DETAILED ACTION
This Office Action is in response to Applicant Amendment and Argument filed on 12/08/2025. This Action is made FINAL.
Claims 1-20 are pending for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
Applicant’s amendments to independent claims 1 and 11 overcome previous rejections under 35 U.S.C. § 101 and therefore the previous rejections are withdrawn.
Response to Arguments
Applicant’s arguments, see Remarks pages , filed 12/08/2025, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102 and 103 have been fully considered and not persuasive and/or moot.
In the Remarks, Applicant argued the following:
Cronin fails to disclose
“estimate the energy consumption of the electrified vehicle” and
Roman fails to teach or suggest “detecting a location of a combustion engine equipped vehicle, estimating an available charging time in response to the location of the combustion engine equipped vehicle being proximate to the location of the electrical vehicle charger, estimating a current battery charge level in response to an initial battery charge level, and the available charging time” and “estimating an electric vehicle range in response to the energy consumption rate and the current battery charge level” recited in amended claim 9.
Regarding point (a)(i), applicant argues Cronin’s teachings of estimating the capacity of the electronic storage unit (ESU) does no teach energy consumption. However, examiner maintains that teaching of an estimate of capacity of an energy storage at least requires an estimate of energy consumption, and therefore, Cronin’s teachings of estimating the capacity of the electronic storage unit teaches an estimate of the energy consumption of the electrified vehicle. Applicant further argues capacity is an entirely different parameter than vehicle energy consumption, however at any time the current capacity of an energy storage is direct function of energy consumption, while the parameters may be used in different context to convey information in different ways, both energy capacity and energy consumption are values which inherently indicate the other in such a way as when given a rate of energy consumption you can determine the energy capacity as well as when given the current energy capacity you can directly determine the energy consumption.
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder (“an external computing system”, “a control system”) that is coupled with functional language (“to execute”, “to train”, “to access” , “to obtain”, “to utilize”, “to generate”) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are as follows:
“an external computing system that is separate from the electrified vehicle and is configured to: execute computer-aided engineering (CAE) software…train an energy consumption model…” recited in claim 1. For the purposes of examination, the examiner will take “an external computing system” as one or more of an application-specific integrated circuit (ASIC) and/or processor with a non-transitory memory having instructions stored which the processor executes or equivalent based on the following excerpt(s) from the specification:
Para [0022] : “It will be appreciated that the terms “controller” and “control system” as used herein refer to any suitable control device or set of multiple control devices that is/are configured to perform at least a portion of the techniques of the present application. Non-limiting examples include an application-specific integrated circuit (ASIC), one or more processors and a non-transitory memory having instructions stored thereon that, when executed by the one or more processors, cause the controller to perform a set of operations corresponding to at least a portion of the techniques of the present application. The one or more processors could be either a single processor or two or more processors operating in a parallel or distributed architecture. ”
”a control system configured to: access the trained energy consumption model… obtain a plurality of input parameters… utilize the trained energy consumption model… generate an output” recited in claim 1. For the purposes of examination, the examiner will take “a control system” as one or more of an application-specific integrated circuit (ASIC) and/or processor with a non-transitory memory having instructions stored which the processor executes or equivalent based on the following excerpt(s) from the specification:
Para [0022] : “It will be appreciated that the terms “controller” and “control system” as used herein refer to any suitable control device or set of multiple control devices that is/are configured to perform at least a portion of the techniques of the present application. Non-limiting examples include an application-specific integrated circuit (ASIC), one or more processors and a non-transitory memory having instructions stored thereon that, when executed by the one or more processors, cause the controller to perform a set of operations corresponding to at least a portion of the techniques of the present application. The one or more processors could be either a single processor or two or more processors operating in a parallel or distributed architecture. ”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Independent claims 1 and 11 recite “execute computer-aided engineering (CAE) software to generate training data across all possible operating ranges of the electrified vehicle” however this limitation does not indicate how it would be possible for the CAE software to generate training data across all possible operating ranges of the electrified vehicle as this limitation could potentially include an infinite number of possible ranges. For the purpose of compact prosecution the examiner will interpret this claim as “generate training data across a plurality of operating ranges”.
Claims 10 and 20 recite “receive a user input based on the generated output” however, it is unclear how it may be determined that the user input is based on generated output in view of the specification.
Claims 2-9 and 12-19 are dependent on claims 1 or 11 and do not cure the eefinciencies thereof and are therefore rejected for the same reason.
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.
Claim(s) 1-2, 5-6, 9-12, 15-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cronin (US 20230182616 A1) henceforth referred to as Cronin and further in view of Chen et al (US 20240174254 A1) henceforth referred to as Chen.
Regarding Claim 1 Cronin teaches An energy consumption estimation and control system for an electrified vehicle, the energy consumption estimation and control system comprising (para [0025] : “Disclosed herein are systems and methods for energy-based vehicle analysis.”):
an computing system that is configured to:
execute computer-aided engineering (CAE) software to generate training data across all possible operating ranges of the electrified vehicle (para [0140] : “Before using the one or more ML models 1325 to generate the capacity 1330 and/or the range 1335 the ML engine 1320 performs initial training 1365 of the one or more ML models 1325 using training data 1370. The training data 1370 can include examples of attribute data (e.g., as in the attribute data 1305) and/or examples of pre-determined capacity and/or the predetermined range (e.g., as in the pre-determined capacity 1340 and/or the pre-determined range 1345). In some examples, the pre-determined capacity and/or the predetermined range in the training data 1370 are optimizations that the one or more ML models 1325 previously generated based on the attribute data in the training data 1370. In the initial training 1365, the ML engine 1320 can form connections and/or weights based on the training data 1370, for instance between nodes of a neural network or another form of neural network. For instance, in the initial training 1365, the ML engine 1320 can be trained to output the pre-determined capacity and/or the predetermined range in the training data 1370 in response to receipt of the corresponding attribute data in the training data 1370.”, as the software generating the training data are optimizations that one or more ML models previously generated it is configured to generate the training data across all operating ranges of the electrified vehicle as the data was generated previously while the vehicle was operated.); and
train an energy consumption model using the generated training data to obtain a trained energy consumption model, wherein the trained energy consumption model is configured to estimate an energy consumption of the electrified vehicle (para [0140] : “Before using the one or more ML models 1325 to generate the capacity 1330 and/or the range 1335 the ML engine 1320 performs initial training 1365 of the one or more ML models 1325 using training data 1370. The training data 1370 can include examples of attribute data (e.g., as in the attribute data 1305) and/or examples of pre-determined capacity and/or the predetermined range (e.g., as in the pre-determined capacity 1340 and/or the pre-determined range 1345). In some examples, the pre-determined capacity and/or the predetermined range in the training data 1370 are optimizations that the one or more ML models 1325 previously generated based on the attribute data in the training data 1370. In the initial training 1365, the ML engine 1320 can form connections and/or weights based on the training data 1370, for instance between nodes of a neural network or another form of neural network. For instance, in the initial training 1365, the ML engine 1320 can be trained to output the pre-determined capacity and/or the predetermined range in the training data 1370 in response to receipt of the corresponding attribute data in the training data 1370.”); and
a control system configured to (para [0025] : “The control system estimates a range that the vehicle is capable of reaching using the propulsion mechanism based on the one or more attributes of the vehicle and the estimated capacity of the energy storage unit. The control system causes an output interface to output an indication of the estimated range.”):
receive and store, at a memory, the trained energy consumption model (para [0025] : “A control system with a processor and a memory estimates a capacity of the energy storage unit based on the one or more attributes of the energy storage unit.”, para [0134] : “FIG. 13 is a block diagram 1300 illustrating use of one or more trained machine learning models 1325 of a machine learning engine 1320 to identify an energy storage unit's capacity 1330 and/or a vehicle's range 1335 based on attribute data.”,);
access the trained energy consumption model from the memory (para [0134] : “FIG. 13 is a block diagram 1300 illustrating use of one or more trained machine learning models 1325 of a machine learning engine 1320 to identify an energy storage unit's capacity 1330 and/or a vehicle's range 1335 based on attribute data.”, it would be required the control system access the trained model from memory in order to operate as described.);
obtain a plurality of input parameters for the trained energy consumption model based on real-time parameters of the electrified vehicle (para [0135] : “Once trained via initial training 1365, the one or more ML models 1325 receive, as an input, attribute data 1305 that identifies attribute(s) of an energy storage unit (ESU) (e.g., type, voltage, discharge curve, capacitance, impedance, current, amperage, capacity, energy density, specific energy density, power density, temperature, temperature dependence, service life, physical attributes, charge cycle, discharge cycle, cycle life, deep discharge ability, discharge rate, charge rate, and the like) and/or attribute(s) of a vehicle (e.g., mileage, efficiency, ergonomics, aerodynamics, shape, geometry, weight, horsepower, brake power, turning radius, type, size, energy consumption rate, and the like). At least some of the attribute data 1305 may be received from one or more sensors, such as sensors to measure voltage, current, resistance, capacitance, inductance, frequency, power, temperature, and/or continuity.”);
utilize the trained energy consumption model and the plurality of input parameters to estimate the energy consumption of the electrified vehicle in real-time (para [0136] : “In response to receiving at least the attribute data 1305 as an input(s), the one or more ML model(s) 1325 estimate the capacity 1330 of the ESU based on the attribute data 1305. The capacity 1330 of the ESU can identify the total capacity of the ESU, the remaining charge and/or remaining capacity of the ESU, or a combination thereof. The capacity may be an indication of the total and/or remaining voltage, current, resistance, capacitance, inductance, frequency, power, and/or charge of the ESU.”, para [0137] : “In response to receiving at least the attribute data 1305, the estimated capacity 1330, and/or the predetermined capacity 1340 as an input(s), the ML model(s) 1325 estimate the range 1335 of that the vehicle can travel given the capacity (e.g., the estimated capacity 1330 or the predetermined capacity 1340) of the ESU and/or given the attribute data 1305.” ); and
generate an output based on the estimated energy consumption of the electrified vehicle (para [0139] : “Once the one or more ML models 1325 generate the capacity 1330 and/or the range 1335, the capacity 1330 and/or the range 1335 can be output to an output interface that can indicate the capacity 1330 and/or the range 1335 to a user (e.g., by displaying the capacity 1330 and/or the range 1335 or playing audio indicative of the capacity 1330 and/or the range 1335) and/or to the vehicle, which can adjust its settings and/or configurations based on the capacity 1330 and/or the range 1335, for instance to improve efficiency of the vehicle to extend the range beyond the estimated range 1335.”). However, Cronin does not explicitly teach an external computing system that is separate from the electrified vehicle.
However, in a similar field of endeavor (machine learning systems for vehicle applciations), Chen teaches an an external computing system that is separate from the electrified vehicle (para [0020] : “In embodiments, the server 106 sends an initialized model to each of the edge nodes 101, 103, 105, 107, and 109. The initialized model may be any model that may be utilized for operating a vehicle, for example, an image processing model, an object detection model, or any other model for advanced driver assistance systems. Each of the edge nodes 101, 103, 105, 107, and 109 trains the received initialized model using local data to obtain an updated local model and sends the updated local model or parameters of the updated local model back to the server 106. The server 106 collects the updated local models, computes a global model based on the updated local models, and sends the global model to each of the edge nodes 101, 103, 105, 107, and 109.”, Fig. 1A, As Chen teaches collection of locally trained models and distribution to other vehicles the combination with Cronin training the machine learned moel with generated training data and Chen teaches the use of locally trained models for distribution amongst other agents, a second vehicle using the system of Cronin would be an external computing system separate from the electrified vehicle carrying out the functions as claimed followed by Chen’s distribution of the trained model to the electrified vehicle.).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to modify the system of Cronin with the system of Chen to increase versatility and improve reliability of machine-learned models.
Regarding Claim 2 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 1, further Cronin teaches wherein the plurality of input parameters include at least one of ambient temperature, wind speed, wind direction, vehicle mass, battery system state of charge (SOC), road grade, vehicle velocity, and vehicle acceleration (para [0135] : “Once trained via initial training 1365, the one or more ML models 1325 receive, as an input, attribute data 1305 that identifies attribute(s) of an energy storage unit (ESU) (e.g., type, voltage, discharge curve, capacitance, impedance, current, amperage, capacity, energy density, specific energy density, power density, temperature, temperature dependence, service life, physical attributes, charge cycle, discharge cycle, cycle life, deep discharge ability, discharge rate, charge rate, and the like) and/or attribute(s) of a vehicle (e.g., mileage, efficiency, ergonomics, aerodynamics, shape, geometry, weight, horsepower, brake power, turning radius, type, size, energy consumption rate, and the like). At least some of the attribute data 1305 may be received from one or more sensors, such as sensors to measure voltage, current, resistance, capacitance, inductance, frequency, power, temperature, and/or continuity.”).
Regarding Claim 5 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 1, further Cronin renders obvious wherein the control system has insufficient computing power to execute the CAE software online (limitations of computing resources are well known and the capacity to carry out functions varies with the functions being performed, therefore running of software while the vehicle is operating may limit available computing power to prevent execution of the CAE software during operation.).
Regarding Claim 6 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 1, further Cronin teaches wherein the trained energy consumption model is a recurrent neural network (RNN) (para [0134] : “The ML engine 1320 and/or the ML model(s) 1325 can include one or more neural network (NNs), one or more convolutional neural networks (CNNs), one or more trained time delay neural networks (TDNNs), one or more deep networks, one or more autoencoders, one or more deep belief nets (DBNs), one or more recurrent neural networks (RNNs), one or more generative adversarial networks (GANs), one or more conditional generative adversarial networks (cGANs), one or more other types of neural networks, one or more trained support vector machines (SVMs), one or more trained random forests (RFs), one or more computer vision systems, one or more deep learning systems, one or more classifiers, one or more transformers, or combinations thereof.”).
Regarding Claim 9 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 1, further Cronin teaches wherein the trained energy consumption model is an artificial neural network (ANN) (para [0134] : “The ML engine 1320 and/or the ML model(s) 1325 can include one or more neural network (NNs), one or more convolutional neural networks (CNNs), one or more trained time delay neural networks (TDNNs), one or more deep networks, one or more autoencoders, one or more deep belief nets (DBNs), one or more recurrent neural networks (RNNs), one or more generative adversarial networks (GANs), one or more conditional generative adversarial networks (cGANs), one or more other types of neural networks, one or more trained support vector machines (SVMs), one or more trained random forests (RFs), one or more computer vision systems, one or more deep learning systems, one or more classifiers, one or more transformers, or combinations thereof.”, a convolutional neural network (CNN) is a type of artificial neural network (ANN)).
Regarding Claim 10 Cronin teaches The energy consumption estimation and control system of claim 1, further Cronin teaches wherein the generated output is an estimated range of the electrified vehicle (para [0139] : “Once the one or more ML models 1325 generate the capacity 1330 and/or the range 1335, the capacity 1330 and/or the range 1335 can be output to an output interface that can indicate the capacity 1330 and/or the range 1335 to a user (e.g., by displaying the capacity 1330 and/or the range 1335 or playing audio indicative of the capacity 1330 and/or the range 1335) and/or to the vehicle, which can adjust its settings and/or configurations based on the capacity 1330 and/or the range 1335, for instance to improve efficiency of the vehicle to extend the range beyond the estimated range 1335.”), and wherein the control system is further configured to (i) receive a user input based on the generated output and (ii) control an operating parameter of the electrified vehicle based on the received user input (para [0103] : “Further, a user may retrieve the store instructions from the energy management database 104 before driving the electric vehicle 102.”, a user driving the electric vehicle would use the controls of the vehicle for input to the vehicle in response to generated output which would control an operating parameters based on the received user input.).
Regarding Claim 11, it recites a method with limitations substantially the same to claim 1 above, therefore it is rejected for the same reason.
Regarding Claim 12, it recites a method with limitations substantially the same to claim 2 above, therefore it is rejected for the same reason.
Regarding Claim 15, it recites a method with limitations substantially the same to claim 5 above, therefore it is rejected for the same reason.
Regarding Claim 16, it recites a method with limitations substantially the same to claim 6 above, therefore it is rejected for the same reason.
Regarding Claim 19, it recites a method with limitations substantially the same to claim 9 above, therefore it is rejected for the same reason.
Regarding Claim 20, it recites a method with limitations substantially the same to claim 10 above, therefore it is rejected for the same reason.
Claim(s) 3-4 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cronin and Chen and further in view of Tseng et al ("Personalized Prediction of Vehicle Energy Consumption based on Participatory Sensing") henceforth referred to as Tseng.
Regarding Claim 3 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 1, further Cronin teaches wherein the plurality of input parameters include ambient temperature, vehicle mass, battery system SOC (para [0135] : “Once trained via initial training 1365, the one or more ML models 1325 receive, as an input, attribute data 1305 that identifies attribute(s) of an energy storage unit (ESU) (e.g., type, voltage, discharge curve, capacitance, impedance, current, amperage, capacity, energy density, specific energy density, power density, temperature, temperature dependence, service life, physical attributes, charge cycle, discharge cycle, cycle life, deep discharge ability, discharge rate, charge rate, and the like) and/or attribute(s) of a vehicle (e.g., mileage, efficiency, ergonomics, aerodynamics, shape, geometry, weight, horsepower, brake power, turning radius, type, size, energy consumption rate, and the like). At least some of the attribute data 1305 may be received from one or more sensors, such as sensors to measure voltage, current, resistance, capacitance, inductance, frequency, power, temperature, and/or continuity.”). However, Cronin does not explicitly teach the input parameters include wind speed, wind direction, road grade, vehicle velocity, and vehicle acceleration.
However, in the same field of endeavor (calculation of vehicle DTE) Tseng teaches parameters affecting DTE including wind speed, wind direction, road grade, vehicle velocity, and vehicle acceleration (pg 3 col 1 : “While there are many factors to determine vehicle energy consumption, they can be classified by three broad areas: • Driver: The driver who controls the vehicle has a direct impact on the vehicle movement. Different drivers exhibit different preferences for stop/start and acceleration, aggression in various scenarios, propensity for hypermiling, etc. Psychological and behavioral traits of drivers also affect vehicle energy efficiency”, pg 4 col 1 : “v is the continuous average speed (i.e., the average speed without idling). The higher powers2 of v like v 2 , ..., vr are also considered.”, pg 11 col 1 : “Road Grade: This is the elevation of roads. There are public mapping APIs to provide road elevation data. This can be added to the future energy consumption model”, pg 11 col 1 : “Weather and Traffic: Our study assumes mild weather and traffic conditions. But our energy consumption model can be extended to incorporate additional parameters to capture the impacts of weather in the vehicle model (e.g., weather types and route conditions). The vehicle speed from participatory sensing data naturally reflect the traffic condition to a certain extent, however, the data would need to be updated more frequently. Wind speed and road surface conditions also affect vehicle energy consumption, but are more difficult to measure.”).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to modify the combination of Cronin and Chen with the parameters of Tseng to increase the accuracy of the determined energy consumption/distance to empty.
Regarding Claim 4 the combination of Cronin and Chen teaches further Cronin teaches wherein the plurality of input parameters include ambient temperature, vehicle mass, battery system SOC (para [0135] : “Once trained via initial training 1365, the one or more ML models 1325 receive, as an input, attribute data 1305 that identifies attribute(s) of an energy storage unit (ESU) (e.g., type, voltage, discharge curve, capacitance, impedance, current, amperage, capacity, energy density, specific energy density, power density, temperature, temperature dependence, service life, physical attributes, charge cycle, discharge cycle, cycle life, deep discharge ability, discharge rate, charge rate, and the like) and/or attribute(s) of a vehicle (e.g., mileage, efficiency, ergonomics, aerodynamics, shape, geometry, weight, horsepower, brake power, turning radius, type, size, energy consumption rate, and the like). At least some of the attribute data 1305 may be received from one or more sensors, such as sensors to measure voltage, current, resistance, capacitance, inductance, frequency, power, temperature, and/or continuity.”). However, Cronin does not explicitly teach the input parameters consists of ambient temperature, vehicle mass, battery system SOC, wind speed, wind direction, road grade, vehicle velocity, and vehicle acceleration.
However, in the same field of endeavor (calculation of vehicle DTE) Tseng teaches parameters affecting DTE including wind speed, wind direction, road grade, vehicle velocity, and vehicle acceleration (pg 3 col 1 : “While there are many factors to determine vehicle energy consumption, they can be classified by three broad areas: • Driver: The driver who controls the vehicle has a direct impact on the vehicle movement. Different drivers exhibit different preferences for stop/start and acceleration, aggression in various scenarios, propensity for hypermiling, etc. Psychological and behavioral traits of drivers also affect vehicle energy efficiency”, pg 4 col 1 : “v is the continuous average speed (i.e., the average speed without idling). The higher powers2 of v like v 2 , ..., vr are also considered.”, pg 11 col 1 : “Road Grade: This is the elevation of roads. There are public mapping APIs to provide road elevation data. This can be added to the future energy consumption model”, pg 11 col 1 : “Weather and Traffic: Our study assumes mild weather and traffic conditions. But our energy consumption model can be extended to incorporate additional parameters to capture the impacts of weather in the vehicle model (e.g., weather types and route conditions). The vehicle speed from participatory sensing data naturally reflect the traffic condition to a certain extent, however, the data would need to be updated more frequently. Wind speed and road surface conditions also affect vehicle energy consumption, but are more difficult to measure.”, as the combination of Cronin and Tseng teaches including input parameters of ambient temperature, wind speed, wind direction, vehicle mass, battery system SOC, road grade, vehicle velocity, and vehicle acceleration and it is a finite list of possible combinations of input parameters for calculation of distance to empty/ energy consumption it would be obvious to a person of ordinary skill in the art to try the closed set of input parameters which consists of ambient temperature, wind speed, wind direction, vehicle mass, battery system SOC, road grade, vehicle velocity, and vehicle acceleration ).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to modify the combination of Cronin and Chen with the parameters of Tseng to increase the accuracy of the determined energy consumption/distance to empty.
Regarding Claim 13, it recites a method with limitations substantially the same to claim 3 above, therefore it is rejected for the same reason.
Regarding Claim 14, it recites a method with limitations substantially the same to claim 4 above, therefore it is rejected for the same reason.
Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cronin and Chen and further in view of Otten ("The Vanishing Gradient Problem, How To Detect & Overcome It" https://spotintelligence.com/2023/02/06/vanishing-gradient-problem/) henceforth referred to as Otten.
Regarding Claim 7 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 6, however Cronin does not explicitly teach wherein the RNN is a long short-term memory (LSTM) type RNN.
However, in a similar field of endeavor (RNNs) Otten teaches wherein the RNN is a long short-term memory (LSTM) type RNN (pg 6 : “Several solutions have been proposed to address this issue, including activation functions such as ReLU, architectures such as LSTMs or GRUs, and gradient clipping.”).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to modify the RNN of the combination of Cronin and Chen with to be an LSTM of Otten to solve the vanishing gradient problem.
Regarding Claim 8 the combination of Cronin and Chen teaches The energy consumption estimation and control system of claim 6, however Cronin does not explicitly teach wherein the RNN is a gated recurrent unit (GRU) type RNN.
However, in a similar field of endeavor (RNNs) Otten teaches wherein the RNN is a long short-term memory (LSTM) type RNN (pg 6 : “Several solutions have been proposed to address this issue, including activation functions such as ReLU, architectures such as LSTMs or GRUs, and gradient clipping.”).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date to modify the RNN the RNN of the combination of Cronin and Chen with to be an GRU of Otten to solve the vanishing gradient problem.
Regarding Claim 17, it recites a method with limitations substantially the same to claim 7 above, therefore it is rejected for the same reason.
Regarding Claim 18, it recites a method with limitations substantially the same to claim 8 above, therefore it is rejected for the same reason.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID HATCH whose telephone number is (571)272-4518. The examiner can normally be reached on Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James J Lee can be reached on 571-270-5965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.H./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668