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 .
Status of Claims
This Office action is in response to correspondence received November 18, 2025.
Claims 1, 3, 5, 6, 8 and 10 are amended. Claim 11 is newly added. Claims 1-11 are pending and have been examined.
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 1-11 are rejected because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s)
Claim 1:
A method for assigning a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types to a user,
the method comprising; (S1) providing a usage behavior of the user; assigning a usage category to the user's usage behavior; determining a usage variable profile of usage parameter corresponding to the usage category, wherein the a usage parameter is indicative of a variable indicative of an operational mode of the technical device affecting a load on the device battery;(S2) determining a predicted load variable profile using a data-based, mathematical, or physically motivated operational model;(S4) simulating a predicted ageing state profile for the predicted load parameter profile for a predetermined amount of time for each type of device of a plurality of device types in order to determine a predicted ageing state at a predetermined end of useful life period; (S5) selecting a device type for the user depending on the predicted ageing state; determining if an irregularity has occurred, wherein a usage parameter of the predicted load differs from measured parameter;
Claim 2:
A method according to claim 1, wherein, in the case of an electrically driven vehicle as a technical device as an operational model, a powertrain model, and/or a battery model is provided
Claim 8:
A use of a method for a simulation of an operational strategy power and thermal behavior in a closed control loop for an electrically driven vehicle, wherein an overall vehicle model, a powertrain model, and/or a battery model is provided as an operational model in order to predict a state of an energy converter or electrochemical energy storage means of the electrically driven vehicle, the method comprising: providing a usage behavior of the user; assigning a usage category to the user's usage behavior; determining a usage variable profile of a usage parameter corresponding to the usage category, wherein the a usage parameter is indicative of a variable indicative of an operational mode of the electrically driven vehicle affecting a load on the vehicle battery; determining a predicted load variable profile using a data-based, mathematical, or physically motivated operational model; simulating a predicted ageing state profile for the predicted load parameter profile for a predetermined amount of time for each type of device of a plurality of device types in order to determine a predicted ageing state at a predetermined end of useful life period; selecting a device type for the user depending on the predicted ageing state; determining if an irregularity has occurred, wherein a usage parameter of the predicted load differs from measured parameter;
Claim 9:
performing a method according to claim 1
Claim 10:
assign a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types to a user, by: providing a usage behavior of the user; assigning a usage category to the user's usage behavior; determining a usage variable profile of a usage variable corresponding to the usage category, wherein the a usage parameter is indicative of a variable indicative of an operational mode of the technical device affecting a load on the device battery; determining a predicted load variable profile using a data-based, mathematical, or physically motivated operational model; simulating a predicted ageing state profile for the predicted load parameter profile for a predetermined amount of time for each type of device of a plurality of device types in order to determine a predicted ageing state at a predetermined end of useful life period; selecting a device type for the user depending on the predicted ageing state; and determining if an irregularity has occurred, wherein a usage parameter of the predicted load differs from measured parameter;
These steps are a mental process because they are steps that are observations or judgments, which can be performed mentally step by step. First, assigning a device type is a judgment saying that this device type (car, drill, generator) is assigned to a user- it’s a choice, like saying someone is a certain career or a prefers a kind of food. Then assigning a usage category is also a choice, saying that a usage is low, medium, or high, for instance. Then, determining a profile of at least one variable corresponding to the character is yet another choice. These choices frame the alleged (alleged patentable) invention, a series of choices that one could make assigning values to something, like numbers or whatever one chooses within the scope. That the variable is something that “affects the load” on the device battery is another way of saying using a device that has a battery, uses the battery. For example a variable could be the RPM of an electromechanical device, where a higher RPM uses more battery than a lower RPM. Then a model is chosen to determine a “profile” so the model could for example assign the amount of RPMs used to a certain group. Then a profile can be simulated, which merely means calculating something into the future (all in one’s head or on paper), to determine when the battery will fail. This is can be done with pen and paper, for example, a line graph where the line extends into the future and intersects another line which is the average failure rate of the device. Then, a device type can be selected which is the final choice made which, like the other choices that frame the invention, are all readily able to be done mentally. Then, an irregularity can be determined by comparing received information to known information to see if it is irregular . Therefore the combination of steps are all able to, one after the other, be done mentally and this invention encompasses (absent the few additional elements) a mental process.
Claim 2 merely describes that a model is provided for an electrically driven vehicle. The model could be linear for example (a line on a graph). This is a further mental process step.
This judicial exception is not integrated into a practical application. The few additional elements listed below merely instruct the user to apply the invention to a computer in any or all ways, the scope of that being unlimited to how much a computer is used. Then, the step of performing predictive maintenance on a sensor lacks details as to the details (as in steps) of the performance maintenance performed, also additionally lacking details on the sensor in question. This is a result-oriented solution that is no more than the idea or outcome of a solution (that predictive maintenance on a sensor is performed). See MPEP 2106.05(f)(1). This is similar to Affinity Labs where wireless delivery is recited but there is no details as to how that is accomplished. Id. (“Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)”). In combination the additional elements are:
Claim 1: computer-implemented method; performing predictive maintenance on a sensor based on the determined irregularity.
Claim 2 discloses the method of claim 1 but does not add further additional elements.
Claim 8 describes the use of a method which describes performing predictive maintenance on a sensor based on the determined irregularity.
Claim 9: An apparatus; performing predictive maintenance on a sensor based on the determined irregularity.
Claim 10: A non-transitory, computer-readable storage medium comprising instructions that, when executed by at least one data processing device, cause the latter to; performing predictive maintenance on a sensor based on the determined irregularity.
These elements are not a practical application because, given that their scope includes a general purpose computer, they are apply it steps, and apply it steps are not a practical application of an abstract idea such as the one claimed by applicant. See MPEP 2106.05(f)(1-2).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the reasoning above is carried over into this section. For the same reasons that there is not a practical application of an abstract idea, there is not significantly more.
Therefore, the independent claims are rejected under 35 USC 101.
Per the dependent claims, claims 3-7, further limit the abstract idea with limitations about inputs to models which further describe the steps that could be performed mentally.
Claim 11 recites replacing a physical component of the sensor. This is a further apply it step as there are no details as to how this functional result or outcome is achieved and this is similar to reciting wireless delivery per Affinity Labs. The scope could include replacing a common off the shelf battery, for example.
Therefore, claims 1-11 are rejected under 35 USC 101.
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.
Claim(s) 1-4, 9, 10, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Budan et al., US Pat No 11527786 B1 ("Budan") in view of Muntes et al., US PGPUB 20190305383 A1 ("Muntes"), further in view of Bertness, US PGPUB 20210141043 (“Bertness”).
Per claims 1, 9, and 10, which are similar in scope, Budan teaches A computer-implemented method (apparatus, computer readable medium) for assigning a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types to a user, the method comprising: (S1) providing a usage behavior of the user in col 10 ln 42-56: “In some embodiments, the processing device may receive fifth data including a user battery usage profile including information pertaining to acceleration, deceleration, braking, mileage in a given time period, speed, or some combination thereof. The fifth data may include charging habits of the user. For example, the charging habits may include information pertaining to a frequency of charging the battery pack and/or a type (e.g., fast or standard) of battery pack charging selected by the user.”
Budan then teaches assigning a usage category to the user's usage behavior in col 20 ln 8-16: “Real battery pack 1002 measurements are obtained from the sensors 131 and stored in the measurement dataset 1008. The real battery pack 1002 measurements are input into a usage profile classification stage 1010 that classifies the user battery usage profile based on the measurements. The measurement dataset 1008 and the usage profile classification 1010 are input to a data formatting 1012 stage as well as physics model data 1004.” Classifying the profile based on measurements of the battery pack teaches assigning a usage category because it is based on the measurements of the battery pack, which is directly related to how the user uses the battery powered device.
Budan then teaches determining a usage variable profile of at least one usage variable corresponding to the usage category, wherein the at least one usage parameter is indicative of a variable indicative of an operational mode of the technical device affecting a load on the device battery in col 19 ln 18-24: “The RUL model 800 depicts cell capacity on the Y axis and time on the X axis. The RUL model 800 depicts where a RUL prediction results for a heavy electric vehicle user (˜30000 miles/year) and for a RUL prediction results for a light electric vehicle user (˜8800 miles/year). The results for each of the light user and the heavy user are based on the physics model outputs.” The usage category, determined by tracking the user’s use (see above), indicates a variable that is indicative of an operational mode because one operation is light and one is heavy, and this affects a load on the battery because the battery.
Budan then teaches (S2) determining a predicted load variable profile using a data-based, mathematical, or physically motivated operational model in col 19 ln 41-49: " The physics edge model 600-2 may receive a temperature, a current, and an initial SOC as inputs and may output the SEI thickness to the machine learning model 132. The machine learning model 132 may use extracted features and the SEI thickness from the physics edge model 600-2 to produce its prediction of RUL as a single shot value.
The RUL model 900 depicts an indication of where the cell capacity is now (dashed line) and a location of time 902 where the RUL is predicted." Because temperature is an input this is a predicted load variable profile using a physically motivated (temperature) operational model.”
Budan then teaches (S4) simulating a predicted ageing state profile for the predicted load parameter profile for a predetermined amount of time for [the device] in order to determine a predicted ageing state at a predetermined end of useful life period in col 19 ln 64 - col 20 ln 3”: “Real-life data is pre-processed and fed to the machine learning model 132 at the inference 1024 stage, and the RUL estimation is produced. The estimated value is shown to fleet manager, teams managing test fleets, OEM teams tracking vehicle status, manufacturers and potentially also individual users. Further, the predicted RUL can be used to perform a preventative action as described herein.” The RUL estimation is the simulating a predicted aging state value because by estimating RUL they are simulating the state profile, which is defined as determining a predicted aging state.
Budan does not teach each type of device of a plurality of device types; and (S5) selecting a device type for the user depending on the predicted ageing state.
Muntes teaches determining battery levels and battery “outlooks” of different computing devices. See Abstract.
Muntes teaches each type of device of a plurality of device types; and (S5) selecting a device type for the user depending on the predicted ageing state in par 004 where devices, plural, and interrelationship are discussed, see also par 003 for different devices. Muntes then teaches that a device type is selected for the user depending on the predicted ageing state in par 006 where outlook corresponds to improving battery usage of management which under a broadest reasonable interpretation teaches ageing state, as ageing state may be of any length of time (one charge, the life of a battery); and then teaches in par 027 that whether a device should be used versus a different device based on the battery state is determined, which teaches selecting a device type for the user based on the predicted ageing state. See par 027: “Based on this calendar entry combined with historical uses, in some embodiments, future potential uses of devices can be determined (e.g., predicted with greater than a threshold level of confidence) 305 and future charging opportunities can be identified (e.g., predicted with greater than a threshold level of confidence). Any of the events can be scaled as appropriate within the determination step 305. A one-hour presentation can be scaled from a simple scaling factor based on historical presentations, e.g., usage during a 30 minute presentation by that user can be doubled, or more sophisticated scaling factors can be used based on a variety of user-specific data, event-specific, or generalized data, such as (1) user biographic data, (2) user location data, (3) future user location data; (4) various environmental conditions (e.g., weather data wherein appropriate factors are determined for increased cold weather discharge of batteries), (5) event type, (6) event length. The system, in some embodiments, analyzes 306 whether a particular device will have sufficient battery to perform the task. If it will (e.g., is predicted to with greater than a threshold confidence), no further action is needed. If not, in various embodiments, the system may be caused to respond differently, as shown in 307, including recommendation of charging the device, using a different device, reconfiguring the device, prioritizing usage of devices, etc. These recommendations, in some cases, may be presented to the user or provided to a process by which recommended actions are taken (e.g., decreasing screen brightness, reducing radio transmit strength, decreasing beaconing frequency, adjusting thresholds to enter lower-power states, and the like).”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery simulation teaching of Budan with the multiple device, each device of a plurality of devices, and selecting a device based on ageing teaching of Muntes because Muntes teaches in pars 003-004 that more effective alternation between devices whose functionality may be increased may increase available usage when charging is not readily available, and more importantly as taught in par 0019 that the battery life can be increased so that the battery powered capabilities are more available. As Budan teaches the RUL modeling of a battery, one would be motivated to modify Budan with Muntes to increase the life of a battery, as battery users generally want to get the most out of a battery and this would accomplish that. Therefore for these reasons one would be motivated to combine Budan with Muntes.
Budan does not teach determining if an irregularity has occurred, wherein a usage parameter of the predicted load differs from measured parameter;
and performing predictive maintenance on a sensor based on the determined irregularity
Bertness teaches performing maintenance on a battery pack of an automotive vehicle. See abstract.
Bertness teaches determining if an irregularity has occurred, wherein a usage parameter of the predicted load differs from measured parameter in par 059: “In yet another aspect, measurements obtained by sensors 122 (see FIG. 1) are retrieved and compared with measurements of individual batteries or cells 140 obtained by maintenance device 100. This information can be used to repair a battery pack 104 in which sensors 122 or controller 120 are failing or otherwise providing inaccurate measurement or outputs”
See also pars 067-068: “At 504 of the method, the results of the verification test are compared to the information received in step 500. If the verification test results match or substantially match (e.g., within 10%) the information received in step 500, then the identified battery module 140 is confirmed as malfunctioning or degrading, as indicated at 506. The particular acceptable range can be user selectable and/or stored in memory 164. The method then continues to step 508 where the malfunctioning module is repaired or replaced in accordance with techniques described herein, and/or described in U.S. Pub. No. 2019/0204392, which is incorporated herein by reference in its entirety. Thus, the method may proceed with the necessary steps for removing the malfunctioning battery module 140 for replacement, for example.
If the verification information does not match or does not substantially match (e.g., within 10%) of the information received in step 500, then, as indicated at 510, it is determined that an error has occurred in deriving and/or communicating the information received in step 500. That is, there may be a fault with the scan tool, the communications interface or other link in the chain of generating and communicating the information relating to the initial testing of the battery pack 104. In some embodiments, the method uses the verification test to determine whether the battery module 140 requires repair or replacement. In some embodiments, additional tests may be performed on the battery module 140. This verification can be used to identify a failing sensor 122 in the battery pack 104.”
See also par 060: “Load testing-based parameters may also be employed.”
Then Bertness teaches and performing predictive maintenance on a sensor based on the determined irregularity in par 068: “This verification can be used to identify a failing sensor 122 in the battery pack 104. Upon such identification, the failing sensor 122 can be replaced thereby repairing the pack 104 such that it can be returned to service.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery simulation teaching of Budan with the identifying irregularity and performing predictive maintenance on a sensor teaching of Bertness because Bertness teaches in par 018 that: “It is not at all apparent how the battery pack can be discharged as there are many different types of battery pack, as well as various techniques used to access the packs. Further, after an accident, systems of the vehicle may not be functioning properly and may prevent maintenance from being performed on the battery pack whereby the battery pack cannot be discharged using normal procedures. In one aspect, the present invention provides an apparatus and method for safely accessing the battery pack of an electrical vehicle and discharges the battery pack. However, the present invention is not limited to this configuration and may be used generally to perform maintenance on the battery pack of an electric vehicle.” This maintenance teaching would motivate one ordinarily skilled to combine Bertness with Budan because one would be motivated, when determining through Budan that a Battery would need service, to use the techniques of Bertness, which are analogous to the well-known techniques of repairing internal combustion engines. Those techniques are well known but as electric vehicles are newer one would be motivated to add steps to Budan so that once information about a battery suggested it, or any related part or component needed repair, one would apply Bertness. Therefore for these reasons one would be motivated to combine Budan and Bertness.
Per claim 2, which is an independent claim, Budan, Muntes, and Bertness is incorporated here (as claim 2 teaches A method according to claim 1). Budan further teaches wherein, in the case of an electrically driven vehicle as a technical device as an operational model, a powertrain model, and/or a battery model is provided in col 19 ln 1-6: “The physics cloud-based model 600-1 may receive as inputs one or more of: theta parameter set (SOH state of health), number of cycles, frequency of RTP cycles, c-rate of RPT cycles, c-rate of charge, termination capacity (% BOL), initial SOC, initial SEC thickness (m), and user battery usage profile.” Electrically driven vehicle is taught in col 7 ln 51 – col 8 ln 12.
Per claim 3, Budan, Muntes, and Bertness teaches the limitations of claim 1, above. Budan further teaches wherein the usage behavior of the user is continuously detected based on usage variable and is derived from historical profiles of a usage variable, wherein the usage behavior is aggregated into usage characteristics, wherein the usage categories are determined by characteristics of the usage characteristics in col 10 ln 46-56: “In some embodiments, the processing device may receive fifth data including a user battery usage profile including information pertaining to acceleration, deceleration, braking, mileage in a given time period, speed, or some combination thereof. The fifth data may include charging habits of the user. For example, the charging habits may include information pertaining to a frequency of charging the battery pack and/or a type (e.g., fast or standard) of battery pack charging selected by the user.” Habits teaches historical behavior, mileage over a given time period also teaches historical behavior. “a frequency of charging the battery pack and/or a type (e.g., fast or standard) of battery pack” teaches usage behavior aggregated into usage characteristics, wherein the usage categories are determined by characteristics of the usage characteristics.
See also col 11 ln 9-14: “At 208, the processing device may receive historical data on a fleet of vehicles that use the battery pack. The historical fleet data may relate to BOLs and expected degradation related to EOL and ROL based on analyzed data of batteries used in the fleet.”
Per claim 4, Budan, Muntes, and Bertness teaches the limitations of claim 3, above. Budan further teaches wherein the usage characteristics comprise an average load during operation, a service duration relative to the calendar age, and a frequency of use, wherein in vehicles acting as technical devices, the usage characteristics comprise a predicted annual mileage, a number and type of charging cycles, a temperature range, and an average load range in col 10 ln 46-56: “In some embodiments, the processing device may receive fifth data including a user battery usage profile including information pertaining to acceleration, deceleration, braking, mileage in a given time period, speed, or some combination thereof. The fifth data may include charging habits of the user. For example, the charging habits may include information pertaining to a frequency of charging the battery pack and/or a type (e.g., fast or standard) of battery pack charging selected by the user.” See also col 11 ln 9-14: “At 208, the processing device may receive historical data on a fleet of vehicles that use the battery pack. The historical fleet data may relate to BOLs and expected degradation related to EOL and ROL based on analyzed data of batteries used in the fleet.” ROL teaches service duration relative to calendar age; frequency of charging teaches frequency of use. Average load is taught by standard or fast charging. Mileage in a given time period teaches annual mileage. Frequency of charging teaches number of charges, fast and standard teach type of charging cycles.
See also col 11 ln 1-13: “At 207, the processing device may receive fourth data pertaining to lab experiments data associated with the battery pack. The lab experiments data may include details of experiments run on the battery pack and results of the experiments. For example, the lab experiments may include testing how well the battery pack performs (e.g., length of life of each cell) under certain conditions (e.g., temperature, moisture, vibration, etc.).” Temperature teaches temperature, length of life teaches load range.
See also col 21 ln 15-20: the mean is taken, teaching average.
Per claim 11, Budan, Muntes, and Bertness teach the limitations of claim 1, above. Budan does not teach wherein the predictive maintenance includes replacing a physical component of the sensor.
Bertness teaches wherein the predictive maintenance includes replacing a physical component of the sensor in par 068: “ In some embodiments, the method uses the verification test to determine whether the battery module 140 requires repair or replacement. In some embodiments, additional tests may be performed on the battery module 140. This verification can be used to identify a failing sensor 122 in the battery pack 104. Upon such identification, the failing sensor 122 can be replaced thereby repairing the pack 104 such that it can be returned to service.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery simulation teaching of Budan with the identifying irregularity and performing predictive maintenance on a sensor teaching of Bertness because Bertness teaches in par 018 that: “It is not at all apparent how the battery pack can be discharged as there are many different types of battery pack, as well as various techniques used to access the packs. Further, after an accident, systems of the vehicle may not be functioning properly and may prevent maintenance from being performed on the battery pack whereby the battery pack cannot be discharged using normal procedures. In one aspect, the present invention provides an apparatus and method for safely accessing the battery pack of an electrical vehicle and discharges the battery pack. However, the present invention is not limited to this configuration and may be used generally to perform maintenance on the battery pack of an electric vehicle.” This maintenance teaching would motivate one ordinarily skilled to combine Bertness with Budan because one would be motivated, when determining through Budan that a Battery would need service, to use the techniques of Bertness, which are analogous to the well-known techniques of repairing internal combustion engines. Those techniques are well known but as electric vehicles are newer one would be motivated to add steps to Budan so that once information about a battery suggested it, or any related part or component needed repair, one would apply Bertness. Therefore for these reasons one would be motivated to combine Budan and Bertness.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Budan et al., US Pat No 11527786 B1 ("Budan") in view of Muntes et al., US PGPUB 20190305383 A1 ("Muntes"), further in view of Bertness, US PGPUB 20210141043 (“Bertness”), further in view of Pjetri et al., US PGPUB 20230315940 (“Pjetri”).
Per claim 5, Budan, Muntes, and Bertness teach the limitations of claim 1, above. Budan further teaches wherein simulating the predicted ageing state profile for the predicted usage variable profile initially includes predicting a load variable profile for the device battery, which profile corresponds to a operational variable profile for the device battery, by means of a predetermined useful life period operational model for each device type, then predicting the ageing state profile by means of an ageing state model in col 20 ln 21-33: “A model validation 1018 stage is performed where one or more machine learning models are validated based on the hyperparameters, and at stage 1020, the machine learning models are trained. At stage 1022, test data is applied each of the trained machine learning models and an optimal machine learning model is selected based performance (e.g., most accurate, fastest, etc.) on the test data. At stage 1024, real-life data (e.g., sensor measurements, user battery usage profile, etc.) 1026 are entered into the selected machine learning model and a RUL is predicted at stage 1028.”
Budan does not teach wherein the ageing state model is based on a differential equation system which determines the ageing state by means of a chronological integration method.
Pjetri teaches a system for monitoring a powering system asset. See abstract.
Pjetri teaches wherein the ageing state model is based on a differential equation system which determines the ageing state by means of a chronological integration method in par 153: “Measurement values may be received at sampling times at the interface 31. The first determination module 32 may be operative to process measurements (e.g., a voltage and a current) measured time-sequentially and/or received at the interface 31 at time-sequential sampling times into one set of first model parameter values. For illustration, the first determination module 32 may be operative to determine a set of first model parameter values for the parameters of the power system asset model by solving a time-discrete differential equation. Measurements (e.g., a voltage and a current) associated with at least two, at least three or more consecutive sampling times may be processed to respectively determine one set of first model parameter values.” By means of a chronological integration method is taught by using two or three or more consecutive sampling times with the values as a result.
See also par 0169-0171: “FIG. 3 shows a system 40 comprising plural power system assets 11a-11c (in the present case, plural BESSs, without being limited thereto), plural local control systems 41a-41c each associated with one of the power system assets 11a-11c, and a computing system 13 that is communicatively coupled with the plural local control systems 41a-41c. The computing system 13 may be operative to determine a series of sets of first model parameter values of a power system asset model for each one of the assets 11a-11c, each of the sets of first model parameter values being determined for a different time or time interval (the parameter values may be different for different ones of the assets 11a-11c even when the same parameterization, i.e., the same power system asset model is used); and at least one set of second model parameter values of the parameter evolution model. The parameter evolution model describes an evolution of one, several or all first model parameter values of power system asset model for the assets 11a-11c.” Batteries are taught in par 002 (rechargeable storage systems).
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery remaining useful life determining teaching of Budan with the time differential to determine an asset teaching of Pjetri because Pjetri teaches in par 002 that for batteries by monitoring the condition as taught above, it will ensure reliable and stable system operation. See also par 007 where Pjetri’s teachings improve over modeling that does not take into account the change of a system over time. Therefore, for these reasons one would be motivated to modify Budan with Pjetri.
Per claim 6, Budan, Muntes, Bertness, and Pjetri teach the limitations of claim 5, above. Budan does not teach wherein a additional of the a operational variables is determined by means of a battery performance model dependent on the a load variable profile, wherein, for the simulation, the model parameters of the battery performance model are adjusted with respect to the respective predicted ageing state.
Pjetri teaches wherein a additional of the a operational variables is determined by means of a battery performance model dependent on the a load variable profile, wherein, for the simulation, the model parameters of the battery performance model are adjusted with respect to the respective predicted ageing state in par 190: “The parameter evolution model and, more specifically, the determined set of second model parameter values may be used in various ways. The set of second model parameter values may be used to quantitatively assess changes in power system asset state that occur over a time period that is longer, in particular much longer than a time interval after which a new set of first model parameter values 71, 72, 73 is being determined.” That the time periods are taken over a long period of time teach adjusting with respect to the predicted ageing state because the battery (taught in par 002) has aged and therefore the modeling later intervals is a more aged battery, predicted by the models taught by Pjetri.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery remaining useful life determining teaching of Budan with the time differential to determine an asset teaching of Pjetri because Pjetri teaches in par 002 that for batteries by monitoring the condition as taught above, it will ensure reliable and stable system operation. See also par 007 where Pjetri’s teachings improve over modeling that does not take into account the change of a system over time. Therefore, for these reasons one would be motivated to modify Budan with Pjetri.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Budan et al., US Pat No 11527786 B1 ("Budan") in view of Muntes et al., US PGPUB 20190305383 A1 ("Muntes"), further in view of Bertness, US PGPUB 20210141043 (“Bertness”), further in view of Buchbinder, US PGPUB 20160092847 ("Buchbinder").
Per claim 7, Budan, Muntes, and Bertness teach the limitations of claim 1, above. Budan does not teach wherein selecting a device type for the user is performed at the end of the predetermined useful life period, depending on the predicted state of ageing, so that a remaining potential use of the device battery is at a maximum.
Buchbinder teaches battery replacement. See abstract.
Buchbinder teaches wherein selecting a device type for the user is performed at the end of the predetermined useful life period, depending on the predicted state of ageing, so that a remaining potential use of the device battery is at a maximum in par 88: “Smart devices that are capable of battery health monitoring and automated replenishment may also be able to squeeze extra life out of batteries that are near end of life. For example, in embodiments a smart device may order a replacement battery when a currently installed battery's estimated remaining useful life is about 5% of its original life. When the replacement battery arrives, it may check the currently installed battery's characteristics again, at more frequent intervals if deemed necessary, until its estimated useful life declines to 2%, 1% or less of its original life. The frequency of battery characteristic checking may be managed so that it is inversely proportional both to the accuracy of the characteristic in determining remaining useful life and the perceived importance of the functionality provided by the smart device, and/or directly proportional to the cost of the battery. Once the currently installed battery's estimated useful life declines to an unacceptably short time (or upon the device ceasing to operate, if consequences of it doing so are small) the battery can then be replaced.” This teaches selecting a device type at the end of the predicted life because under a broadest reasonable interpretation the replacement battery is the device and it is selected, or put in use, at the end of the estimated useful life as taught here.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery remaining useful life determining teaching of Budan with the selection of a replacement device at the end of a useful life teaching of Buchbinder because Buchbinder teaches the problem in par 001 that replacing batteries for battery powered devices is complicated given the number of batteries in existence and that Buchbinder’s teaching, as explained in par 0024, helps to order the correct battery at the correct time so that extra batteries are not stored when they are not needed. This would make maintaining battery devices more efficient overall and therefore one would be motivated to modify Budan with Buchbinder.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Budan et al., US Pat No 11527786 B1 ("Budan") in view of Muntes et al., US PGPUB 20190305383 A1 ("Muntes"), further in view of Bertness, US PGPUB 20210141043 (“Bertness”), further in view of Hyde et al., US PGPUB 20150239365 A1 ("Hyde").
Per claim 8, which is an independent claim, Budan, Muntes, and Bertness teach the method of claim 1 referred to. Further, the similar limitations to claim 1 are to be referred to above for those teachings, which proceed after the teaching of Hyde, below. Budan does not teach A use of a method for a simulation of an operational strategy power and thermal behavior in a closed control loop for an electrically driven vehicle, wherein an overall vehicle model, a powertrain model, and/or a battery model is provided as an operational model in order to predict a state of an energy converter or electrochemical energy storage means of the electrically driven vehicle, the method comprising.
Hyde teaches a method for managing energy storage system such as a vehicle. See abstract.
Hyde teaches A use of a method for a simulation of an operational strategy power and thermal behavior in a closed control loop for an electrically driven vehicle, wherein an overall vehicle model, a powertrain model, and/or a battery model is provided as an operational model in order to predict a state of an energy converter or electrochemical energy storage means of the electrically driven vehicle, the method comprising in par 0137: “The management plan will be created in accordance with the conditions predicted to be encountered by the vehicle in the duty/route according to the capability of the vehicle as configured. For example, with a battery system configured with a battery pack comprising high-rate battery modules, operation of the vehicle by the plan may be directed as follows: (a) advance of anticipated periods of available energy from the regenerative braking system such as on downhill grades or in stop-and-go traffic conditions the battery system will discharge high-rate battery modules so that regenerative power can be accepted and used for charging; (b) in advance of anticipated increased demand such as on uphill grades or entering an expressway from an on-ramp the system will charge the high-rate battery modules so that power is available for discharge and acceleration of the vehicle; (c) identified available periods of reduced intensity of use can be used for management operations such as voltage-level shifting or cell balancing between battery modules. According to an exemplary embodiment, if the vehicle comprises alternative systems/sources of energy supply (such as a waste heat recovery system or a solar panel array configured to provide energy for recharging battery modules), the management system can use route/direction and weather and time of day to plan optimize utilization of the system/source to charge the battery system.” Creating a management plan for a battery model that uses regenerative braking teaches the use of the method (in combination) with energy converter. See also teaching of waste recovery heat system.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the battery remaining useful life determining teaching of Budan with the regenerative teaching of Hyde because Hyde teaches in par 010 that optimizing systems (see also par 011) for energy storage and power provision in cars (see pars 002-009) would optimize the performance of duties to be performed by the vehicle. Because one would want to optimize vehicle performance one would be motivated to modify Budan with Hyde.
Therefore, claims 1-11 are rejected under 35 USC 103.
Response to Remarks:
35 USC 101
Applicant has amended claim 8. Examiner is not clear if a use of a method is a statutory category, but given that the method steps are recited within, Examiner considers the weight that this is a process and the rejection is withdrawn.
Per the judicial exception rejection, it is maintained for the reasons above. While replacing a sensor is not a mental process step, Examiner finds it to be broad to only recite the desired outcome or functional result without reciting sufficient detail to overcome the Apply It interpretation in the rejection above. Therefore, for these reasons the 101 rejection is maintained.
35 USC 103
As the claims are amended new art is applied because of required further search and consideration, rendering the previous rejection moot. The previous rejection would not have overcome the amendments.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
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/RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619