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 08/03/2026 have been fully considered.
In regards to independent claim 1, Applicant argues Nakanishi (US 20250304063) does not anticipate the claim, in particular the newly amended features are not to be found within Nakanishi. Applicant argues none of the cited portions of Nakanishi disclose retaining at least two curvature values from a prior state and interpolating between those prior values to generate a value of a current state and Nakanishi’s predetermined distance is not a distance traveled by the machine after obtaining a prior set of curvature values. Instead, Applicant argues Nakanishi recognized curvature from map and sensor information and generates a target trajectory and speed based on the surroundings and merely basing a later execution upon the determinations of a previous execution does not amount to the newly amended claim. Therefore, Applicant concludes independent claim 1 is patentably distinguished from Nakanishi.
Indeed, it is found that Nakanishi alone does not teach the newly amended features, and therefore the amendment has necessitated the inclusion of new reference non-patent literature Hasberg, which teaches, in brief, estimating curvature through interpolation of data to fit third order polynomial splines representing the road shape using Kalman filtering. As such this argument is moot.
That being said, the amended claim does not require actual interpolation of curvature between two curvature data points, instead it requires interpolation of two data points that are in some generic way representative of curvature which is far broader than what has been argued by the Applicant. If the Applicant firmly believes this to be a fundamental part of their application, the Examiner strongly encourages them to amend the claims to actually recite such features, rather than arguing features the claim simply does not reflect.
Further, Nakanishi teaches the vehicle travels along a trajectory while the curvature operations are repeated where the trajectory is generated based at least in part on the curvature determinations, this necessarily bases the current determinations of curvature on the previous determinations of curvature and the distance traveled between those determinations. By the combination of Nakanishi and Hasberg, one of ordinary skill would have arrived at feeding the data of Nakanishi, including the curvature determinations, into the 3rd order polynomial spline determination processing of Hasberg to interpolate data to fit 3rd order polynomials which define the area in between each point as the vehicle travels incorporating the distance traveled, which is what is required by the claim.
Applicant argues independent claim 17 recite similar features to independent claim 1 and therefore is distinguished for the same reasons.
This argument is unpersuasive for the same reasons as given above.
Applicant argues independent claim 9 recites using a state estimation algorithm to transform previous curvature predictions from a prior time step to a current time step based on the machine’s trajectory and refining the resulting values using the state estimation algorithm and Nakanishi’s repeated recognition of curvature does not disclose using previous curvature predictions as inputs to such a state estimation algorithm. Therefore, Applicant argues claim 9 is patentably distinguished from Nakanishi.
However, Nakanishi teaches using a determined curvature to plan a trajectory for a vehicle and repeating the operations while the vehicle travels such that, once the vehicle has followed the trajectory, newly determined information is used to redetermine curvature and replan the trajectory based at least in part upon the previous iterations curvature determination. This is predicting a curvature of an upcoming road section using an algorithm estimating state based on trajectory motion of the vehicle, such that as the vehicle travels, it travels through the predicted curvature determined at a past time, and repeats the determinations which transforms previous curvature to current estimates of curvature by following the trajectory and overwriting old data, which is precisely what is required by the claim.
As such, this argument is unpersuasive.
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 of the independent claim and therefore this argument is unpersuasive.
Applicant argues the dependent claims are allowable by virtue of their dependency.
This argument is unpersuasive for the reasons as each independent claim has been rejected and for the reasons as given above.
Claim Objections
Claim 7 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. In particular, while references are believed to exist that may teach every individual limitation, it is found that this would require too significant modification to the base reference for one of ordinary skill to have arrived at a conclusion of obviousness. Therefore, were the features of dependent claim 7 amended into independent form including the limitations of each and every intervening claim, independent claim 1 would be found to be allowable. As a further note, were the limitations of claim 7 amended into independent claim 9, the claim would not be found to be allowable because claim 9 does not recite the same limitations as claim 1 and therefore different analysis would be required likely including a rejection.
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 9-12, 16, and 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nakanishi et al. (US 20250304063).
In regards to claim 9, Nakanishi teaches a system comprising: (Fig 1.)
one or more processors to: ([0048] processor performs operations.)
predict, using a state estimation algorithm, one or more values representative of one or more curvature predictions associated with a driving surface traversed by a machine by transforming 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, such that as the vehicle travels along its trajectory the vehicle will arrive at and travel through the predicted curvature determined at a past time and then repeat the determinations, thereby transforming the previous estimates of curvature to current estimates of curvature by following the trajectory and overwriting.)
refine, using the state estimation algorithm, 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 values as refined. ([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 values as refined 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 values as refined 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 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 1, 2, 17, 18, 20, 21, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi, in view of Non-patent Literature Hasberg et al. “Online-Estimation of Road Map Elements using Spline Curves”.
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, a second set of values based at least on a distance traveled by a machine subsequent to obtaining the first set of values, the 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 when the vehicle travels a distance.)
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.)
Nakanishi does not teach:
predicting, using a state estimation algorithm, a second set of values by interpolating between at least two values of the first set of values based at least on a distance traveled by a machine subsequent to obtaining the first set of values,
However, Hasberg teaches estimating the path of a road including its curvature by fitting third order polynomial splines to sampled coordinates through interpolation (Pages 2-3). This determines every point between data points using interpolation to form a polynomial.
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 Hasberg, such that as the curvature is determined, a 3rd order polynomial spline is constructed reflecting the curvature which interpolates points between each point of determined curvature and factors in the distance traveled by the vehicle at least by shifting the spline model using the constant term of the 3rd order equation.
The motivation to do so is that, as acknowledged by Hasberg, this minimizes error in road determination (Page 2).
In regards to claim 2, Nakanishi, as modified by Hasberg, 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 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 current estimations of curvature corresponding to a driving surface, the current estimations of curvature being generated based at least on a distance traveled by the machine between a previous time step and a current time step, and being refined 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, including current curvature, 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. 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 when the vehicle travels a distance.)
Nakanishi does not teach:
the current estimations of curvature being generated by interpolating between at least two prior curvature estimations based at least on a distance traveled by the machine between a previous time step and a current time step,
However, Hasberg teaches estimating the path of a road including its curvature by fitting third order polynomial splines to sampled coordinates through interpolation, such that curvature is smooth at all points (Pages 2-3). This determines every point between data points using interpolation to form a polynomial.
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 Hasberg, such that as the curvature is determined, a 3rd order polynomial spline is constructed at least from the curvature estimations, reflecting the curvature which interpolates curvature points between each point of determined curvature and factors in the distance traveled by the vehicle at least by shifting the spline model using the constant term of the 3rd order equation.
The motivation to do so is that, as acknowledged by Hasberg, this minimizes error in road determination (Page 2).
In regards to claim 18, Nakanishi, as modified by Hasberg, 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, as modified by Hasberg, 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 based at least 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.)
Nakanishi does not teach: the one or more processors further to predict the one or more values by interpolating between one or more values corresponding to the one or more previous curvature predictions based at least on a distance traveled by the machine between the prior time step and the current time step.
However, Hasberg teaches estimating the path of a road including its curvature by fitting third order polynomial splines to sampled coordinates through interpolation, such that curvature is smooth at all points (Pages 2-3). This determines every point between data points using interpolation to form a polynomial.
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 Hasberg, such that as the curvature is determined, a 3rd order polynomial spline is constructed at least from the curvature estimations, reflecting the curvature which interpolates curvature points between each point of determined curvature and factors in the distance traveled by the vehicle at least by shifting the spline model using the constant term of the 3rd order equation.
The motivation to do so is that, as acknowledged by Hasberg, this minimizes error in road determination (Page 2).
In regards to claim 24, Nakanishi, as modified by Hasberg, teaches the method of claim 1.
Nakanishi also teaches 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 ([0057], [0091], [0098]-[0100]).
Hasberg teaches a Kalman filter that is used to estimate curvature state, which is a process model of the Kalman filter, and estimating curvature through fit splines composed of third order polynomials (Page 2-3).
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, as already modified by Hasberg, by further incorporating the teachings of Hasberg, such that a Kalman filter receives the collected data of Hasberg and uses it to estimate the state of curvature as the vehicle travels such that the spline polynomials are propagated between estimations over time according to the distance traveled by the vehicle between every previous and every new estimation.
The motivation to do so is the same as acknowledged by Hasberg in regards to claim 1.
Claims 5, 6, 8, 23 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Nakanishi, in view of Hasberg, in further view of Jeon et al. (US 20220388526).
In regards to claim 5, Nakanishi, as modified by Hasberg, teaches the method of claim 1.
Nakanishi, as modified by Hasberg, 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, as already modified by Hasberg, 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, as modified by Hasberg, teaches the method of claim 1.
Nakanishi, as modified by Hasberg, 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, as already modified by Hasberg, 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, as modified by Hasberg, teaches the method of claim 1.
Nakanishi, as modified by Hasberg, 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, as modified by Hasberg, 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, as modified by Hasberg, 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, as modified by Hasberg, does not teach: the one or more circuits further to determine a difference between at least one of the current estimations of curvature and at least one measured curvature indicated by the map data, and refine the at least one current estimation of curvature based at least 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, as modified by Hasberg, 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.
In regards to claim 25, Nakanishi, as modified by Hasberg, teaches the method of claim 1.
Nakanish, as modified by Hasberg, does not teach: further comprising:
determining the distance traveled based at least on trajectory information indicating a previous location of the machine at which the first set of values was obtained and a current location of the machine at which the second set of values is predicted.
However, Jeon teaches determining a distance between points while a vehicle travels along a trajectory over a processing time period at which analysis is performed ([0075], [0076])
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, as already modified by Hasberg, by incorporating the teachings of Jeon, such that the distance traveled is determined over the period between each processing step of Nakanishi while the vehicle travels along its trajectory.
The motivation to do so is the same as acknowledged by Jeon in regards to claim 5.
Claim 15 is 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 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 that 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.
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.
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/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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