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 .
Priority
The applicant’s claim to priority SE2450750-1 on 07/02/2024 is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/02/2025 and 11/07/2025 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 6, 9-11, 12, 17, and 20-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Conte et al. (20200263622; hereinafter Conte).
Regarding claim 1, Conte teaches a computer-implemented method for detecting changes in operation of a powertrain of a heavy-duty vehicle (Conte: Abstract), comprising:
obtaining information indicating that at least one powertrain sensor signal monitoring sequence is to be performed (Conte: “The method also includes determining raw-sensed data from the sensor at the measurement point” ¶ 2, “The method 100 includes determining input conditions to the engine 12” ¶ 30);
obtaining a set of vehicle powertrain operation parameter settings to be applied during the at least one powertrain sensor signal monitoring sequence (Conte: “Referring to FIG. 2, and with continued reference to FIG. 1, there is shown a method 100 for improved sensing and estimation of temperatures, such as those at different locations of the powertrain 10” ¶ 24);
controlling the operation of the powertrain of the heavy-duty vehicle according to the obtained set of vehicle powertrain operation parameter settings for a determined time period (Conte: “A control system, computer, or controller 20, which may be the ECU for the entire vehicle, is linked to the powertrain 10 and may be linked to other systems of the vehicle. The controller 20 has sufficient memory and processing power to interact with the components, and equivalents thereof, described herein and to execute the process or functions, and equivalents thereof” ¶ 15, see also ¶ 22, 30);
determining virtual sensor values based on sensor signals from one or more powertrain sensors, wherein the sensor signals are obtained during said determined time period (Conte: “executed to create improved virtual data for any sensor data that may be modeled, as explained herein. As used herein, virtual data, or a virtual parameter, refers to data generated through a combination of both sensed and modeled data” ¶ 24, “From the turbine conditions, the method 100 may, optionally, calculate the time constant, τf, for the low-pass filter based on current operating conditions of the engine 12” ¶ 42); and
detecting a change in the operation of the powertrain of the heavy-duty vehicle in case the determined virtual sensor values indicate a different operation of the powertrain of the heavy-duty vehicle as compared to previously determined virtual sensor values using the same at least one powertrain sensor signal monitoring sequence (Conte: “The method 100 calculates a differential between the modeled temperature (determined in step 114) and the filtered temperature (determined in step 116) to determine a compensation term or compensation temperature. The compensation temperature represents the difference between the actual model results” ¶ 46).
Regarding claim 6, Conte teaches the method according to claim 1, wherein each of the at least one powertrain sensor signal monitoring sequence is associated with a determined time period, information indicating the virtual sensor values to be determined based on the sensor signals, and a set of vehicle powertrain operation parameter settings (Conte: “From the turbine conditions, the method 100 may, optionally, calculate the time constant, τf, for the low-pass filter based on current operating conditions of the engine 12, as opposed to a fixed value time constant. The time constant may be determined as a function of the time change of mass flow through the turbine 16” ¶ 42, see also ¶ 38 sampling times).
Regarding claim 9, Conte teaches the method according to claim 1, wherein the powertrain of the heavy-duty vehicle comprise a combustion engine (Conte: “The powertrain 10 includes an internal combustion engine (ICE), which may be referred to as engine 12” ¶ 10).
Regarding claim 10, Conte teaches the method according to claim 9, wherein the one or more operating characteristics or conditions of one or more powertrain components comprise a change in fuel composition (Conte: “The intake conditions may include fuel used, intake temperature, and airflow to the engine 12” ¶ 30, see also ¶ 34).
Regarding claim 11, Conte teaches the method according to claim 9, wherein the set of vehicle powertrain operation parameter settings comprise one or more of: a predetermined setting of an injection system timing, a predetermined setting of an injection pressure, a predetermined setting of an injection strategy, a predetermined setting of intake-and exhaust pressures, and a predetermined setting of a charge air temperature (Conte: “The intake conditions may include fuel used, intake temperature, and airflow to the engine 12. Airflow may refer to volumetric or mass airflow, and may include change in airflow over time (e.g., the airflow input condition may be the time derivative of the mass airflow through the MAF sensor 42). Additional input conditions may also be used by the method 100 and the controller 20” ¶ 30).
Regarding claim 12, Conte teaches a control unit for detecting changes in operation of a powertrain of a heavy-duty vehicle, wherein the control unit comprise a processing circuitry and a memory, the processing circuitry being configured to (Conte: “A control system, computer, or controller 20, which may be the ECU for the entire vehicle, is linked to the powertrain 10 and may be linked to other systems of the vehicle. The controller 20 has sufficient memory and processing power to interact with the components” ¶ 15):
...
In regards to the remainder of claim 12, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
In regards to claim(s) 17 and 20-22, the claim(s) recite analogous limitations to claim(s) 6 and 9-11, respectively, and are therefore rejected under the same premise.
Regarding claim 23, Conte teaches a computer program product comprising program code for performing, when executed by a processing circuitry of a control unit, ECU, on-board a heavy-duty vehicle, the method of claim 1 (Conte: “A control system, computer, or controller 20, which may be the ECU for the entire vehicle, is linked to the powertrain 10 and may be linked to other systems of the vehicle. The controller 20 has sufficient memory and processing power to interact with the components” ¶ 15).
Regarding claim 24, Conte teaches a non-transitory computer-readable storage medium comprising instructions, which when executed by a processing circuitry of a control unit, ECU, on-board a heavy-duty vehicle, cause the processing circuitry to perform the method of claim 1 (Conte: “A control system, computer, or controller 20, which may be the ECU for the entire vehicle, is linked to the powertrain 10 and may be linked to other systems of the vehicle. The controller 20 has sufficient memory and processing power to interact with the components” ¶ 15).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-4 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Conte in view of Bai et al. (20170264691; hereinafter Bai).
Regarding claim 2, Conte teaches the method according to claim 1, further comprising:
identifying one or more operating characteristics or conditions of one or more powertrain components involved in the operation of the powertrain of the heavy-duty vehicle that is causing the detected change in the operation of the powertrain of the heavy-duty vehicle (Conte: “the compensation temperature is non-zero during rapid changes in the modeled temperature, such as those occurring as a result of transient conditions in the engine 12” ¶ 47); and
However, Conte fails to teach providing, to a driver of the heavy-duty vehicle and/or a remote vehicle monitoring system to which the heavy-duty vehicle is connected, information indicating the identified one or more operating characteristics or conditions of one or more powertrain components.
In a similar field of endeavor, Bai teaches providing, to a driver of the heavy-duty vehicle and/or a remote vehicle monitoring system to which the heavy-duty vehicle is connected, information indicating the identified one or more operating characteristics or conditions of one or more powertrain components (Bai: “a virtual-sensor-arrangement client configured to, when executed by a processing unit, communicate with a virtual-sensor-arrangement server of the add-on device. Communications include the client receiving, from the virtual-sensor-arrangement server, the sensor data corresponding to sensing performed at a sensor of the add-on device” ¶ 8).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Conte so that it also includes the element of a remote server, as taught by Bai, in order to provide efficient computations at a centralized location (Bai: ¶ 132).
Regarding claim 3, Conte teaches the method according to claim 1, further comprising:
However, Conte fails to teach transmitting, to a remote vehicle monitoring system to which the heavy-duty vehicle is connected, information indicating the determined virtual sensor values.
In a similar field of endeavor, Bai teaches transmitting, to a remote vehicle monitoring system to which the heavy-duty vehicle is connected, information indicating the determined virtual sensor values (Bai: “application output of the application 270 ... can include communications to a remote system 160, such as an OnStar® server” ¶ 123, “FIGS. 6 and 7 show example algorithms, represented schematically by a process flows 600, 700 for creating and using virtual sensor(s) input at the vehicle 102 based on sensor input from the add-on device 150” ¶ 126).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Conte so that it also includes the element of a remote server, as taught by Bai, in order to provide efficient data storage at a centralized location (Bai: ¶ 132).
Regarding claim 4, Conte in view of Bai teaches the method according to claim 3, further comprising:
However, Conte fails to teach receiving, from the remote vehicle monitoring system, information indicating identified one or more operating characteristics or conditions of one or more powertrain components that is causing the detected change in the operation of the powertrain of the heavy-duty vehicle; and
providing, to a driver of the heavy-duty vehicle, information indicating the identified one or more operating characteristics or conditions of one or more powertrain components.
In a similar field of endeavor, Bai teaches receiving, from the remote vehicle monitoring system, information indicating identified one or more operating characteristics or conditions of one or more powertrain components that is causing the detected change in the operation of the powertrain of the heavy-duty vehicle (Bai: “Road grade estimation can be valuable in vehicle operations such as powertrain-, or propulsion-, efficiency optimization and autonomous driving functions. Applications focused on these functions could use a virtual sensor, virtualizing a sensor the vehicle 102 doesn't already have, or virtualizing a sensor that is in one or more ways (e.g., accuracy) more advanced than a corresponding vehicle sensor” ¶ 206); and
providing, to a driver of the heavy-duty vehicle, information indicating the identified one or more operating characteristics or conditions of one or more powertrain components (Bai: “The operation 708 can include delivery of application output, indicated by path 598 in FIG. 8, to a receiving apparatus 540, such as a vehicle-user interface—e.g., heads-up display (HUD) or other screen, a vehicle speaker” ¶ 201, see also ¶ 200).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Conte so that it also includes the providing information to a driver, as taught by Bai, in order to provide satisfactory results (Bai: ¶ 175).
In regards to claim(s) 13-15, the claim(s) recite analogous limitations to claim(s) 2-4, and are therefore rejected under the same premise.
Claim(s) 5, 7, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Conte in view of Ting (US6275761B1).
Regarding claim 5, Conte teaches the method according to claim 1,
However, Conte fails to teach wherein a machine learning model is trained to identify the one or more operating characteristics or conditions of one or more powertrain components involved in the operation of the powertrain of the heavy-duty vehicle that is causing the detected change in the operation of the powertrain of the heavy-duty vehicle based the determined virtual sensor values and the previously determined virtual sensor values.
In a similar field of endeavor, Ting teaches wherein a machine learning model is trained to identify the one or more operating characteristics or conditions of one or more powertrain components involved in the operation of the powertrain of the heavy-duty vehicle that is causing the detected change in the operation of the powertrain of the heavy-duty vehicle based the determined virtual sensor values and the previously determined virtual sensor values (Ting: “A composite neural network-based virtual torque converter slip sensor is indicated schematically at 18 in FIG. 1. Such a virtual sensor 18 exists as a suitably programmed microprocessor in the powertrain control module of a vehicle as described in the following portion of this specification” Col. 4 Lines 32-37, see also Col. 9).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Conte so that it also includes a machine learning model, as taught by Ting, in order to tailor the system to specific operating conditions (Ting: Col. 2 Line 68 – Col. 3 Line 22).
Regarding claim 7, Conte teaches the method according to claim 1,
However, Conte fails to teach wherein the one or more operating characteristics or conditions of one or more powertrain components comprise damaged or malfunctioning powertrain components.
In a similar field of endeavor, Ting teaches wherein the one or more operating characteristics or conditions of one or more powertrain components comprise damaged or malfunctioning powertrain components (Ting: “A main variable of interest in automatic transmission performance is the slip variable which is defined as the speed difference between the engine speed and the torque converter output speed” Col. 4 Lines 29-32).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the sensor system of Conte so that it also includes identifying a damaged or malfunctioning component, as taught by Ting, in order to improve vehicle efficiency and control (Ting: Col. 5 Lines 11-23).
In regards to claim(s) 16 and 18, the claim(s) recite analogous limitations to claim(s) 5 and 7, and are therefore rejected under the same premise.
Claim(s) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Conte in view of Carruthers et al. (20240132051; hereinafter Carruthers).
Regarding claim 8, Conte teaches the method according to claim 1,
However, Conte fails to teach wherein the powertrain of the heavy-duty vehicle is a battery-electric or fuel-cell electric powertrain.
In a similar field of endeavor, Carruthers teaches wherein the powertrain of the heavy-duty vehicle is a battery-electric or fuel-cell electric powertrain (Carruthers: “A vehicle, such as a battery electric vehicle, a combustion engine hybrid vehicle, a fuel cell electric vehicle, or the like, includes a power supply for providing electrical power to a powertrain of the vehicle to drive/propel the vehicle” ¶ 19).
As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the vehicle system of Conte so that it also includes a battery-electric or fuel-cell electric powertrain, as taught by Carruthers, in order to improve vehicle control of various types of vehicles (Carruthers: ¶ 21).
In regards to claim(s) 19, the claim(s) recite analogous limitations to claim(s) 8, and is therefore rejected under the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Roos et al. (20150314789) is in the similar field of endeavor as the claimed invention of vehicle system detection.
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/C.P./Examiner, Art Unit 3663
/TYLER J LEE/Primary Examiner, Art Unit 3663
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