the electric vehicle charging station. (Par. (0025); detecting spoofing on electric vehicle charging station), (Par. (0049); spoofed data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Choi, Chase, Spalt, and Kim to incorporate the teaching of Liu to utilize the above feature because of the analogous concept of detecting possible spoofing and malware to securely protected the integrity of data in electric vehicles. This prevents security breaches, tampering or falsification associated with the data and protects consumer from harm or damage to charging stations of the electric vehicle. By having a detection mechanism put into place users can be assured cyber threats and impacts on power drawn from charging station will not be affected and their products are safeguarded. (Liu Par. (0003-0004))
Claim 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (U.S Pub. No. 20210101502, hereinafter referred to as “Choi”), Chase et al.(U.S Pub. No. 20200388167, hereinafter referred to as “Chase”), Spalt et al. (U.S Pub. No. 20210373518, hereinafter referred to as “Spalt”), Kim et al. (U.S Pub. No. 20240039320, hereinafter referred to as “Kim”) and Althoff et al. (U.S Pub. No. 20230391350, hereinafter referred to as “Althoff”) further in view of Wang et al. (U.S Pub. No. 20230382254, hereinafter referred to as “Wang”)
In regards to Claim 19, the combination of Choi, Chase, Spalt, Kim and Althoff do not explicitly teach wherein the at least one processor is further configured to: determine that the erroneous data is generated by the first electric vehicle supply equipment and not by the second electric vehicle supply equipment in response to the first difference being greater than the threshold value and the second difference being less than the threshold value.
Wherein Wang teaches wherein: the at least one processor is further configured to: determine that the erroneous data is generated by the first electric vehicle supply equipment and not by the second electric vehicle supply equipment (Par. (0052-0057); determining electric vehicle may fail; not at a second electric vehicle supply (only one vehicle and charging power being determined for fail not two vehicles)
in response to the first difference being greater than the threshold value and the second difference being less than the threshold value. (Par. (0055-0057); greater than or equal to a threshold but less another threshold; difference in temp for charging power 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 have modified Choi, Chase, Spalt, Kim, and Althoff to incorporate the teaching of Wang to utilize the above feature because of the analogous concept of charging battery supply of electric vehicle communication and detecting error within the data, with the motivation of detecting data in a specific vehicle based on difference of a threshold to have an effective way of determining spoof, failure or errors within the data. By implementing a comparison users can detect invalid or unauthentic data in vehicle charging stations for electrical vehicle and in return promote high credibility that sensor readings for electric vehicles are accurate and the power drawn from charging is sufficient without concerns of tampering or error. (Wang Par. (0003-0006))
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Starkey; Hesham (U.S Pub. No. 20240203168) “SYSTEMS AND METHODS FOR AUTOMATICALLY PREDICTING AND SCHEDULING VEHICLE REPAIRS”. Considered this reference because it addressed data models and ignoring certain measurements associated with electrical vehicles.
Chihwan; Kim. (U.S Pub. No. 20240337495) “APPARATUS AND METHOD FOR DISPLAYING INDOOR DRIVING INFORMATION USING MAP INFORMATION AND MOTION SENSOR”. Considered this application because it relates electrical vehicles and a multiplying of a difference associated with error data.
Busse; Timon (U.S Pub. No. 20240132046) “DEVICE AND METHOD FOR THE MODEL-BASED PREDICTED CONTROL OF A COMPONENT OF A VEHICLE”. Considered this application because it addressed predicting using data models the power and charging of electrical vehicles.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.A.H./Examiner, Art Unit 2497
/MALCOLM CRIBBS/Primary Examiner, Art Unit 2497