Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 03/18/2026 have been fully considered but they are not persuasive.
Applicant argues the Office has not shown the amended features of the independent claim 1 are taught within Nakanishi (US 20250304063). At least, Applicant argues, the “predicting…” step is not taught by the reference as there is no mention within the reference of using a state estimation algorithm to make curvature predictions associated with a driving surface based on previous curvature predictions and motion of a machine as recited within the claim. Instead, Applicant argues, Nakanishi at best discloses determining curvature values from map data and perception data at a given time and using those values for vehicle control without transforming prior curvature predictions to a current state based on motion of the machine or performing state based predictions. Therefore, Applicant concludes Nakanishi does not anticipate claim 1 and the rejection should be withdrawn.
However, Nakanishi teaches repeatedly performing a method of estimating curvature of a road and trajectory of a vehicle, which is then followed by the vehicle as the methods repeat, where a current iterations predicted curvature is based at least upon the last iteration’s determinations because the vehicle is controlled based upon that last iteration’s determinations which is the source of the current prediction. Each iteration’s predictions correspond to a particular portion of the driving surface. The curvature is determined based upon either of or a combination of sensor recognized information and map data. As such, this predicts a curvature of an upcoming road section using an algorithm estimating state based on trajectory motion of the vehicle, which is exactly what is required by the claim as written. The claim does not, despite arguments, currently recite transforming prior curvature predictions to a current state based on motion of the machine, but merely recites that curvatures are predicted at multiple timesteps and then updates to values occur which is used for control. This is not the same as transforming prior curvature predictions to a current state based on motion of the machine. If the Applicant firmly believes such features to be an important part of their invention, then the Examiner strongly encourages them to amend the claims to actually recite such features, rather than arguing features the claims simply do not reflect.
As such, this argument is unpersuasive.
Applicant argues independent claims 9 and 17 recite similar features to independent claim 1 and therefore are their rejections should be withdrawn.
This argument is unpersuasive for the same reasons as given above.
Applicant argues the rejections of the dependent claims should be withdrawn by virtue of their dependency.
This argument is unpersuasive as each independent and dependent claim has been fully rejected and for the reasons as given above.
Applicant argues the remaining references do not remedy the deficiencies of the independent claims.
However, none of the remaining references are required to remedy any challenged limitation and therefore this argument is unpersuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In particular, claim 21 recites “predict the one or more values by shifting one or more values corresponding to the one or more previous curvature predictions along the driving surface based on a distance traveled by the machine between the prior time step and the current time step”. The disclosure as originally filed does not appear to disclosure such features such that one of ordinary skill in the art would have been able to conclude that the Applicant was in possession of these features before the filing date of the application. At best, the disclosure as originally filed appears to recite that a distance traveled by a machine from a first time step to a second time step is used to determine a curvature prediction. This does not in any way involve shifting of values based upon distance traveled, nor would one of ordinary skill in the art have found it to do so, instead it merely says that distance traveled is used in calculating a next predicted curvature. As such, this must be found to be new matter of which the Applicant did not have proper written description support within the disclosure as originally filed, and therefore this claim must be rejected for lacking written description.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 9-12, 16-18, and 20-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nakanishi et al. (US 20250304063).
In regards to claim 1, Nakanishi teaches a method comprising: (Fig 7.)
obtaining a first set of values representing a prior state of curvature predictions associated with one or more first portions of a driving surface; ([0057], [0091], [0098]-[0100] methods are performed repeatedly where in a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures include a first portion of the driving surface.)
predicting, using a state estimation algorithm and based at least on motion of a machine, a second set of values representing a current state of curvature predictions associated with one or more second portions of the driving surface; ([0057], [0091], [0098]-[0100] methods are performed repeatedly where in a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures include a first portion of the driving surface and a current iteration includes a second portion of the driving surface. Curvature is recognized in either of or a combination of external sensor data and map data. This predicts a curvature of an upcoming road section using an algorithm estimating state based on trajectory motion of the vehicle.)
updating, as one or more first updated values, one or more first values of the second set of values based at least on perception data indicative of one or more first measured curvatures associated with the driving surface; ([0098] method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. [0057], [0091] curvature may be determined particularly using environmental sensors which provide perception data indicative of measured curvature associated with a particular road section within a predetermined distance of the own vehicle. This determines and overwrites, thereby updating, an environmental sensor recognized curvature associated with a particular road section.)
updating, as one or more second updated values, one or more second values of the second set of values based at least on map data indicative of one or more second measured curvatures associated with the driving surface; ([0098] method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. [0057], [0091] curvature may be determined particularly using map data which provide data indicative of measured curvature associated with a particular road section within a predetermined distance of the own vehicle. This determines and overwrites, thereby updating, an map data recognized curvature associated with a particular road section.) and
performing one or more operations associated with the machine using the second set of values that includes the one or more first updated values and the one or more second updated values. ([0100], [0058] driving control is executed based on trajectory determined from curvature information.)
In regards to claim 2, Nakanishi teaches the method of claim 1, wherein individual values of the second set of values correspond to magnitudes of curvature associated with the curvature predictions for the one or more second portions of the driving surface. ([0057], [0091] curvature and radius of curvature of the sections of road are determined from either or both of environmental sensor data and map data, which includes the magnitude of curvature.)
In regards to claim 3, Nakanishi teaches the method of claim 1, wherein the predicting of the second set of values representative of the current state of curvature predictions is further based at least on a trajectory of the machine and on the first set of values. ([0057], [0091], [0098]-[0100] methods are performed repeatedly where in a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, which include first values of curvature prediction.)
In regards to claim 4, Nakanishi teaches the method of claim 3, wherein the trajectory of the machine comprises a distance of travel of the machine between a first location associated with the prior state and a second location associated with the current state. ([0098]-[0100] operations are executed repeatedly at predetermined intervals or predetermined timing, where the vehicle determines and follows a trajectory based on the determined curvatures which means the vehicle travels a distance according to its speed between the different time intervals at which the operations are executed.)
In regards to claim 9, Nakanishi teaches a system comprising: (Fig 1.)
one or more processors to: ([0048] processor performs operations.)
obtain one or more values representative of one or more curvature predictions associated with a driving surface traversed by a machine, the one or more values predicted using a state estimation algorithm to transform one or more previous curvature predictions from a prior time step to a current time step based on a trajectory of the machine between the prior time step and the current time step; ([0057], [0091], [0098]-[0100] methods are performed repeatedly where in a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures include a first portion of the driving surface and a current iteration includes a second portion of the driving surface. Curvature is recognized in either of or a combination of external sensor data and map data. This predicts a curvature of an upcoming road section using an algorithm estimating state based on trajectory motion of the vehicle.)
refine, as one or more updated values, the one or more values based at least on at least one of: ([0098] method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. [0057], [0091] curvature may be determined associated with a particular road section within a predetermined distance of the own vehicle. This determines and overwrites, thereby updating and refining, an environmental sensor recognized curvature associated with a particular road section.)
map data associated with the driving surface; ([0098] method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. [0057], [0091] curvature may be determined particularly using map data which provide data indicative of measured curvature associated with a particular road section within a predetermined distance of the own vehicle. This determines and overwrites, thereby updating, an map data recognized curvature associated with a particular road section.) or
perception data generated based at least on sensor data obtained using one or more sensors of the machine; ([0098] method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. [0057], [0091] curvature may be determined particularly using environmental sensors which provide perception data indicative of measured curvature associated with a particular road section within a predetermined distance of the own vehicle. This determines and overwrites, thereby updating, an environmental sensor recognized curvature associated with a particular road section.) and
perform one or more operations associated with the machine based at least on the one or more updated values. ([0100], [0058] driving control is executed based on trajectory determined from curvature information.)
In regards to claim 10, Nakanishi teaches the system of claim 9, the one or more processors further to:
determine, based at least on the map data, one or more second values representative of one or more curvature measurements associated with the driving surface, ([0057], [0091], [0098] curvature and radius of curvature of the sections of road are determined from map data, which includes the magnitude of curvature, where operations are repeated and the curvature is determined at multiple times.)
wherein one or more magnitudes of the one or more updated values are based at least on the one or more second values. ([0057], [0091], [0098] curvature and radius of curvature of the sections of road are determined from map data, which includes the magnitude of curvature, where operations are repeated and the curvature is determined at multiple times.)
In regards to claim 11, Nakanishi teaches the system of claim 9, the one or more processors further to:
determine, based at least on the perception data, one or more second values representative of one or more curvature measurements associated with the driving surface, ([0057], [0091], [0098] curvature and radius of curvature of the sections of road are determined from environmental sensor data, which includes the magnitude of curvature, where operations are repeated and the curvature is determined at multiple times.)
wherein one or more magnitudes of the one or more updated values are based at least on the one or more second values. ([0057], [0091], [0098] curvature and radius of curvature of the sections of road are determined from environmental sensor data, which includes the magnitude of curvature, where operations are repeated and the curvature is determined at multiple times.)
In regards to claim 12, Nakanishi teaches the system of claim 9, wherein the one or more values are representative of one or more predicted magnitudes of curvature corresponding to one or more portions of the driving surface. ([0057], [0091], [0098] curvature and radius of curvature of the sections of road are determined from environmental sensor data and map data, which includes the magnitude of curvature, where operations are repeated and the curvature is determined at multiple times for the road ahead of the vehicle which is a prediction of the curvature of that road section.)
In regards to claim 16, Nakanishi teaches the system of claim 9, wherein the system is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine; ([0048] system is composed of at least driving assistance device, which is a semi-autonomous vehicle machine.)
a perception system for an autonomous or semi-autonomous machine; ([0048] system is composed of at least driving assistance device with recognition units, which is a perception system for a semi-autonomous vehicle machine.)
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot; ([0048] system is composed of at least driving assistance device which is using a robot to perform driving assistance.)
a system for performing one or more generative AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing one or more conversational AI operations;
a system for generating synthetic data;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
In regards to claim 17, Nakanishi teaches at least one processor comprising: ([0048] processor performs operations.)
one or more circuits to perform one or more operations associated with a machine based at least on estimations of curvature corresponding to a driving surface, the estimations of curvature generated by transforming prior curvature estimations based at least on motion of the machine between a previous time step and a current time step and refining the estimations of curvature based at least on map data indicative of one or more measured curvatures associated with the driving surface. ([0057], [0091], [0098]-[0100] operations are executed repeatedly to determine road situation, including whether the road is a curved road, of upcoming road, where curvature is recognized in either of or a combination of external sensor data and map data. In a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures include a first portion of the driving surface and a current iteration includes a second portion of the driving surface. This predicts a curvature of an upcoming road section using an algorithm estimating state based on trajectory motion of the vehicle. Curvature may be determined particularly using map data which provides data indicative of measured curvature associated with a particular road section within a predetermined distance of the own vehicle. This determines and overwrites, thereby refining, a map data recognized curvature associated with a particular road section. [0100], [0058] driving control is executed based on trajectory determined from curvature information. These operations are performed by circuit components.)
In regards to claim 18, Nakanishi teaches the processor of claim 17, the one or more circuits to further refine estimations of curvature based at least on perception data generated from at least sensor data obtained using one or more sensors of the machine, the perception data indicative of one or more perceived curvatures associated with the driving surface. ([0057], [0091], [0098]-[0100] operations are performed repeatedly where in a first run through of the operations, curvatures and predicted curvatures are determined from environmental sensor data and map data and then a trajectory is found based on the determined curvatures and predicted curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, predicted curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures and predicted curvatures include a previous portion of the driving surface.)
In regards to claim 20, Nakanishi teaches the processor of claim 17.
Claim 20 recites a processor having substantially the same features of claim 16 above, therefore claim 20 is rejected for the same reasons as claim 16.
In regards to claim 21, Nakanishi teaches the system of claim 9, the one or more processors further to predict the one or more values by shifting one or more values corresponding to the one or more previous curvature predictions along the driving surface based on a distance traveled by the machine between the prior time step and the current time step. ([0057], [0091], [0098]-[0100] operations are executed repeatedly at predetermined intervals or predetermined timing, where the vehicle determines and follows a trajectory based on the determined curvatures which means the vehicle travels a distance according to its speed between the different time intervals at which the operations are executed. Methods are performed repeatedly where in a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s. Because the current predictions are based upon the previous predictions as the vehicle travels, the previous predicted values are transposed as the vehicle travels as well, which is shifting the values based on the distance traveled by the vehicle.)
In regards to claim 22, Nakanishi teaches the system of claim 9, the one or more processors further to refine at least one value of the one or more values based at least on the perception data and subsequently refine the at least one value based at least on the map data. ([0098] [0057], [0091], [0098]-[0100] method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. Curvature may be determined associated with a particular road section within a predetermined distance of the own vehicle. Methods are performed repeatedly where in a first run through of the method, curvatures are determined and then a trajectory is found based on the determined curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures include a first portion of the driving surface and a current iteration includes a second portion of the driving surface. Curvature is recognized in either of or a combination of external sensor data and map data. This determines and overwrites, thereby updating and refining, an environmental sensor recognized curvature associated with a particular road section. This includes a first iteration which refines and updates curvature based upon a combination of external sensor data and map data and then a subsequent second iteration that refines and updates curvature based upon a combination of external sensor data and map data, where the first iteration is prior to the second iteration and refinement then occurs based at least on the perception data in the first iteration and refinement further occurs based at least on the map data in the second iteration.)
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.
Claims 5, 6, 8, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi, in view of Jeon et al. (US 20220388526).
In regards to claim 5, Nakanishi teaches the method of claim 1.
Nakanishi does not teach: further comprising:
determining, based at least on the perception data, that one or more differences between one or more of the current state of curvature predictions and the one or more first measured curvatures meet or exceed a threshold,
wherein the updating of the one or more first values of the second set of values is based at least on the one or more differences meeting or exceeding the threshold.
However, Jeon teaches determining a difference between a predicted curvature and a real curvature of a road section and assessing the difference against a threshold for recognition failure ([0057], [0100]-[0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Nakanishi, by incorporating the teachings of Jeon, such that a difference between the predicted road curvature at a first operation of the method of Nakanishi and the road curvature determined from a subsequent operation is determined, where particularly the difference between the environmental sensor based curvatures is found, which is then used to adjust the trajectory and control, and then subsequent updating of the curvatures of Nakanishi.
The motivation to do so is that, as acknowledged by Jeon, this allows for improved failure determination, which allows for improved control of the vehicle ([0057], [0100]-[0104]).
In regards to claim 6, Nakanishi teaches the method of claim 1.
Nakanishi does not teach: further comprising:
determining, based at least on the map data, that one or more differences between one or more of the current state of curvature predictions and the one or more second measured curvatures meet or exceed a threshold,
wherein the updating of the one or more second values of the second set of values is based at least on the one or more differences meeting or exceeding the threshold.
However, Jeon teaches determining a difference between a predicted curvature and a real curvature of a road section and assessing the difference against a threshold for recognition failure ([0057], [0100]-[0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Nakanishi, by incorporating the teachings of Jeon, such that a difference between the predicted road curvature at a first operation of the method of Nakanishi and the road curvature determined from a subsequent operation is determined, where particularly the difference between the map based curvatures is found, which is then used to adjust the trajectory and control, and then subsequent updating of the curvatures of Nakanishi.
The motivation to do so is the same as acknowledged by Jeon in regards to claim 5.
In regards to claim 8, Nakanishi teaches the method of claim 1.
Nakanishi does not teach:
further comprising updating at least one value of the one or more second values that corresponds to at least one of the one or more first updated values, the at least one value updated, as part of the one or more second updated values, based at least on the map data indicating a difference in the at least one value between the one or more first measured curvatures and the one or more second measured curvatures.
However, Jeon teaches determining a difference between a predicted curvature and a real curvature of a road section and assessing the difference against a threshold for recognition failure ([0057], [0100]-[0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control method of Nakanishi, by incorporating the teachings of Jeon, such that predicted curvature information from map data of Nakanishi is compared with real determined curvature information from sensor data of Nakanishi and a difference is assessed against a threshold, which is then used to adjust the trajectory and control, and then subsequent updating of the curvatures of Nakanishi.
The motivation to do so is the same as acknowledged by Jeon in regards to claim 5.
In regards to claim 23, Nakanishi teaches the processor of claim 17.
Nakanishi also teaches the method is executed repeatedly, such that upon a subsequent execution, data is updated from a previous execution. The curvature may be determined associated with a particular road section within a predetermined distance of the own vehicle. Curvature is recognized in either of or a combination of external sensor data and map data. This determines and overwrites, thereby updating and refining, an environmental sensor recognized curvature and map recognized curvature associated with a particular road section ([0057], [0091], [0098]-[0100]).
Nakanishi does not teach: the one or more circuits further to determine a difference between at least one of the estimations of curvature and at least one measured curvature indicated by the map data, and refine the at least one estimation of curvature based on the difference.
However, Jeon teaches determining a difference between a predicted curvature and a real curvature of a road section and assessing the difference against a threshold for recognition failure ([0057], [0100]-[0104]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle system of Nakanishi, by incorporating the teachings of Jeon, such that a difference between predicted curvature of Nakanishi and map based curvatures of Nakanishi is determined and further used in the updating of curvatures in future iterations by at least controlling the vehicle.
The motivation to do so is the same as acknowledged by Jeon in regards to claim 5.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi, in view of Jeon and Keaton et al. (US 20050100220).
In regards to claim 7, Nakanishi teaches the method of claim 1.
Nakanishi also teaches operations are performed repeatedly where in a first run through of the operations, curvatures and predicted curvatures are determined and then a trajectory is found based on the determined curvatures and predicted curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, predicted curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures and predicted curvatures include a previous portion of the driving surface ([0057], [0091], [0098]-[0100]).
Nakanishi does not teach: wherein:
the updating of the one or more first values of the second set of values reduces one or more first differences between one or more first curvature predictions of the current state of curvature predictions and one or more first curvature measurements from the perception data; and
the updating of the one or more second values of the second set of values reduces one or more second differences between one or more second curvature predictions of the current state of curvature predictions and one or more second curvature measurements from the map data.
However, Jeon teaches determining a difference between a predicted curvature and a real curvature of a road section and assessing the difference against a threshold for recognition failure ([0057], [0100]-[0104]).
Further, Keaton performing iterations to determine geospatial features such as road curvatures and iterating through these curvatures to smooth irregularities (Claim 10).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the application to modify the vehicle control method of Nakanishi, by incorporating the teachings of Jeon and Keaton, such that a difference between the current and next predicted iterations of the environmental sensor based curvatures and the map based curvatures of Nakanishi and iterated through to smooth irregularities thereby reducing differences between the current and predicted curvatures.
The motivation to determine the differences between real and predicted curvatures is the same as acknowledged by Jeon in regards to claim 5. The motivation to iterate curvature to smooth irregularities is that, as acknowledged by Keaton, this allows for improved recognition of features, such as lane boundaries and the like (Abstract), which one of ordinary skill would have recognized allows for improved navigation.
In regards to claim 15, Nakanishi teaches the system of claim 9.
Nakanishi also teaches operations are performed repeatedly where in a first run through of the operations, curvatures and predicted curvatures are determined and then a trajectory is found based on the determined curvatures and predicted curvatures, and the vehicle is controlled to follow the trajectory to a time and new position where the method is executed based on the newly determined information at that time and position to again calculate curvatures, predicted curvatures, trajectory, and control, which bases the current iteration’s determinations on the previous iteration’s, where the previous iterations curvatures and predicted curvatures include a previous portion of the driving surface ([0057], [0091], [0098]-[0100]).
Nakanishi does not teach:
wherein the refinement of the one or more values reduces one or more differences between the one or more values and one or more second values representative of one or more curvature measurements associated with the driving surface, the one or more second values determined based at least on at least one of the map data or the perception data.
However, Jeon teaches determining a difference between a predicted curvature and a real curvature of a road section and assessing the difference against a threshold for recognition failure ([0057], [0100]-[0104]).
Further, Keaton performing iterations to determine geospatial features such as road curvatures and iterating through these curvatures to smooth irregularities (Claim 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify the vehicle control system of Nakanishi, by incorporating the teachings of Jeon and Keaton, such that predicted curvature information from map data of Nakanishi is compared with real determined curvature information from sensor data of Nakanishi over the current and next predicted iterations and a difference is assessed against a threshold, and iterated through to smooth irregularities thereby reducing differences between the current and predicted curvatures, which is then used to adjust the trajectory and control, and then subsequent updating of the curvatures of Nakanishi.
The motivations to do so are the same as acknowledged by Jeon in regards to claim 5 and Keaton in regards to claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Eigel (US 20190025063) teaches predicting a future course of a road including road curvature.
Yin et al. (US 20240310176) teaches determining differences between parameters of current and predicted features of an environment against a threshold, including lane curvature.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHIAS S WEISFELD whose telephone number is (571)272-7258. The examiner can normally be reached Monday-Thursday 7:00 AM - 4:00 PM.
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, Ramya Burgess can be reached at Ramya.Burgess@USPTO.GOV. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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