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 .
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.
Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 14 recites the limitation "the at least one computing system" in line 1. There is insufficient antecedent basis for this limitation in the claim.
For the purpose of further examination on the merits, Examiner will treat the claimed “the at least one computing system” to refer to the “computing device” recited in claim 8.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-12, 14-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the claim recites, in part, “determining, by the at least one computing device, that the vehicle is operating in a steady state based at least in part on the vehicle data; and in response to determining that the vehicle is operating in the steady state: estimating, by the at least one computing device, a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data; applying, by the at least one computing device, the tractive force and a vehicle speed of the vehicle to a surface condition detection model; and determining, by the at least one computing device, a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model.” These limitations, when read in light of the specification, are mental processes in the form of judgements and/or evaluations capable of being performed in the human mind. A human being can mentally determine, from collected data, that the vehicle is operating in a steady state, estimate a tractive force based on the data and a mathematical model, apply the tractive force and speed data to another model to determine a probability that the surface is wet or dry. Mental processes capable of being performed in the human mind have been held as being abstract ideas (see MPEP 2106.04(a)(2)).
This judicial exception is not integrated into a practical application because the claim does not purport the improvement to the functioning of a computer or other technology, is not applied by way of a particular machine, does not effect a tangible transformation in state of a particular article, and is not otherwise applying in some other meaningful way (see MPEP 2106.05).
The claim recites an additional element of “obtaining, by at least one computing device, vehicle data collected from a vehicle controller area network (CAN) bus in communication with one or more vehicle systems of a vehicle traveling along a surface.” This limitation is an act of data gathering. The act of mere data gathering for use in an abstract idea, when not specifying new types of data or techniques of collection, has been held as being insignificant extra-solution activity that does not render a claimed abstract idea patent-eligible (see MPEP 2106.05(g); see also Electric Power Group, LLC. v. Alstom, S.A., 830 f.3d 1350 (Fed. Circ. 2016)).
The claim further recites the use of “at least one computing device.” The invocation of generic computing components to perform an abstract idea does not amount to significantly more than the abstract idea (see MPEP 2106.05(f)).
Regarding claim 2¸ the claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea. The claim merely further specifies the collected “vehicle data,” which does not change the above-noted limitations in claim 1 from being an abstract idea.
Regarding claim 3, the claim recites “determining that the vehicle is operating in the steady state further comprises: determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.” This limitation is a mental process in the form of a judgement capable of being performed in the human mind. A human being can mentally determine, given data, that an engine speed, vehicle speed, and vehicle acceleration all exceed thresholds.
The claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea.
Regarding claim 4, the claim recites “the surface condition detection model comprises a trained classifier.” Recited at a high level of generality, the model being a trained classifier does not change the applying and determining steps in claim 1, using the surface condition detection model, from being mental evaluations (see UPSTO July 2024 Subject Matter Eligibility Example 47, claim 2).
Regarding claim 5, the claim recites “determining a total tractive force; determining a road grade force; and determining an aerodynamic drag force, and wherein the tractive force or the net resistive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.” These limitations, when read in light of the specification, are mental processes in the form of evaluations capable of being performed in the human mind. Additionally or alternatively, these limitations are mathematical operations in the form of calculations.
The claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea.
Regarding claim 7, the claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea. The claim merely specifies the location of the “at least one computing system”, which does not result in an unconventional arrangement.
Regarding claim 8, the claim recites, in part, “determine that the vehicle is operating in a steady state based at least in part on the vehicle data; and in response to determining that the vehicle is operating in the steady state: estimate a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data; apply the tractive force and a vehicle speed of the vehicle to a surface condition detection model; and determine a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model.” These limitations, when read in light of the specification, are mental processes in the form of judgements and/or evaluations capable of being performed in the human mind. A human being can mentally determine, from collected data, that the vehicle is operating in a steady state, estimate a tractive force based on the data and a mathematical model, apply the tractive force and speed data to another model to determine a probability that the surface is wet or dry. Mental processes capable of being performed in the human mind have been held as being abstract ideas (see MPEP 2106.04(a)(2)).
This judicial exception is not integrated into a practical application because the claim does not purport the improvement to the functioning of a computer or other technology, is not applied by way of a particular machine, does not effect a tangible transformation in state of a particular article, and is not otherwise applying in some other meaningful way (see MPEP 2106.05).
The claim recites an additional element of “obtain vehicle data collected from a vehicle controller area network (CAN) bus in communication with one or more vehicle systems of a vehicle traveling along a surface.” This limitation is an act of data gathering. The act of mere data gathering for use in an abstract idea, when not specifying new types of data or techniques of collection, has been held as being insignificant extra-solution activity that does not render a claimed abstract idea patent-eligible (see MPEP 2106.05(g); see also Electric Power Group, LLC. v. Alstom, S.A., 830 f.3d 1350 (Fed. Circ. 2016)).
The claim further includes additional elements of “a processor” and “a memory.” The invocation of generic computing components to perform an abstract idea does not amount to significantly more than the abstract idea (see MPEP 2106.05(f)).
Regarding claim 9¸ the claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea. The claim merely further specifies the collected “vehicle data,” which does not change the above-noted limitations in claim 1 from being an abstract idea.
Regarding claim 10, the claim recites “determining that the vehicle is operating in the steady state further comprises: determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.” This limitation is a mental process in the form of a judgement capable of being performed in the human mind. A human being can mentally determine, given data, that an engine speed, vehicle speed, and vehicle acceleration all exceed thresholds.
The claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea.
Regarding claim 11, the claim recites “the surface condition detection model comprises a trained classifier.” Recited at a high level of generality, the model being a trained classifier does not change the applying and determining steps in claim 1, using the surface condition detection model, from being mental evaluations (see UPSTO July 2024 Subject Matter Eligibility Example 47, claim 2).
Regarding claim 12, the claim recites “determine a total tractive force; determine a road grade force; and determine an aerodynamic drag force, and wherein the tractive force or the net resistive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.” These limitations, when read in light of the specification, are mental processes in the form of evaluations capable of being performed in the human mind. Additionally or alternatively, these limitations are mathematical operations in the form of calculations.
The claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea.
Regarding claim 14, the claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea. The claim merely specifies the location of the “at least one computing system”, which does not result in an unconventional arrangement.
Regarding claim 15, the claim recites, in part, “determine that the vehicle is operating in a steady state based at least in part on the vehicle data; and in response to determining that the vehicle is operating in the steady state: estimate a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data; apply the tractive force and a vehicle speed of the vehicle to a surface condition detection model; and determine a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model.” These limitations, when read in light of the specification, are mental processes in the form of judgements and/or evaluations capable of being performed in the human mind. A human being can mentally determine, from collected data, that the vehicle is operating in a steady state, estimate a tractive force based on the data and a mathematical model, apply the tractive force and speed data to another model to determine a probability that the surface is wet or dry. Mental processes capable of being performed in the human mind have been held as being abstract ideas (see MPEP 2106.04(a)(2)).
This judicial exception is not integrated into a practical application because the claim does not purport the improvement to the functioning of a computer or other technology, is not applied by way of a particular machine, does not effect a tangible transformation in state of a particular article, and is not otherwise applying in some other meaningful way (see MPEP 2106.05).
The claim recites an additional element of “obtain vehicle data collected from a vehicle controller area network (CAN) bus in communication with one or more vehicle systems of a vehicle traveling along a surface.” This limitation is an act of data gathering. The act of mere data gathering for use in an abstract idea, when not specifying new types of data or techniques of collection, has been held as being insignificant extra-solution activity that does not render a claimed abstract idea patent-eligible (see MPEP 2106.05(g); see also Electric Power Group, LLC. v. Alstom, S.A., 830 f.3d 1350 (Fed. Circ. 2016)).
The claim further includes additional elements of “a non-transitory, computer-readable medium” and “a processor.” The invocation of generic computing components to perform an abstract idea does not amount to significantly more than the abstract idea (see MPEP 2106.05(f)).
Regarding claim 16¸ the claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea. The claim merely further specifies the collected “vehicle data,” which does not change the above-noted limitations in claim 1 from being an abstract idea.
Regarding claim 17, the claim recites “determining that the vehicle is operating in the steady state further comprises: determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.” This limitation is a mental process in the form of a judgement capable of being performed in the human mind. A human being can mentally determine, given data, that an engine speed, vehicle speed, and vehicle acceleration all exceed thresholds.
The claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea.
Regarding claim 18, the claim recites “the surface condition detection model comprises a trained classifier.” Recited at a high level of generality, the model being a trained classifier does not change the applying and determining steps in claim 1, using the surface condition detection model, from being mental evaluations (see UPSTO July 2024 Subject Matter Eligibility Example 47, claim 2).
Regarding claim 20, the claim recites “determine a total tractive force; determine a road grade force; and determine an aerodynamic drag force, and wherein the tractive force or the net resistive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.” These limitations, when read in light of the specification, are mental processes in the form of evaluations capable of being performed in the human mind. Additionally or alternatively, these limitations are mathematical operations in the form of calculations.
The claim recites no additional elements that are indicative of integration into a practical application or that amount to significantly more than the abstract idea.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-4, 6-11, and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Myklebust et al. (United States Patent Application Publication No. US 2022/0073042 A1) [hereinafter “Myklebust”] in view of Kakehi (United States Patent Application Publication No. US 2020/0380862 A1) and Sato et al. (United States Patent Application Publication No. US 2023/0382390 A1) [hereinafter “Sato”].
Regarding claim 1, Myklebust teaches a method for determining a road surface condition, comprising:
obtaining, by at least one computing device (processing unit 92), vehicle data collected from a vehicle controller area network bus in communication with one or more vehicle systems of a vehicle traveling along a surface (see [0011]-[0013], [0048]-[0051], [0057], and [0103]-[0106]);
estimating, by the at least one computing device, a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data (see [0011]-[0013], [0040]-[0041], and [0048]-[0051]);
applying, by the at least one computing device, the tractive force and a vehicle speed of the vehicle to a surface condition detection model (see [0011]-[0013], [0038]-[0041], [0048]-[0051], and [0071]-[0075]).
Myklebust does not expressly teach determining, by the at least one computing device, that the vehicle is operating in a steady state based at least in part on the vehicle data, wherein the estimating and applying is performed in response to determining that the vehicle is operating in the steady state.
Kakehi also generally teaches a system for predicting the coefficient of friction between a vehicle tire and a road surface (see Abstract). Kakehi teaches that the friction coefficient is estimated based on vehicle tire-related parameters acquired during a steady traveling state of the vehicle (see at least [0046] and [0087]).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention taught by Myklebust to determine that the vehicle is operating in a steady state based on the vehicle data and perform the estimating and applying as a result, in view of Kakehi, as Kakehi teaches that steady state traveling is required to accurately estimate the coefficient of friction between the vehicle tires and the road surface.
The combination of Myklebust and Kakehi further does not expressly teach determining, by the at least one computing device, a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model. Myklebust teaches the model can output a probability of the friction coefficient (see [0038]-[0042] and [0075]-[0087]).
Sato also generally teaches a system for detecting the condition of a road surface (see Abstract). Sato teaches that the condition of the road surface can be classified as “wet” or “dry” based on an estimated coefficient of friction (see [0030]).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention taught by the combination of Myklebust and Kakehi to determine a probability that the surface is wet or dry based at least in part on an estimated coefficient of friction and its associated probability output of the surface condition detection model, in view of Sato, as Sato teaches the coefficient of friction can reliably be used to determine the condition of the road surface as either wet or dry.
Regarding claim 2, the combination of Myklebust, Kakehi, and Sato further teaches the vehicle data comprises an engine torque, an engine revolutions per minute (RPM), the vehicle speed, a wheel speed, and a vehicle acceleration (see [0011] and [0038] of Myklebust).
Regarding claim 3, the combination of Myklebust, Kakehi, and Sato does not expressly teach determining that the vehicle is operating in the steady state further comprises: determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.
However, as noted above in the rejection of claim 1, Kakehi requires the vehicle to be “travelling” in a steady state in order to determine the road surface condition (see [0046] and [0087]). Thus, as the vehicle must be travelling, the engine speed and vehicle speed must both be greater than a threshold of zero. Furthermore, as Myklebust requires the use of a vehicle acceleration and a tractive force, the vehicle must be in a state of net acceleration, and therefore the vehicle acceleration must be non-zero.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the invention taught by the combination of Myklebust, Kakehi, and Sato to include determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold as determining the vehicle is operating in a steady state as Kakehi requires the vehicle to be travelling and Myklebust requires a net vehicle acceleration and tractive force.
Regarding claim 4, the combination of Myklebust, Kakehi, and Sato further teaches the surface condition detection model comprises a trained classifier (see [0017], [0023], [0048]-[0051], and [0090]-[0093] of Myklebust).
Regarding claim 6, the combination of Myklebust, Kakehi, and Sato further teaches transmitting the probability to at least one vehicle system of the one or more vehicle systems, the at least one vehicle system being configured to adjust an operation of the vehicle based at least in part on the probability (see [0022]-[0026] of Myklebust).
Regarding claim 7, the combination of Myklebust, Kakehi, and Sato further teaches the at least one computing system is located within the vehicle or is located remote from the vehicle (see [0103]-[0108] of Myklebust).
Regarding claim 8, the combination of Myklebust, Kakehi, and Sato, as applied to claim 1 above, teaches a road surface detection system, comprising:
a computing device comprising a processor and a memory (see [0103] of Myklebust); and
machine-readable instructions stored in the memory that, when executed by the processor (see claim 13 of Myklebust), cause the computing device to at least:
obtain vehicle data collected from a vehicle controller area network bus in communication with one or more vehicle systems of a vehicle traveling along a surface (see [0011]-[0013], [0048]-[0051], [0057], and [0103]-[0106] of Myklebust);
determine that the vehicle is operating in a steady state based at least in part on the vehicle data (see [0046] and [0087] of Kakehi and the rejection of claim 1 above); and
in response to determining that the vehicle is operating in the steady state:
estimate a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data (see [0011]-[0013], [0040]-[0041], and [0048]-[0051] of Myklebust);
apply the tractive force and a vehicle speed of the vehicle to a surface condition detection model (see [0011]-[0013], [0038]-[0041], [0048]-[0051], and [0071]-[0075] of Myklebust); and
determine a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model (see [0030] of Sato and the rejection of claim 1 above).
Regarding claim 9, the combination of Myklebust, Kakehi, and Sato further teaches the vehicle data comprises an engine torque, an engine revolutions per minute (RPM), the vehicle speed, a wheel speed, and a vehicle acceleration (see [0011] and [0038] of Myklebust).
Regarding claim 10, the combination of Myklebust, Kakehi, and Sato does not expressly teach determining that the vehicle is operating in the steady state further comprises: determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.
However, as noted above in the rejection of claim 1, Kakehi requires the vehicle to be “travelling” in a steady state in order to determine the road surface condition (see [0046] and [0087]). Thus, as the vehicle must be travelling, the engine speed and vehicle speed must both be greater than a threshold of zero. Furthermore, as Myklebust requires the use of a vehicle acceleration and a tractive force, the vehicle must be in a state of net acceleration, and therefore the vehicle acceleration must be non-zero.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the invention taught by the combination of Myklebust, Kakehi, and Sato to include determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold as determining the vehicle is operating in a steady state as Kakehi requires the vehicle to be travelling and Myklebust requires a net vehicle acceleration and tractive force.
Regarding claim 11, the combination of Myklebust, Kakehi, and Sato further teaches the surface condition detection model comprises a trained classifier (see [0017], [0023], [0048]-[0051], and [0090]-[0093] of Myklebust).
Regarding claim 13, the combination of Myklebust, Kakehi, and Sato further teaches the machine-readable instructions further cause the computing device to at least transmit the probability to at least one vehicle system of the one or more vehicle systems, the at least one vehicle system being configured to adjust an operation of the vehicle based at least in part on the probability (see [0022]-[0026] of Myklebust).
Regarding claim 14, the combination of Myklebust, Kakehi, and Sato further teaches the at least one computing system is located within the vehicle or is located remote from the vehicle (see [0103]-[0108] of Myklebust).
Regarding claim 15, the combination of Myklebust, Kakehi, and Sato, as applied to claim 1 above, teaches a non-transitory, computer-readable medium, comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least (see [0103] and claim 13 of Myklebust):
obtain vehicle data collected from a vehicle controller area network bus in communication with one or more vehicle systems of a vehicle traveling along a surface (see [0011]-[0013], [0048]-[0051], [0057], and [0103]-[0106] of Myklebust);
determine that the vehicle is operating in a steady state based at least in part on the vehicle data (see [0046] and [0087] of Kakehi and the rejection of claim 1 above); and
in response to determining that the vehicle is operating in the steady state:
estimate a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data (see [0011]-[0013], [0040]-[0041], and [0048]-[0051] of Myklebust);
apply the tractive force and a vehicle speed of the vehicle to a surface condition detection model (see [0011]-[0013], [0038]-[0041], [0048]-[0051], and [0071]-[0075] of Myklebust); and
determine a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model (see [0030] of Sato and the rejection of claim 1 above).
Regarding claim 16, the combination of Myklebust, Kakehi, and Sato further teaches the vehicle data comprises an engine torque, an engine revolutions per minute (RPM), the vehicle speed, a wheel speed, and a vehicle acceleration (see [0011] and [0038] of Myklebust).
Regarding claim 17, the combination of Myklebust, Kakehi, and Sato does not expressly teach determining that the vehicle is operating in the steady state further comprises: determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.
However, as noted above in the rejection of claim 1, Kakehi requires the vehicle to be “travelling” in a steady state in order to determine the road surface condition (see [0046] and [0087]). Thus, as the vehicle must be travelling, the engine speed and vehicle speed must both be greater than a threshold of zero. Furthermore, as Myklebust requires the use of a vehicle acceleration and a tractive force, the vehicle must be in a state of net acceleration, and therefore the vehicle acceleration must be non-zero.
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date to modify the invention taught by the combination of Myklebust, Kakehi, and Sato to include determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold as determining the vehicle is operating in a steady state as Kakehi requires the vehicle to be travelling and Myklebust requires a net vehicle acceleration and tractive force.
Regarding claim 18, the combination of Myklebust, Kakehi, and Sato further teaches the surface condition detection model comprises a trained classifier (see [0017], [0023], [0048]-[0051], and [0090]-[0093] of Myklebust).
Regarding claim 19, the combination of Myklebust, Kakehi, and Sato further teaches the machine-readable instructions, when executed by the processor, further cause the computing device to at least: transmit the probability to at least one vehicle system of the one or more vehicle systems, the at least one vehicle system being configured to adjust an operation of the vehicle based at least in part on the probability (see [0022]-[0026] of Myklebust).
Claims 5, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Myklebust, Kakehi, and Sato, as applied to claim 1 above, and further in view of Blandina et al. (United States Patent Application Publication No. US 2024/0294172 A1) [hereinafter “Blandina”].
Regarding claim 5, the combination of Myklebust, Kakehi, and Sato, as applied to claim 1 above, does not expressly teach determining a total tractive force; determining a road grade force; and determining an aerodynamic drag force, and wherein the tractive force or the net resistive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.
Blandina also teaches a method for determining a road surface condition (see Abstract). Blandina teaches determining a total tractive force, determining a road grade force, determining an aerodynamic drag force, and determining a tractive force or net resistive force by subtracting the road grade force and the aerodynamic drag force from the total tractive force (see [0028]-[0049]).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by the combination f Myklebust, Kakehi, and Sato to include determining a total tractive force, determining a road grade force, determining an aerodynamic drag force, and determining a tractive force or net resistive force by subtracting the road grade force and the aerodynamic drag force from the total tractive force, in view of Blandina, as Myklebust requires the net tractive force (see [0040]) and Blandina teaches this is an effective manner of determining the net tractive force.
Regarding claim 12, the combination of Myklebust, Kakehi, Sato, and Blandina, as applied to claim 5 above, teaches the machine-readable instructions further cause the computing device to at least: determine a total tractive force; determine a road grade force; and determine an aerodynamic drag force, wherein the tractive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force (see [0028]-[0049] of Blandina and the rejection of claim 5 above).
Regarding claim 20, the combination of Myklebust, Kakehi, Sato, and Blandina, as applied to claim 5 above, teaches the machine-readable instructions, when executed by the processor, further cause the computing device to at least: determine a total tractive force; determine a road grade force; and determine an aerodynamic drag force, wherein the tractive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force (see [0028]-[0049] of Blandina and the rejection of claim 5 above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kang et al. (US 2024/0416920 A1) generally teaches:
A road surface condition estimation apparatus includes a storage unit configured to store a road surface condition estimation model, and a road surface severity estimator configured to estimate, based on travel information, severity of a condition of a road surface on which a vehicle is travelling using the road surface condition estimation model.
Dallas et al. (US 2024/0034302 A1) generally teaches:
System, methods, and other embodiments described herein relate to adjusting a prediction model for control at handling limits associated with a projected trajectory during automated driving. In one embodiment, a method includes adjusting parameters of a prediction model using friction estimates and sideslip costs associated with a projected trajectory of a vehicle, the friction estimates being derived from Kalman filtering. The method also includes scaling, using the prediction model, handling limits of the vehicle for the projected trajectory according to a friction circle. The method also includes generating, by the prediction model, vehicle dynamics using a load transfer and a brake distribution, the vehicle dynamics being associated with estimated road conditions and the handling limits. The method also includes outputting, by the prediction model using the vehicle dynamics, a driving command to the vehicle for the projected trajectory.
Chrungoo et al. (US 2019/0024781 A1) generally teaches:
A soft underfoot conditions response system for use with a vehicle includes a plurality of sensors configured to transmit signals indicative of live data representing at least one of real time vehicle speed, vehicle acceleration, vehicle pose, vehicle payload, engine torque, engine power output, and engine RPM, and a controller communicatively coupled with the sensors. The controller is programmed to receive the live data, receive reference data representative of soft underfoot conditions from a database, and analyze the live data and the reference data. The controller determines a first set of parameters including measured real time values corresponding to wheel slip ratio and rolling resistance, vehicle speed, and vehicle pose, extracts from the reference data at least one of a first data subset containing vehicle operational parameters identified by an operator as being associated with soft underfoot conditions, and a second data subset containing data extracted using heuristics, and builds and trains a model for use by a classifier that segregates data subsets from the first set of parameters into a first classification that includes parameters that characterize surfaces with soft underfoot conditions, and a second classification that includes parameters that characterize surfaces without soft underfoot conditions. The controller also generates control command signals that cause a change in vehicle operational parameters to reduce or avoid any effects on operation of the vehicle associated with soft underfoot conditions.
Ogawa (US 2008/0319683 A1) generally teaches:
A driving-torque difference value, an inertial-force difference value of the vehicle, and an inertial-force change-amount difference value of the vehicle are calculated. Subsequently, a first determination coefficient by which the inertial-force difference value is to be multiplied or a second determination coefficient by which the driving-torque difference value is to be multiplied is estimated on the basis of a state equation having the inertial-force difference value as a state variable and the driving-torque difference value as an input variable. Subsequently, a road-surface condition is determined on the basis of a comparison between a threshold value and the first determination coefficient or the second determination coefficient.
Svendenius et al. (US 2008/0243348 A1) generally teaches:
The present invention refers to systems and to methods for determining at least one parameter relating to a tire-to-road contact and/or a relation between a wheel and a vehicle motion. In particular, the present invention refers to systems and to methods for determining the coefficient of friction between a tire and a road surface. For that purpose, the systems and methods according to the invention use new estimation filtering procedures.
Jansson (US 2006/0025895 A1) generally teaches:
A control system and method for minimizing tire wear on vehicles such as highway trucks and tractors determines a drive wheel torque limit based on an assumed tire/road friction coefficient and at least an estimated vehicle weight. The drive wheel torque limit is used to control engine torque applied to the drive wheels.
Kogure et al. (US 2004/0267492 A1) generally teaches:
A correlation coefficient computing unit receives front-left and front-right wheel-accelerations from high-pass filters, each having a driver-operating component removed therefrom, and computes a correlation coefficient therebetween. A computing-unit of upper and lower limits of a correlation coefficient of a population sets upper and lower limits of a correlation coefficient of a population. First and second correction-gain setting units set first and second correction-gains varying in accordance with running and driving states, respectively. A correlation coefficient computing unit of a population computes a correlation coefficient of a population of this time based on the correlation coefficients computed as above, a correlation coefficient of a population of the previous time, the upper and lower limits, and the first and second correction-gains. A coefficient of friction on road surface estimating unit estimates a coefficient of friction on road surface by comparing the correlation coefficient of the population of this time with a determining threshold value previously set according to the running state.
Margolis et al. (US 6508102 B1) generally teaches:
An improved estimator is provided for road/tire friction. The friction estimator provides near-real-time friction estimation, even while the car is accelerating, braking or turning. It is desirable to have an instantaneous and continuous estimate of the road/tire friction, but an estimate that occurs over several wheel rotations is more realistic. The estimate relies on easily measured signals such as yaw rate, lateral acceleration, wheel speed, etc. The estimate can be used to give the driver or a closed-loop controller an advanced warning when the tire force limit is being approached.
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/ANSHUL SOOD/ Primary Examiner, Art Unit 3667