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 the application filed on 04/16/2025. Claims 1, 8, 9, 11, 17, 18, and 20 are currently amended. Claims 1-20 are presently pending and are presented for examination.
Response to Amendments
In response to Applicant's Amendments dated 04/16/2025, examiner withdraws the previous prior art rejection. However, a new ground(s) of rejection is made.
Response to Arguments
Applicant's arguments filed 04/16/2025 have been fully considered but they are moot in view of the new ground(s) of rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to ATA 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 9-11 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210162965 (hereinafter, "Mellings"; previously of record), in view of U.S. Pub. No. 20200231167 (hereinafter, "Zhang"; previously of record), and in further view of U.S. Pub. No. 20210396620 (hereinafter, "Jensen"; newly of record).
Regarding claim 1, Mellings discloses a method for controlling performance of a vehicle, the method comprising:
receiving by a vehicle control unit, load parameters describing features of a load in the vehicle (“The processor may additionally comprise an instruction output port for transmitting instructions to the loading apparatus, and may be programmed to use data received from the loading apparatus concerning the weight, dimensions and/or volume of a load to be placed on the vehicle to determine a preferred location in the vehicle for the load, and to transmit to the loading apparatus instructions as to location in or on the vehicle in which the load is to be placed” (para 0011));
collecting, by the vehicle control unit, a load weight measure of a load in the vehicle, from a load sensor, when the vehicle is in a steady state (“the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020));
computing, by the vehicle control unit, longitudinal and lateral positions and height of a center of gravity of the vehicle, … and receiving the load parameters and the load weight measure, and providing the longitudinal and lateral positions and height of the center of gravity (“together with pre-programmed trailer specific information concerning the dimensions and weight distribution of the trailer itself to calculate the centre of gravity of the trailer 14, and to use this and the input signal from the lateral acceleration sensor or yaw rate sensor to calculate if the centre of gravity of the trailer 14 is sufficiently high that it is likely to move outside of the wheel base of the trailer 14 during cornering, thus causing rollover, and initiate a control intervention on the basis of this determination. It will be appreciated that both the weight of the load, and its location (both in the reference plane and its distance from the reference plane, as defined by the x, y and z coordinates described above would be used in determining the centre of gravity of the trailer 14” (para 00100)), …; and
using the longitudinal and lateral positions and height of the center of gravity of the vehicle to adjust control of at least one of vehicle brakes, vehicle engine torque, vehicle stability and vehicle suspension (“the data from the loading apparatus, in the form of load x, y, z, will determine if the slip limits will increase or decrease. The calculation of centre of gravity and/or moment of inertia will be used to determine the thresholds that are used by the automatic intervention system, or as input for making proactive choices as to speed, braking torque, suspension settings and/or steering angle, in order to ensure safe and stable operation” (para 0098)).
However, Mellings does not explicitly teach
…using a neural network trained for a model of the vehicle...,
…wherein the vehicle control unit is further configured to control the position of the center of gravity by comparing the position of the center of gravity with acceptable ranges defined by a three-dimension vehicle model.
Zhang, in the same field of endeavor, teaches
…using a neural network trained for a model of the vehicle...(“The non-linear assignment between sensor data and associated center of gravity positions is preferably learned using simulation data of a model of the target vehicle” (para 0013) and “The learning-based classification algorithm is used to replicate a non-linear assignment between driving dynamics data (standard ESP sensor data) and associated center of gravity positions” (para 0012)),
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide an output of the probability of each individual class; see Zhang at least at [0013].
...a three-dimension vehicle model (“wherein a non-linear assignment between sets of input variables and classes of center of gravity positions is learned using simulation data of a model of the motor vehicle” (claim 2) and “A vertical coordinate of the estimated center of gravity position is preferably determined on the basis of the lateral and longitudinal coordinates calculated first” (para 0053)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide simulation data of a model of the motor vehicle; see Zhang at least at [0053].
Jensen, in the same field of endeavor, teaches
…wherein the vehicle control unit is further configured to control the position of the center of gravity by comparing the position of the center of gravity with acceptable ranges defined by a … (“For example, the center-of-mass system may use a known weight and/or center-of-mass of the trailer to derive the trailer-independent center of mass. Center-of-mass thresholds may be used in a form that corresponds to the type of load center of mass used. For example, a load center of mass that describes a combination of the trailer and the load may be compared to center-of-mass thresholds that are also expressed in terms of the trailer and the load. A load center of mass that describes the load independent of the trailer may be compared to center-of-mass thresholds that are also expressed in terms of the load itself, independent of the trailer” (para 0021)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Jensen in order to use a known weight and/or center-of-mass of the trailer to derive the trailer-independent center of mass; see Jensen at least at [0021].
Regarding claim 9, Mellings discloses the method according to claim 1. Additionally Mellings discloses further comprising providing the position of the center of gravity of the vehicle to other systems of the vehicle (“The calculation of centre of gravity and/or moment of inertia will be used to determine the thresholds that are used by the automatic intervention system, or as input for making proactive choices as to speed, braking torque, suspension settings and/or steering angle, in order to ensure safe and stable operation” (para 0098)).
Regarding claim 10, Mellings discloses the method according to claim 1. However, Mellings does not explicitly teach further comprising:
a test phase for acquiring training data sets each comprising values of the input parameters of the neural network and a corresponding position of the center of gravity measured in a loaded vehicle; and
a training phase of the neural network during which coefficients of the neural network are determined using the training data sets.
Zhang, in the same field of endeavor, teaches
a test phase for acquiring training data sets each comprising values of the input parameters of the neural network and a corresponding position of the center of gravity measured in a loaded vehicle (“The non-linear assignment between sensor data and associated center of gravity positions is preferably learned using simulation data of a model of the target vehicle” (para 0013)); and
a training phase of the neural network during which coefficients of the neural network are determined using the training data sets (“The assignment is performed preferably by means of a training process” (para 0012)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide a training process; see Zhang at least at [0013].
Regarding claim 11, Mellings discloses a system for controlling performance of a vehicle, the system comprising a computing unit, a load sensor and at least one control unit for adjusting at least one of a vehicle brake, a vehicle engine torque, a vehicle stability and a vehicle suspension, the computing unit being configured to:
receive load parameters describing features of a load in the vehicle (“The processor may additionally comprise an instruction output port for transmitting instructions to the loading apparatus, and may be programmed to use data received from the loading apparatus concerning the weight, dimensions and/or volume of a load to be placed on the vehicle to determine a preferred location in the vehicle for the load, and to transmit to the loading apparatus instructions as to location in or on the vehicle in which the load is to be placed” (para 0011));
collect a load weight measure of a load in the vehicle, from a load sensor, when the vehicle is in a steady state (“the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020));
compute longitudinal and lateral positions and height of a center of gravity of the vehicle, … and receiving the load parameters and the load weight measure, and providing the longitudinal and lateral positions and height of the center of gravity (“together with pre-programmed trailer specific information concerning the dimensions and weight distribution of the trailer itself to calculate the centre of gravity of the trailer 14, and to use this and the input signal from the lateral acceleration sensor or yaw rate sensor to calculate if the centre of gravity of the trailer 14 is sufficiently high that it is likely to move outside of the wheel base of the trailer 14 during cornering, thus causing rollover, and initiate a control intervention on the basis of this determination. It will be appreciated that both the weight of the load, and its location (both in the reference plane and its distance from the reference plane, as defined by the x, y and z coordinates described above would be used in determining the centre of gravity of the trailer 14” (para 00100)),...; and
use the longitudinal and lateral positions and height of the center of gravity of the vehicle to adjust control of at least one of vehicle brakes, vehicle engine torque, vehicle stability and vehicle suspension (“ the data from the loading apparatus, in the form of load x, y, z, will determine if the slip limits will increase or decrease. The calculation of centre of gravity and/or moment of inertia will be used to determine the thresholds that are used by the automatic intervention system, or as input for making proactive choices as to speed, braking torque, suspension settings and/or steering angle, in order to ensure safe and stable operation” (para 0098)).
However, Mellings does not explicitly teach
…using a neural network trained for a model of the vehicle...,
…wherein the vehicle control unit is further configured to control the position of the center of gravity by comparing the position of the center of gravity with acceptable ranges defined by a three-dimension vehicle model.
Zhang, in the same field of endeavor, teaches
…using a neural network trained for a model of the vehicle...(“The non-linear assignment between sensor data and associated center of gravity positions is preferably learned using simulation data of a model of the target vehicle” (para 0013) and “The learning-based classification algorithm is used to replicate a non-linear assignment between driving dynamics data (standard ESP sensor data) and associated center of gravity positions” (para 0012)),
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide an output of the probability of each individual class; see Zhang at least at [0013].
...a three-dimension vehicle model (“wherein a non-linear assignment between sets of input variables and classes of center of gravity positions is learned using simulation data of a model of the motor vehicle” (claim 2) and “A vertical coordinate of the estimated center of gravity position is preferably determined on the basis of the lateral and longitudinal coordinates calculated first” (para 0053)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide simulation data of a model of the motor vehicle; see Zhang at least at [0053].
Jensen, in the same field of endeavor, teaches
…wherein the vehicle control unit is further configured to control the position of the center of gravity by comparing the position of the center of gravity with acceptable ranges defined by a … (“For example, the center-of-mass system may use a known weight and/or center-of-mass of the trailer to derive the trailer-independent center of mass. Center-of-mass thresholds may be used in a form that corresponds to the type of load center of mass used. For example, a load center of mass that describes a combination of the trailer and the load may be compared to center-of-mass thresholds that are also expressed in terms of the trailer and the load. A load center of mass that describes the load independent of the trailer may be compared to center-of-mass thresholds that are also expressed in terms of the load itself, independent of the trailer” (para 0021)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Jensen in order to use a known weight and/or center-of-mass of the trailer to derive the trailer-independent center of mass; see Jensen at least at [0021].
Regarding claim 18, Mellings discloses the system according to claim 11. Additionally Mellings discloses wherein the computing unit is further configured to provide the position of the center of gravity of the vehicle to other systems of the vehicle (“The calculation of centre of gravity and/or moment of inertia will be used to determine the thresholds that are used by the automatic intervention system, or as input for making proactive choices as to speed, braking torque, suspension settings and/or steering angle, in order to ensure safe and stable operation” (para 0098)).
Regarding claim 19, Mellings discloses the system according to claim 11. However, Mellings does not explicitly teach wherein the computing unit is further configured to perform:
a test phase for acquiring training data sets each comprising values of the input parameters of the neural network and a corresponding position of the center of gravity measured in a loaded vehicle; and
a training phase of the neural network during which coefficients of the neural network are determined using the training data sets.
Zhang, in the same field of endeavor, teaches
a test phase for acquiring training data sets each comprising values of the input parameters of the neural network and a corresponding position of the center of gravity measured in a loaded vehicle (“The non-linear assignment between sensor data and associated center of gravity positions is preferably learned using simulation data of a model of the target vehicle” (para 0013)); and
a training phase of the neural network during which coefficients of the neural network are determined using the training data sets (“The non-linear assignment between sensor data and associated center of gravity positions is preferably learned using simulation data of a model of the target vehicle” (para 0013)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide a training process; see Zhang at least at [0013].
Regarding claim 20, Mellings discloses a vehicle comprising a computing unit configured to:
receive load parameters describing features of a load in the vehicle (“The processor may additionally comprise an instruction output port for transmitting instructions to the loading apparatus, and may be programmed to use data received from the loading apparatus concerning the weight, dimensions and/or volume of a load to be placed on the vehicle to determine a preferred location in the vehicle for the load, and to transmit to the loading apparatus instructions as to location in or on the vehicle in which the load is to be placed” (para 0011));
collect a load weight measure of a load in the vehicle, from a load sensor, when the vehicle is in a steady state (“the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020));
compute longitudinal and lateral positions and height of a center of gravity of the vehicle, … and receiving the load parameters and the load weight measure, and providing the longitudinal and lateral positions and height of the center of gravity (“together with pre-programmed trailer specific information concerning the dimensions and weight distribution of the trailer itself to calculate the centre of gravity of the trailer 14, and to use this and the input signal from the lateral acceleration sensor or yaw rate sensor to calculate if the centre of gravity of the trailer 14 is sufficiently high that it is likely to move outside of the wheel base of the trailer 14 during cornering, thus causing rollover, and initiate a control intervention on the basis of this determination. It will be appreciated that both the weight of the load, and its location (both in the reference plane and its distance from the reference plane, as defined by the x, y and z coordinates described above would be used in determining the centre of gravity of the trailer 14” (para 00100)),...; and
use the longitudinal and lateral positions and height of the center of gravity of the vehicle to adjust control of at least one of vehicle brakes, vehicle engine torque, vehicle stability and vehicle suspension (“ the data from the loading apparatus, in the form of load x, y, z, will determine if the slip limits will increase or decrease. The calculation of centre of gravity and/or moment of inertia will be used to determine the thresholds that are used by the automatic intervention system, or as input for making proactive choices as to speed, braking torque, suspension settings and/or steering angle, in order to ensure safe and stable operation” (para 0098)).
However, Mellings does not explicitly teach
…using a neural network trained for a model of the vehicle...,
…wherein the vehicle control unit is further configured to control the position of the center of gravity by comparing the position of the center of gravity with acceptable ranges defined by a three-dimension vehicle model.
Zhang, in the same field of endeavor, teaches
…using a neural network trained for a model of the vehicle...(“The non-linear assignment between sensor data and associated center of gravity positions is preferably learned using simulation data of a model of the target vehicle” (para 0013) and “The learning-based classification algorithm is used to replicate a non-linear assignment between driving dynamics data (standard ESP sensor data) and associated center of gravity positions” (para 0012)),
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide an output of the probability of each individual class; see Zhang at least at [0013].
...a three-dimension vehicle model (“wherein a non-linear assignment between sets of input variables and classes of center of gravity positions is learned using simulation data of a model of the motor vehicle” (claim 2) and “A vertical coordinate of the estimated center of gravity position is preferably determined on the basis of the lateral and longitudinal coordinates calculated first” (para 0053)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide simulation data of a model of the motor vehicle; see Zhang at least at [0053].
Jensen, in the same field of endeavor, teaches
…wherein the vehicle control unit is further configured to control the position of the center of gravity by comparing the position of the center of gravity with acceptable ranges (Fig. 4, #) defined by a … (“For example, the center-of-mass system may use a known weight and/or center-of-mass of the trailer to derive the trailer-independent center of mass. Center-of-mass thresholds may be used in a form that corresponds to the type of load center of mass used. For example, a load center of mass that describes a combination of the trailer and the load may be compared to center-of-mass thresholds that are also expressed in terms of the trailer and the load. A load center of mass that describes the load independent of the trailer may be compared to center-of-mass thresholds that are also expressed in terms of the load itself, independent of the trailer” (para 0021)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Jensen in order to use a known weight and/or center-of-mass of the trailer to derive the trailer-independent center of mass; see Jensen at least at [0021].
Claims 4, 6-7, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210162965 (hereinafter,"Mellings"; previously of record), in view of U.S. Pub. No. 20200231167 (hereinafter,"Zhang"; previously of record), in view of U.S. Pub. No. 20210396620 (hereinafter,"Jensen"; newly of record) as applied to claims 1 and 11 above, and in further view of U.S. Pat. No. 11549241 (hereinafter,"Hogan"; previously of record).
Regarding claim 4, Mellings discloses the method according to claim 1. However, Mellings does not explicitly teach further comprising collecting, by the vehicle control unit, dynamic signals from sensors when the vehicle is subjected to a movement, the longitudinal, lateral positions and height of a center of gravity of the vehicle being computed using the dynamic signals.
Hogan, in the same field of endeavor, teaches
further comprising collecting, by the vehicle control unit, dynamic signals from sensors when the vehicle is subjected to a movement, the longitudinal, lateral positions and height of a center of gravity of the vehicle being computed using the dynamic signals ((Fig. 2, #204) and “The controller 120 may determine (e.g., calculate) a kinematic center of gravity (e.g., a weight distribution) of the machine 100 based on the state of the machine 100. That is, the center of gravity may be a dynamic center of gravity of the machine 100. For example, the controller 120 may determine the center of gravity based on a current state of the machine” (Col. 5, lines 52-60)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Hogan in order to determine a kinematic center of gravity of the machine based on the state of the machine; see Hogan at least at (Col. 5, lines 52-60).
Regarding claim 6, Mellings discloses the method according to claim 1. However, Mellings does not explicitly teach further comprising storing vehicle parameters describing features of the vehicle in a memory of the vehicle, the longitudinal and lateral positions and height of the center of gravity of the vehicle, being computed based on the vehicle parameters.
Hogan, in the same field of endeavor, teaches
further comprising storing vehicle parameters describing features of the vehicle in a memory of the vehicle, the longitudinal and lateral positions and height of the center of gravity of the vehicle, being computed based on the vehicle parameters (“The configuration information 202 may be stored in a memory and/or a storage component in communication with the controller 120. The controller 120 may be in communication with the memory and/or the storage component” (Col. 4, lines 33-40) and “The controller 120 may determine (e.g., calculate) a kinematic center of gravity (e.g., a weight distribution) of the machine 100 based on the state of the machine 100. That is, the center of gravity may be a dynamic center of gravity of the machine 100. For example, the controller 120 may determine the center of gravity based on a current state of the machine” (Col. 5, lines 52-60)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Hogan in order to provide a controller may be in communication with the memory and/or the storage component; see Hogan at least at (Col. 4, lines 33-40).
Regarding claim 7, Mellings and Hogan disclose the method according to claim 6. However, Mellings does not explicitly teach wherein the vehicle parameters comprise at least one of:
a vehicle type or model, a distance between a front axle and a rear axle of the vehicle, a distance between the wheels on a same axle of the vehicle, and an unladen weight of the vehicle.
Hogan, in the same field of endeavor, teaches
wherein the vehicle parameters comprise at least one of:
a vehicle type or model, a distance between a front axle and a rear axle of the vehicle, a distance between the wheels on a same axle of the vehicle, and an unladen weight of the vehicle (“The configuration information 202 may indicate an identity of one or more components (e.g., optional components, removable components, modular components, and/or the like) of the machine 100, a location of one or more components on the machine 100 (e.g., front, rear, left side, right side, and/or the like), a size of one or more components of the machine 100 (e.g., an undercarriage width, a rotor width, a boom length, and/or the like), a weight of one or more components of the machine 100, and/or the like” (Col. 5, Lines 1-10)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Hogan in order to provide configuration information; see Hogan at least at (Col. 5, Lines 1-10).
Regarding claim 14, Mellings discloses the system according to claim 11. However, Mellings does not explicitly wherein the computing unit is configured to collect dynamic signals from sensors when the vehicle is subjected to a movement, the longitudinal, lateral positions and height of a center of gravity of the vehicle being computed using the dynamic signals.
Hogan, in the same field of endeavor, teaches
wherein the computing unit is configured to collect dynamic signals from sensors when the vehicle is subjected to a movement, the longitudinal, lateral positions and height of a center of gravity of the vehicle being computed using the dynamic signals ((Fig. 2, #204) and “The controller 120 may determine (e.g., calculate) a kinematic center of gravity (e.g., a weight distribution) of the machine 100 based on the state of the machine 100. That is, the center of gravity may be a dynamic center of gravity of the machine 100. For example, the controller 120 may determine the center of gravity based on a current state of the machine” (Col. 5, lines 52-60)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Hogan in order to determine a kinematic center of gravity of the machine based on the state of the machine; see Hogan at least at (Col. 5, lines 52-60).
Regarding claim 16, Mellings discloses the system according to claim 11. However, Mellings does not explicitly teach wherein the computing unit is configured to store vehicle parameters describing features of the vehicle in a memory of the vehicle, the longitudinal and lateral positions and height of the center of gravity of the vehicle, being computed based on the vehicle parameters.
Hogan, in the same field of endeavor, teaches
wherein the computing unit is configured to store vehicle parameters describing features of the vehicle in a memory of the vehicle, the longitudinal and lateral positions and height of the center of gravity of the vehicle, being computed based on the vehicle parameters (“The configuration information 202 may be stored in a memory and/or a storage component in communication with the controller 120. The controller 120 may be in communication with the memory and/or the storage component” (Col. 4, lines 33-40) and “The controller 120 may determine (e.g., calculate) a kinematic center of gravity (e.g., a weight distribution) of the machine 100 based on the state of the machine 100. That is, the center of gravity may be a dynamic center of gravity of the machine 100. For example, the controller 120 may determine the center of gravity based on a current state of the machine” (Col. 5, lines 52-60)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Hogan in order to provide a controller may be in communication with the memory and/or the storage component; see Hogan at least at (Col. 4, lines 33-40).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210162965 (hereinafter,"Mellings"; previously of record), in view of U.S. Pub. No. 20200231167 (hereinafter,"Zhang"; previously of record), in view of U.S. Pub. No. 20210396620 (hereinafter,"Jensen"; newly of record), and in further view of U.S. Pat. No. 11549241 (hereinafter,"Hogan"; previously of record) as applied to claims 4 and 14 above, and in further view of U.S. Pub. No. 20210048333 (hereinafter,"Kun Zhang"; previously of record).
Regarding claim 5, Mellings discloses the method of claim 4. Additionally Mellings discloses wherein the dynamic signals comprise at least one of:
a load weight measured in the vehicle by a load sensor (“and the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020)),
accelerations measured in the vehicle by acceleration sensors (“The sensor may comprise a steering angle sensor lateral acceleration sensor, a yaw rate sensor or a wheel speed sensor” (para 0043)).
However, Mellings does not explicitly teach
axle load weights measured on axles of the vehicle, and
wheel load weights measured on wheels of the vehicle by wheel load sensors,
Kun Zhang, in the same field of endeavor, teaches
axle load weights measured on axles of the vehicle (“the weight distribution module 165 determines, for each axle, one or more weight distribution values that describe weight or pressure applied on an axle” (para 0034)), and
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Kun Zhang in order to obtain a weight distribution of the vehicle; see Kun Zhang at least at [0034].
wheel load weights measured on wheels of the vehicle by wheel load sensors (“The weight distribution module 165 also receives from the TPMS sensors a second set of values that indicate pressures in tires of the vehicle” (para 0029)),
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Kun Zhang in order to provide pressure measurements that describe the air pressure in each tire; see Kun Zhang at least at [0029].
Regarding claim 15, Mellings discloses the system of claim 14. Additionally Mellings discloses wherein the dynamic signals comprise at least one of:
a load weight measured in the vehicle by a load sensor (“and the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020)),
accelerations measured in the vehicle by acceleration sensors (“The sensor may comprise a steering angle sensor lateral acceleration sensor, a yaw rate sensor or a wheel speed sensor” (para 0043)).
However, Mellings does not explicitly teach
axle load weights measured on axles of the vehicle, and
wheel load weights measured on wheels of the vehicle by wheel load sensors,
Kun Zhang, in the same field of endeavor, teaches
axle load weights measured on axles of the vehicle (“the weight distribution module 165 determines, for each axle, one or more weight distribution values that describe weight or pressure applied on an axle” (para 0034)), and
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Kun Zhang in order to obtain a weight distribution of the vehicle; see Kun Zhang at least at [0034].
wheel load weights measured on wheels of the vehicle by wheel load sensors (“The weight distribution module 165 also receives from the TPMS sensors a second set of values that indicate pressures in tires of the vehicle” (para 0029)),
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Kun Zhang in order to provide pressure measurements that describe the air pressure in each tire; see Kun Zhang at least at [0029].
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210162965 (hereinafter, "Mellings"; previously of record), in view of U.S. Pub. No. 20200231167 (hereinafter, "Zhang"; previously of record), in view of U.S. Pub. No. 20210396620 (hereinafter, "Jensen"; newly of record) as applied to claims 1 and 11 above, in view of U.S. Pub. No. 20210048333 (hereinafter, "Kun Zhang"; previously of record), and in further view of U.S. Pub. No. 20170349166 (hereinafter, "Anderson"; previously of record).
Regarding claim 2, Mellings discloses the method according to claim 1. Additionally, Mellings discloses wherein the load weight measure comprises at least one of:
a load weight measured in the vehicle by a load sensor (“and the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020)),
However, Mellings does not explicitly teach
axle load weights measured on axles, and
an image of the load in the vehicle provided by an image sensor positioned in the vehicle to provide images of the load in or on the vehicle.
Kun Zhang, in the same field of endeavor, teaches
axle load weights measured on axles (“the weight distribution module 165 determines, for each axle, one or more weight distribution values that describe weight or pressure applied on an axle” (para 0034)), and
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Kun Zhang in order to obtain a weight distribution of the vehicle; see Kun Zhang at least at [0034].
Anderson, in the same field of endeavor, teaches
an image of the load in the vehicle provided by an image sensor positioned in the vehicle to provide images of the load in or on the vehicle ((Fig. 2, #216) and “the vehicle controller 201 may compare video images of the cargo captured by cargo bay camera 216 over time” (para 0048)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Anderson in order to evaluate the state of the cargo within the cargo container; see Anderson at least at [0048].
Regarding claim 12, Mellings discloses the system according to claim 11. Additionally, Mellings discloses wherein the load weight measure comprises at least one of:
a load weight measured in the vehicle by a load sensor (“and the processor may be connected to the pressure sensor and be programmed to use data received from the pressure sensor to determine a calculated weight of a load in or on the vehicle” (para 0020)),
However, Mellings does not explicitly teach
axle load weights measured on axles, and
an image of the load in the vehicle provided by an image sensor positioned in the vehicle to provide images of the load in or on the vehicle.
Kun Zhang, in the same field of endeavor, teaches
axle load weights measured on axles (“the weight distribution module 165 determines, for each axle, one or more weight distribution values that describe weight or pressure applied on an axle” (para 0034)), and
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Kun Zhang in order to obtain a weight distribution of the vehicle; see Kun Zhang at least at [0034].
Anderson, in the same field of endeavor, teaches
an image of the load in the vehicle provided by an image sensor positioned in the vehicle to provide images of the load in or on the vehicle ((Fig. 2, #216) and “the vehicle controller 201 may compare video images of the cargo captured by cargo bay camera 216 over time” (para 0048)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Anderson in order to evaluating the state of the cargo within the cargo container; see Anderson at least at [0048].
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210162965 (hereinafter, "Mellings"; previously of record), in view of U.S. Pub. No. 20200231167 (hereinafter, "Zhang"; previously of record), in view of U.S. Pub. No. 20210396620 (hereinafter ,"Jensen"; newly of record) as applied to claims 1 and 11 above, and in further view of U.S. Pub. No. 20130132025 (hereinafter, "Watanabe"; previously of record).
Regarding claim 8, Mellings discloses the method according to claim 1. However, Mellings does not explicitly teach further comprising:
comparing the position of the center of gravity of the vehicle to the three-dimension vehicle model; and
transmitting a warning message if the position of the center of gravity is not within the three-dimension vehicle model.
Watanabe, in the same field of endeavor, teaches
comparing the position of the center of gravity of the vehicle to the …. (“a center-of-gravity detecting system capable of universally deriving the three-dimensional center-of-gravity location of the travelling object during travel as compared to a conventional center-of-gravity detecting system” (para 0025)); and
transmitting a warning message if the position of the center of gravity is not within the … (“The operation setting/display part 15c includes an operation part (e.g., a key board (not shown)) having setting buttons thereon for inputting the constants, and an informing device such as a liquid crystal panel screen and a speaker (which are not shown). The informing device provides the output data of the center-of-gravity of the container cargo vehicle during travel which is output from the arithmetic part 15a to an operator (driver or passenger) by means of visual information or sound information so that the operator can check it” (para 0086)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Watanabe in order to provide a center-of-gravity detecting system; see Watanabe at least at [0025].
Zhang, in the same field of endeavor, teaches
... three-dimension vehicle model (“wherein a non-linear assignment between sets of input variables and classes of center of gravity positions is learned using simulation data of a model of the motor vehicle” (claim 2) and “A vertical coordinate of the estimated center of gravity position is preferably determined on the basis of the lateral and longitudinal coordinates calculated first” (para 0053)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide simulation data of a model of the motor vehicle; see Zhang at least at [0053].
Regarding claim 17, Mellings discloses the system according to claim 11. However, Mellings does not explicitly teach wherein the computing unit is further configured to:
compare the position of the center of gravity of the vehicle to the three-dimension vehicle model; and
transmit a warning message if the position of the center of gravity is not within the three-dimension vehicle model.
Watanabe, in the same field of endeavor, teaches
compare the position of the center of gravity of the vehicle to the … (“a center-of-gravity detecting system capable of universally deriving the three-dimensional center-of-gravity location of the travelling object during travel as compared to a conventional center-of-gravity detecting system” (para 0025)); and
transmit a warning message if the position of the center of gravity is not within the … (“The operation setting/display part 15c includes an operation part (e.g., a key board (not shown)) having setting buttons thereon for inputting the constants, and an informing device such as a liquid crystal panel screen and a speaker (which are not shown). The informing device provides the output data of the center-of-gravity of the container cargo vehicle during travel which is output from the arithmetic part 15a to an operator (driver or passenger) by means of visual information or sound information so that the operator can check it” (para 0086)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Watanabe in order to provide a center-of-gravity detecting system; see Watanabe at least at [0025].
Zhang, in the same field of endeavor, teaches
... three-dimension vehicle model (“wherein a non-linear assignment between sets of input variables and classes of center of gravity positions is learned using simulation data of a model of the motor vehicle” (claim 2) and “A vertical coordinate of the estimated center of gravity position is preferably determined on the basis of the lateral and longitudinal coordinates calculated first” (para 0053)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Zhang in order to provide simulation data of a model of the motor vehicle; see Zhang at least at [0053].
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 20210162965 (hereinafter, "Mellings"; previously of record), in view of U.S. Pub. No. 20200231167 (hereinafter, "Zhang"; previously of record), in view of U.S. Pub. No. 20210396620 (hereinafter, "Jensen"; newly of record) as applied to claims 1 and 11 above, and in further view of U.S. Pub. No. 20220396293 (hereinafter, "Harrington"; previously of record).
Regarding claim 3, Mellings discloses the method according to claim 1. However, Mellings does not explicitly teach wherein the load parameters comprise at least one of a type of load, and a loading ratio of a volume occupied by the load in an available volume for loads in the vehicle.
Harrington, in the same field of endeavor, teaches
wherein the load parameters comprise at least one of a type of load, and a loading ratio of a volume occupied by the load in an available volume for loads in the vehicle (“The autonomous vehicle selection system 116 can determine available space in the cargo storage area based on the real time load data” (para 0032) and “the computing system 214 can be further configured to store information relating to the type of cargo already stored within the autonomous vehicle” para (0046)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Harrington in order to determine the available portion in the cargo storage area; see Harrington at least at [0032].
Regarding claim 13, Mellings discloses the system according to claim 11. However, Mellings does not explicitly teach wherein the load parameters comprise at least one of a type of load, and a loading ratio of a volume occupied by the load in an available volume for loads in the vehicle.
Harrington, in the same field of endeavor, teaches
wherein the load parameters comprise at least one of a type of load, and a loading ratio of a volume occupied by the load in an available volume for loads in the vehicle (“The autonomous vehicle selection system 116 can determine available space in the cargo storage area based on the real time load data” (para 0032) and “the computing system 214 can be further configured to store information relating to the type of cargo already stored within the autonomous vehicle” para (0046)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Mellings with the teachings of Harrington in order to determine the available portion in the cargo storage area; see Harrington at least at [0032].
Conclusion
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 extension fee 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 date of this final action.
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