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 .
DETAILED ACTION
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/05/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-7, 11, and 12 are rejected under 35 U.S.C. 101 because the claimed invention, under its broadest reasonable interpretation, is directed to a judicial exception without significantly more. In particular, the independent claims contain limitations that are directed towards abstract ideas, laws of nature, or mathematical calculations, without claiming significantly more. This analysis will proceed through the Alice/Mayo test to show that the independent claims, as drafted, are not eligible subject matter for a patent under 35 U.S.C. 101.
101 ANALYSIS – STEP 1: Does the claimed invention fall within one of the four statutory categories of invention (process, machine, manufacture or composition matter)?
Yes, the claims are directed to either an apparatus, method, or device.
STEP 2A – Prong One: Does the Claim Recite A Judicial Exception (An Abstract Idea, Law of Nature or Natural Phenomenon)?
Independent claims 1, 11, and 12 recite a series of steps describing systems and methods for gathering and inputting data into a learning model, which is an abstract mental process. Claims 1, 11, and 12 describe a computer system, processing circuitry, a learning model, and a vehicle. The components claimed in claims 1, 11, and 12 are directed to generic computer components that are applied to abstract limitations. This is affirmed by In re Grams (888 F.2d 835, Fed. Cir. 1989) which found that performing routine data gathering steps to obtain input for mathematical processes to be insignificant extra-solution activity. In present, if, for example, the limitations of independent claims 8 and 13, reciting “issue at least a portion of said target brake torque data to at least each one of said service brake and said auxiliary brake” output by the learning model, were implemented into independent claims 1, 11, and 12, then the claims may be integrated into practical application. (Yes, the claims recite an abstract idea.)
STEP 2A – Prong Two: Does the Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application of the Exception?
The independent claims 1, 11, and 12 only recite a computer system, processing circuitry, a learning model, and a vehicle, which are recited at a high level of generality within the claims. These additional elements do not implement the abstract idea into a practical application and do not impose any meaningful limits on practicing the abstract idea. (No, the claims do not recite additional elements that integrate the judicial exception into practical application of the exception.)
STEP 2B: If there is an exception, determine if the claim as a whole recites significantly more than the judicial exception itself.
With respect to step 2B, in which any additional element or combination of elements is considered to be insignificant extra-solution activity in step 2A, prong 2 is re-evaluated, to see if re- evaluation finds that the recited elements are unconventional or otherwise more than well-understood, routine, conventional activity in the art. The examiner finds that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 8 and 13, and their respective dependent claims, do recite additional elements which integrate the judicial exception and thus are not rejected under 35 U.S.C. 101. Independent claims 8 and 13 recite specific systems and methods which are not at a high level of generality. Independent claims 8 and 13 recite issuing target brake torque data to at least each of said service brake and said auxiliary brake, such that the abstract limitations are integrated into a practical application. Dependent claims 2-7 however, further define the abstract ideas presented in independent claims 1, 11, and 12 and are further grouped as mental processes and are abstract for the same reasons as presented above.
The conclusion from going through the Alice/Mayo test is that claims 1-7, 11, and 12 are not integrated into a practical application and are therefore not patent eligible. Claims 1-7, 11, and 12 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 (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 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 and 7-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Feng, et al. (Chinese Patent Application Pub. No. 114987462).
Regarding Claim 1, Feng teaches: A computer system comprising a learning model and processing circuitry configured to train said learning model (Feng, Para. 0034, 0121 – “trained braking model” trained by a “model training module” and utilized by a “calculation module”, where “modules can be embedded in the processor of a computer device”) for use in determination of target brake torque data, indicative of a target brake torque for at least each one of a service brake and an auxiliary brake of a vehicle (Feng, Para. 0038-0039 – calculating a “the minimum”, or target, “braking force” and obtaining a “predicted basic braking force and the predicted auxiliary braking force”), said processing circuitry being configured to:
receive braking condition information (Feng, Para. 0102 – “sample data for the braking model”) comprising the following for each braking condition of a plurality of different braking conditions of said vehicle:
operating condition data indicative of a current or predicted operating condition of said vehicle during said braking condition (Feng, Para. 0072, 0080, 0092 – “determine the current environment of the vehicle and identify environmental information” including “road type information”, “weather information”, etc. and collision information such as “first distance between the current vehicle and the vehicle in front, and the second distance between the current vehicle and the vehicle behind” and “driver's braking intention” based on “brake pedal opening information”);
brake torque data, indicative of an applied brake torque for at least each one of said service brake and said auxiliary brake during said braking condition (Feng, Para. 0032-0034, 0080, 0094 –“brake pedal opening information”, including “brake pedal opening and brake pedal change rate”, “the first vehicle speed, and the corresponding actual vehicle braking force and actual vehicle braking deceleration are input into the braking model for model training”, where the “first vehicle” is the “current vehicle”, and “auxiliary braking configuration file” containing “auxiliary braking force” data), and
vehicle dynamic response data indicative of a vehicle dynamic response of said vehicle during said braking condition (Feng, Para. 0034 – “actual vehicle braking force and actual vehicle braking deceleration are input into the braking model”),
for each braking condition of said plurality of different braking conditions of the vehicle, associate said braking condition with a penalty in response to determining that said vehicle dynamic response data is indicative of a vehicle dynamic response of said vehicle during said braking condition being outside an allowable vehicle dynamic response range (Feng, Para. 0004, 0078-0079, 0112 – determining a “distance between this vehicle and vehicles traveling in front, to the side, and behind this vehicle in the direction of travel”, or vehicle dynamic response range, where if “the distance is less than a preset value, a vehicle collision warning is issued”, where the “value of the safe driving distance varies depending on the current vehicle speed and the specific scenario”; where the device is implemented “for avoiding vehicle collisions”, i.e. “scenarios” such as “a collision with the vehicle in front due to insufficient braking force, or a collision with the vehicle behind due to sudden braking, without colliding with the vehicle in front”, where a collision is a penalty situation),
input training data to the learning model to train the learning model through machine learning (Feng, Para. 0034, 0102, 0117 – where the above information, or “sample data”, is “input into the braking model for model training to obtain the trained braking model” by “a model training module… for model training, so as to obtain the trained braking model”),
wherein the training data comprises at least said operating condition data and said brake torque data for at least each braking condition of said plurality of different braking conditions not being associated with a penalty (Feng, Para. 0078, 0102, 0110-0117 – the training data, including “brake pedal opening”, “brake pedal change rate”, “the current vehicle's first speed”, and various coefficients determined based on sensor information, is input into the model and used for “maintaining a certain safe distance” for “avoiding vehicle collisions”, where the collision is a penalty situation).
In regards to Claim 7, Feng teaches the computer system of Claim 1, and Feng further teaches wherein said target brake torque data is indicative of a distribution of brake request among at least each one of said service brake and said auxiliary brake of said vehicle (Feng, Para. 0102-0110 – obtaining a “predicted braking force” that is the sum of “predicted basic braking force Fa” and “predicted auxiliary braking force Fb”, such that the braking force is distributed between basic braking and auxiliary braking, to meet a “minimum braking force” to “avoid a collision”).
Regarding Claim 8, Feng teaches: A computer system, comprising processing circuitry (Feng, Para. 0034, 0121 – a “processor of a computer device”), for a vehicle, said vehicle comprising at least a service brake and an auxiliary brake (Feng, Para. 0052, 0099-0100 – “brake pedal” for applying a “basic braking force” and an “auxiliary” brake for applying an “auxiliary braking force” of a “current vehicle”), said processing circuitry being configured to:
receive brake request data indicative of a brake request for the vehicle (Feng, Para. 0012, 0019 – “In response to detecting a change in brake pedal opening” applied by the driver, “brake pedal opening information is acquired”);
receive operating condition data indicative of a current or predicted operating condition of said vehicle (Feng, Para. 0019-0032 – “In response to detecting a change in brake pedal opening, brake pedal opening information is acquired” and obtaining “environmental information surrounding the current vehicle”, including “road type information, weather information”, etc., and determining if “the current vehicle will collide with the vehicle behind it but will not collide with the vehicle in front of it, and the current vehicle will not collide with the vehicle behind it but will not collide with the vehicle in front of it”);
input said brake request data and said operating condition data to a trained learning model and obtain target brake torque data, indicative of a target brake torque for at least each one of said service brake and said auxiliary brake, from said trained learning model (Feng, Para. 0096-0101 – “Based on the current brake pedal opening information and the first vehicle speed, call and input the trained braking model to obtain the predicted braking deceleration” including obtaining a “basic braking force” and an “auxiliary braking force”), and
issue at least a portion of said target brake torque data to at least each one of said service brake and said auxiliary brake (Feng, Para. 0092, 0105-0106, 0114 – “execute the control command” to “decelerate the current vehicle” at a “critical braking deceleration” or “expected braking deceleration”, where the expected braking deceleration is “the predicted braking deceleration” adjusted for the environment).
In regards to Claim 9, Feng teaches the computer system of Claim 8, and Feng further teaches wherein said trained learning model has been trained by said computer system (Feng, Para. 0034, 0121 – the “trained braking model” is trained by a “model training module” and utilized by a “calculation module”, where “modules can be embedded in the processor of a computer device”).
In regards to Claim 10, Feng teaches the computer system of Claim 8, and Feng further teaches wherein computer system further comprises a learning model and wherein said processing circuitry is also configured to train said learning model (Feng, Para. 0034, 0102, 0117, 0121 – inputting information, or “sample data”, “into the braking model for model training to obtain the trained braking model” by “a model training module… for model training, so as to obtain the trained braking model”, where “modules can be embedded in the processor of a computer device”).
Regarding Claim 11, Feng teaches: A vehicle (Feng, Para. 0016 – “current vehicle”) comprising at least a service brake and an auxiliary brake (Feng, Para. 0052, 0099-0100 – “brake pedal” for applying a “basic braking force” and an “auxiliary” brake for applying an “auxiliary braking force”), said vehicle also comprising the computer system of claim 1 (See Claim 1 Above).
Regarding Claim 12, Feng teaches: A computer-implemented method for training a learning model for use in determination of target brake torque data (Feng, Para. 0034, 0121 – “trained braking model” trained by a “model training module” and utilized by a “calculation module”, where “modules can be embedded in the processor of a computer device”), indicative of a target brake torque for at least each one of a service brake and an auxiliary brake of a vehicle (Feng, Para. 0038-0039 – calculating a “the minimum”, or target, “braking force” and obtaining a “predicted basic braking force and the predicted auxiliary braking force”), said method comprising:
receiving, by processing circuitry of a computer system, braking condition information (Feng, Para. 0102 – “sample data for the braking model”) comprising the following for each braking condition of a plurality of different braking conditions of said vehicle:
operating condition data indicative of a current or predicted operating condition of said vehicle during said braking condition (Feng, Para. 0072, 0080, 0092 – “determine the current environment of the vehicle and identify environmental information” including “road type information”, “weather information”, etc. and collision information such as “first distance between the current vehicle and the vehicle in front, and the second distance between the current vehicle and the vehicle behind” and “driver's braking intention” based on “brake pedal opening information”);
brake torque data, indicative of an applied brake torque for at least each one of said service brake and said auxiliary brake during said braking condition (Feng, Para. 0032-0034, 0080, 0094 –“brake pedal opening information”, including “brake pedal opening and brake pedal change rate”, “the first vehicle speed, and the corresponding actual vehicle braking force and actual vehicle braking deceleration are input into the braking model for model training”, where the “first vehicle” is the “current vehicle”, and “auxiliary braking configuration file” containing “auxiliary braking force” data), and
vehicle dynamic response data indicative of a vehicle dynamic response of said vehicle during said braking condition (Feng, Para. 0034 – “actual vehicle braking force and actual vehicle braking deceleration are input into the braking model”),
for each braking condition of said plurality of different braking conditions of the vehicle, associating, by the processing circuitry, said braking condition with a penalty in response to determining that said vehicle dynamic response data is indicative of a vehicle dynamic response of said vehicle during said braking condition being outside an allowable vehicle dynamic response range (Feng, Para. 0004, 0078-0079, 0112 – determining a “distance between this vehicle and vehicles traveling in front, to the side, and behind this vehicle in the direction of travel”, or vehicle dynamic response range, where if “the distance is less than a preset value, a vehicle collision warning is issued”, where the “value of the safe driving distance varies depending on the current vehicle speed and the specific scenario”; where the device is implemented “for avoiding vehicle collisions”, i.e. “scenarios” such as “a collision with the vehicle in front due to insufficient braking force, or a collision with the vehicle behind due to sudden braking, without colliding with the vehicle in front”, where a collision is a penalty situation),
inputting, by the processing circuitry, training data to the learning model to train the learning model through machine learning (Feng, Para. 0034, 0102, 0117 – where the above information, or “sample data”, is “input into the braking model for model training to obtain the trained braking model” by “a model training module… for model training, so as to obtain the trained braking model”),
wherein the training data comprises at least said operating condition data and said brake torque data for at least each braking condition of said plurality of different braking conditions not being associated with a penalty (Feng, Para. 0078, 0102, 0110-0117 – the training data, including “brake pedal opening”, “brake pedal change rate”, “the current vehicle's first speed”, and various coefficients determined based on sensor information, is input into the model and used for “maintaining a certain safe distance” for “avoiding vehicle collisions”, where the collision is a penalty situation).
Regarding Claim 13, Feng teaches: A computer-implemented method for braking a vehicle (Feng, Para. 0015-0022 – “a method for avoiding vehicle collisions” by “decelerating the current vehicle” by applying a “braking force”), said vehicle comprising at least a service brake and an auxiliary brake (Feng, Para. 0052, 0099-0100 – “brake pedal” for applying a “basic braking force” and an “auxiliary” brake for applying an “auxiliary braking force” of a “current vehicle”), said method comprising:
receiving, by processing circuitry of a computer system, brake request data indicative of a brake request for the vehicle (Feng, Para. 0012, 0019 – “In response to detecting a change in brake pedal opening” applied by the driver, “brake pedal opening information is acquired”);
receiving, by the processing circuitry, operating condition data indicative of a current or predicted operating condition of said vehicle (Feng, Para. 0019-0032 – “In response to detecting a change in brake pedal opening, brake pedal opening information is acquired” and obtaining “environmental information surrounding the current vehicle”, including “road type information, weather information”, etc., and determining if “the current vehicle will collide with the vehicle behind it but will not collide with the vehicle in front of it, and the current vehicle will not collide with the vehicle behind it but will not collide with the vehicle in front of it”);
inputting, by the processing circuitry, said brake request data and said operating condition data to a trained learning model and obtain target brake torque data indicative of a target brake torque for at least each one of said service brake and said auxiliary brake, from said trained learning model (Feng, Para. 0096-0101 – “Based on the current brake pedal opening information and the first vehicle speed, call and input the trained braking model to obtain the predicted braking deceleration” including obtaining a “basic braking force” and an “auxiliary braking force”), and
issuing, by the processing circuitry, at least a portion of said target brake torque data to at least each one of said service brake and said auxiliary brake (Feng, Para. 0092, 0105-0106, 0114 – “execute the control command” to “decelerate the current vehicle” at a “critical braking deceleration” or “expected braking deceleration”, where the expected braking deceleration is “the predicted braking deceleration” adjusted for the environment).
In regards to Claim 14, Feng teaches the method of Claim 13, and Feng further teaches wherein said trained learning model has been trained by said computer system (Feng, Para. 0034, 0121 – the “trained braking model” is trained by a “model training module” and utilized by a “calculation module”, where “modules can be embedded in the processor of a computer device”).
In regards to Claim 15, Feng teaches the method of Claim 13, and Feng further teaches further comprising training a learning model (Feng, Para. 0034, 0102, 0117 – inputting information, or “sample data”, “into the braking model for model training to obtain the trained braking model” by “a model training module… for model training, so as to obtain the trained braking model”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-6 are rejected under 35 U.S.C. 103 as being unpatentable over Feng in view of Wang, et al. (Chinese Patent Application Pub. No. 109591811).
In regards to Claim 2, Feng teaches the computer system of Claim 1, but Feng does not specifically teach wherein said training data further comprises information whether or not each braking condition is associated with a penalty.
However, Wang teaches wherein said training data further comprises information whether or not each braking condition is associated with a penalty (Wang, Para. 0207-0213 – a method of “training the neural network model” on “multiple sets of braking state information”, where from the “multiple sets of braking state information”, a “set of comfort braking state information” and a “set of emergency braking state information”, are determined, or classified, where the method selects “only the braking state information of the braking process in which no rear-end collision has occurred as the training samples of the neural network model to be trained, so as to ensure the safety of the braking data output by the specified neural network model and thus avoid rear-end collisions”, such that braking state information where a collision has occurred is penalized).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer system of Feng to include wherein said training data further comprises information whether or not each braking condition is associated with a penalty, as taught by Wang, in order to ensure the safety of the braking data output by the model and avoid collisions (Wang, Para. 0213).
In regards to Claim 3, Feng teaches the computer system of Claim 1, and Feng further teaches wherein said braking condition information further comprises brake request data indicative of a brake request for the vehicle for each braking condition of a plurality of different braking conditions of said vehicle (Feng, Para. 0102 – “brake pedal opening, brake pedal change rate, and the current vehicle's first speed are collected as sample data for the braking model”, the sample data comprising “multiple sets of such sample data”, or a plurality of braking conditions), but Feng does not specifically teach said training data also comprises said brake request data for at least each braking condition of said plurality of different braking conditions not being associated with a penalty.
However, Wang teaches said training data also comprises said brake request data (Wang, Para. 0297-0298 – “braking force applied by the driver is detected based on the brake pedal of the target vehicle” which is collected as “braking state information” for training) for at least each braking condition of said plurality of different braking conditions not being associated with a penalty (Wang, Para. 0207-0213 – a method of “training the neural network model” on “multiple sets of braking state information”, where the method selects “only the braking state information of the braking process in which no rear-end collision has occurred as the training samples of the neural network model to be trained, so as to ensure the safety of the braking data output by the specified neural network model and thus avoid rear-end collisions”, such that braking state information where a collision has occurred is penalized).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer system of Feng to include said training data also comprises said brake request data for at least each braking condition of said plurality of different braking conditions not being associated with a penalty, as taught by Wang, in order to ensure the safety of the braking data output by the model and avoid collisions (Wang, Para. 0213).
In regards to Claim 4, Feng teaches the computer system of Claim 1, but Feng does not teach wherein said vehicle dynamic response data comprises oscillation data indicative of an oscillation in speed and/or acceleration of said vehicle during said braking condition, optionally said processing circuitry is configured to determine whether or not said oscillation data falls outside an allowable oscillation data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said oscillation data falls outside said allowable oscillation data range.
However, Wang teaches wherein said vehicle dynamic response data comprises oscillation data indicative of an oscillation in speed and/or acceleration of said vehicle during said braking condition (Wang, Para. 0046 – obtaining “braking acceleration” and a “braking jerk” by “differentiating the braking acceleration with respect to time”), optionally said processing circuitry is configured to determine whether or not said oscillation data falls outside an allowable oscillation data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said oscillation data falls outside said allowable oscillation data range (Wang, Para. 0046, 0208 – “the preset comfort index includes a preset maximum comfort braking acceleration and a preset maximum comfort braking jerk”, or allowable oscillation range, “with the braking jerk being obtained by differentiating the braking acceleration with respect to time”, where when selecting training samples, a “set of comfort braking state information” is selected based on “preset comfort index”, such that any data sets not satisfying the index are penalized).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer system of Feng to include wherein said vehicle dynamic response data comprises oscillation data indicative of an oscillation in speed and/or acceleration of said vehicle during said braking condition, optionally said processing circuitry is configured to determine whether or not said oscillation data falls outside an allowable oscillation data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said oscillation data falls outside said allowable oscillation data range, as taught by Wang, in order to maintain comfortability when braking.
In regards to Claim 5, Feng teaches the computer system of Claim 1, but Feng does not teach wherein said vehicle dynamic response data comprises deceleration error data indicative of an error between a target deceleration determined using said brake request data, and an actual deceleration, optionally said processing circuitry is configured to determine whether or not said deceleration error data falls outside an allowable deceleration error data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said deceleration error data falls outside an allowable deceleration error data range.
However, Wang teaches wherein said vehicle dynamic response data comprises deceleration error data indicative of an error between a target deceleration determined using said brake request data, and an actual deceleration (Wang, Para. 0051, 0059, 0225 – “comparing the actual output data” with “theoretical output data” and obtaining “the model error”; the output including “target braking speed determined based on the speed at the end of braking, braking distance, braking duration, maximum braking force, maximum braking acceleration parameters”), optionally said processing circuitry is configured to determine whether or not said deceleration error data falls outside an allowable deceleration error data range (Wang, Para. 0225 – “iterating through all the sample data, the error between the actual output data Y’ and the theoretical output data Y can be continuously reduced. After a certain period of training, a specified neural network model that meets the requirements”, or allowable error, “can be obtained”), optionally said processing circuitry is configured to assign the braking condition with said penalty if said deceleration error data falls outside an allowable deceleration error data range (Wang, Para. 0208 – where when selecting training samples, a “set of comfort braking state information” is selected based on “preset comfort index” and “only the braking state information of the braking process in which no rear-end collision has occurred as the training samples of the neural network model to be trained, so as to ensure the safety of the braking data output by the specified neural network model and thus avoid rear-end collisions” such that any data sets not satisfying the comfort index or causing a collision are penalized).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer system of Feng to include wherein said vehicle dynamic response data comprises deceleration error data indicative of an error between a target deceleration determined using said brake request data, and an actual deceleration, optionally said processing circuitry is configured to determine whether or not said deceleration error data falls outside an allowable deceleration error data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said deceleration error data falls outside an allowable deceleration error data range, as taught by Wang, in order to ensure the safety of the braking data output by the model and avoid collisions (Wang, Para. 0213).
In regards to Claim 6, Feng teaches the computer system of Claim 1, but Feng does not teach wherein said vehicle dynamic response data comprises integrated deceleration error data indicative of an integrated error between a target deceleration determined using said brake request data, and an actual deceleration during a predetermined time range, optionally said processing circuitry is configured to determine whether or not said integrated deceleration error data falls outside an allowable integrated deceleration error data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said integrated deceleration error data falls outside said allowable integrated deceleration error data range.
However, Wang teaches wherein said vehicle dynamic response data comprises integrated deceleration error data indicative of an integrated error between a target deceleration determined using said brake request data, and an actual deceleration during a predetermined time range (Wang, Para. 0051, 0059, 0225 – “comparing the actual output data” with “theoretical output data” and obtaining “the model error”; the output including “target braking speed determined based on the speed at the end of braking, braking distance, braking duration,” or deceleration during a time range, “maximum braking force, maximum braking acceleration parameters”), optionally said processing circuitry is configured to determine whether or not said integrated deceleration error data falls outside an allowable integrated deceleration error data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said integrated deceleration error data falls outside said allowable integrated deceleration error data range (Wang, Para. 0208 – where when selecting training samples, a “set of comfort braking state information” is selected based on “preset comfort index” and “only the braking state information of the braking process in which no rear-end collision has occurred as the training samples of the neural network model to be trained, so as to ensure the safety of the braking data output by the specified neural network model and thus avoid rear-end collisions” such that any data sets not satisfying the comfort index or causing a collision are penalized).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the computer system of Feng to include wherein said vehicle dynamic response data comprises integrated deceleration error data indicative of an integrated error between a target deceleration determined using said brake request data, and an actual deceleration during a predetermined time range, optionally said processing circuitry is configured to determine whether or not said integrated deceleration error data falls outside an allowable integrated deceleration error data range, optionally said processing circuitry is configured to assign the braking condition with said penalty if said integrated deceleration error data falls outside said allowable integrated deceleration error data range, as taught by Wang, in order to ensure the safety of the braking data output by the model and avoid collisions (Wang, Para. 0213).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Oswald, et al. (U.S. Patent Application Pub. No. 2014/0343767) teaches a computer-implemented method for determining dynamic braking data for use in a braking model of at least one train having at least one locomotive.
Laine, et al. (U.S. Patent Application Pub. No. 2023/0001898) teaches a method for controlling a vehicle brake system for a heavy duty vehicle including a primary brake system and an auxiliary brake system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELEN LI whose telephone number is (703)756-4719. The examiner can normally be reached Monday through Friday, from 9am to 5pm eastern.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hunter Lonsberry can be reached at (571) 272-7298. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/H.L./Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights