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
This action is in response to arguments and remarks filed on 02/05/2026. No amendments were filed; no additional claims canceled, and no new claims filed. Therefore, Claims 1-4 and 6-21 are pending examination, in which Claims 1, 9 and 13 are independent claims.
In light of the instant arguments and remarks:
Regarding the rejection of Claims 1-4 and 6-20 under 35 U.S.C. § 101, the applicant’s arguments have been fully considered but found unpersuasive. [The examiner continues to contend that the claims represent an abstract idea without an aspect of direct vehicle control.]
Further examination resulted in use of the same prior art to reject Claims 1-4 and 6-21 under 35 U.S.C. § 103.
Additional prior art searching resulted in additional relevant prior art being identified, which is summarized in the Conclusion section.
THIS ACTION IS MADE FINAL. Necessitated by amendment.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
Pages 7-9 – applicant’s arguments related to previously filed arguments
Note: Specific support for the following examiner’s response to arguments/remarks can be found in the 103 rejection section below, and corresponds to underlined portions of the examiner’s comments/prior art excerpts.
“Applicant respectfully maintains that amended independent claims 1 and 9 recite subject matter that is not taught or suggested by Fu or Zhang. [The examiner notes that only independent Claim 1 is rejected by the combination of Fu and Zhang. Independent Claim 9 is rejected by the combination of Fu and Klein. Thus, the focus of the comments in this section is addressed specifically to Claim 1] In particular, the claims require "estimating ... a set of model parameters for reduction of battery capacity relative to distance travelled, from a starting battery capacity to a selected end of range battery capacity, the selected end of range battery capacity comprising as a non-zero percentage of a total battery state-of-charge." Fu's discussion of "driving habits" and "range anxiety reduction" does not disclose any model whose terminal point is a driver-selected non-zero state-of-charge, nor any parameter set expressly defined between that starting capacity and a selected non-zero end-of-range capacity.” [The examiner respectfully disagrees, and argues that the applicant is not fully acknowledging that Fu captures the overall methodology embodied in Claim 1 involving the use of a specific mathematical technique [Kalman filtering], with input parameters based on both historical driving data and real-time driving and battery data, to generate an accurate estimate of the remaining battery charge. Which is supplemented by Zhang, who also using the Kalman filtering approach, reduces the error involved in estimating the remaining distance the vehicle can travel on the “residual battery capacity”].
“Fu constructs first and second ensemble learning models using historical battery influence data and characteristic variables to predict endurance mileage, but does not disclose "estimating, using the previous trip data, a set of model parameters for reduction of battery capacity relative to distance travelled, from a starting battery capacity to a selected end of range battery capacity, the selected end of range battery capacity comprising as a non-zero percentage of a total battery state-of-charge," as now recited in claims 1 and 9. Zhang calculates residual mileage from residual capacity and average energy consumption and then recalibrates using accumulated travelled mileage and historical speed data, but again does not teach a parameterization of the model specifically from a starting capacity to a selected non-zero end-of-range capacity using previous-trip data. The Examiner's mapping treats any range-prediction or recalibration scheme as equivalent to the claimed estimation of a bounded "reduction of battery capacity vs. distance" model, which is an over-generalization and does not satisfy the requirement to show that each claimed feature is taught or suggested by the prior art.” [The examiner respectfully disagrees. The combination of Fu and Zhang gathers both previous and real-time data pertaining to driving and battery performance, and then uses comparable mathematics [Kalman filtering with covariance matrices] to generate estimates of remaining battery capacity, and translates this into estimated remaining distance. In response to the applicant’s specifically mentioning “a bounded "reduction of battery capacity vs. distance" model”, the examiner notes that when the application expressed this concept in mathematical form in Claim 8, the examiner provided a prior art reference by Liu, that exactly matched the linear equation of Claim 8, involving the key parameter of state-of-charge. Please see Claim 8 below. ]
“Claim 1 requires that previous-trip data be used to estimate a set of model parameters for battery-capacity reduction versus distance, bounded explicitly between a starting battery capacity and a selected end-of-range capacity comprising a non-zero percentage of total SOC, and further requires calculating an error covariance matrix for that estimated set using a Kalman filter operating on the previous-trip data. Fu's ensemble learning models do not impose a non-zero SOC boundary as a model terminal condition, nor do they disclose Kalman-filter-based error covariance matrices over a distance-vs-capacity model parameter set derived from a single previous trip. Accordingly, Fu does not perform estimation "in the same way" as now claimed, and the rejection fails to show where the art teaches or suggests that particular combination of features.” [The examiner respectfully disagrees. The applicant continues to argue about “parameterization”. In addition, to the previous comments above, the examiner contends that under the broadest reasonable interpretation of Fu and/or Zhang, the input data is not narrowly restricted as the applicant implies, i.e., such narrow restrictions are not explicit in Fu and/or Zhang. In addition, Fu clearly uses error covariance matrix approach on Pg. 9, Lns. 17-28 before using a Kalman filter. ]
“Zhang's teaching of calculating residual mileage from actual residual capacity and average energy consumption, and "recalibrating" remaining mileage using accumulated travelled mileage and historical speed data, does not disclose using previous-trip data to estimate a set of model parameters for reduction of battery capacity versus distance from a starting capacity to a selected non-zero end-of-range capacity, nor calculating and using an error covariance matrix for that specific parameter set with a Kalman filter, as now positively recited in claims 1 and 9. Rather, Zhang adjusts residual range based on aggregate historical behavior, without defining a bounded distance-vs-capacity model anchored at an explicit non-zero SOC end-of-range constraint.” [The examiner respectfully disagrees. Please see the previous examiner’s comments. ]
Pages 9-14 – applicant’s arguments related to the rejection under 35 USC § 101
Turning to Step 2A, Prong 1…For at least the following reasons, Applicant respectfully disagrees.
1. Applicant submits that Claim 1 is directed to an EV-specific range-management method, not to math in the abstract. Claim 1 is directed to "a method of generating estimates of end of range for an electric vehicle," and recites…These limitations define a concrete method of operating an electric vehicle with a non-zero SOC end-of-range constraint, not a disembodied mathematical formula. [The examiner respectfully disagrees. Estimating the remaining distance an electric vehicle can travel before running out of battery power can be done with the human mind, with pen and paper as needed, with sufficient input data. The examiner, further disagrees, that Claim 1 involves an aspect of vehicle control.]
2. Applicant submits that the claimed method cannot reasonably be performed "by the human mind" as alleged. The Examiner asserts that the claim "covers performance of the limitations of the human mind," but a person cannot feasibly perform Kalman-filter-based error covariance calculations on continuous battery and distance data for an ongoing trip, nor continuously update end-of-range estimates in real time while driving, without specialized processors and sensor inputs from the electric vehicle. Under the 2019 PEG, "mental processes" are limited to observations, evaluations, and judgments that can practically be performed in the human mind or with pen and paper; here, the claimed EV-specific Kalman filtering and online update behavior is plainly beyond that scope. [The examiner respectfully disagrees. And contends that Claim 1 represents the abstract idea of a mathematical black-box into which data – corresponding to the specific technical field of electric vehicle – is input to generate an output for display, wherein displaying such an output satisfies the definition of post-solution activity.]
3. Any mathematical operations are part of a larger EV control/monitoring solution. To the extent the claims involve mathematical relationships (e.g., Kalman filter equations or distance-vs-SOC models), those operations are not claimed as ends in themselves. They are recited only as part of a concrete method for generating and updating vehicle-specific end-of-range estimates for an electric vehicle operated under a driver-selected non-zero SOC constraint. Under MPEP §2106.04, such embedded use of mathematics within a specific technological process does not, by itself, render the claim "directed to" a mathematical concept. [The examiner respectfully disagrees, as detailed in the two preceding comments. The examiner also respectfully disagrees with the applicant’s description of Claim 1 limitations as involving “EV control”.]
“Turning to Step 2A, Prong 2…The Examiner finds that the underlined elements are "insignificant extra-solution activity," characterizing data acquisition and display as mere pre-solution and post-solution steps that "do not impose any meaningful limits on practicing the abstract idea. For at least the following reasons, Applicant respectfully disagrees.”
1. Use of EV sensor data is not generic "data gathering," but essential to the EV-specific solution. Claim 1 requires obtaining a set of data "for a previous trip of the electric vehicle" and using that data to estimate model parameters capturing reduction of battery capacity relative to distance travelled between a starting capacity and a selected non-zero end-of-range SOC. This is not generic "data gathering"; the data are EV-specific measurements (e.g., distance travelled and battery behavior) used to fit a model tailored to a particular vehicle and driver's end-of-range constraint. The model parameters and associated Kalman error covariance are then applied to new trips for that vehicle, based on its actual operational history. That is a concrete technical use of the EV's operational data, not an incidental pre-solution step. [The examiner respectfully disagrees. The EV sensors are gathering data for inputting to the Kalman filtering technique/equations. ]
2. Real-time updating and driver display are intrinsic to the EV range-management method, not mere "post-solution" output. Claim 1 requires "displaying the estimated end of range distance to a driver of the vehicle, wherein the displayed prediction is updated as the new trip progresses." Dependent claims further specify updating the model parameters and error covariance based on the actual driven distance and change in battery SOC during the new trip, reapplying the updated model to generate an updated estimated end-of-range distance, and repeatedly displaying this updated estimate to the driver. These features are not incidental "post-solution" reporting; they define how the EV is actually operated in real time, with the driver informed-under a non-zero SOC constraint-of how far the vehicle can travel before reaching the selected end-of-range SOC. This is a practical application that directly affects vehicle operation and driver behavior. [The examiner respectfully disagrees. The output of the Kalman filtering technique/equations is designed to be displayed in the vehicle, as per Fig. 1.]
3. The non-zero end-of-range SOC and driver-determined SOC range impose meaningful constraints on the method. The claims further require that the selected end-of-range battery capacity is a non-zero percentage of total SOC, and claim 21 specifies that it is a driver-determined SOC in a range of 15-30% of total SOC. This is a concrete, EV-specific constraint: the system is configured so that the vehicle is operated to maintain SOC above a driver-selected non-zero threshold and the model parameters and Kalman updates are computed with that boundary condition. Such a constraint meaningfully limits how any mathematical operations are employed and ties them to the real-world EV range-management problem (range anxiety, reserve margin, etc.), rather than allowing unconstrained abstract calculation. [The examiner respectfully disagrees. The considerations stated here again pertain to the data input into the Kalman filter technique, and do not integrate the judicial exception into practical application involving an aspect directly related to vehicle control.]
4. Digital-twin and Kalman-fusion claims likewise recite a practical EV-system implementation. Independent claim 13 recites a "digital twin simulation" that generates a speed profile using a model of the vehicle and road data, predicts battery charge consumption, sets an EOR reference and SOC EOR value, communicates those to the vehicle, then fuses the EOR reference, SOC measurements, and distance measurements in a Kalman filter to update a model relating SOC to distance as the vehicle traverses the path. This is a concrete architecture for remote EV range management, not an abstract equation. The Kalman fusion (claims 14- 17) is performed specifically to update EOR estimates for an actual vehicle in a current trip and display them to the driver. [The examiner respectfully disagrees. The digital-twin is effectively serving as a remote or virtual computer. The courts have indicated that additional elements merely using a computer or processor to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” Additionally, invoking the use of a digital-twin to implement the Kalman filter is unrelated to direct control of the vehicle, per the claim language limitations in Claims 1 and 13.]
“Turning to Step 2B, "Significantly more" than any alleged abstract idea, Applicant submits that the Examiner asserts that the additional elements do not amount to "significantly more" than an abstract idea, characterizing them as "well-understood, routine, conventional" activities specified at a high level of generality.
For at least the following reasons, Applicant respectfully disagrees.
1. The particular combination of features is not shown to be routine or conventional. The Office Action does not provide any evidence (e.g., from the cited references) that it was conventional in the art to:
* Use previous-trip EV data to estimate a capacity-reduction vs distance model bounded
between a starting capacity and a driver-selected non-zero EOR SOC.
* Compute an error covariance matrix for that specific parameter set using a Kalman filter
on the previous-trip data;
* Apply that model and covariance to a new trip, updating the model based on actual driven
distance and SOC changes during the new trip; and
* Repeatedly display updated end-of-range distance to the driver, with the EOR defined by
the non-zero SOC boundary.
[The examiner respectfully disagrees. And contends that the limitations stated here provide only additional granularity to an abstract idea, in the form of additional mathematical steps/formulas and data processing; and thus still represents the abstract idea of a mathematical black-box into which data – corresponding to the specific technical field of electric vehicle – is input to generate an output for display, without any association with vehicle control. And thus does not represent "significantly more" than an abstract idea.]
Nor does the Office action identify any prior art for the digital-twin and Kalman-fusion architecture of claims 13-17 that would render that combination "routine."
2. The Examiner conflates generic data processing with the claimed EV-specific solution. Labeling data acquisition and display as "well-understood, routine, conventional" ignores the EV-specific structure recited in these claims. The combination of: (i) non-zero SOC EOR constraint, (ii) EV-specific previous-trip training, (iii) Kalman-based parameter and covariance estimation, and (iv) real-time driver updating and digital-twin implementation is not shown in the cited art, and therefore cannot simply be dismissed as "conventional" without evidentiary support. [The examiner respectfully disagrees. And contends that the limitations stated here provide only additional granularity to an abstract idea – performed on specific computation hardware, that is comparably performed by a generic computer - without any association with vehicle control. And thus does not represent "significantly more" than an abstract idea. ]
3. The claims provide a specific technological improvement. As reflected in the specification and claims, the disclosed methods improve electric-vehicle range prediction by providing end-of-range estimates that respect a driver-selected non-zero SOC reserve, updated online using Kalman or MHIIO techniques tailored to the vehicle's actual usage. This is a specific improvement in EV range-prediction technology-similar in nature to control-system improvements that the Federal Circuit and the USPTO have recognized as eligible when they improve the functioning of a particular machine, even when they use mathematical tools. Accordingly, even if some claim elements are deemed to involve a judicial exception, the claims as a whole recite additional elements that provide "significantly more" than the exception, and the Examiner has not established that the claimed combination is merely well-understood, routine, and conventional. [The examiner respectfully disagrees. The examiner contents that the reliance of the applicant on post-solution activity – in the form of displaying the results of mathematical calculations, with explicitly controlling the vehicle, does not represent "significantly more" than an abstract idea.]
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 6-21 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
As described in MPEP § 2106, the analyses as to whether a claim qualifies as eligible subject matter under 35 U.S.C. § 101 includes the following determinations:
(1) Whether the claim is to a statutory category, i.e. to a process, machine, manufacture or composition of matter ("Step 1")- see MPEP §§ 2106, subsection III, and 2106.03.
(2) If the claim is to a statutory category, whether the claim recites any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, or mental processes) ("Step 2A, Prong One") - see MPEP §§ 2106, subsection III, and 2106.04.
(3) If the claim recites a judicial exception, whether the claim recites additional elements that integrate the judicial exception into a practical application ("Step 2A, Prong Two") - see MPEP §§ 2106, subsection III, and 2106.04.
(4) If the claim does not recite additional elements that integrate the judicial exception into a practical application, whether the claim recites additional elements that amount to significantly more than the judicial exception ("Step 2B") – see MPEP §§ 2106, subsection III, and 2106.05.
Step 1: Claims 1-4 and 6-21 are a method claims, with claims 1, 9 and 13 being independent claims. Thus, each independent claim, on its face, is directed to one of the four statutory categories of 35 U.S.C. §101 (MPEP 2106.03).
Claim 1 is considered a representative independent claim. The examiner has determined the following analysis is applicable to each independent claim. With regard to Claim 1:
A method of generating estimates of end of range for an electric vehicle, comprising: obtaining a set of data for a previous trip of the electric vehicle; estimating, using the previous trip data, a set of model parameters for reduction of battery capacity relative to distance travelled, from a starting battery capacity to a selected end of range battery capacity, the selected end of range battery capacity comprising as a non-zero percentage of a total battery state-of-charge; calculating an error covariance matrix for the estimated set of model parameters using the previous trip data, wherein the error covariance matrix is calculated with a Kalman filter operating on the set of data for the previous trip of the electric vehicle; for a new trip, applying the set of model parameters and error covariance matrix to generate an estimated end of range distance; and displaying the estimated end of range distance to a driver of the vehicle, wherein the displayed prediction is updated as the new trip progresses.
Step 2A, Prong 1:
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The examiner submits that the foregoing bolded limitations constitute “mathematical concepts”, because under its broadest reasonable interpretation, the claim covers performance of the limitations of the human mind. “Mathematical concepts” - per MPEP § 2106.04(a)(2), subsection I – includes mathematical relationships, formulas, equations and calculations, without regard to the level of mathematical simplicity or sophistication. The foregoing bolded limitations, while involving sophisticated mathematics (i.e., “calculating an error covariance matrix…wherein the error covariance matrix is calculated with a Kalman filter”) represents a set of mathematical steps. Claim 1 recites the general idea of estimating a driving range (“generating estimates of end of range for an electric vehicle”) with the input data being from a prior trip (“obtaining a set of data for a previous trip of the electric vehicle”) and data associated with the performance of a battery (“obtaining a set of data for a previous trip of the electric vehicle”), and the output being an estimated driving range (“displaying the estimated end of range distance to a driver of the vehicle”). Thus, the claim recites a straight-forward, mathematics-based process, which under its broadest reasonable interpretation, recites an abstract idea capable of being performed by the limitations of the human mind.
Step 2A, Prong 2:
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer or processor to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The examiner submits that the foregoing underlined additional limitation does not integrate the above-noted abstract idea into a practical application. The examiner contends that the additional limitations constitutes insignificant extra-solution activity [MPEP 2106.05(g)]; specifically, pre-solution activity in the form of data gathering (“obtaining a set of data for a previous trip of the electric vehicle; estimating, using the previous trip data, a set of model parameters for reduction of battery capacity”) and post-solution activity in the form of displaying the calculation results (“displaying the estimated end of range distance to a driver of the vehicle”). Thus, these additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The examiner further submits that the aforementioned underlined elements in Claim 1 are not sufficient to amount to significantly more than the judicial exception for the same reason discussed above for Step 2A, Prong 2. Simply gathering the necessary data for a specific type of mathematical calculation and displaying the results of the calculation, does not elevate the judicial exception to the level of an inventive-concept, since it represents well-understood, routine, conventional activities, specified at a high level of generality to the judicial exception [MPEP 2106.05(d) and 2106.07(a)III]. Hence, the claim is not patent eligible.
The examiner finds that independent Claims 9 and 13 include the same limitations as Claim 1 associated with estimating driving range (discussed above under Step 2A, Prong 1). Thus, each of these claims recites a simple process, which under its broadest reasonable interpretation, recites an abstract idea.
Dependent: Claims 2-4, 6-8, 10-12 and 14-21 do not recite any further limitations that cause the claims to be patent eligible. Rather, the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. For example, dependent claims 2 and 12 involve the gathering of current battery and vehicle data. Claims 3 and 4 extends the steps of Claim 1 by gathering additional real-time data, performing additional calculations or iterations, and displaying the updated results, corresponding to mere additional instructions for implementing an abstract idea.
Claim 13 involves the use of digital-twin to perform the computations, which is effectively serving as a remote or virtual computer. The courts have indicated that additional elements merely using a computer or processor to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application” as it is equivalent to a human performing the abstract idea:
Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59, 113 USPQ2d 1097, 1105-07 (Fed. Cir. 2014).
Claims 4, 8, 11 and 14-15 deal with the introduction of mathematical concepts corresponding to different aspects of a specific algorithm (“Kalman filter”) or expressed explicitly as mathematical formulas (“Distance=a*SOCu+b”, and Kalman filter equations for an R-Matrix, Q-Matrix, real-time end-of-range estimates, y1,k, etc.). Claims 6, 7, 10, 12 and 16-21 provide some additional granularity to the corresponding independent claim, however, this additional granularity involves mere additional instructions for implementing an abstract idea.
The examiner thus finds that dependent Claims 2-4, 6-8 and 10-21 all fail to integrate a judicial exception into a practical application, or provide additional elements significantly beyond the scope of the judicial exception and amounting to an inventive concept. Therefore, Claims 1-4 and 6-21 are ineligible under 35 USC §101.
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.
Claims 1-4, 7, 13, 18 and 20-21 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Fu et al. (CN 113335131 B, henceforth Fu), and Zhang et al. (CN 115610226 B, henceforth Zhang).
Regarding Claim 1, Fu teaches the limitations: a method of generating estimates of end of range for an electric vehicle {“a method, a device, equipment and a storage medium for predicting the endurance mileage of a vehicle”, Abstract, and “a vehicle driving range prediction apparatus, including: the system comprises a state information acquisition module, a running state information screening module and a vehicle endurance mileage prediction module”, Pg. 2, Lns. 23-25}, comprising: obtaining a set of data for a previous trip of the electric vehicle {“S530, obtaining the driving state history information of the vehicle; S540, the travel state history information as input information, constructing a first integrated learning model; s550, screening the historical driving state information based on an importance degree sorting algorithm preset in the first integrated learning model to obtain historical battery state influence data of the vehicle; specifically, each vehicle identification number is sorted according to the travel starting time, and the historical information of the running state of the vehicle is obtained.”, Pg. 11, Lns. 15-21}; estimating, using the previous trip data {“s510, acquiring battery state historical information and corresponding vehicle travel information”, Pg. 7, Lns. 33-34, and “S560, taking the historical influence data of the battery state as input information to construct a second learning model; and S570, taking the driving mileage information of the unit electric quantity as target information, taking the historical influence data of the battery state as characteristic information, and training a second ensemble learning model to predict the driving mileage., Pg.13, Lns. 10-14}, a set of model parameters for reduction of battery capacity {characteristic variables/data and battery state data are used to estimate residual mileage: “wherein the real-time battery state influence data are characteristic data representing road conditions, driving habits, vehicle conditions and running time; and predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage.”, Pg. 2, Lns. 17-21, and “The input characteristic information is battery state real-time influence data and battery state real-time information obtained by screening of the first ensemble learning model, and the mileage that the residual electric quantity of the battery in the battery state real-time information can continue driving, namely the vehicle driving mileage, is deduced according to the battery state real-time influence data.”, Pg. 7, Lns. 23-27}, from a starting battery capacity to a selected end of range battery capacity, the selected end of range battery capacity comprising as a non-zero percentage of a total battery state-of-charge {Fu teaches of the need to anticipate “end of range” in a manner to relieve driver anxiety (i.e., “The remaining driving mileage of the electric automobile is used for indicating the mileage of the automobile which can run before the energy is exhausted, so that the automobile owner is reminded to prepare the energy in advance before driving the automobile or in the process of driving the automobile, and the half-way energy is prevented from being exhausted to influence a trip plan”, Pg. 1, Lns. 26-30), and combines this with taking into account the driving habits of the driver as reflected in historical data on battery state and travel (“the real-time information of the driving state when the vehicle is running is screened to obtain the real-time impact data of the battery state, and the real-time impact data of the battery state is characteristic data representing road conditions, driving habits, vehicle conditions and driving time”, Pg. 6, Lns. 10-13, and “s510, acquiring battery state historical information and corresponding vehicle travel information of different types of vehicles; s520, determining the driving mileage information of unit electric quantity based on the battery state historical information and the vehicle travel information; further, referring to Fig. 6, the determining the mileage per unit charge based on the battery status history information and the vehicle trip information”, Pg. 7, Lns. 29-34)}; calculating an error covariance matrix {in-depth use of Kalman filtering described starting on Pg. 9 and going through the middle of Pg. 10} for the set of model parameters using the previous trip data {“S550, based on the preset important degree ordering algorithm in the first integrated learning model, the travel state history information is filter out to obtain the battery state history influence data of the vehicle”, Pg. 11, Lns. 18-20}, wherein the error covariance matrix is calculated with a Kalman filter operating on the set of data for the previous trip of the electric vehicle {“the preset filtering algorithm may be Kalman filtering, and a linear regression model of the SOC consumption values and the electric quantity values is established for different vehicle types according to the acquired historical single trip data of the electric vehicle, so as to obtain a linear relationship between the SOC consumption values and the electric quantities of the different vehicle types”, Pg. 9, Lns. 1-5}; for a new trip, applying the set of model parameters {“acquiring the real-time information of the battery state and the real-time information of the running state of the vehicle when the vehicle runs currently”, Pg. 2, Lns. 14-15} and error covariance matrix to generate an estimated end of range distance {a second integrated learning model is used to predict vehicle endurance mileage: “the battery state real-time influence data and the battery state real-time information are input into the second integrated learning model to predict the driving mileage so as to obtain the driving mileage of the vehicle”, Pg. 3, Lns. 15-18, after receiving battery input data from a Kalman/preset filtering algorithm, described on Pgs. 9-10, to correct, i.e., improve the accuracy of, battery state information: “modifying the battery state history information based on a preset filtering algorithm to obtain the battery state modification”, Pg. 8, Lns. 23-24}; and displaying the estimated end of range distance to a driver of the vehicle, wherein the displayed prediction is updated as the new trip progresses {“the vehicle network 120 pushes the driving range prediction result to the vehicle-mounted terminal 110”, Pg. 5, Lns. 19-20}.
Fu does not appear to explicitly recite the limitations: estimating a set of model parameters for reduction of battery capacity relative to distance travelled, and applying the set of model parameters and error covariance matrix to generate an estimated end of range distance.
However, Zhang explicitly recites the limitation: estimating a set of model parameters for reduction of battery capacity relative to distance travelled {residual capacity related to actual mileage: “the residual mileage is calculated according to the actual residual capacity and the average energy consumption of the electric vehicle…and the remaining mileage is recalibrated through the accumulated traveled mileage data and the historical speed data, so that the prediction error of the endurance mileage can be reduced, and the accuracy of the endurance mileage prediction is improved”, Pg. 3, Lns. 10-18}, and applying the set of model parameters and error covariance matrix to generate an estimated end of range distance {“the optimal values of the first travel distance and the second travel distance are based on the Kalman filter model. In one possible embodiment, when starting the trip, two distances are tracked by Kalman: and (3) iteratively calculating the distance error according to the first travel distance (namely the distance obtained by the difference of two adjacent accumulated mileage) and the second travel distance (namely the distance calculated by integrating the speed and the time)”, Pg. 12, Lns. 20-26}.
Fu and Zhang are analogous art because they both deal with estimating the remaining useful battery charge, or driving range, of an electric vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Fu and Zhang before them, to modify the teachings of Fu to include the teachings of Zhang to improve the accuracy of an endurance mileage prediction {Abstract}.
Regarding Claim 2, the combination of Fu and Zhang discloses all the limitations of Claim 1, as discussed supra. In addition, Fu explicitly recites the limitation: further comprising recording {“a storage medium is provided, which includes a processor and a memory…to implement a vehicle range prediction method as described above”, Pg. 3, Lns. 7-10, and “vehicle-mounted terminal 110 collects driving state data and battery state data of a vehicle,”, Pg. 5, Ln. 14}, distance {“the battery state real-time information are input into the second integrated learning model to predict the driving mileage so as to obtain the driving mileage of the vehicle”, Pg. 3, Lns. 16-18}, speed {“calculating the maximum value, the minimum value and the mean value of the average speed of the last 10 trips per time aiming at each trip.”, Pg. 11, Lns. 27-28}, and battery parameters {“SOC value collected by the sensor is a measured value of a true SOC value, and the sum of the measured value and the measurement error is a true value”, Pg. 9, Lns. 12-13} during the new trip for use in performing the estimating and calculating steps for a subsequent trip {data collection: “vehicle-mounted terminal 110 collects driving state data and battery state data of a vehicle, the data collected by the vehicle-mounted terminal 110 is sent to the internet of vehicles 120, the data is reported to the server 130 by the internet of vehicles 120, the server 130 invokes a first ensemble learning model and a second ensemble learning model, performs driving range prediction according to the data collected by the vehicle-mounted terminal 110”, Pg. 5, Lns. 14-18}.
Regarding Claim 3, the combination of Fu and Zhang discloses all the limitations of Claim 1, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the displayed prediction is updated as the new trip progresses by: determining actual driven distance of the vehicle and change in battery state of charge (SOC) during the new trip {“a method for predicting the driving range of a vehicle, which can be applied to a server side, is shown, and the method includes: s210, acquiring real-time battery state information and real-time running state information of a vehicle during current running”, Pg. 6, Lns. 4-7, and “S230, predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage”, Pg. 7, Lns. 1-3; also, Pg. 10, Lns. 20-24}; using the actual driven distance and change in battery SOC during the new trip to update the set of model parameters and the error covariance matrix and reapplying the updated set of model parameters and the updated error covariance matrix to generate an updated estimated end of range distance {“S610, based on a preset filtering algorithm, correcting the historical information of the battery state to obtain battery state correction information; and S620, determining the mileage information of unit electric quantity according to the vehicle travel information and the battery state correction information.”, Pg. 8, Lns. 5-8}; and displaying the updated estimated end of range distance to the driver of the vehicle {“the vehicle network 120 pushes the driving range prediction result to the vehicle-mounted terminal 110”, Pg. 5, Lns. 19-20}.
Regarding Claim 4, the combination of Fu and Zhang discloses all the limitations of Claim 3, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the error covariance matrix is calculated with a Kalman filter operating on the set of data for the previous trip of the electric vehicle {“the preset filtering algorithm may be Kalman filtering”, Pg. 9, Ln. 1, and “referring to Fig. 7, the modifying the battery state history information based on a preset filtering algorithm to obtain the battery state modification information….obtaining battery state correction information according to the battery state estimation information, the battery state historical information, the estimation error and the measurement error”, Pg. 8, Lns. 23-33}, and wherein the step of using the actual driven distance and change in battery SOC during the new trip to update the set of model parameters and the error covariance matrix comprises updating the model parameters in a discrete time model by calculating and adding to each of the model parameters a process noise of the respective parameter, as calculated by the Kalman filter {a set of Kalman filtering equations, including noise parameters, is detailed on Pgs. 9-10: “the preset filtering algorithm may be Kalman filtering, and a linear regression model of the SOC consumption values and the electric quantity values”, Pg. 9, Lns. 1-2, which are used to update SOC values for then updating the mileage estimate: “referring to Fig. 9, obtaining the mileage per unit of electric energy according to the vehicle trip information and the battery state correction information includes: s910, carrying out differential calculation on the vehicle travel information and the battery state correction information to obtain driving mileage information and electric quantity consumption information”, Pg. 10, Lns. 20-24}.
Regarding Claim 7, the combination of Fu and Zhang discloses all the limitations of Claim 1, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the step of applying the set of model parameters and error covariance matrix to generate an estimated end of range distance for the new trip {“vehicle trip information”, Pg. 8, Ln. 4} is performed without a destination known to a control apparatus of the electric vehicle {calculation based on historical data is independent of any future trip destination: “the preset filtering algorithm may be Kalman filtering, and a linear regression model of the SOC consumption values and the electric quantity values is established for different vehicle types according to the acquired historical single trip data of the electric vehicle, so as to obtain a linear relationship between the SOC consumption values and the electric quantities of the different vehicle types”, Pg. 9, Lns. 1-5}.
Regarding Claim 13, Fu discloses the limitations: a method of generating updated estimates of end of range for an electric vehicle {“a method, a device, equipment and a storage medium for predicting the endurance mileage of a vehicle”, Abstract, and “a vehicle driving range prediction apparatus, including: the system comprises a state information acquisition module, a running state information screening module and a vehicle endurance mileage prediction module”, Pg. 2, Lns. 23-25}, comprising: receiving a destination {“vehicle trip information”, Pg. 8, Ln. 4} for a current trip of the vehicle {“predicting the endurance mileage of a vehicle”, Abstract}; in a digital twin simulation {communication with external servers, where computationally intensive calculations are performed:“ a vehicle-mounted terminal 110, an internet of vehicles 120, and a server 130, the vehicle-mounted terminal 110 collects driving state data and battery state data of a vehicle, the data collected by the vehicle-mounted terminal 110 is sent to the internet of vehicles 120, the data is reported to the server 130 by the internet of vehicles 120, the server 130 invokes a first ensemble learning model and a second ensemble learning model, performs driving range prediction according to the data collected by the vehicle-mounted terminal 110, feeds a driving range prediction result back to the internet of vehicles 120”, Pg. 5, Lns. 13-19}: generating a speed profile for the vehicle to reach the destination using a model of the vehicle, and road data {“wherein the real-time battery state influence data are characteristic data representing road conditions, driving habits, vehicle conditions and running time; and predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage.”, Pg. 2, Lns. 17-21} for a path between a current position of the vehicle and the destination {Pg. 11, Lns. 18-34 discusses generating velocity data, including: “calculating the maximum value, the minimum value and the mean value of the average speed of the last 10 trips per time aiming at each trip.”; also, “the first ensemble learning model is used for screening data….so that a scoring result of the driving state real-time data is obtained…weights may be set according to the actual meanings of the data in the real-time information of the driving state, for example, weights may be set for the information related to the speed, so that the information related to the speed is scored higher”, Pg. 6, Lns. 23-33}; generating a battery charge consumption using a battery model for the vehicle to determine expected charge consumption to reach the destination or an end of range position {“The second ensemble learning model is trained to predict the driving range through the input feature information. The input characteristic information is battery state real-time influence data and battery state real-time information obtained by screening of the first ensemble learning model, and the mileage that the residual electric quantity of the battery in the battery state real-time information can continue driving, namely the vehicle driving mileage, is deduced according to the battery state real-time influence data”, Pg. 7, Lns. 22-27}; and setting an end of range (EOR) reference and battery state of charge (SOC) EOR value {trip target values: “the vehicle travel information may be mainly obtained from a vehicle single-travel schedule, a vehicle travel tag table, and a trip driving condition table. And according to the difference calculation, obtaining the total driving range and the total SOC consumption of the single trip, wherein the total SOC consumption is the electric quantity consumption information…wherein the driving range information of the unit electric quantity is a target variable which is input into a second integrated learning model for training subsequently”, Pg. 11, Lns. 1-8}; communicating the EOR reference and the battery SOC EOR value to the vehicle from the digital twin {a digital twin is represented by the running of Kalman filter algorithm, on Pgs. 9-10, on server 11 in Fig. 11 and Pg. 15, Ln. 9, and additional calculations to determine the predicted mileage: “the vehicle endurance mileage predicting module is used for predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage”, Pg. 2, Lns. 31-34}; as the vehicle traverses the path: collecting actual distance traveled and battery state of charge measurements {“acquiring the real-time information of the battery state and the real-time information of the running state of the vehicle when the vehicle runs currently”, Pg. 2, Lns. 14-15}; fusing the EOR reference, the battery SOC EOR value, the battery SOC measurement and estimating EOR for the vehicle in the current trip {method steps 510, 520, 610 and 620, Pg. 7, Ln. 33 through Pg. 8, Ln. 8, represents taking historical data, which may be from previous trips or previous time intervals of a current trip, running it through first and second integrated learning models, Pg. 3, Lns. 12-25, wherein increased accuracy is achieved through the use of the Kalman filtering algorithm, Pgs. 9-10, which corresponds to the correction step discussed on Pg. 8, Lns. 10-21; the second integrated learning model, which is used to predict vehicle endurance mileage - “the vehicle endurance mileage predicting module is used for predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage”, Pg. 2, Lns. 31-34, uses the Kalman filtering algorithm, described on Pgs. 9-10, to correct, i.e., improve the accuracy of, battery state information, whether historical or real-time: “The method also constructs an integrated learning model on the basis of correcting the battery state information and selecting the characteristic data, trains the data in an integrated learning model mode”, Pg. 3, Lns. 19-21}; and displaying the estimated EOR for the vehicle in the current trip to a driver of the vehicle {“the vehicle network 120 pushes the driving range prediction result to the vehicle-mounted terminal 110”, Pg. 5, Lns. 19-20}.
Fu does not appear to explicitly recite the limitations: wherein fusing data includes the actual distance travelled to update a model relating battery SOC to distance travelled.
However, Zhang explicitly recites the limitation: wherein fusing data includes the actual distance travelled to update a model relating battery SOC to distance travelled {residual capacity related to actual mileage: “the residual mileage is calculated according to the actual residual capacity and the average energy consumption of the electric vehicle, the distance error is acquired based on the accumulated form mileage data and the historical speed data acquired in the first time period, and the residual mileage is corrected according to the distance error to determine the actual driving mileage of the electric vehicle. The actual remaining capacity is obtained based on the temperature data correction, and the remaining mileage is recalibrated through the accumulated traveled mileage data and the historical speed data, so that the prediction error of the endurance mileage can be reduced, and the accuracy of the endurance mileage prediction is improved”, Pg. 3, Lns. 10-18, determined using a Kalman filter modeling approach: “the optimal values of the first travel distance and the second travel distance are based on the Kalman filter model. In one possible embodiment, when starting the trip, two distances are tracked by Kalman: and (3) iteratively calculating the distance error according to the first travel distance (namely the distance obtained by the difference of two adjacent accumulated mileage) and the second travel distance (namely the distance calculated by integrating the speed and the time)”, Pg. 12, Lns. 20-26}.
Regarding Claim 18, the combination of Fu and Zhang discloses all the limitations of Claim 13, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the digital twin uses traffic data in addition to road data {“the real-time battery state influence data are characteristic data representing road conditions, driving habits, vehicle conditions and driving time”, Pg. 14, Lns. 3-4} to generate the speed profile {Pg. 11, Lns. 22-34 discusses generating velocity data, including: “calculating the maximum value, the minimum value and the mean value of the average speed of the last 10 trips per time aiming at each trip.”; also, “the first ensemble learning model is used for screening data….so that a scoring result of the driving state real-time data is obtained…weights may be set according to the actual meanings of the data in the real-time information of the driving state, for example, weights may be set for the information related to the speed, so that the information related to the speed is scored higher”, Pg. 6, Lns. 23-33}, and the collecting, fusing, estimating and displaying steps are performed without obtaining or using traffic or road data {calculation based on historical data is independent of any future trip destination: “the preset filtering algorithm may be Kalman filtering, and a linear regression model of the SOC consumption values and the electric quantity values is established for different vehicle types according to the acquired historical single trip data of the electric vehicle, so as to obtain a linear relationship between the SOC consumption values and the electric quantities of the different vehicle types”, Pg. 9, Lns. 1-5}.
Regarding Claim 20, the combination of Fu and Zhang discloses all the limitations of Claim 13, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the digital twin is calculated in a fleet monitor or data processing center remote from the electric vehicle {a digital twin is represented by the running of Kalman filter algorithm, on Pgs. 9-10, on server 11 in Fig. 11 and Pg. 15, Ln. 7, and additional calculations to determine the predicted mileage: “the vehicle endurance mileage predicting module is used for predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage”, Pg. 3, Lns. 22-25}.
Regarding Claim 21, the combination of Fu and Zhang discloses all the limitations of Claim 1, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the selected end of range battery capacity comprises a driver-determined battery state-of-charge, and is in a range from 15% to 30% of total battery state-of-charge {an input parameter related to driving habits (“wherein the real-time battery state influence data are characteristic data representing road conditions, driving habits, vehicle conditions and running time; and predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage.”, Pg. 2, Lns. 17-21) can be one of the parameters used in the Kalman filtering approach described on Pgs. 9-10; one skilled in the art will appreciate that driving habits can include a driver’s need for “driving range anxiety reduction” (Pg. 1, Lns. 25-29), which can take the form of driver not wanting the state-of-charge of the battery system to get close to zero}.
Claims 6 and 19 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Fu, Zhang, and Trancik et al. (US 11,346,678 B2, henceforth Trancik).
Regarding Claim 6, the combination of Fu and Zhang discloses all the limitations of Claim 1, as discussed supra. The combination of Fu and Zhang does not appear to explicitly disclose limitation: wherein the set of data for the previous trip of the electric vehicle is for a previous trip having at least a minimum distance traveled or a minimum duration of travel.
However, Trancik explicitly recites the limitation: wherein the set of data for the previous trip of the electric vehicle is for a previous trip having at least a minimum distance traveled or a minimum duration of travel {“wherein the driving behavior database contains information from driving trips of more than one vehicle for specific distances and specific durations”, Col. 14, Lns. 62-65}.
The combination of Fu and Zhang along with Trancik are analogous art because they deal with vehicle mileage endurance.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Fu, Zhang and Trancik before them, to modify the teachings of the combination of Fu and Zhang to include the teachings of Trancik to provide robust input data to improve the accuracy of an endurance mileage prediction {Abstract}.
Regarding Claim 19, the combination of Fu and Zhang discloses all the limitations of Claim 13, as discussed supra. The combination of Fu and Zhang does not appear to explicitly disclose limitation: wherein the set of data for the previous trip of the electric vehicle is for a previous trip having at least a minimum distance traveled or a minimum duration of travel.
However, Trancik explicitly recites the limitation: wherein the set of data for the previous trip of the electric vehicle is for a previous trip having at least a minimum distance traveled or a minimum duration of travel {“wherein the driving behavior database contains information from driving trips of more than one vehicle for specific distances and specific durations”, Col. 14, Lns. 62-65}.
Claims 8 and 14-17 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Fu, Zhang, and Liu et al. (CN 114889492 B, henceforth Liu).
Regarding Claim 8, the combination of Fu and Zhang discloses all the limitations of Claim 1, as discussed supra. The combination of Fu and Zhang does not appear to explicitly disclose limitation: wherein the set of model parameters are the parameters a and b in the following formula: Distance = a*SOCU + b wherein SOCU is a usable battery charge at a start of the new trip.
However, Liu explicitly recites the limitation: wherein the set of model parameters are the parameters a and b in the following formula: Distance = a*SOCU + b wherein SOCU is a usable battery charge at a start of the new trip {a linear expression between distance and state-of-charge/SOC: “ Dleft =K*SOC”, Pg. 6, Lns. 11-12}.
The combination of Fu and Zhang along with Liu are analogous art because they aim to improve the accuracy of estimating the range of electric vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Fu, Zhang and Liu before them, to modify the teachings of the combination of Fu and Zhang to include the teachings of Liu because to improve the accuracy of an endurance mileage prediction {Abstract}.
Regarding Claim 14, the combination of Fu and Zhang discloses all the limitations of Claim 13, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the fusing step is performed in a Kalman filter {“obtaining an iterative formula of the posterior estimation according to the correlation of the prior estimation and the posterior estimation, and fusing the formula of the Kalman filtering. The formulas of Kalman filtering can be finally simplified into an iterative formula…and randomly selecting 500 continuous sampling points of a certain travel, and performing Kalman filtering on the SOC”, Pg. 10, Lns. 1-17} having an R matrix {Pg. 9, Ln. 20}, a Q matrix {Pg. 9, Ln. 21}, and an error covariance matrix {“the covariance of the prior estimation error”, Pg. 9, Lns. 25-31}, by: constructing a first model using the EOR reference and the battery SOC EOR value {“obtaining the mileage per unit of electric energy according to the vehicle trip information and the battery state correction information”, Pg. 10, Lns. 16-25} as: a*SOCEOR + b + v1,k, where y1,k is the EOR reference, SOCEOR, is the battery SOC at EOR, and v1,k is a white noise defined by a variance of the Kalman filter R matrix, and a and b are model parameters {first formula of Kalman filtering: xk ＝xk-1 +Buk-1 +wk-1 …. wk-1 Noise, i.e. estimation error in the estimation, Pg. 9, Lns. 1-7}; pairing the first model with a second model of the form: y2,k = a*SOCk + b + v2,k where y2,k is the actually driven distance at a sample, k, SOCk, is the battery SOC at sample k, and v2,k is a white noise defined by a variance of the Kalman filter R matrix {second formula of Kalman filtering: zk ＝xk +vk, wherein vk To observe noise, Pg. 9, Lns. 11-13}.
The combination of Fu and Zhang does not appear to explicitly disclose limitation: using equations with distance directly proportional to the state of charge, such as, y1,k = a*SOCEOR+b+v1,k and y2,k = a*SOCk+b+v2,k.
However, Liu explicitly recites the limitation: a method using equations with distance directly proportional to the state of charge, such as, y1,k = a*SOCEOR+b+v1,k and y2,k = a*SOCk+b+v2,k {a linear expression between distance and state-of-charge/SOC: “Dleft =K*SOC”, Pg. 6, Lns. 11-12}.
Regarding Claim 15, the combination of Fu, Zhang, and Liu discloses all the limitations of Claim 14, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the model parameters a and b are treated as constants and are updated from one sample to a next sample using formulas given by: pk = pk-1 + wp,k-1, bk = bk-1 + wb,k-1 and wp,k-1 is the process noise of the a parameter and wb,k-1 is the process noise of the b parameters, each given by the Kalman filter Q matrix {second Kalman filtering processes, including an error covariance associated with a normal white noise distribution: Pg. 9, Lns. 6-22}.
Regarding Claim 16, the combination of Fu, Zhang, and Liu discloses all the limitations of Claim 15, as discussed supra. In addition, Fu explicitly recites the limitation: further comprising updating the estimated EOR for the vehicle in the current trip using the updated model parameters {“obtaining the mileage per unit of electric energy according to the vehicle trip information and the battery state correction information”, Pg. 10, Lns. 16-25}, and displaying to a driver of the vehicle an updated estimated EOR at least once {“the driving state information screening module is used for screening the driving state real-time information of the vehicle during driving based on the first integrated learning model to obtain battery state real-time influence data, and the battery state real-time influence data”, Pg. 14, Lns. 16-19}.
Regarding Claim 17, the combination of Fu, Zhang, and Liu discloses all the limitations of Claim 15, as discussed supra. In addition, Fu explicitly recites the limitation: wherein the steps of collecting, fusing, estimating and displaying are performed repeatedly at sample times as the vehicle traverses the path {“And obtaining an iterative formula of the posterior estimation according to the correlation of the prior estimation and the posterior estimation, and fusing the formula of the Kalman filtering. The 5 formulas of Kalman filtering can be finally simplified into an iterative formula”, Pg. 10, Lns. 7-9}.
Claims 9 and 12 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Fu and Klein et al. (US 9,960,625 B2, henceforth Klein).
Regarding Claim 9, Fu teaches the limitations: a method of generating estimates of end of range for an electric vehicle {“a method, a device, equipment and a storage medium for predicting the endurance mileage of a vehicle”, Abstract, and “a vehicle driving range prediction apparatus, including: the system comprises a state information acquisition module, a running state information screening module and a vehicle endurance mileage prediction module”, Pg. 2, Lns. 23-25}, comprising: obtaining a set of data for a previous trip of the electric vehicle {S530, obtaining the driving state history information of the vehicle; S540, the travel state history information as input information, constructing a first integrated learning model; s550, screening the historical driving state information based on an importance degree sorting algorithm preset in the first integrated learning model to obtain historical battery state influence data of the vehicle; specifically, each vehicle identification number is sorted according to the travel starting time, and the historical information of the running state of the vehicle is obtained.”, Pg. 11, Lns. 15-20}; estimating a set of model parameters for reduction of battery capacity relative to distance travelled, from a starting battery capacity to a selected end of range battery capacity using the previous trip data {“s510, acquiring battery state historical information and corresponding vehicle travel information of different types of vehicles”, Pg. 7, Lns. 33-34}, by entering the previous trip data as inputs into a Kalman filter algorithm {in-depth use of Kalman filtering described starting on Pg. 9 and going through the middle of Pg. 10; “the preset filtering algorithm may be Kalman filtering, and a linear regression model of the SOC consumption values and the electric quantity values is established for different vehicle types according to the acquired historical single trip data of the electric vehicle, so as to obtain a linear relationship between the SOC consumption values and the electric quantities of the different vehicle types”, Pg. 9, Lns. 1-5}; wherein the selected end of range battery capacity comprising as a non-zero percentage of a total battery state-of-charge {Fu teaches of the need to anticipate “end of range” in a manner to relieve driver anxiety (i.e., “The remaining driving mileage of the electric automobile is used for indicating the mileage of the automobile which can run before the energy is exhausted, so that the automobile owner is reminded to prepare the energy in advance before driving the automobile or in the process of driving the automobile, and the half-way energy is prevented from being exhausted to influence a trip plan”, Pg. 1, Lns. 26-30), and combines this with taking into account the driving habits of the driver as reflected in historical data on battery state and travel (“the real-time information of the driving state when the vehicle is running is screened to obtain the real-time impact data of the battery state, and the real-time impact data of the battery state is characteristic data representing road conditions, driving habits, vehicle conditions and driving time”, Pg. 6, Lns. 10-13, and “s510, acquiring battery state historical information and corresponding vehicle travel information of different types of vehicles; s520, determining the driving mileage information of unit electric quantity based on the battery state historical information and the vehicle travel information; further, referring to Fig. 6, the determining the mileage per unit charge based on the battery status history information and the vehicle trip information”, Pg. 7, Lns. 29-34)}; for a new trip, applying the set of model parameters {“acquiring the real-time information of the battery state and the real-time information of the running state of the vehicle when the vehicle runs currently”, Pg. 2, Lns. 14-15} to generate an estimated end of range distance {the second integrated learning model, which is used to predict vehicle endurance mileage - “the vehicle endurance mileage predicting module is used for predicting the endurance mileage of the battery state real-time influence data and the battery state real-time information based on a second integrated learning model to obtain the vehicle endurance mileage”, Pg. 2, Lns. 31-34, uses the Kalman filtering algorithm, described on Pgs. 9-10, to correct, i.e., improve the accuracy of, battery state information, whether historical or real-time: “The method also constructs an integrated learning model on the basis of correcting the battery state information and selecting the characteristic data, trains the data in an integrated learning model mode”, Pg. 3, Lns. 18-21}; and displaying the estimated end of range distance to a driver of the vehicle, wherein the displayed prediction is updated as the new trip progresses by obtaining new data from the new trip {“the vehicle network 120 pushes the driving range prediction result to the vehicle-mounted terminal 110”, Pg. 5, Lns. 19-20}.
Fu does not appear to explicitly recite the limitations: wherein the algorithm is based on a moving horizon observer; and comprising: entering the new data in the moving horizon observer while removing oldest data from the moving horizon observer.
However, Klein explicitly recites the limitations: wherein the algorithm is based on a moving horizon observer, and entering the new data in the moving horizon observer while removing oldest data from the moving horizon observer {use of a Moving Horizon technique rather than a Kalman filter, which uses a sliding window of recent past measurements to estimate the current state of a system: “Alternative methods for obtaining internal state estimates could also be based on Kalman filtering theory (extended KF, unscented KF, sigma point KF, iterated KF, etc.), moving horizon estimator theory”, Col. 6, Lns. 62-66; and Col. 7, Lns. 11-29}.
Fu and Klein are analogous art because they both deal with battery charge estimation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Fu and Klein before them, to modify the teachings of the combination of Fu to include the teachings of Klein to provide an alternative algorithmic approach to a Kalman filter to estimating internal battery states {Col. 5, Lns. 62-66}.
Regarding Claim 12, the combination of Fu and Klein discloses all the limitations of Claim 9, as discussed supra. In addition, Fu explicitly recites the limitation: further comprising recording {“a storage medium is provided, which includes a processor and a memory…to implement a vehicle range prediction method as described above”, Pg. 3, Lns. 7-10, and “vehicle-mounted terminal 110 collects driving state data and battery state data of a vehicle,”, Pg. 5, Ln. 14}, distance {“the battery state real-time information are input into the second integrated learning model to predict the driving mileage so as to obtain the driving mileage of the vehicle”, Pg. 3, Lns. 16-18}, speed {“calculating the maximum value, the minimum value and the mean value of the average speed of the last 10 trips per time aiming at each trip.”, Pg. 11, Lns. 27-28}, and battery parameters {“SOC value collected by the sensor is a measured value of a true SOC value, and the sum of the measured value and the measurement error is a true value”, Pg. 9, Lns. 12-13} during the new trip for use in performing the estimating and calculating steps for a subsequent trip {data collection: “vehicle-mounted terminal 110 collects driving state data and battery state data of a vehicle, the data collected by the vehicle-mounted terminal 110 is sent to the internet of vehicles 120, the data is reported to the server 130 by the internet of vehicles 120, the server 130 invokes a first ensemble learning model and a second ensemble learning model, performs driving range prediction according to the data collected by the vehicle-mounted terminal 110”, Pg. 5, Lns. 14-18}.
Claim 10 is rejected under 35 U.S.C. §103 as being unpatentable over the combination of Fu, Klein and Trancik.
Regarding Claim 10, the combination of Fu and Klein discloses all the limitations of Claim 9, as discussed supra. The combination of Fu and Klein does not appear to explicitly disclose limitation: wherein the set of data for the previous trip of the electric vehicle is for a previous trip having at least a minimum distance traveled or a minimum duration of travel.
However, Trancik explicitly recites the limitation: wherein the set of data for the previous trip of the electric vehicle is for a previous trip having at least a minimum distance traveled or a minimum duration of travel {“wherein the driving behavior database contains information from driving trips of more than one vehicle for specific distances and specific durations”, Col. 14, Lns. 62-65}.
The combination of Fu and Klein along with Trancik are analogous art because they deal with vehicle mileage endurance.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Fu, Klein and Trancik before them, to modify the teachings of the combination of Fu and Klein to include the teachings of Trancik to provide robust input data to improve the accuracy of an endurance mileage prediction {Abstract}.
Claim 11 is rejected under 35 U.S.C. §103 as being unpatentable over the combination of Fu, Klein and Liu.
Regarding Claim 11, the combination of Fu and Klein discloses all the limitations of Claim 9, as discussed supra. The combination of Fu and Klein does not appear to explicitly disclose limitation: wherein the set of model parameters are the parameters a and b in the following formula: Distance = a*SOCU + b wherein SOCU is a usable battery charge at a start of the new trip.
However, Liu explicitly recites the limitation: wherein the set of model parameters are the parameters a and b in the following formula: Distance = a*SOCU + b wherein SOCU is a usable battery charge at a start of the new trip {a linear expression between distance and state-of-charge/SOC: “ Dleft =K*SOC”, Pg. 6, Lns. 11-12}.
The combination of Fu and Klein along with Liu are analogous art because they aim to improve the accuracy of estimating the range of electric vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Fu, Klein and Liu before them, to modify the teachings of the combination of Fu and Klein to include the teachings of Liu because to improve the accuracy of an endurance mileage prediction {Abstract}.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
GB 2505663 A – A residual range indicator is provided that directly displays an estimated range (Figs. 2-3), with the maximum and minimum range being determined on different set of conditions or “range modifiers, with computations being provided by a processor with range and driving style algorithms, which take into account a variety of factors as input data (“The processor can be configured to select the first range modifier and/or the second range modifier based on one or more of the following: driving style data relating to the present journey and/or at least one historic journey; historic usage data associated with a driver of the vehicle; predicted route data based on historic route data; user-defined criteria; ambient conditions; road conditions; battery temperature etc.”, Pg. 2, Lns. 18-22).
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/R.E.G./Examiner, Art Unit
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/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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