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 . In the event the determination of the status of the application as subject to 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.
Status of Claims
Claims 1-5 and 7-14 are pending. Claims 1 and 8 are independent. Claims 1-2, 4-5, and 8-14 are amended.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 November 2025 has been entered.
Response to Amendment/Remarks
The Examiner received remarks and amendments to the claims dated 10 November 2025 in response Final Rejection office action dated 12 August 2025 (hereinafter, “prior office action”).
All objections from the prior office action are withdrawn. There are new objections issued (see below).
Applicant’s argument with respect to the rejection under 35 U.S.C. 101 has been considered, but is not persuasive. Applicant argues “Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations” (MPEP 2106.04(a)(2)(III)(A)). Examiner notes these claims do include mental processes, and therefore this argument is not persuasive. From the same section of the MPEP, examples of claims that do recite mental processes are “limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions”. As one specific example, Claim 8 recites “associate (S2) a usage category with the usage behavior” (there is a similar limitation in Claim 1). This limitation fits the definition of “observations, evaluations, judgments, and opinions” and includes steps easily completed in the human mind (for example, evaluating/judging if the number of miles driven is “harsh” or “not harsh”). Therefore, since both independent claims are directed to mental processes, then all claims are directed to mental processes. Additionally, while many limitations require either a computer or a non-transitory computer-readable storage medium; this does not mean the claims are not directed to mental processes because, per MPEP 2106.04(a)(2)(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”.
Applicant’s argument with respect to the rejections under 35 U.S.C. 103 has been considered, and is persuasive, accordingly the rejections under 35 U.S.C. 103 from the prior office action are withdrawn. Upon further consideration, new means of rejection under 35 U.S.C. 103 have been issued (see below). The new means of rejection were necessitated by amendment.
Claim Objections
Claim 1 is objected to because of the following informalities:
“the operational parameter profile” should be “the at least one operational parameter profile”.
“of the corresponding device battery” should be “of the
Claim 2 is objected to because of the following informalities:
“based on at least one usage parameter” should be “based on the at least one usage parameter”.
“of at least one usage parameter” should be “of the at least one usage parameter”.
Claim 4 is objected to because of the following informality:
“at least one operational parameter profile” should be “the at least one operational parameter profile”.
Claim 8 is objected to because of the following informality:
“of the corresponding device battery” should be “of the
Claim 9 is objected to because of the following informality:
“the operational parameter profile” should be “the at least one operational parameter profile”.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5 and 7-14 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-5 and 7-14 are rejected because the term “irregularity” in Claims 1 and 8 is a relative term which renders the claim, and all its dependent claims, as indefinite. For the purposes of compact prosecution, this is interpreted as “in an event a deviation between the operational parameter data and the at least one operational parameter profile occurs,
Claims 1-5 and 7-14 are further rejected because the term “types” (“plurality of various technical device types”) in Claims 1 and 8 is a relative term which renders the claim, and all its dependent claims, as indefinite. See MPEP 2173.05(b)(III)(E). For the purposes of compact prosecution, this is interpreted as “plurality of various technical devices
Claim 3 is further rejected because the term “type” (“type of charging cycles”) is a relative term which renders the claim indefinite. See MPEP 2173.05(b)(III)(E). For the purposes of compact prosecution, “type of charging cycles” is interpreted as “[type] proportion of fast charging cycles”. Appropriate corrections are required.
Claims 4-5 are further rejected because it is indefinite if “which profile” (in Claim 4) corresponds to “at least one load parameter profile”, “the predicted usage parameter profile”, one of the other introduced profiles, or a not-yet-introduced profile. For the purposes of compact prosecution, Examiner will assume it refers to any of these. Applicant should ensure there is support in the original disclosure for any amendments. Appropriate corrections are required.
Claims 4-5 are further rejected because it is indefinite if “wherein simulating the ageing state profile” (in Claim 4) should instead be “wherein simulating the predicted ageing state profile” or “wherein [[simulating]] determining the ageing state profile”. (Applicant has introduced these as two separate limitations in Claim 1). For the purposes of compact prosecution, Examiner will assume either of these interpretations reads on the claims. Applicant should ensure there is support in the original disclosure for any amendments. Appropriate corrections are required.
Claim 5 is further rejected because Applicant’s intended meaning (“wherein determining the at least one operational parameter profile comprises determining by means of a battery performance model dependent on the at least one load parameter profile, wherein model parameters of the battery performance model are adjusted with respect to the respective predicted ageing state profile”) does not state what is being determined as written, therefore making the claim indefinite. For the purposes of compact prosecution, Examiner has interpreted this as “wherein determining the at least one operational parameter profile comprises ,
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that use the word “means” but are nonetheless not being interpreted under 35 U.S.C. 112(f) because the claim limitations recite sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitations are: …wherein simulating the ageing state profile for the predicted usage parameter profile initially includes predicting at least one load parameter profile for the device battery, which profile corresponds to at least one operational parameter profile for the device battery, by means of a predetermined useful life period operational model for each technical device… in Claim 4. Also, …wherein determining the at least one operational parameter profile comprises determining by means of a battery performance model dependent on the at least one load parameter profile, wherein model parameters of the battery performance model are adjusted with respect to the respective predicted ageing state profile… in Claim 5.
Because these claim limitations are not being interpreted under 35 U.S.C. 112(f) they are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitations interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitations to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitations do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-5 and 7-14 are rejected under 35 U.S.C. 101, because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5 and 11-14 are directed to a method. Claim 7 is directed to an apparatus. Claims 8-10 are directed to a non-transitory computer-readable storage medium. Therefore, Claims 1-5 and 7-14 meet the requirements to be considered a statutory category.
Step 2A Prong 1: The independent claims (Claim 1 and 8) each recite at least one abstract idea. The following limitations are considered abstract ideas because they could be reasonably completed in the human mind:
selecting a technical device having a device battery and belonging to a plurality of various technical device types (Claim 1)
obtaining (S1)…a usage behavior from a database (Claim 1) / obtain (S1) a usage behavior from a database (Claim 8)
associating (S2)…a usage category with the usage behavior (Claim 1) / associate (S2) a usage category with the usage behavior (Claim 8)
determining (S3)…a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a mode of operation affecting a load on the device battery for powering the technical device (Claim 1) / determine (S3) a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a mode of operation of a technical device belonging to a plurality of technical devices affecting a load on the device battery for powering the technical device (Claim 8)
obtaining…a predetermined useful life period operational model for each technical device belonging to the plurality of various technical device types (Claim 1) / obtain a predetermined useful life period for each technical device (Claim 8)
determining…at least one operational parameter profile for the device battery based on the predicted usage parameter profile and the predetermined useful life period operational model for each technical device (Claim 1) / determine at least one operational parameter profile for the device battery based on the predicted usage parameter profile and the predetermined useful life period for each technical device (Claim 8)
determining (S4)…an ageing state profile for the device battery over a predetermined amount of time based on a differential equation system, wherein the differential equation system utilizes a chronological integration method to determine an ageing state at a specific time depending on the operational parameter profile up to the specific time (Claim 1) / determine (S4) an ageing state profile for the device battery over a predetermined amount of time based on the at least one operational parameter profile (Claim 8)
simulating (S5) for each technical device…a predicted ageing state profile for the predicted usage parameter profile for the predetermined amount of time in order to determine a predicted ageing state at a predetermined end of useful life period (Claim 1) / simulate a predicted ageing state profile for the predicted usage parameter profile for the predetermined amount of time for each technical device in order to determine a predicted ageing state at a predetermined end of useful life period (Claim 8)
determining…a residual value associated with each technical device based on the predicted ageing state (Claim 1) / determine a residual value associated with the selected technical device based on the predicted ageing state (Claim 8)
selecting (S6)…a technical device from the plurality of technical devices, with a maximum residual value at the predetermined end of useful life period (Claim 1) / select (S6) a technical device from the plurality of technical devices, with a maximum residual value at the predetermined end of useful life period (Claim 8)
comparing…the at least one operational parameter profile to the operational parameter data (Claim 1) / compare the at least one operational parameter profile to the operational parameter data (Claim 8)
in an event a deviation between the operational parameter data and the at least one operational parameter profile occurs, determining an irregularity and planning predictive maintenance based on the irregularity (Claim 1) / determine an irregularity and plan predictive maintenance based on the irregularity when a deviation between the operational parameter data and the at least one operational parameter profile occurs (Claim 8)
The additional elements in Claims 1-5 and 7-14 are grouped together as follows:
A computer-implemented method of… (Claim 1) / …via a computer… (Claim 1)
sampling, via the computer and during operation of the selected technical device, operational parameter data of the corresponding device battery (Claim 1) / sample during operation of the selected technical device, operational parameter data of the corresponding device battery (Claim 8) / wherein sampling the operational parameter data occurs at a temporal resolution in the range of 1 to 100 HZ (Claim 11)
An apparatus for performing a method according to claim 1 (Claim 7)
A non-transitory computer-readable storage medium comprising instructions that, when executed by a computer cause the computer to… (Claim 8)
wherein the predicted ageing state profile comprises a hybrid ageing state model including a physical ageing model and a data-based model including an artificial intelligence-based probabilistic regression model (Claim 12)
Step 2A Prong 2: the additional elements recited above, when taken individually and in combination, do not result in the claim as a whole being integrated into a practical application. The additional elements in groupings a, c, and d are merely apply the abstract idea to one or more generic computing components (see MPEP 2106.05(f)). The additional elements in grouping b merely add insignificant extra-solution activity (see MPEP 2106.05(g)). The additional element in grouping e is merely generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Step 2B: the additional elements recited above, when taken individually and in combination, do not result in the claim as a whole amounting to significantly more than the judicial exception because the office takes Official Notice they are well-understood, routine, and conventional activity previously known to the industry, specified at a high level of generality (see MPEP 2106.05(d)), or else they are insignificant pre-solution and/or post-solution activity in the form of mere data gathering (additional elements in grouping b) (see numerous court decisions pertaining to observations, evaluations, judgements, and opinions, such as the findings from Electric Power Group where it was found that collecting information, analyzing it, and outputting certain results of the collection and analysis was not significantly more than the judicial exception – see MPEP 2106.05(d)(II)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2023/0288489 (Jin et al., hereinafter, Jin) with effective filing date 28 January 2022 in view of U.S. Pub. No. 2015/0213420 (Krishnamurthy et al., hereinafter, Krishnamurthy).
Regarding Claim 8, Jin discloses A non-transitory computer-readable storage medium comprising instructions (See at least [0069] and FIG. 3). that, when executed by a computer cause the computer to
obtain (S1) a usage behavior from a database (see at least [0011]-[0018], [0070]-[0071], and [0083]: “The system may further comprise a database which stores the historical vehicle driving information. This can allow vehicle driving information to be collected and accumulated over time to form the historical vehicle driving information.”);
associate (S2) a usage category with the usage behavior (see at least [0070]-[0072] and [0083]: “clustering algorithms, for example based on GPS, road grade and/or velocity information”);
determine (S3) a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a mode of operation of a technical device belonging to a plurality of technical devices affecting a load on the device battery for powering the technical device (see at least [0019]-[0020] and [0072]: “The vehicle route profile generator 40 is used to generate a route profile for individual vehicles. The vehicle route profile is a predicted profile of how that vehicle will be driven over a predetermined period of time in the future. The vehicle route profile is generated based on historic driving information for that vehicle and other vehicles stored in the database 38. Predicted route characteristics, such as weather forecasts and traffic forecasts, may also be used. Techniques for generating the vehicle route profile include: clustering algorithms, for example based on GPS, road grade and/or velocity information; Markov chain based algorithms; and machine learning algorithms such as generative adversarial networks. These algorithms may be implemented using a suitably programmed processor. The vehicle route profile may indicate, for example, predicted operating times, predicted operating conditions, and predicted rest times for the vehicle.”);
obtain a predetermined useful life period for each technical device (see at least [0111]: “a plurality of time windows of a predetermined length is defined. The time windows can be any appropriate length, and may be for example days, weeks or months (for example, 30, 60 or 90 days or any other appropriate value)”);
determine at least one operational parameter profile for the device battery based on the predicted usage parameter profile and the predetermined useful life period for each technical device (see at least [0073]-[0075]: “The vehicle digital twin 42 receives a vehicle route profile from the vehicle route profile generator 40 and coverts it into a battery demand profile. The battery demand profile is a predicted profile of the demands that will be placed on the battery over a predetermined period of time. For example, the battery demand profile may be a predicted profile of battery current and/or power request with respect to time. The vehicle digital twin 42 uses a model of the vehicle powertrain to produce the battery demand profile from the vehicle route profile. The power train model may include physics based transfer equations for the powertrain, and machine learning (ML) models. In one embodiment, the model is a semi-empirical model using a combination of physics equations and data tables. The model may be implemented using a suitably programmed processor. The output of the vehicle digital twin 42 is a battery demand profile which comprises predictive battery duty cycle information based on factors such as future weather, traffic, route, and usage information.”; “The system-to-cell converter 45 receives the battery demand profile from the vehicle digital twin 42, and coverts it to a cell-level operating profile. This conversion is carried out because the vehicle digital twin operates at a system (vehicle) level, whereas the RUL estimator 46 operates at the level of the battery cells. The system-to-cell converter 45 uses a model of the battery system which relates system level demand power/current to cell level current demand. The model of the battery system may be provided in advance based on laboratory data and/or may be updated during use based on data received from the vehicle’s battery management system. Where the battery system comprises a plurality of battery packs, the conversion is carried out for each battery pack. As indicated by the dashed line in FIG. 3, values from the system-to-cell converter 45 can be fed back to the battery digital twin 42 to help improve the accuracy of the modelling. In one embodiment, the output of the system to cell converter 45 is a cell level state of charge (SOC) cycle for the vehicle over the predetermined period of time. However, the output of the system to cell converter 45 could comprise other cyclic information such as current against time, voltage against time and/or temperature against time as well or instead.”);
determine (S4) an ageing state profile for the device battery over a predetermined amount of time based on the at least one operational parameter profile (see at least [0076]-[0078] and FIG. 4: “The SOH profile generator 48 uses the RUL estimates to produce an SOH profile. The SOH profile is a trendline of the predicted SOH of the vehicle’s battery system”);
simulate a predicted ageing state profile for the predicted usage parameter profile for the predetermined amount of time for each technical device in order to determine a predicted ageing state at a predetermined end of useful life period (see at least [0076]-[0078] and FIG. 4: “The SOH profile generator 48 uses the RUL estimates to produce an SOH profile. The SOH profile is a trendline of the predicted SOH of the vehicle’s battery system”);
select (S6) a technical device from the plurality of technical devices, based on an economic factor at the predetermined end of useful life period (see at least [0004], [0026]-[0027], [0067], [0081]-[0082], and [0100]-[0108]: “At a fleet level, fleet operation may be optimized by scheduling vehicles with batteries that are close to end of life to have a more favorable driving characterization profile. This may allow the fleet to extend the life of the batteries and perform scheduled downtimes. This may be achieved by checking the predicted state of health profiles of a plurality of batteries in the fleet and prioritizing and optimizing vehicle operation according to the predicted state of health profiles.”; “Thus, the vehicle may be a vehicle in a fleet of vehicles and the system may further comprise a fleet scheduling module arranged to generate fleet scheduling commands based on a predicted state of health profile for each of a plurality of vehicles in the fleet. The fleet scheduling module may be arranged to compare the predicted state of health profiles for the plurality of vehicles in the fleet and to determine which vehicles have more severely aged batteries in comparison to other vehicles in the fleet. The scheduling commands may comprise at least one of: prioritize vehicles with less severely aged batteries; schedule vehicles with more severely aged batteries to avoid operating under harsh ambient conditions (for example, at noon or early in the afternoon when high temperatures accelerate the aging); schedule vehicles with more severely aged batteries to avoid operating on demanding routes (for example, routes with a large number of hills or long distances), and schedule vehicles with severely aged batteries to avoid operating with heavy loads.”; “When a battery no longer meets electric vehicle performance standards it may need to be replaced. Typically, a battery replacement or servicing date is scheduled in advance. The battery’s SOH may also be monitored to assist with organising maintenance and replacement schedules. However, the useful life of a battery will depend on how the vehicle is driven. Therefore, there is a risk that on one hand a battery may be replaced or serviced sooner than necessary, or on the other hand that the battery could fail while in service. Battery failure could result in significant warranty cost and downtime cost to the operator. It would therefore be desirable to provide an alert a certain amount of time before the battery reaches its end of life to allow the option of performing a scheduled downtime.”);
sample during operation of the selected technical device, operational parameter data of the corresponding device battery (see at least [0030] and [0113]-[0114]: “By charging the battery at a known current and determining the amount of time it takes for the battery to reach full charge, an estimate of the battery capacity can be obtained. This estimate can then be transmitted from the vehicle to the fleet management system.”);
compare the at least one operational parameter profile to the operational parameter data (see at least [0111]-[0118], FIG. 8, and FIG. 9: “In step 130, the estimates of usable capacity received from the vehicle are used to update a battery model in the fleet management system. The battery model is a model of how the battery capacity changes with battery use. The battery model may comprise a system-to-cell converter and a remaining useful life estimator in the form described above with reference to FIG. 3. By using actual measurements received from the vehicle to update the battery model, the accuracy of the battery model can be increased over time.”); and
determine an irregularity and plan predictive maintenance based on the irregularity when a deviation between the operational parameter data and the at least one operational parameter profile occurs (see at least [0111]-[0118], FIG. 8, and FIG. 9: “In step 130, the estimates of usable capacity received from the vehicle are used to update a battery model in the fleet management system. The battery model is a model of how the battery capacity changes with battery use. The battery model may comprise a system-to-cell converter and a remaining useful life estimator in the form described above with reference to FIG. 3. By using actual measurements received from the vehicle to update the battery model, the accuracy of the battery model can be increased over time.”; “If it is determined that the battery will reach end of life within the next time window, then in step 138 an alert is provided that the battery will reach end of life. The operations manager and sales, distribution and servicing center are then advised that the battery should be replaced”).
Jin does not explicitly disclose determine a residual value associated with the selected technical device based on the predicted ageing state;
based on an economic factor is with a maximum residual value.
Jin discloses the predicted ageing state of the battery is a vehicle condition factor. Krishnamurthy, in the same field of vehicle information management, and therefore analogous art, teaches determine a residual value associated with the selected technical device based on a vehicle condition factor (see at least [0027] and [0064]: “The extrapolated residual value at the end of the lease term is extrapolated periodically throughout the term using the vehicle condition data that is continuously collected and extrapolated to adjust the previously determined residual value to calculate the periodic discount”; “The data is sent from the TCU 14 to the data center 30 to manipulate the data and determine the extrapolated residual value”);
based on an economic factor is with a maximum residual value (see at least [0023]: “ If all factors used to determine residual value for two vehicles of the same make, model and year are the same or equal, the vehicle in the better condition at the end of the lease will have a higher residual value”).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to combine the teachings of Jin and Krishnamurthy with the motivation of providing economic-benefit to both the driver and owner (when the vehicle is being driven by a non-owner): “the driver's monthly payment decreases” and “The owner of the vehicle benefits financially from the program. The owner will generate additional revenue from a higher residual value for the vehicle at the end of the term due to the improved vehicle condition at the end of the term.” (see at least Krishnamurthy [0080]-[0081]).
Regarding Claim 10, the Jin and Krishnamurthy combination teaches the limitations of Claim 8. Furthermore, Krishnamurthy further teaches (with the same motivation to combine as Claim 8) further comprising instructions to output a lease rate for the technical device based on the maximum residual value (see at least [0080]: “Therefore, for the next three months of the lease, the driver's monthly payment decreases based on the decrease in effective interest rate”).
Claims 1-5, 7, 9, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Jin in view of Krishnamurthy in view of U.S. Pub. No. 2020/0009988 (Basler et al., hereinafter, Basler).
Regarding Claim 1, Jin discloses A computer-implemented method, of selecting a technical device having a device battery and belonging to a plurality of various technical device types (see at least [0026]-[0027] and [0063]: “fleet operation may be optimized by scheduling vehicles with batteries that are close to end of life to have a more favorable driving characterization profile”; “the system comprises a plurality of electric vehicles 10, a network 12, one or more charging depots 13, an operations manager 14, a sales, distribution and servicing center 15 and a fleet management system 16. Each electric vehicle 10 may be an electric or hybrid electric vehicle, and may comprise a traction motor powered by one or more battery packs”), said method comprising the following steps:
obtaining (S1), via a computer, a usage behavior from a database (see at least [0011]-[0018], [0070]-[0071], and [0083]: “ The system may further comprise a database which stores the historical vehicle driving information. This can allow vehicle driving information to be collected and accumulated over time to form the historical vehicle driving information.”);
associating (S2), via the computer, a usage category with the usage behavior (see at least [0070]-[0072] and [0083]: “clustering algorithms, for example based on GPS, road grade and/or velocity information”);
determining (S3), via the computer, a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a mode of operation affecting a load on the device battery for powering the technical device (see at least [0019]-[0020] and [0072]: “The vehicle route profile generator 40 is used to generate a route profile for individual vehicles. The vehicle route profile is a predicted profile of how that vehicle will be driven over a predetermined period of time in the future. The vehicle route profile is generated based on historic driving information for that vehicle and other vehicles stored in the database 38. Predicted route characteristics, such as weather forecasts and traffic forecasts, may also be used. Techniques for generating the vehicle route profile include: clustering algorithms, for example based on GPS, road grade and/or velocity information; Markov chain based algorithms; and machine learning algorithms such as generative adversarial networks. These algorithms may be implemented using a suitably programmed processor. The vehicle route profile may indicate, for example, predicted operating times, predicted operating conditions, and predicted rest times for the vehicle.”);
obtaining, via the computer, a predetermined useful life period operational model for each technical device belonging to the plurality of various technical device types (see at least [0073]: “vehicle digital twin 42 is a model of how the battery demand for the vehicle will vary in dependence on the vehicle route profile”);
determining, via the computer, at least one operational parameter profile for the device battery based on the predicted usage parameter profile and the predetermined useful life period operational model for each technical device (see at least [0073]-[0075]: “The vehicle digital twin 42 receives a vehicle route profile from the vehicle route profile generator 40 and coverts it into a battery demand profile. The battery demand profile is a predicted profile of the demands that will be placed on the battery over a predetermined period of time. For example, the battery demand profile may be a predicted profile of battery current and/or power request with respect to time. The vehicle digital twin 42 uses a model of the vehicle powertrain to produce the battery demand profile from the vehicle route profile. The power train model may include physics based transfer equations for the powertrain, and machine learning (ML) models. In one embodiment, the model is a semi-empirical model using a combination of physics equations and data tables. The model may be implemented using a suitably programmed processor. The output of the vehicle digital twin 42 is a battery demand profile which comprises predictive battery duty cycle information based on factors such as future weather, traffic, route, and usage information.”; “The system-to-cell converter 45 receives the battery demand profile from the vehicle digital twin 42, and coverts it to a cell-level operating profile. This conversion is carried out because the vehicle digital twin operates at a system (vehicle) level, whereas the RUL estimator 46 operates at the level of the battery cells. The system-to-cell converter 45 uses a model of the battery system which relates system level demand power/current to cell level current demand. The model of the battery system may be provided in advance based on laboratory data and/or may be updated during use based on data received from the vehicle’s battery management system. Where the battery system comprises a plurality of battery packs, the conversion is carried out for each battery pack. As indicated by the dashed line in FIG. 3, values from the system-to-cell converter 45 can be fed back to the battery digital twin 42 to help improve the accuracy of the modelling. In one embodiment, the output of the system to cell converter 45 is a cell level state of charge (SOC) cycle for the vehicle over the predetermined period of time. However, the output of the system to cell converter 45 could comprise other cyclic information such as current against time, voltage against time and/or temperature against time as well or instead.”);
simulating (S5) for each technical device, via the computer, a predicted ageing state profile for the predicted usage parameter profile for the predetermined amount of time in order to determine a predicted ageing state at a predetermined end of useful life period (see at least [0032], [0076]-[0078], [0118], FIG. 4, FIG. 7, and FIG. 9: “The SOH profile generator 48 uses the RUL estimates to produce an SOH profile. The SOH profile is a trendline of the predicted SOH of the vehicle’s battery system”; “Then in step 140 the various estimates of battery capacity are combined and used to produce or update the SOH profile.”);
selecting (S6), via the computer, a technical device from the plurality of technical devices, based on an economic factor at the predetermined end of useful life period (see at least [0004], [0026]-[0027], [0067], [0081]-[0082], and [0100]-[0108]: “At a fleet level, fleet operation may be optimized by scheduling vehicles with batteries that are close to end of life to have a more favorable driving characterization profile. This may allow the fleet to extend the life of the batteries and perform scheduled downtimes. This may be achieved by checking the predicted state of health profiles of a plurality of batteries in the fleet and prioritizing and optimizing vehicle operation according to the predicted state of health profiles.”; “Thus, the vehicle may be a vehicle in a fleet of vehicles and the system may further comprise a fleet scheduling module arranged to generate fleet scheduling commands based on a predicted state of health profile for each of a plurality of vehicles in the fleet. The fleet scheduling module may be arranged to compare the predicted state of health profiles for the plurality of vehicles in the fleet and to determine which vehicles have more severely aged batteries in comparison to other vehicles in the fleet. The scheduling commands may comprise at least one of: prioritize vehicles with less severely aged batteries; schedule vehicles with more severely aged batteries to avoid operating under harsh ambient conditions (for example, at noon or early in the afternoon when high temperatures accelerate the aging); schedule vehicles with more severely aged batteries to avoid operating on demanding routes (for example, routes with a large number of hills or long distances), and schedule vehicles with severely aged batteries to avoid operating with heavy loads.”; “When a battery no longer meets electric vehicle performance standards it may need to be replaced. Typically, a battery replacement or servicing date is scheduled in advance. The battery’s SOH may also be monitored to assist with organising maintenance and replacement schedules. However, the useful life of a battery will depend on how the vehicle is driven. Therefore, there is a risk that on one hand a battery may be replaced or serviced sooner than necessary, or on the other hand that the battery could fail while in service. Battery failure could result in significant warranty cost and downtime cost to the operator. It would therefore be desirable to provide an alert a certain amount of time before the battery reaches its end of life to allow the option of performing a scheduled downtime.”);
sampling, via the computer and during operation of the selected technical device, operational parameter data of the corresponding device battery (see at least [0030] and [0113]-[0114]: “By charging the battery at a known current and determining the amount of time it takes for the battery to reach full charge, an estimate of the battery capacity can be obtained. This estimate can then be transmitted from the vehicle to the fleet management system.”);
comparing, via the computer, the at least one operational parameter profile to the operational parameter data (see at least [0111]-[0118], FIG. 8, and FIG. 9: “In step 130, the estimates of usable capacity received from the vehicle are used to update a battery model in the fleet management system. The battery model is a model of how the battery capacity changes with battery use. The battery model may comprise a system-to-cell converter and a remaining useful life estimator in the form described above with reference to FIG. 3. By using actual measurements received from the vehicle to update the battery model, the accuracy of the battery model can be increased over time.”); and
in an event a deviation between the operational parameter data and the at least one operational parameter profile occurs, determining an irregularity and planning predictive maintenance based on the irregularity (see at least [0111]-[0118], FIG. 8, and FIG. 9: “In step 130, the estimates of usable capacity received from the vehicle are used to update a battery model in the fleet management system. The battery model is a model of how the battery capacity changes with battery use. The battery model may comprise a system-to-cell converter and a remaining useful life estimator in the form described above with reference to FIG. 3. By using actual measurements received from the vehicle to update the battery model, the accuracy of the battery model can be increased over time.”; “If it is determined that the battery will reach end of life within the next time window, then in step 138 an alert is provided that the battery will reach end of life. The operations manager and sales, distribution and servicing center are then advised that the battery should be replaced”).
Jin does not explicitly disclose determining (S4), via the computer, an ageing state profile for the device battery over a predetermined amount of time based on a differential equation system, wherein the differential equation system utilizes a chronological integration method to determine an ageing state at a specific time depending on the operational parameter profile up to the specific time;
determining, via the computer, a residual value associated with each technical device based on the predicted ageing state;
based on an economic factor is with a maximum residual value.
As previously discussed, Jin discloses the predicted ageing state of the battery is a vehicle condition factor. Krishnamurthy, in the same field of vehicle information management, and therefore analogous art, teaches determining, via the computer, a residual value associated with each technical device based on a vehicle condition factor (see at least [0027] and [0064]: “The extrapolated residual value at the end of the lease term is extrapolated periodically throughout the term using the vehicle condition data that is continuously collected and extrapolated to adjust the previously determined residual value to calculate the periodic discount”; “The data is sent from the TCU 14 to the data center 30 to manipulate the data and determine the extrapolated residual value”);
based on an economic factor is with a maximum residual value (see at least [0023]: “ If all factors used to determine residual value for two vehicles of the same make, model and year are the same or equal, the vehicle in the better condition at the end of the lease will have a higher residual value”).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to combine the teachings of Jin and Krishnamurthy with the motivation of providing economic-benefit to both the driver and owner (when the vehicle is being driven by a non-owner): “the driver's monthly payment decreases” and “The owner of the vehicle benefits financially from the program. The owner will generate additional revenue from a higher residual value for the vehicle at the end of the term due to the improved vehicle condition at the end of the term.” (see at least Krishnamurthy [0080]-[0081]).
Additionally, Basler, in the same field of battery degradation prediction, and therefore analogous art, teaches determining (S4), via the computer, an ageing state profile for the device battery over a predetermined amount of time based on a differential equation system, wherein the differential equation system utilizes a chronological integration method to determine an ageing state at a specific time depending on the operational parameter profile up to the specific time (see at least [0030], [0103]-[0104], and FIG. 6: “The traction battery model is preferably a mathematical model that illustrates the ageing condition of the traction battery depending on time”; dR(t) is an input to SOHR).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to combine the teachings of Jin and Krishnamurthy with the teachings of Basler with the motivation of using the specific calculations of Basler with the motivation of accurately determining the ageing condition as time progresses (see at least Basler[0006]).
Regarding Claim 2, the Jin, Krishnamurthy, and Basler combination teaches the limitations of Claim 1. Furthermore, Jin discloses wherein the usage behavior is continuously recorded based on at least one usage parameter and derived from historical profiles of at least one usage parameter, wherein the usage behavior is aggregated into usage characteristics, wherein the usage categories are determined by characteristics of the usage characteristics (see at least [0071]-[0072] and [0083]: “The database 38 is continually updated with new vehicle driving information. This allows the database 38 to build up historic records of driving information for the vehicles in the fleet. The historical data can be accessed from the database by database queries.”; “Techniques for generating the vehicle route profile include: clustering algorithms, for example based on GPS, road grade and/or velocity information”; “a driving characterization profile is created for each vehicle/fleet using historical data such that it is characterized into vehicle speed, vehicle acceleration, weather and traffic information clusters”).
Regarding Claim 3, the Jin, Krishnamurthy, and Basler combination teaches the limitations of Claim 2. Furthermore, Jin further discloses wherein the usage characteristics comprise an average load during operation, a service duration relative to a calendar age, and a frequency of use, wherein in vehicles acting as technical devices, the usage characteristics comprise …, a number and type of charging cycles, a temperature range, and an average load range (see at least [0018]-[0019], [0072], and [0083]: “The historical vehicle driving information may comprise at least one of: vehicle speed; environmental data (such as temperature, pressure, precipitation etc); traffic data (such as traffic jams, roadworks etc); road grade data; charging data (such as rate and/or frequency of charging); state of charge data; state of health data; vehicle driver information (such as driver ID); and any other appropriate data concerning past vehicle driving conditions.”; “The vehicle route profile generator may be arranged to generate the vehicle specific route profile based further on predicted vehicle driving information. The predicted vehicle driving information may comprise, for example: predicted environmental data (such as temperature, pressure, precipitation etc); predicted traffic data (such as traffic jams, roadworks etc); predicted driver; predicted routes; and any other data concerning future vehicle driving conditions which can be predicted in advance”; “Techniques for generating the vehicle route profile include: clustering algorithms, for example based on GPS, road grade and/or velocity information”; “vehicle speed, vehicle acceleration, weather and traffic information clusters”) and Krishnamurthy further teaches (with the same motivation to combine as Claim 1) wherein the usage characteristics comprise an average load during operation, a service duration relative to a calendar age, and a frequency of use, wherein in vehicles acting as technical devices, the usage characteristics comprise a predicted annual mileage, …, a temperature range, and an average load range (see at least [0049]-[0060], [0067]-[0073], and FIG. 7: “number of miles drive